811
11
Agriculture, Forestry and
Other Land Use (AFOLU)
Coordinating Lead Authors:
Pete Smith (UK), Mercedes Bustamante (Brazil)
Lead Authors:
Helal Ahammad (Australia), Harry Clark (New Zealand), Hongmin Dong (China), Elnour A. Elsiddig
(Sudan), Helmut Haberl (Austria), Richard Harper (Australia), Joanna House (UK), Mostafa Jafari
(Iran), Omar Masera (Mexico), Cheikh Mbow (Senegal), Nijavalli H. Ravindranath (India), Charles
W. Rice (USA), Carmenza Robledo Abad (Switzerland / Colombia), Anna Romanovskaya (Russian
Federation), Frank Sperling (Germany / Tunisia), Francesco N. Tubiello (FAO / USA / Italy)
Contributing Authors:
Göran Berndes (Sweden), Simon Bolwig (Denmark), Hannes Böttcher (Austria / Germany), Ryan
Bright (USA / Norway), Francesco Cherubini (Italy / Norway), Helena Chum (Brazil / USA), Esteve
Corbera (Spain), Felix Creutzig (Germany), Mark Delucchi (USA), Andre Faaij (Netherlands), Joe
Fargione (USA), Gesine Hänsel (Germany), Garvin Heath (USA), Mario Herrero (Kenya), Richard
Houghton (USA), Heather Jacobs (FAO / USA), Atul K. Jain (USA), Etsushi Kato (Japan), Oswaldo
Lucon (Brazil), Daniel Pauly (France / Canada), Richard Plevin (USA), Alexander Popp (Germany),
John R. Porter (Denmark / UK), Benjamin Poulter (USA), Steven Rose (USA), Alexandre de Siqueira
Pinto (Brazil), Saran Sohi (UK), Benjamin Stocker (USA), Anders Strømman (Norway), Sangwon Suh
(Republic of Korea / USA), Jelle van Minnen (Netherlands)
Review Editors:
Thelma Krug (Brazil), Gert-Jan Nabuurs (Netherlands)
Chapter Science Assistant:
Marina Molodovskaya (Canada / Uzbekistan)
812812
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Chapter 11
This chapter should be cited as:
Smith P., M. Bustamante, H. Ahammad, H. Clark, H. Dong, E. A. Elsiddig, H. Haberl, R. Harper, J. House, M. Jafari, O. Masera,
C. Mbow, N. H. Ravindranath, C. W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agricul-
ture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of
Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R.
Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J.
Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.
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Chapter 11
Contents
Executive Summary � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 816
11�1 Introduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 818
11�2 New developments in emission trends and drivers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 819
11�2�1 Supply and consumption trends in agriculture and forestry
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 822
11�2�2 Trends of GHG emissions from agriculture
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 822
11�2�3 Trends of GHG fluxes from forestry and other land use
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 825
11�3 Mitigation technology options and practices, and behavioural aspects � � � � � � � � � � � � � � � � � � � � � � � � � � � � 829
11�3�1 Supply-side mitigation options
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 829
11�3�2 Mitigation effectiveness (non- permanence: saturation,
human and natural impacts, displacement)
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 832
11�4 Infrastructure and systemic perspectives� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 836
11�4�1 Land: a complex, integrated system
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 836
11�4�2 Mitigation in AFOLU — feedbacks with land-use competition
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 837
11�4�3 Demand-side options for reducing GHG emissions from AFOLU
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 838
11�4�4 Feedbacks of changes in land demand
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 841
11�4�5 Sustainable development and behavioural aspects
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 842
11�5 Climate change feedback and interaction with adaptation (includes vulnerability) � � � � � � � � � � � � 843
11�5�1 Feedbacks between ALOFU and climate change
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 845
11�5�2 Implications of climate change on terrestrial carbon pools and mitigation potential of forests
� � � � � � � � � 845
11�5�3 Implications of climate change on peatlands, grasslands, and croplands
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 845
11�5�4 Potential adaptation options to minimize the impact of climate change on carbon stocks in forests and
agricultural soils
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 846
11�5�5 Mitigation and adaptation synergies and tradeoffs
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 846
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11�6 Costs and potentials � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 847
11�6�1 Approaches to estimating economic mitigation potentials
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 848
11�6�2 Global estimates of costs and potentials in the AFOLU sector
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 848
11�6�3 Regional disaggregation of global costs and potentials in the AFOLU sector
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � 849
11�7 Co-benefits, risks, and spillovers � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 852
11�7�1 Socio-economic effects
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 853
11�7�2 Environmental effects
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 855
11�7�3 Public perception
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 857
11�7�4 Spillovers
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 858
11�8 Barriers and opportunities � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 858
11�8�1 Socio-economic barriers and opportunities
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 858
11�8�2 Institutional barriers and opportunities
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 858
11�8�3 Ecological barriers and opportunities
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 859
11�8�4 Technological barriers and opportunities
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 859
11�9 Sectoral implications of transformation pathways and sustainable development � � � � � � � � � � � � � � 859
11�9�1 Characterization of transformation pathways
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 860
11�9�2 Implications of transformation pathways for the AFOLU sector
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 862
11�9�3 Implications of transformation pathways for sustainable development
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 862
11�10 Sectoral policies � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 862
11�10�1 Economic incentives
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 864
11�10�2 Regulatory and control approaches
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 864
11�10�3 Information schemes
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 868
11�10�4 Voluntary actions and agreements
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 868
11�11 Gaps in knowledge and data � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 868
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11�12 Frequently Asked Questions � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 869
11�13 Appendix Bioenergy: Climate effects, mitigation options, potential and
sustainability implications
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 870
11�13�1 Introduction
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 870
11�13�2 Technical bioenergy potential
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 870
11�13�3 Bioenergy conversion: technologies and management practices
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 873
11�13�4 GHG emission estimates of bioenergy production systems
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 877
11�13�5 Aggregate future potential deployment in integrated models
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 882
11�13�6 Bioenergy and sustainable development
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 883
11�13�7 Tradeoffs and synergies with land, water, food, and biodiversity
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 883
References � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 887
816816
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Executive Summary
Agriculture, Forestry, and Other Land Use (AFOLU) is unique
among the sectors considered in this volume, since the mitiga-
tion potential is derived from both an enhancement of removals
of greenhouse gases (GHG), as well as reduction of emissions
through management of land and livestock (robust evidence;
high agreement). The land provides food that feeds the Earth’s human
population of ca. 7 billion, fibre for a variety of purposes, livelihoods
for billions of people worldwide, and is a critical resource for sustain-
able development in many regions. Agriculture is frequently central to
the livelihoods of many social groups, especially in developing coun-
tries where it often accounts for a significant share of production. In
addition to food and fibre, the land provides a multitude of ecosystem
services; climate change mitigation is just one of many that are vital
to human well-being (robust evidence; high agreement). Mitigation
options in the AFOLU sector, therefore, need to be assessed, as far as
possible, for their potential impact on all other services provided by
land. [Section 11.1]
The AFOLU sector is responsible for just under a quarter
(~10 – 12 GtCO
2
eq / yr) of anthropogenic GHG emissions mainly
from deforestation and agricultural emissions from livestock,
soil and nutrient management (robust evidence; high agreement)
[11.2]. Anthropogenic forest degradation and biomass burning (forest
fires and agricultural burning) also represent relevant contributions.
Annual GHG emissions from agricultural production in 2000 – 2010
were estimated at 5.0 – 5.8 GtCO
2
eq / yr while annual GHG flux from
land use and land-use change activities accounted for approximately
4.3 – 5.5 GtCO
2
eq / yr. Leveraging the mitigation potential in the sec-
tor is extremely important in meeting emission reduction targets
(robust evidence; high agreement) [11.9]. Since publication of the IPCC
Fourth Assessment Report (AR4), emissions from the AFOLU sector
have remained similar but the share of anthropogenic emissions has
decreased to 24 % (in 2010), largely due to increases in emissions in
the energy sector (robust evidence, high agreement). In spite of a large
range across global Forestry and Other Land Use (FOLU) flux estimates,
most approaches indicate a decline in FOLU carbon dioxide (CO
2
) emis-
sions over the most recent years, largely due to decreasing defores-
tation rates and increased afforestation (limited evidence, medium
agreement). As in AR4, most projections suggest declining annual net
CO
2
emissions in the long run. In part, this is driven by technological
change, as well as projected declining rates of agriculture area expan-
sion, which, in turn, is related to the expected slowing in population
growth. However, unlike AR4, none of the more recent scenarios proj-
ects growth in the near-term [11.9].
Opportunities for mitigation include supply-side and demand-
side options� On the supply side, emissions from land-use change
(LUC), land management and livestock management can be reduced,
terrestrial carbon stocks can be increased by sequestration in soils and
biomass, and emissions from energy production can be saved through
the substitution of fossil fuels by biomass (robust evidence; high agree-
ment) [11.3]. On the demand side, GHG emissions could be mitigated
by reducing losses and wastes of food, changes in diet and changes in
wood consumption (robust evidence; high agreement) [11.4] though
quantitative estimates of the potential are few and highly uncertain.
Increasing production without a commensurate increase in emissions
also reduces emission intensity, i. e., the GHG emissions per unit of
product that could be delivered through sustainable intensification;
another mechanism for mitigation explored in more detail here than in
AR4. Supply-side options depend on the efficacy of land and livestock
management (medium evidence; high agreement) [11.6]. Considering
demand-side options, changes in human diet can have a significant
impact on GHG emissions from the food production lifecycle (medium
evidence; medium agreement) [11.4]. There are considerably different
challenges involved in delivering demand-side and supply-side options,
which also have very different synergies and tradeoffs.
The nature of the sector means that there are potentially many
barriers to implementation of available mitigation options,
including accessibility to AFOLU financing, poverty, institutional,
ecological, technological development, diffusion and transfer
barriers (medium evidence; medium agreement) [11.7, 11.8]. Simi-
larly, there are important feedbacks to adaptation, conservation of nat-
ural resources, such as water and terrestrial and aquatic biodiversity
(robust evidence; high agreement) [11.5, 11.8]. There can be competi-
tion between different land uses if alternative options to use available
land are mutually exclusive, but there are also potential synergies, e. g.,
integrated systems or multi-functionality at landscape scale (medium
evidence; high agreement) [11.4]. Recent frameworks, such as those
for assessing environmental or ecosystem services, provide one mecha-
nism for valuing the multiple synergies and tradeoffs that may arise
from mitigation actions (medium evidence; medium agreement) [11.1].
Sustainable management of agriculture, forests, and other land is an
underpinning requirement of sustainable development (robust evi-
dence; high agreement) [11.4].
AFOLU emissions could change substantially in transformation
pathways, with significant mitigation potential from agriculture,
forestry, and bioenergy mitigation measures (medium evidence;
high agreement). Recent multi-model comparisons of idealized imple-
mentation transformation scenarios find land emissions (nitrous oxide,
N
2
O; methane, CH
4
; CO
2
) changing by – 4 to 99 % through 2030, and 7
to 76 % through 2100, with the potential for increased emissions from
land carbon stocks. Land-related mitigation, including bioenergy, could
contribute 20 to 60 % of total cumulative abatement to 2030, and 15 to
40 % to 2100. However, policy coordination and implementation issues
are challenges to realizing this potential [11.9]. Large-scale biomass
supply for energy, or carbon sequestration in the AFOLU sector provide
flexibility for the development of mitigation technologies in the energy
supply and energy end-use sectors, as many technologies already exist
and some of them are commercial (limited evidence; medium agree-
ment) [11.3], but there are potential implications for biodiversity, food
security, and other services provided by land (medium evidence, high
817817
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
agreement) [11.7]. Implementation challenges, including institutional
barriers and inertia related to governance issues, make the costs and
net emission reduction potential of near-term mitigation uncertain. In
mitigation scenarios with idealized comprehensive climate policies,
agriculture, forestry, and bioenergy contribute substantially to the
reduction of global CO
2
, CH
4
, and N
2
O emissions, and to the energy
system, thereby reducing policy costs (medium evidence; high agree-
ment) [11.9]. More realistic partial and delayed policies for global land
mitigation have potentially significant spatial and temporal leakage,
and economic implications, but could still be cost-effectively deployed
(limited evidence; limited agreement) [11.9].
Economic mitigation potential of supply-side measures in the
AFOLU sector is estimated to be 7�18 to 10�60 (full range:
0�49 – 10�60) GtCO
2
eq / yr in 2030 for mitigation efforts consis-
tent with carbon prices up to 100 USD / tCO
2
eq, about a third of
which can be achieved at < 20 USD / tCO
2
eq (medium evidence;
medium agreement) [11.6]. These estimates are based on studies that
cover both forestry and agriculture and that include agricultural soil
carbon sequestration. Estimates from agricultural sector-only studies
range from 0.3 to 4.6 GtCO
2
eq / yr at prices up to 100 USD / tCO
2
eq, and
estimates from forestry sector-only studies from 0.2 to 13.8 GtCO
2
eq / yr
at prices up to 100 USD / tCO
2
eq (medium evidence; medium agree-
ment) [11.6]. The large range in the estimates arises due to widely
different collections of options considered in each study, and because
not all GHGs are considered in all of the studies. The composition of
the agricultural mitigation portfolio varies with the carbon price, with
the restoration of organic soils having the greatest potential at higher
carbon prices (100 USD / tCO
2
eq) and cropland and grazing land man-
agement at lower (20 USD / tCO
2
eq). In forestry there is less difference
between measures at different carbon prices, but there are significant
differences between regions, with reduced deforestation dominat-
ing the forestry mitigation potential in Latin America and Caribbean
(LAM) and Middle East and Africa (MAF), but very little potential in
the member countries of the Organisation for Economic Co-operation
and Development (OECD-1990) and Economies in Transition (EIT). For-
est management, followed by afforestation, dominate in OECD-1990,
EIT, and Asia (medium evidence, strong agreement) [11.6]. Among
demand-side measures, which are under-researched compared to sup-
ply-side measures, changes in diet and reductions of losses in the food
supply chain can have a significant, but uncertain, potential to reduce
GHG emissions from food production (0.76 – 8.55 GtCO
2
eq / yr by 2050),
with the range being determined by assumptions about how the
freed land is used (limited evidence; medium agreement) [11.4]. More
research into demand-side mitigation options is merited. There are
significant regional differences in terms of mitigation potential, costs,
and applicability, due to differing local biophysical, socioeconomic, and
cultural circumstances, for instance between developed and develop-
ing regions, and among developing regions (medium evidence; high
agreement) [11.6].
The size and regional distribution of future mitigation potential
is difficult to estimate accurately because it depends on a num-
ber of inherently uncertain factors� Critical factors include popu-
lation (growth), economic and technological developments, changes
in behaviour over time (depending on cultural and normative back-
grounds, market structures and incentives), and how these translate
into demand for food, fibre, fodder and fuel, as well as development in
the agriculture, aquaculture and forestry sectors. Other factors impor-
tant to mitigation potential are potential climate change impacts on
carbon stocks in soils and forests including their adaptive capacity
(medium evidence; high agreement) [11.5]; considerations set by bio-
diversity and nature conservation requirements; and interrelations with
land degradation and water scarcity (robust evidence; high agreement)
[11.8].
Bioenergy can play a critical role for mitigation, but there are
issues to consider, such as the sustainability of practices and
the efficiency of bioenergy systems (robust evidence, medium
agreement) [11.4.4, Box 11.5, 11.13.6, 11.13.7]. Barriers to large-scale
deployment of bioenergy include concerns about GHG emissions from
land, food security, water resources, biodiversity conservation and live-
lihoods. The scientific debate about the overall climate impact related
to land use competition effects of specific bioenergy pathways remains
unresolved (robust evidence, high agreement) [11.4.4, 11.13]. Bioen-
ergy technologies are diverse and span a wide range of options and
technology pathways. Evidence suggests that options with low lifecy-
cle emissions (e. g., sugar cane, Miscanthus, fast growing tree species,
and sustainable use of biomass residues), some already available, can
reduce GHG emissions; outcomes are site-specific and rely on efficient
integrated ‘biomass-to-bioenergy systems’, and sustainable land-use
management and governance. In some regions, specific bioenergy
options, such as improved cookstoves, and small-scale biogas and
biopower production, could reduce GHG emissions and improve liveli-
hoods and health in the context of sustainable development (medium
evidence, medium agreement) [11.13].
Policies governing practices in agriculture and in forest conser-
vation and management need to account for both mitigation
and adaptation� One of the most visible current policies in the AFOLU
sector is the implementation of REDD+ (see Annex I), that can repre-
sent a cost-effective option for mitigation (limited evidence; medium
agreement) [11.10], with economic, social, and other environmental
co-benefits (e. g., conservation of biodiversity and water resources).
818818
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11.1 Introduction
Agriculture, Forestry, and Other Land Use (AFOLU
1
) plays a central role
for food security and sustainable development (Section 11.9). Plants take
up carbon dioxide (CO
2
) from the atmosphere and nitrogen (N) from the
soil when they grow, re-distributing it among different pools, including
above and below-ground living biomass, dead residues, and soil organic
matter. The CO
2
and other non-CO
2
greenhouse gases (GHG), largely
methane (CH
4
) and nitrous oxide (N
2
O), are in turn released to the atmo-
sphere by plant respiration, by decomposition of dead plant biomass
and soil organic matter, and by combustion (Section 11.2). Anthropo-
genic land-use activities (e. g., management of croplands, forests, grass-
lands, wetlands), and changes in land use / cover (e. g., conversion of for-
est lands and grasslands to cropland and pasture, afforestation) cause
changes superimposed on these natural fluxes. AFOLU activities lead to
both sources of CO
2
(e. g., deforestation, peatland drainage) and sinks of
CO
2
(e. g., afforestation, management for soil carbon sequestration), and
to non-CO
2
emissions primarily from agriculture (e. g., CH
4
from livestock
and rice cultivation, N
2
O from manure storage and agricultural soils and
biomass burning (Section 11.2).
The main mitigation options within AFOLU involve one or more of
three strategies: reduction / prevention of emissions to the atmosphere
by conserving existing carbon pools in soils or vegetation that would
otherwise be lost or by reducing emissions of CH
4
and N
2
O (Section
11.3); sequestration — enhancing the uptake of carbon in terrestrial
reservoirs, and thereby removing CO
2
from the atmosphere (Section
11.3); and reducing CO
2
emissions by substitution of biological prod-
ucts for fossil fuels (Appendix 1) or energy-intensive products (Sec-
tion 11.4). Demand-side options (e. g., by lifestyle changes, reducing
losses and wastes of food, changes in human diet, changes in wood
consumption), though known to be difficult to implement, may also
play a role (Section 11.4).
Land is the critical resource for the AFOLU sector and it provides food
and fodder to feed the Earth’s population of ~7 billion, and fibre and
fuel for a variety of purposes. It provides livelihoods for billions of
people worldwide. It is finite and provides a multitude of goods and
ecosystem services that are fundamental to human well-being (MEA,
2005). Human economies and quality of life are directly dependent on
the services and the resources provided by land. Figure 11.1 shows the
many provisioning, regulating, cultural and supporting services pro-
vided by land, of which climate regulation is just one. Implementing
mitigation options in the AFOLU sector may potentially affect other
services provided by land in positive or negative ways.
In the Intergovernmental Panel on Climate Change (IPCC) Second
Assessment Report (SAR) (IPCC, 1996) and in the IPCC Fourth Assess-
1
The term AFOLU used here consistent with the (IPCC, 2006) Guidelines is also
consistent with Land Use, Land-Use Change and Forestry (LULUCF) (IPCC, 2003),
and other similar terms used in the scientific literature.
ment Report (AR4) (IPCC, 2007a), agricultural and forestry mitigation
were dealt with in separate chapters. In the IPCC Third Assessment
Report (TAR) (IPCC, 2001), there were no separate sectoral chapters
on either agriculture or forestry. In the IPCC Fifth Assessment Report
(AR5), for the first time, the vast majority of the terrestrial land surface,
comprising agriculture, forestry and other land use (AFOLU) (IPCC,
2006), is considered together in a single chapter, though settlements
(which are important, with urban areas forecasted to triple in size from
2000 global extent by 2030; Section 12.2), are dealt with in Chapter
12. This approach ensures that all land-based mitigation options can
be considered together; it minimizes the risk of double counting or
inconsistent treatment (e. g., different assumptions about available
land) between different land categories, and allows the consideration
of systemic feedbacks between mitigation options related to the land
surface (Section 11.4). Considering AFOLU in a single chapter allows
phenomena common across land-use types, such as competition for
land (Smith etal., 2010; Lambin and Meyfroidt, 2011) and water (e. g.,
Jackson etal., 2007), co-benefits (Sandor etal., 2002; Venter etal.,
2009), adverse side-effects (Section 11.7) and interactions between
mitigation and adaptation (Section 11.5) to be considered consistently.
The complex nature of land presents a unique range of barriers and
opportunities (Section 11.8), and policies to promote mitigation in the
AFOLU sector (Section 11.10) need to take account of this complexity.
In this chapter, we consider the competing uses of land for mitigation
and for providing other services (Sections 11.7; 11.8). Unlike the chap-
ters on agriculture and forestry in AR4, impacts of sourcing bioenergy
from the AFOLU sector are considered explicitly in a dedicated appen-
dix (Section 11.13). Also new to this assessment is the explicit con-
sideration of food / dietary demand-side options for GHG mitigation in
the AFOLU sector (Section 11.4), and some consideration of freshwa-
ter fisheries and aquaculture, which may compete with the agriculture
and forestry sectors, mainly through their requirements for land and / or
water, and indirectly, by providing fish and other products to the same
markets as animal husbandry.
This chapter deals with AFOLU in an integrated way with respect to
the underlying scenario projections of population growth, economic
growth, dietary change, land-use change (LUC), and cost of mitigation.
We draw evidence from both ‘bottom-up’ studies that estimate mitiga-
tion potentials at small scales or for individual options or technologies
and then scale up, and multi-sectoral ‘top-down’ studies that consider
AFOLU as just one component of a total multi-sector system response
(Section 11.9). In this chapter, we provide updates on emissions trends
and changes in drivers and pressures in the AFOLU sector (Section 11.2),
describe the practices available in the AFOLU sector (Section 11.3),
and provide refined estimates of mitigation costs and potentials for
the AFOLU sector, by synthesising studies that have become available
since AR4 (Section 11.6). We conclude the chapter by identifying gaps in
knowledge and data (Section 11.11), providing a selection of Frequently
Asked Questions (Section 11.12), and presenting an Appendix on bioen-
ergy to update the IPCC Special Report on Renewable Energy Sources
and Climate Change Mitigation (SRREN) (IPCC, 2011; see Section 11.13).
819819
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11.2 New developments
in emission trends
and drivers
Estimating and reporting the anthropogenic component of gross and
net AFOLU GHG fluxes to the atmosphere, globally, regionally, and
at country level, is difficult compared to other sectors. First, it is not
always possible to separate anthropogenic and natural GHG fluxes
from land. Second, the input data necessary to estimate GHG emis-
sions globally and regionally, often based on country-level statistics
or on remote-sensing information, are very uncertain. Third, methods
for estimating GHG emissions use a range of approaches, from simple
default methodologies such as those specified in the IPCC GHG Guide-
lines
2
(IPCC, 2006), to more complex estimates based on terrestrial car-
bon cycle modelling and / or remote sensing information. Global trends
in total GHG emissions from AFOLU activities between 1971 and 2010
are shown in Figure 11.2; Figure 11.3 shows trends of major drivers of
emissions.
2
Parties to the United Nations Framework Convention on Climate Change
(UNFCCC) report net GHG emissions according to IPCC methodologies (IPCC,
2006). Reporting is based on a range of methods and approaches dependent on
available data and national capacities, from default equations and emission fac-
tors applicable to global or regional cases and assuming instantaneous emissions
of all carbon that will be eventually lost from the system following human action
(Tier 1) to more complex approaches such as model-based spatial analyses (Tier 3).
Figure 11�1 | Multiple ecosystem services, goods and benefits provided by land (after MEA, 2005; UNEP-WCMC, 2011). Mitigation actions aim to enhance climate regulation, but
this is only one of the many functions fulfilled by land.
Land
Goods and
Benefits
Policies and
Drivers
Primary Production
Decomposition
Soil Formation, Nutrient Cycling
Water Cycling, Weathering
Ecological Interactions
Evolutionary Processes
Climate Regulation
Hazard Regulation
Noise Regulation
Pollution Control
Air, Soil and Water Quality
Disease/Pest Regulation
Pollination
Food
Fibre
Water
Energy
Biodiversity
Recreation
Tourism
Spiritual
Religious
Cultural
Services
Provisioning
Services
Regulating
Services
Supporting
Services
AFOLU
+/- +/-
820820
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�2 | Top: AFOLU emissions for the last four decades. For the agricultural sub-sectors emissions are shown for separate categories, based on FAOSTAT, (2013). Emissions
from crop residues, manure applied to soils, manure left on pasture, cultivated organic soils, and synthetic fertilizers are typically aggregated to the category ‘agricultural soils’ for
IPCC reporting. For the Forestry and Other Land Use (FOLU) sub-sector data are from the Houghton bookkeeping model results (Houghton etal., 2012). Emissions from drained
peat and peat fires are, for the 1970s and the 1980s, from JRC / PBL (2013), derived from Hooijer etal. (2010) and van der Werf etal. (2006) and for the 1990s and the 2000s, from
FAOSTAT, 2013. Bottom: Emissions from AFOLU for each RC5 region (see Annex II.2) using data from JRC / PBL (2013), with emissions from energy end-use in the AFOLU sector
from IEA (2012a) included in a single aggregated category, see Annex II.9, used in the AFOLU section of Chapter 5.7.4 for cross-sectoral comparisons. The direct emission data
from JRC / PBL (2013; see Annex II.9) represents land-based CO
2
emissions from forest and peat fires and decay that approximate to CO
2
flux from anthopogenic emission sources
in the FOLU sub-sector. Differences between FAOSTAT / Houghton data and JRC / PBL (2013) are discussed in the text. See Figures 11.4 and 11.6 for the range of differences among
available databases for AFOLU emissions.
0
2
4
6
8
10
18
16
14
12
1970-1979 1980-1989 1990-1999 2000-2009
Average Annual GHG Emissions [GtCO
2
eq/yr]
Crop Residues and Savannah
Burning (N
2
O,CH
4
)
Cultivated Organic Soils (N
2
O)
Crop Residues (N
2
O)
Manure Applied to Soils (N
2
O)
Manure on Pasture (N
2
O)
Synthetic Fertilizers (N
2
O)
Manure Management (CH
4
and N
2
O)
Rice Cultivation (CH
4
)
Enteric Fermentation (CH
4
)
Drained Peat and Peat Fires
(CO
2
, N
2
O, CH
4
)
Land Use Change and Forestry (CO
2
)
GHG Emissions [GtCO
2
eq/yr]
4.1
3.0
2.2
1.5
1.6
4.7
3.3
1.9
0.62
1.4
2.6
3.1
1.8
1.2
1.2
Total 10
Total 13
Total 12
ASIA (Indirect Emissions)
MAF (Indirect Emissions)
LAM (Indirect Emissions)
EIT (Indirect Emissions)
OECD-1990 (Indirect Emissions)
ASIA
MAF
LAM
EIT
OECD-1990
2010200520001995199019851980
19751970
0
5
10
15
20
821821
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�3 | Global trends from 1971 to 2010 in (top) area of land use (forest land — available only from 1990; 1000 Mha) and amount of N fertilizer use (million tonnes), and
(bottom) number of livestock (million heads) and poultry (billion heads). Data presented by regions: 1) Asia, 2) LAM, 3) MAF, 4) OECD-1990, 5) EIT (FAOSTAT, 2013). The area extent
of AFOLU land-use categories, from FAOSTAT, (2013): ‘Cropland’ corresponds to the sum of FAOSTAT categories ‘arable land’ and ‘temporary crops’ and coincides with the IPCC
category (IPCC, 2003); ‘Forest’ is defined according to FAO (2010); countries reporting to UNFCCC may use different definitions. ‘Permanent meadows and pasture’, are a subset of
IPCC category ‘grassland’ (IPCC, 2003), as the latter, by definition, also includes unmanaged natural grassland ecosystems.
EITOECD-1990MAFLAMASIA
1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 20101970 1990 2010
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Forest Land
Permanent Meadows and Pastures
Cropland
N Fertilizers
Area of Land Use [1000 Mha]
0
10
20
30
40
50
60
70
1.6
1.8
2.0
80
90
100
Fertilizers Application [Million t]
0
1
2
3
4
5
0
100
150
50
[1000 Mha]
[Million t]
Global Trends from 1970 to 2010
1970 1990 2010
Poultry [Billion Heads]
Animals [Million Heads]
EITOECD-1990MAFLAMASIA
1970 1990 2010 1970 1990 2010 1970 1990 2010 1970 1990 20101970 1990 2010
0
100
200
300
400
500
600
700
800
0
2
4
6
8
10
12
Horses, Mules, Assess, Camels
Sheep and Goats
Pigs
Cattle and Buffaloes
Poultry (Billion Heads)
900
1000
1100
1200
1300
14
16
18
20
0
5
10
15
20
25
0
500
1000
1500
2500
2000
Global Trends from 1970 to 2010
[Billion Heads]
[Million Heads]
1970 1990 2010
822822
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11�2�1 Supply and consumption trends in
agriculture and forestry
In 2010 world agricultural land occupied 4889 Mha, an increase of
7 % (311 Mha) since 1970 (FAOSTAT, 2013). Agricultural land area has
decreased by 53 Mha since 2000 due to a decline of the cropland area
(Organisation for Economic Co-operation and Development (OECD)-
1990, Economies in Transition (EIT)) and a decrease in permanent
meadows and pastures (OECD-1990 and Asia). The average amount of
cropland and pasture land per capita in 1970 was 0.4 and 0.8 ha and
by 2010 this had decreased to 0.2 and 0.5 ha per capita, respectively
(FAOSTAT, 2013).
Changing land-use practices, technological advancement and varietal
improvement have enabled world grain harvests to double from 1.2 to
2.5 billion tonnes per year between 1970 and 2010 (FAOSTAT, 2012).
Average world cereal yields increased from 1600 to 3030 kg / ha over
the same period (FAOSTAT, 2012) while there has also been a 233 %
increase in global fertilizer use from 32 to 106 Mt / yr, and a 73 %
increase in the irrigated cropland area (FAOSTAT, 2013).
Globally, since 1970, there has been a 1.4-fold increase in the num-
bers of cattle and buffalo, sheep and goats (which is closely linked to
the trend of CH
4
emissions in the sector; Section 11.2.2), and increases
of 1.6- and 3.7-fold for pigs and poultry, respectively (FAOSTAT, 2013).
Major regional trends between 1970 and 2010 include a decrease in
the total number of animals in Economies in Transition (EIT) and OECD-
1990 (except poultry), and continuous growth in other regions, particu-
larly Middle East and Africa (MAF) and Asia (Figure 11.3, bottom panel).
The soaring demand for fish has led to the intensification of freshwater
and marine fisheries worldwide, and an increased freshwater fisheries
catch that topped 11 Mt in 2010, although the marine fisheries catch
has slowly declined (78 Mt in 2010; FAOSTAT, 2013). The latter is, how-
ever, compensated in international markets by tremendous growth of
aquaculture production to 60 Mt wet weight in 2010, of which 37 Mt
originate from freshwater, overwhelmingly in Asia (FAOSTAT, 2013).
Between 1970 and 2010, global daily per capita food availability,
expressed in energy units, has risen from 10,008 to 11,850 kJ (2391 to
2831 kcal), an increase of 18.4 %; growth in MAF (10,716 kJ in 2010)
has been 22 %, and in Asia, 32 % (11,327 kJ in 2010; FAOSTAT, 2013).
The percentage of animal products in daily per capita total food con-
sumption has increased consistently in Asia since 1970 (7 to 16 %),
remained constant in MAF (8 %) and, since 1985, has decreased in
OECD-1990 countries (32 to 28 %), comprising, respectively, 1,790,
870 and 3,800 kJ in 2010 (FAOSTAT, 2013).
11�2�2 Trends of GHG emissions from
agriculture
Organic and inorganic material provided as inputs or output in the
management of agricultural systems are typically broken down
through bacterial processes, releasing significant amounts of CO
2
, CH
4
,
and N
2
O to the atmosphere. Only agricultural non-CO
2
sources are
reported as anthropogenic GHG emissions, however. The CO
2
emitted
is considered neutral, being associated to annual cycles of carbon fixa-
tion and oxidation through photosynthesis. The agricultural sector is
the largest contributor to global anthropogenic non-CO
2
GHGs,
accounting for 56 % of emissions in 2005 (U. S. EPA, 2011). Other
important, albeit much smaller non-CO
2
emissions sources from other
AFOLU categories, and thus not treated here, include fertilizer applica-
tions in forests. Annual total non-CO
2
GHG emissions from agriculture
in 2010 are estimated to be 5.2 – 5.8 GtCO
2
eq / yr (FAOSTAT, 2013; Tubi-
ello etal., 2013) and comprised about 10 – 12 % of global anthropo-
genic emissions. Fossil fuel CO
2
emissions on croplands added another
Figure 11�4 | Data comparison between FAOSTAT (2013), U. S. EPA (2006), and EDGAR (JRC / PBL, 2013) databases for key agricultural emission categories, grouped as agricultural
soils, enteric fermentation, manure management systems, and rice cultivation, for 2005 | Whiskers represent 95 % confidence intervals of global aggregated categories, computed
using IPCC guidelines (IPCC, 2006) for uncertainty estimation (from Tubiello etal., 2013).
GHGEmissions[GtCO
2
eq/yr]
0
1
2
3
4
5
6
7
8
TotalRice CultivationManure Management SystemsEnteric FermentationAgricultural Soils
EPA2006
EPA2011
EDGAR
FAO
823823
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
0.4 – 0.6 GtCO
2
eq / yr
in 2010 from agricultural use in machinery, such
as tractors, irrigation pumps, etc. (Ceschia etal., 2010; FAOSTAT, 2013),
but these emissions are accounted for in the energy sector rather than
the AFOLU sector. Between 1990 and 2010, agricultural non-CO
2
emis-
sions grew by 0.9 % / yr, with a slight increase in growth rates after
2005 (Tubiello etal., 2013).
Three independent sources of disaggregated non-CO
2
GHG emissions
estimates from agriculture at global, regional, and national levels are
available. They are mostly based on FAOSTAT activity data and IPCC
Tier 1 approaches (IPCC, 2006; FAOSTAT, 2012; JRC / PBL, 2013; U. S.
EPA, 2013). EDGAR and FAOSTAT also provide data at country level.
Estimates of global emissions for enteric fermentation, manure man-
agement and manure, estimated using IPCC Tier 2 / 3 approaches are
also available (e. g., (Herrero etal., 2013). The FAOSTAT, EDGAR and
U. S. EPA estimates are slightly different, although statistically con-
sistent given the large uncertainties in IPCC default methodologies
(Tubiello et al., 2013). They cover emissions from enteric fermenta-
tion, manure deposited on pasture, synthetic fertilizers, rice cultivation,
manure management, crop residues, biomass burning, and manure
applied to soils. Enteric fermentation, biomass burning, and rice cul-
tivation are reported separately under IPCC inventory guidelines, with
the remaining categories aggregated into ‘agricultural soils’. According
to EDGAR and FAOSTAT, emissions from enteric fermentation are the
largest emission source, while US EPA lists emissions from agricultural
soils as the dominant source (Figure 11.4).
The following analyses refer to annual total non-CO
2
emissions by all
categories. All three databases agree that that enteric fermentation
and agricultural soils represent together about 70 % of total emis-
sions, followed by paddy rice cultivation (9 – 11 %), biomass burning
(6 – 12 %) and manure management (7 – 8 %). If all emission catego-
ries are disaggregated, both EDGAR and FAOSTAT agree that the larg-
est emitting categories after enteric fermentation (32 – 40 % of total
agriculture emissions) are manure deposited on pasture (15 %) and
synthetic fertilizer (12 %), both contributing to emissions from agricul-
tural soils. Paddy rice cultivation (11 %) is a major source of global CH
4
emissions, which in 2010 were estimated to be 493 – 723 MtCO
2
eq / yr.
The lower end of the range corresponds to estimates by FAO (FAOSTAT,
2013), with EDGAR and US EPA data at the higher end. Independent
analyses suggest that emissions from rice may be at the lower end of
the estimated range (Yan etal., 2009).
Figure 11�5 | Regional data comparisons for key agricultural emission categories in 2010 | Whiskers represent 95 % confidence intervals computed using IPCC guidelines (IPCC,
2006; Tubiello etal., 2013). The data show that most of the differences between regions and databases are of the same magnitude as the underlying emission uncertainties. [FAO-
STAT, 2013; JRC/PBL, 2013; U.S. EPA, 2013]
Manure Management Systems Rice Cultivation
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
ASIA LAM MAF OECD-1990 EIT
GHG Emissions [GtCO
2
eq/yr]
GHG Emissions [GtCO
2
eq/yr]GHG Emissions [GtCO
2
eq/yr]
GHG Emissions [GtCO
2
eq/yr]
Agricultural Soils Enteric Fermentation
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
ASIA LAM MAF OECD-1990 EIT
ASIA LAM MAF OECD-1990 EIT ASIA LAM MAF OECD-1990 EIT
FAO EDGAR EPA
824824
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Enteric Fermentation. Global emissions of this important category
grew from 1.4 to 2.1 GtCO
2
eq / yr between 1961 and 2010, with aver-
age annual growth rates of 0.70 % (FAOSTAT, 2013). Emission growth
slowed during the 1990s compared to the long-term average, but
became faster again after the year 2000. In 2010, 1.0 – 1.5 GtCO
2
eq / yr
(75 % of the total emissions), were estimated to come from devel-
oping countries (FAOSTAT, 2013). Over the period 2000 – 2010, Asia
and the Americas contributed most, followed by Africa and Europe
(FAOSTAT, 2013); see Figure 11.5). Emissions have grown most in
Africa, on average 2.4 % / yr. In both Asia (2.0 % / yr) and the Ameri-
cas (1.1 % / yr), emissions grew more slowly, and decreased in Europe
(– 1.7 % / yr). From 2000 to 2010, cattle contributed the largest share
(75 % of the total), followed by buffalo, sheep and goats (FAOSTAT,
2013).
Manure. Global emissions from manure, as either organic fertilizer
on cropland or manure deposited on pasture, grew between 1961
and 2010 from 0.57 to 0.99 GtCO
2
eq / yr. Emissions grew by 1.1 % / yr
on average. Manure deposited on pasture led to far larger emissions
than manure applied to soils as organic fertilizer, with 80 % of emis-
sions from deposited manures coming from developing countries (FAO-
STAT, 2013; Herrero et al., 2013). The highest emitting regions from
2000 – 2010 were the Americas, Asia and Africa. Growth over the same
period was most pronounced in Africa, with an average of 2.5 % / yr,
followed by Asia (2.3 % / yr), and the Americas (1.2 % / yr), while there
was a decrease in Europe of – 1.2 % / yr. Two-thirds of the total came
from grazing cattle, with smaller contributions from sheep and goats.
In this decade, emissions from manure applied to soils as organic fertil-
izer were greatest in Asia, then in Europe and the Americas. Though the
continent with the highest growth rates of 3.4 % / yr, Africa’s share in
total emissions remained small. In this sub-category, swine and cattle
contributed more than three quarters (77 %) of the emissions. Emis-
sions from manure management grew from 0.25 to 0.36 GtCO
2
eq / yr,
resulting in average annual growth rates of only 0.6 % / yr during the
period 1961 – 2010. From 2000 – 2010 most emissions came from Asia,
then Europe, and the Americas (Figure 11.5).
Synthetic Fertilizer. Emissions from synthetic fertilizers grew at an aver-
age rate of 3.9 % / yr from 1961 to 2010, with absolute values increas-
ing more than 9-fold, from 0.07 to 0.68 GtCO
2
eq / yr (Tubiello et al.,
2013). Considering current trends, synthetic fertilizers will become a
larger source of emissions than manure deposited on pasture in less
than 10 years and the second largest of all agricultural emission cat-
egories after enteric fermentation. Close to three quarters (70 %) of
these emissions were from developing countries in 2010. In the decade
2000 – 2010, the largest emitter by far was Asia, then the Americas
and then Europe (FAOSTAT, 2012). Emissions grew in Asia by 5.3 % / yr,
in Africa by 2.0 % / yr, and in the Americas by 1.5 % / yr. Emissions
decreased in Europe (– 1.8 % / yr).
Rice. Emissions from rice are limited to paddy rice cultivation. From
1961 to 2010, global emissions increased with average annual growth
rates of 0.4 % / yr (FAOSTAT, 2013) from 0.37 to 0.52 GtCO
2
eq / yr. The
growth in global emissions has slowed in recent decades, consistent
with trends in rice cultivated area. During 2000 – 2010, the largest
share of emissions (94 %) came from developing countries, with Asia
being responsible for almost 90 % of the total (Figure 11.5). The larg-
est growth of emissions took place in in Africa (2.7 % / yr), followed by
Europe (1.4 % / yr). Growth rates in Asia and the Americas were much
smaller over the same period (0.4 – 0.7 % / yr).
Figure 11�6 | Global net CO
2
emission estimates from FOLU including LUC. Black line:
Houghton bookkeeping model approach updated to 2010 as in (Houghton etal., 2012),
including LUC and forest management but no peatlands. Red lines: EDGAR ‘LULUCF’
emissions derived from the GFED 2.0 database (van der Werf etal., 2006) of emissions
due to all forest fires (includes both FOLU and non-FOLU fires), with (solid line) and
without (dotted line) peat fires and decay. Green lines: emissions from land-use change
and management from FAO agricultural and forest inventory data (FAOSTAT, 2013),
shown with (solid line) and without (dotted line) peat fires and peat degradation. Dark
red line: deforestation and degradation fires only based on satellite fire data from GFED
3.0 database (van der Werf etal., 2010). Light blue lines: a selection of process-based
vegetation model results, updated for WGI Chapter 6; (Le Quéré etal., 2013) include
LUC, some include forest management, none include peatlands. LPJ-wsl: (Poulter etal.,
2010); BernCC: (Stocker etal., 2011); VISIT: (Kato etal., 2011); ISAM: (Jain etal., 2013),
IMAGE 2.4 (Van Minnen etal., 2009, deforestation only). The symbols and transparent
rectangles represent mean values for the tropics only. Circles: tropical deforestation and
forest management (Pan etal., 2011), using the Houghton (2003) bookkeeping model
approach and FAO data. Triangle: tropical deforestation only, based on satellite forest
area and biomass data (Baccini etal., 2012; Harris etal., 2012). Square: tropical defor-
estation and forest management, based on satellite forest area and biomass data and
FAO data using bookkeeping model (Baccini etal., 2012; Harris etal., 2012).
Pan 1990-1999
and 2000 to 2007
Harris 2000 to 2005
Baccini 2000 to 2010GFED 3.0 Deforestation Fires only
EDGAR 4.2 all Forest Fires
and Peat
EDGAR 4.2 all Forest Fires
FAOSTAT 2013: incl. Peat
FAOSTAT 2013: excl. Peat
Process Modells
Average Values
Houghton Bookkeeping Model
1970 1980 1990 2000 2010
0
1
2
3
4
5
6
7
8
9
10
Carbon Dioxide Net Flux [GtCO
2
/yr]
825825
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11�2�3 Trends of GHG fluxes from forestry and
other land use
3
This section focuses on the most significant non-agricultural GHG
fluxes to the atmosphere for which there are global trend data. Fluxes
resulting directly from anthropogenic FOLU activity are dominated by
CO
2
fluxes, primarily emissions due to deforestation, but also uptake
due to reforestation / regrowth. Non-CO
2
greenhouse gas emissions
from FOLU are small in comparison, and mainly arise from peat degra-
dation through drainage and biomass fires (Box 11.1; Box 11.2).
FOLU accounted for about a third of anthropogenic CO
2
emissions
from 1750 to 2011 and 12 % of emissions in 2000 to 2009 (Table
11.1). At the same time, atmospheric measurements indicate the land
as a whole was a net sink for CO
2
, implying a ‘residual’ terrestrial
sink offsetting FOLU emissions (Table 11.1). This sink is confirmed by
inventory measurements in both managed and unmanaged forests in
temperate and tropical regions (Phillips etal., 1998; Luyssaert etal.,
2008; Lewis etal., 2009; Pan etal., 2011). A sink of the right order of
magnitude has been accounted for in models as a result of the indirect
effects of human activity on ecosystems, i. e., the fertilizing effects of
increased levels of CO
2
and N in the atmosphere and the effects of
climate change (WGI Chapter 6; (Le Quéré etal., 2013), although some
of it may be due to direct AFOLU activities not accounted for in current
estimates (Erb etal., 2013). This sink capacity of forests is relevant to
AFOLU mitigation through forest protection.
3
The term ‘forestry and other land use’ used here, is consistent with AFOLU in the
(IPCC, 2006) Guidelines and consistent with LULUCF (IPCC, 2003).
Global FOLU CO
2
flux estimates (Table 11.1 and Figure 11.6) are based
on a wide range of data sources, and include different processes, defi-
nitions, and different approaches to calculating emissions (Houghton
etal., 2012; Le Quéré etal., 2013; Pongratz etal., 2013). This leads
to a large range across global FOLU flux estimates. Nonetheless, most
approaches agree that there has been a decline in FOLU CO
2
emissions
over the most recent years. This is largely due to a decrease in the rate
of deforestation (FAO, 2010; FAOSTAT, 2013).
Regional trends in FOLU CO
2
emissions are shown in Figure 11.7.
Model results indicate FOLU emissions peaked in the 1980s in Asia and
LAM regions and declined thereafter. This is consistent with a reduced
rate of deforestation, most notably in Brazil
4
, and some areas of affor-
estation, the latter most notably in China, Vietnam and India (FAO-
STAT, 2013). In MAF the picture is mixed, with the Houghton model
(Houghton etal., 2012) showing a continuing increase from the 1970s
to the 2000s, while the VISIT model (Kato etal., 2011) indicates a small
sink in the 2000s. The results for temperate and boreal areas repre-
sented by OECD and EIT regions are very mixed ranging from large
net sources (ISAM) to small net sinks. The general picture in temperate
and boreal regions is of declining emissions and / or increasing sinks.
These regions include large areas of managed forests subjected to har-
vest and regrowth, and areas of reforestation (e. g., following cropland
abandonment in the United States and Europe). Thus results are sensi-
tive to whether and how the models include forest management and
environmental effects on regrowing forests.
4
For annual deforestation rates in Brazil see http: / / www. obt. inpe. br / prodes / index.
php
Table 11�1 | Net global CO
2
flux from AFOLU.
1750 – 2011 1980 – 1989 1990 – 1999 2000 – 2009
Cumulative GtCO
2
GtCO
2
/ yr GtCO
2
/ yr GtCO
2
/ yr
IPCC WGI Carbon Budget, Table 6�1
a
:
Net AFOLU CO
2
flux
b
660 ± 293 5.13 ± 2.93 5.87 ± 2.93 4.03 ± 2.93
Residual terrestrial sink
c
– 550 ± 330 – 5.50 ± 4.03 – 9.90 ± 4.40 – 9.53 ± 4.40
Fossil fuel combustions and cement production
d
1338 ± 110 20.17 ± 1.47 23.47 ± 1.83 28.60 ± 2.20
Meta-analyses of net AFOLU CO
2
flux:
WGI, Table 6.2
e
4.77 ± 2.57 4.40 ± 2.20 2.93 ± 2.20
Houghton et al., 2012
f
4.18 ± 1.83 4.14 ± 1.83 4.03 ± 1.83
Notes: Positive fluxes represent net emissions and negative fluxes represent net sinks.
(a)
Selected components of the carbon budget in IPCC WGI AR5, Chapter 6, Table 6.1.
(b)
From the bookkeeping model accounting method of Houghton (2003) updated in Houghton etal., (2012), uncertainty based on expert judgement; 90 % confidence uncer-
tainty interval.
(c)
Calculated as residual of other terms in the carbon budget.
(d)
Fossil fuel flux shown for comparison (Boden etal., 2011).
(e)
Average of estimates from 12 process models, only 5 were updated to 2009 and included in the 2000 – 2009 mean. Uncertainty based on standard deviation across models,
90 % confidence uncertainty interval (WGI Chapter 6).
(f)
Average of 13 estimates including process models, bookkeeping model and satellite / model approaches, only four were updated to 2009 and included in the 2000 – 2009
mean. Uncertainty based on expert judgment.
826826
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�7 | Regional trends in net CO
2
fluxes from FOLU (including LUC). Houghton bookkeeping model approach updated to 2010 as in Houghton etal., (2012) and five
process-based vegetation models updated to 2010 for WGI Chapter 6; (Le Quéré etal., 2013): LPJ-wsl: (Poulter etal., 2010); BernCC: (Stocker etal., 2011); VISIT: (Kato etal., 2011);
ISAM: (Jain etal., 2013), IMAGE 2.4: ((Van Minnen etal., 2009), deforestation only). Only the FAO estimates (FAOSTAT, 2013) include peatlands.
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1970-
1979
1980-
1989
1990-
1999
2000-
2009
1970-
1979
1980-
1989
1990-
1999
2000-
2009
1970-
1979
1980-
1989
1990-
1999
2000-
2009
1970-
1979
1980-
1989
1990-
1999
2000-
2009
1970-
1979
1980-
1989
1990-
1999
2000-
2009
Max
OECD-1990 EIT ASIA MAF LAM
Min
Mean
FAOSTAT
BernCC
IMAGE 2.4
ISAM
VISIT
LPJ-wsl
Houghton
Net CO
2
Flux [GtCO
2
/yr]
The bookkeeping model method (Houghton, 2003; Houghton etal.,
2012) uses regional biomass, growth and decay rates from the inven-
tory literature that are not varied to account for changes in climate
or CO
2
. It includes forest management associated with shifting cul-
tivation in tropical forest regions as well as global wood harvest
and regrowth cycles. The primary source of data for the most recent
decades is FAO forest area and wood harvest (FAO, 2010). FAOSTAT
(2013) uses the default IPCC methodologies to compute stock-differ-
ence to estimate emissions and sinks from forest management, car-
bon loss associated with forest conversion to other land uses as a
proxy for emissions from deforestation, GFED4 data on burned area to
estimate emissions from peat fires, and spatial analyses to determine
emissions from drained organic soils (IPCC, 2007b). The other mod-
els in Figures 11.6 and 11.7 are process-based terrestrial ecosystem
models that simulate changing plant biomass and carbon fluxes, and
include climate and CO
2
effects, with a few now including the nitro-
gen cycle (Zaehle etal., 2011; Jain etal., 2013). Inclusion of the nitro-
gen cycle results in much higher modelled net emissions in the ISAM
model (Jain etal., 2013) as N limitation due to harvest removals lim-
its forest regrowth rates, particularly in temperate and boreal forests.
Change in land cover in the process models is from the HYDE dataset
(Goldewijk etal., 2011; Hurtt etal., 2011), based on FAO cropland and
pasture area change data. Only some process models include forest
management in terms of shifting cultivation (VISIT) or wood harvest
and forest degradation (ISAM); none account for emissions from peat-
lands (see Box 11.1).
Satellite estimates of change in land cover have been combined with
model approaches to calculate tropical forest emissions (Hansen etal.,
2010). The data is high resolution and verifiable, but only covers recent
decades, and does not account for fluxes due to LUC that occurred
prior to the start of the study period (e. g., decay or regrowth). Sat-
ellite data alone cannot distinguish the cause of change in land use
(deforestation, natural disturbance, management), but can be used in
conjunction with activity data for attribution (Baccini etal., 2012). A
recent development is the use of satellite-based forest biomass esti-
mates (Saatchi etal., 2011) together with satellite land cover change
in the tropics to estimate ‘gross deforestation’ emissions (Harris etal.,
2012) or further combining it with FAO and other activity data to esti-
mate net fluxes from forest area change and forest management (Bac-
cini etal., 2012).
A detailed breakdown of the component fluxes in (Baccini etal., 2012)
is shown in Figure 11.8. Where there is temporary forest loss through
management, ‘gross’ forest emissions can be as high as for permanent
forest loss (deforestation), but are largely balanced by ‘gross’ uptake
in regrowing forest, so net emissions are small. When regrowth does
not balance removals, it leads to a degradation of forest carbon stocks.
In Baccini etal. (2012) this degradation was responsible for 15 % of
total net emissions from tropical forests (Houghton, 2013; Figure 11.8).
Huang and Asner (2010) estimated that forest degradation in the Ama-
zon, particularly from selective logging, is responsible for 15 – 19 %
higher C emissions than reported from deforestation alone. Pan etal.
827827
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�8 | Breakdown of mean annual CO
2
fluxes from deforestation and forest management in tropical countries (GtCO
2
/ yr). Pan etal. (2011) estimates are based on FAO
data and the Houghton bookkeeping model (Houghton, 2003). Baccini etal. (2012) estimates are based on satellite land cover change and biomass data with FAO data, and the
Houghton (2003) bookkeeping model, with the detailed breakdown of these results shown in Houghton, (2013). Harris etal. (2012) estimates are based on satellite land cover
change and biomass data.
2.35
0.30
0.84
0.31
1.65
0.55
0.55
2.97
2.97
2.97
-0.06 Afforestation
-0.06 Afforestation
-1.64
-0.54
-2.05
5.32
4.11
11.11
10.30
-5.79
-6.20
-8
-6
-4
-2
0
2
4
6
8
10
12
Pan et al. "Gross
Deforestation
Emissions" and
"Regrowth Forest"
1990s
Pan et al. "Gross
Deforestation
Emissions" and
"Regrowth Forest"
Uptake 2000-2007
Pan et al. "Land Use
Change Emission"
i.e. Net Flux 1990s
Pan et al. "Land Use
Change Emission"
i.e. Net Flux
2000-2007
Baccini et al. Gross
Fluxes 2000-2007
Baccini et al. Net
Fluxes 2000-2007
Harris et al. "Gross
Deforestation"
Flux 2000-2007
Fluxes (+ Emissions, - Uptake) [GtCO
2
/yr]
“Gross Deforestation Emissions”
“Regrowth Forest”
Net “Land Use Change Emissions”
Shifting Cultivation
Fuelwood Harvest
Industrial Logging
Afforestation
Deforestation
Soils
828828
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
(2011) separated ‘gross emissions’ from deforestation and forest man-
agement on the one hand, from uptake in regrowing vegetation on the
other. Deforestation emissions decline from the 1990s to 2000 – 2007,
and uptake in regrowing vegetation increases, both contributing to the
decline in net tropical CO
2
emissions.
Satellite fire data have also been used to estimate FOLU emissions
(van der Werf etal., 2006); Box 11.2). The EDGAR
5
database ‘Land-
5
http: / / edgar.jrc.ec.europa.eu / index.php
Use Change and Forestry’ emissions are based on forest and peat
fire data from GFED 2.0 (van der Werf etal., 2006), with additional
estimates of post-burn decay, and emissions from degraded peat-
lands based on (Joosten, 2010); Box 11.1). However, GFED 2.0 fire
data does not distinguish anthropogenic AFOLU fires from other fires,
unlike GFED 3.0 (van der Werf etal., 2010); Box 11.2). Fire data also
does not capture significant additional AFOLU fluxes due to land
clearing and forest management that is by harvest rather than fire
(e. g., deforestation activities outside the humid tropics) or regrowth
following clearing. Thus EDGAR data only approximates the FOLU
flux.
Box 11�1 | AFOLU GHG emissions from peatlands and mangroves
Undisturbed waterlogged peatlands (organic soils) store a large
amount of carbon and act as small net sinks (Hooijer etal., 2010).
Drainage of peatlands for agriculture and forestry results in a
rapid increase in decomposition rates, leading to increased emis-
sions of CO
2
, and N
2
O, and vulnerability to further GHG emissions
through fire. The FAO emissions database estimates globally
250,000 km
2
of drained organic soils under cropland and grass-
land, with total GHG emissions of 0.9 GtCO
2
eq / yr in 2010 — with
the largest contributions from Asia (0.44 GtCO
2
eq / yr) and Europe
(0.18 GtCO
2
eq / yr) (FAOSTAT, 2013). Joosten (2010), estimated
that there are >500,000 km
2
of drained peatlands in the world
including under forests, with CO
2
emissions having increased
from 1.06 GtCO
2
/ yr in 1990 to 1.30 GtCO
2
/ yr in 2008, despite a
decreasing trend in Annex I countries, from 0.65 to 0.49 GtCO
2
/ yr,
primarily due to natural and artificial rewetting of peatlands.
In Southeast Asia, CO
2
emissions from drained peatlands in
2006 were 0.61±0.25 GtCO
2
/ yr (Hooijer etal., 2010). Satel-
lite estimates indicate that peat fires in equatorial Asia emitted
on average 0.39 GtCO
2
eq / yr over the period 1997 – 2009 (van
der Werf etal., 2010), but only 0.2 GtCO
2
eq / yr over the period
1998 – 2009. This lower figure is consistent with recent indepen-
dent FAO estimates over the same period and region. Mangrove
ecosystems have declined in area by 20 % (36 Mha) since 1980,
although the rate of loss has been slowing in recent years, reflect-
ing an increased awareness of the value of these ecosystems (FAO,
2007). A recent study estimated that deforestation of mangroves
released 0.07 to 0.42 GtCO
2
/ yr (Donato etal., 2011).
Box 11�2� | AFOLU GHG emissions from fires
Burning vegetation releases CO
2
, CH
4
, N
2
O, ozone-precursors
and aerosols (including black carbon) to the atmosphere. When
vegetation regrows after a fire, it takes up CO
2
and nitrogen.
Anthropogenic land management or land conversion fire activities
leading to permanent clearance or increasing levels of disturbance
result in net emissions to the atmosphere over time. Satellite-
detection of fire occurrence and persistence has been used to
estimate fire emissions (e. g., GFED 2.0 database; (van der Werf
etal., 2006). It is hard to separate the causes of fire as natural
or anthropogenic, especially as the drivers are often combined.
An update of the GFED methodology now distinguishes FOLU
deforestation and degradation fires from other management fires
(GFED 3.0 database; (van der Werf etal., 2010); Figure 11.6). The
estimated tropical deforestation and degradation fire emissions
were 1.39 GtCO
2
eq / yr during 1997 to 2009 (total carbon including
CO
2
, CH
4
, CO and black carbon), 20 % of all fire emissions. Carbon
dioxide FOLU fire emissions are already included as part of the
global models results such as those presented in Table 1.1 and
Figures 11.6 and 11.7. According to (FAOSTAT, 2013)
1
, in 2010 the
non-CO
2
component of deforestation and forest degradation fires
totalled 0.1 GtCO
2
eq / yr, with forest management and peatland
fires (Box 11.1) responsible for an additional 0.2 GtCO
2
eq / yr.
1
FOLU GHG emissions by fires include, as per IPCC GHG guidelines, all fires
on managed land. Most current FOLU estimates are limited however to fires
associated to deforestation, forest management and peat fires. Emissions
from prescribed burning of savannahs are reported under agriculture. Both
CO
2
and non-CO
2
emissions are accounted under these FOLU components,
but CO
2
emissions dominate.
829829
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
FAO estimates AFOLU GHG emissions (FAOSTAT, 2013)
6
based on
IPCC Tier 1 methodology
7
. With reference to the decade 2001 – 2010,
total GHG FOLU emissions were 3.2 GtCO
2
eq / yr including defor-
estation (3.8 GtCO
2
eq / yr), forest degradation and forest manage-
ment (– 1.8 GtCO
2
eq / yr), biomass fires including peatland fires
(0.3 GtCO
2
eq / yr), and drained peatlands (0.9 GtCO
2
eq / yr). The FAO
estimated total mean net GHG FOLU flux to the atmosphere decreased
from 3.9 GtCO
2
eq / yr in 1991 – 2000 to 3.2 GtCO
2
eq / yr in 2001 – 2010
(FAOSTAT, 2013).
11.3 Mitigation technology
options and practices,
and behavioural aspects
Greenhouse gases can be reduced by supply-side mitigation options (i. e.,
by reducing GHG emissions per unit of land / animal, or per unit of prod-
uct), or by demand-side options (e. g., by changing demand for food and
fibre products, reducing waste). In AR4, the forestry chapter (Nabuurs
etal., 2007) considered some demand-side options, but the agriculture
chapter focused on supply-side options only (Nabuurs etal., 2007; Smith
etal., 2007). In this section, we discuss only supply-side options (Section
11.3.1). Demand-side options are discussed in Section 11.4.
Mitigation activities in the AFOLU sector can reduce climate forcing in
different ways:
• Reductions in CH
4
or N
2
O emissions from croplands, grazing lands,
and livestock.
• Conservation of existing carbon stocks, e. g., conservation of forest
biomass, peatlands, and soil carbon that would otherwise be lost.
• Reductions of carbon losses from biota and soils, e. g., through
management changes within the same land-use type (e. g., reduc-
ing soil carbon loss by switching from tillage to no-till cropping) or
by reducing losses of carbon-rich ecosystems, e. g., reduced defor-
estation, rewetting of drained peatlands.
• Enhancement of carbon sequestration in soils, biota, and long-
lived products through increases in the area of carbon-rich eco-
systems such as forests (afforestation, reforestation), increased
carbon storage per unit area, e. g., increased stocking density in
6
http: / / faostat.fao.org /
7
Parties to the UNFCCC report net GHG emissions according to IPCC method-
ologies (IPCC, 2003, 2006). Reporting is based on a range of methods and
approaches dependent on available data and national capacities, from default
equations and emission factors applicable to global or regional cases and assum-
ing instantaneous emissions of all carbon that will be eventually lost from the
system following human action (Tier 1) to more complex approaches such as
model-based spatial analyses (Tier 3).
forests, carbon sequestration in soils, and wood use in construction
activities.
• Changes in albedo resulting from land-use and land-cover change
that increase reflection of visible light.
• Provision of products with low GHG emissions that can replace
products with higher GHG emissions for delivering the same ser-
vice (e. g., replacement of concrete and steel in buildings with
wood, some bioenergy options; see Section 11.13).
• Reductions of direct (e. g., agricultural machinery, pumps, fishing
craft) or indirect (e. g., production of fertilizers, emissions result-
ing from fossil energy use in agriculture, fisheries, aquaculture, and
forestry or from production of inputs); though indirect emission
reductions are accounted for in the energy end-use sectors (build-
ings, industry, energy generation, transport) so are not discussed
further in detail in this chapter.
11�3�1 Supply-side mitigation options
Mitigation potentials for agricultural mitigation options were given on
a ‘per-area’ and ‘per-animal’ in AR4 (Nabuurs etal., 2007; Smith etal.,
2007). All options are summarized in Table 11.2 with impacts on each
GHG noted, and a categorization of technical mitigation potential,
ease of implementation, and availability (supported by recent refer-
ences). These mitigation options can have additive positive effects, but
can also work in opposition, e. g., zero tillage can reduce the effective-
ness of residue incorporation. Most mitigation options were described
in detail in AR4 so are not described further here; additional practices
that were not considered in AR4, i. e., biochar, reduced emissions from
aquaculture, and bioenergy are described in Boxes 11.3, 11.4, and
11.5, respectively.
In addition to the per-area and per-animal mitigation options described
in AR4, more attention has recently been paid to options that reduce
emissions intensity by improving the efficiency of production (i. e., less
GHG emissions per unit of agricultural product; (Burney etal., 2010;
Bennetzen etal., 2012); a reduction in emissions intensity has long
been a feature of agricultural emissions reduction and is one compo-
nent of a process more broadly referred to as sustainable intensifica-
tion (Tilman etal., 2009; Godfray et al., 2010; Smith, 2013; Garnett
etal., 2013). This process does not rely on reducing inputs per se, but
relies on the implementation of new practices that result in an increase
in product output that is larger than any associated increase in emis-
sions (Smith, 2013). Even though per-area emissions could increase,
there is a net benefit since less land is required for production of the
same quantity of product. The scope to reduce emissions intensity
appears considerable since there are very large differences in emis-
sions intensity between different regions of the world (Herrero etal.,
2013). Sustainable intensification is discussed further in Section 11.4.2,
and trends in changes in emissions intensity are discussed further in
Section 11.6.
830830
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Table 11�2 | Summary of supply-side mitigation options in the AFOLU sector. Technical Mitigation Potential: Area = (tCO
2
eq / ha) / yr; Animal = percent reduction of enteric emissions.
Low = < 1; <5 % (white), Medium = 1 – 10; 5 – 15 % (light grey), High = >10, >15 % (grey); Ease of Implementation (acceptance or adoption by land manager): Difficult (white),
Medium (light grey), Easy, i. e., universal applicability (grey); Timescale for Implementation: Long-term (at research and development stage; white), Mid-term (trials in place, within
5 – 10 years; light grey), Immediate (technology available now, grey).
Categories Practices and Impacts
Technical Mitiga-
tion Potential
Ease of Imple-
mentation
Timescale for
implementation
References
Forestry
Reducing deforestation
C: Conservation of existing C pools in forest vegetation and soil by controlling deforestation
protecting forest in reserves, and controlling other anthropogenic disturbances such as
fire and pest outbreaks. Reducing slash and burn agriculture, reducing forest fires.
1
CH
4,
N
2
O: Protection of peatland forest, reduction of wildfires. 2
Afforestation / Reforestation
C: Improved biomass stocks by planting trees on non-forested agricultural lands.
This can include either monocultures or mixed species plantings. These activities may
also provide a range of other social, economic, and environmental benefits.
3, 4, 5
Forest management
C: Management of forests for sustainable timber production including
extending rotation cycles, reducing damage to remaining trees, reducing
logging waste, implementing soil conservation practices, fertilization, and using
wood in a more efficient way, sustainable extortion of wood energy
6, 7, 8, 9
CH
4,
N
2
O: Wildfire behaviour modification. 10, 11, 12
Forest restoration
C: Protecting secondary forests and other degraded forests whose biomass and soil
C densities are less than their maximum value and allowing them to sequester C by
natural or artificial regeneration, rehabilitation of degraded lands, long-term fallows.
13, 14
CH
4,
N
2
O
: Wildfire behaviour modification.
Land-based agriculture
Cropland management
Croplands — plant
management
C: High input carbon practices, e. g., improved crop varieties, crop rotation, use
of cover crops, perennial cropping systems, agricultural biotechnology.
15, 16, 17
N
2
O: Improved N use efficiency. 18
Croplands — nutrient
management
C: Fertilizer input to increase yields and residue inputs
(especially important in low-yielding agriculture).
19, 20
N
2
O: Changing N fertilizer application rate, fertilizer type,
timing, precision application, inhibitors.
21, 22, 23, 24, 25, 105, 106
Croplands — tillage / residues
management
C: Reduced tillage intensity; residue retention. 17, 24, 26, 27
N
2
O: 28, 96, 97
CH
4:
96
Croplands — water
management
C: Improved water availability in cropland including water harvesting and application. 29
CH
4
: Decomposition of plant residues.
N
2
O: Drainage management to reduce emissions, reduce N runoff leaching.
Croplands — rice management
C: Straw retention. 30
CH
4
: Water management, mid-season paddy drainage. 31, 32, 98
N
2
O: Water management, N fertilizer application rate,
fertilizer type, timing, precision application.
32, 98, 99
Rewet peatlands drained
for agriculture
C: Ongoing CO
2
emissions from reduced drainage (but CH
4
emissions may increase). 33
Croplands — set-aside and LUC
C: Replanting to native grasses and trees. Increase C sequestration. 34, 35, 36, 37, 38
N
2
O: N inputs decreased resulting in reduced N
2
O.
Biochar application
C: Soil amendment to increase biomass productivity, and sequester C
(biochar was not covered in AR4 so is described in Box 11.3).
39, 40, 41
N
2
O: Reduced N inputs will reduce emissions. 39, 42
⇒
831831
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Categories Practices and Impacts
Technical Mitiga-
tion Potential
Ease of Imple-
mentation
Timescale for
implementation
References
Grazing Land Management
Grazing lands — plant
management
C: Improved grass varieties / sward composition, e. g., deep rooting grasses,
increased productivity, and nutrient management. Appropriate stocking densities,
carrying capacity, fodder banks, and improved grazing management.
43, 44, 45
N
2
O 46
Grazing lands — animal
management
C: Appropriate stocking densities, carrying capacity management, fodder banks and
improved grazing management, fodder production, and fodder diversification.
43, 47
CH
4
N
2
O: Stocking density, animal waste management.
Grazing land — fire
management
C: Improved use of fire for sustainable grassland management.
Fire prevention and improved prescribed burning.
Revegetation
Revegetation
C: The establishment of vegetation that does not meet the definitions
of afforestation and reforestation (e. g., Atriplex spp.).
48
CH
4
: Increased grazing by ruminants may increase net emissions.
N
2
O: Reduced N inputs will reduce emissions.
Other
Organic soils — restoration
C: Soil carbon restoration on peatlands; and avoided net soil
carbon emissions using improved land management.
49
CH
4
: May increase.
Degraded soils — restoration
Land reclamation (afforestation, soil fertility management, water
conservation soil nutrients enhancement, improved fallow).
100, 101, 102, 103, 104
Biosolid applications
C: Use of animal manures and other biosolids for improved management
of nitrogen; integrated livestock agriculture techniques.
26
N
2
O
Livestock
Livestock — feeding
CH
4
: Improved feed and dietary additives to reduce emissions from
enteric fermentation; including improved forage, dietary additives
(bioactive compounds, fats), ionophores / antibiotics, propionate
enhancers, archaea inhibitors, nitrate and sulphate supplements.
50, 51, 52, 53, 54,
55, 56, 57, 58, 59
Livestock — breeding and
other long-term management
CH
4
: Improved breeds with higher productivity (so lower emissions per unit
of product) or with reduced emissions from enteric fermentation; microbial
technology such as archaeal vaccines, methanotrophs, acetogens, defaunation
of the rumen, bacteriophages and probiotics; improved fertility.
54, 55, 56, 58, 60, 61,
62, 63, 64, 65, 66,
67, 68, 69, 70, 71
Manure management
CH
4
: Manipulate bedding and storage conditions, anaerobic
digesters; biofilters, dietary additives.
56, 58, 72, 73
N
2
O: Manipulate livestock diets to reduce N excreta, soil applied and animal
fed nitrification inhibitors, urease inhibitors, fertilizer type, rate and timing,
manipulate manure application practices, grazing management.
56, 58, 72, 74, 75, 76, 77, 78
Integrated systems
Agroforestry (including
agropastoral and
agrosilvopastoral systems)
C: Mixed production systems can increase land productivity and
efficiency in the use of water and other resources and protect against
soil erosion as well as serve carbon sequestration objectives.
79, 80, 81, 82, 83,
84, 85, 86, 87, 88
N
2
O: Reduced N inputs will reduce emissions.
Other mixed biomass
production systems
C: Mixed production systems such as double-cropping systems and mixed crop-livestock
systems can increase land productivity and efficiency in the use of water and other resources
as well as serve carbon sequestration objectives. Perennial grasses (e. g., bamboo) can in
the same way as woody plants be cultivated in shelter belts and riparian zones / buffer strips
provide environmental services and supports C sequestration and biomass production.
82, 89, 90
N
2
O: Reduced N inputs will reduce emissions.
⇒
832832
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11�3�2 Mitigation effectiveness
(non- permanence: saturation,
human and natural impacts,
displacement)
Since carbon sequestration in soil and vegetation and the retention of
existing carbon stocks forms a significant component of the mitigation
potential in the AFOLU sector, this section considers the factors affect-
ing this strategy compared to avoided GHG emissions.
Non-permanence / reversibility. Reversals are the release of previously
sequestered carbon, which negates some or all of the benefits from
sequestration that has occurred in previous years. This issue is some-
times referred to as ‘non-permanence’ (Smith, 2005). Various types of
carbon sinks (e. g., afforestation / reforestation, agricultural soil C) have
an inherent risk of future reversals.
Certain types of mitigation activities (e. g., avoided N
2
O from fertilizer,
emission reductions from changed diet patterns or reduced food-chain
losses) are effectively permanent since the emissions, once avoided,
cannot be re-emitted. The same applies to the use of bioenergy to dis-
place fossil-fuel emissions (Section 11.13) or the use of biomass-based
products to displace more emissions-intensive products (e. g., wood in
place of concrete or steel) in construction.
Reversals may be caused by natural events that affect yields / growth.
In some cases (e. g., frost damage, pest infestation, or fire; (Reichstein
etal., 2013), these effects may be temporary or short-term. Although
these events will affect the annual increment of C sequestration, they
may not result in a permanent decline in carbon stocks. In other cases,
such as stand replacing forest fires, insect or disease outbreaks, or
drought, the declines may be more profound. Although a substantial
loss of above-ground stored carbon could occur following a wildfire,
whether this represents a loss depends on what happens following the
fire and whether the forest recovers, or changes to a lower carbon-
storage state (see Box 11.2). Similarly, some systems are naturally
adapted to fire and carbon stocks will recover following fire, whereas
in other cases the fire results in a change to a system with a lower
carbon stock (e. g., Brown and Johnstone, 2011). For a period of time
following fire (or other disruptive event), the stock of carbon will be
less than that before the fire. Similarly, emissions of non-CO
2
gases
also need to be considered.
The permanence of the AFOLU carbon stock relates to the longevity of
the stock, i. e., how long the increased carbon stock remains in the soil
or vegetation. This is linked to consideration of the reversibility of the
increased carbon stock (Smith, 2005), as discussed in Section 11.5.2.
Saturation. Substitution of fossil fuel and material with biomass, and
energy-intensive building materials with wood can continue in perpe-
tuity. In contrast, it is often considered that carbon sequestration in
soils (Guldea etal., 2008) or vegetation cannot continue indefinitely.
The carbon stored in soils and vegetation reaches a new equilibrium
(as the trees mature or as the soil carbon stock saturates). As the
Categories Practices and Impacts
Technical Mitiga-
tion Potential
Ease of Imple-
mentation
Timescale for
implementation
References
Integration of biomass
production with subsequent
processing in food and
bioenergy sectors
C: Integrating feedstock production with conversion, typically producing animal feed
that can reduce demand for cultivated feed such as soy and corn and can also reduce
grazing requirements. Using agricultural and forestry residues for energy production.
91, 92, 93, 94, 95
N
2
O: Reduced N inputs will reduce emissions.
Bioenergy (see Box 11�5 and Section 11�13)
1
Van Bodegom etal., 2009;
2
Malmsheimer etal., 2008;
3
Reyer etal., 2009;
4
Sochacki etal., 2012;
5
IPCC, 2000;
6
DeFries and Rosenzweig, 2010;
7
Takimoto etal., 2008;
8
Masera
etal., 2003;
9
Silver etal., 2000;
10
Dezzeo etal., 2005;
11
Ito, 2005;
12
Sow etal., 2013;
13
Reyer etal., 2009;
14
Palm etal., 2004;
15
Godfray etal., 2010;
16
Burney etal., 2010;
17
Conant
etal., 2007;
18
Huang and Tang, 2010;
19
Lemke etal., 2010;
20
Eagle and Olander, 2012;
21
Snyder etal., 2007;
22
Akiyama etal., 2010;
23
Barton etal., 2011;
24
Powlson etal., 2011;
25
van Kessel etal., 2013;
26
Farage etal., 2007;
27
Smith, 2012;
28
Abdalla etal., 2013;
29
Bayala etal., 2008;
30
Yagi etal., 1997;
31
Tyagi etal., 2010;
32
Feng etal., 2013;
33
Lohila
etal., 2004;
34
Seaquist etal., 2008;
35
Mbow, 2010;
36
Assogbadjo etal., 2012;
37
Laganiere etal., 2010;
38
Bayala etal., 2011;
39
Singh etal., 2010;
40
Woolf etal., 2010;
41
Lehmann
etal., 2003;
42
Taghizadeh-Toosi etal., 2011;
43
Franzluebbers and Stuedemann, 2009;
44
Follett and Reed, 2010;
45
McSherry and Ritchie, 2013;
46
Saggar etal., 2004;
47
Thornton
and Herrero, 2010;
48
Harper etal., 2007;
49
Smith and Wollenberg, 2012;
50
Beauchemin etal., 2008;
51
Beauchemin etal., 2009;
52
Martin etal., 2010;
53
Grainger and Beauchemin,
2011;
54
Clark, 2013;
55
Cottle etal., 2011;
56
Eckard etal., 2010;
57
Sauvant and Giger-Reverdin, 2007;
58
Hristov etal., 2013;
59
Bryan etal., 2013;
60
Attwood and McSweeney, 2008;
61
Attwood etal., 2011;
62
Hegarty etal., 2007;
63
Hook etal., 2010;
64
Janssen and Kirs, 2008;
65
Martin etal., 2010;
66
Morgavi etal., 2008;
67
Morgavi etal., 2010;
68
Place and Mit-
loehner, 2010;
69
Waghorn and Hegarty, 2011;
70
Wright and Klieve, 2011;
71
Yan etal., 2010
72
Chadwick etal., 2011;
73
Petersen and Sommer, 2011;
74
de Klein etal., 2010;
75
de
Klein and Eckard, 2008;
76
Dijkstra etal., 2011
77
Schils etal., 2013;
78
VanderZaag etal., 2011;
79
Oke and Odebiyi, 2007;
80
Rice, 2008;
81
Takimoto etal., 2008;
82
Lott etal., 2009;
83
Sood and Mitchell, 2011;
84
Assogbadjo etal., 2012;
85
Wollenberg etal., 2012;
86
Semroc etal., 2012;
87
Souza etal. 2012;
88
Luedeling and Neufeldt, 2012;
89
Heggenstaller etal.,
2008;
90
Herrero etal., 2010;
91
Dale etal., 2009;
92
Dale etal., 2010;
93
Sparovek etal. 2007;
94
Sood and Mitchell, 2011;
95
Vermeulen etal., 2012;
96
Metay etal., 2007 ;
97
Rochette,
2008;
98
Ma etal., 2009;
99
Yao etal., 2010;
100
Arnalds, 2004;
101
Batjes, 2004;
102
Hardner etal., 2000;
103
May etal., 2004;
104
Zhao etal., 2005;
105
Huang and Tang, 2010;
106
Kim
etal., 2013.
833833
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Box 11�3 | Biochar
This box summarizes the mitigation potential for biochar tech-
nologies, which were not considered in AR4. Biomass C stabiliza-
tion could be combined with (or substitute) bioenergy capture
as part of a land-based mitigation strategy (Lehmann, 2007).
Heating biomass with air excluded (pyrolysis) generates energy-
containing volatiles and gases. Hydrogen and O are preferentially
eliminated, creating a stable (biologically recalcitrant) C-rich co-
product (char). By adding char to soil as ‘biochar’ a system can be
established that may have a higher carbon abatement than typical
bioenergy alternatives (Woolf etal., 2010). The gain is probably
highest where efficient bioenergy is constrained by a remote, sea-
sonal, or diffuse biomass resource (Shackley etal., 2012). The ben-
efit of pyrolysis-biochar systems (PBS) is increased considerably if
allowance is made for the indirect effects of using biochar via the
soil. These effects include increased crop and biomass production
and decreased N
2
O and CH
4
emissions. Realizing the mitigation
potential for biochar technologies will be constrained by the need
for sustainable feedstock acquisition, competing biomass use
options are an important influence of the production process on
biochar properties. Considering sustainable feedstock production
and targeting biochar deployment on less fertile land, Woolf etal.
(2010) calculated maximum global abatement of 6.6 GtCO
2
eq / yr
from 2.27 Gt biomass C. Allowing for competition for virgin non-
waste biomass the value was lower (3.67 GtCO
2
eq / yr from 1.01
Gt biomass C), accruing 240 – 480 GtCO
2
eq abatement within 100
years.
Meta-analysis shows that in experimental situations crop produc-
tivity has, on average, been enhanced by circa 15 % near-term,
but with a wide range of effects (Jeffery etal., 2011; Biederman
and Harpole, 2013). This range is probably explained by the nature
and extent of pre-existing soil constraints. The Woolf etal. (2010)
analysis accordingly assumed crop yield increases of 0 – 90 % (rela-
tive). Relaxing this assumption by one-half decreased projected
abatement by 10 %. Decreasing an assumed 25 % suppression on
soil N
2
O flux by the same proportion had a smaller impact. Ben-
eficial interactions of biochar and the soil N cycle are beginning
to be understood with effects on mineralization, nitrification,
denitrification, immobilization and adsorption persisting at least
for days and months after biochar addition (Nelissen etal., 2012;
Clough etal., 2013). Although the often large suppression of soil
N
2
O flux observed under laboratory conditions can be increasingly
explained (Cayuela etal., 2013), this effect is not yet predictable
and there has been only limited validation of N
2
O suppression by
biochar in planted field soils (Liu etal., 2012; Van Zwieten etal.,
2013) or over longer timeframes (Spokas, 2013). The potential to
gain enhanced mitigation using biochar by tackling gaseous emis-
sions from manures and fertilizers before and after application to
soil are less well-explored (Steiner etal., 2010; Angst etal., 2013).
The abatement potential for PBS remains most sensitive to the
absolute stability of the C stored in biochar. Estimates of ‘half-
life’ have been inferred from wildfire charcoal (Lehmann, 2007)
or extrapolated from direct short-term observation. These give
values that range from <50 to >10,000 years, but predominantly
between 100 – 1000 years (Singh etal., 2012; Spokas, 2013).
Nonetheless, the assumption made by Woolf etal. (2010) for the
proportion of biochar C that is stable long-term (85 %) is subject
to refinement and field validation.
Demonstration of the equipment and infrastructure required for
effective use of energy products from biomass pyrolysis is still
limited, especially across large and small unit scales. Preliminary
analyses shows, however, that the break-even cost of biochar
production is likely to be location- and feedstock-specific (Shack-
ley etal., 2012; Field etal., 2013). Until economic incentives are
established for the stabilization of C, biochar adoption will depend
on predictable, positive effects on crop production. This requires
more research on the use of biochar as a regular low-dose soil
input, rather than single applications at rates >10t / ha, which
have so far been the norm (Sohi, 2012). Product standards are
also required, to ensure that biochar is produced in a way that
does not create or conserve problematic concentrations of toxic
contaminants, and to support regulated deployment strategies (IBI
Biochar, 2012; Downie etal., 2012).
soils / vegetation approach the new equilibrium, the annual removal
(sometimes referred to as the sink strength) decreases until it becomes
zero at equilibrium. This process is called saturation (Smith, 2005;
Körner, 2006, 2009; Johnston etal., 2009b) , and the uncertainty asso-
ciated with saturation has been estimated (Kim and McCarl, 2009). An
alternative view is that saturation does not occur, with studies from
old-growth forests, for example, showing that they can continue to
sequester C in soil and dead organic matter even if net living biomass
increment is near zero (e. g., Luyssaert et al., 2008). Peatlands are
unlikely to saturate in carbon storage, but the rate of C uptake may be
very slow (see Box 11.1).
Human and natural impacts. Soil and vegetation carbon sinks can be
impacted upon by direct human-induced, indirect human-induced, and
natural changes (Smith, 2005). All of the mitigation practices discussed
in Section 11.3.1 arise from direct human-induced impacts (deliberate
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management). Both sink processes and carbon stocks can be affected
by natural factors such as soil and hydrological conditions. Indirect
human-induced changes can impact carbon sinks and are influenced by
human activity, but are not directly related to the management of that
piece of land; examples include climate change and atmospheric nitro-
gen deposition. For some tree species, rising concentrations of tropo-
spheric ozone caused by human activities may counteract the effects of
increased atmospheric CO
2
or N deposition on tree growth (Sitch etal.,
2008; Matyssek etal., 2010). Natural changes that threaten to impact
the efficacy of mitigation measures are discussed in Section 11.5.
Displacement / leakage. Displacement / leakage arises from a change in
land use or land management that causes a positive or negative change
in emissions elsewhere. This can occur within or across national bound-
aries, and the efficacy of mitigation practices must consider the leak-
age implications. For example, if reducing emissions in one place leads
to increased emissions elsewhere, no net reduction occurs; the emis-
sions are simply displaced (Powlson etal., 2011; Kastner etal., 2011b;
a). However, this assumes a one-to-one correspondence. Murray etal.
(2004) estimated the leakage from different forest carbon programmes
and this varied from <10 % to >90 % depending on the nature of the
activity. West etal. (2010a) examined the impact of displaced activities
in different geographic contexts; for example, land clearing in the trop-
ics will release twice the carbon, but only produce half the crop yield
of temperate areas. Indirect land-use change is an important compo-
nent to consider for displaced emissions and assessments of this are an
emerging area. Indirect land-use change is discussed further in Section
11.4 and in relation to bioenergy in Section 11.13.
The timing of mitigation benefits from actions (e. g., bioenergy, forest
management, forest products use / storage) can vary as a result both of
the nature of the activity itself (e. g., from the temporal pattern of soil
or forest sequestration compared to biomass substitution), and rates
of adoption. Timing thus needs to be considered when judging the
effectiveness of a mitigation action. Cherubini etal. (2012) modelled
the impact of timing of benefits for three different wood applications
(fuel, non-structural panels, and housing construction materials) and
showed that the options provide mitigation over different timeframes,
and thus have different impacts on CO
2
concentrations and radiative
forcing. The temporal pattern of emissions and removals is especially
important in mitigating emissions of short-lived gases through carbon
sequestration (Lauder etal., 2013).
Box 11�4 | Aquaculture
Aquaculture is defined as the farming of fish, shellfish, and
aquatic plants (Hu etal., 2013). Although it is an ancient practice
in some parts of world, this sector of the food system is growing
rapidly. Since the mid-1970s, total aquaculture production has
grown at an average rate of 8.3 % per year (1970 – 2008; (Hu
etal., 2013). The estimated aquaculture production in 2009 was
55.10 Mt, which accounts for approximately 47 % of all the fish
consumed by humans (Hu etal., 2013). The sector is diverse, being
dominated by shellfish and herbivorous and omnivorous pond
fish, either entirely or partly utilizing natural productivity, but
globalizing trade and favourable economic conditions are driving
intensive farming at larger scales (Bostock etal., 2010). Potential
impacts of aquaculture, in terms emissions of N
2
O, have recently
been considered (Williams and Crutzen, 2010; Hu etal., 2012).
Global N
2
O emissions from aquaculture in 2009 were estimated to
be 93 ktN
2
O-N (~43 MtCO
2
eq), and will increase to 383 ktN
2
O-N
(~178 MtCO
2
eq) by 2030, which could account for 5.7 % of
anthropogenic N
2
O-N emissions if aquaculture continues to grow
at the present growth rate (~7.1 % / yr; Hu etal., 2012).
Some studies have focused on rice-fish farming, which is a
practice associated with wet rice cultivation in Southeast Asia,
providing protein, especially for subsistence-oriented farmers
(Bhattacharyya etal., 2013). Cultivation of fish along with rice
increases emissions of CH
4
(Frei etal., 2007; Bhattacharyya etal.,
2013), but decreases N
2
O emissions, irrespective of the fish spe-
cies used (Datta etal., 2009; Bhattacharyya etal., 2013). Although
rice-fish farming systems might be globally important in terms
of climate change, they are also relevant for local economy, food
security, and efficient water use (shared water), which makes
it difficult to design appropriate mitigation measures, because
of the tradeoffs between mitigation measures and rice and fish
production (Datta etal., 2009; Bhattacharyya etal., 2013). Feeding
rate and dissolved oxygen (DO) concentration could affect N
2
O
emissions from aquaculture systems significantly, and nitrifica-
tion and denitrification processes were equally responsible for the
emissions of N
2
O in these systems. Measures to control N
2
O from
aquaculture are described by Hu etal. (2012), and include the
maintenance of optimal operating conditions of the system, such
as appropriate pH and temperature, sufficient DO and good qual-
ity feed. Additionally, two potential ways to minimize N
2
O emis-
sions from aquaculture systems include ‘Aquaponic Aquaculture’
(polyculture consisting of fish tanks (aquaculture) and plants that
are cultivated in the same water cycle (hydroponic)), and Bioflocs
Technology (BFT) Aquaculture (which involves the development
and control of heterotrophic bacteria in flocs within the fish
culture component), where the growth of heterotrophic bacteria is
stimulated, leading to nitrogen uptake (Hu etal., 2012).
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Box 11�5 | Bioenergy
Bioenergy deployment offers significant potential for climate
change mitigation, but also carries considerable risks. The SRREN
(IPCC, 2011) suggested potential bioenergy deployment levels to
be between 100 – 300 EJ. This assessment agrees on a technical
bioenergy potential of around 100 EJ, and possibly 300 EJ and
higher. Integrated models project between 15 – 245 EJ / yr deploy-
ment in 2050, excluding traditional bioenergy. Achieving high
deployment levels would require, amongst others, extensive use
of agricultural residues and second-generation biofuels to miti-
gate adverse impacts on land use and food production, and the
co-processing of biomass with coal or natural gas with carbon
dioxide capture and storage (CCS) to produce low net GHG-
emitting transportation fuels and / or electricity. Integration of
crucial sectoral research (albedo effects, evaporation, counterfac-
tual land carbon sink assumptions) into transformation pathways
research, and exploration of risks of imperfect policy settings
(for example, in absence of a global CO
2
price on land carbon)
is subject of further research (Sections 11.9, 11.13.2, 11.13.4).
Small-scale bioenergy systems aimed at meeting rural energy
needs synergistically provide mitigation and energy access
benefits. Decentralized deployment of biomass for energy, in
combination with improved cookstoves, biogas, and small-scale
biopower, could improve livelihoods and health of around 2.6
billion people. Both mitigation potential and sustainability hinges
crucially on the protection of land carbon (high-density carbon
ecosystems), careful fertilizer application, interaction with food
markets, and good land and water management. Sustainability
and livelihood concerns might constrain beneficial deployment of
dedicated biomass plantations to lower values (Sections 11.13.3,
11.13.5, 11.13.7).
Lifecycle assessments for bioenergy options demonstrate a
plethora of pathways, site-specific conditions and technologies
that produce a wide range of climate-relevant effects. Specifically,
LUC emissions, N
2
O emissions from soil and fertilizers, co-prod-
ucts, process design and process fuel use, end-use technology,
and reference system can all influence the total attributional
lifecycle emissions of bioenergy use. The large variance for specific
pathways points to the importance of management decisions in
reducing the lifecycle emissions of bioenergy use. The total mar-
ginal global warming impact of bioenergy can only be evaluated
in a comprehensive setting that also addresses equilibrium effects,
e. g., indirect land-use change (iLUC) emissions, actual fossil fuel
substitution, and other effects. Structural uncertainty in modelling
decisions renders such evaluation exercises uncertain. Available
data suggest a differentiation between options that offer low
lifecycle emissions under good land-use management (e. g., sug-
arcane, Miscanthus, and fast-growing tree species) and those that
are unlikely to contribute to climate change mitigation (e. g., corn
and soybean), pending new insights from more comprehensive
consequential analyses (Sections 8.7, 11.13.4).
Coupling bioenergy and CCS (BECCS) has attracted particular
attention since AR4 because it offers the prospect of negative
emissions. Until 2050, the economic potential is estimated to be
between 2 – 10 GtCO
2
per year. Some climate stabilization sce-
narios see considerable higher deployment towards the end of the
century, even in some 580 – 650 ppm scenarios, operating under
different time scales, socioeconomic assumptions, technology
portfolios, CO
2
prices, and interpreting BECCS as part of an overall
mitigation framework. Technological challenges and potential
risks of BECCS include those associated with the provision of the
biomass feedstock as well as with the capture, transport and long-
term underground storage of CO
2
. BECCS faces large challenges in
financing and currently no such plants have been built and tested
at scale (Sections 7.5.5, 7.9, 11.13.3).
Land demand and livelihoods are often affected by bioenergy
deployment. Land demand for bioenergy depends on (1) the share
of bioenergy derived from wastes and residues; (2) the extent to
which bioenergy production can be integrated with food and fibre
production, and conservation to minimize land-use competition;
(3) the extent to which bioenergy can be grown on areas with
little current production; and (4) the quantity of dedicated energy
crops and their yields. Considerations of tradeoffs with water,
land, and biodiversity are crucial to avoid adverse effects. The total
impact on livelihood and distributional consequences depends on
global market factors, impacting income and income-related food-
security, and site-specific factors such as land tenure and social
dimensions. The often site-specific effects of bioenergy deploy-
ment on livelihoods have not yet been comprehensively evaluated
(Section 11.13.7).
Additionality: Another consideration for gauging the effectiveness of
mitigation is determining whether the activity would have occurred
anyway, with this encompassed in the concept of ‘additionality’ (see
Glossary).
Impacts of climate change: An area of emerging activity is predicting
the likely impacts of climate change on mitigation potential, both in
terms of impacts on existing carbon stocks, but also on the rates of
carbon sequestration. This is discussed further in Section 11.5.
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Agriculture, Forestry and Other Land Use (AFOLU)
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Chapter 11
11.4 Infrastructure and
systemic perspectives
Only supply-side mitigation options are considered in Section 11.3.
In this section, we consider infrastructure and systemic perspec-
tives, which include potential demand-side mitigation options in the
AFOLU sector. Since infrastructure is a minor issue in AFOLU com-
pared to energy end-use sectors, this section focusses on systemic
perspectives.
11�4�1 Land: a complex, integrated system
Mitigation in the AFOLU sector is embedded in the complex interactions
between socioeconomic and natural factors simultaneously affecting
land systems (Turner etal., 2007). Land is used for a variety of purposes,
including housing and infrastructure (Chapter 12), production of goods
and services through agriculture, aquaculture and forestry, and absorp-
tion or deposition of wastes and emissions (Dunlap and Catton, Jr., 2002).
Agriculture and forestry are important for rural livelihoods and employ-
ment (Coelho etal., 2012), while aquaculture and fisheries can be region-
ally important (FAO, 2012). More than half of the planet’s total land area
Figure 11�9 | Global land use and biomass flows arising from human economic activity in 2000 from the cradle to the grave. Values in Gt dry matter biomass / yr. Figure source:
(Smith etal., 2013b). If a source reported biomass flows in energy units, the numbers were converted to dry matter assuming a gross energy value of 18.5MJ / kg. The difference
between inputs and outputs in the consumption compartment is assumed to be released to the atmosphere (respiration, combustion); small differences may result from rounding.
Note that data sources a) area: (Erb etal., 2007; Schneider etal., 2009; FAO, 2010) ; b) biomass flows: (Wirsenius, 2003; Sims etal., 2006; Krausmann etal., 2008; FAOSTAT, 2012;
Kummu etal., 2012) are incomplete; more research is needed to close data gaps between different statistical sources such as agricultural, forestry, and energy statistics (Section
11.11). ‘Unused forests’ are pristine forests not harvested or otherwise used.
Unused
Forests
LAND
WASTE/RESIDUES
LIVESTOCK SYSTEM
TRADE
CONSUMPTIONPROCESSING
Urban
Cropland
Grazing
Land
Unused
- Natural
- Regenerating
Crops and Residues
Grazing and Hay
Forestry Products
Fuelwood from Non-Forests
Animal Raw Products
Final Products
Waste Flows and Residues
Recycling
6.36
1.78
0.16
13 Mkm²
2 Mkm²
2 Mkm
2
34 Mkm²
12 Mkm²
30 Mkm²
11 Mkm²
26 Mkm²
1.28
0.26
2.04
0.99
0.37
3.87
1.94
0.43
0.75
0.14
1.19
0.43
0.28
0.14
0.47
0.12
0.14
0.18
0.5
?
?
0.23
0.61
0.26
0.22
1.52
0.61
2.35
2.18
0.79
Food
Intake
Energy
Materials
Vegetable
Animal
Forestry
Monogastrics
Pigs
Poultry
Ruminants
Cattle, Sheep,
Goats, etc.
Food
Bioenergy
Other
Industries
and Manu-
facturing
0.67
Values in
Gt Dry Matter Biomass/yr
Extensive
Grazing
Cropland Fallows
837837
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
(134 Mkm
2
) is used for urban and infrastructure land, agriculture, and for-
estry. Less than one quarter shows relatively minor signs of direct human
use (Erb etal., 2007; Ellis etal., 2010; Figure 11.9). Some of the latter
areas are inhabited by indigenous populations, which depend on the land
for the supply of vitally important resources (Read etal., 2010).
Land-use change is a pervasive driver of global environmental change
(Foley etal., 2005, 2011) . From 1950 to 2005, farmland (cropland plus
pasture) increased from 28 to 38 % of the global land area excluding
ice sheets and inland waters (Hurtt etal., 2011). The growth of farmland
area (+33 %) was lower than that of population, food production, and
gross domestic product (GDP) due to increases in yields and biomass
conversion efficiency (Krausmann etal., 2012). In the year 2000, almost
one quarter of the global terrestrial net primary production (one third
of the above-ground part) was ‘appropriated’ by humans. This means
that it was either lost because the net primary productivity (the bio-
mass production of green plants, net primary production, NPP) of agro-
ecosystems or urban areas was lower than that of the vegetation they
replaced or it was harvested for human purposes, destroyed during har-
vest or burned in human-induced fires (Imhoff etal., 2004; Haberl etal.,
2007). The fraction of terrestrial NPP appropriated by humans doubled
in the last century (Krausmann etal., 2013), exemplifying the increasing
human domination of terrestrial ecosystems (Ellis etal., 2010). Growth
trajectories of the use of food, energy, and other land-based resources,
as well as patterns of urbanization and infrastructure development are
influenced by increasing population and GDP, as well as the on-going
agrarian-industrial transition (Haberl etal., 2011b; Kastner etal., 2012).
Growing resource use and land demand for biodiversity conservation
and carbon sequestration (Soares-Filho etal., 2010), result in increas-
ing competition for land (Harvey and Pilgrim, 2011; Section 11.4.2).
Influencing ongoing transitions in resource use is a major challenge
(WBGU, 2011; Fischer-Kowalski, 2011). Changes in cities, e. g., in terms
of infrastructure, governance, and demand, can play a major role in
this respect (Seto etal., 2012b; Seitzinger etal., 2012; Chapter 12).
Many mitigation activities in the AFOLU sector affect land use or land
cover and, therefore, have socioeconomic as well as ecological con-
sequences, e. g., on food security, livelihoods, ecosystem services or
emissions (Sections 11.1; 11.4.5; 11.7). Feedbacks involved in imple-
menting mitigation in AFOLU may influence different, sometimes
conflicting, social, institutional, economic, and environmental goals
(Madlener etal., 2006). Climate change mitigation in the AFOLU sector
faces a complex set of interrelated challenges (Sections 11.4.5; 11.7):
• Full GHG impacts, including those from feedbacks (e. g., iLUC) or
leakage, are often difficult to determine (Searchinger etal., 2008).
• Feedbacks between GHG reduction and other important objectives
such as provision of livelihoods and sufficient food or the mainte-
nance of ecosystem services and biodiversity are not completely
understood.
• Maximizing synergies and minimizing negative effects involves
multi-dimensional optimization problems involving various social,
economic, and ecological criteria or conflicts of interest between
different social groups (Martinez-Alier, 2002).
• Changes in land use and ecosystems are scale-dependent and may
proceed at different speeds, or perhaps even move in different
directions, at different scales.
11�4�2 Mitigation in AFOLU — feedbacks with
land-use competition
Driven by economic and population growth, increased demand for food
and bioenergy as well as land demand for conservation and urbanization
(e. g., above-ground biomass carbon losses associated with land-clearing
from new urban areas in the pan-tropics are estimated to be 5 % of the
tropical deforestation and land-use change emissions, (Seto etal., 2012a;
Section 12.2), competition for land is expected to intensify (Smith etal.,
2010; Woods etal., 2010). Maximization of one output or service (e. g.,
crops) often excludes, or at least negatively affects, others (e. g., conser-
vation; (Phalan etal., 2011). Mitigation in the AFOLU sector may affect
land-use competition. Reduced demand for AFOLU products generally
decreases inputs (fertilizer, energy, machinery) and land demand. The
ecological feedbacks of demand-side options are mostly beneficial since
they reduce competition for land and water (Smith etal., 2013b).
Some supply-side options, though not all, may intensify competition
for land and other resources. Based on Figure 11.9 one may distinguish
three cases:
• Optimization of biomass-flow cascades; that is, increased use
of residues and by-products, recycling of biogenic materials and
energetic use of wastes (WBGU, 2009). Such options increase
resource use efficiency and may reduce competition, but there may
also be tradeoffs. For example, using crop residues for bioenergy
or roughage supply may leave less C and nutrients on cropland,
reduce soil quality and C storage in soils, and increase the risk of
losses of carbon through soil erosion. Residues are also often used
as forage, particularly in the tropics. Forest residues are currently
also used for other purposes, e. g., chipboard manufacture, pulp
and paper production (González-Estrada etal., 2008; Blanco-Can-
qui and Lal, 2009; Muller, 2009; Ceschia etal., 2010).
• Increases in yields of cropland (Burney etal., 2010; Foley etal.,
2011; Tilman etal., 2011; Mueller etal., 2012; Lobell etal., 2013),
grazing land or forestry and improved livestock feeding efficiency
(Steinfeld et al., 2010; Thornton and Herrero, 2010) can reduce
land competition if yield increases relative to any additional inputs
and the emission intensity (i. e., GHG emissions per unit of prod-
uct) decreases. This may result in tradeoffs with other ecological,
social, and economic costs (IAASTD, 2009) although these can to
some extent be mitigated if intensification is sustainable (Tilman
etal., 2011). Another caveat is that increases in yields may result in
rebound effects that increase consumption (Lambin and Meyfroidt,
2011; Erb, 2012) or provide incentives to farm more land (Matson
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Agriculture, Forestry and Other Land Use (AFOLU)
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and Vitousek, 2006), and hence may fail to spare land (Section
11.10).
• Land-demanding options reduce GHG emissions by harness-
ing the potential of the land for either C sequestration or growing
energy crops (including food crops used as feedstocks for bioenergy
production). These options result in competition for land (and some-
times other resources such as water) that may have substantial
social, economic, and ecological effects (positive or negative; (UNEP,
2009; WBGU, 2009; Chum etal., 2011; Coelho etal., 2012). Such
options may increase pressures on ecosystems (e. g., forests) and
GHG emissions related to direct and indirect LUC, contribute to price
increases of agricultural products, or negatively affect livelihoods
of rural populations. These possible impacts need to be balanced
against possible positive effects such as GHG reduction, improved
water quality (Townsend etal., 2012), restoration of degraded land
(Harper etal., 2007), biodiversity protection (Swingland etal., 2002),
and job creation (Chum etal., 2011; Coelho etal., 2012).
Therefore, an integrated energy / agriculture / land-use approach for mit-
igation in AFOLU can help to optimize synergies and mitigate negative
effects (Popp etal., 2011; Smith, 2012; Creutzig etal., 2012a; Smith
etal., 2013b).
11�4�3 Demand-side options for reducing GHG
emissions from AFOLU
Some changes in demand for food and fibre can reduce GHG emissions
in the production chain (Table 11.3) through (i) a switch to the con-
sumption of products with higher GHG emissions in the process chain to
products with lower GHG emissions and (ii) by making land available for
other GHG reduction activities e. g., afforestation or bioenergy (Section
11.4.4). Food demand change is a sensitive issue due to the prevalence
of hunger, malnutrition, and the lack of food security in many regions
(Godfray etal., 2010). Sufficient production of, and equitable access to,
food are both critical for food security (Misselhorn etal., 2012). GHG
emissions may be reduced through changes in food demand without
jeopardizing health and well-being by (1) reducing losses and wastes of
food in the supply chain as well as during final consumption; (2) chang-
ing diets towards less GHG-intensive food, e. g., substitution of animal
products with plant-based food, while quantitatively and qualitatively
maintaining adequate protein content, in regions with high animal
product consumption; and (3) reduction of overconsumption in regions
where this is prevalent. Substituting plant-based diets for animal-based
diets is complex since, in many circumstances, livestock can be fed on
plants not suitable for human consumption or growing on land with
high soil carbon stocks not suitable for cropping; hence, food produc-
tion by grazing animals contributes to food security in many regions of
the world (Wirsenius, 2003; Gill etal., 2010).
Reductions of losses in the food supply chain — Globally, rough esti-
mates suggest that ~30 – 40 % of all food produced is lost in the sup-
ply chain from harvest to consumption (Godfray etal., 2010). Energy
embodied in wasted food is estimated at ~36 EJ / yr (FAO, 2011). In
developing countries, up to 40 % is lost on farm or during distribution
due to poor storage, distribution, and conservation technologies and
procedures. In developed countries, losses on farm or during distribu-
tion are smaller, but the same amount is lost or wasted in service sec-
tors and at the consumer level (Foley etal., 2005; Parfitt etal., 2010;
Godfray etal., 2010; Gustavsson etal., 2011; Hodges etal., 2011). How-
ever, uncertainties related to losses in the food supply chain are large
and more research is needed.
Not all losses are (potentially) avoidable because losses in households
also include parts of products normally not deemed edible (e. g., peels of
some fruits and vegetables). According to Parfitt etal. (2010), in the UK,
18 % of the food waste is unavoidable, 18 % is potentially avoidable,
and 64 % is avoidable. Data for Austria, Netherlands, Turkey, the United
Kingdom, and the United States, derived with a variety of methods,
show that food wastes at the household level in industrialized countries
are 150 – 300 kg per household per year (Parfitt etal., 2010). According
to a top-down mass-flow modelling study based on FAO commodity
balances completely covering the food supply chain, but excluding non-
edible fractions, food loss values range from 120 – 170 kg / cap / yr in Sub-
Saharan Africa to 280 – 300 kg / cap / yr in Europe and North America.
Table 11�3 | Overview of demand-side mitigation options in the AFOLU sector.
Measure Description References
Reduced losses in the
food supply chain
Reduced losses in the food supply chain and in final consumption reduces energy use and GHG
emissions from agriculture, transport, storage and distribution, and reduce land demand.
(Godfray etal., 2010; Gustavsson
etal., 2011), see text.
Changes in human diets
towards less emission-
intensive products
Where appropriate, reduced consumption of food items with high GHG emissions per unit
of product, to those with low GHG products can reduce GHG emissions. Such demand
changes can reduce energy inputs in the supply chain and reduces land demand.
(Stehfest etal., 2009;
FAO, 2011), see text
Demand-side options related
to wood and forestry
Wood harvest in forests releases GHG and at least temporarily reduces forest C stocks. Conservation of wood (products)
through more efficient use or replacement with recycled materials and replacing wood from illegal logging or destructive
harvest with wood from certified sustainable forestry (Section 11.10) can save GHG emissions. Substitution of wood
for non-renewable resources can reduce GHG emissions, e. g., when wood is substituted for emission-intensive
materials such as aluminium, steel, or concrete in buildings. Integrated optimization of C stocks in forests and in
long-lived products, as well as the use of by-products and wastes for energy, can deliver the highest GHG benefits.
(Gustavsson etal., 2006;
Werner etal., 2010;
Ingerson, 2011), see text.
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Losses ranging from 20 % in Sub-Saharan Africa to more than 30 % in
the industrialized countries were calculated (Gustavsson etal., 2011).
A range of options exist to reduce wastes and losses in the supply
chain: investments into harvesting, processing and storage technolo-
gies in the developing countries, awareness raising, taxation and other
incentives to reduce retail and consumer-related losses primarily in the
developed countries. Different options can help to reduce losses (i. e.,
increase efficiency) in the supply chain and at the household level. Sub-
stantial GHG savings could be realized by saving one quarter of the
wasted food according to (Gustavsson etal., 2011); see Table 11.4.
Changes in human diets — Land use and GHG effects of changing diets
require widespread behavioural changes to be effective; i. e., a strong
deviation from current trajectories (increasing demand for food, in par-
ticular for animal products). Cultural, socioeconomic and behavioural
aspects of implementation are discussed in Sections 11.4.5 and 11.7.
Studies based on Lifecycle Assessment (LCA) methods show substan-
tially lower GHG emissions for most plant-based food than for ani-
mal products (Carlsson-Kanyama and González, 2009; Pathak et al.,
2010; Bellarby etal., 2012; Berners-Lee etal., 2012), although there
are exceptions, e. g., vegetables grown in heated greenhouses or
transported by airfreight (Carlsson-Kanyama and González, 2009).
A comparison of three meals served in Sweden with similar energy
and protein content based on (1) soy, wheat, carrots, and apples, (2)
pork, potatoes, green beans, and oranges, and (3) beef, rice, cooked
frozen vegetables, and tropical fruits revealed GHG emissions of 0.42
kgCO
2
eq for the first option, 1.3 kgCO
2
eq for the second, and 4.7
kgCO
2
eq for the third, i. e., a factor of >10 difference (Carlsson-Kan-
yama and González, 2009). Most LCA studies quoted here use attribu-
tional LCA; differences to results from consequential LCA (see Annex
II) are generally not large enough to reverse the picture (Thomassen
etal., 2008). The GHG benefits of plant-based food over animal prod-
ucts hold when compared per unit of protein (González etal., 2011).
In addition to plant-based foods having lower emissions than animal-
based ones, GHG emissions of livestock products also vary consider-
ably; emissions per unit of protein are highest for beef and lower for
pork, chicken meat, eggs and dairy products (de Vries and de Boer,
2010) due to their feed and land-use intensities. Figure 11.10 presents
a comparison between milk and beef for different production systems
and regions of the world (Herrero etal., 2013). Beef production can use
up to five times more biomass for producing 1 kg of animal protein
than dairy. Emissions intensities for the same livestock product also
Figure 11�10 | Biomass use efficiencies for the production of edible protein from (top) beef and (bottom) milk for different production systems and regions of the world (Herrero
etal., 2013).
0
500
1000
1500
2000
0
50
100
150
200
250
300
350
Biomass Use Efficiencies for Beef
[kg Feed / kg Protein]
Biomass Use Efficiencies for Milk
[kg Feed per kg Protein]
North America, Europe WorldAfricaAsiaLatin America
Livestock Systems
Temperate
Humid
Arid
Temperate
Humid
Arid
Grazing
Mixed Crop-Livestock
840840
Agriculture, Forestry and Other Land Use (AFOLU)
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Chapter 11
vary largely between different regions of the world due to differences
in agro-ecology, diet quality, and intensity of production (Herrero etal.,
2013). In overall terms, Europe and North America have lower emis-
sions intensities per kg of protein than Africa, Asia, and Latin America.
This shows that the highest potential for improving emissions inten-
sities lies in developing countries, if intensification strategies can be
matched to local resources and contexts.
Studies based on integrated modelling show that changes in diets
strongly affect future GHG emissions from food production (Stehfest
etal., 2009; Popp etal., 2010; Davidson, 2012). Popp etal. (2010) esti-
mated that agricultural non-CO
2
emissions (CH
4
and N
2
O) would triple
by 2055 to 15.3 GtCO
2
eq / yr if current dietary trends and population
growth were to continue. Technical mitigation options on the sup-
ply side, such as improved cropland or livestock management, alone
could reduce that value to 9.8 GtCO
2
eq / yr, whereas emissions were
reduced to 4.3 GtCO
2
eq / yr in a ‘decreased livestock product’ scenario
and to 2.5 GtCO
2
eq / yr if both technical mitigation and dietary change
were assumed. Hence, the potential to reduce GHG emissions through
changes in consumption was found to be substantially higher than
that of technical mitigation measures. Stehfest et al., (2009) evalu-
ated effects of dietary changes on CO
2
(including C sources / sinks of
ecosystems), CH
4
, and N
2
O emissions. In a ‘business-as-usual’ scenario
largely based on FAO (2006), total GHG emissions were projected to
reach 11.9 GtCO
2
eq / yr in 2050. The following changes were evaluated:
no ruminant meat, no meat, and a diet without any animal products.
Changed diets resulted in GHG emission savings of 34 – 64 % compared
to the ‘business-as-usual’ scenario; a switch to a ‘healthy diet’ recom-
mended by the Harvard Medical School would save 4.3 GtCO
2
eq / yr
(– 36 %). Adoption of the ‘healthy diet’ (which includes a meat, fish and
egg consumption of 90 g / cap / day) would reduce global GHG abatement
costs to reach a 450 ppm CO
2
eq concentration target by ~50 % com-
pared to the reference case (Stehfest etal., 2009). The analysis assumed
nutritionally sufficient diets; reduced supply of animal protein was com-
pensated by plant products (soy, pulses, etc.). Considerable cultural and
social barriers against a widespread adoption of dietary changes to low-
GHG food may be expected (Davidson, 2012; Smith etal., 2013, 11.4.5).
A limitation of food-related LCA studies is that they have so far sel-
dom considered the emissions resulting from LUC induced by chang-
ing patterns of food production (Bellarby etal., 2012) . A recent study
(Schmidinger and Stehfest, 2012) found that cropland and pastures
required for the production of beef, lamb, calf, pork, chicken, and
milk could annually sequester an amount of carbon equivalent to
30 – 470 % of the GHG emissions usually considered in LCA of food
products if the land were to be reforested. Land-related GHG costs dif-
fer greatly between products and depend on the time horizon (30 – 100
yr) assumed (Schmidinger and Stehfest, 2012). If cattle production
contributes to tropical deforestation (Zaks et al., 2009; Bustamante
etal., 2012; Houghton etal., 2012), land-use related GHG emissions
are particularly high (Cederberg etal., 2011). These findings underline
the importance of diets for GHG emissions in the food supply chain
(Garnett, 2011; Bellarby etal., 2012). A potential co-benefit is a reduc-
tion in diet-related health risks in regions where overconsumption of
animal products is prevalent (McMichael etal., 2007).
Demand-side options related to wood and forestry — A comprehen-
sive global, long-term dataset on carbon stocks in long-lived wood
Table 11�4 | Changes in global land use and related GHG reduction potentials in 2050 assuming the implementation of options to increase C sequestration on farmland, and use
of spared land for either biomass production for energy or afforestation. Afforestation and biomass for bioenergy are both assumed to be implemented only on spare land and are
mutually exclusive (Smith etal., 2013b).
Cases Food crop area
Livestock
grazing area
C sink on
farmland
1
Afforestation of
spare land
2,3
Biomass for
bioenergy on
spare land
2,4
Total mitigation
potential
Difference in
mitigation from
reference case
[Gha] GtCO
2
eq / yr
Reference 1.60 4.07 3.5 6.1 1.2 – 9.4 4.6 – 12.9 0
Diet change 1.38 3.87 3.2 11.0 2.1 – 17.0 5.3 – 20.2 0.7 – 7.3
Yield growth 1.49 4.06 3.4 7.3 1.4 – 11.4 4.8 – 14.8 0.2 – 1.9
Feeding efficiency 1.53 4.04 3.4 7.2 1.4 – 11.1 4.8 – 14.5 0.2 – 1.6
Waste reduction 1.50 3.82 3.3 10.1 1.9 – 15.6 5.2 – 18.9 0.6 – 6.0
Combined 1.21 3.58 2.9 16.5 3.2 – 25.6 6.1 – 28.5 1.5 – 15.6
Notes:
1
Potential for C sequestration on cropland for food production and livestock grazing land with improved soil C management. The potential C sequestration rate was derived
from Smith etal., (2008).
2
Spare land is cropland or grazing land not required for food production, assuming increased but still sustainable stocking densities of livestock based on Haberl etal., (2011);
Erb etal., (2012).
3
Assuming 11.8 (tCO
2
eq / ha) / yr (Smith etal., 2000).
4
Assumptions were as follows. High bioenergy value: short-rotation coppice or energy grass directly replaces fossil fuels, energy return on investment 1:30, dry-matter biomass
yield 190 GJ / ha / yr
(WBGU, 2009). Low bioenergy value: ethanol from maize replaces gasoline and reduces GHG by 45 %, energy yield 75 GJ / ha / yr (Chum etal., 2011).
Some energy crops may, under certain conditions, sequester C in addition to delivering bioenergy; the effect is context-specific and was not included. Whether bioenergy or
afforestation is a better option to use spare land for mitigation needs to be decided on a case-by-case basis.
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Chapter 11
products in use (excluding landfills) shows an increase from approxi-
mately 2.2 GtC in 1900 to 6.9 GtC in 2008 (Lauk etal., 2012). Per
capita, carbon stored in wood products amounted to ~1.4 tC / cap in
1900 and ~1.0 tC / cap in 2008. The net yearly accumulation of long-
lived wood products in use varied between 35 and 91 MtC / yr in the
period 1960 – 2008 (Lauk etal., 2012). The yearly accumulation of C in
products and landfills was ~200 MtC / yr in the period 1990 – 2008 (Pan
etal., 2011). If more long-lived wood products were used, C sequestra-
tion and mitigation could be enhanced.
Increased wood use does not reduce GHG emissions under all circum-
stances because wood harvest reduces the amount of carbon stored in
the forest, at least temporarily, and increases in wood harvest levels
may result in reduced long-term carbon storage in forests (Ingerson,
2011; Böttcher etal., 2012; Holtsmark, 2012; Lamers and Junginger,
2013). Reducing wood consumption, e. g., through paper recycling, can
reduce GHG emissions (Acuff and Kaffine, 2013), as may the use of
wood from sustainable forestry in place of emission-intensive materi-
als such as concrete, steel, or aluminium. Recent studies suggest that,
where technically possible, substitution of wood from sustainably
managed forests for non-wood materials in the construction sector
(concrete, steel, etc.) in single-family homes, apartment houses, and
industrial buildings, reduces GHG emissions in most cases (Werner
etal., 2010; Sathre and O’Connor, 2010; Ximenes and Grant, 2013).
Most of the emission reduction results from reduced production emis-
sions, whereas the role of carbon sequestration in products is relatively
small (Sathre and O’Connor, 2010). Werner etal. (2010) show that GHG
benefits are highest when wood is primarily used for long-lived prod-
ucts, the lifetime of products is maximized, and energy use of woody
biomass is focused on by-products, wood wastes, and end-of-lifecycle
use of long-lived wood products.
11�4�4 Feedbacks of changes in land demand
Mitigation options in the AFOLU sector, including options such as bio-
mass production for energy, are highly interdependent due to their
direct and indirect impacts on land demand. Indirect interrelation-
ships, mediated via area demand for food production, which in turn
affects the area available for other purposes, are difficult to quan-
tify and require systemic approaches. Table 11.4 (Smith etal., 2013b)
shows the magnitude of possible feedbacks in the land system in
2050. It first reports the effect of single mitigation options compared
to a reference case, and then the combined effect of all options. The
reference case is similar to the (FAO, 2006a) projections for 2050 and
assumes a continuation of on-going trends towards richer diets, con-
siderably higher cropland yields (+54 %) and moderately increased
cropland areas (+9 %). The diet change case assumes a global con-
tract-and-converge scenario towards a nutritionally sufficient low
animal product diet (8 % of food calories from animal products). The
yield growth case assumes that yields in 2050 are 9 % higher than
those in the reference case, according to the ‘Global Orchestration’
scenario in (MEA, 2005). The feeding efficiency case assumes on aver-
age 17 % higher livestock feeding efficiencies than the reference case.
The waste reduction case assumes a reduction of the losses in the
food supply chain by 25 % (Section 11.4.3). The combination of all
options results in a substantial reduction of cropland and grazing
areas (Smith etal., 2013b), even though the individual options cannot
simply be added up due to the interactions between the individual
compartments.
Table 11.4 shows that demand-side options save GHG by freeing up
land for bioenergy or afforestation and related carbon sequestration.
The effect is strong and non-linear, and more than cancels out reduced
C sequestration potentials on farmland. Demand-side potentials are
substantial compared to supply-side mitigation potentials (Section
11.3), but implementation may be difficult (Sections 11.7; 11.8). Esti-
mates of GHG savings from bioenergy are subject to large uncertain-
ties related to the assumptions regarding power plants, utilization
pathway, energy crop yields, and effectiveness of sustainability criteria
(Sections 11.4.5; 11.7; 11.13).
The systemic effects of land-demanding mitigation options such as
bioenergy or afforestation depend not only on their own area demand,
but also on land demand for food and fibre supply (Chum etal., 2011;
Coelho etal., 2012; Erb etal., 2012b). In 2007, energy crops for trans-
port fuels covered about 26.6 Mha or 1.7 % of global cropland (UNEP,
2009). Assumptions on energy crop yields (Section 11.13) are the main
reason for the large differences in estimates of future area demand
of energy crops in the next decades, which vary from <100 Mha to
>1000 Mha, i. e., 7 – 70 % of current cropland (Sims etal., 2006; Smeets
etal., 2007; Pacca and Moreira, 2011; Coelho etal., 2012). Increased
pressure on land systems may also emerge when afforestation claims
land, or forest conservation restricts farmland expansion (Murtaugh
and Schlax, 2009; Popp etal., 2011).
Land-demanding mitigation options may result in feedbacks such as
GHG emissions from land expansion or agricultural intensification,
higher yields of food crops, higher prices of agricultural products,
reduced food consumption, displacement of food production to other
regions and consequent land clearing, as well as impacts on biodiver-
sity and non-provisioning ecosystem services (Plevin etal., 2010; Popp
etal., 2012).
Restrictions to agricultural expansion due to forest conservation,
increased energy crop area, afforestation and reforestation may
increase costs of agricultural production and food prices. In a model-
ling study, conserving C-rich natural vegetation such as tropical forests
was found to increase food prices by a factor of 1.75 until 2100, due
to restrictions of cropland expansion, even if no growth of energy crop
area was assumed (Wise etal., 2009). Food price indices (weighted
average of crop and livestock products) are estimated to increase until
2100 by 82 % in Africa, 73 % in Latin America, and 52 % in Pacific Asia
if large-scale bioenergy deployment is combined with strict forest con-
servation, compared to a reference scenario without forest conserva-
tion and bioenergy (Popp etal., 2011). Further trade liberalization can
842842
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
lead to lower costs of food, but also increases the pressure on land,
especially on tropical forests (Schmitz etal., 2011).
Increased land demand for GHG mitigation can be partially compen-
sated by higher agricultural yield per unit area (Popp et al., 2011).
While yield increases can lead to improvements in output from less
land, generate better economic returns for farmers, help to reduce
competition for land, and alleviate environmental pressures (Burney
etal., 2010; Smith et al., 2010), agricultural intensification if poorly
implemented incurs economic costs (Lotze-Campen etal., 2010) and
may also create social and environmental problems such as nutrient
leaching, soil degradation, pesticide pollution, impact on animal wel-
fare, and many more (IAASTD, 2009). Maintaining yield growth while
reducing negative environmental and social effects of agricultural
intensification is, therefore, a central challenge, requiring sustainable
management of natural resources as well as the increase of resource
efficiency (DeFries and Rosenzweig, 2010), two components of sustain-
able intensification (Garnett etal., 2013).
Additional land demand may put pressures on biodiversity, as LUC
is one of the most important drivers of biodiversity loss (Sala etal.,
2000). Improperly managed large-scale agriculture (or bioenergy) may
negatively affect biodiversity (Groom etal., 2008), which is a key pre-
requisite for the resilience of ecosystems, i. e., their ability to adapt to
changes such as climate change, and to continue to deliver ecosystem
services in the future (Díaz etal., 2006; Landis etal., 2008). However,
implementing appropriate management, such as establishing bioen-
ergy crops or plantations for carbon sequestration in already degraded
ecosystems areas represents an opportunity where bioenergy can be
used to achieve positive environmental outcomes (e. g., Hill etal., 2006;
Semere and Slater, 2007; Campbell et al., 2008; Nijsen etal., 2012).
Because climate change is also an important driver of biodiversity loss
(Sala etal., 2000), bioenergy for climate change mitigation may also be
beneficial for biodiversity if it is planned with biodiversity conservation
in mind (Heller and Zavaleta, 2009; Dawson etal., 2011; Section 11.13).
Tradeoffs related to land demand may be reduced through multifunc-
tional land use, i. e., the optimization of land to generate more than
one product or service such as food, animal feed, energy or materials,
soil protection, wastewater treatment, recreation, or nature protection
(de Groot, 2006; DeFries and Rosenzweig, 2010; Section 11.7). This
also applies to the potential use of ponds and other small water bodies
for raising fish fed with agricultural waste (Pullin etal., 2007).
11�4�5 Sustainable development and behaviou-
ral aspects
The assessment of impacts of AFOLU mitigation options on sustainable
development requires an understanding of a complex multilevel system
where social actors make land-use decisions aimed at various develop-
ment goals, one of them being climate change mitigation. Depending
on the specific objectives, the beneficiaries of a particular land-use
choice may differ. Thus tradeoffs between global, national, and local
concerns and various stakeholders need to be considered (see also Sec-
tion 4.3.7 and WGII Chapter 20). The development context provides
opportunities or barriers for AFOLU (May etal., 2005; Madlener etal.,
2006; Smith and Trines, 2006; Smith etal., 2007; Angelsen, 2008; How-
den etal., 2008; Corbera and Brown, 2008; Cotula etal., 2009; Catta-
neo etal., 2010; Junginger etal., 2011; Section 11.8 and Figure 11.11).
Further, AFOLU measures have additional effects on development,
beyond improving the GHG balance (Foley et al., 2005; Alig et al.,
2010; Calfapietra etal., 2010; Busch etal., 2011; Smith etal., 2013b;
Branca etal., 2013; Albers and Robinson, 2013). These effects can be
positive (co-benefits) or negative (adverse side-effects) and do not
necessarily overlap geographically, socially or in time (Section 11.7
and Figure 11.11). This creates the possibility of tradeoffs, because an
AFOLU measure can bring co-benefits to one social group in one area
(e. g., increasing income), while bringing adverse side-effects to others
somewhere else (e. g., reducing food availability).
Table 11.5 summarizes the issues commonly considered when assess-
ing the above-mentioned interactions at various levels between sus-
tainable development and AFOLU.
Social complexity: Social actors in the AFOLU sector include indi-
viduals (farmers, forest users), social groups (communities, indigenous
groups), private companies (e. g., concessionaires, food-producer multi-
nationals), subnational authorities, and national states (see Table 11.6).
Figure 11�11 | Dynamic interactions between the development context and AFOLU.
AFOLU
Measures
Institutional
Arrangements
Social and
Human Assets
Natural Assets
State of and Access to
Infrastructure and Technology
Economic Assets
Development
Context
Effects of AFOLU measures
on sustainable development
(Section 11.7 Co-benefits,
risks and spillovers)
Enabling conditions to AFOLU
measures as provided by the
development context (Section
11.8 Barriers and opportunities)
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Agriculture, Forestry and Other Land Use (AFOLU)
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Chapter 11
Table 11�5 | Issues related to AFOLU measures and sustainable development.
Dimensions Issues
Social and human assets Population growth and migration, level of education, human capacity, individual skills, indigenous and traditional
knowledge, cultural values, equity and health, animal welfare, organizational capacity
Natural assets Availability of natural resources (land, forest, water, agricultural land, minerals, fauna), GHG balance, ecosystem integrity,
biodiversity conservation, ecosystem services, the productive capacity of ecosystems, ecosystem health and resilience
State of infrastructure
and technology
Availability of infrastructure and technology and industrial capacity, technology development, appropriateness, acceptance
Economic factors Credit capacity, employment creation, income, wealth distribution / distribution mechanisms, carbon finance, available capital / investments, market access
Institutional arrangements Land tenure and land-use rights, participation and decision making mechanisms (e. g., through Free, Prior and Informed Consent), sectoral and cross-
sectoral policies, investment in research, trade agreements and incentives, benefit sharing mechanisms, existence and forms of social organization
Based on Madlener etal. (2006), Sneddon etal. (2006), Pretty (2008), Corbera and Brown (2008), Macauley and Sedjo (2011), and de Boer etal. (2011).
Spatial scale refers on the one hand to the size of an intervention
(e. g., in number of hectares) and on the other hand to the biophysical
characterization of the specific land (e. g., soil type, water availability,
slope). Social interactions tend to become more complex the bigger
the area of an AFOLU intervention, on a social-biophysical continuum:
family / farm — neighbourhood — community — village — city — prov-
ince — country — region — globe. Impacts from AFOLU measures on
sustainable development are different along this spatial-scale con-
tinuum (Table 11.6). The challenge is to provide landscape governance
that responds to societal needs as well as biophysical capacity at dif-
ferent spatial scales (Görg, 2007; Moilanen and Arponen, 2011; van
der Horst and Vermeylen, 2011).
Temporal scale: As the concept of sustainable development includes
current and future generations, the impacts of AFOLU over time need
to be considered (see Chapter 4). Positive and negative impacts of
AFOLU measures can be realized at different times. For instance, while
reducing deforestation has an immediate positive impact on reducing
GHG emissions, reforestation will have a positive impact on C seques-
tration over time. Further, in some circumstances, there is the risk of
reversing current emission reductions in the future (see Section 11.3.2
on non-permanence).
Behavioural aspects: Level of education, cultural values and tradi-
tion, as well as access to markets and technology, and the decision
power of individuals and social groups, all influence the perception of
potential impacts and opportunities from AFOLU measures, and con-
sequently have a great impact on local land management decisions
(see Chapters 2, 3, and 4; Guthinga, 2008; Durand and Lazos, 2008;
Gilg, 2009; Bhuiyan etal., 2010; Primmer and Karppinen, 2010; Durand
and Vázquez, 2011). When decisions are taken at a higher adminis-
trative level (e. g., international corporations, regional authorities or
national states), other factors or values play an important role, includ-
ing national and international development goals and priorities, poli-
cies and commitments, international markets or corporate image (see
Chapters 3 and 4). Table 11.7 summarizes the emerging behavioural
aspects regarding AFOLU mitigation measures.
Land-use policies (Section 11.10) have the challenge of balancing
impacts considering these parameters: social complexity, spatial scale,
temporal scale, and behavioural aspects. Vlek and Keren (1992) and
Vlek (2004) indicate the following dilemmas relevant to land-manage-
ment decisions: Who should take the risks, when (this generation or
future generations) and where (specific place) co-benefits and poten-
tial adverse effects will take place, and how to mediate between indi-
vidual vs. social benefits. Addressing these dilemmas is context-spe-
cific. Nevertheless, the fact that a wide range of social actors need to
face these dilemmas explains, to a certain extent, disagreements about
environmental decision making in general, and land-management
decisions in particular (Villamor etal., 2011; Le etal., 2012; see Section
11.10) .
11.5 Climate change feedback
and interaction with
adaptation (includes
vulnerability)
When reviewing the inter-linkages between climate change mitiga-
tion and adaptation within the AFOLU sector the following issues need
to be considered: (i) the impact of climate change on the mitigation
potential of a particular activity (e. g., forestry and agricultural soils)
over time, (ii) potential synergies / tradeoffs within a land-use sector
between mitigation and adaptation objectives, and (iii) potential trad-
eoffs across sectors between mitigation and adaptation objectives.
Mitigation and adaptation in land-based ecosystems are closely inter-
linked through a web of feedbacks, synergies, and tradeoffs (Section
11.8). The mitigation options themselves may be vulnerable to climatic
change (Section 11.3.2) or there may be possible synergies or tradeoffs
between mitigation and adaptation options within or across AFOLU
sectors.
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Chapter 11
Table 11�6 | Characterization of social actors in AFOLU.
Social actors Characterization
Individuals (legal and illegal forest users, farmers) Rather small-scale interventions, although some can be medium-scale
Decisions taken rather at the local level
Social groups (communities, indigenous peoples) Small to medium interventions
Decisions taken at the local or regional levels
Sub-national authorities (provinces, states) Medium to large interventions
Decisions taken at the national or sub-national level, depending on the governance structure
State (national level) Rather large interventions
Decisions taken at the national level, often in line with international agreements
Corporate (at the national or multinational levels) Rather large interventions. Decisions can be taken within a specific region / country, in another country, or at global
level (e. g., for multinational companies). National and international markets play a key role in decision making
Table 11�7 | Emerging behavioural aspects relevant for AFOLU mitigation measures.
Change in Emerging behavioural aspects in AFOLU
Consumption patterns Dietary change: Several changes in diet can potentially reduce GHG emissions, including reduction of food waste and reduction of or
changes in meat consumption (especially in industrialized countries). On the other hand, increasing income and evolving lifestyles with
increasing consumption of animal protein in developing countries are projected to increase food-related GHG emissions.
The potential of reducing GHG emissions in the food sector needs to be understood in a wider and changing socio-cultural context that determines nutrition.
Potential drivers of change: Health awareness and information, income increase, lifestyle
References 1, 2,3, 4, 5
Production patterns Large-scale land acquisition: The acquisition of (long-term rights) of large areas of farmland in lower-income countries, by
transnational companies, agribusiness, investments funds or government agencies. There are various links between these acquisitions
and GHG emissions in the AFOLU sector. On one hand because some acquisitions are aimed at producing energy crops (through non-
food or ‘flex-crops’), on the other because these can cause the displacement of peoples and activity, increasing GHG leakage.
Impacts on livelihood, local users rights, local employment, economic activity, or on biodiversity conservation are of concern.
Potential drivers of change: International markets and their mechanisms, national and international policies
References 6, 7, 8
Production and
consumption patterns
Switching to low-carbon products: Land managers are sensitive to market changes. The promotion of low-carbon products as a means for
reducing GHG emissions can increase the land area dedicated to these products. Side-effects from this changes in land management (positive and
negative), and acceptability of products and technologies at the production and consumption sides are context-related and cannot be generalized
Potential drivers of change: International agreements and markets, accessibility to rural energy, changes in energy demand
References 9, 10, 11
Relation between
producers and consumers
Certification: Labelling, certification, or other information-based instruments have been developed for promoting
behavioural changes towards more sustainable products (Section 11.10). Recently, the role of certification in reducing
GHG while improving sustainability has been explored, especially for bioenergy (Section 11.13).
Potential drivers of change: Consumer awareness, international agreements, cross-national sector policies and initiatives.
References 11, 12, 13, 14
Management priorities Increasing interest in conservation and sustainable (land) management: Changing management practices towards
more sustainable ones as alternative for gaining both environmental and social co-benefits, including climate change mitigation,
is gaining recognition. Concerns about specific management practices, accountability methods of co-benefits, and sharing
mechanisms seem to be elements of concerns when promoting a more sustainable management of natural resources.
Potential drivers of change: Policies and international agreements and their incentive mechanisms, schemes for payments for environmental services.
References 15, 16, 17, 18, 19
1
Stehfest etal. (2009);
2
Roy etal. (2012);
3
González etal. (2011);
4
Popp etal. (2010);
5
Schneider etal. (2011);
6
Cotula (2012);
7
Messerli etal. (2013);
8
German etal. (2013);
9
Muys
etal. (2014);
10
MacMillan Uribe etal. (2012);
11
Chakrabarti (2010);
12
Karipidis etal. (2010);
13
Auld etal. (2008);
14
Diaz-Chavez (2011);
15
Calegari etal. (2008);
16
Deal etal. (2012);
17
DeFries and Rosenzweig (2010);
18
Hein and van der Meer (2012);
19
Lippke etal. (2003).
845845
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
The IPCC WGI presents feedbacks between climate change and the car-
bon cycle (WGI Chapter 6; Le Quéré etal., 2013), while WGII assesses
the impacts of climate change on terrestrial ecosystems (WGII Chapter
4) and crop production systems (WGII Chapter 7), including vulnerabil-
ity and adaptation. This section focuses particularly on the impacts of
climate change on mitigation potential of land-use sectors and inter-
actions that arise with adaptation, linking to the relevant chapters of
WGI and WGII reports.
11�5�1 Feedbacks between ALOFU and climate
change
AFOLU activities can either reduce or accelerate climate change by
affecting biophysical processes (e. g., evapotranspiration, albedo) and
change in GHG fluxes to and from the atmosphere (WGI). Whether a
particular ecosystem is functioning as sink or source of GHG emission
may change over time, depending on its vulnerability to climate change
and other stressors and disturbances. Hence, mitigation options avail-
able today (Section 11.3) in the AFOLU sectors may no longer be avail-
able in the future.
There is robust evidence that human-induced land-use changes have
led to an increased surface albedo (WGI Chapter 8; Myhre and Shin-
dell, 2013). Changes in evapotranspiration and surface roughness may
counteract the effect of changes in albedo. Land-use changes affect
latent heat flux and influence the hydrological cycle. Biophysical cli-
mate feedbacks of forest ecosystems differ depending on regional
climate regime and forest types. For example, a decrease in tropical
forests has a positive climate forcing through a decrease in evapora-
tive cooling (Bala etal., 2007; Bonan, 2008). An increase in conifer-
ous-boreal forests compared to grass and snow provides a positive cli-
mate forcing through lowering albedo (Bala etal., 2007; Bonan, 2008;
Swann etal., 2010). There is currently low agreement on the net bio-
physical effect of land-use changes on the global mean temperature
(WGI Chapter 8; Myhre and Shindell, 2013). By contrast, the biogeo-
chemical effects of LUC on radiative forcing through emissions of GHG
is positive (WGI Chapter 8; Sections 11.2.2; 11.2.3).
11�5�2 Implications of climate change on
terrestrial carbon pools and mitigation
potential of forests
Projections of the global carbon cycle to 2100 using ‘Coupled Model
Intercomparison Project Phase 5 (CMIP5) Earth System Models’ (WGI
Chapter 6; Le Quéré etal., 2013) that represent a wider range of com-
plex interactions between the carbon cycle and the physical climate
system consistently estimate a positive feedback between climate and
the carbon cycle, i. e., reduced natural sinks or increased natural CO
2
sources in response to future climate change. Implications of climate
change on terrestrial carbon pools biomes and mitigation potential of
forests.
Rising temperatures, drought, and fires may lead to forests becoming a
weaker sink or a net carbon source before the end of the century (Sitch
etal., 2008). Pervasive droughts, disturbances such as fire and insect
outbreaks, exacerbated by climate extremes and climate change put
the mitigation benefits of the forests at risk (Canadell and Raupach,
2008; Phillips etal., 2009; Herawati and Santoso, 2011). Forest distur-
bances and climate extremes have associated carbon balance implica-
tions (Millar etal., 2007; Kurz etal., 2008; Zhao and Running, 2010;
Potter etal., 2011; Davidson, 2012; Reichstein etal., 2013). Allen etal.
(2010) suggest that at least some of the world’s forested ecosystems
may already be responding to climate change.
Experimental studies and observations suggest that predicted changes
in temperature, rainfall regimes, and hydrology may promote the die-
back of tropical forests (e. g., Nepstad et al., 2007). The prolonged
drought conditions in the Amazon region during 2005 contributed to
a decline in above-ground biomass and triggered a release of 4.40 to
5.87 GtCO
2
(Phillips etal., 2009). Earlier model studies suggested Ama-
zon die-back in the future (Cox etal., 2013; Huntingford etal., 2013).
However, recent model estimates suggest that rainforests may be more
resilient to climate change, projecting a moderate risk of tropical forest
reduction in South America and even lower risk for African and Asian
tropical forests (Gumpenberger etal., 2010; Cox etal., 2013; Hunting-
ford etal., 2013).
Arcidiacono-Bársony etal., (2011) suggest that the mitigation benefits
from deforestation reduction under REDD+ (Section 11.10.1) could be
reversed due to increased fire events, and climate-induced feedbacks,
while Gumpenberger et al., (2010) conclude that the protection of
forests under the forest conservation (including REDD) programmes
could increase carbon uptake in many tropical countries, mainly due to
CO
2
fertilization effects, even under climate change conditions.
11�5�3 Implications of climate change on
peatlands, grasslands, and croplands
Peatlands: Wetlands, peatlands, and permafrost soils contain higher
carbon densities relative to mineral soils, and together they comprise
extremely large stocks of carbon globally (Davidson and Janssens,
2006). Peatlands cover approximately 3 % of the Earth’s land area
and are estimated to contain 350 – 550 Gt of carbon, roughly between
20 to 25 % of the world’s soil organic carbon stock (Gorham, 1991;
Fenner etal., 2011). Peatlands can lose CO
2
through plant respiration
and aerobic peat decomposition (Clair etal., 2002) and with the onset
of climate change, may become a source of CO
2
(Koehler etal., 2010).
Large carbon losses are likely from deep burning fires in boreal peat-
lands under future projections of climate warming and drying (Flanni-
gan etal., 2009). A study by Fenner etal. (2011) suggests that climate
change is expected to increase the frequency and severity of drought
in many of the world’s peatlands which, in turn, will release far more
GHG emissions than thought previously. Climate change is projected
to have a severe impact on the peatlands in northern regions where
846846
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
most of the perennially frozen peatlands are found (Tarnocai, 2006).
According to Schuur etal. (2008), the thawing permafrost and conse-
quent microbial decomposition of previously frozen organic carbon, is
one of the most significant potential feedbacks from terrestrial ecosys-
tems to the atmosphere in a changing climate. Large areas of perma-
frost will experience thawing (WGI Chapter 12), but uncertainty over
the magnitude of frozen carbon losses through CO
2
or CH
4
emissions
to the atmosphere is large, ranging between 180 and 920 GtCO
2
by
the end of the 21st century under the Representative Concentration
Pathways (RCP)8.5 scenario (WGI Chapter 6; Le Quéré etal., 2013).
Grasslands: Tree cover and biomass in savannah has increased over
the past century (Angassa and Oba, 2008; Witt etal., 2009; Lunt etal.,
2010; Rohde and Hoffman, 2012) leading to increased carbon storage
per hectare (Hughes etal., 2006; Liao etal., 2006; Throop and Archer,
2008; Boutton etal., 2009), which has been attributed to land man-
agement, rising CO
2
, climate variability, and climate change. Climate
change and CO
2
may affect grazing systems by altering species compo-
sition; for example, warming will favour tropical (C4) species over tem-
perate (C3) species but CO
2
increase would favour C3 grasses (Howden
etal., 2008).
Croplands: Climate change impacts on agriculture will affect not only
crop yields, but also soil organic carbon (SOC) levels in agricultural
soils (Rosenzweig and Tubiello, 2007). Such impacts can be either posi-
tive or negative, depending on the particular effect considered, which
highlights the uncertainty of the impacts. Elevated CO
2
concentrations
alone are expected to have positive effects on soil carbon storage,
because of increased above- and below-ground biomass production
in agro-ecosystems. Similarly, the lengthening of the growing season
under future climate will allow for increased carbon inputs into soils.
Warmer temperatures could have negative impacts on SOC, by speed-
ing decomposition and by reducing inputs by shortening crop lifecycles
(Rosenzweig and Tubiello, 2007), but increased productivity could
increase SOC stocks (Gottschalk etal., 2012).
11�5�4 Potential adaptation options to
minimize the impact of climate change
on carbon stocks in forests and
agricultural soils
Forests: Forest ecosystems require a longer response time to adapt.
The development and implementation of adaptation strategies is also
lengthy (Leemans and Eickhout, 2004; Ravindranath, 2007). Some
examples of the adaptation practices (Murthy etal., 2011) are as fol-
lows: anticipatory planting of species along latitude and altitude,
assisted natural regeneration, mixed-species forestry, species mix
adapted to different temperature tolerance regimes, fire protection
and management practices, thinning, sanitation and other silvicultural
practices, in situ and ex situ conservation of genetic diversity, drought
and pest resistance in commercial tree species, adoption of sustain-
able forest management practices, increase in Protected Areas and
linking them wherever possible to promote migration of species, for-
ests conservation and reduced forest fragmentation enabling species
migration, and energy-efficient fuel-wood cooking devices to reduce
pressure on forests.
Agricultural soils: On current agricultural land, mitigation and adap-
tation interaction can be mutually re-enforcing, particularly for improv-
ing resilience to increased climate variability under climate change
(Rosenzweig and Tubiello, 2007). Many mitigation practices imple-
mented locally for soil carbon sequestration will increase the ability
of soils to hold soil moisture and to better withstand erosion and will
enrich ecosystem biodiversity by establishing more diversified crop-
ping systems, and may also help cropping systems to better withstand
droughts and floods, both of which are projected to increase in fre-
quency and severity under a future warmer climate (Rosenzweig and
Tubiello, 2007).
11�5�5 Mitigation and adaptation synergies and
tradeoffs
Mitigation choices taken in a particular land-use sector may further
enhance or reduce resilience to climate variability and change within or
across sectors, in light of the multiple, and often competing, pressures
on land (Section 11.4), and shifting demographics and consumption
patterns (e. g., (O’Brien etal., 2004; Sperling etal., 2008; Hunsberger
and Evans, 2012). Land-use choices driven by mitigation concerns
(e. g., forest conservation, afforestation) may have consequences for
adaptive responses and / or development objectives of other sectors
(e. g., expansion of agricultural land). For example, reducing emis-
sions from deforestation and degradation may also yield co-benefits
for adaptation by maintaining biodiversity and other ecosystem goods
and services, while plantations, if they reduce biological diversity may
diminish adaptive capacity to climate change (e. g., Chum etal., 2011).
Primary forests tend to be more resilient to climate change and other
human-induced environmental changes than secondary forests and
plantations (Thompson etal., 2009). The impact of plantations on the
carbon balance is dependent on the land-use system they replace.
While plantation forests are often monospecies stands, they may be
more vulnerable to climatic change (see IPCC WGII Chapter 4). Smith
and Olesen (2010) identified a number of synergies between options
that deliver mitigation in agriculture while also enhancing resilience to
future climate change, the most prominent of which was enhancement
of soil carbon stocks.
Adaptation measures in return may help maintain the mitigation
potential of land-use systems. For example, projects that prevent
fires and restore degraded forest ecosystems also prevent release of
GHGs and enhance carbon stocks (CBD and GiZ, 2011). Mitigation
and adaptation benefits can also be achieved within broader-level
objectives of AFOLU measures, which are linked to sustainable devel-
opment considerations. Given the exposure of many livelihoods and
communities to multiple stressors, recommendations from case stud-
847847
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
ies suggest that climate risk-management strategies need to appreci-
ate the full hazard risk envelope, as well as the compounding socio-
economic stressors (O’Brien etal., 2004; Sperling etal., 2008). Within
this broad context, the potential tradeoffs and synergies between
mitigation, adaptation, and development strategies and measures
need to be considered. Forest and biodiversity conservation, protected
area formation, and mixed-species forestry-based afforestation are
practices that can help to maintain or enhance carbon stocks, while
also providing adaptation options to enhance resilience of forest
ecosystems to climate change (Ravindranath, 2007). Use of organic
soil amendments as a source of fertility could potentially increase
soil carbon (Gattinger etal., 2012). Most categories of adaptation
options for climate change have positive impacts on mitigation. In
the agriculture sector, cropland adaptation options that also contrib-
ute to mitigation are ‘soil management practices that reduce fertilizer
use and increase crop diversification; promotion of legumes in crop
rotations; increasing biodiversity, the availability of quality seeds and
integrated crop / livestock systems; promotion of low energy produc-
tion systems; improving the control of wildfires and avoiding burning
of crop residues; and promoting efficient energy use by commercial
agriculture and agro-industries’ (FAO, 2008, 2009a). Agroforestry is
an example of mitigation-adaptation synergy in the agriculture sec-
tor, since trees planted sequester carbon and tree products provide
livelihood to communities, especially during drought years (Verchot
etal., 2007).
11.6 Costs and potentials
This section deals with economic costs and potentials of climate
change mitigation (emission reduction or sequestration of carbon)
within the AFOLU sector. Economic mitigation potentials are distin-
guished from technical or market mitigation potentials (Smith, 2012).
Technical mitigation potentials represent the full biophysical potential
of a mitigation option, without accounting for economic or other con-
straints. These estimates account for constraints and factors such as
land availability and suitability (Smith, 2012), but not any associated
costs (at least explicitly). By comparison, economic potential refers to
mitigation that could be realized at a given carbon price over a specific
period, but does not take into consideration any socio-cultural (for
example, lifestyle choices) or institutional (for example, political, policy,
and informational) barriers to practice or technology adoption. Eco-
nomic potentials are expected to be lower than the corresponding
technical potentials. Also, policy incentives (e. g., a carbon price; see
also Section 11.10) and competition for resources across various miti-
gation options, tend to affect the size of economic mitigation poten-
tials in the AFOLU sector (McCarl and Schneider, 2001). Finally, market
potential is the realized mitigation outcome under current or forecast
market conditions encompassing biophysical, economic, socio-cultural,
and institutional barriers to, as well as policy incentives for, technologi-
cal and / or practice adoption, specific to a sub-national, national or
supra-national market for carbon. Figure 11.12 (Smith, 2012) provides
a schematic view of the three types of mitigation potentials.
Economic (as well as market) mitigation potentials tend to be con-
text-specific and are likely to vary across spatial and temporal scales.
Unless otherwise stated, in the rest of this section, economic potentials
are expressed in million tonnes (Mt) of mitigation in carbon dioxide
equivalent (CO
2
eq) terms, that can arise from an individual mitiga-
tion option or from an AFOLU sub-sector at a given cost per tonne of
CO
2
eq. (USD / tCO
2
eq) over a given period to 2030, which is ‘additional’
to the corresponding baseline or reference case levels.
Various supply-side mitigation options within the AFOLU sector are
described in Section 11.3, and Section 11.4 considers a number of
potential demand-side options. Estimates for costs and potentials are
not always available for the individual options described. Also, aggre-
gate estimates covering both the supply- and demand-side options for
mitigation within the AFOLU sector are lacking, so this section mostly
focuses on the supply-side options. Key uncertainties and sensitivities
around mitigation costs and potentials in the AFOLU sector are (1) car-
bon price, (2) prevailing biophysical and climatic conditions, (3) existing
management heterogeneity (or differences in the baselines), (4) man-
agement interdependencies (arising from competition or co-benefits
across tradition production, environmental outcomes and mitigation
strategies or competition / co-benefits across mitigation options), (5) the
extent of leakage, (6) differential impact on different GHGs associated
with a particular mitigation option, and (7) timeframe for abatement
activities and the discount rate. In this section, we (a) provide aggregate
mitigation potentials for the AFOLU sector (because these wereprovided
separately for agriculture and forestry in AR4), (b) provide estimates of
Figure 11�12 | Relationship between technical, economic, and market potential (based
on Smith, 2012).
Physical /
Biological
(e.g. Land
Suitability)
Economic
Social /
Political /
Institutional /
Educational
Market
Technical Potential
Physically / Biologically
Constrained Potential
Economic Potential
Socially / Politically
Constrained Potential
Market Potential
Maximum Value
GHG Mitigation
Potential
Minimum Value
Barriers: Potential:
848848
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
global mitigation costs and potentials published since AR4, and (c) pro-
vide a regional disaggregation of the potentials to show how potential,
and the portfolio of available options, varies in different world regions.
11�6�1 Approaches to estimating economic
mitigation potentials
Bottom-up and top-down modelling approaches are used to estimate
AFOLU mitigation potentials and costs. While both approaches provide
useful estimates for mitigation costs and potentials, comparing bot-
tom-up and top-down estimates is not straightforward.
Bottom-up estimates are typically derived for discrete abatement
options in agriculture at a specific location or time, and are often based
on detailed technological, engineering and process information, and
data on individual technologies (DeAngelo etal., 2006). These studies
provide estimates of how much technical potential of particular AFOLU
mitigation options will become economically viable at certain carbon
dioxide-equivalent prices. Bottom-up mitigation responses are typically
restricted to input management (for example, changing practices with
fertilizer application and livestock feeding) and mitigation costs esti-
mates are considered ‘partial equilibrium’ in that the relevant input-
output prices (and, sometimes, quantities such as area or production
levels) are held fixed. As such, unless adjusted for potential overlaps
and tradeoffs across individual mitigation options, adding up various
individual estimates to arrive at an aggregate for a particular land-
scape or at a particular point in time could be misleading.
With a ‘systems’ approach, top-down models (described in Chapter 6;
Section 11.9) typically take into account possible interactions between
individual mitigation options. These models can be sector-specific or
economy-wide, and can vary across geographical scales: sub-national,
national, regional, and global. Mitigation strategies in top-down mod-
els may include a broad range of management responses and practice
changes (for example, moving from cropping to grazing or grazing to
forestry) as well as changes in input-output prices (for example, land
and commodity prices). Such models can be used to assess the cost
competitiveness of various mitigation options and implications across
input-output markets, sectors, and regions over time for large-scale
domestic or global adoption of mitigation strategies. In top-down
modelling, dynamic cost-effective portfolios of abatement strategies
are identified incorporating the lowest cost combination of mitigation
strategies over time from across sectors, including agricultural, forestry,
and other land-based sectors across the world that achieve the climate
stabilization target (see Chapter 6). Top-down estimates for 2030 are
included in this section, and are revisited in Section 11.9 when consid-
ering the role of the AFOLU sector in transformation pathways.
Providing consolidated estimates of economic potentials for mitigation
within the AFOLU sector as a whole is complicated because of complex
interdependencies, largely stemming from competing demands on land
for various agricultural and forestry (production and mitigation) activi-
ties, as well as for the provision of many ecosystem services (Smith etal.,
2013a). These interactions are discussed in more detail in Section 11.4.
11�6�2 Global estimates of costs and potentials
in the AFOLU sector
Through combination of forestry and agriculture potentials from AR4,
total mitigation potentials for the AFOLU sector are estimated to be ~3
to ~7.2 GtCO
2
eq / yr in 2030 at 20 and 100 USD / tCO
2
eq, respectively
(Figure 11.13), including only supply-side options in agriculture (Smith
etal., 2007) and a combination of supply- and demand-side options for
forestry (Nabuurs etal., 2007).
Estimates of global economic mitigation potentials in the AFOLU sec-
tor published since AR4 are shown in Figure 11.14, with AR4 estimates
shown for comparison (IPCC, 2007a).
Table 11.8 summarizes the ranges of global economic mitigation
potentials from AR4 (Nabuurs et al., 2007; Smith et al., 2007), and
studies published since AR4 that are shown in full in Figure 11.14, for
agriculture, forestry, and AFOLU combined.
As described in Section 11.3, since AR4, more attention has been paid
to options that reduce emissions intensity by improving the efficiency
of production (i. e., less GHG emissions per unit of agricultural prod-
uct; Burney etal., 2010; Bennetzen etal., 2012). As agricultural and
silvicultural efficiency have improved over recent decades, emissions
intensities have declined (Figure 11.15). Whilst emissions intensity has
increased (1960s to 2000s) by 45 % for cereals, emissions intensities
have decreased by 38 % for milk, 50 % for rice, 45 % for pig meat,
76 % for chicken, and 57 % for eggs.
The implementation of mitigation measures can contribute to further
decrease emission intensities of AFOLU commodities (Figure 11.16;
which shows changes of emissions intensities when a commodity-
specific mix of mitigation measures is applied). For cereal production,
mitigation measures considered include improved cropland agronomy,
nutrient and fertilizer management, tillage and residue management,
and the establishment of agro-forestry systems. Improved rice manage-
ment practices were considered for paddy rice cultivation. Mitigation
measures applied in the livestock sector include improved feeding and
dietary additives. Countries can improve emission intensities of AFOLU
commodities through increasing production at the same level of input,
the implementation of mitigation measures, or a combination of both.
In some regions, increasing current yields is still an option with a signifi-
cant potential to improve emission intensities of agricultural production.
Foley etal. (2011) analyzed current and potential yields that could be
achieved for 16 staple crops using available agricultural practices and
technologies and identified large ‘yield gaps’, especially across many
parts of Africa, Latin America, and Eastern Europe. Better crop manage-
ment practices can help to close yield gaps and improve emission inten-
sities if measures are selected that also have a mitigation potential.
Figure 11�13 | Mitigation potential for the AFOLU sector, plotted using data from AR4 (Nabuurs etal., 2007; Smith etal., 2007). Whiskers show the range of estimates (+ / - 1
standard deviation) for agricultural options for which estimates are available.
Mitigation Potential [MtCO
2
eq/yr (2030)]
-250
0
250
750
500
1250
1000
1750
1500
Cropland
Management
Manure
Management
LivestockRestore
Degraded Lands
Restore Cultivated
Organic Soils
Grazing Land
Management
Agroforestry and
Regrowth on
Remaining Land
Rice
Management
-1SD
Mean
+1SD
Up to
100 USD
Up to
50 USD
Up to
20 USD
0
1
2
3
4
5
6
ForestryAgriculture Total
Mitigation Potential [GtCO
2
eq/yr (2030)]
849849
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
ties, as well as for the provision of many ecosystem services (Smith etal.,
2013a). These interactions are discussed in more detail in Section 11.4.
11�6�2 Global estimates of costs and potentials
in the AFOLU sector
Through combination of forestry and agriculture potentials from AR4,
total mitigation potentials for the AFOLU sector are estimated to be ~3
to ~7.2 GtCO
2
eq / yr in 2030 at 20 and 100 USD / tCO
2
eq, respectively
(Figure 11.13), including only supply-side options in agriculture (Smith
etal., 2007) and a combination of supply- and demand-side options for
forestry (Nabuurs etal., 2007).
Estimates of global economic mitigation potentials in the AFOLU sec-
tor published since AR4 are shown in Figure 11.14, with AR4 estimates
shown for comparison (IPCC, 2007a).
Table 11.8 summarizes the ranges of global economic mitigation
potentials from AR4 (Nabuurs et al., 2007; Smith et al., 2007), and
studies published since AR4 that are shown in full in Figure 11.14, for
agriculture, forestry, and AFOLU combined.
As described in Section 11.3, since AR4, more attention has been paid
to options that reduce emissions intensity by improving the efficiency
of production (i. e., less GHG emissions per unit of agricultural prod-
uct; Burney etal., 2010; Bennetzen etal., 2012). As agricultural and
silvicultural efficiency have improved over recent decades, emissions
intensities have declined (Figure 11.15). Whilst emissions intensity has
increased (1960s to 2000s) by 45 % for cereals, emissions intensities
have decreased by 38 % for milk, 50 % for rice, 45 % for pig meat,
76 % for chicken, and 57 % for eggs.
The implementation of mitigation measures can contribute to further
decrease emission intensities of AFOLU commodities (Figure 11.16;
which shows changes of emissions intensities when a commodity-
specific mix of mitigation measures is applied). For cereal production,
mitigation measures considered include improved cropland agronomy,
nutrient and fertilizer management, tillage and residue management,
and the establishment of agro-forestry systems. Improved rice manage-
ment practices were considered for paddy rice cultivation. Mitigation
measures applied in the livestock sector include improved feeding and
dietary additives. Countries can improve emission intensities of AFOLU
commodities through increasing production at the same level of input,
the implementation of mitigation measures, or a combination of both.
In some regions, increasing current yields is still an option with a signifi-
cant potential to improve emission intensities of agricultural production.
Foley etal. (2011) analyzed current and potential yields that could be
achieved for 16 staple crops using available agricultural practices and
technologies and identified large ‘yield gaps’, especially across many
parts of Africa, Latin America, and Eastern Europe. Better crop manage-
ment practices can help to close yield gaps and improve emission inten-
sities if measures are selected that also have a mitigation potential.
Figure 11�13 | Mitigation potential for the AFOLU sector, plotted using data from AR4 (Nabuurs etal., 2007; Smith etal., 2007). Whiskers show the range of estimates (+ / - 1
standard deviation) for agricultural options for which estimates are available.
Mitigation Potential [MtCO
2
eq/yr (2030)]
-250
0
250
750
500
1250
1000
1750
1500
Cropland
Management
Manure
Management
LivestockRestore
Degraded Lands
Restore Cultivated
Organic Soils
Grazing Land
Management
Agroforestry and
Regrowth on
Remaining Land
Rice
Management
-1SD
Mean
+1SD
Up to
100 USD
Up to
50 USD
Up to
20 USD
0
1
2
3
4
5
6
ForestryAgriculture Total
Mitigation Potential [GtCO
2
eq/yr (2030)]
Mitigation potentials and costs differ largely between AFOLU com-
modities (Figure 11.16). While average abatement costs are low for
roundwood production under the assumption of perpetual rotation,
costs of mitigation options applied in meat and dairy production sys-
tems have a wide range (1:3 quartile range: 58 – 856 USD / tCO
2
eq).
Calculations of emission intensities are based on the conservative
assumption that production levels stay the same after the applica-
tion of the mitigation option. However, some mitigation options can
increase production. This would not only improve food security but
could also increase the cost-effectiveness of mitigation actions in the
agricultural sector.
Agriculture and forestry-related mitigation could cost-effectively
contribute to transformation pathways associated with long-run
climate change management (Sections 11.9 and 6.3.5). Transforma-
tion pathway modelling includes LUC, as well as land-management
options that reduce emissions intensities and increase sequestration
intensities. However, the resulting transformation pathway emissions
(sequestration) intensities are not comparable to those discussed
here. Transformation pathways are the result of integrated modelling
and the resulting intensities are the net result of many effects. The
intensities capture mitigation technology adoption, but also changes
in levels of production, land-cover change, mitigation technology
competition, and model-specific definitions for sectors / regions / and
assigned emissions inventories. Mitigation technology competition,
in particular, can lead to intensification (and increases in agricultural
emissions intensities) that support cost-effective adoption of other
mitigation strategies, such as afforestation or bioenergy (Sections
11.9 and 6.3.5).
11�6�3 Regional disaggregation of global costs
and potentials in the AFOLU sector
Figure 11.17 shows the economically viable mitigation opportunities
in AFOLU in 2030 by region and by main mitigation option at carbon
prices of up to 20, 50, and 100 USD / tCO
2
eq. The composition of the
agricultural mitigation portfolio varies greatly with the carbon price
(Smith, 2012), with low cost options such as cropland management
being favoured at low carbon prices, but higher cost options such as
restoration of cultivated organic soils being more cost-effective at
higher prices. Figure 11.17 also reveals some very large differences in
mitigation potential, and different ranking of most effective options,
between regions. Across all AFOLU options, Asia has the largest mitiga-
tion potential, with the largest mitigation in both forestry and agricul-
ture, followed by LAM, OECD-1990, MAF, and EIT.
850850
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�14 | Estimates of economic mitigation potentials in the AFOLU sector published since AR4, (AR4 estimates shown for comparison, denoted by arrows), including
bottom-up, sectoral studies, and top-down, multi-sector studies. Some studies estimate potential for agriculture and forestry, others for one or other sector. Supply-side mitigation
potentials are estimated for around 2030, but studies range from estimates for 2025 (Rose etal., 2012) to 2035 (Rose and Sohngen, 2011). Studies are collated for those reporting
potentials at carbon prices of up to ~20 USD / tCO
2
eq (actual range 1.64 – 21.45), up to ~50 USD / tCO
2
eq (actual range 31.39 – 50.00), and up to ~100 USD / tCO
2
eq (actual range
70.0 – 120.91). Demand-side options (shown on the right-hand side of the figure) are for ~2050 and are not assessed at a specific carbon price, and should be regarded as techni-
cal potentials. Smith etal. (2013) values are mean of the range. Not all studies consider the same options or the same GHGs; further details are given in the text.
Mitigation Potential [GtCO
2
eq/yr]
0
3
6
9
12
15
Smith et al. (2013) - Diet and All Measures
Smith et al. (2013) - Feed Improvement
Popp et al. (2011)
Stehfest et al. (2009) - High [No Animal Products]
Stehfest et al. (2009) - Low [Waste Reduction Only]
Kindermann et al. (2008)
Rose et al. (2012) - IMAGE 2.3 450 ppm
Sohngen (2009)
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
Smith et al. (2008)
IPCC AR4 (2007)
UNEP (2011)
McKinsey & Co (2009)
Kindermann et al. (2008)
Sohngen (2009)
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
Smith et al. (2008)
IPCC AR4 (2007)
Rose et al. (2012) - IMAGE 2.3 550 ppm
Rose et al. (2012) - GTEM EMF-21 4.5 W/m
2
Rose et al. (2012) - MESSAGE EMF-21 3.0 W/m
2
Kindermann et al. (2008)
Rose et al. (2012) - IMAGE 2.3 650 ppm
Rose and Songhen (2011) - Ideal Policy Scenario
Rose and Songhen (2011) - Policy DC1
Golub et al. (2009)
UNEP (2011)
Smith et al. (2008)
IPCC AR4 (2007)
Rose et al. (2012) - IMAGE 2.2 EMF-21 4.5 W/m
2
Rose et al. (2012) - MESSAGE A2r-21 4.5W/m
2
Rose et al. (2012) - MESSAGE EMF-21 4.5 W/m
2
Rose et al. (2012) - GRAPE EMF-21 4.5 W/m
2
Forestry
Agriculture
Up to 20 USD/tCO
2
eq Up to 50 USD/tCO
2
eq Up to 100 USD/tCO
2
eq Demand-Side
Measures -
Technical
Potentials
851851
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�15 | GHG emissions intensities of selected major AFOLU commodities for decades 1960s – 2000s, based on (Tubiello etal., 2012). i) Cattle meat, defined as GHG (enteric
fermentation + manure management of cattle, dairy and non-dairy) / meat produced; ii) Pig meat, defined as GHG (enteric fermentation + manure management of swine, market
and breeding) / meat produced; iii) Chicken meat, defined as GHG (manure management of chickens) / meat produced; iv) Milk, defined as GHG (enteric fermentation + manure man-
agement of cattle, dairy) / milk produced; v) Eggs, defined as GHG (manure management of chickens, layers) / egg produced; vi) Rice, defined as GHG (rice cultivation) / rice produced;
vii) Cereals, defined as GHG (synthetic fertilizers) / cereals produced; viii) Wood, defined as GHG (carbon loss from harvest) / roundwood produced. Data Source: (FAOSTAT, 2013).
0
1
2
3
4
5
6
7
8
GHG Emissions Intensities
[kgCO
2
eq/kg of Commodity;
kgCO
2
eq/m
3
Roundwood]
Cattle Meat
Pig Meat
Chicken
Eggs
Rice
Milk
Cereals
Roundwood
1960-1970 1970-1980 1980-1990 1990-2000 2000-2010
Figure 11�16 | Potential changes of emission intensities of major AFOLU commodities through implementation of commodity-specific mitigation measures (left panel) and related
mitigation costs (right panel). Commodities and GHG emission sources are defined as in Figure 11.15, except for roundwood, expressed as the amount of carbon sequestered
per unit roundwood from reforestation and afforestation within dedicated plantation cycles. Agricultural emission intensities represent regional averages, calculated based on
2000 – 2010 data (FAOSTAT, 2013) for selected commodities. Data on mitigation potentials and costs of measures are calculated using the mean values reported by (Smith etal.,
2008) and the maximum and minimum are defined by the highest and lowest values for four climate zones for cereals and rice, or five geographical regions for milk and cattle
meat. Emission intensities and mitigation potentials of roundwood production are calculated using data from Sathaye etal. (2005; 2006), FAO (2006), and IPCC (2006); maximum
and minimum values are defined by the highest and lowest values for 10 geographical regions. The right panel shows the mitigation costs (in USD / tCO
2
eq) of commodity-specific
mitigation measures (25
th
to 75
th
percentile range).
-7-6-5-4-3-2-10123456710 9 8
Roundwood
Cereals
Rice
Milk
Cattle Meat
Indicative Levelized Cost of Conserved Carbon [USD2010/tCO2eq]Emission Intensity [tCO
2
eq/t Product or tCO
2
eq/m³ Roundwood]
-200 0 200 400 600 800 1000
Minimum
75
th
percentile
Maximum Median
25
th
percentile
2000-2010 Global Average
Emission Intensities
2100
6200
Table 11�8 | Ranges of global mitigation potential (GtCO
2
eq / yr) reported since AR4 | All values are for 2030 except demand-side options that are for ~2050 (full data shown in
Figure 11.14).
up to 20 USD / tCO
2
eq up to 50 USD / tCO
2
eq up to 100 USD / tCO
2
eq Technical potential only
Agriculture onl y
1
0 – 1.59 0.03 – 2.6 0.26 – 4.6 -
Forestry only 0.01 – 1.45 0.11 – 9.5 0.2 – 13.8 -
AFOLU tota l
1,2
0.12 – 3.03 0.5 – 5.06 0.49 – 10.6 -
Demand-side options - - - 0.76 – 8.55
Notes:
1
All lower range values for agriculture are for non-CO
2
GHG mitigation only and do not include soil C sequestration
2
AFOLU total includes only estimates where both agriculture and forestry have been considered together.
852852
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Differences between the most effective forestry options in each region
(Figure 11.18) are particularly striking, with reduced deforestation
dominating the forestry mitigation potential LAM and MAF, but very
little potential in OECD-1990 and EIT. Forest management, followed
by afforestation, dominate in OECD-1990, EIT, and Asia (Figure 11.18).
Among agricultural options, among the most striking of regional dif-
ferences are the rice management practices for which almost all of the
global potential is in Asia, and the large potential for restoration of
organic soils also in Asia (due to cultivated Southeast Asian peats), and
OECD-1990 (due to cultivated northern peatlands; Figure 11.18).
11.7 Co-benefits, risks,
and spillovers
Implementation of AFOLU mitigation measures (Section 11.3) will
result in a range of outcomes beyond changes in GHG balances with
respect to institutional, economic, social, and environmental objectives.
To the extent these effects are positive, they can be deemed ‘co-bene-
fits’; if adverse and uncertain, they imply risks.
8
A global assessment of
8
Co-benefits and adverse side-effects describe effects in non-monetary units
without yet evaluating the net effect on overall social welfare. Please refer to the
respective sections in the framing chapters as well as to the glossary in Annex I for
concepts and definitions — particularly Sections 2.4, 3.6.3, and 4.8.
the co-benefits and adverse side-effects of AFOLU mitigation measures
is challenging for a number of reasons. First, co-benefits and adverse
side-effects depend on the development context and the scale of the
intervention (size), i. e., implementing the same AFOLU mitigation mea-
sure in two different areas (different countries or different regions
within a country) can have different socio-economic, institutional, or
environmental effects (Forner etal., 2006; Koh and Ghazoul, 2008; Tra-
bucco etal., 2008; Zomer etal., 2008; Alves Finco and Doppler, 2010;
Alig etal., 2010, p.201; Colfer, 2011; Davis etal., 2013; Albers and
Robinson, 2013; Muys etal., 2014). Thus the effects are site-specific
and generalizations are difficult. Second, these effects do not necessar-
ily overlap geographically, socially, or over the same time scales (Sec-
tion 11.4.5). Third, there is no general agreement on attribution of co-
benefits and adverse side-effects to specific AFOLU mitigation
measures; and fourth there are no standardized metrics for quantifying
many of these effects. Modelling frameworks are being developed that
allow an integrated assessment of multiple outcomes at landscape
(Bryant et al., 2011), project (Townsend et al., 2012), and smaller
(Smith etal., 2013a) scales. Table 11.9 presents an overview of the
potential effects from AFOLU mitigation measures, while the text pres-
ents the most relevant co-benefits and potential adverse side-effects
from the recent literature.
Maximizing co-benefits of AFOLU mitigation measures can increase
efficiency in achieving the objectives of other international agree-
ments, including the United Nations Convention to Combat Desertifi-
cation (UNCCD, 2011), or the Convention on Biological Diversity (CBD),
and mitigation actions may also contribute to a broader global sus-
Figure 11�18 | Regional differences in forestry options, shown as a proportion of total potential available in forestry in each region. Global forestry activities (annual amount
sequestered or emissions avoided above the baseline for forest management, reduced deforestation and afforestation), at carbon prices up to 100 USD / tCO
2
are aggregated to
regions from results from three models of global forestry and land use: the Global Timber Model (GTM; Sohngen and Sedjo, 2006), the Generalized Comprehensive Mitigation
Assessment Process (Sathaye etal., 2006), and the Dynamic Integrated Model of Forestry and Alternative Land Use (Benítez etal., 2007).
0
20
40
60
80
100
100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD
OECD-1990 EIT LAM MAF ASIA
Proportion from Forest Management
Proportion from Reduced Deforestation
Proportion from Afforestation
Share of Forestry Options [%]
Figure 11�17 | Economic mitigation potentials in the AFOLU sector by region. Agriculture values are from Smith etal. (2007). Forestry values are from Nabuurs etal. (2007). For
forestry, 20 USD values correspond to ‘low’, and 100 USD values correspond to ‘high’ values from Nabuurs etal. (2007). Values of 50 USD represent the mean of the ‘high’ and
‘low’ values from Nabuurs etal. (2007).
Economic Mitigation Potential [GtCO
2
eq/yr (2030)]
0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD
OECD-1990 EIT LAM MAF ASIA
Forestry
Manure Management
Livestock
Restoration of Degraded Lands
Restoration of Cultivated Organic Soils
Grazing Land Management
Rice Management
Cropland Management
Carbon Price per tCO
2
eq
853853
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
tainability agenda (Harvey etal., 2010; Gardner etal., 2012; see Chap-
ter 4). In many cases, implementation of these agendas is limited by
capital, and mitigation may provide a new source of finance (Tubiello
etal., 2009).
11�7�1 Socio-economic effects
AFOLU mitigation measures can affect institutions and living conditions
of the various social groups involved. This section includes potential
effects of AFOLU mitigation measures on three dimensions of sustain-
able development: institutional, social, and economic (Section 11.4.5).
AFOLU mitigation measures may have impacts on land tenure and
land-use rights for several social groups including indigenous peoples,
local communities and other social groups, dependant on natural
assets. Co-benefits from AFOLU mitigation measures can be clarifica-
tion of land tenure and harmonization of rights, while adverse side-
effects can be lack of recognition of customary rights, loss of tenure or
possession rights, and even displacement of social groups (Sunderlin
etal., 2005; Chhatre and Agrawal, 2009; Blom etal., 2010; Sikor etal.,
2010; Robinson etal., 2011; Rosemary, 2011; Larson, 2011; Rosendal
and Andresen, 2011). Whether an impact on land tenure and use rights
is positive or negative depends upon two factors: (a) the institutions
regulating land tenure and land-use rights (e. g., laws, policies), and
(b) the level of enforcement by such institutions (Corbera and Brown,
2008; Araujo etal., 2009; Rosemary, 2011; Larson etal., 2013; Albers
and Robinson, 2013). More research is needed on specific tenure forms
(e. g., individual property, state ownership or community rights), and
on the specific effects from tenure and rights options, on enabling
AFOLU mitigation measures and co-benefits in different regions under
specific circumstances (Sunderlin et al., 2005; Katila, 2008; Chhatre
and Agrawal, 2009; Blom etal., 2010; Sikor etal., 2010; Robinson etal.,
2011; Rosemary, 2011; Larson, 2011; Rosendal and Andresen, 2011).
AFOLU mitigation measures can support enforcement of sectoral
policies (e. g., conservation policies) as well as cross-sectoral coordi-
nation (e. g., facilitating a landscape view for policies in the agricul-
ture, energy, and forestry sectors (Brockhaus etal., 2013). However,
AFOLU mitigation activities can also introduce or reduce clashes with
existing policies in other sectors (e. g., if a conservation policy covers
a forest area, where agricultural land is promoted by another policy
(Madlener etal., 2006; Halsnæs and Verhagen, 2007; Smith et al.,
2007; Beach etal., 2009; Alig etal., 2010; Jackson and Baker, 2010;
DeFries and Rosenzweig, 2010; Pettenella and Brotto, 2011; Section
11.10).
An area of increasing concern since AR4 is the potential impact of
AFOLU mitigation measures on food security. Efforts to reduce hun-
ger and malnutrition will increase individual food demand in many
developing countries, and population growth will increase the num-
ber of individuals requiring secure and nutritionally sufficient food
production. Thus, a net increase in food production is an essential
component for securing sustainable development (Ericksen et al.,
2009; FAO, WFP, and IFAD, 2012). AFOLU mitigation measures linked
to increases in food production (e. g., agroforestry, intensification
of agricultural production, or integrated systems) can increase food
availability and access especially at the local level, while other mea-
sures (e. g., forest or energy crop plantations) can reduce food pro-
duction at least locally (Foley etal., 2005; McMichael et al., 2007;
the co-benefits and adverse side-effects of AFOLU mitigation measures
is challenging for a number of reasons. First, co-benefits and adverse
side-effects depend on the development context and the scale of the
intervention (size), i. e., implementing the same AFOLU mitigation mea-
sure in two different areas (different countries or different regions
within a country) can have different socio-economic, institutional, or
environmental effects (Forner etal., 2006; Koh and Ghazoul, 2008; Tra-
bucco etal., 2008; Zomer etal., 2008; Alves Finco and Doppler, 2010;
Alig etal., 2010, p.201; Colfer, 2011; Davis etal., 2013; Albers and
Robinson, 2013; Muys etal., 2014). Thus the effects are site-specific
and generalizations are difficult. Second, these effects do not necessar-
ily overlap geographically, socially, or over the same time scales (Sec-
tion 11.4.5). Third, there is no general agreement on attribution of co-
benefits and adverse side-effects to specific AFOLU mitigation
measures; and fourth there are no standardized metrics for quantifying
many of these effects. Modelling frameworks are being developed that
allow an integrated assessment of multiple outcomes at landscape
(Bryant et al., 2011), project (Townsend et al., 2012), and smaller
(Smith etal., 2013a) scales. Table 11.9 presents an overview of the
potential effects from AFOLU mitigation measures, while the text pres-
ents the most relevant co-benefits and potential adverse side-effects
from the recent literature.
Maximizing co-benefits of AFOLU mitigation measures can increase
efficiency in achieving the objectives of other international agree-
ments, including the United Nations Convention to Combat Desertifi-
cation (UNCCD, 2011), or the Convention on Biological Diversity (CBD),
and mitigation actions may also contribute to a broader global sus-
Figure 11�18 | Regional differences in forestry options, shown as a proportion of total potential available in forestry in each region. Global forestry activities (annual amount
sequestered or emissions avoided above the baseline for forest management, reduced deforestation and afforestation), at carbon prices up to 100 USD / tCO
2
are aggregated to
regions from results from three models of global forestry and land use: the Global Timber Model (GTM; Sohngen and Sedjo, 2006), the Generalized Comprehensive Mitigation
Assessment Process (Sathaye etal., 2006), and the Dynamic Integrated Model of Forestry and Alternative Land Use (Benítez etal., 2007).
0
20
40
60
80
100
100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD 100 USD50 USD20 USD
OECD-1990 EIT LAM MAF ASIA
Proportion from Forest Management
Proportion from Reduced Deforestation
Proportion from Afforestation
Share of Forestry Options [%]
854854
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Pretty, 2008; Godfray etal., 2010; Jackson and Baker, 2010; Graham-
Rowe, 2011; Jeffery etal., 2011).
Regarding human health reduced emissions from agriculture and for-
estry may also improve air, soil, and water quality (Smith etal., 2013a),
thereby indirectly providing benefits to human health and well-being.
Demand-side measures aimed at reducing the proportion of livestock
products in human diets that are high in animal products are also
associated with multiple health benefits (McMichael etal., 2007; Ste-
hfest etal., 2009; Marlow etal., 2009). AFOLU mitigation measures,
particularly in the livestock sector, can have an impact on animal wel-
fare (Sundrum, 2001; Lund and Algers, 2003; Keeling etal., 2011; Kehl-
bacher etal., 2012; Koknaroglu and Akunal, 2013).
A major area of concern is related to the potential impacts of
AFOLU mitigation measures on equity (Sections 3.3; 4.2; 4.7;
Box 11�6 | Challenges for mitigation in developing countries in the AFOLU sector
Mitigation challenges related to the AFOLU sector
The contribution of developing countries to future GHG emissions
is expected to be very significant due to projected increases in food
production by 2030 driving short-term land conversion in these
countries. Mitigation efforts in the AFOLU sector rely mainly on
reduction of GHG emissions and an increase in carbon sequestra-
tion (Table 11.2). Potential activities include reducing deforesta-
tion, increasing forest cover, agroforestry, agriculture and livestock
management, and production of sustainable renewable energy
(Sathaye etal., 2005; Smith etal., 2013b). Although agriculture and
forestry are important sectors for GHG abatement (Section 11.2.3),
it is likely that technology alone will not be sufficient to deliver the
necessary transitions to a low-GHG future (Alig etal., 2010; Section
11.3.2). Other barriers include access to market and credits, techni-
cal capacities to implement mitigation options, including accurate
reporting of emission levels and emission factors based on activity
data, and institutional frameworks and regulations (Corbera and
Schroeder, 2011; Mbow etal., 2012; Sections 11.7; 11.8). Addition-
ally, the diversity of circumstances among developing countries
makes it difficult to establish the modelled relationships between
GDP and CO
2
emissions per capita found by using the Kaya
identity. This partly arises from the wide gap between rural and
urban communities, and the difference in livelihoods (e. g., the use
of fuel wood, farming practices in various agro-ecological condi-
tions, dietary preferences with a rising middle class in developing
countries, development of infrastructure, and behavioural change,
etc.; Lambin and Meyfroidt, 2011). Also, some mitigation pathways
raise the issue of non-permanence and leakage that can lead to
the transfer activities to non-protected areas, which may threaten
conservation areas in countries with low capacities (Lippke etal.,
2003; Jackson and Baker, 2010; Section 11.3.2).
Critical issues to address are the co-benefits and adverse side-
effects associated with changed agricultural production, the
necessary link between mitigation and adaptation, and how to
manage incentives for a substantial GHG abatement initiative
without compromising food security (Smith and Wollenberg, 2012;
Sections 11.5; 11.7). The challenge is to strike a balance between
emissions reductions / adaptation and development / poverty
alleviation priorities, or to find policies that co-deliver. Mitigation
pathways in developing countries should address the dual need
for mitigation and adaptation through clear guidelines to manage
multiple options (Section 11.5.4). Prerequisites for the successful
implementation of AFOLU mitigation projects are ensuring that
(a) communities are fully engaged in implementing mitigation
strategies, (b) any new strategy is consistent with ongoing policies
or programmes, and (c) a priori consent of small holders is given.
Extra effort is required to address equity issues including gender,
challenges, and prospects (Mbow etal., 2012).
Mitigation challenges related to the bioenergy sector
Bioenergy has a significant mitigation potential, provided that the
resources are developed sustainably and that bioenergy sys-
tems are efficient (Chum etal., 2011; Section 11.9.1). Bioenergy
production can be integrated with food production in developing
countries, e. g., through suitable crop rotation schemes, or use of
by-products and residues (Berndes etal., 2013). If implemented
sustainably this can result in higher food and energy outcomes
and hence reduce land-use competition. Some bioenergy options
in developing countries include perennial cropping systems, use of
biomass residues and wastes, and advanced conversion systems
(Beringer etal., 2011; Popp etal., 2011; Box 7.1). Agricultural and
forestry residues can provide low-carbon and low-cost feedstock
for bioenergy. Biomass from cellulosic bioenergy crops feature
substantially in future energy systems, especially in the framework
of global climate policy that aims at stabilizing CO
2
concentration
at low levels (Popp etal., 2011; Section 11.13). The large-scale
use of bioenergy is controversial in the context of developing
countries because of the risk of reducing carbon stocks and
releasing carbon to the atmosphere (Bailis and McCarthy, 2011),
threats to food security in Africa (Mbow, 2010), and threats to
biodiversity via the conversion of forests to biofuel (e. g., palm oil)
plantations. Several studies underline the inconsistency between
the need for bioenergy and the requirement for, e. g., Africa, to
use its productive lands for sustainable food production (Cotula
etal., 2009). Efficient biomass production for bioenergy requires a
range of sustainability requirements to safeguard food production,
biodiversity, and terrestrial carbon storage.
855855
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
4.8). Depending on the actual and perceived distribution of socio-
economic benefits, responsibilities (burden sharing), as well the
access to decision making, financing mechanisms, and technology,
AFOLU mitigation measures can promote inter- and intra-genera-
tional equity (Di Gregorio etal., 2013). Conversely, depending on
the policy instruments and the implementation schemes of these
mitigation measures, they can increase inequity and land conflicts,
or marginalize small-scale farm / forest owners or users (Robinson
etal., 2011; Kiptot and Franzel, 2012; Huettner, 2012; Mattoo and
Subramanian, 2012). Potential impacts on equity and benefit-shar-
ing mechanisms arise for AFOLU activities using forestry measures
in developing countries including conservation, restoration, reduced
deforestation and degradation, as well as sustainable management
and afforestation / reforestation (Combes Motel etal., 2009; Catta-
neo etal., 2010; Rosemary, 2011).
Large-scale land acquisition (often referred to as ‘land grabbing’)
related to the promotion of AFOLU mitigation measures (especially for
production of bioenergy crops) and its links to sustainable develop-
ment in general, and equity in particular, are emerging issues in the
literature (Cotula etal., 2009; Scheidel and Sorman, 2012; Mwakaje,
2012; Messerli etal., 2013; German etal., 2013).
In many cases, the implementation of agricultural and forestry systems
with positive impacts mitigating climate change are limited by capi-
tal, and carbon payments or compensation mechanisms may provide
a new source of finance (Tubiello etal., 2009). For instance, in some
cases, mitigation payments can help to make production of non-timber
forest products (NTFP) economically viable, further diversifying income
at the local level (Singh, 2008). However, depending on the accessibil-
ity of the financing mechanisms (payments, compensation, or other)
economic benefits can become concentrated, marginalizing many
local stakeholders (Combes Motel etal., 2009; Alig etal., 2010; Asante
etal., 2011; Asante and Armstrong, 2012; Section 11.8). The realiza-
tion of economic co-benefits is related to the design of the specific
mechanisms and depends upon three main variables: (a) the amount
and coverage of these payments, (b) the recipient of the payments, and
(c) timing of payments (ex-ante or ex-post; Corbera and Brown, 2008;
Skutsch etal., 2011)� Further considerations on financial mechanisms
and carbon payments, both within and outside UNFCCC agreements,
are described in Section 11.10.
Financial flows supporting AFOLU mitigation measures (e. g., those
resulting from the REDD+) can have positive effects on conserving
biodiversity, but could eventually create conflicts with conservation
of biodiversity hotspots, when their respective carbon stocks are low
(Gardner etal., 2012; Section 11.10). Some authors propose that car-
bon payments can be complemented with biodiversity payments as an
option for reducing tradeoffs with biodiversity conservation (Phelps
etal., 2010a). Bundling of ecosystem service payments, and links to
carbon payments, is an emerging area of research (Deal and White,
2012).
11�7�2 Environmental effects
Availability of land and land competition can be affected by AFOLU
mitigation measures. Different stakeholders may have different views
on what land is available, and when considering several AFOLU mitiga-
tion measures for the same area, there can be different views on the
importance of the goods and ecosystem services provided by the land,
e. g., some AFOLU measures can increase food production but reduce
water availability or other environmental services. Thus decision mak-
ers need to be aware of potential site-specific tradeoffs within the sec-
tor. A further potential adverse side-effect is that of increasing land
rents and food prices due to a reduction in land availability for agricul-
ture in developing countries (Muller, 2009; Smith etal., 2010, 2013b;
Rathmann etal., 2010; Godfray etal., 2010; de Vries and de Boer, 2010;
Harvey and Pilgrim, 2011; Amigun etal., 2011; Janzen, 2011; Cotula,
2012; Scheidel and Sorman, 2012; Haberl etal., 2013a).
AFOLU mitigation options can promote conservation of biological
diversity (Smith etal., 2013a) both by reducing deforestation (Chhatre
etal., 2012; Murdiyarso etal., 2012; Putz and Romero, 2012; Visseren-
Hamakers et al., 2012), and by using reforestation / afforestation to
restore biodiverse communities on previously developed farmland
(Harper et al., 2007). However, promoting land-use changes (e. g.,
through planting monocultures on biodiversity hot spots) can have
adverse side-effects, reducing biodiversity (Koh and Wilcove, 2008;
Beringer etal., 2011; Pandit and Grumbine, 2012; Ziv etal., 2012; Hert-
wich, 2012; Gardner etal., 2012).
In addition to potential climate impacts, land-use intensity drives the
three main N loss pathways (nitrate leaching, denitrification, and ammo-
nia volatilization) and typical N balances for each land use indicate that
total N losses also increase with increasing land-use intensity (Steven-
son etal., 2010). Leakages from the N cycle can cause air (e. g., ammo-
nia (NH
3
+
), nitrogen oxides (NO
x
))
9
, soil nitrate (NO
3
-
) and water pollu-
tion (e. g., eutrophication), and agricultural intensification can lead to
a variety of other adverse environmental impacts (Smith etal., 2013a).
Combined strategies (e. g., diversified crop rotations and organic N
sources) or single-process strategies (e. g., reduced N rates, nitrification
inhibitors, and changing chemical forms of fertilizer) can reduce N losses
(Bambo etal., 2009; Gardner and Drinkwater, 2009). Integrated systems
may be an alternative approach to reduce leaching (Section 11.10).
AFOLU mitigation measures can have either positive or negative
impacts on water resources, with responses dependant on the miti-
gation measure used, site conditions (e. g., soil thickness and slope,
hydrological setting, climate; Yu etal., 2013) and how the particular
mitigation measure is managed. There are two main components:
water yield and water quality. Water yields can be manipulated
with forest management, through afforestation, reforestation, for-
9
Please see Section 7.9.2 and WGII Section 11.9 for a discussion of health effects
related to air pollution.
856856
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Table 11�9 | Summary of potential co-benefits (green arrows) and adverse side-effects (orange arrows) from AFOLU mitigation measures; arrows pointing up / down denote posi-
tive / negative effect on the respective issue. These effects depend on the specific context (including bio-physical, institutional, and socio-economic aspects) as well as on the scale
of implementation. For an assessment of macroeconomic, cross-sectoral effects associated with mitigation policies (e. g., on energy prices, consumption, growth, and trade), see
Sections 3.9, 6.3.6, 13.2.2.3, and 14.4.2. Note: Co-benefits / adverse side-effects of bioenergy are discussed in Section 11.13.
Issue Potential co-benefit or adverse side-effect Scale AFOLU mitigation measure
Institutional
Land tenure and
use rights
Improving (↑) or diminishing (↓) tenure and use rights for local
communities and indigenous peoples, including harmonization of
land tenure and use regimes (e.g., with customary rights)
Local to national Forestry (4, 5, 6, 8, 9, 12, 20)
Sectoral policies
Promoting (↑) or contradicting (↓) the enforcement of
sectoral (forest and/or agriculture) policies
National Forestry (2, 5, 6, 9, 20); land-based
agriculture (7, 11, 20)
Cross-sectoral
policies
Cross-sectoral coordination (↑) or clashes (↓) between
forestry, agriculture, energy, and/or mining policies
Local to national Forestry (7, 20); agriculture (7, 11, 20)
Participative
mechanisms
Creation/use of participative mechanisms (↑) for decision making
regarding land management (including participation of various
social groups, e.g., indigenous peoples or local communities)
Local to national Forestry (4, 5, 6, 8, 9, 14, 20); agriculture
(20, 32); integrated systems (20, 34)
Benefit sharing
mechanisms
Creation/use of benefits-sharing mechanisms (↑) from AFOLU mitigation measures
Local to national Forestry (4, 5, 6, 8, 20)
Social
Food security
Increase (↑) or decrease (↓) on food availability and access
Local to national Forestry (18, 19); agriculture (7, 15, 18,
19, 23, 28, 30); livestock (2, 3, 19, 35, 36);
integrated systems (18, 19); biochar (17, 26)
Local/traditional
knowledge
Recognition (↑) or denial (↓) of indigenous and local
knowledge in managing (forest/agricultural) land
Local/sub-national Forestry (4, 5, 6, 8, 20); agriculture (20, 28);
integrated systems (2); livestock (2, 3, 35); biochar (2)
Animal welfare Changes in perceived or measured animal welfare (perceived due to
cultural values or measured, e.g., through amount of stress hormones)
Local to national Livestock (2, 31, 35, 37, 38)
Cultural values
Respect and value cultural habitat and traditions (↑), reduce (↓), or
increase (↑) existing conflicts or social discomfort (4, 5, 6, 20, 8)
Local to trans-
boundary
Forestry (4, 5, 6, 9, 20)
Human health Impacts on health due to dietary changes, especially in societies
with a high consumption of animal protein (↓)
Local to global Changes in demand patterns (31, 36)
Equity
Promote (↑) or not (↓) equal access to land, decision making, value chain,
and markets as well as to knowledge- and benefit-sharing mechanisms
Local to global Forestry (4, 5, 6, 8, 9, 10, 20); agriculture (11, 23, 32)
Economic
Income
Increase (↑) or decrease (↓) in income. There are
concerns regarding income distribution (↑)
Local Forestry (6, 7, 8, 16, 20, 21, 22); agriculture (16, 19,
20, 23, 28); livestock (2, 3); integrated systems (7,
20); biochar (24); changes in demand patterns (2)
Employment
Employment creation (↑) or reduction of employment
(especially for small farmers or local communities) (↓)
Local Forestry (8, 20); agriculture (20, 23); livestock
(2, 3); integrated systems (7, 20)
Financing
mechanisms
Access (↑) or lack of access (↓) to new financing schemes
Local to global Forestry (6, 8, 16, 20); agriculture
(16, 20); livestock (2, 3)
Economic activity
Diversification and increase in economic activity (↑) while concerns on equity (↑)
Local Forestry (6, 7, 8, 20); land-based agriculture
(16, 19, 20, 23, 28); livestock (2, 3)
Environmental
Land availability
Competition between land uses and risk of activity or community displacement (↑)
Local to trans-
boundary
Forestry and land-based-agriculture (5, 6, 15,
18, 20, 29, 30); livestock (2, 3, 29, 40)
Biodiversity
Monocultures can reduce biodiversity (↓). Ecological restoration
increases biodiversity and ecosystem services (↑) by 44 and 25%
respectively (28). Conservation, forest management, and integrated
systems can keep biodiversity (↑) and/or slow desertification (↓)
Local to trans-
boundary
Forestry (1, 19, 20, 27); on conservation and
forest management (1, 19, 21, 27, 30); agriculture
and integrated systems (15, 19, 20, 28, 30)
Albedo
Positive impacts (↑) on albedo and evaporation and interactions with ozone
Local to global See Section 11.5
N and P cycles
Impacts on N and P cycles in water (↓/↑) especially from
monocultures or large agricultural areas
Local to trans-
boundary
Agriculture (19, 23, 30, 35); livestock (2, 3, 30)
Water resources Monocultures and /or short rotations can have negative impacts on water availability
(↓). Potential water depletion due to irrigation (↓). Some management practices
can support regulation of the hydrological cycle and protection of watersheds (↑)
Local to trans-
boundary
Forestry (1, 19, 20, 27); land-based agriculture
(30, 44); integrated systems (2, 30, 44)
Soil
Soil conservation (↑) and improvement of soil quality and fertility (↑). Reduction
of erosion. Positive or negative carbon mineralization priming effect (↑/↓)
Local Forestry (44, 45); land-based agriculture (13, 19,
23, 28, 30); integrated systems biochar (39, 40)
New products
Increase (↑) or decrease (↓) on fibre availability as well
as non-timber/non-wood products output
Local to national Forestry (18, 19, 41, 42); agriculture (7, 15, 18,
19, 23, 28, 30); integrated systems (18, 19)
Ecosystem resilience
Increase (↑) or reduction (↓) of resilience, reduction of disaster risks (↓)
Local to trans-
boundary
Forestry, integrated systems (11, 33; see Section 11.5)
⇒
857857
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
est thinning, or deforestation. In general, reduction in water yields
in afforestation / reforestation projects has been reported in both
groundwater or surface catchments (Jackson etal., 2005), or where
irrigation water is used to produce bioenergy crops. For water sup-
ply security, it is important to consider the relative yield reduction
and this can have severe consequences in dry regions with inherent
water shortages (Wang etal., 2011c). Where there is a water imbal-
ance, however, this additional water use can be beneficial by reduc-
ing the efflux of salts (Jackson etal., 2005). Another aspect of water
yield is the reduction of flood peaks, and also prolonged periods of
water flow, because discharge is stabilized (Jackson et al., 2005),
however low flows can be reduced because of increased forest
water use. Water quality can be affected by AFOLU in several ways.
For example, minimum tillage systems have been reported to reduce
water erosion and thus sedimentation of water courses (Lal, 2011).
Deforestation is well known to increase erosion and thus efflux of
silt; avoiding deforestation will prevent this. In other situations,
watershed scale reforestation can result in the restoration of water
quality (e. g., Townsend et al., 2012). Furthermore, strategic place-
ment of tree belts in lands affected by dryland salinity can remedi-
ate the affected lands by lowering the water table (Robinson etal.,
2004) . Various types of AFOLU mitigation can result in degradation
of water sources through the losses of pesticides and nutrients to
water (Smith etal., 2013a).
AFOLU mitigation measures can have several impacts on soil. Increas-
ing or maintaining carbon stocks in living biomass (e. g., through forest
or agroforestry systems) will reduce wind erosion by acting as wind
breaks and may increase crop production; and reforestation, conserva-
tion, forest management, agricultural systems, or bioenergy systems
can be used to restore degraded or abandoned land (Smith etal., 2008;
Stickler et al., 2009; Chatterjee and Lal, 2009; Wicke et al., 2011b;
Sochacki etal., 2012). Silvo-pastoral systems can help to reverse land
degradation while providing food (Steinfeld etal., 2008, 2010; Janzen,
2011). Depending on the soil type, production temperature regimes,
the specific placement and the feedstock tree species, biochar can
have positive or negative carbon mineralization priming effects over
time (Zimmerman etal., 2011; Luo etal., 2011).
AFOLU mitigation options can promote innovation, and many technolog-
ical supply-side mitigation options outlined in Section 11.3 also increase
agricultural and silvicultural efficiency. At any given level of demand for
agricultural products, intensification increases output per unit area and
would therefore, if all else were equal, allow the reduction in farmland
area, which would in turn free land for C sequestration and / or bioen-
ergy production (Section 11.4). For example, a recent study calculated
potentially large GHG reductions from global agricultural intensification
by comparing the past trajectory of agriculture (with substantial yield
improvements), with a hypothetical trajectory with constant technol-
ogy (Burney etal., 2010). However, in real-world situations increases in
yield may result in feedbacks such as increased consumption (‘rebound
effects’; see Section 11.4; Lambin and Meyfroidt, 2011; Erb, 2012).
11�7�3 Public perception
Mitigation measures that support sustainable development are likely to
be viewed positively in terms of public perception, but a large-scale drive
towards mitigation without inclusion of key stakeholder communities
involved would likely not be greeted favourably (Smith and Wollenberg,
2012). However, there are concerns about competition between food and
AFOLU outcomes, either because of an increasing use of land for biofuel
plantations (Fargione et al., 2008; Alves Finco and Doppler, 2010), or
afforestation / reforestation (Mitchell etal., 2012), or by blocking the trans-
formation of forest land into agricultural land (Harvey and Pilgrim, 2011).
Further, lack of clarity regarding the architecture of the future inter-
national climate regime and the role of AFOLU mitigation measures is
perceived as a potential threat for long-term planning and long-term
investments (Streck, 2012; Visseren-Hamakers etal., 2012). Certain tech-
Issue Potential co-benefit or adverse side-effect Scale AFOLU mitigation measure
Technology
Infrastructure
Increase (↑) or decrease (↓) in availability of and access to infrastructure.
Competition for infrastructure for agriculture (↑), can increase social conflicts
Local Agriculture (20, 46, 47)
Technology
innovation and
transfer
Promote (↑) or delay (↓) technology development and transfer
Local to global Forestry (7, 13, 25); agriculture (23); livestock (2, 3)
Technology
Acceptance
Can facilitate acceptance of sustainable technologies (↑)
Local to national Forestry (7, 13, 25); livestock (2, 3, 35)
Notes: AFOLU mitigation measures are grouped following the structure given in Table 11.2
Sources:
1
Trabucco etal., 2008;
2
Steinfeld etal., 2010;
3
Gerber etal., 2010;
4
Sikor etal., 2010;
5
Rosemary, 2011;
6
Pettenella and Brotto, 2011;
7
Jackson and Baker, 2010;
8
Corbera
and Schroeder, 2011;
9
Colfer, 2011;
10
Blom etal., 2010;
11
Halsnæs and Verhagen, 2007;
12
Larson, 2011;
13
Lichtfouse etal., 2009;
14
Thompson etal., 2011;
15
Graham-Rowe, 2011;
16
Tubiello etal., 2009;
17
Barrow, 2012;
18
Godfray etal., 2010;
19
Foley etal., 2005;
20
Madlener etal., 2006;
21
Strassburg etal., 2012;
22
Canadell and Raupach, 2008;
23
Pretty, 2008;
24
Galinato etal., 2011;
25
Macauley and Sedjo, 2011;
26
Jeffery etal., 2011;
27
Benayas etal., 2009;
28
Foley etal., 2011;
29
Haberl etal., 2013;
30
Smith etal., 2013;
31
Stehfest etal.,
2009;
32
Chhatre etal., 2012;
33
Seppälä etal., 2009;
34
Murdiyarso etal., 2012;
35
de Boer etal., 2011;
36
McMichael etal., 2007;
37
Koknaroglu and Akunal, 2013;
38
Kehlbacher etal.,
2012;
39
Zimmerman etal., 2011;
40
Luo etal., 2011;
41
Mirle, 2012;
42
Albers and Robinson, 2013;
43
Smith etal., 2013a;
44
Chatterjee and Lal, 2009;
45
Smith, 2008;
46
Ziv etal., 2012;
47
Beringer etal., 2011;
48
Douglas etal., 2009
858858
Agriculture, Forestry and Other Land Use (AFOLU)
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Chapter 11
nologies, such as animal feed additives and genetically modified organ-
isms are banned in some jurisdictions due to perceived health and / or
environmental risks. Public perception is often as important as scien-
tific evidence of hazard / risk in considering government policy regarding
such technologies (Royal Society, 2009; Smith and Wollenberg, 2012).
11�7�4 Spillovers
Emerging knowledge on the importance of ecosystems services as a
means for addressing climate change mitigation and adaptation have
brought attention to the role of ecosystem management for achiev-
ing several development goals, beyond climate change adaptation
and mitigation. This knowledge has enhanced the creation of ecosys-
tem markets (Section 11.10). In some jurisdictions ecosystem markets
are developing (MEA, 2005; Engel etal., 2008; Deal and White, 2012;
Wünscher and Engel, 2012)and these allow valuation of various com-
ponents of land-use changes, in addition to mitigation (Mayrand and
Paquin, 2004; Barbier, 2007). Different approaches are used; in some
cases the individual components (both co-benefits and adverse side-
effects) are considered singly (bundled), in other situations they are
considered together (stacked) (Deal and White, 2012). Ecosystem mar-
ket approaches can serve as a framework to assess the benefits of miti-
gation actions from project, to regional and national level (Farley and
Costanza, 2010). Furthermore, designing ecosystem market approaches
yields methodologies for the evaluation of individual components (e. g.,
water quality response to reforestation, timber yield), and other types of
ecosystem service (e. g., biodiversity, social amenity; Bryan etal., 2013).
11.8 Barriers and opportunities
Barriers and opportunities refer to the conditions provided by the devel-
opment context (Section 11.4.5). These conditions can enable and facil-
itate (opportunities) or hinder (barriers) the full use of AFOLU mitiga-
tion measures. AFOLU programmes and policies can help to overcome
barriers, but countries being affected by many barriers will need time,
financing, and capacity support. In some cases, international negotia-
tions have recognized these different circumstances among countries
and have proposed corresponding approaches (e. g., a phased approach
in the REDD+, Green Climate Fund; Section 11.10). Corresponding to
the development framework presented in Section 11.4.5, the following
types of barriers and benefits are discussed: socio-economic, environ-
mental, institutional, technological, and infrastructural.
11�8�1 Socio-economic barriers and
opportunities
The design and coverage of the financing mechanisms is key to suc-
cessful use of the AFOLU mitigation potential (Section 11.10; Chapter
16). Questions remain over which costs will be covered by such mecha-
nisms. If financing mechanisms fail to cover at least transaction and
monitoring costs, they will become a barrier to the full implementation
of AFOLU mitigation. According to some studies, opportunity costs also
need to be fully covered by any financing mechanism for the AFOLU
sector, especially in developing countries, as otherwise AFOLU mitiga-
tion measures would be less attractive compared to returns from other
land uses (Angelsen, 2008; Cattaneo etal., 2010; Böttcher etal., 2012).
Conversely, if financing mechanisms are designed to modify economic
activity, they could provide an opportunity to leverage a larger propor-
tion of AFOLU mitigation potential.
Scale of financing sources can become either a barrier (if a relevant
financial volume is not secured) or create an opportunity (if finan-
cial sources for AFOLU suffice) for using AFOLU mitigation potential
(Streck, 2012; Chapter 16). Another element is the accessibility to
AFOLU financing for farmers and forest stakeholders (Tubiello etal.,
2009, p. 200; Havemann, 2011; Colfer, 2011). Financial concerns,
including reduced access to loan and credits, high transaction costs or
reduced income due to price changes of carbon credits over the project
duration, are potential risks for AFOLU measures, especially in develop-
ing countries, and when land holders use market mechanisms (e. g.,
Afforestation and Reforestation (A / R) Clean Development Mechanism
(CDM); Madlener etal., 2006).
Poverty is characterized not only by low income, but also by insuf-
ficient food availability in terms of quantity and / or quality, limited
access to decision making and social organization, low levels of
education and reduced access to resources (e. g., land or technology;
UNDP International Poverty Centre, 2006). High levels of poverty can
limit the possibilities for using AFOLU mitigation options, because of
short-term priorities and lacking resources. In addition, poor communi-
ties have limited skills and sometimes lack of social organization that
can limit the use and scaling up of AFOLU mitigation options, and can
increase the risk of displacement, with other potential adverse side-
effects (Smith and Wollenberg, 2012; Huettner, 2012). This is especially
relevant when forest land sparing competes with other development
needs e. g., increasing land for agriculture or promoting some types of
mining (Forner etal., 2006), or when large-scale bioenergy compro-
mises food security (Nonhebel, 2005; Section 11.13).
Cultural values and social acceptance can determine the feasibility of
AFOLU measures, becoming a barrier or an opportunity depending of
the specific circumstances (de Boer etal., 2011).
11�8�2 Institutional barriers and opportunities
Transparent and accountable governance and swift institutional estab-
lishment are very important for a sustainable implementation of AFOLU
mitigation measures. This includes the need to have clear land tenure
and land-use rights regulations and a certain level of enforcement, as
well as clarity about carbon ownership (Palmer, 2011; Thompson etal.,
859859
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
2011; Markus, 2011; Rosendal and Andresen, 2011; Murdiyarso etal.,
2012 Sections 11.4.5; 11.10; Chapters 14; 15).
Lack of institutional capacity (as a means for securing creation of equal
institutions among social groups and individuals) can reduce feasibil-
ity of AFOLU mitigation measures in the near future, especially in areas
where small-scale farmers or forest users are the main stakeholders
(Laitner etal., 2000; Madlener etal., 2006; Thompson etal., 2011a). Lack
of an international agreement that supports a wide implementation of
AFOLU measures can become a major barrier for realizing the mitiga-
tion potential from the sector globally (Section 11.10; Chapter 13).
11�8�3 Ecological barriers and opportunities
Mitigation potential in the agricultural sector is highly site-specific,
even within the same region or cropping system (Baker etal., 2007;
Chatterjee and Lal, 2009). Availability of land and water for different
uses need to be balanced, considering short- and long-term priorities,
and global differences in resource use. Consequently, limited resources
can become an ecological barrier and the decision of how to use them
needs to balance ecological integrity and societal needs (Jackson,
2009).
At the local level, the specific soil conditions, water availability, GHG
emission-reduction potential as well as natural variability and resil-
ience to specific systems will determine the level of realization of miti-
gation potential of each AFOLU measure (Baker etal., 2007; Halvorson
etal., 2011). Frequent droughts in Africa and changes in the hydro-
meteorological events in Asia and Central and South America are
important in defining the specific regional potential (Bradley et al.,
2006; Rotenberg and Yakir, 2010). Ecological saturation (e. g., soil car-
bon or yield) means that some AFOLU mitigation options have their
own limits (Section 11.5). The fact that many AFOLU measures can
provide adaptation benefits provides an opportunity for increasing
ecological efficiency (Guariguata etal., 2008; van Vuuren etal., 2009;
Robledo etal., 2011; Section 11.5).
11�8�4 Technological barriers and
opportunities
Technological barriers refer to the limitations in generating, procuring,
and applying science and technology to identify and solve an environ-
mental problem. Some mitigation technologies are already applied now
(e. g., afforestation, cropland, and grazing land management, improved
livestock breeds and diets) so there are no technological barriers for
these options, but others (e. g., some livestock dietary additives, crop
trait manipulation) are still at the development stage (see Table 11.2).
The ability to manage and re-use knowledge assets for scientific com-
munication, technical documentation and learning is lacking in many
areas where mitigation could take place. Future developments pres-
ent opportunities for additional mitigation to be realized if efforts to
deliver ease-of-use and range-of-use are guaranteed. There is also a
need to adapt technology to local needs by focusing on existing local
opportunities (Kandji etal., 2006), as proposed in Nationally Appropri-
ate Mitigation Actions (NAMAs) (Section 11.10).
Barriers and opportunities related to monitoring, reporting, and veri-
fication of the progress of AFOLU mitigation measures also need be
considered. Monitoring activities, aimed at reducing uncertainties, pro-
vide the opportunity of increasing credibility in the AFOLU sector. How-
ever there are technical challenges. For instance, monitoring carbon
in forests with high spatial variability in species composition and tree
density can pose a technical barrier to the implementation of some
AFOLU activities (e. g., REDD+; Baker etal., 2010; Section 11.10). The
IPCC National Greenhouse Gas Inventory Guidelines (Paustian etal.,
2006) also provide an opportunity, because they offer standard sci-
entific methods that countries already use to report AFOLU emissions
and removals under the UNFCCC. Also, field research in high-biomass
forests (Gonzalez etal., 2010) shows that remote sensing data and
Monte Carlo quantification of uncertainty offer a technical opportu-
nity for implementing REDD+ (Section 11.10). Exploiting the exist-
ing human skills within a country is essential for realizing full AFOLU
potential. A lack of trained people can therefore become a barrier to
implementation of appropriate technologies (Herold and Johns, 2007).
Technology improvement and technology transfer are two crucial
components for the sustainable increase of agricultural production
in developed and developing regions with positive impacts in terms
of mitigation, soil, and biodiversity conservation (Tilman etal., 2011).
International and national policy instruments are relevant to foster
technology transfer and to support research and development (Section
11.10.4), overcoming technological barriers.
11.9 Sectoral implications
of transformation
pathways and sustain-
able development
Some climate change management objectives require large-scale
transformations in human societies, in particular in the produc-
tion and consumption of energy and the use of the land resource.
Chapter 6 describes alternative ‘transformation pathways’ of societ-
ies over time from now into the future, consistent with different cli-
mate change outcomes. Many pathways that foresee large efforts in
mitigation will have implications for sustainable development, and
corrective actions to move toward sustainability may be possible.
However, impacts on development are context specific and depend
upon scale and institutional agreements of the AFOLU options, and
not merely on the type of option (see Sections 11.4 for development
860860
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
context and systemic view, 11.7 for potential co-benefits and adverse
effects, and 11.8 for opportunities and challenges). To evaluate sec-
toral implications of transformation pathways, it is useful to first
characterize the pathways in terms of mitigation technologies and
policy assumptions.
11�9�1 Characterization of transformation
pathways
Uncertainty about reference AFOLU emissions is significant both his-
torically (Section 11.2) and in projections (Section 6.3.1.3). The trans-
formation projections of the energy system, AFOLU emissions and
land-use are characterized by the reference scenario, as well as the
abatement policy assumptions regarding eligible abatement options,
regions covered, and technology costs over time. Many mitigation
scenarios suggest a substantial cost-effective mitigation role for land
related mitigation assuming idealized policy implementation, with
immediate, global, and comprehensive availability of land-related miti-
gation options. However, policy implementation of large-scale land-
based mitigation will be challenging. In addition, the transformation
pathways often ignore, or only partially cover, important mitigation
risks, costs, and benefits (e. g., transaction costs or Monitoring Report-
ing and Verification (MRV) costs), and other developmental issues
including intergenerational debt or non-monetary benefits (Ackerman
etal., 2009; Lubowski and Rose, 2013).
In recent idealized implementation scenarios from a model compari-
son study, land-related changes can represent a significant share of
emissions reductions (Table 11.10). In these scenarios, models assume
an explicit terrestrial carbon stock incentive, or a global forest protec-
tion policy, as well as an immediate global mitigation policy in general.
Bioenergy is consistently deployed (because it is considered to reduce
net GHG emissions over time; see Section 6.3.5), and agricultural emis-
sions are priced. Note that bioenergy related mitigation is not captured
in Table 11.10. The largest land emission reductions occur in net CO
2
emissions, which also have the greatest variability across models.
Some models exhibit increasing land CO
2
emissions under mitigation,
as bioenergy feedstock production leads to LUC, while other models
exhibit significant reductions with protection of existing terrestrial car-
bon stocks and planting of new trees to increase carbon stocks. Land-
related CO
2
and N
2
O mitigation is more important in the nearer-term
Table 11�10 | Cumulative land-related emissions reductions, land reduction share of global reductions, and percent of baseline land emissions reduced for CH
4
, CO
2
, and N
2
O in
idealized implementation 550 and 450 ppm CO
2
eq scenarios. The number of scenarios is indicated for each GHG and atmospheric concentration goal. Negative values represent
increases in emissions (Kriegler etal., 2013). Bioenergy-related mitigation is not captured in the table.
550 ppm 450 ppm
2010 – 2030 2010 – 2050 2010 – 2100 2010 – 2030 2010 – 2050 2010 – 2100
Cumulative global land-related
emissions reductions (GtCO
2
eq)
CH
4
min 3.5 17.5 51.4 0.0 4.5 52.3
(n = 5 / 5) max 9.8 46.0 201.7 12.7 50.5 208.6
CO
2
min – 20.2 – 43.2 – 129.8 – 20.3 – 50.8 – 153.9
(n = 11 / 10) max 280.9 543.0 733.4 286.6 550.5 744.6
N
2
O min 3.1 8.4 25.5 3.1 8.4 25.5
(n = 4 / 4) max 8.2 27.7 96.6 9.7 29.3 96.8
Sum
min – 8.7 2.5 53.9 – 3.7 5.6 69.7
(n = 4 / 4)
max 295.2 587.7 903.5 301.4 596.9 940.3
Land reductions share of total
global emissions reductions
CH
4
min 25 % 20 % 20 % 22 % 20 % 16 %
max 37 % 40 % 42 % 30 % 31 % 36 %
CO
2
min – 43 % – 12 % – 4 % – 20 % -8 % -4 %
max 74 % 48 % 17 % 73 % 47 % 15 %
N
2
O
min 52 % 61 % 65 % 53 % 61 % 65 %
max 95 % 90 % 87 % 78 % 83 % 85 %
Sum
min -11 % 0 % 1 % -2 % 1 % 1 %
max 70 % 47 % 19 % 69 % 46 % 17 %
Percent of baseline land
emissions reduced
CH
4
min 3 % 8 % 10 % 0 % 2 % 10 %
max 8 % 16 % 28 % 10 % 18 % 30 %
CO
2
min -42 % -89 % 0 % -42 % -104 % 0 %
max 373 % 417 % 504 % 381 % 423 % 512 %
N
2
O
min 4 % 6 % 8 % 4 % 6 % 8 %
max 10 % 16 % 22 % 12 % 17 % 22 %
Sum
min -4 % 1 % 7 % -2 % 1 % 8 %
max 97 % 100 % 73 % 99 % 101 % 76 %
861861
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11
Chapter 11
for some models. Land-related N
2
O and CH
4
reductions are a signifi-
cant part of total N
2
O and CH
4
reductions, but only a small fraction
of baseline emissions, suggesting that models have cost-effective rea-
sons to keep N
2
O and CH
4
emissions. Emissions reductions from land
increase only slightly with the stringency of the atmospheric concen-
tration goal, as energy and industry emission reductions increase faster
with target stringency. This result is consistent with previous studies
(Rose etal., 2012). Land-based CO
2
reductions can be over 100 % of
baseline emissions, from the expansion of managed and unmanaged
forests for sequestration.
Emissions reductions from individual land-related technologies, espe-
cially bioenergy, are not generally reported in transformation path-
way studies. In part, this is due to emphasis on the energy system, but
also other factors that make it difficult to uniquely quantify mitiga-
tion by technology. An exception is Rose etal. (2012) who reported
agriculture, forest carbon, and bioenergy abatement levels for vari-
ous atmospheric concentration goals. Cumulatively, over the century,
bioenergy was the dominant strategy, followed by forestry, and then
agriculture. Bioenergy cumulatively generated approximately 5 to
52 GtCO
2
eq and 113 to 749 GtCO
2
eq mitigation by 2050 and 2100,
respectively. In total, land-related strategies contributed 20 to 60 %
of total cumulative abatement to 2030, 15 to 70 % to 2050, and 15
to 40 % to 2100.
Within models, there is a positive correlation between emissions
reductions and GHG prices. However, across models, it is less clear, as
some estimate large reductions with a low GHG price, while others
estimate low reductions despite a high GHG price (Rose etal., 2012).
For the most part, these divergent views are due to differences in
model assumptions and are difficult to disentangle. Overall, while a
tighter target and higher carbon price results in a decrease in land-use
emissions, emissions decline at a decreasing rate. This is indicative of
the rising relative cost of land mitigation, the increasing demand for
bioenergy, and subsequent increasing need for overall energy system
GHG abatement and energy consumption reductions. For additional
discussion of land’s potential role in transformation pathways, espe-
cially regarding physical land-use and bioenergy, see sections 6.3.2.4
and 6.3.5.
Models project increased deployment of, and dependence on, modern
bioenergy (i. e., non-traditional bioenergy that is produced centrally to
service communities rather than individual household production for
heat and cooking), with some models projecting up to 95 EJ per year
by 2030, and up to 245 EJ per year by 2050. Models universally project
that the majority of agriculture and forestry mitigation, and bioenergy
primary energy, will occur in developing and transitional economies
(Section 6.3.5).
More recently, the literature has begun analyzing more realistic policy
contexts. This work has identified a number of policy coordination and
implementation issues. There are many dimensions to policy coordina-
tion: technologies, sectors, regions, climate and non-climate policies,
and timing. There are three prominent issues. First, there is coordina-
tion between mitigation activities. For instance, increased bioenergy
incentives without global terrestrial carbon stock incentives or global
forest protection policy, could result in substantial land conversion
and emissions with large-scale deployment of energy crops. The pro-
jected emissions come primarily from the displacement of pasture,
grassland, and natural forest (Sections 6.3.5 and 11.4.3). Energy crop-
land expansion also results in non-energy cropland conversion. These
studies find that ignoring land conversion emissions with energy crop
expansion, results in the need for deeper emissions reductions in the
fossil and industrial sectors, and increased total mitigation costs.
However, illustrative scenarios by (Calvin etal., 2013a) suggest that
extensive forest protection policies may be needed for managing bio-
energy driven deforestation. Note that providing energy crops, espe-
cially while protecting terrestrial carbon stocks, could result in a sig-
nificant increase in food prices, potentially further exacerbated if also
expanding forests (Wise etal., 2009; Popp etal., 2011; Reilly etal.,
2012; Calvin et al., 2013a; see also Sections 11.4.3 and 11.13.7).
In addition to competition between energy crops and forest carbon
strategies, there is also competition between avoided deforestation
and afforestation mitigation strategies, but synergies between forest
management and afforestation (Rose and Sohngen, 2011). Bioenergy
sustainability policies across sectors also need to be coordinated
(Frank etal., 2013).
The second major concern is coordination of mitigation activity over
time. The analyses noted in the previous paragraph assume the abil-
ity to globally protect or incentivize all, or a portion, of forest carbon
stocks. A few studies to date have evaluated the implications of stag-
gered forest carbon incentives — across regions and forest carbon
activities. For instance, (Calvin etal., 2009) estimate land CO
2
emis-
sions increases of 4 and 6 GtCO
2
/ yr in 2030 and 2050, respectively,
from scenarios with staggered global regional climate policies that
include forest carbon incentives. And, Rose and Sohngen (2011) find
that fragmented or delayed forest carbon policy could accelerate defor-
estation. They project 60 – 100 GtCO
2
of leakage by 2025 with a carbon
price of 15 USD
2010
/ tCO
2
that rises at 5 % per year. Regional agriculture
and forestry mitigation supply costs are also affected by regional par-
ticipation / non-participation, with non-participating regions potentially
increasing the mitigation costs for participating regions (Golub etal.,
2009). Staggered adoption of land-mitigation policies will likely have
institutional and socioeconomic implications as well (Madlener etal.,
2006). Institutional issues, especially clarification of land tenure and
property rights and equity issues (Section 11.7), will also be critical for
successful land mitigation in forestry over time (Palmer, 2011; Gupta,
2012; Karsenty etal., 2014).
Finally, the type of incentive structure has implications. International
land-related mitigation projects are currently regarded as high risk car-
bon market investments, which may affect market appeal. Also, mitiga-
tion scenarios assume that all emissions and sequestration changes
are priced (similar to capping all emissions). However, mitigation,
especially in agriculture and forestry, may be sought through volun-
862862
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
tary markets, where mitigation suppliers choose whether to participate
(Section 11.10). For instance, Rose etal. (2013) estimate reduced miti-
gation potential, as well as over-crediting, for United States agriculture
and forestry with voluntary mitigation supply incentives, e. g., mitiga-
tion decreased 25 – 55 % at 15 USD
2010
/ tCO
2
eq due to non-participant
leakage and non-additional crediting.
11�9�2 Implications of transformation pathways
for the AFOLU sector
Transformation pathways indicate that a combination of forces can
result in very different projected landscapes relative to today, even
in baseline scenarios (Section 6.3.5). For instance, Popp etal. (2013)
evaluate three models, and show that projected 2030 baseline changes
from today alone vary sharply across models in all regions (Figure
11.19). See Section 6.3.5 for global land cover change results for a
broader set of studies and policy contexts. In the examples in Figure
11.19, projections exhibit growth and reductions in both non-energy
cropland (e. g., ASIA), and energy cropland (e. g., ASIA, OECD-1990,
EIT). Furthermore, different kinds of land are converted when baseline
cropland expands (e. g., MAF). Mitigation generally induces greater
land cover changes than in baseline scenarios, but there are very differ-
ent potential transformation visions. Overall, it is difficult to generalize
on regional land cover effects of mitigation. For the same atmospheric
concentration goal, some models convert significant area, some do
not. There is energy cropland expansion in many regions that supports
the production of bioenergy. Less consistent is the response of forest
land, primarily due to differences in the land carbon options / policies
modelled (Section 6.3.5). Finally, there is relatively modest additional
land conversion in the 450 ppm, compared to the 550 ppm, scenarios,
which is consistent with the declining role of land-related mitigation
with policy stringency.
The implications of transformation pathway scenarios with large
regional expansion of forest cover for carbon sequestration, depends
in part on how the forest area increases (Figure 11.19; Popp etal.,
2013). If forest areas increase through the expansion of natural veg-
etation, biodiversity and a range of other ecosystem services pro-
vided by forests could be enhanced. If afforestation occurs through
large-scale plantation, however, some negative impacts on biodiver-
sity, water, and other ecosystem services could arise, depending on
what land cover the plantation replaces and the rotation time (Sec-
tion 11.7). Similar issues arise with large-scale bioenergy, and envi-
ronmental impacts of energy crop plantations, which largely depend
upon where, how, and at what scale they are implemented, and how
they are managed (Davis etal., 2013; see Section 11.13.6). Not sur-
prisingly, the realistic policy coordination and implementation issues
discussed in Section 11.9.1 could have significant land-use conse-
quences, and additional policy design research is essential to better
characterize mitigation costs, net emissions, and other social implica-
tions.
11�9�3 Implications of transformation pathways
for sustainable development
The implications of the transformation pathways on sustainable
development are context- and time-specific. A detailed discussion of
the implications of large-scale LUC, competition between different
demands for land, and the feedbacks between LUC and other services
provided by land is provided in Section 11.4, potential co-benefits
and adverse side-effects are discussed in Section 11.7, and Section
6.6 compares potential co-benefits and adverse side-effects across
sectors, while Section 11.8 presents the opportunities and barriers
for promoting AFOLU mitigation activities in the future. Finally, Sec-
tion 11.13 discusses the specific implications of increasing bioenergy
crops.
11.10 Sectoral policies
Climate change and different policy and management choices inter-
act. The interrelations are particularly strong in agriculture and for-
estry: climate has a strong influence on these sectors that also con-
stitute sources of GHG as well as sinks (Golub etal., 2009). The land
provides a multitude of ecosystem services, climate change mitigation
being just one of many services that are vital to human well-being.
The nature of the sector means that there are, potentially, many bar-
riers and opportunities as well as a wide range of potential impacts
related to the implementation of AFOLU mitigation options (Sections
11.7 and 11.8). Successful mitigation policies need to consider how
to address the multi-functionality of the sector. Furthermore, physi-
cal environmental limitations are central for the implementation of
mitigation options and associated policies (Pretty, 2013). The cost-
effectiveness of different measures is hampered by regional variabil-
ity. National and international agricultural and forest climate policies
have the potential to redefine the opportunity costs of international
land-use in ways that either complement or hinder the attainment
of climate change mitigation goals (Golub etal., 2009). Policy inter-
actions could be synergistic (e. g., research and development invest-
ments and economic incentives for integrated production systems) or
conflicting (e. g., policies promoting land conversion vs. conservation
policies) across the sector (see Table 11.11). Additionally, adequate
policies are needed to orient practices in agriculture and in forestry
toward global sharing of innovative technologies for the efficient use
of land resources to support effective mitigation options (see Table
11.2).
Forty-three countries in total (as of December 2010) have pro-
posed NAMAs to the UNFCCC. Agriculture and forestry activities
were considered as ways to reduce their GHG emissions in 59 and
94 % of the proposed NAMAs. For the least developed countries,
the forestry sector was quoted in all the NAMAs, while the agricul-
863863
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
tural sector was represented in 70 % of the NAMAs (Bockel etal.,
2010). Policies related to the AFOLU sector that affect mitigation are
discussed below according to the instruments through which they
may be implemented (economic incentives, regulatory and control
approaches, information, communication and outreach, research
and development). Economic incentives (e. g., special credit lines for
low-carbon agriculture, sustainable agriculture and forestry prac-
tices, tradable credits, payment for ecosystem services) and regula-
tory approaches (e. g., enforcement of environmental law to reduce
deforestation, set-aside policies, air and water pollution control
reducing nitrate load and N
2
O emissions) have been effective in dif-
ferent cases. Investments in research, development, and diffusion
(e. g., improved fertilizer use efficiency, livestock improvement, better
forestry management practices) could result in positive and synergis-
tic impacts for adaptation and mitigation (Section 11.5). Emphasis
is given to REDD+, considering its development in recent years, and
relevance for the discussion of mitigation policies in the forestry sec-
tor.
Figure 11�19 | Regional land cover change by 2030 from 2005 from three models for baseline (left) and idealized policy implementation 550 ppm CO
2
eq (centre) and 450 ppm
CO
2
eq (right) scenarios. (Popp etal., 2013).
Other arable
Other
Pasture
Energy crops ForestAbandoned
Non-energy crops
450 ppm CO
2
eq550 ppm CO
2
eqBaseline
GCAM
IMAGE
REMIND
-MAgPIE
REMIND
-MAgPIE
REMIND
-MAgPIE
REMIND
-MAgPIE
REMIND
-MAgPIE
ASIA
GCAM
IMAGE
LAM
GCAM
IMAGE
MAF
GCAM
IMAGE
OECD-1990
-300 -100 100 300
GCAM
IMAGE
Million Hectares
EIT
-300 -100 100 300
Million Hectares
-300 -100 100 300
Million Hectares
Type of Land Cover Change
864864
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
11�10�1 Economic incentives
Emissions trading: Carbon markets occur under both compliance
schemes and as voluntary programmes. A review of existing offset
programmes was provided by Kollmuss etal. (2010). More details are
also presented in Section 15.5.3. Compliance markets (Kyoto offset
mechanisms, mandatory cap-and-trade systems, and other manda-
tory GHG systems) are created and regulated by mandatory national,
regional, or international carbon reduction regimes (Kollmuss etal.,
2010). The three Kyoto Protocol mechanisms are very important for
the regulatory market: CDM, Joint Implementation (JI) and the Emis-
sions Trading System (ETS). Currently, AFOLU projects in CDM only
include specific types of projects: for agriculture — methane avoid-
ance (manure management), biogas projects, agricultural residues
for biomass energy; for forestry — reforestation and afforestation. By
June 2013, the total number of registered CDM projects was 6989,
0.6 and 2.5 % of this total being related to afforestation / reforestation
and agriculture, respectively (UNFCCC — CDM); therefore, finance
streams coming from A / R CDM Projects are marginal from the global
perspective. An analysis of A / R CDM projects suggests crucial fac-
tors for the performance of these projects are initial funding support,
design, and implementation guided by large organizations with tech-
nical expertise, occurrence on private land (land with secured prop-
erty rights attached), and that most revenue from Certified Emission
Reductions (CERs) is directed back to local communities (Thomas
etal., 2010).
Table 11�11 | Some regional and global programs and partnerships related to illegal logging, forest management and conservation and REDD+.
Programme / Institution / Source Context Objectives and Strategies
Forest Law Enforcement and
Governance (FLEG) /
World Bank /
www. worldbank. org / eapfleg
Illegal logging and lack of appropriate forest governance
are major obstacle to countries to alleviate poverty, to
develop their natural resources and to protect global
and local environmental services and values
Support regional forest law enforcement and governance (FLEG)
Improving Forest Law Enforcement
and Governance in the European
Neighbourhood Policy East Countries
and Russia (ENPI-FLEG) / EU /
www. enpi-fleg. org
Regional cooperation in the European Neighbourhood
Policy Initiative East Countries (Armenia, Azerbaijan,
Belarus, Georgia, Moldova, and Ukraine), and Russia
following up on the St. Petersburg Declaration
Support governments, civil society, and the private sector in participating
countries in the development of sound and sustainable forest management
practices, including reducing the incidence of illegal forestry activities.
Forest Law Enforcement, Governance
and Trade (FLEGT) / European Union /
www. euflegt. efi. int /
Illegal logging has a devastating impact on some of the
world’s most valuable forests. It can have not only serious
environmental, but also economic and social consequences.
Exclude illegal timber from markets, to improve the supply of legal timber and to increase
the demand for responsible wood products. Central elements are trade accords to
ensure legal timber trade and support good forest governance in the partner countries.
There are a number of countries in Africa, Asia, South and Central America currently
negotiating FLEGT Voluntary Partnership Agreements (VPAs) with the European Union.
Program on Forests (PROFOR) / multiple
donors including the European
Union, European countries,
Japan and the World Bank /
www. profor. info
Well-managed forests have the potential to reduce
poverty, spur economic development, and contribute
to a healthy local and global environment
Provide in-depth analysis and technical assistance on key forest questions
related to livelihoods, governance, financing, and cross-sectoral issues. PROFOR
activities comprise analytical and knowledge generating work that support the
strategy’s objectives of enhancing forests‘ contribution to poverty reduction,
sustainable development and the protection of environmental services.
UN-REDD Programme / United Nations /
www. un-redd. org
The UN collaborative initiative on Reducing Emissions from
Deforestation and forest Degradation (REDD) in developing
countries was launched in 2008 and builds on the convening
role and technical expertise of the FAO, UNDP, and the UNEP.
The Programme supports national REDD+ readiness efforts in 46 partner
countries (Africa, Asia-Pacific, and Latin America) through (i) direct support
to the design and implementation of REDD+ National Programmes;
and (ii) complementary support to national REDD+ action (common
approaches, analyses, methodologies, tools, data, and best practices).
REDD+ Partnership / International
effort (50 different countries) /
www. reddpluspartnership. org
The UNFCCC has encouraged the Parties to coordinate their
efforts to reduce emissions from deforestation and forest
degradation. As a response, countries attending the March
2010 International Conference on the Major Forest Basins,
hosted by the Government of France, agreed on the need
to forge a strong international partnership on REDD+.
The REDD+ Partnership serves as an interim platform for its partner countries to
scale up actions and finance for REDD+ initiatives in developing countries (including
improving the effectiveness, efficiency, transparency, and coordination of REDD+
and financial instruments), to facilitate knowledge transfer, capacity enhancement,
mitigation actions and technology development, and transfer among others.
Forest Investment Program
(FIP) / Strategic Climate Fund (a
multi-donor Trust Fund within the
Climate Investment Funds)
www. climateinvestmentfunds. org / cif /
Reduction of deforestation and forest degradation
and promotion of sustainable forest management,
leading to emission reductions and the
protection of carbon terrestrial sinks.
Support developing countries’ efforts to REDD and promote sustainable
forest management by providing scaled-up financing to developing
countries for readiness reforms and public and private investments,
identified through national REDD readiness or equivalent strategies.
Forest Carbon Partnership
(FCPF) / World Bank /
www. forestcarbonpartnership. org
Assistance to developing countries to implement
REDD+ by providing value to standing forests.
Builds the capacity of developing countries to reduce emissions from deforestation
and forest degradation and to tap into any future system of REDD+.
Indonesia-Australia Forest
Carbon Partnership /
www. iafcp. or. id
Australia’s assistance on climate change and builds on long-
term practical cooperation between Indonesia and Australia.
The Partnership supports strategic policy dialogue on climate change,
the development of Indonesia‘s National Carbon Accounting System,
and implementing demonstration activities in Central Kalimantan.
865865
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
There are compliance schemes outside the scope of the Kyoto Proto-
col, but these are carried out exclusively at the national level, with no
relation to the Protocol. In 2011, Australia started the Carbon Farming
Initiative (CFI) that allows farmers and investors to generate tradable
carbon offsets from farmland and forestry projects. This followed sev-
eral years of state-based and voluntary activity that resulted in 65,000
ha of A / R projects (Mitchell etal., 2012). Another example is The West-
ern Arnhem Land Fire Abatement Project (WALFA), a fire management
project in Australia initiated in 2006 that produces a tradable carbon
offset through the application of improved fire management using tra-
ditional management practices of indigenous land owners (Whitehead
etal., 2008; Bradstock etal., 2012). Alberta’s offset credit system is
a compliance mechanism for entities regulated under the province’s
mandatory GHG emission intensity-based regulatory system (Koll-
muss etal., 2010). In the case of N
2
O emissions from agriculture, the
Alberta Quantification Protocol for Agricultural N
2
O Emissions Reduc-
tions issues C offset credits for on-farm reductions of N
2
O emissions
and fuel use associated with the management of fertilizer, manure, and
crop residues for each crop type grown. Other N
2
O emission reduction
protocols (e. g., Millar etal., 2010) are being considered for the Veri-
fied Carbon Standard, the American Carbon Registry, and the Climate
Action Reserve (Robertson etal., 2013).
Agriculture and Forestry activities are not covered by the European
Union Emissions Trading Scheme (EU ETS), which is by far the largest
existing carbon market. Forestry entered the New Zealand Kyoto Pro-
tocol compliant ETS in 2008, and mandatory reporting for agriculture
began in 2012, although full entry of agriculture into the scheme has
been delayed indefinitely. Agricultural participants include meat pro-
cessors, dairy processors, nitrogen fertilizer manufacturers and import-
ers, and live animal exporters, although some exemptions apply (Gov-
ernment of New Zealand). California’s Cap-and-Trade Regulation took
effect on January 1, 2012, with amendments to the Regulation effec-
tive September 1, 2012. The enforceable compliance obligation began
on January 1, 2013. Four types of projects were approved as eligible
to generate carbon credits to regulated emitters in California: avoid-
ance of methane emissions from installation of anaerobic digesters on
farms, carbon sequestration in urban and rural forestry, and destruc-
tion of ozone depleting substances (California Environmental Protec-
tion Agency).
Voluntary carbon markets operate outside of the compliance markets.
By enabling businesses, governments, non-governmental organizations
(NGOs), and individuals to purchase offsets that were created either
in the voluntary market or through the CDM, they can offset their
emissions (Verified or Voluntary Emissions Reductions (VERs)). The vol-
untary offset market includes a wide range of programmes, entities,
standards, and protocols (e. g., Community & Biodiversity Standards,
Gold Standard, Plan Vivo among others) to improve the quality and
credibility of voluntary offsets. The most common incentives for the
quantity buyers of carbon credits in the private sector are corporate
social responsibility and public relations. Forest projects are increas-
ing in the voluntary markets. Transactions of carbon credits from this
sector totalled 133 million USD in 2010, 95 % of them in voluntary
markets (Peters-Stanley etal., 2011).
Reducing emissions from deforestation; reducing emissions from forest
degradation; conservation of forest carbon stocks; sustainable man-
agement of forests; and enhancement of forest carbon stocks (REDD+):
REDD+ consists of forest-related activities implemented voluntarily by
developing countries that may, in isolation or jointly lead to significant
climate change mitigation
10
. REDD+ was introduced in the agenda of
the UNFCCC in 2005, and has since evolved to an improved under-
standing of the potential positive and negative impacts, methodologi-
cal issues, safeguards, and financial aspects associated with REDD+
implementation. Here, we first address the REDD+ discussions under
the UNFCCC, but also introduce other REDD+-related initiatives. The
novel aspects of REDD+ under the Convention, relative to previous
forest-related mitigation efforts by developing countries under the
UNFCCC are its national and broader coverage, in contrast to project-
based mitigation activities
11
(e. g., under the CDM of the Kyoto Proto-
col). Its main innovation is its results-based approach, in which pay-
ments are done ex post in relation to a mitigation outcome already
achieved, as opposed to project-based activities, where financing is
provided ex ante in relation to expected outcomes. A phased approach
to REDD+ was agreed at the UNFCCC, building from the develop-
ment of national strategies or action plans, policies and measures,
and evolving into results-based actions that should be fully measured,
reported, and verified — MRV (UNFCCC Dec. 1 / 16). REDD+ payments
are expected for results-based actions, and although the UNFCCC has
already identified potential ways to pay for these
12
, the financing archi-
tecture for the REDD+ mechanism is still under negotiation under the
UNFCCC.
Meanwhile, and as a result to the explicit request from the UNFCCC for
early actions in REDD+, different regional and global programmes and
partnerships address forest management and conservation and readi-
ness for REDD+ (Table 11.11), while some REDD+ strategies have
started in countries with significant forest cover (see Box 11.7 for
examples). Initiatives include multilateral activities (e. g., UN-REDD
10
Decision 1 / CP.16 (FCCC / CP / 2010 / 7 / Add.1 , paragraph 70) “Encourages
developing countries to contribute to mitigation actions in the forest sector by
undertaking the following activities, as deemed appropriate by each Party and in
accordance with their respective capabilities and national circumstances — reduc-
ing emissions from deforestation; reducing emissions from forest degradation;
conservation of forest carbon stocks; sustainable management of forests; and
enhancement of forest carbon stocks”.
11
Decision 1 / CP.16 (FCCC / CP / 2010 / 7 / Add.1 , paragraph 73) “Decides that the
activities undertaken by Parties referred to in paragraph 70 above should be
implemented in phases, beginning with the development of national strategies
or action plans, policies and measures, and capacity-building, followed by the
implementation of national policies and measures and national strategies or
action plans that could involve further capacity-building, technology development
and transfer and results-based demonstration activities, and evolving into results-
based actions that should be fully measured, reported and verified”.
12
Decision 2 / CP.17 (FCCC / CP / 2011 / 9 / Add.1, paragraph 65) “Agrees that results-
based finance provided to developing country Parties that is new, additional and
predictable may come from a wide variety of sources, public and private, bilateral
and multilateral, including alternative sources”.
866866
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Programme, Forest Carbon Partnership Facility, Forest Investment Pro-
gram), bilateral activities (e. g., Tanzania-Norway, Indonesia-Norway),
country driven initiatives (in addition to 16 UN-REDD Programme
countries, the Programme also supports 31 other partner countries
across Africa, Asia-Pacific, and Latin America and the Caribbean; UN-
REDD Programme — Support to Partner Countries).
REDD+ can be a very cost-effective option for mitigating climate change
and could supply a large share of global abatement of emissions from
the AFOLU sector from the extensive margin of forestry, especially
through reducing deforestation in tropical regions (Golub etal., 2009).
Issues of concern for REDD+ implementation have been captured under
REDD+ safeguards in line with the UNFCCC Cancun Agreement. To
respond to the requirements outlined in the UNFCCC agreement, a num-
ber of steps need to be considered in the development of country-level
safeguard information systems for REDD+ including defining social and
environmental objectives, assessing potential benefits and risks from
REDD+, assessing current safeguard systems, drafting a strategic plan or
policy, and establishing a governance system.
A growing body of literature has analyzed different aspects related to
the implementation, effectiveness, and scale of REDD+, as well as the
interactions with other social and environmental co-benefits (e. g.,
Angelsen etal., 2008; Levin etal., 2008; Larson, 2011; Gardner etal.,
2012). Results-based REDD+ actions, which are entitled to results-
based finance, require internationally agreed rules for MRV. Measur-
ing and monitoring the results will most likely rely on a combination of
remotely-sensed data with ground-based inventories. The design of a
REDD policy framework (and specifically its rules) can have a significant
impact on monitoring costs (Angelsen etal., 2008; Böttcher etal., 2009).
Forest governance is another central aspect in recent studies, including
debate on decentralization of forest management, logging concessions
in public-owned commercially valuable forests, and timber certifica-
tion, primarily in temperate forests (Agrawal etal., 2008). Although the
majority of forests continue to be formally owned by governments, there
are indications that the effectiveness of forest governance is increas-
ingly independent of formal ownership (Agrawal etal., 2008). However,
there are widespread concerns that REDD+ will increase costs on forest-
dependent peoples and in this context, stakeholders rights, including
rights to continue sustainable traditional land-use practices, appear as a
precondition for REDD development (Phelps etal., 2010b).
Some studies have addressed the potential displacement of emissions,
i. e., a reduction of emissions in one place resulting in an increase
of emissions elsewhere (or leakage) (Santilli et al., 2005; Forner
etal., 2006; Nabuurs etal., 2007; Strassburg etal., 2008, 2009; Sec-
tion 11.3.2). The national coverage of REDD+ might ameliorate the
issue of emissions displacement, a major drawback of project-based
approaches (Herold and Skutsch, 2011). To minimize transnational dis-
placement of emissions, REDD+ needs to stimulate the largest number
of developing countries to engage voluntarily. There are also concerns
about the impacts of REDD+ design and implementation options on
biodiversity conservation, as areas of high C content and high biodi-
versity are not necessarily coincident. Some aspects of REDD+ imple-
mentation that might affect biodiversity include site selection, man-
agement strategies, and stakeholder engagement (Harvey etal., 2010).
From a conservation biology perspective, it is also relevant where
the displacement occurs, as deforestation and exploitation of natural
Box 11�7 | Examples of REDD+ initiatives at national scale in different regions with significant
extension of forest cover
Amazon Fund: The Amazon Fund in Brazil was officially cre-
ated in 2008 by a presidential decree. The Brazilian Development
Bank (BNDES) was given the responsibility of managing it. The
Norwegian government played a key role in creating the fund by
donating funds to the initiative in 2009. Since then, the Amazon
Fund has received funds from two more donors: the Federal
Republic of Germany and Petrobrás, Brazil’s largest oil company.
As of February 2013, 1.03 billion USD has been pledged, with 227
million USD approved for activities (Amazon Fund).
UN-REDD Democratic Republic of Congo: The Congo Basin
rainforests are the second largest after Amazonia. In 2009,
Democratic Republic of the Congo (DRC), with support of UN-
REDD Programme and Forest Carbon Partnership Facility (FCPC),
started planning the implementation stages of REDD+ readiness.
The initial DRC National Programme transitioned into the full
National Programme (Readiness Plan) after it was approved by
the UN-REDD Programme Policy Board in 2010 (UN-REDD Pro-
gramme). The budget comprises 5.5 million USD
2010
and timeframe
is 2010 – 2013.
Indonesia-Norway REDD+ Partnership: In 2010, the Indo-
nesia-Norway REDD+ Partnership was established through an
agreement between governments of the two countries. The
objective was to ‘support Indonesia’s efforts to reduce emissions
from deforestation and degradation of forests and peatlands.
Indonesia agreed to take systematic and decisive action to
reduce its forest and peat-related GHG emissions, whereas Nor-
way agreed to support those efforts by making available up to
1 billion USD
2010
, exclusively on a payment-for-results basis over
the next few years’ (UN-REDD Programme). In 2013, Indonesia’s
government has extended the moratorium on new forest conces-
sions for a further two years, protecting an additional 14.5 Mha
of forest.
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resources could move from areas of low conservation value to those of
higher conservation value, or to other natural ecosystems, threatening
species native to these ecosystems (Harvey etal., 2010). Additionally,
transnational displacement could cause deforestation to move into
relatively intact areas of high biodiversity value, or into countries that
currently have little deforestation (Putz and Redford, 2009).
Taxes, charges, subsidies: Financial regulations are another approach
to pollution control. A range of instruments can be used: pollution
charges, taxes on emission, taxes on inputs, and subsidies (Jakobsson
etal., 2002). Nitrogen taxes are one possible instrument, since agri-
cultural emissions of N
2
O mainly derive from the use of nitrogenous
fertilizers. An analysis of the tax on the nitrogen content of synthetic
fertilizers in Sweden indicated that direct N
2
O emissions from agri-
cultural soils in Sweden (the tax abolished in 2010) would have been
on average 160 tons or 2 % higher without the tax (Mohlin, 2013).
Additionally, the study showed that removal of the N tax could com-
pletely counteract the decreases in CO
2
emissions expected from
the future tax increase on agricultural CO
2
. The mitigation potential
of GHG-weighted consumption taxes on animal food products was
estimated for the EU using a model of food consumption (Wirsenius
etal., 2011). A 7 % reduction of current GHG emission in European
Union (EU) agriculture was estimated with a GHG-weighted tax on
animal food products of 79 USD
2010
/ tCO
2
eq (60 EUR
2010
/ tCO
2
eq). Low-
interest loans can also support the transition to sustainable agricul-
tural practices as currently implemented in Brazil, the second largest
food exporter, through the national programme (launched in 2010;
Plano ABC).
11�10�2 Regulatory and control approaches
Deforestation control and land planning (protected areas and land
sparing / set-aside policies): The rate of deforestation in the tropics and
relative contribution to anthropogenic carbon emissions has been
declining (Houghton, 2012; see Section 11.2 for details). Public policies
have had a significant impact by reducing deforestation rates in some
tropical countries (see, e. g., Box 11.8).
Since agricultural expansion is one of the drivers of deforestation (espe-
cially in tropical regions), one central question is if intensification of
agriculture reduces cultivated areas and results in land sparing by con-
centrating production on other land. Land sparing would allow released
lands to sequester carbon, provide other environmental services, and
protect biodiversity (Fischer etal., 2008). In the United States, over 13
Mha of former cropland are enrolled in the US Conservation Reserve
Program (CRP), with biodiversity, water quality, and carbon sequestra-
tion benefits (Gelfand etal., 2011). In 1999, China launched the Grain
for Green Program or Sloping Land Conversion Program as a national
measure to increase vegetation cover and reduce erosion. Cropland and
barren land were targeted and over 20 Mha of land were converted into
mostly tree-based plantations. Over its first 10 years between ~800 to
1700 MtCO
2
eq (Moberg, 2011) were sequestered.
Environmental regulation (GHG and their precursors emissions con-
trol): In many developed countries, environmental concerns related
to water and air pollution since the mid-1990s led to the adoption of
laws and regulations that now mandate improved agricultural nutrient
management planning (Jakobsson etal., 2002). Some policy initiatives
deal indirectly with N leakages and thus promote the reduction of N
2
O
emissions. The EU Nitrates Directive (1991) sets limits on the use of fer-
tilizer N and animal manure N in nitrate-vulnerable zones. Across the
27 EU Member States, 39.6 % of territory is subject to related action
programmes. However, in terms of the effectiveness of environmen-
tal policies and agriculture, there has been considerable progress in
controlling point pollution, but efforts to control non-point pollution of
nutrients have been less successful, and potential synergies from vari-
ous soil-management strategies could be better exploited. Emission
targets for the AFOLU sector were also introduced by different coun-
tries (e. g., Climate Change Acts in UK and Scotland; European Union).
Bioenergy targets: Many countries worldwide, by 2012, have set tar-
gets or mandates or both for bioenergy, to deliver to multiple policy
objectives, such as climate change mitigation, energy security, and
rural development. The bulk of mandates continue to come from the
EU-27 but 13 countries in the Americas, 12 in Asia-Pacific, and 8 in
Africa have mandates or targets in place (Petersen, 2008; www.
biofuelsdigest. com). For the sustainability of biofuels implementation,
land-use planning and governance are central (Tilman etal., 2009), as
related policy and legislation, e. g., in agriculture, forestry, environment
and trade, can strongly influence the development of bioenergy pro-
grammes (Jull etal., 2007). A recent study analyzed the consequences
of renewable targets of EU member states on the CO
2
sink of EU for-
ests, and indicated a decrease in the forest sink by 4 – 11 % (Böttcher
etal., 2012). Another possible tradeoff of biofuel targets is related to
international trade. Global trade in biofuels might have a major impact
on other commodity markets (e. g., vegetable oils or animal fodder)
and has already caused a number of trade disputes, because of subsi-
dies and non-tariff barriers (Oosterveer and Mol, 2010).
Box 11�8 | Deforestation control in Brazil
The Brazilian Action Plan for the Prevention and Control of
Deforestation in the Legal Amazon (PPCDAm) includes coor-
dinated efforts among federal, state, and municipal govern-
ments, and civil organizations, remote-sensing monitoring,
significant increase of new protected areas (Soares-Filho
etal., 2010), and combination of economic and regulatory
approaches. For example, since 2008 federal government
imposed sanctions to municipalities with very high deforesta-
tion rates, subsidies were cut and new credit policies made
rural credit dependent on compliance with environmental
legislation (Macedo etal., 2012; Nolte etal., 2013).
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11�10�3 Information schemes
Acceptability by land managers and practicability of mitigation mea-
sures (Table 11.2) need to be considered, because the efficiency of a
policy is determined by the cost of achieving a given goal (Sections
11.4.5; 11.7). Therefore, costs related to education and communica-
tion of policies should be taken into account (Jakobsson etal., 2002).
Organizations created to foster the use of science in environmental
policy, management, and education can facilitate the flow of informa-
tion from science to society, increasing awareness of environmental
problems (Osmond etal., 2010). In the agriculture sector, non-profit
conservation organizations (e. g., The Sustainable Agriculture Network
(SAN)) and governments (e. g., Farming for a Better Climate, Scotland)
promote the social and environmental sustainability of activities by
developing standards and educational campaigns.
Certification schemes also support sustainable agricultural practices
(Sections 11.4.5; 11.7). Climate-friendly criteria reinforce existing cer-
tification criteria and provide additional value. Different certification
systems also consider improvements in forest management, reduced
deforestation and carbon uptake by regrowth, reforestation, agrofor-
estry, and sustainable agriculture. In the last 20 years, forest certifica-
tion has been developed as an instrument for promoting sustainable
forest management. Certification schemes encompass all forest types,
but there is a concentration in temperate forests (Durst etal., 2006).
Approximately 8 % of global forest area has been certified under a
variety of schemes and 25 % of global industrial roundwood comes
from certified forests (FAO, 2009b). Less than 2 % of forest area in
African, Asian, and tropical American forests are certified, and most
certified forests (82 %) are large and managed by the private sector
(ITTO, 2008). In the forestry sector, many governments have worked
towards a common understanding of sustainable forest management
(Auld et al., 2008). Certification bodies certify that farms or groups
comply with standards and policies (e. g., Rainforest Alliance Certified).
In some, specific voluntary climate change adaptation and mitigation
criteria are included.
Forest certification as an instrument to promote sustainable forest
management (SFM) and biodiversity maintenance was evaluated by
(Rametsteiner and Simula, 2003) they indicated that standards used
for issuing certificates upon compliance are diverse, but often include
elements that set higher than minimum standards.
Further, independent audits are an incentive for improving forest
management. In spite of many difficulties, forest certification was
considered successful in raising awareness, disseminating knowledge
on the SFM concept worldwide, and providing a tool for a range of
applications other than the assessment of sustainability, e. g., verify-
ing carbon sinks. Another evaluation of certification schemes for con-
serving biodiversity (Harvey etal., 2008) indicated some constraints
that probably also apply to climate-friendly certification: weakness
of compliance or enforcement of standards, transaction costs and
paperwork often limit participation, and incentives are insufficient to
attract high levels of participation. Biofuel certification is a specific
case as there are multiple actors and several successive segments
of biofuel production pathways: feedstock production, conversion of
the feedstock to biofuels, wholesale trade, retail, and use of biofuels
in engines (Gnansounou, 2011). Because of the length and the com-
plexity of biofuel supply chains assessing sustainability is challenging
(Kaphengst etal., 2009).
11�10�4 Voluntary actions and agreements
Innovative agricultural practices and technologies can play a central
role in climate change mitigation and adaptation, with policy and insti-
tutional changes needed to encourage the innovation and diffusion of
these practices and technologies to developing countries. Under the
UNFCCC, the 2007 Bali Action Plan identified technology development
and transfer as a priority area. A Technology Mechanism was estab-
lished by Parties at the COP16 in 2010 “to facilitate the implementation
of enhanced action on technology development and transfer, to sup-
port action on mitigation and adaptation, in order to achieve the full
implementation of the Convention” (UNFCCC). For agriculture, Burney
etal., (2010) indicated that investment in yield improvements compared
favourably with other commonly proposed mitigation strategies.
Additionally, adaptation measures in agriculture can also generate
significant mitigation effects. Lobell et al. (2013) investigated the
co-benefits of adaptation measures on farm level that reduced GHG
emissions from LUC. The study focused on investments in research
for developing and deploying new technologies (e. g., disease-resis-
tant or drought-tolerant crops, or soil-management techniques). It
concluded that broad-based efforts to adapt agriculture to climate
change have mitigation co-benefits that are associated with lower
costs than many activities focusing on mitigation, especially in devel-
oped countries.
11.11 Gaps in knowledge
and data
Data and knowledge gaps include:
• Improved global high-resolution data sets of crop production
systems (including crop rotations, variety selection, fertilization
practices, and tillage practices), grazing areas (including quality,
intensity of use, management), and freshwater fisheries and aqua-
culture, also comprising subsistence farming.
• Globally standardized and homogenized data on soil as well as
forest degradation and a better understanding of the effects of
degradation on carbon balances and productivity.
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• Improved understanding of the mitigation potential, interplay, and
costs as well as environmental and socio-economic consequences
of land use-based mitigation options such as improved agricul-
tural management, forest conservation, bioenergy production, and
afforestation on the national, regional, and global scale.
• Better understanding of the effect of changes in climate param-
eters, rising CO
2
concentrations and N deposition on productivity
and carbon stocks of different types of ecosystems, and the related
consequences for land-based climate change mitigation potentials.
11.12 Frequently Asked
Questions
FAQ 11�1 How much does AFOLU contribute
to GHG emissions and how is this
changing?
Agriculture and land-use change, mainly deforestation of tropical for-
ests, contribute greatly to anthropogenic greenhouse gas emissions
and are expected to remain important during the 21st century. Annual
GHG emissions (mainly CH
4
and N
2
O) from agricultural production in
2000 – 2010 were estimated at 5.0 – 5.8 GtCO
2
eq / yr, comprising about
10 – 12 % of global anthropogenic emissions. Annual GHG flux from
land use and land-use change activities accounted for approximately
4.3 – 5.5 GtCO
2
eq / yr, or about 9 – 11 % of total anthropogenic green-
house gas emissions. The total contribution of the AFOLU sector to
anthropogenic emissions is therefore around one quarter of the global
anthropogenic total.
FAQ 11�2 How will mitigation actions in AFOLU
affect GHG emissions over different
timescales?
There are many mitigation options in the AFOLU sector that are already
being implemented, e. g., afforestation, reducing deforestation, crop-
land and grazing land management, fire management, and improved
livestock breeds and diets. These can be implemented now. Others
(such as some forms of biotechnology and livestock dietary additives)
are still in development and may not be applicable for a number of
years. In terms of the mode of action of the options, in common with
other sectors, non-CO
2
greenhouse gas emission reduction is immedi-
ate and permanent. However, a large portion of the mitigation poten-
tial in the AFOLU sector is carbon sequestration in soils and vegetation.
This mitigation potential differs, in that the options are time-limited
(the potential saturates), and the enhanced carbon stocks created are
reversible and non-permanent. There is, therefore, a significant time
component in the realization and the duration of much of the mitiga-
tion potential available in the AFOLU sector.
FAQ 11�3 What is the potential of the main
mitigation options in AFOLU for
reducing GHG emissions?
In general, available top-down estimates of costs and potentials sug-
gest that AFOLU mitigation will be an important part of a global cost-
effective abatement strategy. However, potentials and costs of these
mitigation options differ greatly by activity, regions, system boundaries,
and the time horizon. Especially, forestry mitigation options — includ-
ing reduced deforestation, forest management, afforestation, and
agro-forestry — are estimated to contribute 0.2 – 13.8 GtCO
2
/ yr of
economically viable abatement in 2030 at carbon prices up to 100
USD / tCO
2
eq. Global economic mitigation potentials in agriculture in
2030 are estimated to be up to 0.5 – 10.6 GtCO
2
eq / yr. Besides supply-
side-based mitigation, demand-side mitigation options can have a sig-
nificant impact on GHG emissions from food production. Changes in
diet towards plant-based and hence less GHG-intensive food can result
in GHG emission savings of 0.7 – 7.3 GtCO
2
eq / yr in 2050, depending
on which GHGs and diets are considered. Reducing food losses and
waste in the supply chain from harvest to consumption can reduce
GHG emissions by 0.6 – 6.0 GtCO
2
eq / yr.
FAQ 11�4 Are there any co-benefits associated
with mitigation actions in AFOLU?
In several cases, the implementation of AFOLU mitigation measures
may result in an improvement in land management and there-
fore have socio-economic, health, and environmental benefits: For
example, reducing deforestation, reforestation, and afforestation
can improve local climatic conditions, water quality, biodiversity
conservation, and help to restore degraded or abandoned land. Soil
management to increase soil carbon sequestration may also reduce
the amount of wind and water erosion due to an increase in surface
cover. Further considerations on economic co-benefits are related to
the access to carbon payments either within or outside the UNFCCC
agreements and new income opportunities especially in developing
countries (particularly for labour-intensive mitigation options such as
afforestation).
FAQ 11�5 What are the barriers to reducing
emissions in AFOLU and how can these
be overcome?
There are many barriers to emission reduction. Firstly, mitigation prac-
tices may not be implemented for economic reasons (e. g., market
failures, need for capital investment to realize recurrent savings), or a
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range of factors including risk-related, political / bureaucratic, logistical,
and educational / societal barriers. Technological barriers can be over-
come by research and development; logistical and political / bureau-
cratic barriers can be overcome by better governance and institutions;
education barriers can be overcome through better education and
extension networks; and risk-related barriers can be overcome, for
example, through clarification of land tenure uncertainties.
11.13 Appendix Bioenergy:
Climate effects,
mitigation options,
potential and sustain-
ability implications
11�13�1 Introduction
SRREN (IPCC, 2011) provided a comprehensive overview on bioenergy
(Chum etal., 2011). However, a specific bioenergy Appendix in the
context of the WGIII AR5 contribution is necessary because (1) many
of the more stringent mitigation scenarios (resulting in 450 ppm, but
also 550 ppm CO
2
eq concentration by 2100, see Section 11.9.1) heav-
ily rely on a large-scale deployment of bioenergy with carbon dioxide
capture and storage (BECCS); (2) there has been a large body of lit-
erature published since SRREN, which complements and updates the
analysis presented in this last report; (3) bioenergy is important for
many chapters (Chapters 6; 7; 8; 10; 11), which makes it more use-
ful to treat it in a single section instead of in many scattered chapter
sections throughout the report. Chapter 11 is the appropriate location
for the Appendix, as bioenergy analysis relies crucially on land-use
assessments.
Bioenergy is energy derived from biomass, which can be deployed
as solid, liquid, and gaseous fuels for a wide range of uses, includ-
ing transport, heating, electricity production, and cooking (Chum etal.,
2011). Bioenergy has a significant mitigation potential, but there are
issues to consider, such as the sustainability of practices and the effi-
ciency of bioenergy systems (Chum etal., 2011). Bioenergy systems can
cause both positive and negative effects and their deployment needs
to balance a range of environmental, social, and economic objectives
that are not always fully compatible. The consequences of bioenergy
implementation depend on (1) the technology used; (2) the location,
scales, and pace of implementation; (3) the land category used (for-
est, grassland, marginal lands, and crop lands); and (4) the business
models and practices adopted — including how these integrate with or
displace the existing land use.
As an update to the SRREN, this report presents (1) a more fine-grained
assessment of the technical bioenergy potential reflecting diverse per-
spectives in the literature; (2) recent potential estimates on techno-
logical solutions such as BECCS; (3) an in-depth description of differ-
ent lifecycle emission accounting methods and their results; (4) a small
increase in uncertainty on the future economic bioenergy potential; (5)
a comprehensive assessment of diverse livelihood and sustainability
effects of bioenergy deployment, identifying the need for systematic
aggregation.
11�13�2 Technical bioenergy potential
The technical bioenergy potential, also known as the technical pri-
mary biomass potential for bioenergy, is the amount of the theoretical
bioenergy output obtainable by full implementation of demonstrated
technologies or practices (IPCC, 2011). Unfortunately there is no
standard methodology to estimate the technical bioenergy potential,
which leads to diverging estimates. Most of the recent studies estimat-
ing technical bioenergy potentials assume a ‘food / fibre first principle’
and exclude deforestation, eventually resulting in an estimate of the
‘environmentally sustainable bioenergy potential’ when a comprehen-
sive range of environmental constraints is considered (Batidzirai etal.,
2012).
Recently published estimates that are based in this extended defini-
tion of global technical bioenergy potentials in 2050 span a range of
almost three orders of magnitude, from <50 EJ / yr to >1,000 EJ / yr
(Smeets etal., 2007; Field etal., 2008; Haberl etal., 2010; Batidzirai
etal., 2012). For example, Chum etal. reported global technical bioen-
ergy potentials of 50 – 500 EJ / yr for the year 2050 (IPCC, 2011), and the
Global Energy Assessment gave a range of 160 – 270 EJ / yr (Johansson
etal., 2012). The discussion following the publication of these global
reports has not resulted in a consensus on the magnitude of the future
global technical bioenergy potential, but has helped to better under-
stand some of its many structural determinants (Wirsenius etal., 2011;
Berndes, 2012; Erb et al., 2012a). How much biomass for energy is
technically available in the future depends on the evolution of a mul-
titude of social, political, and economic factors, e. g., land tenure and
regulation, trade, and technology (Dornburg etal., 2010).
Figure 11.20 shows estimates of the global technical bioenergy poten-
tial in 2050 by resource categories. Ranges were obtained from assess-
ing a large number of studies based on a food / fibre first principle and
various restrictions regarding resource limitations and environmental
concerns but no explicit cost considerations (Hoogwijk et al., 2005;
Smeets etal., 2007; Smeets and Faaij, 2007; van Vuuren etal., 2009;
Hakala etal., 2009; Dornburg etal., 2010; Haberl etal., 2010, 2011a;
Gregg and Smith, 2010; Chum etal., 2011; GEA, 2012; Rogner etal.,
2012). Most studies agree that the technical bioenergy potential in
2050 is at least approximately 100 EJ / yr with some modelling assump-
tions leading to estimates exceeding 500 EJ / yr (Smeets etal., 2007). As
stated, different views about sustainability and socio-ecological con-
straints lead to very different estimates, with some studies reporting
much lower figures.
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As shown in Figure 11.20, the total technical bioenergy potential is
composed of several resource categories that differ in terms of their
absolute potential, the span of the ranges — which also reflect the
relative agreement / disagreement in the literature — and the implica-
tions of utilizing them. Regional differences — which are not addressed
here — are also important as the relative size of each biomass resource
within the total potential and its absolute magnitude vary widely
across countries and world regions.
Forest and Agriculture residues� Forest residues (Smeets and Faaij,
2007; Smeets etal., 2007; Dornburg etal., 2010; Haberl etal., 2010;
Gregg and Smith, 2010; Rogner etal., 2012) include residues from silvi-
cultural thinning and logging; wood processing residues such as saw-
dust, bark, and black liquor; and dead wood from natural disturbances,
such as storms and insect outbreaks (irregular source). The use of these
resources is in general beneficial and any adverse side-effects can be
mitigated by controlling residue removal rates considering biodiversity,
climate, topography, and soil factors. There is a near-term tradeoff, par-
ticularly within temperate and boreal regions, in that organic matter
retains organic C for longer if residues are left to decompose slowly
instead of being used for energy. Agricultural residues (Smeets etal.,
2007; Hakala etal., 2009; Haberl etal., 2010, 2011a; Gregg and Smith,
2010; Chum etal., 2011; Rogner etal., 2012) include manure, harvest
residues (e. g., straw), and processing residues (e. g., rice husks from
rice milling) and are also in general beneficial. However, mitigating
potential adverse side-effects — such as the loss of soil C — associated
to harvesting agriculture residues is more complex as they depend on
the different crops, climate, and soil conditions (Kochsiek and Knops,
2012; Repo etal., 2012). Alternative uses of residues (bedding, use
as fertilizer) need to be considered. Residues have varying collection
and processing costs (in both agriculture and forestry) depending on
residue quality and dispersal, with secondary residues often having the
benefits of not being dispersed and having relatively constant qual-
ity. Densification and storage technologies would enable cost-effective
collections over larger areas. Optimization of crop rotation for food
and bioenergy output and the use of residues in biogas plants may
result in higher bioenergy yields from residues without food-energy
competition.
Optimal forest harvesting is defined as the fraction of sustainable
harvest levels (often set equal to net annual increment) in forests
available for wood extraction, which is additional to the projected bio-
mass demand for producing other forest products. This includes both
biomass suitable for other uses (e. g., pulp and paper production) and
biomass that is not used commercially (Smeets and Faaij, 2007; Chum
etal., 2011). The resource potential depends on both environmental
and socio-economic factors. For example, the change in forest man-
agement and harvesting regimes due to bioenergy demand depends
on forest ownership and the structure of the associated forest indus-
try. Also, the forest productivity — and C stock — response to changes
in forest management and harvesting depends on the character of
the forest ecosystem, as shaped by historic forest management and
events such as fires, storms, and insect outbreaks, but also on the
management scheme (e. g., including replanting after harvest, soil
protection, recycling of nutrients, and soil types (Jonker etal., 2013;
Lamers etal., 2013). In particular, optimizing forest management for
mitigation is a complex issue with many uncertainties and still sub-
ject to scientific debate. Intensive forest management activities of the
early- to mid-twentieth century as well as other factors such as recov-
ery from past overuse, have led to strong forest C sinks in many OECD
regions (Pan etal., 2011; Loudermilk etal., 2013; Nabuurs etal., 2013;
Erb etal., 2013). However, the capacity of these sinks is being reduced
as forests approach saturation (Smith, 2005; Körner, 2006; Guldea
etal., 2008; Nabuurs etal., 2013; Sections 11.2.3, 11.3.2). Active for-
est management, including management for bioenergy, is therefore
important for sustaining the strength of the forest carbon sink well
into the future (Nabuurs etal., 2007, 2013; Canadell and Raupach,
2008; Ciais etal., 2008), although countries should realize that for
some old forest areas, conserving carbon stocks may be preferential,
and that the actively managed forests may for some time (decades)
act as sources.
Organic wastes include waste from households and restaurants,
discarded wood products such as paper, construction, and demolition
wood waste, and waste waters suitable for anaerobic biogas produc-
tion (Haberl etal., 2010; Gregg and Smith, 2010). Organic waste may
be dispersed and also heterogeneous in quality but the health and
environmental gains from collection and proper management through
Figure 11�20 | Global Technical Bioenergy Potential by main resource category for the
year 2050 | The figure shows the ranges in the estimates by major resource category of
the global technical bioenergy potential. The color grading is intended to show quali-
tatively the degree of agreement in the estimates, from blue (large agreement in the
literature) to purple (medium agreement) to red (small agreement). In addition, reduc-
ing traditional biomass demand by increasing its use efficiency could release the saved
biomass for other energy purposes with large benefits from a sustainable development
perspective.
0
75
25
50
125
100
150
Dedicated
Crops
Forest and
Agriculture
Residues
Industrial
Organic
Residues
Optimal
Forest
Harvesting
Projected
Reduced
Demand of
Traditional
Biomass
Global Primary Biomass Energy Supply [EJ/yr]
Global Bioenergy Use in 2010
High
Medium
Agreement in the Literature
Low
675
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Detailed country studies have estimated the fraction of non-renewable
biomass from traditional bioenergy use to vary widely, e. g., from 1.6 %
for the Democratic Republic of Congo to 73 % for Burundi (CDM-SSC
WG, 2011) with most countries in the range between 10 – 30 % (i. e.,
meaning that 70 – 90 % of total traditional bioenergy use is managed
sustainably). Thus a fraction of the traditional biomass saved through
better technology, should not be used for other energy purposes but
simply not consumed to help restore the local ecosystems.
11�13�3 Bioenergy conversion: technologies and
management practices
Numerous conversion technologies can transform biomass to heat,
power, liquid, and gaseous fuels for use in the residential, industrial,
transport, and power sectors (see Chum etal., 2011; GEA, 2012) for a
comprehensive coverage of each alternative, and Figure 11.21 for the
pathways concerning liquid and gaseous fuels). Since SRREN, the
major advances in the large-scale production of bioenergy include the
increasing use of hybrid biomass-fossil fuel systems. For example, cur-
rent commercial coal and biomass co-combustion technologies are the
lowest-cost technologies for implementing renewable energy policies,
enabled by the large-scale pelletized feedstocks trade (REN21, 2013;
Junginger etal., 2014). Direct biopower use is also increasing commer-
cially on a global scale (REN21, 2013, p.21). In fact, using biomass for
electricity and heat, for example, co-firing of woody biomass with coal
in the near term and large heating systems coupled with networks for
district heating, and biochemical processing of waste biomass, are
among the most cost-efficient and effective biomass applications for
GHG emission reduction in modern pathways (Sterner and Fritsche,
2011).
Integrated gasification combined cycle (IGCC) technologies for co-
production of electricity and liquid fuels from coal and biomass with
higher efficiency than current commercial processes are in demonstra-
tion phase to reduce cost (Williams etal., 2011; GEA, 2012; Larson
etal., 2012). Coupling of biomass and natural gas for fuels is another
option for liquid fuels (Baliban etal., 2013) as the biomass gasification
technology development progresses. Simulations suggest that inte-
grated gasification facilities are technically feasible (with up to 50 %
biomass input; Meerman et al., 2011), and economically attractive
with a CO
2
price of about 66 USD
2010
/ tCO
2
(50 EUR
2010
/ tCO
2
) (Meerman
etal., 2012). Many gasification technology developments around the
world are in pilot, demonstration, operating first commercial scale for
a variety of applications (see examples in Bacovsky etal., 2013; Balan
etal., 2013).
Many pathways and feedstocks (Figure 11.21) can lead to biofuels for
aviation. The development of biofuel standards started and enabled
testing of 50 % biofuel in jet fuel for commercial domestic and trans-
atlantic flights by consortia of governments, aviation industry, and
associations (IEA, 2010; REN21, 2013). Advanced ‘drop in’ fuels, such
as iso-butanol, synthetic aviation kerosene from biomass gasification
Figure 11�21 | Production pathways to liquid and gaseous fuels from biomass and, for comparison from fossil fuels (adapted from GEA, 2012; Turkenburg etal., 2012).
LPG
Jet
Fuel
Petrol Diesel
CNG,
LNG
H₂
Coal
Lignocellulosic
Biomass
Sugar and
Starch Crops
Oil Crops
Hydrolysis
Anaerobic
Digestion
(Biogas)
Extraction
Esterification
Gasification Pyrolysis
Synthesis
Chemical or
Bio Refining
Fossil Fuels Biomass
Fermentation
Re-
newable
Diesel
Refining
Ethanol
Butanol
FTL
DME MeOH
Drop-in
Biofuels
Biodiesel
Mixed
Alcohols
Hydro
Thermal
Upgrade
Wet Biomass
Refining
Blending
Crude Oil Natural Gas
Developing Technologies
Commercial Technologies
combustion or anaerobic digestion can be significant. Competition
with alternative uses of the wastes may limit this resource potential.
Dedicated biomass plantations include annual (cereals, oil, and
sugar crops) and perennial plants (e. g., switchgrass, Miscanthus) and
tree plantations (both coppice and single-stem plantations (e. g., wil-
low, poplar, eucalyptus, pine; (Hoogwijk et al., 2005, 2009; Smeets
etal., 2007; van Vuuren etal., 2009; Dornburg etal., 2010; Wicke etal.,
2011b; Haberl etal., 2011a). The range of estimates of technical bio-
energy potentials from that resource in 2050 is particularly large (<50
to > 500 EJ / yr). Technical bioenergy potentials from dedicated bio-
mass plantations are generally calculated by multiplying (1) the area
deemed available for energy crops by (2) the yield per unit area and
year (Batidzirai etal., 2012; Coelho etal., 2012). Some studies have
identified a sizable technical potential (up to 100 EJ) for bioenergy pro-
duction using marginal and degraded lands (e. g., saline land) that are
currently not in use for food production or grazing (Nijsen etal., 2012).
However, how much land is really unused and available is contested
(Erb etal., 2007; Haberl etal., 2010; Coelho etal., 2012). Contrasting
views on future technical bioenergy potentials from dedicated biomass
plantations can be explained by differences in assumptions regarding
feasible future agricultural crop yields, livestock feeding efficiency,
land availability for energy crops and yields of energy crops (Dornburg
etal., 2010; Batidzirai etal., 2012; Erb etal., 2012a). Most scientists
agree that increases in food crop yields and higher feeding efficiencies
and lower consumption of animal products results in higher techni-
cal bioenergy potential. Also, there is a large agreement that careful
policies for implementation focused on land-use zoning approaches
(including nature conservation and biodiversity protection), multifunc-
tional land use, integration of food and energy production, avoidance
of detrimental livelihood impacts, e. g., on livestock grazing and subsis-
tence farming, and consideration of equity issues, and sound manage-
ment of impacts on water systems are crucial for sustainable solutions.
Reduced traditional biomass demand� A substantial quantity of
biomass will become available for modern applications by improving
the end-use efficiency of traditional biomass consumption for energy,
mostly in households but also within small industries (such as char-
coal kilns, brick kilns, etc.). Traditional bioenergy represents approxi-
mately 15 % of total global energy use and 80 % of current bioenergy
use (≈35 EJ / yr) and helps meeting the cooking needs of ~2.6 billion
people (Chum etal., 2011; IEA, 2012b). Traditional bioenergy use cov-
ers several end-uses including cooking, water, and space heating, and
small-industries (such as brick and pottery kilns, bakeries, and many
others). Cooking is the dominant end use; it is mostly done in open
fires and rudimentary stoves, with approximately 10 – 20 % conversion
efficiency, leading to very high primary energy consumption. Advanced
woodburning and biogas stoves can potentially reduce biomass fuel
consumption by 60 % or more (Jetter etal., 2012) and further lower
the atmospheric radiative forcing, reducing CO
2
emissions, and in many
cases black carbon emissions, by up to 90 % (Anenberg etal., 2013).
Assuming that actual savings reach on average 30 – 60 % of current
consumption, the total bioenergy potential from reducing traditional
bioenergy demand can be estimated at 8 – 18 EJ / yr. An unknown frac-
tion of global traditional biomass is consumed in a non-environmen-
tally sustainable way, leading to forest degradation and deforestation.
873873
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Detailed country studies have estimated the fraction of non-renewable
biomass from traditional bioenergy use to vary widely, e. g., from 1.6 %
for the Democratic Republic of Congo to 73 % for Burundi (CDM-SSC
WG, 2011) with most countries in the range between 10 – 30 % (i. e.,
meaning that 70 – 90 % of total traditional bioenergy use is managed
sustainably). Thus a fraction of the traditional biomass saved through
better technology, should not be used for other energy purposes but
simply not consumed to help restore the local ecosystems.
11�13�3 Bioenergy conversion: technologies and
management practices
Numerous conversion technologies can transform biomass to heat,
power, liquid, and gaseous fuels for use in the residential, industrial,
transport, and power sectors (see Chum etal., 2011; GEA, 2012) for a
comprehensive coverage of each alternative, and Figure 11.21 for the
pathways concerning liquid and gaseous fuels). Since SRREN, the
major advances in the large-scale production of bioenergy include the
increasing use of hybrid biomass-fossil fuel systems. For example, cur-
rent commercial coal and biomass co-combustion technologies are the
lowest-cost technologies for implementing renewable energy policies,
enabled by the large-scale pelletized feedstocks trade (REN21, 2013;
Junginger etal., 2014). Direct biopower use is also increasing commer-
cially on a global scale (REN21, 2013, p.21). In fact, using biomass for
electricity and heat, for example, co-firing of woody biomass with coal
in the near term and large heating systems coupled with networks for
district heating, and biochemical processing of waste biomass, are
among the most cost-efficient and effective biomass applications for
GHG emission reduction in modern pathways (Sterner and Fritsche,
2011).
Integrated gasification combined cycle (IGCC) technologies for co-
production of electricity and liquid fuels from coal and biomass with
higher efficiency than current commercial processes are in demonstra-
tion phase to reduce cost (Williams etal., 2011; GEA, 2012; Larson
etal., 2012). Coupling of biomass and natural gas for fuels is another
option for liquid fuels (Baliban etal., 2013) as the biomass gasification
technology development progresses. Simulations suggest that inte-
grated gasification facilities are technically feasible (with up to 50 %
biomass input; Meerman et al., 2011), and economically attractive
with a CO
2
price of about 66 USD
2010
/ tCO
2
(50 EUR
2010
/ tCO
2
) (Meerman
etal., 2012). Many gasification technology developments around the
world are in pilot, demonstration, operating first commercial scale for
a variety of applications (see examples in Bacovsky etal., 2013; Balan
etal., 2013).
Many pathways and feedstocks (Figure 11.21) can lead to biofuels for
aviation. The development of biofuel standards started and enabled
testing of 50 % biofuel in jet fuel for commercial domestic and trans-
atlantic flights by consortia of governments, aviation industry, and
associations (IEA, 2010; REN21, 2013). Advanced ‘drop in’ fuels, such
as iso-butanol, synthetic aviation kerosene from biomass gasification
or upgrading of pyrolysis liquids, can be derived through a number
of possible conversion routes such as hydro treatment of vegetable
oils, iso-butanol, and Fischer-Tropsch synthesis from gasification of
biomass (Hamelinck and Faaij, 2006; Bacovsky etal., 2010; Meerman
etal., 2011, 2012; Rosillo-Calle etal., 2012); see also Chapter 8). In
specific cases, powering electric cars with electricity from biomass has
higher land-use efficiency and lower global-warming potential (GWP)
effects than the usage of bioethanol from biofuel crops for road trans-
port across a range of feedstocks, conversion technologies, and vehicle
classes (Campbell etal., 2009; Schmidt etal., 2011)
13
, though costs
are likely to remain prohibitive for considerable time (van Vliet etal.,
2011a; b; Schmidt etal., 2011).
The number of routes from biomass to a broad range of biofuels,
shown in Figure 11.21, includes hydrocarbons connecting today’s fos-
sil fuels industry in familiar thermal / catalytic routes such as gasifica-
tion (Williams etal., 2011; Larson etal., 2012) and pyrolysis (Brown
et al., 2011; Bridgwater, 2012; Elliott, 2013; Meier et al., 2013). In
addition, advances in genomic technology, the emphasis in systems
approach, and the integration between engineering, physics, chem-
istry, and biology bring together many new approaches to biomass
conversion (Liao and Messing, 2012) such as (1) biomolecular engi-
neering (Li etal., 2010; Favaro etal., 2012; Peralta-Yahya etal., 2012;
Lee etal., 2013; Yoon et al., 2013); (2) deconstruction of lignocellu-
losic biomass through combinations of mild thermal and biochemi-
cal routes in multiple sequential or consolidated steps using similar
biomolecular engineering tools (Rubin, 2008; Chundawat etal., 2011;
Beckham etal., 2012; Olson etal., 2012; Tracy etal., 2012; Saddler and
Kumar, 2013; Kataeva etal., 2013); and (3) advances in (bio)catalysis
and basic understanding of the synthesis of cellulose are leading to
routes for many fuels and chemicals under mild conditions (Serrano-
Ruiz etal., 2010; Carpita, 2012; Shen etal., 2013; Triantafyllidis etal.,
2013; Yoon etal., 2013). Fundamental understanding of biofuel pro-
duction increased for microbial genomes by forward engineering of
cyanobacteria, microalgae, aiming to arrive at minimum genomes for
synthesis of biofuels or chemicals (Chen and Blankenship, 2011; Eckert
etal., 2012; Ungerer et al., 2012; Jones and Mayfield, 2012; Kontur
etal., 2012; Lee etal., 2013).
Bioenergy coupled with CCS (Spath and Mann, 2004; Liu etal., 2010)
is seen as an option to mitigate climate change through negative emis-
sions if CCS can be successfully deployed (Cao and Caldeira 2010;
Lenton and Vaughan 2009). BECCS features prominently in long-run
mitigation scenarios (Sections 6.3.2 and 6.3.5) for two reasons: (1) The
potential for negative emissions may allow shifting emissions in time;
and (2) in scenarios, negative emissions from BECCS compensate for
residual emissions in other sectors (most importantly transport) in the
second half of the 21st century. As illustrated in Figure 11.22, BECCS
is markedly different than fossil CCS because it not only reduces CO
2
emissions by storing C in long-term geological sinks, but it continu-
13
Biomass can be used for electric transport and biofuels within one pathway
(Macedo et al., 2008)
Figure 11�21 | Production pathways to liquid and gaseous fuels from biomass and, for comparison from fossil fuels (adapted from GEA, 2012; Turkenburg etal., 2012).
LPG
Jet
Fuel
Petrol Diesel
CNG,
LNG
H₂
Coal
Lignocellulosic
Biomass
Sugar and
Starch Crops
Oil Crops
Hydrolysis
Anaerobic
Digestion
(Biogas)
Extraction
Esterification
Gasification Pyrolysis
Synthesis
Chemical or
Bio Refining
Fossil Fuels Biomass
Fermentation
Re-
newable
Diesel
Refining
Ethanol
Butanol
FTL
DME MeOH
Drop-in
Biofuels
Biodiesel
Mixed
Alcohols
Hydro
Thermal
Upgrade
Wet Biomass
Refining
Blending
Crude Oil Natural Gas
Developing Technologies
Commercial Technologies
874874
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�22 | Illustration of the sum of CO
2
eq (GWP
100
)* emissions from the process chain of alternative transport and power generation technologies both with and without
CCS. (*Differences in C-density between forest biomass and switchgrass are taken into account but not calorific values (balance-of-plant data are for switchgrass, ref. Larson etal.,
2012). Specific emissions vary with biomass feedstock and conversion technology combinations, as well as lifecycle GHG calculation boundaries. For policy relevant purposes,
counterfactual and market-mediated aspects (e. g., iLUC), changes in soil organic carbon, or changes in surface albedo need also to be considered, possibly leading to significantly
different outcomes, quantitatively (Section 11.13.4, Figures 11.23 and 11.24).Unit: gCO
2
eq / MJ
El
(left y-axis, electricity); gCO
2
eq / MJ combusted (right y-axis, transport fuels). Direct
CO
2
emissions from energy conversion (‘vented’ and ‘stored’) are adapted from the mean values in Tables 12.7, 12.8, and 12.15 of ref. [1], which are based on the work of refs.
[2, 3], and characterized with the emission metrics in ref. [4]. Impacts upstream in the supply chain associated with feedstock procurement (i. e., sum of GHGs from mining / cultiva-
tion, transport, etc.) are adapted from refs. [5, 6] and Figure 11.23 (median values).
1
Larson, etal. (2012);
2
Woods, etal., (2007) ;
3
Liu etal. (2010);
4
Guest etal. (2013);
5
Turconi etal. (2013);
6
Jaramillo etal. (2008)
Notes:
*
Global Warming Potential over 100 years. See Glossary and Section 1.2.5.
ALCA GHG Emissions [gCO
2
eq/MJ
el
]
ALCA GHG Emissions [gCO
2
eq/MJ
Fuel Combusted
]
Coal IGCC Natural Gas CC Switchgrass Forest Biomass* Coal Coal +
F.
Biomass
Coal +
Switch-
grass
Forest Biomass*Switchgrass
Electricity CTLs BTLs C + BTLs
0
50
100
150
200
250
300
Fossil Combustion CO
2
Vented
Biogenic CO
2
Vented
Biogenic CO
2
Stored
CO
2
Transport and Storage
Net
Value Chain GHGs
-250
-200
-150
-100
-50
CCS CCSNo CCS CCSNo CCS CCSNo CCS CCSNo CCS CCSNo CCS CCSNo CCS CCSNo CCS CCS
875875
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
ally sequesters CO
2
from the air through regeneration of the biomass
resource feedstock.
BECCS deployment is in the development and exploration stages. The
most relevant BECCS project is the ‘Illinois Basin — Decatur Project’
that is projected to inject 1 MtCO
2
/ yr (Gollakota and McDonald, 2012;
Senel and Chugunov, 2013). In the United States, two ethanol fuel pro-
duction by fermentation facilities are currently integrated commercially
with carbon dioxide capture, pipeline transport, and use in enhanced
oil recovery in nearby facilities at a rate of about 0.2 MtCO
2
/ yr (DiP-
ietro etal., 2012). Altogether, there are 16 global BECCS projects in
exploration stage (Karlsson and Byström, 2011).
Critical to overall CO
2
storage is the realization of a lignocellulosic
biomass supply infrastructure for large-scale commodity feedstock
production and efficient advanced conversion technologies at scale;
both benefit from cost reductions and technological learning as does
the integrated system with CCS, with financial and institutional con-
ditions that minimize the risks of investment and facilitate dissemi-
nation (Eranki and Dale, 2011; IEA, 2012c, 2013). Integrated analy-
sis is needed to capture system and knock-on effects for bioenergy
potentials. A nascent feedstock infrastructure for densified biomass
trading globally could indicate decreased pressure on the need for
closely co-located storage and production (IEA, 2011; Junginger
etal., 2014).
The overall technical potential is estimated to be around 10 GtCO
2
storage per year for both Integrated Gasification Combined Cycle
(IGCC)-CCS co-firing (IGCC with co-gasification of biomass), and Bio-
mass Integrated Gasification Combined Cycle (BIGCC)-CCS dedicated,
and around 6 GtCO
2
storage for biodiesel based on gasification and
Fischer-Tropsch synthesis (FT diesel), and 2.7 GtCO
2
for biomethane
production (Koornneef etal., 2012, 2013). Another study estimates the
potential capacity (similar to technical potential) to be between 2.4
and 10 GtCO
2
per year for 2030 – 2050 (McLaren, 2012). The economic
potential, at a CO
2
price of around 70 USD / t is estimated to be around
3.3 GtCO
2
, 3.5 GtCO
2
, 3.1 GtCO
2
and 0.8 GtCO
2
in the corresponding
four cases, judged to be those with highest economic potential (Koorn-
neef etal., 2012, 2013). Potentials are assessed on a route-by-route
basis and cannot simply be added, as they may compete and substitute
each other. Practical figures might be not much higher than 2.4 GtCO
2
per year at 70 – 250 USD / tCO
2
(McLaren, 2012). Altogether, until 2050,
the economic potential is anywhere between 2 – 10 GtCO
2
per year.
Some climate stabilization scenarios see considerable higher deploy-
ment towards the end of the century, even in some 580 – 650 ppm sce-
narios, operating under different time scales, socioeconomic assump-
tions, technology portfolios, CO
2
prices, and interpreting BECCS as part
of an overall mitigation framework (e. g., Rose etal., 2012; Kriegler
etal., 2013; Tavoni and Socolow, 2013).
Possible climate risks of BECCS relate to reduction of land carbon
stock, feasible scales of biomass production and increased N
2
O emis-
sions, and potential leakage of CO
2
, which has been stored in deep
geologic reservoirs (Rhodes and Keith, 2008). The assumptions of suf-
ficient spatially appropriate CCS capture, pipeline, and storage infra-
structure are uncertain. The literature highlights that BECCS as well as
CCS deployment is dependent on strong financial incentives, as they
are not cost competitive otherwise (Sections 7.5.5; 7.6.4; 7.9; 7.12).
Figure 11.22 illustrates some GHG effects associated with BECCS
pathways. Tradeoffs between CO
2
capture rate and feedstock conver-
sion efficiency are possible. Depicted are pathways with the highest
removal rate but not necessarily with the highest feedstock conver-
sion rate. Among all BECCS pathways, those based on integrated gas-
ification combined cycle produce most significant geologic storage
potential from biomass, alone (shown in Figure 11.23, electricity) or
coupled with coal. Fischer-Tropsch diesel fuel production with biomass
as feedstock and CCS attached to plant facilities could enable BECCS
for transport; uncertainties in input factors, and output metrics warrant
further research (van Vliet etal., 2009). Fischer-Tropsch diesel would
also allow net removal but at lower rates than BIGCC.
Economics of scale in power plant size are crucial to improve economic
viability of envisaged BECCS projects. Increasing power plant size
requires higher logistic challenges in delivering biomass.
Scales of 4,000 to 10,000 Mg / day needed for >600 MW power plants
could become feasible as the biomass feedstock supply logistic devel-
opment with manageable logistic costs if biomass is derived from
high-yield monocrops; logistical costs are more challenging when bio-
mass is derived from residues (e. g., Argo etal., 2013; Junginger etal.,
2014). Large-scale biomass production with flexible integrated poly-
generation facilities for fuels and / or power can improve the techno-
economic performance, currently above market prices to become more
economically competitive over time (Meerman et al., 2011). In the
future, increased operating experience of BECCS IGCC-CCS through
technological improvements and learning could enable carbon neutral
electricity and, in combination with CCS, could result in net removal of
CO
2
(Figure 11.22). BECCS is among the lowest cost CCS options for a
number of key industrial sectors (Meerman etal., 2013). It should be
noted that primary empiric cost and performance data for dedicated
bioenergy plants are not yet available and needed for comprehensively
assessing BECCS. The current status of CCS and on-going research
issues are discussed in Sections 7.5.5 and 7.6.4. Social concerns con-
stitute a major barrier for implement demonstration and deployment
projects.
Integrated bio-refineries continue to be developed; for instance, 10 %
of the ethanol or corresponding sugar stream goes into bio-products
in Brazil (REN21, 2012) including making ethylene for polymers (IEA-
ETSAP and IRENA, 2013). Multi product bio-refineries could produce a
wider variety of co-products to enhance the economics of the overall
process, facilitating learning in the new industry (IEA, 2011); Lifecycle
Analyses (LCAs) for these systems are complex (Pawelzik etal., 2013).
876876
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�23 | Direct CO
2
eq (GWP
100
) emissions from the process chain or land-use disturbances of major bioenergy product systems, not including impacts from LUC (see Figure
11.24). The interpretation of values depends also on baseline assumption about the land carbon sink when appropriate and the intertemporal accounting frame chosen, and should
also consider information from Figure 11.24. The lower and upper bounds of the bars represent the minimum and the maximum value reported in the literature. Whenever possible,
peer-reviewed scientific literature published post SRREN is used (but results are comparable). Note that narrow ranges may be an artefact of the number of studies for a given case.
Results are disaggregated in a manner showing the impact of Feedstock production (in gCO
2
eq / MJ lower heating value (LHV) of feedstock) and the contributions from end prod-
uct / conversion technology.Results from conversion into final energy products Heat, Power, and Transport fuels include the contribution from Feedstock production and are shown
in gCO
2
eq / MJ of final product. For some pathways, additional site-specific climate forcing agents apply and are presented as separate values to be added or subtracted from the
value indicated by the median in the Feedstock bar (dark grey). Final products are also affected by these factors, but this is not displayed here. References: Corn 1 – 7; Oil crops 1,
8, 8 – 12; Crop residues 1, 4, 13 – 24; Sugarcane 2, 3, 5, 6, 25 – 27; Palm Oil 2, 3, 10, 28 – 31; Perennial grasses 1, 3, 11, 18, 22, 32 – 40; Short Rotation Woody Crops 1, 3, 6, 12, 22,
33, 35, 37, 38, 41 – 53; Forestry 5, 6, 38, 49, 54 – 66; Biogas, open storage: 67 – 69; Biogas, closed storage 69 – 71; Waste cooking oil: 22, 72 – 74. Note that the biofuels technolo-
gies for transport from lignocellulosic feedstocks, short rotation woody crops, and crop residues, including collection and delivery, are developing so larger ranges are expected than
for more mature commercial technologies such as sugarcane ethanol and waste cooking oil (WCO) biodiesel. The biogas electricity bar represents scenarios using LCAs to explore
treating mixtures of a variety of lignocellulosic feedstocks (e. g., ensiled grain or agricultural residues or perennial grasses) with more easily biodegradable wastes (e. g., from animal
husbandry), to optimize multiple outputs. Some of the scenarios assume CH
4
leakage, which leads to very high lifecycle emissions.
1
Gelfand etal. (2013);
2
Nemecek etal. (2012);
3
Hoefnagels etal. (2010);
4
Kaufman etal. (2010);
5
Cherubini etal. (2009);
6
Cherubini (2012);
7
Wang etal. (2011b);
8
Milazzo etal.
(2013);
9
Goglio etal. (2012);
10
Stratton etal. (2011);
11
Fazio and Monti (2011);
12
Börjesson and Tufvesson (2011);
13
Cherubini and Ulgiati (2010);
14
Li etal. (2012);
15
Luo etal.
(2009);
16
Gabrielle and Gagnaire (2008);
17
Smith etal. (2012b);
18
Anderson-Teixeira etal. (2009);
19
Nguyen etal. (2013);
20
Searcy and Flynn (2008);
21
Giuntoli etal. (2013);
22
Whita-
ker etal. (2010);
23
Wang etal. (2013a);
24
Patrizi etal. (2013);
25
Souza etal. (2012a);
26
Seabra etal. (2011);
27
Walter etal. (2011);
28
Choo etal. (2011);
29
Harsono etal. (2012);
30
Sian-
gjaeo etal. (2011);
31
Silalertruksa and Gheewala (2012);
32
Smeets etal. (2009b);
33
Tiwary and Colls (2010);
34
Wilson etal. (2011);
35
Brandão etal. (2011);
36
Cherubini and Jungmeier
(2010);
37
Don etal. (2012);
38
Pucker etal. (2012);
39
Monti etal. (2012);
40
Bai etal. (2010);
41
Bacenetti etal. (2012);
42
Budsberg etal. (2012);
43
González-García etal. (2012a);
44
González-García (2012b) ;
45
Stephenson etal. (2010);
46
Hennig and Gawor (2012);
47
Buonocore etal. (2012);
48
Gabrielle etal. (2013);
49
Dias and Arroja (2012);
50
González-García
etal. (2012b);
51
Roedl (2010);
52
Djomo etal. (2011);
53
Njakou Djomo etal. (2013);
54
McKechnie etal. (2011);
55
Pa etal. (2012);
56
Puettmann etal. (2010);
57
Guest etal. (2011);
58
Valente etal. (2011);
59
Whittaker etal. (2011);
60
Bright and Strømman (2009);
61
Felder and Dones (2007);
62
Solli etal. (2009);
63
Lindholm etal. (2011);
64
Mallia and Lewis (2013);
65
Bright etal. (2010);
66
Bright and Strømman (2010);
67
Rehl etal. (2012);
68
Blengini etal. (2011);
69
Boulamanti etal. (2013);
70
Lansche and Müller (2012);
71
De Meester etal. (2012);
72
Sunde etal. (2011);
73
Thamsiriroj and Murphy (2011);
74
Talens Peiró etal. (2010)
ALCA GHG Emissions [gCO
2
eq/MJ]
∆Albedo
Biogenic CO
2
Electricity
Feedstock
Median
Max
Min
Transportation
Feedstock Production and Site-Specific Climate Forcing AgentsEnd-Use Lifecycle GHG Emissions
∆SOC
Heat
Corn
(USA)
Oil Crops
(Europe)
Crop Residues
(North America, Europe)
Sugarcane
(Brazil)
Palm Oil
(Southeast
Asia)
Perennial Grasses
(USA and Europe)
Short Rotation Woody
Crops
(USA, Europe,
South America)
Forestry
(North America and Europe)
Biogas
(Europe),
Closed
Storage*
Biogas
(Europe),
Open
Storage*
Waste
Cooking
Oil
(World)
Annuals Perennials Woody
# of Cases:
# of Ref.:
*Digestate
43354611964551632157676662N/A63
1015292013145530281311530314361417171082512N/A710
4753
2621136
-90
-60
-30
0
30
60
90
120
150
269
N.A.
Waste
877877
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
There are alternatives to land-based bioenergy. Microalgae, for exam-
ple, offer a high-end technical potential. However, it might be com-
promised by water supply, if produced in arid land, or by impacts on
ocean ecosystems. To make microalgae cost competitive, maximizing
algal lipid content (and then maximizing growth rate) requires techno-
logical breakthroughs (Davis etal., 2011a; Sun etal., 2011; Jonker and
Faaij, 2013). The market potential depends on the co-use of products
for food, fodder, higher value products, and fuel markets (Chum etal.,
2011).
Similarly, lignocellulosic feedstocks produced from waste or residues,
or grown on land unsupportive of food production (e. g., contaminated
land for remediation as in previously mined land) have been suggested
to reduce socio-environmental impact. In addition, lignocellulosic
feedstocks can be bred specifically for energy purposes, and can be
harvested by coupling collection and pre-processing (densification and
others) in depots prior to final conversion, which could enable deliv-
ery of more uniform feedstocks throughout the year (Eranki and Dale,
2011; U. S. DOE, 2011; Argo etal., 2013).
Various conversion pathways are in research and development (R&D),
near commercialization, or in early deployment stages in several coun-
tries (see Section 2.6.3 in Chum etal., 2011). More productive land is
also more economically attractive for cellulosic feedstocks, in which
case competition with food production is more likely. Depending on
the feedstock, conversion process, prior land use, and land demand,
lignocellulosic bioenergy can be associated with high or low GHG
emissions (e. g., Davis etal., 2011b). Improving agricultural lands and
reducing non-point pollution emissions to watersheds remediate nitro-
gen run off and increase overall ecosystems’ health (Van Dam etal.,
2009a; b; Gopalakrishnan et al., 2012). Also regeneration of saline
lands by salt-tolerant tree and grass species can have a large potential
on global scale as demonstrated by Wicke etal. (2011).
A range of agro-ecological options to improve agricultural practices
such as no / low tillage conservation, agroforestry, etc., have potential
to increase yields (e. g., in sub-Saharan Africa), while also providing a
range of co-benefits such as increased soil organic matter. Such options
require a much lower level of investment and inputs and are thus more
readily applicable in developing countries, while also holding a low risk
of increased GHG emissions (Keating etal., 2013).
Substantial progress has also been achieved in the last four years in
small-scale bioenergy applications in the areas of technology inno-
vation, impact evaluation and monitoring, and in large-scale imple-
mentation programmes. For example, advanced combustion biomass
cookstoves, which reduce fuel use by more than 60 % and hazardous
pollutant as well as short-lived climate pollutants by up to 90 %, are
now in the last demonstration stages or commercial (Kar etal., 2012;
Anenberg et al., 2013). Innovative designs include micro-gasifiers,
stoves with thermoelectric generators to improve combustion efficiency
and provide electricity to charge LED lamps while cooking, stoves with
advanced combustion chamber designs, and multi-use stoves (e. g.,
cooking and water heating for bathing (Ürge-Vorsatz et al., 2012;
Anenberg etal., 2013). Biogas stoves, in addition to providing clean
combustion, help reduce the health risks associated with the disposal
of organic wastes. There has also been a boost in cookstove dissemi-
nation efforts ranging from regional (multi-country) initiatives (Wang
etal., 2013b) to national, and project-level interventions. In total, more
than 200 large-scale cookstove projects are in place worldwide, with
several million efficient cookstoves installed each year (Cordes, 2011).
A Global Alliance for Clean Cookstoves has been launched that is pro-
moting the adoption of 100 million clean and efficient cookstoves per
year by 2030 and several countries have launched National Cookstove
Programs in recent years (e. g., Mexico, Peru, Honduras, and others).
Many cookstove models are now manufactured in large-scale indus-
trial facilities using state-of-the-art materials and combustion design
technology. Significant efforts are also in place to develop interna-
tional standards and regional stove testing facilities. In addition to pro-
viding tangible local health and other sustainable benefits, replacing
traditional open fires with efficient biomass cookstoves has a global
mitigation potential estimated to be between 0.6 and 2.4 GtCO
2
eq / yr
(Ürge-Vorsatz etal., 2012).
Small-scale decentralized biomass power generation systems based on
biomass combustion and gasification and biogas production systems
have the potential to meet the electricity needs of rural communities in
the developing countries. The biomass feedstocks for these small-scale
systems could come from residues of crops and forests, wastes from
livestock production, and / or from small-scale energy plantations (Faaij,
2006).
11�13�4 GHG emission estimates of bioenergy
production systems
The combustion of biomass generates gross GHG emissions roughly
equivalent to the combustion of fossil fuels. If bioenergy production
is to generate a net reduction in emissions, it must do so by offset-
ting those emissions through increased net carbon uptake of biota and
soils. The appropriate comparison is then between the net biosphere
flux in the absence of bioenergy compared to the net biosphere flux in
the presence of bioenergy production. Direct and indirect effects need
to be considered in calculating these fluxes.
Bioenergy systems directly influence local and global climate through
(i) GHG emissions from fossil fuels associated with biomass produc-
tion, harvest, transport, and conversion to secondary energy carriers
(von Blottnitz and Curran, 2007; van der Voet etal., 2010); (ii) CO
2
and
other GHG emissions from biomass or biofuel combustion (Cherubini
etal., 2011); (iii) atmosphere-ecosystem exchanges of CO
2
following
land disturbance (Berndes etal., 2013; Haberl, 2013); (iv) climate forc-
ing resulting from emissions of short-lived GHGs like black carbon and
other chemically active gases (NO
x
, CO, etc.) (Tsao etal., 2012; Jetter
etal., 2012); (v) climate forcing resulting from alteration of biophysi-
cal properties of the land surface affecting the surface energy balance
878878
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Figure 11�24 | Estimates of GHG
LUC
emissions — GHG emissions from biofuel production-induced LUC (as gCO
2
eq / MJ
fuel produced
) over a 30-year time horizon organized by fuel(s),
feedstock, and study.Assessment methods, LUC estimate types and uncertainty metrics are portrayed to demonstrate the diversity in approaches and differences in results within
and across any given category. Points labeled ‘a’ on the Y-axis represent a commonly used estimate of lifecycle GHG emissions associated with the direct supply chain of petroleum
gasoline (frame A) and diesel (frame B). These emissions are not directly comparable to GHG
LUC
because the emission sources considered are different, but are potentially of inter-
est for scaling comparison. Based on Warner etal. (2013). Please note: These estimates of global LUC are highly uncertain, unobservable, unverifiable, and dependent on assumed
policy, economic contexts, and inputs used in the modelling. All entries are not equally valid nor do they attempt to measure the same metric despite the use of similar naming
conventions (e. g., iLUC). In addition, many different approaches to estimating GHG
LUC
have been used. Therefore, each paper has its own interpretation and any comparisons should
be made only after careful consideration. *CO
2
eq includes studies both with and without CH
4
and N
2
O accounting.
-
0
LUC Estimate Type Assessment Method Uncertainty Bars
Scenarios, Marginal LUC
Miscanthus
Maize Stover
Switchgrass
Scenarios, Average LUC
iLUC Factor
Arithmetic Mean, Marginal LUC
Switchgrass
Median, Marginal LUC
Maximum
Minimum
Deterministic (Simplified)
Causal Descriptive
Optimization Modeling
Economic Equilibrium Modeling
Meta-analysis
Distribution
Statistics
97.5
th
Percentile
2.5
th
Percentile
iLUC
Factor
+iLUC 100%
+iLUC 75%
+iLUC 50%
+iLUC 25%
dLUC
150
100
50
0
-50
Soy Palm Rapeseed
SunflowerJatropha
Yeh 2010 Unnasch 2009
(D) Petroleum Fuel
(GHG Emissions from
Land Disturbance Only)
US EPA 2010 Fritsche 2010 US EPA 2010 Taheripouri 2013
Butanol - Maize Fisher-Tropsch Diesel – Short
Rotation Forest
Fischer-Tropsch Diesel –
Lignocellulose
Renewable Gasoline –
Lignocellulose
2012
2017
2022
2017
2022
(a)
Arable
Grass
2012
2017
2022
Degraded
Grass
Forest
Arable
Grass
Degraded
Arable
Grass
Savannah
(a)
Maize Wheat Sugarbeet Sugarcane Lignocellulose
2012
2017
2022
(1 of 4 Shown)
(3 of 4 Shown)
2017
2022
Degraded
Grass
Savannah
Arable
Grass
(a)
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Panichelli 2008
Laborde 2011
CARB 2010
US EPA 2010
Searchinger 2008b
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Labrode 2011
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Labrode 2011
Fritsche 2010
Laborde 2011
500
300
200
150
100
50
0
-50
-100
(B) Biodiesel
[gCO
2
eq/MJ
Fuel Produced
]*
150
100
50
-50
(C) Butanol and Fischer-Tropsch Diesel
[gCO
2
eq/MJ
fuel produced
]*
GHG
LUC
Emissions per Unit Fuel Produced
[gCO
2
eq/MJ
fuel produced
]*
Lywood 2008
Tipper 2010
Kim 2012
Kim 2009
Taheripouri 2013
Mueller 2012
Laborde 2011
Kløverpris 2012
Tyner 2010
CARB 2010
Hertel 2010a
US EPA 2010
Dumortier 2009
Searchinger 2008a
Plevin 2010
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Laborde 2011
Lywood 2008
Tipper 2010
Laborde 2011
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
US EPA 2010
Laborde 2011
CARB 2010
Teheripouri 2013
Mueller 2012
US EPA 2010
300
250
200
150
100
50
0
-50
-100
(A) Ethanol
⇒
879879
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
(e. g., from changes in surface albedo, heat and water fluxes, surface
roughness, etc.; (Bonan, 2008; West et al., 2010a; Pielke Sr. et al.,
2011); and (vi) GHGs from land management and perturbations to soil
biogeochemistry, e. g., N
2
O from fertilizers, CH
4
, etc. (Cai, 2001; Allen
etal., 2009). Indirect effects include the partial or complete substitu-
tion of fossil fuels and the indirect transformation of land use by equi-
librium effects. Hence, the total climate forcing of bioenergy depends
on feedstock, site-specific climate and ecosystems, management con-
ditions, production pathways, end use, and on the interdependencies
with energy and land markets.
In contrast, bioenergy systems have often been assessed (e. g., in LCA
studies, integrated models, policy directives, etc.) under the assump-
tion that the CO
2
emitted from biomass combustion is climate neutral
14
because the carbon that was previously sequestered from the atmo-
sphere will be re-sequestered if the bioenergy system is managed sus-
tainably (Chum etal., 2011; Creutzig etal., 2012a; b). The shortcomings
of this assumption have been extensively discussed in environmental
impact studies and emission accounting mechanisms (Searchinger
etal., 2009; Searchinger, 2010; Cherubini etal., 2011; Haberl, 2013).
Studies also call for a consistent and case-specific carbon stock / flux
change accounting that integrates the biomass system with the
global carbon cycle (Mackey etal., 2013). As shown in Chapter 8 of
WGI (Myhre and Shindell, 2013) and (Plattner etal., 2009; Fuglestvedt
etal., 2010), the climate impacts can be quantified at different points
along a cause-effect chain, from emissions to changes in temperature
and sea level rise. While a simple sum of the net CO
2
fluxes over time
can inform about the skewed time distribution between sources and
sinks (‘C debt’; Marland and Schlamadinger, 1995; Fargione et al.,
2008; Bernier and Paré, 2013), understanding the climate implication
as it relates to policy targets (e. g., limiting warming to 2 °C) requires
models and / or metrics that also include temperature effects and cli-
mate consequence (Smith etal., 2012c; Tanaka etal., 2013). While the
warming from fossil fuels is nearly permanent as it persists for thou-
sands of years, direct impacts from renewable bioenergy systems cause
a perturbation in global temperature that is temporary and even at
14
The neutrality perception is linked to a misunderstanding of the guidelines for
GHG inventories, e. g., IPCC — Land Use, Land-Use Change and Forestry (2000)
states “Biomass fuels are included in the national energy and carbon dioxide
emissions accounts for informational purposes only. Within the energy module
biomass consumption is assumed to equal its regrowth. Any departures from this
hypothesis are counted within the Land Use Change and Forestry Model.” Carbon
neutrality is valid if the countries account for LUC in their inventories for self-
produced bioenergy.
times cooling if terrestrial carbon stocks are not depleted (House etal.,
2002; Cherubini etal., 2013; Joos etal., 2013; Mackey etal., 2013). The
direct, physical climate effects at various end-points need to be fully
understood and characterized — despite the measurement challenges
that some climate forcing mechanisms can entail (West etal., 2010b;
Anderson-Teixeira etal., 2012), and coherently embedded in mitiga-
tion policy scenarios along with the possible counterfactual effects. For
example, in the specific case of existing forests that may continue to
grow if not used for bioenergy, some studies employing counterfactual
baselines show that forest bioenergy systems can temporarily have
higher cumulative CO
2
emissions than a fossil reference system (for a
time period ranging from a few decades up to several centuries; (Repo
etal., 2011; Mitchell etal., 2012; Pingoud et al., 2012; Bernier and
Paré, 2013; Guest etal., 2013; Helin etal., 2013; Holtsmark, 2013).
In some cases, cooling contributions from changes in surface albedo
can mitigate or offset these effects (Arora and Montenegro, 2011;
O’Halloran etal., 2012; Anderson-Teixeira etal., 2012; Hallgren etal.,
2013).
Accounting always depends on the time horizon adopted when assess-
ing climate change impacts, and the assumed baseline, and hence
includes value judgements (Schwietzke etal., 2011; Cherubini etal.,
2013; Kløverpris and Mueller, 2013).
Two specific contributions to the climate forcing of bioenergy, not
addressed in detail in SRREN include N
2
O and biogeophysical factors.
Nitrous oxide emissions: For first-generation crop-based biofuels, as
with food crops (see Chapter 11), emissions of N
2
O from agricultural
soils is the single largest contributor to direct lifecycle GHG emissions,
and one of the largest contributors across many biofuel production
cycles (Smeets etal., 2009a; Hsu etal., 2010). Emission rates can vary
by as much as 700 % between different crop types for the same site,
fertilization rate, and measurement period (Kaiser and Ruser, 2000;
Don etal., 2012; Yang etal., 2012). Increased estimates of N
2
O emis-
sions alone can convert some biofuel systems from apparent net sinks
to net sources (Crutzen etal., 2007; Smith etal., 2012c). Improvements
in nitrogen use efficiency and nitrogen inhibitors can substantially
reduce emissions of N
2
O (Robertson and Vitousek, 2009). For some
specific crops, such as sugarcane, N
2
O emissions can be low (Macedo
etal., 2008; Seabra etal., 2011) or high (Lisboa etal., 2011). Other
bioenergy crops require minimal or zero N fertilization and can reduce
GHG emissions relative to the former land use where they replace con-
ventional food crops (Clair etal., 2008).
Figure 11�24 | Estimates of GHG
LUC
emissions — GHG emissions from biofuel production-induced LUC (as gCO
2
eq / MJ
fuel produced
) over a 30-year time horizon organized by fuel(s),
feedstock, and study.Assessment methods, LUC estimate types and uncertainty metrics are portrayed to demonstrate the diversity in approaches and differences in results within
and across any given category. Points labeled ‘a’ on the Y-axis represent a commonly used estimate of lifecycle GHG emissions associated with the direct supply chain of petroleum
gasoline (frame A) and diesel (frame B). These emissions are not directly comparable to GHG
LUC
because the emission sources considered are different, but are potentially of inter-
est for scaling comparison. Based on Warner etal. (2013). Please note: These estimates of global LUC are highly uncertain, unobservable, unverifiable, and dependent on assumed
policy, economic contexts, and inputs used in the modelling. All entries are not equally valid nor do they attempt to measure the same metric despite the use of similar naming
conventions (e. g., iLUC). In addition, many different approaches to estimating GHG
LUC
have been used. Therefore, each paper has its own interpretation and any comparisons should
be made only after careful consideration. *CO
2
eq includes studies both with and without CH
4
and N
2
O accounting.
-
0
LUC Estimate Type Assessment Method Uncertainty Bars
Scenarios, Marginal LUC
Miscanthus
Maize Stover
Switchgrass
Scenarios, Average LUC
iLUC Factor
Arithmetic Mean, Marginal LUC
Switchgrass
Median, Marginal LUC
Maximum
Minimum
Deterministic (Simplified)
Causal Descriptive
Optimization Modeling
Economic Equilibrium Modeling
Meta-analysis
Distribution
Statistics
97.5
th
Percentile
2.5
th
Percentile
iLUC
Factor
+iLUC 100%
+iLUC 75%
+iLUC 50%
+iLUC 25%
dLUC
150
100
50
0
-50
Soy Palm Rapeseed
SunflowerJatropha
Yeh 2010 Unnasch 2009
(D) Petroleum Fuel
(GHG Emissions from
Land Disturbance Only)
US EPA 2010 Fritsche 2010 US EPA 2010 Taheripouri 2013
Butanol - Maize Fisher-Tropsch Diesel – Short
Rotation Forest
Fischer-Tropsch Diesel –
Lignocellulose
Renewable Gasoline –
Lignocellulose
2012
2017
2022
2017
2022
(a)
Arable
Grass
2012
2017
2022
Degraded
Grass
Forest
Arable
Grass
Degraded
Arable
Grass
Savannah
(a)
Maize Wheat Sugarbeet Sugarcane Lignocellulose
2012
2017
2022
(1 of 4 Shown)
(3 of 4 Shown)
2017
2022
Degraded
Grass
Savannah
Arable
Grass
(a)
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Panichelli 2008
Laborde 2011
CARB 2010
US EPA 2010
Searchinger 2008b
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Labrode 2011
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Labrode 2011
Fritsche 2010
Laborde 2011
500
300
200
150
100
50
0
-50
-100
(B) Biodiesel
[gCO
2
eq/MJ
Fuel Produced
]*
150
100
50
-50
(C) Butanol and Fischer-Tropsch Diesel
[gCO
2
eq/MJ
fuel produced
]*
GHG
LUC
Emissions per Unit Fuel Produced
[gCO
2
eq/MJ
fuel produced
]*
Lywood 2008
Tipper 2010
Kim 2012
Kim 2009
Taheripouri 2013
Mueller 2012
Laborde 2011
Kløverpris 2012
Tyner 2010
CARB 2010
Hertel 2010a
US EPA 2010
Dumortier 2009
Searchinger 2008a
Plevin 2010
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
Laborde 2011
Lywood 2008
Tipper 2010
Laborde 2011
Lywood 2008
Bauen 2010
Tipper 2010
Fritsche 2010
US EPA 2010
Laborde 2011
CARB 2010
Teheripouri 2013
Mueller 2012
US EPA 2010
300
250
200
150
100
50
0
-50
-100
(A) Ethanol
880880
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
Biogeophysical factors: Land cover changes or land-use distur-
bances of the surface energy balance, such as surface albedo, sur-
face roughness, and evapotranspiration influence the climate system
(Betts, 2001; Marland etal., 2003; Betts etal., 2007; Bonan, 2008;
Jackson etal., 2008; Mahmood etal., 2013). Perturbations to these
can lead to both direct and indirect climate forcings whose impacts
can differ in spatial extent (global and / or local) (Bala etal., 2007;
Davin et al., 2007). Surface albedo is found to be the dominant
direct biogeophysical climate impact mechanism linked to land
cover change at the global scale, especially in areas with seasonal
snow cover (Claussen etal., 2001; Bathiany etal., 2010), with radia-
tive forcing effects possibly stronger than those of the co-occuring
C-cycle changes (Randerson etal., 2006; Lohila etal., 2010; Bright
et al., 2011; Cherubini et al., 2012; O’Halloran et al., 2012). Land
cover changes can also affect other biogeophysical factors like
evapotranspiration and surface roughness, which can have important
local (Loarie etal., 2011; Georgescu etal., 2011) and global climatic
consequences (Bala etal., 2007; Swann etal., 2010, 2011). Biogeo-
physical climate impacts from changes in land use are site-specific
and show variations in magnitude across different geographic
regions and biomes (Bonan, 2008; Anderson, 2010; Pielke Sr. etal.,
2011; Anderson-Teixeira etal., 2012). Biogeophysical impacts should
be considered in climate impact assessments and in the design of
land-use policies to adequately assess the net impacts of land-use
mitigation options (Jackson etal., 2008; Betts, 2011; Arora and Mon-
tenegro, 2011) as their size may be comparable to impacts from
changes to the C cycle.
Figure 11.23 illustrates the range of lifecycle global direct climate
impact (in g CO
2
equivalents per MJ, after characterization with GWP
time horizon=100 years) attributed to major global bioenergy products
reported in the peer-reviewed literature after 2010. Results are broadly
comparable to those of Chapter 2 in SRREN (Figures 2.10 and 2.11 in
SRREN; Chum etal., 2011) Those figures displayed negative emissions,
resulting from crediting emission reduction due to substitution effects.
This appendix refrains from allocating credits to feedstocks to avoid
double accounting.
Significant variation in the results reflects the wide range of conver-
sion technologies and the reported performances in addition to analyst
assumptions affecting system boundary completeness, emission inven-
tory completeness, and choice of allocation method (among others).
Additional ‘site-specific’ land-use considerations such as changes in soil
organic carbon stocks (∆SOC), changes in surface albedo (∆albedo),
and the skewed time distribution of terrestrial biogenic CO
2
fluxes
can either reduce or compound land-use impacts and are presented
to exemplify that, for some bioenergy systems, these impacts can be
greater in magnitude than lifecycle impacts from feedstock cultivation
and bioenergy product conversion. ‘Site-specific’ land-use consider-
ations are geographically explicit and highly sensitive to background
climate conditions, soil properties, biomass yields, and land manage-
ment regimes. The figure reveals that studies find very different values
depending on the boundaries of analysis chosen, site-specific effects,
and management methods. Nonetheless, it is clear that fuels from
sugarcane, perennial grasses, crop residues, and waste cooking oil are
more beneficial than other fuels (LUC emissions can still be relevant,
see Figure 11.23). Another important result is that albedo effects and
site-specific CO
2
fluxes are highly variable for different forest systems
and environmental conditions and determine the total climate forcing
of bioenergy from forestry.
Direct and indirect land-use change: Direct land-use change
occurs when bioenergy crops displace other crops or pastures or for-
ests, while iLUC results from bioenergy deployment triggering the
conversion to cropland of lands, somewhere on the globe, to replace
some portion of the displaced crops (Searchinger etal., 2008; Kløver-
pris etal., 2008; Delucchi, 2010; Hertel etal., 2010). Direct LUC to
establish biomass cropping systems can increase the net GHG emis-
sions, for example, if carbon-rich ecosystems such as wetlands, for-
ests, or natural grasslands are brought into cultivation (Gibbs etal.,
2008; UNEP, 2009, p.2009; Chum etal., 2011). Biospheric C losses
associated with LUC from some bioenergy schemes can be, in some
cases, more than hundred times larger than the annual GHG savings
from the assumed fossil fuel replacement (Gibbs etal., 2008; Chum
etal., 2011). Impacts have been shown to be significantly reduced
when a dynamic baseline includes future trends in global agricultural
land use (Kløverpris and Mueller, 2013). Albeit at lower magnitude,
beneficial LUC effects can also be observed, for example, when some
semi-perennial crops, perennial grasses or woody plants replace
annual crops grown with high fertilizer levels, or where such plants
are produced on lands with carbon-poor soils (Tilman etal., 2006;
Harper etal., 2010; Sterner and Fritsche, 2011; Sochacki etal., 2012).
In particular, Miscanthus improves soil organic carbon reducing over-
all GHG emissions (Brandão et al., 2011); degraded USA Midwest
land for economic agriculture, over a 20-year period, shows succes-
sional perennial crops without the initial carbon debt and indirect
land-use costs associated with food-based biofuels (Gelfand etal.,
2013). Palm oil, when grown on more marginal grasslands, can
deliver a good GHG balance and net carbon storage in soil (Wicke
etal., 2008). Such lands represent a substantial potential for palm
oil expansion in Indonesia without deforestation and draining peat
lands (Wicke etal., 2011a).
In long-term rotation forests, the increased removal of biomass for
bioenergy may be beneficial or not depending on the site-specific
forest conditions (Cherubini et al., 2012b). For long-term rotation
biomass, the carbon debt (increased cumulative CO
2
emissions for a
duration in the order of a rotation cycle or longer) becomes increas-
ingly important (Schlamadinger and Marland, 1996; Marland and
Schlamadinger, 1997; Fargione etal., 2008; McKechnie et al., 2011;
Hudiburg etal., 2011). Calculations of specific GHG emissions from
long-term rotation forests need to account for the foregone CO
2
-accu-
mulation (Searchinger, 2010; Holtsmark, 2012; Pingoud etal., 2012;
Haberl etal., 2012).
881881
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
If part of a larger forest is used as a feedstock for bioenergy while
the overall forest carbon stock increases (the so-called landscape
perspective), then the overall mitigation effects are positive, in par-
ticular over several harvesting cycles making use of the faster car-
bon sequestration rates of younger forests (Daigneault etal., 2012;
Ximenes etal., 2012; Lamers and Junginger, 2013; Latta etal., 2013).
Nabuurs etal. (2013) observe first signs of a carbon sink saturation
in European forest biomass and suggest to focus less on the forest
biomass sink strength but to consider a mitigation strategy that max-
imizes the sum of all the possible components: (1) carbon sequestra-
tion in forest biomass; (2) soil and wood products; and (3) the effects
of material and energy substitution of woody biomass. In general, the
use of easily decomposable residues and wastes for bioenergy can
produce GHG benefits (Zanchi etal., 2012), similarly to increasing the
biomass outtake from forests affected by high mortality rates (Lamers
etal., 2013), whereas the removal of slowly decomposing residues
reduces soil carbon accumulation at a site and results in net emis-
sions (Repo etal., 2011). The anticipation of future bioenergy mar-
kets may promote optimized forest management practices or affor-
estation of marginal land areas to establish managed plantations,
thus contributing to increased forest carbon stocks (Sedjo and Tian,
2012). Rather than leading to wide-scale loss of forest lands, growing
markets for tree products can provide incentives for maintaining or
increasing forest stocks and land covers, and improving forest health
through management (Eisenbies et al., 2009; Dale et al., 2013). If
managed to maximize CO
2
storage rate over the long-term, long-term
rotation forests offer low-cost mitigation options, in particular, when
woody products keep carbon within the human-built environment
over long time-scales (e. g., wood substituting for steel joist; (Lippke
etal., 2011).
Indirect land-use change is difficult to ascertain because the magni-
tude of these effects must be modelled (Nassar etal., 2011) raising
important questions about model validity and uncertainty (Liska and
Perrin, 2009; Plevin etal., 2010; Khanna etal., 2011; Gawel and Lud-
wig, 2011; Wicke etal., 2012) and policy implications (DeCicco, 2013;
Finkbeiner, 2013; Plevin etal., 2013). Available model-based studies
have consistently found positive and, in some cases, high emissions
from LUC and iLUC, mostly of first-generation biofuels (Figure 11.23),
albeit with high variability and uncertainty in results (Hertel et al.,
2010; Taheripour etal., 2011; Dumortier etal., 2011; Havlík etal., 2011;
Chen etal., 2012; Timilsina etal., 2012; Warner etal., 2014). Causes
of the great uncertainty include: incomplete knowledge on global
economic dynamics (trade patterns, land-use productivity, diets, use
of by-products, fuel prices, and elasticities); selection of specific poli-
cies modelled; and the treatment of emissions over time (O’Hare etal.,
2009; Khanna etal., 2011; Wicke etal., 2012). In addition, LUC mod-
elling philosophies and model structures and features (e. g., dynamic
vs. static model) differ among studies. Variations in estimated GHG
emissions from biofuel-induced LUC are also driven by differences
in scenarios assessed, varying assumptions, inconsistent definitions
across models (e. g., LUC, land type), specific selection of reference sce-
narios against which (marginal) LUC is quantified, and disparities in
data availability and quality. The general lack of thorough sensitivity
and uncertainty analysis hampers the evaluation of plausible ranges of
estimates of GHG emissions from LUC.
Wicke etal. (2012) identified the need to incorporate the impacts of
iLUC prevention or mitigation strategies in future modelling efforts,
including the impact of zoning and protection of carbon stocks, selec-
tive sourcing from low risk-areas, policies and investments to improve
agricultural productivity, double cropping, agroforestry schemes, and
the (improved) use of degraded and marginal lands (see Box 7.1).
Indirect land-use change is mostly avoided in the modelled mitiga-
tion pathways in Chapter 6. The relatively limited fuel coverage in
the literature precludes a complete set of direct comparisons across
alternative and conventional fuels sought by regulatory bodies and
researchers.
GHG emissions from LUC can be reduced, for instance through pro-
duction of bioenergy co-products that displace additional feedstock
requirements, thus decreasing the net area needed (e. g., for corn,
Wang etal., 2011a; for wheat, Berndes et al., 2011). Proper man-
agement of livestock and agriculture can lead to improved resource
efficiency, lower GHG emissions, and lower land use while releas-
ing land for bioenergy production as demonstrated for Europe (de
Wit etal., 2013) and Mozambique (van der Hilst etal., 2012b). For
land transport, cellulosic biomass, such as Miscanthus, has been sug-
gested as a relatively low-carbon source for bioethanol that could
be produced at scale, but only if iLUC can be avoided by not displac-
ing food and other commodities and if comprehensive national land
management strategies are developed (e. g., Dornburg etal., 2010;
Scown etal., 2012). Negative iLUC values are theoretically possible
(RFA, 2008). Producing biofuels from wastes and sustainably har-
vested residues, and replacing first-generation biofuel feedstocks
with lignocellulosic crops (e. g., grasses) would induce little or no
iLUC (Davis etal., 2011b; Scown etal., 2012). While iLUC quanti-
fications remain uncertain, lower agricultural yields, land-intensive
diets, and livestock feeding efficiencies, stronger climate impacts and
higher energy crop production levels can result in higher LUC-related
GHG emissions. Strong global and regional governance (forest pro-
tection, zoning), technological change in agriculture and biobased
options, and high-yield bioenergy crops and use of residues and
degraded land (if available) could all reduce iLUC (Van Dam etal.,
2009a; b; Wicke etal., 2009; Fischer etal., 2010; de Wit etal., 2011,
2013; van der Hilst etal., 2012a; Rose etal., 2013). As with any other
renewable fuel, bioenergy can replace or complement fossil fuel.
The fossil fuel replacement effect, relevant when a global cap on
CO
2
emissions is absent, is discussed in Chapter 8.7. Indirect effects
are not restricted to indirect GHG effects of production of biomass
in agricultural systems; there are also indirect (market-mediated)
effects of wood energy, but also effects in terms of biodiversity
threats, environmental degradation, and external social costs, which
are not considered here.
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11�13�5 Aggregate future potential deployment
in integrated models
In SRREN scenarios (IPCC, 2011), bioenergy is projected to contribute
80 – 190 EJ / yr to global primary energy supply by 2050 for 50 % of
the scenarios in the two mitigation levels modelled. The min to max
ranges were 20 – 265 EJ / yr for the less stringent scenarios and 25 – 300
EJ for the tight mitigation scenarios (<440 ppm). Many of these
scenarios coupled bioenergy with CCS. The Global Energy Assessment
(GEA, 2012) scenarios project 80 – 140 EJ by 2050, including extensive
use of agricultural residues and second-generation bioenergy to try to
reduce the adverse impacts on land use and food production, and the
co-processing of biomass with coal or natural gas with CCS to make
low net GHG-emitting transport fuels and or electricity.
Traditional biomass demand is steady or declines in most scenarios
from 34 EJ / yr. The transport sector increases nearly ten-fold from 2008
to 18 – 20 EJ / yr while modern uses for heat, power, combinations, and
industry increase by factors of 2 – 4 from 18 EJ in 2008 (Fischedick
etal., 2011). The 2010 International Energy Agency (IEA) model proj-
ects a contribution of 12 EJ / yr (11 %) by 2035 to the transport sector,
including 60 % of advanced biofuels for road and aviation. Bioenergy
supplies 5 % of global power generation in 2035, up from 1 % in 2008.
Modern heat and industry doubles their contributions from 2008 (IEA,
2010). The future potential deployment level varies at the global and
national level depending on the technological developments, land
availability, financial viability, and mitigation policies.
The WGIII AR5 transformation pathway studies suggest that modern
bioenergy could play a significant role within the energy system (Sec-
tion 6.3.5) providing 5 to 95 EJ / yr in 2030, 10 to 245 EJ / yr in 2050, and
105 to 325 EJ / yr in 2100 under idealized full implementation scenarios
(see also Figure 7.12), with immediate, global, and comprehensive
incentives for land-related mitigation options. The scenarios project
increasing deployment of bioenergy with tighter climate change tar-
gets, both in a given year as well as earlier in time (see Figure 6.20).
Models project increased dependence, as well as increased deploy-
ment, of modern bioenergy, with some models projecting 35 % of total
primary energy from bioenergy in 2050, and as much as 50 % of total
primary energy from modern bioenergy in 2100. Bioenergy’s share of
regional total electricity and liquid fuels could be significant — up to
35 % of global regional electricity from biopower by 2050, and up to
70 % of global regional liquid fuels from biofuels by 2050. However,
the cost-effective allocation of bioenergy within the energy system
varies across models. Several sectoral studies, focusing on biophysical
constraints, model assumptions (e. g., estimated increase in crop yields
over large areas) and current observations, suggest to focus on the
lower half of the ranges reported above (Field etal., 2008; Campbell
etal., 2008; Johnston etal., 2009a, 2011; Haberl etal., 2013b).
BECCS features prominently in many mitigation scenarios. BECCS
is deployed in greater quantities and earlier in time the more strin-
gent the climate policy (Section 6.3.5). Whether BECCS is essential for
mitigation, or even sufficient, is unclear. In addition, the likelihood of
BECCS deployment is difficult to evaluate and depends on safety con-
Box 11�9 | Examples of co-benefits from biofuel production
Brazilian sugar cane ethanol production provides six times more
jobs than the Brazilian petroleum sector and spreads income bene-
fits across numerous municipalities (de Moraes etal., 2010). Worker
income is higher than in nearly all other agricultural sectors (de
Moraes etal., 2010; Satolo and Bacchi, 2013) and several sustain-
ability standards have been adopted (Viana and Perez, 2013). When
substituting gasoline, ethanol from sugar cane also eliminates lead
compounds and reduces noxious emissions (Goldemberg etal.,
2008). Broader strategic planning, understanding of cumulative
impacts, and credible and collaborative decision making processes
can help to enhance biodiversity and reverse ecological fragmenta-
tion, address direct and iLUC, improve the quality and durability of
livelihoods, and other sustainability issues (Duarte etal., 2013).
Co-benefits of palm oil production have been reported in the
major producer countries, Malaysia and Indonesia (Sumathi etal.,
2008; Lam etal., 2009) as well as from new producer countries
(Garcia-Ulloa etal., 2012). Palm oil production results in employ-
ment creation as well as in increment state and individual income
(Sumathi etal., 2008; Tan etal., 2009; Lam etal., 2009; Sayer
etal., 2012; von Geibler, 2013). When combined with agroforestry,
palm oil plantations can increase food production locally and
have a positive impact on biodiversity (Lam etal., 2009; Garcia-
Ulloa etal., 2012) and when palm oil plantations are installed
on degraded land further co-benefits on biodiversity and carbon
enhancement (Sumathi etal., 2008; Garcia-Ulloa etal., 2012;
Sayer etal., 2012). Further, due to its high productivity, palm oil
plantations can produce the same bioenergy input using less
land than other bio-energy crops (Sumathi etal., 2008; Tan etal.,
2009). Certification in palm oil production can become a means
for increasing sustainable production of biofuels (Tan etal., 2009;
Edser, 2012; von Geibler, 2013).
Similarly, co-benefits from the production of Jatropha as a biofuel
crop in developing countries have been reported, mainly when
Jatropha is planted on degraded land. These include increases
in individuals’ incomes (Garg etal., 2011; Arndt etal., 2012),
improvement in energy security at the local level (von Maltitz and
Setzkorn, 2013; Muys etal., 2014), and reducing soil erosion (Garg
etal., 2011).
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firmations, affordability and public acceptance (see Section 11.13.3 for
details). BECCS may also affect the cost-effective emissions trajectory
(Richels etal., In Review; Rose etal., 2013).
Some integrated models are cost-effectively trading off lower land car-
bon stocks and increased land N
2
O emissions for the long-run mitiga-
tion benefits of bioenergy (Rose etal., 2013; Popp etal., 2013). The
models find that bioenergy could contribute effectively to climate
change mitigation despite land conversion and intensification emis-
sions. However, as discussed below and in Section 11.9, policy imple-
mentation and coordination are factors to consider. In these models,
constraining bioenergy has a cost. For instance, limiting global bioen-
ergy availability to 100 EJ / year tripled marginal abatement costs and
doubled consumption losses associated with transformation pathways
(Rose etal., 2013). Overall outcomes may depend strongly on gover-
nance of land use and deployment of best practices in agricultural pro-
duction (see sections above). Progressive developments in governance
of land and modernization of agriculture and livestock and effective
sustainability frameworks can help realize large parts of the technical
bioenergy potential with low associated GHG emissions.
With increasing scarcity of productive land, the growing demand for
food and bioenergy could induce substantial LUC causing high GHG
emissions and / or increased agricultural intensification and higher N
2
O
emissions unless wise integration of bioenergy into agriculture and for-
estry landscapes occurs (Delucchi, 2010). Consideration of LUC emis-
sions in integrated models show that valuing or protecting global ter-
restrial carbon stocks reduces the potential LUC-related GHG emissions
of energy crop deployment, and could lower the cost of achieving cli-
mate change objectives, but could exacerbate increases in agricultural
commodity prices (Popp etal., 2011; Reilly etal., 2012). Many inte-
grated models are investigating idealized policy implementation path-
ways, assuming global prices on GHG (including the terrestrial land
carbon stock); if such conditions cannot be realized, certain types of
bioenergy could lead to additional GHG emissions. More specifically, if
the global terrestrial land carbon stock remains unprotected, large GHG
emissions from bioenergy-related LUC alone are possible (Melillo etal.,
2009; Wise etal., 2009; Creutzig etal., 2012a; Calvin etal., 2013b).
In summary, recent integrated model scenarios project between
10 – 245 EJ / yr modern bioenergy deployment in 2050. Good gover-
nance and favourable conditions for bioenergy development may facil-
itate higher bioenergy deployment while sustainability and livelihood
concerns might constrain deployment of bioenergy scenarios to low
values (see Section 11.13.6).
11�13�6 Bioenergy and sustainable development
The nature and extent of the impacts of implementing bioenergy
depend on the specific system, the development context, and on the
size of the intervention (Section 11.4.5). The effects on livelihoods
have not yet been systematically evaluated in integrated models (Davis
etal., 2013; Creutzig etal., 2012b; Creutzig etal., 2013; Muys etal.,
2014), even if human geography studies have shown that bioenergy
deployment can have strong distributional impacts (Davis etal., 2013;
Muys etal., 2014). The total effects on livelihoods will be mediated
by global market dynamics, including policy regulations and incentives,
the production model and deployment scale, and place-specific factors
such as governance, land tenure security, labour and financial capabili-
ties, among others (Creutzig etal., 2013).
Bioenergy projects can be economically beneficial, e. g., by raising and
diversifying farm incomes and increasing rural employment through
the production of biofuels for domestic use (Gohin, 2008) or export
markets (Wicke etal., 2009; Arndt etal., 2011).
The establishment of large-scale biofuels feedstock production can
also cause smallholders, tenants, and herders to lose access to pro-
ductive land, while other social groups such as workers, investors,
company owners, biofuels consumers, and populations who are
more responsible for GHG emission reductions enjoy the benefits of
this production (van der Horst and Vermeylen, 2011). This is particu-
larly relevant where large areas of land are still unregistered or are
being claimed and under dispute by several users and ethnic groups
(Dauvergne and Neville, 2010). Furthermore, increasing demand for
first-generation biofuels is partly driving the expansion of crops like
soy and oil palm, which in turn contribute to promote large-scale agri-
businesses at the expense of family and community-based agriculture,
in some cases (Wilkinson and Herrera, 2010). Biofuels deployment can
also translate into reductions of time invested in on-farm subsistence
and community-based activities, thus translating into lower produc-
tivity rates of subsistence crops and an increase in intra-community
conflicts as a result of the uneven share of collective responsibilities
(Mingorría etal., 2010).
Bioenergy deployment is more beneficial when it is not an additional
land-use activity expanding over the landscape, but rather integrates
into existing land uses and influences the way farmers and forest
owners use their land. Various studies indicate the ecosystem services
and values that perennial crops have in restoring degraded lands, via
agroforestry systems, controlling erosion, and even in regional climate
effects such as improved water retention and precipitation (Faaij, 2006;
Wicke etal., 2011c; Immerzeel etal., 2013). Examples include adjust-
ments in agriculture practices where farmers, for instance, change their
manure treatment to produce biogas, reduce methane and N losses.
Changes in management practice may swing the net GHG balance
of options and also have clear sustainable development implications
(Davis etal., 2013).
Small-scale bioenergy options can provide cost-effective alternatives
for mitigating climate change, at the same time helping advance sus-
tainable development priorities, particularly in rural areas of devel-
oping countries. IEA (2012b) estimates that 2.6 billion people world-
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Table 11�12 | Potential institutional, social, environmental, economic and technological implications of bioenergy options at local to global scale.
Institutional Scale
May contribute to energy independence (+), especially at the local level (reduce dependency on fossil fuels) (2, 20, 32, 39,50) + Local to national
Can improve (+) or decrease (–) land tenure and use rights for local stakeholders (2, 17, 38, 50) + / – Local
Cross-sectoral coordination (+) or conflicts (–) between forestry, agriculture, energy, and / or mining (2, 13, 26, 31, 60) + / – Local to national
Impacts on labor rights among the value chain (2, 6, 17) + / – Local to national
Promoting of participative mechanisms for small-scale producers (14, 15) + Local to national
Social Scale
Competition with food security including food availability (through reduced food production at the local level), food
access (due to price volatility), usage (as food crops can be diverted towards biofuel production), and consequently to
food stability. Bio-energy derived from residues, wastes, or by-products is an exception (1,2, 7, 9, 12, 18, 23)
– Local to global
Integrated systems (including agroforestry) can improve food production at the local level creating a positive impact towards food security (51, 52,
53, 69, 73, 74). Further, biomass production combined with improved agricultural management can avoid such competition and bring investment
in agricultural production systems with overall improvements of management as a result (as observed in Brazil) (60, 63 66, 67, 70, 71)
+ Local
Increasing (+) or decreasing (–) existing conflicts or social tension (9, 14, 19, 26) + / – Local to national
Impacts on traditional practices: using local knowledge in production and treatment of bioenergy
crops (+) or discouraging local knowledge and practices (–) (2, 50)
+ / – Local
Displacement of small-scale farmers (14, 15, 19). Bioenergy alternatives can also empower local farmers by creating local income opportunities + / – Local
Promote capacity building and new skills (3, 15, 50) + Local
Gender impacts (2, 4, 14, 15, 27) + / – Local to national
Efficient biomass techniques for cooking (e. g., biomass cookstoves) can have positive impacts on
health, especially for women and children in developing countries (42, 43, 44)
+ Local to national
Environmental Scale
Biofuel plantations can promote deforestation and / or forest degradation, under weak or no regulation (1, 8, 22) – Local to global
When used on degraded lands, perennial crops offer large-scale potential to improve soil carbon and structure, abate
erosion and salinity problems. Agroforestry schemes can have multiple benefits including increased overall biomass
production, increase biodiversity and higher resilience to climate changes. (59, 64, 65, 69, 73)
+ Local to global
Some large-scale bio-energy crops can have negative impacts on soil quality, water pollution, and biodiversity. Similarly potential adverse side-effects
can be a consequence of increments in use of fertilizers for increasing productivity (7, 12, 26, 30). Experience with sugarcane plantations has shown
that they can maintain soil structure (56) and application of pesticides can be substituted by the use of natural predators and parasitoids (57, 71)
– / + Local to transboundary
Can displace activities or other land uses (8, 26) – Local to global
Smart modernization and intensification can lead to lower environmental impacts and more efficient land use (75, 76) + Local to transboundary
Creating bio-energy plantations on degraded land can have positive impacts on soil and biodiversity (12) + Local to transboundary
There can be tradeoffs between different land uses, reducing land availability for local stakeholders (45, 46, 47,48, 49). Multicropping
system provide bioenergy while better maintaining ecological diversity and reducing land-use competition (58)
– / + Local to national
Ethanol utilization leads to the phaseout of lead addititives and methyl tertiary-butyl ether (MBTE)
and reduces sulfur, particulate matter, and carbon monoxide emissions (55)
+ Local to global
Economic Scale
Increase in economic activity, income generation, and income diversification (1, 2, 3, 12, 20, 21, 27, 54) + Local
Increase (+) or decrease (–) market opportunities (16, 27, 31) + / – Local to national
Contribute to the changes in prices of feedstock (2, 3, 5, 21) + / – Local to global
May promote concentration of income and / or increase poverty if sustainability criteria and strong governance is not in place (2, 16, 26) – Local to regional
Using waste and residues may create socio-economic benefits with little environmental risks (2, 41, 36) + Local to regional
Uncertainty about mid- and long-term revenues (6, 30) – National
Employment creation (3, 14, 15) + Local to regional
Technological Scale
Can promote technology development and / or facilitate technology transfer (2, 27, 31) + Local to global
Increasing infrastructure coverage (+). However if access to infrastructure and / or technology is
reduced to few social groups it can increase marginalization (–) (27, 28, 29)
+ / – Local
Bioenergy options for generating local power or to use residues may increase labor demand, creating new job opportunities.
Participatory technology development also increases acceptance and appropriation (6, 8, 10, 37, 40)
+ Local
Technology might reduce labor demand (–). High dependent of tech. transfer and / or acceptance – Local
⇒
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wide depend on traditional biomass for cooking, while 84 % of these
belong to rural communities. Use of low-quality fuels and inefficient
cooking and heating devices leads to pollution resulting in nearly 4
million premature deaths every year, and a range of chronic illnesses
and other health problems (Lim et al., 2012; see Section 9.7.3.1).
Modern small-scale bioenergy technologies such as advanced / effi-
cient cook stoves, biogas for cooking and village electrification, bio-
mass gasifiers, and bagasse-based co-generation systems for decen-
tralized power generation, can provide energy for rural communities
with energy services that also promote rural development (IEA, 2011).
Such bioenergy systems reduce CO
2
emissions from unsustainable
biomass harvesting and short-lived climate pollutants, e. g., black car-
bon, from cleaner combustion (Chung etal., 2012). Scaling up clean
cookstove initiatives could not only save 2 million lives a year, but
also significantly reduce GHG emissions (Section 11.13.3). Efficient
biomass cook stoves and biogas stoves at the same time provide mul-
tiple benefits: They reduce the pressure on forests and biodiversity;
they reduce exposure to smoke-related health hazards; they reduce
drudgery for women in collection fuelwood; and they save money if
fuel needs to be purchased (Martin etal., 2011). Benefits from the
dissemination of improved cookstoves outweigh their costs by seven-
fold, when their health, economic, and environmental benefits are
accounted for (Garcia-Frapolli etal., 2010).
Table 11.12 presents the implications of bioenergy options in the light
of social, institutional, environmental, economic, and technological
conditions. The relationship between bioenergy and these conditions is
complex and there could be negative or positive implications, depend-
ing on the type of bioenergy option, the scale of the production sys-
tem and the local context. While biofuels can allow the reduction of
fossil fuel use and of GHG emissions, they often shift environmental
burdens towards land use-related impacts (i. e., eutrophication, acidifi-
cation, water depletion, ecotoxicity; EMPA, 2012; Smith and Torn, 2013;
Tavoni and Socolow, 2013). Co-benefits and adverse side-effects do
not necessarily overlap, neither geographically nor socially (Dauvergne
and Neville, 2010; Wilkinson and Herrera, 2010; van der Horst and Ver-
meylen, 2011). The main potential co-benefits are related to access
to energy and impacts on the economy and well-being, jobs creation,
and improvement of local resilience (Walter etal., 2011; Creutzig etal.,
2013). Main risks of crop-based bioenergy for sustainable develop-
ment and livelihoods include competition for arable land (Haberl etal.,
2013b) and consequent impact on food security, tenure arrangements,
displacement of communities and economic activities, creation of a
driver of deforestation, impacts on biodiversity, water, and soil, or incre-
ment in vulnerability to climate change, and unequal distribution of
benefits (Sala etal., 2000; Hall etal., 2009; German etal., 2011; Thomp-
son etal., 2011b; IPCC, 2012).
Good governance is an essential component of a sustainable energy
system. Integrated studies that compare impacts of bioenergy produc-
tion between different crops and land management strategies show
that the overall impact (both ecological and socio-economic) depends
strongly on the governance of land use and design of the bioenergy
system see van der Hilst etal. (2012) in the European context, and Van
Dam etal. (2009a; b) for different crops and scenarios in Argentina).
Van Eijck etal. (2012) show similar differences in impacts between
the production and use of Jatropha based on smallholder production
versus plantation models. This implies that governance and planning
have a strong impact on the ultimate result and impact of large-scale
bioenergy deployment. Legislation and regulation of bioenergy as well
as voluntary certification schemes are required to guide bioenergy
production system deployment so that the resources and feedstocks
be put to best use, and that (positive and negative) socioeconomic
and environmental issues are addressed as production grows (van
Dam etal., 2010). There are different options, from voluntary to legal
and global agreements, to improve governance of biomass markets
and land use that still require much further attention (Verdonk etal.,
2007). The integration of bioenergy systems into agriculture and for-
est landscapes can improve land and water use efficiency and help
address concerns about environmental impacts of present land use
(Berndes etal., 2004, 2008; Börjesson and Berndes, 2006; Sparovek
etal., 2007; Gopalakrishnan et al., 2009, 2011a; b, 2012; Dimitriou
etal., 2009, 2011; Dornburg etal., 2010; Batidzirai etal., 2012; Parish
etal., 2012; Baum et al., 2012; Busch, 2012), but the global poten-
tials of such systems are difficult to determine (Berndes and Börjesson,
2007; Dale and Kline, 2013). Similarly, existing and emerging guiding
principles and governance systems influence biomass resources avail-
ability (Stupak etal., 2011). Certification approaches can be useful, but
they should be accompanied by effective territorial policy frameworks
(Hunsberger etal., 2012).
1
Alves Finco and Doppler (2010);
2
Amigun etal. (2011);
3
Arndt etal. (2012);
4
Arndt etal. (2011);
5
Arndt etal.(2012);
6
Awudu and Zhang (2012);
7
Beringer etal. (2011);
8
Borzoni
(2012);
9
Bringezu etal. (2012);
10
Cacciatore etal. (2012);
11
Cançado etal. (2006);
12
Danielsen etal. (2009);
13
Diaz-Chavez (2011);
14
Duvenage etal. (2013);
15
Ewing and Msangi
(2009);
16
Gasparatos etal. (2011);
17
German and Schoneveld (2012);
18
Haberl etal. (2011a);
19
Hall etal. (2009);
20
Hanff etal. (2011);
21
Huang etal. (2012);
22
Koh and Wilcove
(2008);
23
Koizumi (2013);
24
Kyu etal. (2010);
25
Madlener etal. (2006);
26
Martinelli and Filoso (2008);
27
Mwakaje (2012);
28
Oberling etal. (2012);
29
Schut etal. (2010);
30
Selfa etal.
(2011);
31
Steenblik (2007);
32
Stromberg and Gasparatos (2012);
33
Searchinger etal. (2009);
34
Searchinger etal. (2008);
35
Smith and Searchinger (2012);
36
Tilman etal. (2009);
37
Van
de Velde etal. (2009);
38
von Maltitz and Setzkorn (2013);
39
Wu and Lin (2009);
40
Zhang etal. (2011);
41
Fargione etal. (2008);
42
Jerneck and Olsson (2013);
43
Gurung and Oh (2013);
44
O’Shaughnessy etal. (2013);
45
German etal. (2013);
46
Cotula (2012);
47
Mwakaje (2012);
48
Scheidel and Sorman (2012);
49
Haberl etal.(2013b);
50
Muys etal. (2014);
51
Egeskog
etal. (2011);
52
Diaz-Chavez (2012);
53
Ewing and Msangi (2009);
54
de Moraes etal. (2010);
55
Goldemberg (2007);
56
Walter etal. (2011);
57
Macedo (2005);
58
Langeveld etal. (2013);
59
Van Dam etal. (2009a; b);
60
van Dam etal. (2010);
61
van Eijck etal. (2012);
62
van Eijck etal. (2014);
63
Martínez etal. (2013);
64
van der Hilst etal. (2010);
65
van der Hilst etal.
(2012);
66
van der Hilst and Faaij (2012);
67
van der Hilst etal. (2012b);
68
Hoefnagels etal. (2013);
69
Immerzeel etal. (2013);
70
Lynd etal. (2011);
71
Smeets etal. (2008);
72
Smeets and
Faaij (2010);
73
Wicke etal. (2013);
74
Wiskerke etal. (2010);
75
De Wit etal. (2011);
76
de Wit etal. (2013)
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11�13�7 Tradeoffs and synergies with land,
water, food, and biodiversity
This section summarizes results from integrated models (models that
have a global aggregate view, but cannot disaggregate place-specific
effects in biodiversity and livelihoods discussed above) on land, water,
food, and biodiversity. In these models, at any level of future bioenergy
supply, land demand for bioenergy depends on (1) the share of bioen-
ergy derived from wastes and residues (Rogner etal., 2012); (2) the
extent to which bioenergy production can be integrated with food or
fiber production, which ideally results in synergies (Garg etal., 2011;
Sochacki etal., 2013) or at least mitigates land-use competition (Ber-
ndes etal., 2013); (3) the extent to which bioenergy can be grown
on areas with little current or future production, taking into account
growing land demand for food (Nijsen etal., 2012); and (4) the vol-
ume of dedicated energy crops and their yields (Haberl etal., 2010;
Batidzirai etal., 2012; Smith etal., 2012d). Energy crop yields per unit
area may differ by factors of >10 depending on differences in natural
fertility (soils, climate), energy crop plants, previous land use, manage-
ment and technology (Johnston etal., 2009a; Lal, 2010; Beringer etal.,
2011; Pacca and Moreira, 2011; Smith etal., 2012a; Erb etal., 2012a).
Assumptions on energy crop yields are one of the main reasons for
the large differences in estimates of future area demand of energy
crops (Popp etal., 2013). Likewise, assumptions on yields, strategies,
and governance on future food / feed crops have large implications for
assessments of the degree of land competition between biofuels and
these land uses (Batidzirai etal., 2012; de Wit etal., 2013).
However, across models, there are very different potential landscape
transformation visions in all regions (Sections 6.3.5 and 11.9.). Overall,
it is difficult to generalize on regional land cover effects of mitigation.
Some models assume significant land conversion while other models
do not. In idealized implementation scenarios, there is expansion of
energy cropland and forest land in many regions, with some models
exhibiting very strong forest land expansion and others very little by
2030. Land conversion is increased in the 450 ppm scenarios compared
to the 550 ppm scenarios, but at a declining share, a result consistent
with a declining land-related mitigation rate with policy stringency.
The results of these integrated model studies need to be interpreted
with caution, as not all GHG emissions and biogeophysical or socio-
economic effects of bioenergy deployment are incorporated into these
models, and as not all relevant technologies are represented (e. g., cas-
cade utilization).
Large-scale bioenergy production from dedicated crops may affect
water availability and quality (see Section 6.6.2.6), which are highly
dependent on (1) type and quantity of local freshwater resources;
(2) necessary water quality; (3) competition for multiple uses (agri-
cultural, urban, industrial, power generation), and (4) efficiency in all
sector end uses (Gerbens-Leenes etal., 2009; Coelho etal., 2012). In
many regions, additional irrigation of energy crops could further inten-
sify existing pressures on water resources (Popp etal., 2011). Studies
indicate that an exclusion of severe water scarce areas for bioenergy
production (mainly to be found in the Middle East, parts of Asia, and
western United States) would reduce global technical bioenergy poten-
tials by 17 % until 2050 (van Vuuren etal., 2009). A model compari-
son study with five global economic models shows that the aggregate
food price effect of large-scale lignocellulosic bioenergy deployment
(i. e., 100 EJ globally by the year 2050) is significantly lower (+5 %
on average across models) than the potential price effects induced
by climate impacts on crop yields (+25 % on average across models
(Lotze-Campen etal., 2013). Possibly hence, ambitious climate change
mitigation need not drive up global food prices much, if the extra land
required for bioenergy production is accessible or if the feedstock, e. g.,
from forests, does not directly compete for agricultural land. Effective
land-use planning and strict adherence to sustainability criteria need to
be integrated into large-scale bioenergy projects to minimize competi-
tions for water (for example, by excluding the establishment of biofuel
projects in irrigated areas). If bioenergy is not managed properly, addi-
tional land demand and associated LUC may put pressures on biodi-
versity (Groom etal., 2008; see Section 6.6.2.5). However, implement-
ing appropriate management, such as establishing bioenergy crops
in degraded areas represents an opportunity where bioenergy can be
used to achieve positive environmental outcomes (Nijsen etal., 2012).
887887
Agriculture, Forestry and Other Land Use (AFOLU)
11
Chapter 11
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