659
10
Key Economic Sectors
and Services
Coordinating Lead Authors:
Douglas J. Arent (USA), Richard S.J. Tol (UK)
Lead Authors:
Eberhard Faust (Germany), Joseph P. Hella (Tanzania), Surender Kumar (India),
Kenneth M. Strzepek (UNU/USA), Ferenc L. Tóth (IAEA/Hungary), Denghua Yan (China)
Contributing Authors:
Francesco Bosello (Italy), Paul Chinowsky (USA), Kristie L. Ebi (USA), Stephane Hallegatte
(France), Robert Kopp (USA), Simone Ruiz Fernandez (Germany), Armin Sandhoevel
(Germany), Philip Ward (Netherlands), Eric Williams (IAEA/USA)
Review Editors:
Amjad Abdulla (Maldives), Haroon Kheshgi (USA), He Xu (China)
Volunteer Chapter Scientist:
Julius Ngeh (Cameroon)
This chapter should be cited as:
Arent
, D.J., R.S.J. Tol, E. Faust, J.P. Hella, S. Kumar, K.M. Strzepek, F.L. Tóth, and D. Yan, 2014: Key economic
sectors and services. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and
Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir,
M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,
P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, pp. 659-708.
10
660
Executive Summary............................................................................................................................................................ 662
10.1. Introduction and Context ....................................................................................................................................... 664
10.2. Energy ..................................................................................................................................................................... 664
10.2.1. Energy Demand ................................................................................................................................................................................ 664
10.2.2. Energy Supply ................................................................................................................................................................................... 665
10.2.3. Transport and Transmission of Energy ............................................................................................................................................... 668
10.2.4. Macroeconomic Impacts ................................................................................................................................................................... 669
10.2.5. Summary .......................................................................................................................................................................................... 672
10.3. Water Services ........................................................................................................................................................ 672
10.3.1. Water Infrastructure and Economy-Wide Impacts ............................................................................................................................. 672
10.3.2. Municipal and Industrial Water Supply ............................................................................................................................................. 673
10.3.3. Wastewater and Urban Stormwater ................................................................................................................................................. 673
10.3.4. Inland Navigation ............................................................................................................................................................................. 673
10.3.5. Irrigation ........................................................................................................................................................................................... 673
10.3.6. Nature Conservation ......................................................................................................................................................................... 674
10.3.7. Recreation and Tourism .................................................................................................................................................................... 674
10.3.8. Water Management and Allocation .................................................................................................................................................. 674
10.3.9. Summary .......................................................................................................................................................................................... 674
10.4. Transport ................................................................................................................................................................. 674
10.4.1. Roads ................................................................................................................................................................................................ 674
10.4.2. Rail ................................................................................................................................................................................................... 675
10.4.3. Pipeline ............................................................................................................................................................................................. 675
10.4.4. Shipping ........................................................................................................................................................................................... 675
10.4.5. Air ..................................................................................................................................................................................................... 676
10.5. Other Primary and Secondary Economic Activities ................................................................................................. 676
10.5.1. Primary Economic Activities .............................................................................................................................................................. 676
10.5.1.1. Crop and Animal Production ............................................................................................................................................. 676
10.5.1.2. Forestry and Logging ......................................................................................................................................................... 676
10.5.1.3. Fisheries and Aquaculture ................................................................................................................................................. 676
10.5.1.4. Mining and Quarrying ....................................................................................................................................................... 676
10.5.2. Secondary Economic Activities .......................................................................................................................................................... 677
10.5.2.1. Manufacturing ................................................................................................................................................................... 677
10.5.2.2. Construction and Housing ................................................................................................................................................. 677
Table of Contents
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Key Economic Sectors and Services Chapter 10
10
10.6. Recreation and Tourism .......................................................................................................................................... 677
10.6.1. Recreation and Tourism Demand ...................................................................................................................................................... 677
10.6.1.1. Recreation ......................................................................................................................................................................... 677
10.6.1.2. Tourism .............................................................................................................................................................................. 678
10.6.2. Recreation and Tourism Supply ......................................................................................................................................................... 679
10.6.3. Market Impacts ................................................................................................................................................................................. 679
10.7. Insurance and Financial Services ............................................................................................................................ 680
10.7.1. Main Results of the Fourth Assessment Report and IPCC Special Report on Managing the Risks of Extreme Events
and Disasters to Advance Climate Change Adaptation on Insurance ................................................................................................ 680
10.7.2. Fundamentals of Insurance Covering Weather Hazards .................................................................................................................... 680
10.7.3. Observed and Projected Insured Losses from Weather Hazards ........................................................................................................ 680
10.7.4. Fundamental Supply-Side Challenges and Sensitivities .................................................................................................................... 683
10.7.5. Products and Systems Responding to Changes in Weather Risks ..................................................................................................... 684
10.7.6. Governance, Public-Private Partnerships, and Insurance Market Regulation ..................................................................................... 686
10.7.7. Financial Services .............................................................................................................................................................................. 686
10.7.8. Summary .......................................................................................................................................................................................... 687
10.8. Services Other than Tourism and Insurance ............................................................................................................ 687
10.8.1. Sectors Other than Health ................................................................................................................................................................ 687
10.8.2. Health ............................................................................................................................................................................................... 687
10.9. Impacts on Markets and Development ................................................................................................................... 689
10.9.1. Effects of Markets ............................................................................................................................................................................. 689
10.9.2. Aggregate Impacts ........................................................................................................................................................................... 690
10.9.3. Social Cost of Carbon ....................................................................................................................................................................... 690
10.9.4. Effects on Growth ............................................................................................................................................................................. 691
10.9.4.1. The Rate of Economic Growth ........................................................................................................................................... 691
10.9.4.2. Poverty Traps ..................................................................................................................................................................... 692
10.9.5. Summary .......................................................................................................................................................................................... 692
10.10. Summary; Research Needs and Priorities .............................................................................................................. 693
References ......................................................................................................................................................................... 694
Frequently Asked Questions
10.1: Why are key economic sectors vulnerable to climate change? ......................................................................................................... 664
10.2: How does climate change impact insurance and financial services? ................................................................................................ 680
10.3: Are other economic sectors vulnerable to climate change too? ....................................................................................................... 688
662
Chapter 10 Key Economic Sectors and Services
10
Executive Summary
This chapter assesses the implications of climate change on economic activity in key economic sectors and services, on economic welfare, and
on economic development.
For most economic sectors, the impact of climate change will be small relative to the impacts of other drivers (medium evidence,
high agreement). Changes in population, age, income, technology, relative prices, lifestyle, regulation, governance, and many other aspects of
socioeconomic development will have an impact on the supply and demand of economic goods and services that is large relative to the impact
of climate change. {10.10}
Climate change will reduce energy demand for heating and increase energy demand for cooling in the residential and commercial
sectors (robust evidence, high agreement); the balance of the two depends on the geographic, socioeconomic, and technological conditions.
Increasing income will allow people to regulate indoor temperatures to a comfort level that leads to fast growing energy demand for air
conditioning even in the absence of climate change in warm regions with low income levels at present. Energy demand will be influenced by
changes in demographics (upward by increasing population and decreasing average household size), lifestyles (upward by larger floor area of
dwellings), the design and heat insulation properties of the housing stock, the energy efficiency of heating/cooling devices, and the abundance
and energy efficiency of other electric household appliances. The relative importance of these drivers varies across regions and will change over
time. {10.2}
Climate change will affect different energy sources and technologies differently, depending on the resources (water flow, wind,
insolation), the technological processes (cooling), or the locations (coastal regions, floodplains) involved (robust evidence, high
agreement).
Gradual changes in various climate attributes (temperature, precipitation, windiness, cloudiness, etc.) and possible changes in the
frequency and intensity of extreme weather events will progressively affect operation over time. Climate-induced changes in the availability
and temperature of water for cooling are the main concern for thermal and nuclear power plants. Several options are available to cope with
reduced water availability but at higher cost; however, decreased efficiency of thermal conversion remains a primary concern. Similarly, already
available or newly developed technological solutions allow firms to reduce the vulnerability of new structures and enhance the climate suitability
of existing energy installations. {10.2}
Climate change may influence the integrity and reliability of pipelines and electricity grids (medium evidence, medium agreement).
Pipelines and electric transmission lines have been designed and operated for more than a century in diverse and often extreme climatic conditions
on land from hot deserts to permafrost areas and increasingly at sea. Owing to the private nature and high economic value to the energy sector,
they have been designed to higher tolerance levels than most transportation infrastructure. Climate change may require changes in design
standards for the construction and operation of pipelines and power transmission and distribution lines. Adopting existing technology from
other geographical and climatic conditions may reduce the cost of adapting new infrastructure as well as the cost of retrofitting existing
pipelines and grids to the changing climate, sea level, and weather conditions, which is likely to become more intense over time. {10.2}
Climate change will have impacts, positive and negative and varying in scale and intensity, on water supply infrastructure and
water demand (robust evidence, high agreement), but the economic implications are not well understood.
Economic impacts
include flooding, scarcity, and cross-sectoral competition. Flooding can have major economic costs, both in term of impacts (capital destruction,
disruption) and adaptation (construction, defensive investment). Water scarcity and competition for water—driven by institutional, economic,
or social factors—may mean that water is not available in sufficient quantity or quality for some uses or locations. {10.3}
Climate change may negatively affect transport infrastructure (limited evidence, high agreement). Transport infrastructure
malfunctions if the weather is outside the design range, which would happen more frequently as the climate continues to change. All
infrastructure is vulnerable to freeze-thaw cycles. Paved roads are particularly vulnerable to temperature extremes, and unpaved roads and
bridges to precipitation extremes. Transport infrastructure on ice or permafrost is especially vulnerable. {10.4}
663
10
Key Economic Sectors and Services Chapter 10
Climate change will affect tourism resorts, particularly ski resorts, beach resorts, and nature resorts (robust evidence, high
agreement) and tourists may spend their holidays at higher altitudes and latitudes (medium evidence, high agreement).
The
economic implications of climate change-induced changes in tourism demand and supply entail gains for countries closer to the poles and
higher up the mountains and losses for other countries. The demand for outdoor recreation is affected by weather and climate, and impacts will
vary geographically and seasonally. {10.6}
Climate change will affect insurance systems (robust evidence, high agreement). More frequent and/or intensive weather disasters as
projected for some regions/hazards will increase losses and loss variability in various regions and challenge insurance systems to offer affordable
coverage while raising more risk-based capital, particularly in low- and middle-income countries. Economic-vulnerability reduction through
insurance has proven effective. Large-scale public-private risk prevention initiatives and government insurance of the non-diversifiable portion
of risk offer example mechanisms for adaptation. Commercial reinsurance and risk-linked securitization markets also have a role in ensuring
financially resilient insurance and risk transfer systems. {10.7}
Climate change will affect the health sector (medium evidence, high agreement) through increases in the frequency, intensity, and
extent of extreme weather events as well as increasing demands for health care services and facilities, including public health programs,
disease prevention activities, health care personnel, infrastructure, and supplies related to treatment of infectious diseases and temperature-
related events. {10.8}
Well-functioning markets provide an additional mechanism for adaptation and thus tend to reduce negative impacts and
increase positive ones for any specific sector or country (medium evidence, high agreement). The impacts of climate on one sector of
the economy of one country in turn affect other sectors and other countries though product and input markets. Markets increase overall welfare,
but not necessarily welfare in every sector and country. {10.9}
The impacts of climate change may decrease productivity and economic growth, but the magnitude of this effect is not well
understood (limited evidence, high agreement). Climate could be one of the causes why some countries are trapped in poverty, and
climate change may make it harder to escape poverty. {10.9}
Global economic impacts from climate change are difficult to estimate. Economic impact estimates completed over the past 20 years
vary in their coverage of subsets of economic sectors and depend on a large number of assumptions, many of which are disputable, and many
estimates do not account for catastrophic changes, tipping points, and many other factors. With these recognized limitations, the incomplete
estimates of global annual economic losses for additional temperature increases of ~2°C are between 0.2 and 2.0% of income (±1 standard
deviation around the mean) (medium evidence, medium agreement). Losses are more likely than not to be greater, rather than smaller, than
this range (limited evidence, high agreement). Additionally, there are large differences between and within countries. Losses accelerate with
greater warming (limited evidence, high agreement), but few quantitative estimates have been completed for additional warming around 3°C
or above. Estimates of the incremental economic impact of emitting carbon dioxide lie between a few dollars and several hundreds of dollars
per tonne of carbon (robust evidence, medium agreement). Estimates vary strongly with the assumed damage function and discount rate.
{10.9}
Not all key economic sectors and services have been subject to detailed research. Few studies have evaluated the possible impacts of
climate change on mining, manufacturing, or services (apart from health, insurance, and tourism). Further research, collection, and access to
more detailed economic data and the advancement of analytic methods and tools will be required to assess further the potential impacts of
climate on key economic systems and sectors. {10.5, 10.8, 10.10}
664
Chapter 10 Key Economic Sectors and Services
10
10.1. Introduction and Context
This chapter discusses the implications of climate change on key economic
sectors and services, for example, economic activity. Other chapters discuss
i
mpacts from a physical, chemical, biological, or social perspective.
Economic impacts cannot be isolated; therefore, there are a large
number of cross-references to sections in other chapters of this report.
In some cases, particularly agriculture, the discussion of the economic
impacts is integrated with the other impacts.
Focusing on the potential impact of climate change on economic activity,
this chapter addresses questions such as: How does climate change
affect the demand for a particular good or service? What is the impact
on its supply? How do supply and demand interact in the market? What
are the effects on producers and consumers? What is the effect on the
overall economy, and on welfare?
An inclusive approach was taken, discussing all sectors of the economy.
Section SM10.1 found in this chapter’s on-line supplementary material
shows the list of sectors according to the International Standard Industrial
Classification. This assessment reflects the breadth and depth of the
state of knowledge across these sectors; many of which have not been
evaluated in the literature. We extensively discuss five sectors: energy
(Section 10.2), water (Section 10.3), transport (Section 10.4), tourism
(Section 10.6), and insurance (Section 10.7). Other primary and secondary
sectors are discussed in Section 10.5, and Section 10.8 is devoted to
other service sectors. Food and agriculture is addressed in Chapter 7.
Sections 10.2 through 10.8 discuss individual sectors in isolation. Markets
are connected, however. Section 10.9 therefore assesses the implications
of changes in any one sector on the rest of the economy. It also discusses
the effect of the impacts of climate change on economic growth and
development. Chapter 19 assesses the impact of climate change on
economic welfare—that is, the sum of changes in consumer and
producer surplus, including for goods and services not traded within the
formal economy. This is not attempted here. The focus is on economic
activity. Section 10.10 discusses whether there may be vulnerable sectors
that have yet to be studied.
P
revious assessment reports by the IPCC did not have a chapter on “key
economic sectors and services.” Instead, the material assembled here
was spread over a number of chapters. The Fourth Assessment Report
(AR4) is referred to in the context of the sections below. In some cases,
however, the literature is so new that previous IPCC reports did not
discuss these impacts at any length.
10.2. Energy
Studies conducted since AR4 and assessed here confirm the main insights
about the impacts of climate change on energy demand as reported in
the Second Assessment Report (SAR; Acosta et al., 1995) and reinforced
by the Third Assessment Report (TAR; Scott et al., 2001) and AR4 (Wilbanks
et al., 2007): ceteris paribus, in a warming world, energy demand for
heating will decline and energy demand for cooling will increase; the
balance of the two depends on the geographic, socioeconomic, and
technological conditions. The relative importance of temperature changes
among the drivers of energy demand varies across regions and will
change over time. Earlier IPCC assessments did not write much about
energy supply, but an increasing number of studies now explore its
vulnerability, impacts, and adaptation options (Karl et al., 2009; Troccoli,
2010; Ebinger and Vergara, 2011). The energy sector will be transformed
by climate policy (WGIII AR5 Chapter 7) but impacts of climate changes
too will be important for secure and reliable energy supply.
10.2.1. Energy Demand
Most studies conducted since AR4 explore the impacts of climate
change on residential energy demand, particularly electricity (Mideksa
and Kallbekken, 2010). Some studies encompass the commercial sector
as well but very few deal with industry and agriculture. In addition to a
few global studies based on global energy or integrated assessment
models, the new studies tend to focus on specific countries or regions
(Zachariadis, 2010; Olonscheck et al., 2011), rely on improved methods
(more advanced statistical techniques; de Cian et al., 2013) and data (both
Frequently Asked Questions
FAQ 10.1 | Why are key economic sectors vulnerable to climate change?
Many key economic sectors are affected by long-term changes in temperature, precipitation, sea level rise, and
extreme events, all of which are impacts of climate change. For example, energy is used to keep buildings warm in
winter and cool in summer. Changes in temperature would thus affect energy demand. Climate change also affects
energy supply through the cooling of thermal plants, through wind, solar, and water resources for power, and
through transport and transmission infrastructure. Water demand increases with temperature but falls with rising
carbon dioxide (CO
2
) concentrations as CO
2
fertilization improves the water use efficiency plant respiration. Water
supply depends on precipitation patterns and temperature, and water infrastructure is vulnerable to extreme
weather, while transport infrastructure is designed to withstand a particular range of weather conditions, and climate
change would expose this infrastructure to weather outside historical design criteria. Recreation and tourism are
weather-dependent. As holidays are typically planned in advance, tourism depends on the expected weather and
will thus be affected by climate change. Health care systems are also impacted, as climate change affects a number
of diseases and thus the demand for and supply of health care.
665
10
Key Economic Sectors and Services Chapter 10
h
istorical and regional climate projections), and many of them explicitly
include non-climatic drivers of energy demand (e.g., sources). A few
studies consider changes in demand together with changes in climate-
dependent energy sources, such as hydropower (Hamlet et al., 2010).
Sorting the assessed studies according to the present climate (represented
by mean annual temperature based on 1971–2000 climatology) and
current income (represented by gross domestic product (GDP) per capita
in 2009), the general patterns are as follows. In countries and regions
with already high incomes, climate-related changes in energy demand
will be driven primarily by increasing temperatures. In countries/regions
with high incomes and warm climates, increasing temperatures will be
associated with heavier use of air conditioning. In countries/regions with
high incomes and temperate and cold climates, increasing temperatures
will result in lower demands for various energy forms (electricity, gas,
coal, oil). Increasing incomes will play a marginal role in these countries
and regions. In contrast, changes in income will be the main driver of
increasing demand for energy (mainly electricity for air conditioning
and transportation fuels) in present-day low-income countries in warm
climates. Neither indicator is ideal because country-level mean annual
temperatures for large countries can hide large regional differences and
average incomes may conceal large disparities, but they help cluster
the national and regional studies in the search for general finding.
At the global scale, energy demand for residential air conditioning in
summer is projected to increase rapidly in the 21st century under the
reference climate change scenario (medium population and economic
growth globally, but faster economic growth in developing countries;
no mitigation policies in addition to those in place in 2008) by the Targets
IMAGE Energy Regional Model/Integrated Model to Assess the Global
Environment (TIMER/IMAGE) model (Isaac and Van Vuuren, 2009). The
increase is from nearly 300 TWh in 2000 to about 4000 TWh in 2050
and more than 10,000 TWh in 2100, about 75% of which is due to
increasing income in emerging market countries and 25% is due to
climate change. Energy demand for heating in winter increases too,
but much less rapidly, since in most regions with the highest need for
heating, incomes are already high enough for people to heat their
homes to the desired comfort level (except in some poor households).
In these regions, energy demand for heating will decrease.
These general patterns and especially the quantitative results of the
projected shifts in energy and electricity demand can be modified by
many other factors. In addition to changes in temperatures and incomes,
the actual energy demand will be influenced by changes in demographics
(upward by increasing population and decreasing average household
size, mixed effects from urbanization), lifestyles (upward by larger floor
area of dwellings), building codes and regulations for the design and
insulation of the housing stock, the energy efficiency of heating/cooling
devices, the abundance and energy efficiency of other electric household
appliances, the price of energy, and so forth.
10.2.2. Energy Supply
Changes in climate attributes (temperature, precipitation, windiness,
cloudiness, etc.) will affect different energy sources and technologies
differently. Gradual climate change will progressively affect the operation
o
f energy installations and infrastructure over time. Possible changes
in the frequency and intensity of extreme weather events (EWEs) as a
result of climate change represent a different kind of hazard for them.
(EWEs are weather events that are rare at a particular place and time
of the year; they are usually defined as rare or rarer than the 10th and
90th percentiles of a probability density function estimated from
observations; see Glossary). Rummukainen (2013) and Mika (2013)
summarize recent trends and prospects relevant for the energy sector.
This section assesses the most important impacts and adaptation options
in both categories. Table 10-1 provides an overview.
Currently, thermal power plants provide about 80% of global electricity
and their share is projected to remain high in most mitigation scenarios
(IEA, 2010a). Thermal power plants can be designed to operate under
diverse climatic conditions, from the cold Arctic to the hot tropical regions
and are normally well adapted to the prevailing conditions. However,
they might face new challenges and will need to respond by hard
(design or structural methods) or soft (operating procedures) measures
as a result of climate change.
A general impact of climate change on thermal power generation
(including combined heat and power) is the decreasing efficiency of
thermal conversion as a result of rising temperature that cannot be
offset per se. Yet there is much room to improve the efficiency of
currently operating subcritical steam power plants (IEA, 2010b). As new
materials allow higher operating temperatures in coal-fired power plants
(Gibbons, 2012), supercritical and ultra-supercritical steam-cycle plants
(operating at much higher pressure and temperature conditions than
conventional power plants) will reach even higher efficiency that can
more than compensate the efficiency losses due to higher temperatures.
Yet in the absence of climate change, these efficiency gains from
improved technology would reduce the costs of energy, so there is still
a net economic loss due to climate change. Another problem facing
thermal power generation in many regions is the decreasing volume
and increasing temperature of water for cooling, leading to reduced
power generation, operation at reduced capacity, and even temporary
shutdown of power plants (Ott and Richter, 2008; Hoffmann et al., 2010;
IEA, 2012; Sieber, 2013). Both problems will be exacerbated if carbon
dioxide (CO
2
) capture and handling equipment is added to fossil-fired
power plants: energy efficiency declines by 8 to 14% (IPCC, 2005) and
water requirement per MWh electricity generated can double (Macknick
et al., 2011). Using partial equilibrium river basin models, (Hurd et al.,
2004; Strzepek et al., 2013) estimate USA welfare loses due to thermal
cooling water changes at US$622 million per year up to 2100, a 6.5%
welfare loss in the energy sector. Van Vliet et al. (2012) find that the
southeastern United States, Europe, eastern China, southern Africa, and
southern Australia could potentially be affected by reduced water
available for thermoelectric power and drinking water, inducing changes
to dry or hybrid cooling (with concomitant loss in electric output), or plant
shut downs, with associated impacts on local and regional economic
activity.
Adaptation possibilities range from relatively simple and low-cost
options such as exploiting non-traditional water sources and re-using
process water to measures such as installing dry cooling towers, heat
pipe exchangers, and regenerative cooling (Ott and Richter, 2008; De
Bruin et al., 2009), all which increase costs. Water use regulation, heat
666
Chapter 10 Key Economic Sectors and Services
10
Technology
Changes in climatic
or related attributes
Possible impacts Adaptation options
Thermal
and nuclear
power plants
Increasing air temperature Reduces effi ciency of thermal conversion by 0.1– 0.2% in
t
he USA; by 0.1– 0.5% in Europe, where the capacity loss
i
s estimated in the range of 1– 2% per 1°C temperature
increase, accounting for decreasing cooling effi ciency and
r
educed operation level /shutdown
Siting at locations with cooler local climates where possible
Changing (lower) precipitation and
increasing air temperature increases
t
emperature and reduces the
availability of water for cooling.
Less power generation; annual average load reduction by
0.1– 5.6% depending on scenario
Use of non-traditional water sources (e.g., water from oil and
gas fi elds, coal mines and treatment, treated sewage); re-use
o
f process water from fl ue gases (can cover 25 – 37% of the
power plant’s cooling needs), coal drying, condensers (drier coal
h
as higher heating value, cooler water enters cooling tower),
fl ue-gas desulfurization; using ice to cool air before entering the
gas turbine increases effi ciency and output, melted ice used in
c
ooling tower; condenser mounted at the outlet of cooling tower
to reduce evaporation losses (by up to 20%). Alternative cooling
t
echnologies: dry cooling towers, regenerative cooling, heat
pipe exchangers; costs of retrofi tting cooling options depend on
f
eatures of existing systems, distance to water, required additional
equipment, estimated at US$250,000 – 500,000 per megawatt
Increasing frequency of extreme hot
t
emperatures
Exacerbating impacts of warmer conditions: reduced thermal
a
nd cooling effi ciency; limited cooling water discharge;
overheating buildings; self-ignition of coal stockpiles
Cooling of buildings (air conditioning) and of coal stockpiles
(
water spraying)
D
rought: reduced water availability Exacerbating impacts of warmer conditions, reduced
operation and output, shutdown
S
ame as reduced water availability under gradual climate change
Hydropower
I
ncrease /decrease in average water
a
vailability
I
ncreased / reduced power output Schedule release to optimize income
Changes in seasonal and inter-annual
variation in infl ows (water availability)
Shifts in seasonal and annual power output; fl oods and lost
output in the case of higher peak fl ows
Soft: adjust water management
Hard: build additional storage capacity, improve turbine runner
capacity
Extreme precipitation causing fl oods Direct and indirect (by debris carried from fl ooded areas)
damage to dams and turbines, lost output due to releasing
water through bypass channels
Soft: adjust water management
Debris removal
Hard: increase storage capacity
Solar energy
Increasing mean temperature Improving performance of TH (especially in colder regions),
reducing effi ciency of PV and CSP with water cooling; PV
effi ciency drops by ~0.5% per 1°C temperature increase
for crystalline silicon and thin-fi lm modules as well, but
performance varies across types of modules, with thin fi lm
modules performing better; long-term exposure to heat
causes faster aging.
Changing cloudiness Increasing unfavorable (reduced output), decreasing
benefi cial (increased output) for all types, but evacuated tube
collectors for TH can use diffuse insolation.
CSP more vulnerable (cannot use diffuse light)
Apply rougher surface for PV panels that use diffuse light better;
optimize fi xed mounting angle for using diffuse light, apply
tracking system to adjust angle for diffuse light conditions;
install / increase storage capacity
Hot spells Material damage for PV, reduced output for PV and CSP;
CSP effi ciency decreases by 3 – 9% as ambient temperature
increases from 30 to 50°C and drops by 6% (tower) to 18%
(trough) during the hottest 1% of time
Cooling PV panels passively by natural air fl ows or actively by
forced air or liquid coolants
Hail Material damage to TH: evacuated tube collectors are more
vulnerable than fl at plate collectors.
Fracturing as glass plate cover, damage to photoactive
material
Flat plate collectors: using reinforced glass to withstand
hailstones of 35 mm (all of 15 tested) or even 45 mm (10 of 15
tested); only 1 in 26 evacuated tube collectors withstood 45-mm
hailstones.
Increase protection to current standards or beyond them
Wind power
Windiness: total wind resource
(multi-year annual mean wind power
densities); likely to remain within ±50%
of current values in Europe and North
America; within ±25% of 1979 – 2000
historical values in contiguous USA
Change in wind power potential Site selection
Wind speed extremes: gust, direction
change, shear
Structural integrity from high structural loads; fatigue,
damage to turbine components; reduced output
Turbine design, lidar-based protection
Table 10-1 | Main projected impacts of climate change and extreme weather events on energy supply and the related adaptation options.
Notes: CSP = concentrating solar power; PV = photovoltaic; TH = thermal heating.
Sources: EPA (2001); Parkpoom et al. (2005); Norton (2006); Pryor et al. (2006); Walter et al. (2006); Christensen and Busuioc (2007); DOE (2007); NETL National Energy
Technology Laboratory (2007); Schaefl i et al. (2007); Bloom et al. (2008); Feeley III et al. (2008); Haugen and Iversen (2008); Leckebusch et al. (2008); Markoff and Cullen (2008);
Ott and Richter (2008); Sailor et al. (2008); Droogers (2009); Förster and Lilliestam (2009); Honeyborne (2009); Kurtz et al. (2009); SPF (2009); Hoffmann et al. (2010); Pryor and
Barthelmie (2010, 2011, 2013); Pryor and Schoof (2010); Kurtz et al. (2011); Linnerud et al. (2011); Mukheibir (2013); Patt et al. (2013); Sieber (2013); Williams (2013).
667
10
Key Economic Sectors and Services Chapter 10
d
ischarge restrictions, and occasional exemptions might be an institutional
adaptation (Eisenack and Stecker, 2012). Though it is easier to plan for
changing climatic conditions and select the site and the conforming
cost-efficient cooling technology for new builds, response options are
more limited for existing power plants, especially for those toward the
end of their economic lifetime.
Climate change impacts on thermal efficiency and cooling water
availability affect nuclear power plants as well but the safety regulations
are stricter than for fossil-fired plants (Williams and Toth, 2013). A range
of alternative cooling options are available to deal with water deficiency,
ranging from re-using wastewater and recovering evaporated water
(Feeley III et al., 2008) to installing dry cooling (EPA, 2001).
The implications of EWEs for nuclear plants can be severe if not properly
addressed. Reliable interconnection (on-site power and instrumentation
connections) of intact key components (reactor vessel, cooling equipment,
control instruments, back-up generators) is indispensable for the safe
operation and/or shutdown of a nuclear reactor. For most of the existing
global nuclear fleet, a reliable connection to the grid for power to run
cooling systems and control instruments in emergency situations is
another crucial item (IAEA, 2011). Several EWEs can damage the
components or disrupt their interconnections. Preventive and protective
measures include technical and engineering solutions (circuit insulation,
shielding, flood protection) and adjusting operation to extreme conditions
(reduced capacity, shutdown) (Williams and Toth, 2013).
Hydropower is by far the largest of renewable energy sources in the
current electricity mix. It is projected to remain important in the future,
irrespective of the climate change mitigation targets in many countries
(IEA, 2010a,b). The resource base of hydropower is the hydrologic cycle
driven by prevailing climate and topology. The former makes the
resource base and hence hydropower generation highly dependent on
future changes in climate and related changes in extreme weather
events (Ebinger and Vergara, 2011; Mukheibir, 2013).
Assessing the impacts of climate change on hydropower generation is
highly complex. A series of nonlinear and region-specific changes in
mean annual and seasonal precipitation and temperatures, the resulting
evapotranspiration losses, shifts in the share of precipitation falling as
snow and the timing of its release from high elevation, and the climate
response of glaciers make resource estimates difficult (see Chapters 2
and 3) while regional changes in water demand due to changes in
population and economic activities (especially irrigation demand for
agriculture) present competition for water resources that are hard to
project (see Section 10.3). Further complications stem from the possibly
increasing need to combine hydropower generation with changing flood
control and ecological (minimum dependable flow) objectives induced
by changing climate regimes. For hydropower locations, adaption to
climate change to maintain output has been reported; in Ethiopia, Block
and Strzepek (2012) report that capital expenditures through 2050 may
either decrease by approximately 3% under extreme wet scenarios or
increase by up to 4% under a severe dry scenario. In the Zambezi river
basin, hydropower may fall by 10% by 2030, and by 35% by 2050 under
the driest scenario (Strzepek et al., 2012). Lower generation is likely in
the upstream power stations of the Zambezi basin and increases are
likely downstream (Fant et al., 2013).
F
ocusing on the possible impacts of climate change on hydroelectricity
and the adaptation options in the sector in response to the changes in
the amount, the seasonal and interannual variations of available water,
and in other demands, the conclusion from the literature is that the
overall impacts of climate change and EWEs on hydropower generation
by 2050 is expected to be slightly positive in most regions (e.g., in Asia,
by 0.27%) and negative in some (e.g., in Europe, by –0.16%), with
diverging patterns across regions, watersheds within regions, and even
river basins within watersheds (IPCC, 2011). Adaptation responses and
planning tools for long-term hydrogeneration may need to be enhanced
to cope with slow but persistent shifts in water availability. Short-term
management models may need to be enhanced to deal with the impacts
of EWEs. A series of hard (raising dam walls, adding bypass channels)
and soft (adjusting water release) measures are available to protect the
related infrastructure (dams, channels, turbines, etc.) and optimize incomes
by timing generation when electricity prices are high (Mukheibir, 2013).
Solar energy is expected to increase from its currently small share in
the global energy balance across a wide range of mitigation scenarios
(IEA, 2008, 2009, 2010a,b). The three main types of technologies for
harnessing energy from insolation include thermal heating (TH; by flat
plate, evacuated tube, and unglazed collectors), photovoltaic (PV) cells
(crystalline silicon and thin film technologies), and concentrating solar
power (CSP; power tower and power trough producing heat to drive a
steam turbine for generating electricity). The increasing body of literature
exploring the vulnerability and adaptation options of solar technologies
to climate change and EWEs is reviewed by Patt et al. (2013).
All types of solar energy are sensitive to changes in climatic attributes
that directly or indirectly influence the amount of insolation reaching
them. If cloudiness increases under climate change (WGI AR5 Chapters
11, 12), the intensity of solar radiation and hence the output of heat or
electricity would be reduced. Efficiency losses in cloudy conditions are
less for technologies that can operate with diffuse light (evacuated tube
collectors for TH, PV collectors with rough surface). Since diffuse light
cannot be concentrated, CSP output would cease under cloudy conditions
but the easy and relatively inexpensive possibility to store heat reduces
this vulnerability if sufficient volume of heat storage is installed (Khosla,
2008; Richter et al., 2009).
The exposure of sensitive material to harsh weather conditions is another
source of vulnerability for all types of solar technologies. Windstorms
can damage the mounting structures directly and the conversion units
by flying debris, whereby technologies with smaller surface areas are
less vulnerable. Hail can also cause material damage and thus reduced
output and increased need for repair. Depending on regional conditions,
strong wind can deposit sand and dust on the collector’s surface, reducing
efficiency and increasing the need for cleaning.
Climate change and EWE hazards per se do not pose any particular
constraints for the future deployment of solar technologies. Technological
development continues in all three solar technologies toward new
designs, models, and materials. An objective of these development efforts
is to make the next generation of solar technologies less vulnerable to
existing physical challenges, changing climatic conditions, and the
impacts of EWEs. Technological development also results in a diverse
portfolio of models to choose from according to the climatic and
668
Chapter 10 Key Economic Sectors and Services
10
w
eather characteristics of the deployment site. These development
efforts can be integrated in addressing the key challenge for solar
technologies today: reducing the costs.
Harnessing wind energy for power generation is an important part of
the climate change mitigation portfolio in many countries. Assessing
the possible impacts of climate change and EWEs and identifying
possible adaptation responses for wind energy is complicated by the
complex dynamics characterizing this generation source. Relevant
attributes of climate are expected to change; the technology is evolving
(blade design, other components); see Kong et al. (2005) and Barlas and
Van Kuik (2010); there is an increasing deployment offshore and a
transition to larger turbines (Garvey, 2010) and to larger sites (multi
megawatt arrays) (Barthelmie et al., 2008).
The key question concerning the impacts of a changing climate regime
on wind power is related to the resource base: how climate change will
rearrange the temporal (inter- and intra-annual variability) and spatial
(geographical distribution) characteristics of the wind resource. In the
next few decades, wind resources (measured in terms of multi-annual
wind power densities) are estimated to remain within the ±50% of the
mean values over the past 20 years in Europe and North America (Pryor
and Barthelmie, 2010). The wide range of the estimates results from the
circulation and flow regimes in different General Circulation Models
(GCMs) and Regional Climate Models (RCMs) (Bengtsson et al., 2006;
Pryor and Barthelmie, 2010). A set of four GCM-RCM combinations for
the period 2041–2062 indicates that average annual mean energy
density will be within ±25% of the 1979–2000 values in all 50-km grid
cells over the contiguous USA (Pryor et al., 2011; Pryor and Barthelmie,
2013). Yet, little is known about changes in the interannual, seasonal,
or diurnal variability of wind resources.
Wind turbines already operate in diverse climatic and weather conditions.
As shown in Table 10-1, siting, design, and engineering solutions are
available to cope with various impacts of gradual changes in relevant
climate attributes over the coming decades. The requirements to
withstand extreme loading conditions resulting from climate change
are within the safety margins prescribed in the design standards,
although load from combinations of extreme events may exceed the
design thresholds (Pryor and Barthelmie, 2013). In summary, the wind
energy sector does not face insurmountable challenges resulting from
climate change.
In the coal fuel cycle, vulnerability in mining depends on mining method.
Surface mining might be particularly affected by high precipitation
extremes and related floods and erosion, and temperature extremes,
especially extreme cold that might encumber extraction for some time,
whereas impacts on coal cleaning and operation of underground mines
will probably be less severe (Ekman, 2013). Changes in drainage and
runoff regulation for on-site coal storage as well as in coal handling
might be required due to the increased moisture content of coal and
more energy might be required for coal drying before transportation
(CCSP, 2007). At the back end of the fuel cycle, the management of fly-
ash, bottom ash, and boiler slag may need to be modified in response
to changes in some EWE patterns such as wind, precipitation, and
floods. Impacts on biomass-based energy sources are discussed in
Chapter 7 of this report.
C
limate- and weather-related hazards in the oil and gas sector include
tropical cyclones with potentially severe effects on offshore platforms
and onshore infrastructure as well, leading to more frequent production
interruptions and evacuation (Cruz and Krausmann, 2013). Gradual
changes in air temperature and precipitation are projected to generate
risk and opportunities for the oil and gas industry. For example, new
areas for oil and gas exploration could open in the Arctic, potentially
increasing the technically recoverable resource base (Cruz and
Krausmann, 2013). Reduced sea ice thickness and coverage might open
new shipping routes, thus reducing shipping costs, while ice scour and
ice pack loading on marine structures would increase. However, most
changes involve increased risks, such as thawing permafrost would
increase construction costs on unstable ground relative to ice-based
construction, while thaw subsidence would trigger increased maintenance
costs. Sea level rise (SLR) and coastal erosion would degrade coastal
barriers, damage facilities, and trigger relocation (Dell and Pasteris,
2010).
10.2.3. Transport and Transmission of Energy
Primary energy sources (coal, oil, gas, uranium), secondary energy forms
(electricity, hydrogen, warm water), and waste products (CO
2
, coal ash,
radioactive waste) are transported in diverse ways to distances ranging
from a few to thousands of kilometers. The transport of energy-related
materials by ships (ocean and inland waters), rail, and road are exposed
to the same impacts of climate change as the rest of the transport sector
(see Section 10.4). This subsection deals only with transport modes that
are unique to the energy sector (power grid) or predominantly used by
it (pipelines). Table 10-2 provides an overview of the impacts of climate
change and EWEs on energy transmission, together with the options to
reduce vulnerability.
Pipelines play a central role in the energy sector by transporting oil and
gas from the wells to processing and distributing centers to distances
from a few hundred to thousands of kilometers. With the potential
spread of CO
2
capture and storage (CCS) technology, another important
function will be to deliver CO
2
from the capture site (typically fossil
power plants) to the storage site onshore or offshore. Pipelines have
been operated for over a century in diverse climatic conditions on land
from hot deserts to permafrost areas and increasingly at sea. This
implies that technological solutions are available for the construction
and operation of pipelines under diverse geographical and climatic
conditions. Yet adjustments may be needed in existing pipelines and
improvements in the design and deployment of new ones in response
to the changing climate and weather conditions.
In addition to reduced line-heating and dilution needs due to reduced
viscosity of liquid fuels under warmer temperatures, pipelines will be
affected mainly by secondary impacts of climate change: SLR in coastal
regions, melting permafrost in cold regions, floods washing away
infrastructure, landslides triggered by heavy rainfall, and bushfires
caused by heat waves or extreme temperatures in hot regions. A
proposed way to reduce vulnerability to these events is to amend land
zoning codes, risk-based design, and construction standards for new
pipelines, and structural upgrades to existing infrastructure (Antonioni
et al., 2009; Cruz and Krausmann, 2013).
669
10
Key Economic Sectors and Services Chapter 10
Owing to the very function of the electricity grid to transmit power from
generation units to consumers, the bulk of its components (overhead
lines, substations, transformers) are located outdoors and exposed to
EWE. The power industry has developed numerous technical solutions
and related standards to protect assets and provide reliable electricity
supply under existing climate and weather conditions worldwide.
However, these assets and the reliability of supply may be vulnerable
to changes in the frequency and intensity of EWEs under changing
climate conditions (DOE, 2013). Higher average temperatures increase
transmission efficiency and reduce current carrying capacity, but this
effect is relatively small compared to the physical and monetary
damages that can be caused by EWEs (Ward, 2013). Historically, high
wind conditions, including storms, hurricanes, and tornados, have been
the most frequent cause of grid disruptions (mainly due to damages to
the distribution networks); and more than half of the damage was
caused by trees (Reed, 2008). Other impacts include freezing precipitation,
ice and winter storms, wildfires caused by higher temperatures, less
precipitation, and increased tree death caused by pests. If the frequency
and power of high wind conditions, as well as extreme precipitation
events, will increase in the future, vegetation management along
existing power lines, and rerouting new transmission lines along roads
or across open fields or moving them underground might help reduce
related risks. An important institutional option is to redefine technical
standards to provide incentives for grid operators to implement
appropriate adaptation measures. Such measures are less expensive to
implement as part of the maintenance-renewal cycle than as independent
retrofit measures.
The economic importance of a reliable transmission and distribution
network is highlighted by the fact that the damage to customers tends
to be much higher than the price of electricity not delivered (lost
production, electricity enabled commerce, service delivery, food spoilage,
lost or restricted water availability). Losses can be minimized through
efficient rationing of electricity (de Nooij et al., 2009) if generation is the
limiting factor. Designing and building climate-resilient infrastructure
will depend on technical standards, market governance, and the type
and degree of liberalization and deregulation of grid services.
10.2.4. Macroeconomic Impacts
Most economic research related to climate change impacts on the energy
sector has focused on mitigation rather than the economic implications
of climate change itself. Table 10-3 summarizes the recent studies on the
economic implications of climate change and extreme weather impacts
in the energy sector.
Assessing across a broad array of studies that focus on different regions
and regional divisions, examine different climate change impacts,
include a different mix of sectors, model different time frames, make
different assumptions about adaptation, and employ different types of
models with different output metrics leads to the overall conclusion
that the macroeconomic impact of climate change on energy demand
is likely to be minimal in developed countries (Bosello et al., 2007a,
2009; Aaheim et al., 2009; Jochem et al., 2009; Eboli et al., 2010).
The current literature sheds less light on the implications for developing
countries and on other climate impacts in the energy sector beyond
those related to changes in energy demand. Europe is the focus of most
of the literature so far. Only two studies focus on developing countries:
Mexico and Brazil (Boyd and Ibarraran, 2009; de Lucena et al., 2010).
Asia and Africa are not well represented, appearing as aggregated
regions in only three global studies (Bosello et al., 2007a, 2009; Eboli
et al., 2010). The limited results indicate that developing countries likely
face a greater negative GDP impact with respect to climate change
implications for the energy sector than developed countries, largely
because of higher expected temperature changes (Aaheim et al., 2009;
Boyd and Ibarraran, 2009; Eboli et al., 2010).
Technology Changes in climatic or related attribute Impacts Adaptation options
Pipelines
Melting permafrost Destabilizing pillars, obstructing access for
m
aintenance and repair
Adjust design code and planning criteria, install
d
isaster mitigation plans
Increasing high wind, storms, hurricanes Damage to offshore and onshore pipelines and
r
elated equipment, spills; lift and blow heavy
objects against pipelines, damage equipment
Enhance design criteria, update disaster preparedness
F
looding caused by heavy rain, storm surge, or sea level rise Damage to pipelines, spills Siting (exclude fl ood plains), waterproofi ng
Electricity grid
I
ncreasing average temperature Increased transmission line losses Include increasing temperature in the design
calculation for maximum temperature
/
rating
I
ncreasing high wind,
storms, hurricanes
D
irect mechanical damage to overhead lines
,
towers
, poles, substations, fl ashover caused by
live cables galloping and thus touching or getting
t
oo close to each other;
indirect mechanical
damage and short circuit by trees blown over or
d
ebris blown against overhead lines
A
djust wind loading standards
,
reroute lines alongside
roads or across open fi
elds; manage vegetation;
improve storm and hurricane forecasting
Extreme high temperatures Lines and transformers may overheat and trip off;
fl
ashover to trees underneath expanding cable
Increase system capacity,
increase tension in the line to
r
educe sag,
add external coolers to transformers
Combination of low temperature
, wind and rain, ice storm
Physical damage (including collapse) of overhead
lines and towers caused by ice build-up on them
Enhance design standard to withstand larger ice and
wind loading,
reroute lines alongside roads or across
o
pen fi
elds; improve forecasting of ice storms impacts
on overhead lines and on transmission circuits
Table 10-2 | Main impacts of climate change and extreme weather events on pipelines and the electricity grid.
Sources: Bayliss (1996); Krausmann and Mushtaq (2008); Reed (2008); Hines et al. (2009); Winkler et al. (2010); Vlasova and Rakitina (2010); McColl (2012); Cruz and
Krausmann (2013); Ward (2013).
670
Chapter 10 Key Economic Sectors and Services
10
Study
Model
type
Climate impacts modeled Energy /economic impacts Regions
Sectors
studied
B
osello et al.
(2009)
I
AM Rising temperatures /changing demand for energy; impacts from
four other sectors /events (Global, 2001– 2050)
C
hange in gross domestic product (GDP) in 2050 due to
rising temperatures and changing energy demand: 0 – 0.75%
(
+1.2°C); – 0.1% to 1.2% (+3.1°C)
1
4 4
J
orgenson et al.
(2004)
C
GE Rising temperatures /changing demand for energy; climate
impacts from three other sectors (USA, 2000 – 2100)
O
ptimistic adaptation: 4 – 6.7% higher energy productivity per
year (2000 – 2100)
O
utput from electricity: – 6% in 2050; GDP is +0.7% (aggregate
all sectors, average annual 2000 – 2100)
P
essimistic adaptation: 0.5 – 2.2% lower energy productivity
per year
O
utput from electricity: +2% in 2050; GDP is – 0.6% (aggregate
impact all sectors)
1
35
B
osello et al.
(2007a)
C
GE Rising temperatures /changing demand for energy (Global, 2050) Change in GDP in 2050 (perfect competition): – 0.297% to
0.027%
C
hange in GDP in 2050 (imperfect competition): – 0.303% to
0.027%
8
1
Aaheim et al.
(
2009)
CGE Change in precipitation
affects share of hydroelectric power;
r
ising temperatures /changing demand for energy; impacts from
four other sectors (Western Europe, 2071– 2100)
Impact from all sectors in 2100: GDP in cooler regions: – 1%
t
o – 0.25%
GDP in warmer regions: – 3% to – 0.5%
Adaptation can mitigate 80 – 85% of economic impact
8 11
Boyd and
I
barraran
(2009)
CGE Drought scenario affecting hydroelectric plus three other sectors
(
Mexico, 2005 – 2026)
• Generation output in 2026: – 2.1%
•
R
efi ning output: – 10.1%
• Coal output: – 7.8%
•
N
G output: – 2%
• Crude oil output: +1.7%
• GDP: – 3%
With adaptation:
• Generation output in 2026: 0.24%
• Refi ning output: 1.36%
• Coal output: 1.09%
• NG output: 0.34%
• Crude oil output: 0.22%
• GDP: 0.33%
1 2
Jochem et al.
(2009)
PE /CGE Rising temperatures /changing demand for energy; change in
technical potential of renewables; change in rainfall induces
change in hydroelectric production; high temperatures induce
water temperatures exceeding regulatory limits (Europe); high
temperatures induce greater electric grid losses and lower
thermal effi ciency; generic extreme events induce reduced
capital stock in CGE model (EU27+2, 2005 – 2050)
• GDP (Europe): – 50 billion € p.a. in 2035
• GDP (Europe): – 240 billion € p.a. in 2050
• GDP (EU regions): – 0.1% to – 0.4% in 2035
• GDP (EU regions): – 0.6% to – 1.3% in 2050
• Jobs (Europe): – 380K in 2035
• Jobs (Europe): – 1 million in 2050
25 1
Eboli et al.
(2010a)
CGE Rising temperatures /changing demand for energy; climate
impacts in four other sectors modeled (Global, 2002 – 2100)
By 2100, change in GDP due to climate impacts on energy
demand vary by country between about – 0.15% and 0.7%.
USA and Japan were negative and all other countries positive.
Overall economic impact from all sectors is neutral to positive
for developed countries and negative for developing ones.
8 17
Golombek et al.
(2011)
PE Rising temperatures /changing demand for energy; rising
temperatures /reduced thermal effi ciency; change in water
infl ow (Western Europe, 2030)
Net impact on the price of electricity is a 1% increase.
Generation decreases by 4%.
13 4
de Lucena et al.
(2010)
PE Changing precipitation induces change in hydroelectric
production; rising temperatures induce lower NG thermal
effi ciency; rising temperatures induce change in demand for
energy (Brazil, 2010 – 2035)
New generating capacity needed to produce additional
153 – 162 TWh per year.
Capital investment of US$48 – 51 billion, which is equivalent to
10 years of capital expenditures in Brazil’s long-term energy
plan.
US$6.9 – 7.2 billion in additional annual operating expenses for
each year in which worst-case hydroelectric production occurs
1 11
Bye et al. (2008) PE Water shortages (Nordic countries, hypothetical 2-year period) Water shortage scenarios can lead to a 100% increase in
electricity prices at peak demand over a 2-year period. Higher
prices lead to marginal reductions in demand (about 1 – 2.25%).
41
Koch et al.
(2012)
PE High temperatures induce water temperatures exceeding
regulatory limits (Berlin, 2010 – 2050)
Thermal plant outages amounting to 60 million € for plants in
Berlin through 2050
11
Gabrielsen et al.
(2005)
Econometric Rising temperatures /changing demand for energy; change
in water infl ow; change in wind speeds (Nordic countries,
2000 – 2040)
Net change in electricity supply in 2040: 1.8%
Change in electricity demand: 1.4%
Change in electricity price: – 1.0%
41
Table 10-3 | Economy-wide implications of impacts of climate change and extreme weather on the energy sector.
671
10
Key Economic Sectors and Services Chapter 10
Despite the considerable number of potential climate change and
extreme weather phenomena—higher mean temperatures, changes in
rainfall patterns, changes in wind patterns, changes in cloud cover and
average insolation, lightning, high winds, hail, sand storms and dust,
extreme cold, extreme heat, floods, drought, fire, and SLR—and their
potential impacts on electricity generation and transmission systems,
fuel infrastructure and transport systems, and energy demand (Williams,
2013), the range of impacts modeled in the literature (Table 10-3) is quite
limited. Most studies consider changing energy demand (specifically,
changes in electricity and fuel consumption for space heating/cooling)
resulting from rising temperatures as the only or primary climate change
impact. These studies draw on recent literature refining the relationship
between climate change and energy demand: the demand for natural
gas and oil in residential and commercial sectors tends to decline with
climate change because of less need for space heating, and demand for
electricity tends to increase because of greater need for space cooling
(Gabrielsen et al., 2005; Kirkinen et al., 2005; Mansur et al., 2005;
Eskeland and Mideksa, 2010; Mideksa and Kallbekken, 2010; Rübbelke
and Vögele, 2010).
Studies using a Computable General Equilibrium (CGE) model that
consider only climate impacts in the energy sector find that the effect
on GDP in 2050 is in the range of –0.3% to 0.03% (Bosello et al., 2007a)
and –1.3% to –0.6% (Jochem et al., 2009). These findings are largely
consistent despite the fact that Bosello et al. (2007a, 2009) are global
studies that model only the change in demand due to rising temperatures,
whereas Jochem et al. (2009) focus on the European Union (EU) and
model the change in demand plus six other climate impacts.
Studies using CGE models that examine the aggregate changes in GDP
brought on by climate impacts in energy and several other sectors have
also primarily found similar shifts in GDP. Aaheim et al. (2009) conclude
that in 2100 in cooler regions in the EU, GDP changes by –1% to –0.25%
and in warmer regions changes by –3% to –0.5%. Boyd and Ibarraran
(2009) project a –3% change in GDP in 2026 for Mexico, consistent
with the warmer regions modeled by Aaheim et al. (2009). Roughly
consistent with each other, Aaheim et al. (2009) and Eboli et al. (2010)
find GDP impacts for the predominantly cooler regions of Japan, the EU,
Eastern Europe and the Former Soviet Union (EEFSU), and Rest of Annex I
as having a “significant positive impact,” while the predominantly
warmer regions of the USA, EEx (China/India, Middle East/Most of
Africa/Mexico/parts of Latin America), and the Rest of the World have
a “significantly negative impact.” Jorgenson et al. (2004) find that
overall GDP impacts are –0.6% to 0.7% in 2050 for the USA, which
stands in contrast to Eboli et al. (2010) with a “significantly negative
impact” in the USA.
Several CGE studies attempt to evaluate how adaptation changes in
the energy sector impact GDP but do not examine specific adaptation
options since CGE models lack the necessary technological detail. They
make general assumptions about the effectiveness of adaptation policy
in reducing climate impacts. Jorgenson et al. (2004) find that pessimistic
assumptions about adaptation imply a 0.6% reduction in GDP in 2050
but optimistic assumptions lead to a 0.7% gain in GDP. Aaheim et al.
(2009) conclude that adaptation can mitigate the costs of climate change
by 80% to 85%, and Boyd and Ibarraran (2009) find that adaptation
can shift a 3% GDP loss in 2026 in Mexico to a gain in GDP of 0.33%.
Partial equilibrium models, by their nature, do not have a full
macroeconomic representation and therefore rarely report changes in
GDP. Instead, these models focus on details in the energy sector, such
as price and quantity effects for fuels and electricity (and the mix of
generation). For example, Rübbelke and Vögele (2013) conclude that the
short-term effects of climate-related problems affecting water cooling
and hydropower production can have negative distributional effects. de
Lucena et al. (2010) find that rising temperature and changing precipitation
lead to the need for an additional 153 to 162 TWh per year by 2035
with a capital investment of US$48 to 51 billion.
Golombek et al. (2011) report a 1% increase in the price of electricity
for Western Europe in 2030 stemming from rising temperatures that
affect demand and thermal efficiency of supply, as well as water inflow.
UNDP (2011) finds between a 0.06% and 1.74% increase in electricity
system costs for Macedonia resulting from temperature changes.
Gabrielsen et al. (2005) conclude that for Nordic countries in 2040, as
a result of rising temperatures that affect demand, changes in water
Study
Model
type
Climate impacts modeled Energy /economic impacts Regions
Sectors
studied
UNDP (2011) PE Damage Case 1 (DC1): hotter in both winter and summer—
d
ecreased demand for heating and increased demand for
cooling;
D
amage Case 2 (DC2): colder in both winter and summer—
increased demand for heating and decreased demand for
c
ooling;
Damage Case 3 (DC3): colder in the winter and hotter in the
s
ummer—increased demand for heating and increased demand
for cooling (Macedonia, 2009 – 2030)
Change in electricity demand in residential and commercial
s
ectors:
• DC1: 3.5%
•
D
C2: 0.3%
• DC3: 8%
C
hange in electricity system cost:
• DC1: 0.8%
•
D
C2: 0.06%
• DC3: 1.74%
95
D
OE (2009) PE Drought scenario (Western Electric Coordinating Council, USA,
2010 – 2020)
I
n 2020, 3.7% reduction in coal generation; 43.4% increase in
NG generation; 29.3% reduction in hydroelectric generation.
Production cost increase of US$3.5 billion. Average monthly
e
lectricity prices up 8.1% (Nov) to 24.1% (July)
1
1
Note: The regions indicated in the Regions column vary in size and are model-specifi c. CGE = Computable General Equilibrium; PE = Partial Equilibrium; IAM = Integrated
Assessment Model.
Table 10-3 (continued)
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Chapter 10 Key Economic Sectors and Services
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i
nflow, and changes in wind speeds, the wholesale price of electricity
will decline by 10%. Koch et al. (2012) conclude that thermal plant
outages in Berlin resulting from heat wave-driven water temperatures
that exceed regulatory limits can amount to a cumulative cost of about
US$80 million over the period 2010 through 2050 for 2850 MW of
capacity. Assuming an 80% capacity factor, the premium for high water
temperatures in Berlin is US$0.1 per MWh. The magnitude of change
in electricity price is small in each of the previously mentioned studies
that evaluate gradual temperature increases.
In contrast, studies that consider shorter-term heat waves and water
shortages find considerably higher price impacts. Bye et al. (2008)
consider a hypothetical water shortage scenario—25% lower inflow
over 2 years—in Nordic countries and conclude that the price of
electricity can double over a 2-year period and then return to normal
as water flow returns. McDermott and Nilsen (2013) find more generally
that electricity prices in Germany increase by 1% for every degree that
water temperatures rise above 25°C and by 1% for every 1% that river
levels fall. DOE (2009) also finds that a drought scenario can lead to
average monthly electricity prices that are 8.1% (November) to 24.1%
(July) higher. Pechan and Eisenack (2013) find that an equivalent of the
2006 German heat wave can result in an increase in electricity prices
of 11% or even 24% (affected plants running at minimum output) and
50% (affected plants at zero output).
10.2.5. Summary
The balance of evidence emerging from the literature assessed in this
section suggests that climate change per se will likely increase the
demand for energy in most regions of the world. At the same time,
increasing temperature will decrease the thermal efficiency of fossil,
nuclear, biomass, and solar power generation technologies (Mideksa
and Kallbekken, 2010). However, gradual temperature-induced impacts
on energy supply will probably make a relatively small contribution to
the cost of energy and electricity. Acute heat waves and droughts can
have a much greater, albeit short-term, impact on electricity prices. In
addition, many other potential climate impacts on energy supply are
possible but have not been fully studied, leading to cost estimates to
date, based only on temperature change, that underestimate the full cost
of climate change on energy supply. Preexisting subsidies may distort
signals for adaptation. Climate change impacts on energy supply will
be part of an evolving picture dominated by technological development
in the pursuit for safer, less expensive, and more reliable energy sources
and technologies as well as mitigation and adaptation response pathways.
Given the limitations in the literature, sweeping conclusions about
results may be premature on macroeconomic implications. However,
some narrow conclusions are possible. The change in GDP due to
temperature-induced changes in energy demand—even if combined
with other climate impacts—range from –3% to 1.2%. Jochem et al.
(2009) provide the most detailed and comprehensive study, and report
only a 1.3% drop in GDP in 2050 in Europe due to at least seven climate
impacts in the energy sector. The GDP impact in warmer regions tends
to be greater than in cooler regions, which benefit from less need for
space cooling. Energy-related economic impact is anticipated to be
negative for developing countries and positive in developed countries.
A
daptation within the energy sector can lower the cost of climate
change, but these results may be driven largely by assumption because
specific policies have not been modeled in these macroeconomic impact
studies. Results from some of the partial equilibrium models suggests
that CGE modeling studies, which largely focus on changes in energy
demand, may be neglecting some potentially costly impacts from
extreme weather events such as drought (see, e.g., Box CC-WE), which,
if modeled, may lead to greater GDP losses than reported thus far in
the literature.
Much research is still needed to understand the implications of climate
change and extreme weather on the energy sector and to identify cost-
effective adaptation options. The best understood area is the implications
of climate on energy demand. A comprehensive evaluation of a full
range of supply-side climate change impacts and adaptation options for
all aspects of energy infrastructure is needed. This information will lead
to an improved assessment of climate impacts due to the use of better,
empirically based assumptions about the relationship of climate impacts
and the economy, as well as about the effectiveness of adaptation options.
10.3. Water Services
This section focuses on economic aspects of climate change in water-
intensive sectors and infrastructure to provide water services. The climate
change impacts on biophysical water system, including the engineering
aspects of water infrastructure, are assessed in Chapter 3. There is a
limited set of studies published in this area and conclusions are limited
by the scope of information to date.
10.3.1. Water Infrastructure and Economy-Wide Impacts
Between the 1950s and the 1990s, the annual economic losses from
large extreme events, including floods and droughts, increased 10-fold,
with developing countries being hardest hit (Kabat et al., 2003). Over
the past few decades, flood damage constitutes about a third of the
economic losses inflicted by natural hazards worldwide (Munich Re,
2005). The economic losses associated with floods worldwide have
increased by a factor of five between the periods 1950–1980 and 1996–
2005 (Kron and Berz, 2007). In 1990–1996 alone, there were six major
floods throughout the world in which the number of fatalities exceeded
1000, and 22 floods with losses exceeding US$1 billion each (Kabat et
al., 2003). Although these increases are primarily due to several non-
climatic drivers, climatic factors are also partly responsible (Kundzewicz
et al., 2007). Chapter 4 of the IPCC Special Report on Managing the
Risks of Extreme Events and Disasters to Advance Climate Change
Adaptation (SREX) provides a comprehensive look at the impacts of
extreme events on water supply (IPCC, 2012) and flooding at a wide
range of spatial scales.
Most of the studies examining the economic impacts of climate change
on the water sector have been carried out at the local, national, or
river-basin scale; and the global distribution of such studies is skewed
toward developed countries (Schreider et al., 2000; Chen et al., 2001;
Middelkoop et al., 2001; Choi and Fisher, 2003; Hall et al., 2005; Hurd
and Rouhi-Rad, 2013). In other studies, the economic impacts of climate
673
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v
ariability on floods and droughts in developing countries were reported
as substantial. These studies address climate variability; climate change
may impact both mean and variability of the hydro-climatic system. The
floods associated with the 1997–1998 El Niño and the drought associated
with the 1998–2000 La Niña show a cost to Kenya of 11% and 16%
of GDP, respectively (Mogaka et al., 2006). Floods and droughts are
estimated to cost Kenya about 2.4% of GDP annually at mid-century,
and water resources degradation a further 0.5% (Mogaka et al., 2006).
For Ethiopia, economy-wide models incorporating hydrological variability
show a drop in projected GDP growth by up to 38% compared to when
hydrological variability is not included (World Bank, 2006). Syria is
projected to experience reduction in economy-wide growth and incomes
of urban households (Breisinger et al., 2013). However, it is not
hydrological variability per se that causes the problem, but rather a lack
of the necessary capacity, infrastructure, and institutions to mitigate
the impacts (Grey and Sadoff, 2007). Similarly, future flood damages
will depend not only on changes in the climate regime, but also on
settlement patterns, land use decisions, flood forecasting quality,
warning and response systems, and other adaptive measures (Pielke
and Downton, 2000; Changnon, 2005; Ward et al., 2008). In many
developing countries, water-related impacts are likely to be more
pronounced with climate change (Chapter 3) and associated economic
costs can be expected to be more substantial in the future, holding all
other factors constant.
Climate change could increase the annual cost of flooding in the UK
almost 15-fold by the 2080s under high emission scenarios. If climate
change increased European flood losses by a similar magnitude, annual
costs could increase by up to US$120 to 150 billion, for the same high
emission scenarios (ABI, 2005). Feyen et al. (2012) project average
annual damage in the EU to increase to US$18 to 28 billion by 2100
depending on the scenario, compared to US$8.5 billion today. Continental
U.S. mean annual flood damages may increase by US$5 billion and US$12
billion in 2050 and 2100, respectively (Wobus et al., 2013). Ntelekos et al.
(2010) estimate a range of US$7 to 19 billion, depending on the economic
growth rate and the emissions scenarios. Dasgupta et al. (2010) report
that by 2050 Bangladesh will face incremental cost to flood protection
(against both sea and river floods) of US$2.6 billion initial costs and
US$54 million annual recurring costs. Ward et al. (2008) found that the
average annual costs to adapt to a 1-in-50-year river flood to range
from US$3.5 to 6.0 billion per year for low- to upper-middle-income
countries over the period 2010–2050 for the SRES A2 scenario.
10.3.2. Municipal and Industrial Water Supply
Municipal and industrial water supply economic systems are also
impacted through changes in precipitation patterns and quantities. These
impacts are evaluated as current costs of building in resiliency to the
system to adapt to anticipated future changes. For example, the costs of
adaptation to maintain supply and quality of water for municipal and
industrial uses have been reported for the Assabet River near Boston
(Kirshen et al., 2006), Toronto (Dore and Burton, 2001), and Quito (Vergara
et al., 2007). Initial analysis indicates that adaptation measures may be
beneficial for water infrastructure with an economic and engineering
life of more than 25 years. Nassopoulos et al. (2012) suggest that
neglecting to account for future climate change while designing water
s
upply reservoirs can cost 0.2 to 2.8% of the net present value, based
on analysis for Greece. For sub-Saharan Africa, adapting urban water
infrastructure (storage facilities, wastewater, and additional supply
infrastructure) to a 30% reduction in runoff could be US$2 to 5 billion
per year (Muller, 2007). Climate change impacts on the Berg River in
South Africa are estimated to account for 20% revenue loss for the water
supply provider and 15.2% loss in social welfare (Callaway et al., 2012).
For the Organisation for Economic Co-operation and Development
(OECD), the cost of adaption in the water supply sector is 1 to 2% of
base costs and would save US$6 to 12 billion per year (Hughes et al.,
2010). U.S. impacts are estimated to be less than 1% of municipal and
industrial welfare (Hurd et al., 2004; Strzepek et al., 2013). In Colorado,
a 30% decrease in annual runoff will result in a 12% treatment cost
increase and a 22% rise in residential costs (Towler et al., 2011).
Ward et al. (2010) estimate the costs of adaptation to climate change
to ensure enough raw water to meet future industrial and municipal
water demand for each country to 2050. Increased demand is assumed
to be met through a combination of increased reservoir yield and
alternative backstop measures. The global adaptation costs are estimated
to be US$12 billion per year (0.04 to 0.06% of GDP), on top of US$73
billion per year to meet the needs of development, with 83 to 90% in
developing countries. The highest costs are in sub-Saharan Africa, and
may be as high as 16% of the global total. Adding adaptive measures
to water infrastructure adds 10 to 20% to the total costs of developing
countries meeting the water-related millennium goals (Ward et al.,
2010).
10.3.3. Wastewater and Urban Stormwater
More frequent heavy rainfall events may overload the capacity of sewer
systems and water and wastewater treatment plants more often, and
increased occurrences of low flows will lead to higher pollutant
concentrations. It is projected for USA in 2100 that national wastewater
treatment costs will increase by US$0.6 to 8 billion per year (Henderson
et al., 2013). The annual costs of urban stormwater system adaptation,
averaged costs over 17 climate models simulating the SRES A2 emissions
scenario, is US$3 billion per year in low- to upper-middle-income nations
over the period 2010–2050 (Hughes et al., 2010). Adaptation costs
estimates (for a 10-year, 24-hour storm in 2100) for various locations in
the USA are relatively low; for example, US$135 million for Los Angeles,
US$7 million for Boston, and US$40 million for Chicago (Neumann et
al., 2013). Adapting bridges to altered urban floods could cost US$140
to 250 billion in the USA through the 21st century (Wright et al., 2012).
10.3.4. Inland Navigation
See Section 10.4.4.
10.3.5. Irrigation
Climate change impacts on the economics of irrigation reflect the
anticipated change in temperature, precipitation, and agricultural demand
and practices. Assessments of surface, ground, and gray water irrigation
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Chapter 10 Key Economic Sectors and Services
10
s
upplies are addressed in Chapter 3; implications for food production
are covered in Chapter 7. By 2080, the global annual costs of additional
irrigation water withdrawals for currently existing irrigated land are
estimated at US$24 to 27 billion (Fischer et al., 2007). The global cost
of improved irrigation efficiency to maintain yields is US$1.5 to 2.0
billion per year for the A2 scenario in developing countries in 2050
(Nelson et al., 2009).
Adaptation to maintain agricultural production in Ethiopia would be
best achieved by better soil water management with the application of
integrated irrigation and drainage systems, improved irrigation efficiency,
and research related to on-farm practices; adaptation costs range from
US$68 million per year for the dry scenario dominated by irrigation, to
US$71 million per year under the wet scenario dominated by drainage
(Strzepek et al., 2010).
10.3.6. Nature Conservation
Climate change is expected to worsen many forms of water pollution,
including the load of sediments, nutrients, dissolved organic carbon,
pathogens, pesticides, and salt, as well as thermal pollution, increased
precipitation intensity, and low flow periods (Kundzewicz et al., 2007).
Future water demands for nature conservation will be different than
today’s (see Chapter 4). There is no published assessment of the economic
implications.
10.3.7. Recreation and Tourism
Tourism and recreation use substantial amounts of water but the
implications of climate change-induced changes in tourism and recreation
on water demand have yet to be quantified. See Section 10.6.
10.3.8. Water Management and Allocation
Water scarcity and competition for water, driven by institutional,
economic, or social factors, may mean that water assumed to be
available for a sector is not and thus economic analyses at the sectoral
level are crucial; inter-sectoral and economy-wide assessments are
needed for comprehensive economic impacts of water services.
Changes in water availability, demand, and quality due to climate
change would impact water management and allocation decisions.
Traditionally, water managers and users have relied on historical
experience when planning water supplies and distribution (Adger et al.,
2007; UNFCCC, 2007). Under a changing climate, existing allocations may
no longer be appropriate. Arndt et al. (2012) examine the implications
of alternative development paths and water allocations to suggest
climate-smart development strategies in Africa; under stress situations,
allocations of water to energy-generation and irrigation may have
economy-wide welfare implications. Water resource-related climate change
impacts on the U.S. economy measured as cumulative undiscounted
welfare changes over the 21st century range from plus US$3 trillion for
wet scenarios to minus US$13 trillion under dry scenarios (in US$
2000
;
Henderson et al., 2013).
10.3.9. Summary
Globally, greenhouse gas-induced increases in flooding and droughts
may have substantial economic impacts (capital destruction, sectoral
disruption) while estimates of adaptation costs (construction, defensive
investment) range from relatively modest to relative high levels (see
Box CC-WE).
10.4. Transport
The impact of climate change and sea level rise on transport has received
qualitative, but limited quantitative, focus in the published literature.
The impact depends greatly on the climatic zone the infrastructure is in
and how climate change will be manifest. There are three major zones:
Changes in Climate
Geographic Zone Expected to Impact Vulnerability
Freezing/Frost Zone Permafrost, freeze-thaw cycles, precipitation,
flooding, SLR, and storms (coastal)
Temperate Zone Precipitation intensity, flooding, maximum
daily precipitation, SLR, and storms (coastal)
Tropical Zone Precipitation intensity, flooding, maximum
daily precipitation, SLR, and storms (coastal)
As detailed in Sections 10.4.1, 10.4.2, 10.4.4, and 10.4.5, several studies
have explored the potential impacts of climate change on the transport
sector—focusing, for example, on safety or disruptions of service.
Quantitative, economic analyses of the impact on physical infrastructure
include Larsen et al. (2008), Chinowsky et al. (2010, 2011), and Hunt and
Watkiss (2010) and on wider economic implications, Arndt et al. (2012).
Adaptation options for each sub-sector of transport infrastructure have
been studied. Existing literature includes CCSP (2008) and Chinowsky et
al. (2011), with proposed strategies ranging from technical to political,
including focus on upgraded design specifications during new construction,
retrofitting structures, and modified land use planning in coastal areas.
Adaptation and resiliency to extreme events is of particular interest as
they may have a cascading impact, in that the loss of critical infrastructure
assets will negatively affect the recovery and resiliency of a community
(Kirshen et al., 2008a,b).
10.4.1. Roads
Studies on the direct effects of climate change on road networks are
focused primarily on qualitative predictions and surveys concerning
impacts on road durability (National Research Council, 2008; Koetse
and Rietveld, 2009; Eisenack et al., 2012; Ryley and Chapman, 2012);
with some studies of the quantitative effects (Nemry and Demirel, 2012;
Chinowsky et al., 2013). Noted impacts from changes in precipitation
and temperature include changes in required road maintenance. These
quantitative studies focus on specific impacts such as maintenance in
an effort to quantify the long-term costs that need to be assumed by
national and regional road agencies. Examples of the metrics used
include kilometers of roads lost over time, redistribution requirements of
transport funds, and benefits from adaptation on long-term maintenance.
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Key Economic Sectors and Services Chapter 10
C
hapter 8 addresses the indirect effects of climate change on roads in
the areas of congestion and safety. As an example, increases in heavy
precipitation events will negatively affect driving safety through
decreased driver visibility and changing surface conditions (Qiu and
Nixon, 2008).
Paved road degradation is directly related to heat stress that can lead
to softening of the pavement as temperatures exceed design thresholds
(Lavin, 2003), and an increase in the number of freeze-thaw cycles
impacts both the base and pavement surface (FHWA, 2006). The melting
of permafrost in northern climates, as well as increased precipitation
and flooding, threaten the integrity of road base and sub-bases (Qin et
al., 2005). Drainage presents a specific problem for urban areas that
experience rainfall above their built capacity and will influence new
design standards and costs for urban transport (City of Chicago, 2008;
Hunt and Watkiss, 2010; Lemmen and Warren, 2010). Increased fire
danger from droughts could also pose a threat to roads.
Unpaved roads are vulnerable to a number of climate-based factors
especially to increasingly intense precipitation, leading to wash out and
disruption of service (Chinowsky and Arndt, 2012). Increased precipitation
in agricultural areas may have negative economic impacts in addition
to the direct impact on infrastructure. In cold climates, temporary winter
roads are susceptible to warming and associated lower connectivity of
rural areas and reduced economic activity in northern climates (Mills
and Andrey, 2002). Warming could imply that ice roads can no longer
be maintained.
Bridges form a core component of any nation’s infrastructure. However,
highway bridges that cross water, ubiquitous to most highway networks,
are exposed to climate changes via flood events and associated changes
in long-term flow regimes. The potential disruptions that could occur
due to the loss of or damage to these bridges are numerous. Estimates
in the USA range from US$140 to 250 billion to address adaptation
requirements for bridge infrastructure over the next 50 years (Wright
et al., 2012). Similarly, European estimates range from US$350 to 500
million per year to adapt bridge infrastructure (Nemry and Demirel,
2012). Once again, the potential cascading effects of these failures will
affect the economic conditions of multiple sectors.
10.4.2. Rail
Rail beds are susceptible to increases in precipitation, flooding and
subsidence, SLR, extreme events, and incidence of freeze-thaw cycles
(Nemry and Demirel, 2012). In northern climates, the melting of
permafrost (URS, 2010) may lead to ground settlement, undermining
stability (Larsen et al., 2008). Increased temperatures pose a threat to
rail through thermal expansion. In urban areas, increased temperatures
pose a threat to underground transport systems that will see a burden
on increased need for cooling systems (Hunt and Watkiss, 2010). For
example, US$290 million has been allocated to finding a workable
solution for increasing the capacity of London’s underground cooling
system (Arkell and Darch, 2006). The complexity of addressing rail
infrastructure is increased through differences in design specifications,
multiple types of rail and materials used, and uncertainty about the
changes in future temperatures.
10.4.3. Pipeline
Increases in precipitation and temperature affect pipelines through
scouring of base areas and unearthing of buried pipelines (URS, 2010),
compromised stability of bases built on permafrost, and increases in
necessary maintenance (National Research Council, 2008; URS, 2010).
Temperature increase can result in thermal expansion of the pipelines,
causing cracking at material connection points. In tropical areas,
increased precipitation may lead to landslides that can compromise
pipeline infrastructure (Sweeney et al., 2005). There has been no economic
assessment of the impacts.
10.4.4. Shipping
Impacts on inland navigation vary widely due to projected rise or fall in
water levels. Overall, the effects on inland navigation are projected to
be negative, and are region specific.
Increased frequency of flood periods will stop ship traffic on the Rhine
more often; longer periods of low flow will also increase the average
annual number of days during which inland navigation is hampered or
stagnates due to limited load carrying capacity of the river; channel
improvements can only partly alleviate these problems (Middelkoop et
al., 2001). Economic impact could be substantial given the value of
navigation on the Rhine (Krekt et al., 2011). See Chapter 23.
Virtually all scenarios of future climate change project reduced Great
Lakes water levels and connecting channel flows, mainly because of
increased evaporation resulting from higher temperatures. The potential
economic impact may result in reductions in vessel cargo capacities and
increases in shipping costs. The lower water levels predicted as a result
of a doubling of atmospheric CO
2
could increase annual transportation
costs by 29%, while more moderate climate change could result in a 13%
increase in annual shipping costs. The impacts vary across commodities
and routes (Millerd, 2010).
Warming leads to increased ice-free navigation and a longer shipping
season, but also to lower water levels from reduced runoff (Lemmen
and Warren, 2010). In cold regions, increased days of ice-free navigation
and a longer shipping season could impact shipping and reduce
transportation costs (National Research Council, 2008; Koetse and
Rietveld, 2009; UNCTAD, 2009; UNECE and UNCTAD, 2010), although
movement in ice waters such as the Canada Arctic sea could become
more difficult (Wilson et al., 2004; Stewart et al., 2007).
Ports will be affected by climate changes including higher temperatures,
SLR, increasingly severe storms, and increased precipitation (Becker et al.,
2011; Nursey-Bray and Miller, 2012). However, (the need to prioritize)
adaptation of ports has been overshadowed by a focus on potential
impacts. Training of port personnel is needed to begin the adaptation
process. More than US$3 trillion in port infrastructure assets in 136 of
the world’s largest port cities are vulnerable to weather events (CCSP,
2008; UNCTAD, 2009; UNECE and UNCTAD, 2010).
Increased storminess in certain routes may raise cost of shipping
through additional safety measures or longer routes that are less storm
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Chapter 10 Key Economic Sectors and Services
10
p
rone (UNCTAD, 2009; UNECE and UNCTAD, 2010). Transport costs
would increase or new routes sought if storms disrupt supply chains by
destroying port infrastructure connecting road or rail (Becker et al.,
2011). Increased storminess may also affect passage through lock
systems (CCSP, 2008; UNCTAD, 2009). Increased storminess may increase
maintenance costs for ships and ports and result in more frequent
weather-related delays.
10.4.5. Air
Hotter air is less dense. In summer months, especially at airports located
at high altitudes, this may result in limitations for freight capacity, safety
issues, and weather-related delays, unless runways are lengthened
(National Research Council, 2008; Pejovic et al., 2009). Chapman (2007)
suggests that technological innovations will negate the challenges
posed by extreme temperatures.
Increased storminess at airports, particularly those located in coastal
regions, may increase the number of weather-related delays and
cancellations (Pejovic et al., 2009; Lemmen and Warren, 2010) and increase
maintenance and repair costs (Gusmao, 2010). Clear-air turbulence will
increase in the Atlantic corridor, leading to longer and bumpier trips
(Williams and Joshi, 2013). The impact of climate change on airport
pavement is very similar to paved roads (DOT, 2002; Allard et al., 2007).
The effect of temperature and increased precipitation intensity on
airports imposes a risk to the entire facility if pavements are not adapted
to these increases (Pejovic et al., 2009).
10.5. Other Primary and Secondary
Economic Activities
This section assesses the impact of climate change on primary (agriculture,
mining) and secondary economic activities (manufacturing, construction),
unless they are discussed elsewhere in the chapter or the report.
10.5.1. Primary Economic Activities
Primary economic activities (e.g., agriculture, forestry, fishing, mining)
are particularly sensitive to the consequences of climate change because
of their immediate dependence on the natural environment. In some
regions, these activities dominate the economy.
10.5.1.1. Crop and Animal Production
Chapters 7 and 9 assess the impact of climate change on agriculture,
including the effects on (international) markets for crops.
10.5.1.2. Forestry and Logging
Chapter 4 assesses the biophysical impact of climate change on forestry.
Including adaptation in forest management, climate change will accelerate
tree growth. This will reduce prices to the benefit of consumers everywhere.
L
ow to mid latitude producers will benefit too as they switch to short-
rotation forest plantations. Mid- to high-latitude producers will be hurt
by lower prices while their productivity increases only modestly (Sohngen
and Mendelsohn, 1997, 1998; Sohngen et al., 2001; Perez-Garcia et al.,
2002; Lee and Lyon, 2004; Seppala et al., 2009). The value of the forest
land in Europe would fall between 14 and 50% by 2100 (Hanewinkel
et al., 2013). Different trees will be affected differently (Aaheim et al.,
2011a,b). Higher biomass prices differentially impact different forest-
based industries (Moiseyev et al., 2011).
10.5.1.3. Fisheries and Aquaculture
Chapter 4 assesses impacts of climate change on freshwater ecosystems,
and Chapters 5, 6, and 30 on marine ecosystems. These assessments
include the effects on commercially valuable fish stocks, but exclude
the effects on markets. Adaptation and markets will substantially
change the effect of climate change on fisheries (Link and Tol, 2009;
Yazdi and Fashandi, 2010).
Allison et al. (2009), using an indicator-based approach, analyzed the
vulnerability of capture fishery of 132 economies. Incongruously, they
find that the sign and size of climate-driven change for particular fish
stocks and fisheries are uncertain but are expected to lead to either
increased economic hardship or missed opportunities for development
in countries that depend on fisheries but lack the capacity to adapt. A
major part of the gross turnover of nine key fish and cephalopod species
in the Bay of Biscay remains potentially unaffected by climate change
(Le Floc’h et al., 2008). In contrast, Iberian-Atlantic sardine biomass and
profitability declines due to climate change (Garza-Gil et al., 2011). The
economic impact of climate change on fisheries is dominated by the
impact of management regime and market (Eide and Heen, 2002;
McGoodwin, 2007; Eide, 2008; McIlgorm, 2010; Merino et al., 2010).
Ocean acidification has a range of impacts on the biological systems
(Doney et al., 2009), but the studies on the economic impacts of ocean
acidification are rare (Cooley and Doney, 2009; Hilmi et al., 2013). Using
a partial equilibrium model, Narita et al. (2012) estimate the economic
impact of ocean acidification on shellfish. By the turn of this century
the aggregate cost could be greater than US$100 billion.
10.5.1.4. Mining and Quarrying
Climate change will affect exploration, extraction, production, and
shipping in the mining and quarrying industry (Pearce et al., 2011). An
increase in climate-related hazards (such as forest fires, flooding,
windstorm) affects the viability of mining operations and potentially
increases operating, transportation, and decommissioning costs.
Most infrastructure was built based on presumption of a stable climate,
and is thus not adapted to climate change (Ford et al., 2010, 2011;
Pearce et al., 2011). Damigos (2012) estimates the damages due to
climate change under the SRES A1B scenario for the period 2021–2050
of the extent of US$0.8 billion for the Mediterranean Region. Note that
other factors such as research and development might influence the
viability of mining operations by lowering the cost of adaptation.
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Key Economic Sectors and Services Chapter 10
10.5.2. Secondary Economic Activities
10.5.2.1. Manufacturing
Climate change will impact manufacturing through three channels. First,
climate change affects primary economic activities (see Section 10.5.1),
and this means that prices and qualities of inputs are different. Second,
the supply chain is affected, or the quality of the product. The impact of
climate change on energy demand is well understood (see Section 10.2).
Using a biophysical model of the human body, Kjellstrom et al. (2009)
project labor productivity to fall, particularly of manual labor in humid
climates. Labor productivity losses will be accentuated by increased
incidences of malaria and vector-borne diseases. Note that the loss in
labor productivity can be offset by the technological progress. Hübler
et al. (2008) uphold the finding with a German case study, and Hsiang
(2010) corroborates it with a statistical analysis of weather data and
labor productivity in the Caribbean for 1970–2006. Some manufacturing
activity is location specific, perhaps because it is tied to an input or
product market, and will thus have to cope with the current and future
climate; other manufacturing has discretion over its location (and hence
its climate). Third, climate change affects the demand for products. This
is pronounced for manufactures that supply primary sectors (Kingwell
and Farré, 2009) and construction material (see Section 10.5.2.2).
Unfortunately, there are only a few studies that quantify these effects
(see Section SM10.1 of the on-line supplementary material).
10.5.2.2. Construction and Housing
Climate and climate change affect construction in three ways. First,
weather conditions are one of the key factors in construction delays
and thus costs. Climate change will change the length of the building
season. In addition, precipitation affects the cost of construction
through temporary flood protection (coffer) structures, slope stabilization
management, and dewatering of foundations. There are adaptation
measures that may reduce some of the costs. Apipattanavis et al. (2010)
show a reduction in the expected value of road construction delays and
associated costs. Second, buildings and building materials are designed
and selected to withstand a particular range of weather conditions. As
climate changes, design standards will change too. Exterior building
components including windows, roofing, and siding are all specified
according to narrow environmental constraints. Climate change will
introduce conditions that are outside the prescribed operating environment
for many materials, resulting in increased failures of window seals,
increased leaks in roofing materials, and reduced lifespan of timber or
glass-based cladding materials. Similarly, the interior building systems
that allow for proper airflow in a facility face significant issues with climate
change. For example, the increases in temperature and precipitation will
lead to increased humidity as well as indoor temperatures. This requires
increased airflow in facilities such as hospitals, schools, and office
buildings—that is, upgrades to air conditioning and fan units, and
perhaps further renovations that may be significant in scope and cost.
Third, a change in the pattern of natural disasters will imply a change
in the demand for rebuilding and repair. Unfortunately, these impacts
have yet to be quantified (Hertin et al., 2003). Note that the direction
and magnitude of the effect on construction and housing costs will
possibly vary geographically. Cost impacts due to changing precipitation
a
nd storms patterns (magnitude, frequency, and/or variation) will vary
as these changes are expected to vary by region as well. Air to air heat
exchangers, heat recovery ventilators, and dehumidifiers and other
technologies may be useful in adapting indoor air quality.
10.6. Recreation and Tourism
Recreation and tourism is one of the largest sectors of the world economy.
In 2011, it accounted for 9% of global expenditure, and employed 260
million people (WTTC, 2011). Supply of tourism services is the dominant
activity in many regional economies.
Recreation and tourism encompass many activities, some of which are
more sensitive to weather and climate than others: compare sunbathing
to angling, gambling, business seminars, family visits, and pilgrimage.
Climate change would affect the place, time, and nature of these
activities.
There is a large literature on the impact of climate change on tourism
(Gössling et al., 2012; Scott et al., 2012a; Pang et al., 2013). Some studies
focus on the changes in the behavior of tourists—that is, the demand
for recreation and tourism services (see Section 10.6.1). Other studies
look at the implications for tourist operators and destinations—that is,
the supply of recreation and tourism services (see Section 10.6.2). A
few studies consider the interactions between changes in supply and
demand (see Section 10.6.3).
10.6.1. Recreation and Tourism Demand
Conventionally, recreation does not involve an overnight stay whereas
tourism does. That implies that recreation, unlike tourism, is done close
to home. Whereas tourists, to a degree, chose the climate of their holidays,
recreationists do not (although climate is a consideration in the choice
where to live). Tourists would adapt to climate change by changing the
region, timing, and activities of their holidays; recreationists would
adapt only timing and activities (Becken and Hay, 2007).
10.6.1.1. Recreation
There has been no research on systematic differences of recreational
behavior due to differences in climate at large spatial scales. The impact
of climate change on recreation is therefore largely unknown. The
economic impact is probably limited, as people will tend to change the
composition rather than the level of their time and money spent on
recreation. For instance, Shaw and Loomis (2008) argue that climate
change would increase boating, golfing, and beach recreation at the
expense of skiing.
There are case studies that indicate the impact of climate change on
recreation. Buckley and Foushee (2012) find a trend toward earlier visits
to U.S. national parks between 1979 and 2008. They argue this is due
to climate change, but do not rigorously test this hypothesis nor control
for other explanations. Whitehead et al. (2009) find a substantial decrease
in the recreational value of sea shore fishing in North Carolina due to
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Chapter 10 Key Economic Sectors and Services
10
S
LR. Daugherty et al. (2011) conclude that climate change will make it
more difficult to guarantee adequate water levels for boating and
angling in artificial reservoirs. Pouta et al. (2009) project a reduction in
cross-country skiing in Finland, particularly among women, the lower
classes, and urban dwellers. Shih et al. (2009) find that weather affects
the demand for ski lift trips. Hamilton et al. (2007) highlight the importance
of “backyard snow” to induce potential skiers to visit ski slopes. One
could expect people to adopt other ways of enjoying themselves but
such alternatives were excluded from these studies.
There are positive effects too (Richardson and Loomis, 2005). Scott and
Jones (2006, 2007) foresee an increase in golf in Canada due to climate
change. Kulshreshtha (2011) sees positive impacts on recreation on the
Canadian Prairies, and Coombes et al. (2009) predict an increase in
beach tourism in East Anglia. Graff Zivin and Neidell (2010) find that
people recreate indoors when the weather is inclement.
Scott et al. (2007) estimate the relationship between visitors to Waterton
Lakes National Park and weather variables for 8 years of monthly
observations, and use this to project an increase in visitor numbers due
to climate change. A survey among current visitors indicates that a
deterioration of the quality of nature would reduce visitor numbers.
Jones et al. (2006) study the impact of climate change on three festivals
in Ottawa. They argue for heat wave preparedness for Canada Day, find
that skating on natural ice may become impossible for Winterlude, and
that the dates of the Tulip Festival may need to be shifted to reflect
changing phenology.
10.6.1.2. Tourism
Climate (Becken and Hay, 2007; WTO and UNEP, 2008) and weather
(Álvarez-Díaz and Rosselló-Nadal, 2010; Rosselló-Nadal et al., 2010;
Rossello, 2011; Førland et al., 2012; Day et al., 2013; Falk, 2013) are
important factors in tourist destination choice, and the tourist sector is
susceptible to extreme weather (Forster et al., 2012; Hamzah et al.,
2012; Tsai et al., 2012). Eijgelaar et al. (2010), for instance, argue that
so-called “last chance tourism” is a strong pull for tourists to visit
Antarctica to admire the glaciers while they still can. Farbotko (2010)
and Prideaux and Mcnamara (2012) use a similar mechanism to explain
the rise in popularity of Tuvalu as a destination choice. Huebner (2012)
find no impact of future climate change on current travel choices. Taylor
and Ortiz (2009) show that domestic tourists in the UK often respond
to past weather; the hot summer of 2003 had a positive impact on
revenues of the tourist sector. Denstadli et al. (2011) find that tourists
in the Arctic do not object to the weather in the Arctic; Gössling et al.
(2006) reaches the same conclusion for tourists on Zanzibar; and
Moreno (2010) for tourists in the Mediterranean.
There are a number of biometeorological studies of the impact of climate
change on tourism. Yu et al. (2009a) find that Alaska has become more
attractive over the last 50 years and Florida less attractive to tourists.
Yu et al. (2009b) conclude that the climate for sightseeing has improved
in Alaska, while the climate for skiing has deteriorated. Matzarakis et
al. (2010) construct a composite index of temperature, humidity, wind
speed, and cloud cover, and use this to map tourism potential. Lin and
Matzarakis (2008, 2011) apply the index to Taiwan POC and eastern
C
hina. Endler and Matzarakis (2010a,b, 2011) use an index to study the
Black Forest in Germany in detail, highlighting the differences between
summer and winter tourism, and between high and low altitudes (Endler
et al., 2010). Zaninović and Matzarakis (2009) and Matzarakis and
Endler (2010) use this method to study Freiburg and Hvar. Matzarakis
et al. (2007) project this potential into the future, finding that the
Mediterranean will probably become less attractive to tourists. Hein et
al. (2009), Perch-Nielsen et al. (2009), Giannakopoulos et al. (2011),
Amelung and Moreno (2012), and Amengual et al. (2012) reach the
same conclusion, but also point out that Mediterranean tourism may
shift from summer to the other seasons. Giannakopoulos et al. (2011)
note that coastal areas in Greece may be affected more than inland
areas because, although temperature would be lower, humidity would
be higher. Moreno and Amelung (2009), on the other hand, conclude
that climate change will not have a major impact (before 2050) on
beach tourism in the Mediterranean because sunbathers like it hot
(Moreno, 2010; Rutty and Scott, 2010). Amelung et al. (2007) use a
weather index for a global study of the impact of climate change on
tourism, finding shifts from equator to pole, summer to spring and
autumn, and low to high altitudes. Perch-Nielsen (2010) combines a
meteorological indicator of exposure with indicators of sensitivity and
adaptive capacity, and uses this to rank the vulnerability of beach
tourism in 51 countries. India stands out as the most vulnerable, and
Cyprus as the least vulnerable.
The main criticism of most biometeorological studies is that the
predicted gradients and changes in tourism attractiveness have rarely
been tested to observations of tourist behavior. De Freitas et al. (2008)
validate their proposed meteorological index to survey data. Moreno et
al. (2008) and Ibarra (2011) use beach occupancy to test meteorological
indices for beach tourism. Gómez-Martín (2006) tests meteorological
indices against visitor numbers and occupancy rates. All four studies
find that weather and climate affects tourists, but in a different way
than typically assumed by biometeorologists.
Maddison (2001) estimates a statistical model of the holiday destinations
of British tourists, Lise and Tol (2002) for Dutch tourists, Bujosa and
Rosselló (2012) for Spanish tourists in Spain, and Bigano et al. (2006)
for international tourists from 45 countries. These models control for as
many other variables as possible; their focus on the average tourist may
be misleading, and their representation of climate may be oversimplified
(Gössling and Hall, 2006). Tourists have a clear preference for the climate
that is currently found in southern France, northern Italy, and northern
Spain. People from hot climates care more about the climate in which
they spend their holidays than people from cool climates. Whereas
(Bigano et al., 2006) find regularity in revealed preferences, Scott et al.
(2008b) find pronounced differences in stated preferences between
types of people.
Bigano et al. (2007) and Hamilton et al. (2005a,b) construct a simulation
model of domestic and international tourism and climate change (but
not SLR), considering the simultaneous change in the attractiveness of
all potential holiday destinations (Dawson and Scott, 2013); Hamilton
and Tol (2007) downscale these national results to the regions of
selected countries. Two main findings emerge. First, climate change
would drive tourists to higher latitudes and altitudes. International
tourist arrivals would fall, relative to the scenario without warming, in
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h
otter countries, and rise in colder countries. Tourists from northwestern
Europe, the main origin worldwide of international travelers at present,
would be more inclined to spend the holiday in their home country, so
that the total number of international tourists falls. Second, the impact
of climate change is dominated by the impact of population growth
and, particularly, economic growth. In the worst affected countries,
climate change slows down, but nowhere reverses, growth in the tourism
sector.
10.6.2. Recreation and Tourism Supply
Studies on the supply side often focus on ski tourism. Warming is
expected to raise the altitude of snow-reliable ski resorts, and fewer
resorts will be snow reliable (Dawson et al., 2009; Hendrikx et al., 2012,
2013; Steger et al., 2012). Snowmobiling will be negatively affected too
(Mcboyle et al., 2007; Scott et al., 2008a). Artificial snow-making cannot
fully offset the loss in natural snowfall (Elsasser and Bürki, 2002;
Scott et al., 2006; Hoffmann et al., 2009), particularly in lower areas
(Wolfsegger et al., 2008; Morrison and Pickering, 2012; Schmidt et al.,
2012), and water scarcity and the costs of snowmaking will be increasingly
large problems (Scott et al., 2003, 2007; Steiger and Mayer, 2008;
Hendrikx and Hreinsson, 2012; Matzarakis et al., 2012; Pons-Pons et al.,
2012); skiers prefer natural over artificial snow (Pickering et al., 2010).
Tourism alternatives to skiing or non-tourism alternatives need to be
considered as a source of economic development (Bicknell and Mcmanus,
2006; Moen and Fredman, 2007; Scott and McBoyle, 2007; Tervo, 2008;
Bourdeau, 2009; Potocka and Zajadacz, 2009; Hill et al., 2010; Pickering
and Buckley, 2010; Steiger, 2010; Serquet and Rebetez, 2011; Landauer
et al., 2012; Matzarakis et al., 2012). Other socioeconomic trends
dominate the impact of climate change (Hopkins et al., 2012; Steiger,
2012).
Other studies consider beach tourism. Scott et al. (2012b) highlight the
vulnerability of coastal tourism facilities to SLR. Hamilton (2007) finds
that tourists are averse to artificial coastlines, so that hard protection
measures against SLR would reduce the attractiveness of an area.
Raymond and Brown (2011) survey tourists on the Southern Fleurieu
Peninsula. They conclude that tourists who are there for relaxation worry
about climate change, particularly SLR, while tourists who are there to
enjoy nature (inland) do not share that concern. Becken (2005) finds
that tourist operators have adapted to weather events, and argues that
this helps them to adapt to climate change. Belle and Bramwell (2005)
find that tourist operators on Barbados are averse to public adaptation
policies. Uyarra et al. (2005) find that tourists on Barbados would
consider holidaying elsewhere if there is severe beach erosion. Buzinde
et al. (2010a,b) find that there is a discrepancy between the marketing
of destinations as pristine and the observations of tourists, at least for
Mexican beach resorts subject to erosion. They conclude that tourists
have a mixed response to environmental change, contrary to the officials’
view that tourists respond negatively. Jopp et al. (2013) find that an
increase in tourism in the shoulder season may offset losses in the peak
season in Victoria.
Some studies focus on nature tourism. Cavan et al. (2006) find that
climate change may have a negative effect on the visitor economy of
the Scottish uplands as natural beauty deteriorates through increased
w
ild fires. Saarinen and Tervo (2006) interviewed nature-based tourism
operators in Finland, and found that about half of them do not believe
that climate change is real, and that few have considered adaptation
options. Nyaupane and Chhetri (2009) argue that climate change would
increase weather hazards in the Himalayas and that this would endanger
tourists. Uyarra et al. (2005) find that tourists on Bonaire would not return
if coral were bleached. Hall (2006) finds that small tourist operators in
New Zealand do not give high priority to climate change, unless they were
personally affected by extreme weather in recent times. The interviewed
operators generally think that adaptation is a sufficient response to
climate change for the tourism sector. Klint et al. (2012) find that tourist
operators in Vanuatu give low priority to adaptation to climate change
and Jiang et al. (2012) find Fiji poorly prepared. Saarinen et al. (2012) find
that tourist operators in Botswana think that climate change would not
affect them. Wang et al. (2010) note that glacier tourism is particularly
vulnerable to climate change, highlighting the Baishiu Glacier in China.
Brander et al. (2012) estimate the economic impacts of ocean acidification
on coral reefs under four IPCC marker scenarios using value transfer
function approach and find that the annual economic impacts increase
rapidly overtime, though it remains a small fraction of total income.
While the case studies reviewed above provide rich detail, it is hard to
draw overarching conclusions. A few studies consider all aspects of the
impact of climate change for particular countries or regions (Ren Guoyu,
1996; Harrison et al., 1999). In France, the Riviera may benefit because
it is slightly cooler than the competing coastal resorts in Italy and Spain;
the Atlantic Coast, although warming, would not become more attractive
because of increased rainfall; it is not probable that the increase in summer
tourism in the mountains would offset the decrease in winter tourism
(Ceron and Dubois, 2005). In the Great Lakes regions, there is a reduced
tourism potential in winter but increased opportunities in summer (Dawson
and Scott, 2010). Tourist operators in Australia find the uncertainty
about climate change too large for early investment in adaptation
(Turton et al., 2010).
10.6.3. Market Impacts
There are only two papers that consider the economic impacts of rather
stylized climate change-induced changes in tourism supply and demand.
Both studies use a global computable general equilibrium model,
assessing the effects on the tourism sector as well as all other markets.
Berrittella et al. (2006) consider the consumption pattern of tourists
and their destination choice. They find that the economic impact is
qualitatively the same as the impact on tourist flows (discussed above):
Colder countries benefit from an expanded tourism sector, and warmer
countries lose. They also find a drop in global welfare, because of the
redistribution of tourism supply from warmer (and poorer) to colder
(and richer) countries.
Bigano et al. (2008) extend the analysis with the implications of sea
level rise. The impact on tourism is limited because coastal facilities
used by tourists typically are sufficiently valuable to be protected
against SLR. The economic impacts on the tourism sector are reinforced
by the economic impacts on the coastal zone; the welfare losses due to
the impact of climate change on tourism are larger than the welfare
losses due to SLR.
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Chapter 10 Key Economic Sectors and Services
10
10.7. Insurance and Financial Services
1
0.7.1. Main Results of the Fourth Assessment Report and
IPCC Special Report on Managing the Risks of
Extreme Events and Disasters to Advance Climate
Change Adaptation on Insurance
More intense or frequent weather-related disaster would affect property
insurance, of which coverage is expanding with economic growth
(WGII AR4 Section 7.4.2.2.4). Insurability can be preserved through risk-
reducing measures. Adaptation to climate change can be incentivized
through risk-commensurate insurance premiums. Improved risk
management would further financial resilience (WGII AR4 Sections
7.4.2.2.4, 7.6.3). Insurance is linked to disaster risk reduction and climate
change adaptation, because it enables recovery, reduces vulnerability,
and provides knowledge and incentives for reducing risk (IPCC, 2012).
10.7.2. Fundamentals of Insurance
Covering Weather Hazards
Insurance is organized either through private markets, publicly, or
public-private partnerships. It internalizes catastrophe risk costs prior
to catastrophic events, reducing the economic impact of weather-related
and other disasters to individuals, enterprises, and governments—thus
stabilizing income and consumption, and decreasing societal vulnerability
(Melecky and Raddatz, 2011; see also Section 17.5.1). Insurance is based
on the law of large numbers: the larger the portfolio of uncorrelated
and relatively small risks, the more accurately the average loss per policy
can be predicted and charged accordingly, allowing for a lower premium
than with a smaller ensemble. Besides spreading risk over a diversified
insured population, insurance spreads risk over time. However, weather-
related disasters such as floods simultaneously affect many, and thus
violate the principle of uncorrelated risks. Consequently, large losses
are much more probable, the loss variance is greater, and the tail risk is
higher (Kousky and Cooke, 2012).
If insurance coverage is to be maintained, insurers would need more
risk-based capital to indemnify catastrophic losses and remain financially
solvent. This coverage is purchased in the reinsurance and capital
markets. The capital costs account for a substantial portion of premiums
and the affordability and viability of weather insurance are subjects of
o
ngoing research given future climate change (Charpentier, 2008; Clarke
and Grenham, 2012; Maynard and Ranger, 2012).
Increasing volatility and burden of losses in many regions are expected
to fundamentally impact the industry, leading insurers to adapt their
business to the changing risk (Herweijer et al., 2009; Phelan et al., 2011;
Mills, 2012; Paudel, 2012). However, prevailing short-term contracts
facilitate adaptation to changing circumstances (Botzen et al., 2010a).
10.7.3. Observed and Projected Insured Losses
from Weather Hazards
Direct and insured losses from weather-related disasters have increased
substantially in recent decades, both globally and regionally (Bouwer
et al., 2007; Crompton and McAneney, 2008; IPCC, 2012; Munich Re,
2013; Smith and Katz, 2013; Swiss Re, 2013c). Global insured weather-
related losses in the period 1980–2008 increased by US$
2008
1.4 billion
per year on average (Barthel and Neumayer, 2012). As a rule, insured
loss figures are more accurate than direct economic loss estimates,
because insurance payouts are closely monitored. Often they are the
basis for estimates of direct overall losses (Kron et al., 2012; Smith and
Katz, 2013). Economic growth, including greater concentrations of
people and wealth in periled areas and rising insurance penetration, is
the most important driver of increasing losses.
Growth-induced changes in past losses are removed by normalizing to
current levels of destructible wealth. So far, only one study analyzes
normalized global weather-related insured losses (Barthel and Neumayer,
2012), but the period is too short (1990–2008) to support a meaningful
analysis of trends. A few studies focus on specific perils and regions, in
particular Australia, USA, and Europe. Trends were detected for the USA
and Germany, but not for Australia and Spain (Table 10-4). Such trends
can be influenced by changing damage sensitivities, adaptive measures,
different normalization, and changes in insurance—besides changing
hazards (Crompton and McAneney, 2008; Bouwer, 2011; Barthel and
Neumayer, 2012; IPCC, 2012). Prevention measures such as flood control
structures or improved building standards would offset an increase in
hazard (Kunreuther et al., 2009, 2012). Given such confounding factors,
it can be challenging to estimate to what degree developments in losses
convey a climate signal (IPCC, 2012; Kron, 2012). Nonetheless, normalized
direct natural disaster losses have already been demonstrated to properly
Frequently Asked Questions
FAQ 10.2 | How does climate change impact insurance and financial services?
Insurance buys financial security against, among other perils, weather hazards. Climate change, including changed
weather variability, is anticipated to increase losses and loss variability in various regions through more frequent
and/or intensive weather disasters. This will challenge insurance systems to offer coverage for premiums that are
still affordable, while at the same time requiring more risk-based capital. Adequate insurance coverage will be
challenging in low- and middle-income countries. Other financial service activities can be affected depending on
the exposure of invested assets/loan portfolios to climate change. This exposure includes not only physical damage
but also regulatory/reputational effects, liability, and litigation risks.
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Key Economic Sectors and Services Chapter 10
r
eflect climate variability on various time scales (Pielke and Landsea,
1999; Welker and Faust, 2013).
Studies analyzing changes in climate variables and insured losses in
parallel are still rare. Variability and mean level of thunderstorm-related
insured losses in the USA in the period 1970–2009 have substantially
increased, while meteorological thunderstorm forcing has risen in parallel
(Sander et al., 2013). The number of days that a regional insurer in
southwest Germany sustains hail losses displays an upward trend since
1986, while meteorological severe storm indicators also show upward
trends (Kunz et al., 2009). Although more studies find increases of large
hail in Europe, general data and monitoring issues hindered assessing
more than low confidence in observed meteorological trends (WGI AR5
Section 2.6.2.4). Corti et al. (2009) found an increase in modeled and
partly observed insured subsidence losses in France over the period
1961–2002, consistent with a likely increase in dryness in Mediterranean
regions (WGI AR5 Section 2.6.2.3). The observed rise in U.S. normalized
insured flood losses (Barthel and Neumayer, 2012) may partly correspond
t
o very likely increased heavy precipitation events in central North
America (WGI AR5 Section 2.6.2.1), while the evidence for climate-
driven changes in river floods is not compelling (WGI AR5 Section
2.6.2.2). Declining anthropogenic aerosol emissions may partly explain
the recent upswing in hurricane hazard and losses (WGI AR5 Sections
2.6.3, 14.6; Table 10-4). Apart from detection, loss trends have not been
conclusively attributed to anthropogenic climate change; most such
discussions are not based on scientific attribution methods.
Many GCM-based projection studies agree that extreme winter storm
wind speeds fall in the Mediterranean and increase in west, central, and
northern Europe (WGI AR5 Section 14.6.2.2). Loss ratios—that is, insured
loss divided by insured value—follow the same pattern (Schwierz et al.,
2010; Donat et al., 2011; Pinto et al., 2012; see also Table 10-5). Return
periods per loss level are projected to shorten in large parts of Europe,
indicating more frequent high losses (Pinto et al., 2012; see Table 10-5).
Projected overall losses and fatalities develop accordingly (Narita et al.,
2010; IPCC, 2012). Across three modeling approaches calibrated to
Region
Peril accounted for in normalized insured
property losses
Observation
period
Trend in insured losses—otherwise specifi ed
(aggregation mode)
Reference
World
A
ll weather-related 1990–2008 No trend (annual aggregates) 1
Australia
Aggregate of bushfi re, fl ood, hailstorm,
thunderstorm, tropical cyclone
1967–2006 No trend (annual aggregates) 7
West
Germany
All weather-related
1980–2008 Positive trend (annual aggregates)
1
Winter storms
Floods
1980–2008 No trend (annual aggregates)
Convective events
Southwest
Germany
Hailstorm 1986–2004 Positive trends in annual frequency of days exceeding thresholds of daily damage
claim counts
Increase in annual count of hail damage claims
8
Spain
Floods 1971–2008 No trend (annual aggregates) 2
USA east
of 109°W
Convective events (hail, heavy precipitation and
fl ash fl ood, straight-line wind, tornado)
1970–2009
(March to
September)
Standard deviation (variability) by a factor 1.65 greater for 1990–2009 than for
1970–1989
Mean annual loss by a factor 2.67 greater for 1990–2009 than for 1970–1989
Data: normalized insured loss exceeding US$150 million per event, annual aggregates
9
USA
Winter storms (ice storms, blizzards and snow
storms)
1949–2003 Positive trend (pentade totals)
Positive trend (average loss per state, pentade totals)
3
All fl ood (“fl ood only” and fl oods specifi cally caused
by convective storms, tropical cyclones, snow melt)
1972–2006 Positive trend (annual aggregates) 4
Tropical cyclones 1949–2004 Increase (7-year totals)
No statistical trend assessment.
5
Hailstorm 1951–2006 Focus on top-ten major hailstorm losses of the period 1951–2006. Increase in
frequency and loss in the 1992–2006 period as compared to 1951–1991. No
statistical trend assessment
6
All weather-related
1973–2008 Positive trend (annual aggregates)
1
Floods
Convective events
Winter storms
Tropical cyclones
Heat episodes
Cold spells 1973–2008 No trend (annual aggregates)
Table 10-4 | Observed normalized insured losses from weather hazards (trends signifi cant at the 10% level are indicated as a trend).
Sources:
1
Barthel and Neumayer (2012);
2
Barredo et al. (2012);
3
Changnon (2007);
4
Changnon (2008);
5
Changnon (2009a);
6
Changnon (2009b);
7
Crompton and McAneney
(2008);
8
Kunz et al. (2009);
9
Sander et al. (2013).
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Chapter 10 Key Economic Sectors and Services
10
German insurance data, the 25-year loss is projected (SRES A1B) to
change by –10% to +26% (2011–2040), +5% to +41% (2041–2070),
and +45% to +58% (2071–2100) against 1971–2000, keeping
exposures and damage sensitivities constant (Held et al., 2013).
Although it is unlikely that the North Atlantic response to climate change
is just a simple poleward shift of the storm track, overall confidence in
the magnitude of regional storm track changes is low (WGI AR5 Section
14.6.3).
Direct losses and fatalities from flooding will increase with climate
change in various locations in the absence of adequate adaptation,
given very likely widespread increases in heavy precipitation (WGI AR5
Sections 11.3.2.5.2, 12.4.5.4; see also IPCC, 2012). This is selectively
reflected in studies projecting mean annual insured heavy rainfall and
flood losses to rise with climate change in the UK, the Netherlands,
Germany, southern Norway, and the Canadian province of Ontario
(Table 10-5).
Direct losses and fatalities from tropical cyclones will increase with
exposure and may increase with the frequency of very intense cyclones
in some basins (WGI AR5 Section 14.6; Nordhaus, 2010; IPCC, 2012;
Peduzzi et al., 2012). Ranger and Niehoerster (2012), Kunreuther et al.
(2012), and Raible et al. (2012) found insured hurricane losses change
in opposite directions across a range of dynamical and statistical model
projections, whereas a high-resolution approach tends to support a long-
term increase (Emanuel, 2011). Here, increased probabilities of upward
shifted accumulated loss might be detectable by 2025 at earliest,
whereas a significant loss trend might emerge much later (Crompton
et al., 2011; Emanuel, 2011).
Insured typhoon-related property losses in China are projected to increase
(Dailey et al., 2009). Averaged across four GCMs, Mendelsohn et al.
(2012) project rising direct losses for Central America, the Caribbean,
North America, and East Asia. Narita et al. (2009) report an increase in
damages and fatalities in all parts of the world.
Hazard Insurance line Region
Projected changes in future time slices relative to current climate
(spatial distribution and vulnerability of insured values assumed to be unchanged over time)
Winter storm Homeowners’
i
nsurance
Europe Projected increases in mean annual loss ratio lie in a range from one- to two-digit percentages in time slices before
a
nd around 2050 for regions such as France, Belgium / Netherlands, UK / Ireland, Germany, and Poland, with larger
i
ncreases at the end of the century. Southern European regions expect decreases, such as Portugal / Spain (SRES A1B,
A2).
4
,5,8,13
–
1
5,19
C
urrently rare and high annual loss ratios are projected to occur more often: today’s 20-year, 10-year, and 5-year
return periods appear strongly reduced by the end of the century for individual countries. For entire Europe they will
r
oughly be halved (SRES A2).
16
Accordingly, return periods will have higher loss levels associated,
10,19
e.g., the 25-year loss in Germany is expected to
r
ise by 5 – 41% in 2041 – 2070 (SRES A1B).
8,10
River fl ood, maritime
fl ood, fl ash fl ood
f
rom rainfall, melting
snow
Property and
business interruption
i
nsurances
Europe, North America Germany: projected increases in mean annual insured fl ood loss according to a seven-member dynamical
downscaling ensemble mean (SRES B1, A1B, A2) are 84% (2011 – 2040), 91% (2041 – 2070), and 114% (2071 – 2100).
7
United Kingdom: projected increases in mean annual insured fl ood loss are 8% (for a +2°C rise in global mean
temperature) and 14% (for a +4°C rise), with the one-in-hundred-year loss higher by 18% and 30%, respectively.
4
Norway, Canada: losses from heavy precipitation in property and business interruption insurances in three city areas
in Canada are projected to rise by 13% (2016 – 2035), 20% (2046 – 2065), and 30% (2081 – 2100) in a fi ve-member
e
nsemble mean (IS92a, SRES A2 / B2, A2)
3
.
In three counties across southern Norway precipitation and snow melt
insurance losses are expected to be higher by approximately 10 – 21% (SRES A2) and 17 – 32% (SRES B2) at the end
o
f the century.
9
The Netherlands: expected annual property loss caused by increasing river discharge and sea level with an assumed
fl
ood insurance system is projected to lie by 125% higher in 2040 relative to 2015 (corresponding to 24 cm sea level
rise) and by 1784% higher in 2100 (85 cm sea level rise).
1
T
ropical cyclone Foremost property
insurance lines
N
orth America, Asia USA: three of four GCMs driving a specifi c tropical cyclone and loss model entail increasing insured hurricane losses
over time (SRES A1B).
6
Two GCM outputs at coarser resolution for the end of the century produce contradictory
results of prolonged (ECHAM5 / MPIOM A2) versus shortened (MRI / JMA A1B) return periods of current loss levels.
1
7
A
nalogously, a wide range of model projections is refl ected in price levels of Florida’s hurricane wind insurance that
are projected to change by – 20% to +5% (2020s) and – 28% to +10% (2040s) (under the assumptions of strained
reinsurance capacity, i.e., hard market conditions, and current adaptation).
1
2,18
These approaches demonstrate
uncertainty in the sign of change.
China: projected increases of insured typhoon losses are 20% (for a +2°C rise in global mean temperature) and 32%
(for a +4°C scenario), with the one-in-hundred-year loss higher by 7% and 9%, respectively.
4
Hailstorm Homeowners’
insurance,
agricultural
insurances
Europe The Netherlands: losses from outdoor farming insurance and greenhouse horticulture insurance are projected to
increase by 25 – 29% and 116 – 134%, respectively, for a +1°C rise in global mean temperature. For a +2°C scenario,
projected increases will be higher at 49% to 58% and 219% to 269%, respectively (statistical model).
2
Germany: projected increases in mean annual loss ratios from homeowners’ insurance due to hail are 15%
(2011 – 2040) and 47% (2041 – 2070) (SRES A1B, statistical model).
8
Storms, pests,
diseases
Paddy rice insurance Asia Japan: paddy rice insurance payouts are projected to decrease by 13% by the 2070s, on the basis of changes in
standard yield and yield loss (A2).
11
Table 10-5 | Climate change projections of insured losses and /or insurance prices.
Notes: GCM = General Circulation Model; ECHAM5 = European Centre for Medium Range Weather Forecasts and (Max Planck Institute of Meteorology) Hamburg, fi fth GCM
generation; MRI = Meteorological Research Institute of Japan Meteorological Agency (JMA); SRES = Special Report on Emission Scenarios.
Sources:
1
Aerts and Botzen (2011);
2
Botzen et al. (2010b);
3
Cheng et al. (2012);
4
Dailey et al. (2009);
5
Donat et al. (2011);
6
Emanuel (2011);
7
German Insurance Association
(Gesamtverband der Deutschen Versicherungswirtschaft) (2011);
8
Gerstengarbe et al. (2013);
9
Haug et al. (2011);
10
Held et al. (2013);
11
Iizumi et al. (2008);
12
Kunreuther et al.
(2012);
13
Leckebusch et al. (2007);
14
Pinto et al. (2007);
15
Pinto et al. (2009);
16
Pinto et al. (2012);
17
Raible et al. (2012);
18
Ranger and Niehoerster (2012);
19
Schwierz et al. (2010).
683
10
Key Economic Sectors and Services Chapter 10
H
ailstorm insurance losses in the Netherlands (Botzen et al., 2010b)
and Germany (Gerstengarbe et al., 2013) are projected to increase,
consistent with more severe thunderstorms (WGI AR5 Section 12.4.5.5).
Paddy rice insurance payouts in Japan are projected to decrease (Iizumi
et al., 2008; see Table 10-5).
Rising insured wealth will increase both losses and premium income,
not necessarily altering the ratio of both. Such automatic compensation
is not effective for changing hazards. Hence, projected ratios of losses
to premiums or sums insured (while assuming constant insured property)
are an approximation of the climate change impact (Donat et al., 2011).
Additional impact factors such as future economic growth (Aerts and
Botzen, 2011) or changing vulnerability are rarely projected.
10.7.4. Fundamental Supply-Side Challenges
and Sensitivities
10.7.4.1. High-Income Countries
The provision of weather hazard insurance is contingent on an insurer’s
ability to find a balance between affordability of the premiums and costs
that have to be covered by the revenue. Costs include the expected level
of losses, expenses for risk assessment, product development, marketing,
operating, and claims processing. Moreover, the revenue must provide
a return on shareholders’ equity and allow for the purchase of external
capital to cover large losses (Charpentier, 2008; Kunreuther et al., 2009).
The balance between affordability and profitability is sensitive to climate
change. Increases in large weather-related losses may corrode an insurer’s
s
olvency if it fails to adjust its risk management, or is hampered in doing
so by price regulation (Grace and Klein, 2009). In addition, misguided
incentives for development in hazard-prone areas, as with the U.S.
National Flood Insurance Program (Michel-Kerjan, 2010; Kousky and
Kunreuther, 2010; GAO, 2011) can aggravate the situation (see Table
10-6).
The additional uncertainty induced by climate change translates into a
need for more risk capital (Charpentier, 2008; Grace and Klein, 2009;
Kunreuther et al., 2009). This raises insurance premiums and affects the
economy (Table 10-6). Health and life insurance may also be affected
through the health impacts of climate change (Hecht, 2008). Liability
insurance, too, may be susceptible to climate change. So far, no damages
have been awarded for greenhouse gas emissions as such, but litigation
where damages are sought is pending (Heintz et al., 2009; Mills, 2009;
Patton, 2011). Defense cost coverage under liability insurance in such
cases depends on the specific contractual wording (Supreme Court of
Virginia, USA, 2012; see Table 10-6).
10.7.4.2. Middle- and Low-Income Countries
Middle- and low-income countries account for a small share of
worldwide non-life insurance: approximately 14% of premiums in 2012
(Swiss Re, 2013b). In high-income countries, some 37% of direct natural
disaster losses have been covered by insurance in the period 1980–
2011, about 4% in middle-income countries, and even less in low-
income countries (Wirtz et al., 2013). For instance, only about 1% of
direct overall losses in the 2010 floods in Pakistan were insured (Munich
Re., 2011).
Challenges that might
increase in the climate
change context
E x a m p l e / e x p l a n a t i o n
Failure to refl ect temporal
changes in hazard condition in
risk management
After the devastating 2004 and 2005 hurricane seasons, the losses of Florida’s homeowners’ insurance accumulated since 1985 exceeded the cumulative
direct premiums earned by 31%. Consequences of the upswing and peak in hurricane activity: one insurer liquidated, two seized by regulation due to
insolvency; reduced coverage availability in high-risk areas.
9
Misguided incentives
additionally increasing risk
US National Flood Insurance Program (NFIP) allows for a vicious circle of built-up areas already existing within fl ood plains pressing authorities to construct
or improve protecting levees that in turn lead to even more development attracted by NFIP premium discounts, although exposed to extreme fl ooding
events.
11,22
In addition, the large majority of older properties situated within fl ood plains and accounting for 16% of losses in the period 1978 – 2008 pay
premiums substantially below the risk-adequate level.
14; see also 1,6,7,11,15
In this respect, premium incentives to reduce residual fl ood risk have been missing.
Policyholders residing in fl ood plains where fl ood cover was made precondition for mortgage drop the cover after only 2 – 4 years, accounting for missing
insurance penetration and insuffi cient build-up of NFIP risk capital.
11,14,15
All these features, among others, account for the fact that NFIP has continuously
been running a cumulative operating defi cit, reaching more than US$20 billion in 2006, after the big hurricanes.
14
Non-quantifi able uncertainties
increasing risk
There is ambiguity as to what degree climate change may modify regional weather hazards—model projections are not unequivocal,
2,3
and there is
uncertainty about prospects of post-disaster regulatory/ jurisdictional pressures, e.g., to extend claims payments beyond the original coverage.
9
Such
uncertainties materialize in risk-based capital loadings.
12
Liability insurance impacted by
new climate risk
Chances of success for claims based on CO
2
emissions in the USA seem small, owing to legal obstacles,
4,5,8,18
even though allocation schemes to overcome
these hurdles are being discussed.
17,20
Defense costs could be covered by liability insurance.
20
CO
2
emissions were declared pollution (US Supreme
Court / EPA). Existing and future regulation on limits for CO
2
emissions could continue to displace liability claims for CO
2
emissions and at the same time
create new liability risks in case of non-compliance. These risks have not yet been adequately taken into account, somewhat similar to the early stages
of environmental liability claims in the USA in the 20th century.
10,16
The Supreme Court of Virginia ruled in 2012 that coverage under liability insurance
for claims based on CO
2
emissions and defense costs depends on the specifi c occurrence-defi nition underlying the contract (e.g., if the cover pertains to
accident, warming due to CO
2
emissions and resulting damage does not match this defi nition).
19
Share of insurance in national
risk fi nancing
In the years following weather-related disasters countries with high insurance penetration show almost no impact on sovereign defi cit and increasing
economic output (GDP), whereas low-penetration countries experience substantially rising government defi cit and missing positive change in output.
13,21
The absence of developed insurance systems, as is the case in many middle- and low-income countries, translates into greater macroeconomic vulnerability
than with developed insurance systems.
Table 10-6 | Fundamental supply-side challenges and sensitivities.
Sources:
1
Burby (2006);
2
Charpentier (2008);
3
Collier et al. (2009);
4
Ebert (2010);
5
Faure and Peeters (2011);
6
GAO (2010);
7
GAO (2011);
8
Gerrard (2007);
9
Grace and Klein (2009);
10
Hecht (2008);
11
Kousky and Kunreuther (2010);
12
Kunreuther et al. (2009);
13
Melecky and Raddatz (2011);
14
Michel-Kerjan (2010);
15
Michel-Kerjan and Kunreuther (2011);
16
Mills
(2009);
17
Patton (2011);
18
Stewart and Willard (2010);
19
Supreme Court of Virginia USA (2012);
20
Taylor and Tollin (2009);
21
von Peter et al. (2012);
22
Zahran et al. (2009).
684
Chapter 10 Key Economic Sectors and Services
10
T
he small share of insurance in risk financing in middle- and low-income
countries may be insufficient because other options, such as external
credit or donor assistance, can be unreliable and late. This leaves a
financial gap in the months immediately following an EWE, often
exacerbated by overstretched public finances. Pre-disaster financing
instruments such as insurance or trigger-based risk-transfer products
have proven to be effective means of providing prompt liquidity for
households, businesses, and governments (Ghesquiere and Mahul,
2007; Linnerooth-Bayer et al., 2011; Melecky and Raddatz, 2011; IPCC,
2012; von Peter et al., 2012; see Table 10-6). These may become more
important if disaster incidence increases with climate change (Collier
et al., 2009; Hochrainer et al., 2010; IPCC, 2012).
It is challenging to increase catastrophe insurance coverage because
of low business volumes, high transaction costs, and high reinsurance
premiums following large disasters. Small-scale insurance schemes in
middle- and low-income countries may find it difficult to obtain sufficient
risk capital (Cummins and Mahul, 2009; Mahul and Stutley, 2010).
Microinsurance schemes, keeping transaction costs at the lowest operable
level, mainly provide health and life insurance to households and small
enterprises in low-income markets. Supply of property insurance suffers
from correlated weather risks, although weather-related agricultural
damages are covered. Such weather coverage is growing, typically with
government and non-governmental organization (NGO) assistance or
cross-subsidies from local insurers (Linnerooth-Bayer et al., 2011; Qureshi
and Reinhard, 2011). These schemes may be particularly sensitive to a
rise in disaster risk due to climate change (Collier et al., 2009; Leblois
and Quirion, 2011; Clarke and Grenham, 2012).
Adverse selection is another challenge: clients do not always disclose
their true risk, for example, a floodplain site, to the insurer so as to
benefit from lower rates. Lower-risk participants may be charged too
high premiums and leave the scheme, thus increasing overall risk; and
in low-income countries, where data to establish homogeneous risk
groups are not available, this can cause disaster insurance markets to
fail. Moral hazard is another issue, where the insured adopt more risky
behavior than anticipated by the insurer, particularly in the absence of
proper monitoring (Barnett et al., 2008; Mahul and Stutley, 2010).
10.7.5. Products and Systems Responding
to Changes in Weather Risks
10.7.5.1. High-Income Countries
A rise in weather-related disaster risk may drive the need for more risk-
based capital to cover the losses. There are several options that sustain
insurability. Reducing vulnerability often makes sense even if expected
climate change impacts will not materialize. Theoretically, risk-based
premiums incentivize policyholders to reduce their vulnerability (Hecht,
2008; Kunreuther et al., 2009; IPCC, 2012; see Table 10-7). Premium
discounts for loss prevention can further promote this (Ward et al., 2008;
Kunreuther et al., 2009; see Table 10-7). Moral hazard can be reduced
by involving the policyholder in the payment of losses, for example, via
deductibles or upper limits of insurance coverage (Botzen and van den
Bergh, 2009; Botzen et al., 2009). Coordinated efforts of insurers and
g
overnments on damage prevention decrease risk (Ward et al., 2008;
Reinhold et al., 2012). For example, new building standards in Florida
reduced mean damage per house by 42% in the period 1996–2004
relative to pre-1996; risks can be further reduced, and premium discounts
contingent on building standard are offered (Kunreuther et al., 2009,
2012). However, risk-based premiums required to incentivize vulnerability
reduction are often hampered (see also Sections 15.4.4, 17.5.1). Price
regulation, subsidies, competitive pressures, and bundling of perils in
one product (implying cross-subsidies) have fostered underpricing. Also,
availability of sufficient on-site risk information limits price adequacy,
for example, for flood insurance (Maynard and Ranger, 2012).
Most commercial risk-assessment models only incipiently factor in
changes in weather hazards, mainly to reflect higher hurricane frequencies
(Seo and Mahul, 2009), assuming unchanging conditions for other
weather hazards. Ignoring changing hazard conditions results in biased
estimates of expected loss, loss variability, and risk capital requirements
(Charpentier, 2008; Herweijer et al., 2009; see also Section 10.7.3). Other
confounding factors, for example, systemic economic impact, in recent
large losses have been addressed (Muir-Wood and Grossi, 2008; see
Table 10-7). Geospatial risk-assessment tools, such as flood-recurrence
zoning with premium differentiation, counteract adverse selection
(Kunreuther et al., 2009; Mahul and Stutley, 2010). Some insurers have
offered weather alert systems to clients (Niesing, 2004). Further, credit
rating agencies and Solvency II insurance regulations in Europe contribute
to enhanced disaster resilience (Michel-Kerjan and Morlaye, 2008;
Grace and Klein, 2009; Kunreuther et al., 2009). Finally, insurers and
researchers have projected climate change-driven losses to allow for
adaptation of the industry (Section 10.7.3).
Reinsurers are key to the supply of disaster risk capital. They operate
globally to diversify the regional risks of hurricanes and other disasters.
Access to reinsurance enhances risk diversification of insurers. Periodic
shortages in reinsurance capacity following major disasters have
moderated over the last 2 decades because of easier new capital inflow
(Cummins and Mahul, 2009).
Global diversification potential of large losses has fallen over recent
decades because of increasing dependence between major insurance
markets. For instance, the floods in Thailand in 2011 disrupted industrial
hubs and global supply chains (Courbage et al., 2012). This process may
continue with climate change (Sherement and Lucas, 2009; Kousky and
Cooke, 2012). However, global diversification potential can be increased
by developing insurance markets in middle- and low-income countries
(Cummins and Mahul, 2009).
Very large loss events, say in excess of US$100 billion, may make
additional capacity desirable. These disasters can be diversified in the
financial securitization market (IPCC, 2012). Natural catastrophe risks
do not correlate with capital market risks and hence are attractive to
institutional investors. For instance, a catastrophe bond assures the
investor above-market returns as long as a parametric index (e.g., wind-
based) does not exceed a threshold, but pays the insurer’s loss otherwise.
The catastrophe bond market reached critical mass after the hurricanes
of 2004 and 2005, with some US$11 billion of risk capital in effect by
June 2011 (Michel-Kerjan and Morlaye, 2008; Cummins and Weiss, 2009;
see Table 10-7).
685
10
Key Economic Sectors and Services Chapter 10
10.7.5.2. Middle- and Low-Income Countries
Index-based weather insurance is often considered well-suited to the
agricultural sector in developing countries (Collier et al., 2009; IPCC,
2012). Payouts depend on a physical trigger, for example, cumulative
rainfall at a nearby weather station, instead of the policyholder’s
condition. Thus, they can be timely; costly loss assessments and moral
hazard are avoided; and adverse selection reduced (Barnett et al., 2008).
Risk-based premiums can encourage adaptive responses (Mahul and
Stutley, 2010; see Table 10-7). However, basis risk, where losses occur
but no payout is triggered, provokes distrust. Misunderstanding and
scaling up of pilots pose further difficulties (Patt et al., 2010; Leblois
and Quirion, 2011; Clarke and Grenham, 2012). Suggested improvements
include area-yield indices and coverage at aggregate levels to reduce
basis risk, and a cooperative design (Biener and Eling, 2012; Clarke and
Grenham, 2012; see Table 10-7). Application of indemnity-based insurance
and index-based concepts depend on the insured’s characteristics and
the market setting (Herbold, 2013a; Swiss Re, 2013a). Insurance-linked
services can strengthen farmers’ resilience by seasonal-forecast-based
agricultural guidance (AgroClima, 2013).
Improved building standards at high-risk sites in the Caribbean
substantially reduce damages from tropical cyclones and increase
benefits twofold over costs over a 20-year period, assuming scenarios
of changing hazard inferred from past decades (Michel-Kerjan et al.,
2013; Ou-Yang et al., 2013). Insurance coverage linked to credit for
retrofitting could improve adaptation (Mechler et al., 2006).
Sovereign insurance is deemed appropriate in developing countries
suffering from post-disaster financing gaps (see Section 10.7.4). Current
schemes include government disaster reserve funds (FONDEN, Mexico)
and pools of developing states’ sovereign risks (e.g., CCRIF, Caribbean;
Response option Example /explanation
R
isk-adjusted premiums convey the risk
to the insured, encouraging them to
p
ursue adaptive measures.
F
lood hazard insurance zoning systems, e.g., HORA (Austria), SIGRA (Italy), and ZÜRS (Germany), hamper development in high-risk zones by
allocating adequately high premiums.
2
6
Prior to Germany’s disastrous River Elbe fl ood in 2002, 48.5% of insured households had obtained
i
nformation on fl ood mitigation or were involved in emergency networks and 28.5% implemented one of several mitigation measures compared
with 33.9% and 20.5%, respectively, of uninsured households.
42
However, perceptions that motivate fl ood insurance uptake range from risk
a
wareness
9
to pure peer group expectation
32
—
the latter might blur the role of the risk-premiums-nexus in some societal contexts.
C
onditions of insurance policies
incentivizing vulnerability reduction
P
remium discounts for compliance with local building codes or other prevention options
2
7,45
;
share of the insured in claims payment by deductibles
or upper coverage limits, and exclusion of systematically affected property
1,7,8,10,11,15,21
; long-term natural-hazard insurance tied to the property
a
nd linked to mortgages and loans granted for prevention measures.
27,28,36
The latter is contested by modeled high-risk capital requirements and
ambiguity loadings, rendering multi-year policies relatively expensive and less fl exible for the insurance market.
34
A
mplifying factors in large disaster
losses included in risk models
E
vacuation and systemic economic catastrophe impacts, adversely affecting regional workforce and repair capacity, or knock-on catastrophes
following initial catastrophes, e.g., long-term fl ooding following hurricane landfall.
38
D
iversifying large disaster risk across
securitization markets
F
ollowing the hurricane disasters of 2004 and 2005, securitization instruments, e.g., catastrophe bonds, industry loss warranties, and sidecars,
acquired greater prominence, and have been recovering again from the market break in 2008.
16,18,20
Investors in insurance linked securities are
attracted by the lack of correlation to typical fi nancial market risks (e.g., currency risks) and the well defi ned loss-per-index structure. The higher
t
ransparency relative to other asset-backed securities, such as mortgage-backed securities, contributed to the better performance of catastrophe
bonds following the fi nancial crisis of 2007/ 2008.
16,18
As bonds typically cover large losses, the basis risk, i.e., suffering damage without parametric
t
riggering, is reduced
44
;
further reduction may be feasible by optimizing index measurements.
16
Weather derivatives are further instruments used to
transfer risks to the capital markets.
17,27,37
Also, multiple-trigger “hybrid” products are available, combining a parametric trigger-based catastrophe
b
ond with a trigger-based protection against a simultaneous drop in stock market prices, thereby hedging against a double hit from direct disaster
loss and losses incurred by the asset management side.
5,18
Index-based weather crop insurance
p
roducts
Agricultural insurances predominantly cover crops, but also livestock, forestry, aquaculture, and greenhouses. Main products are indemnity-based
c
rop insurance (covers for single perils and multiple-peril events), and index-based crop insurance.
41
The latter is available in 40% of middle-income
countries, with enlarged systems beyond pilot implementation in India and Mexico, and growth in China.
2
3,33,40,46
Risk-based price signals may
b
etter foster adaptation if schemes are coupled with access to advanced technology, e.g., drought-resistant seed.
4,15,23,33
Various index defi nitions
(cumulative rainfall, area-yield, etc.) and applications exist or have been proposed.
4
,29,30,31
Adjusting to uncertain regional changes in temporal hazard
c
ondition is a basic challenge with climate change.
14,24,29
Improvements in index-based weather
insurance
Basis risk, i.e., weak correlation between index and damage, can be reduced if the index scheme is applied to an area-yield trigger in a region with
homogeneous production potential (e.g., based on a sample) and /or to the uppermost disaster risk layer only.
14,15,22
It can be better absorbed if index
insurance works at aggregate level, e.g., to cover crop-credit portfolios, cooperatives, or informal networks,
43
and if satellite-based remote-sensing
technology can be used to establish plot identifi cation and yield estimation and loss assessment.
2
2
Satellite-based forage estimation is already used
for livestock index insurance in East Africa.
13
Pooling local schemes across climate regions under one cooperative parent organization, thus realizing
central management, economics of scale, and risk diversifi cation, can reduce capital requirements and advance performance.
6
,12,35
The disaster risk
layer and high start-up costs (weather data collection, risk modeling, education) necessitate subsidies from the state or donors.
15,33
Sovereign insurance schemes Economic theory about the public sector’s risk neutrality argues (1) that risks borne publicly render the social cost of risk-bearing insignifi cant and
(2) that disaster loss is seen small in comparison with a government’s portfolio of diversifi ed assets.
3
This theory proved inadequate if applied to
relatively vulnerable small-sized middle- to low-income countries,
19
thereby rehabilitating sovereign insurance. For the Caribbean scheme CCRIF,
which pools states, the reduction in premium cost per country is expected to be 45 – 50%.
31
Similar pooling schemes are being developed (e.g.,
African Risk Capacity, Pacifi c Catastrophe Risk Insurance Pilot).
2,39
Pooling natural catastrophe risks across an array of megacities has also been
proposed.
2
5
Table 10-7 | Products and systems responding to changes in weather risks.
Sources:
1
Aakre et al. (2010);
2
Wilcox et al. (2010);
3
Arrow and Lind (1970);
4
Barnett et al. (2008);
5
Barrieu and Loubergé (2009);
6
Biener and Eling (2012);
7
Botzen and van den
Bergh (2008);
8
Botzen and van den Bergh (2009);
9
Botzen and van den Bergh (2012);
10
Botzen et al. (2009);
11
Botzen et al. (2010a);
12
Candel (2007);
13
Chantarat et al. (2013);
14
Clarke and Grenham (2012);
15
Collier et al. (2009);
16
Cummins (2012);
17
Cummins and Mahul (2009);
18
Cummins and Weiss (2009);
19
Ghesquiere and Mahul (2007);
20
Guy
Carpenter (2011);
21
Hecht (2008);
22
Herbold (2013b);
23
Hess and Hazell (2009);
24
Hochrainer et al. (2010);
25
Hochrainer and Mechler (2011);
26
Kron (2009);
27
Kunreuther et al.
(2009);
28
Kunreuther and Michel-Kerjan (2009);
29
Leblois and Quirion (2011);
30
Leiva and Skees (2008);
31
Linnerooth-Bayer and Mechler (2009);
32
Lo (2013);
33
Mahul and Stutley
(2010);
34
Maynard and Ranger (2012);
35
Meze-Hausken et al. (2009);
36
Michel-Kerjan and Kunreuther (2011);
37
Michel-Kerjan and Morlaye (2008);
38
Muir-Wood and Grossi
(2008);
39
The World Bank (2013);
40
Prabhakar et al. (2013);
41
Swiss Re (2013a);
42
Thieken et al. (2006);
43
Trærup (2012);
44
Van Nostrand and Nevius (2011);
45
Ward et al. (2008);
46
Zhu (2011).
686
Chapter 10 Key Economic Sectors and Services
10
I
PCC, 2012). In both cases, peak risk is transferred to reinsurance and
catastrophe bonds (Table 10-7).
10.7.6. Governance, Public–Private Partnerships,
and Insurance Market Regulation
1
0.7.6.1. High-Income Countries
Theory favors an arrangement where individual risk is insured, but the
non-diversifiable component of risk (that may rise with climate change)
is public (Borch, 1962; Kunreuther et al., 2009). Accordingly, many high-
income states have public-private arrangements involving government
intervention on peak risk (Aakre et al., 2010; Bruggeman et al., 2010;
Schwarze et al., 2011; Paudel, 2012), or even public statutory insurance
systems (Quinto, 2011; see Table 10-8). Expected governmental post-
disaster relief has been shown to counteract insurance uptake (Raschky
et al., 2013). The pro-adaptive, risk-reducing features of insurance are
more effective if the price reflects the risk and the pool of insureds is
larger, for example, through bundled perils (Bruggeman et al., 2010;
Paudel, 2012). People who cannot afford premiums can be covered
by vouchers, leaving the price signal undistorted, or by subsidies
(Kunreuther et al., 2009; Aakre et al., 2010; see Table 10-8).
Insurance regulation ensures availability, affordability, and solvency,
but often adopts only short- to medium-term views. Because of climate
change, the role of regulators has changed to include risk-adequate
pricing, risk education, and risk-reduction in the long term (Hecht, 2008;
Grace and Klein, 2009; Mills, 2009).
10.7.6.2. Middle- and Low-Income Countries
A key element of risk financing is the transfer of private risks to an
insurance system. This reduces the governments’ burden and uncertainty
due to weather disasters (Ghesquiere and Mahul, 2007; Melecky and
Raddatz, 2011). Interest in public-private partnerships may evolve, for
example, between government, farmers, rural banks, and insurers, in
order to expedite agricultural development and resilience, for example,
b
y means of subsidies for start-up costs and peak risk (Collier et al.,
2009; Mahul and Stutley, 2010; see Table 10-8). Previously implemented
systems have suffered from adverse selection and moral hazard (Makki
and Somwaru, 2001; Glauber, 2004), suggesting an improved design is
needed. For instance, group policies foster mutual monitoring. Programs
or legislative actions that encourage purchase of insurance may
increase participation rates. Further, insurance pools can diversify
weather risks across larger regions, reduce premiums, and improve
access to external risk capital (Mendoza, 2009; Hochrainer and Mechler,
2011; Biener and Eling, 2012; IPCC, 2012).
In least developed countries, domestic insurance markets are rare.
Climate change-related disaster risk management was proposed for
inclusion in the adaptation regime of the United Nations Framework
Convention on Climate Change (UNFCCC). Besides prevention, insurance
is a central element in these concepts, partly funded from a UNFCCC
adaptation fund according to the principles of “equity and […] common
but differentiated responsibilities and respective capabilities” (UNFCCC
Art. 3.1; Linnerooth-Bayer et al., 2009; Warner and Spiegel, 2009; IPCC,
2012; see Table 10-8).
For insurance systems in developing markets, challenges include adequate
public-private partnership framing, improved risk assessment with
sufficient detail and appropriate dynamics, development of markets and
regulation, and scaling-up of successful schemes. Regulatory requirements
for risk-based capital, and access to reinsurance and securitization
markets, further contribute to a resilient insurance system.
10.7.7. Financial Services
The financial industry apart from insurance is vulnerable to both slow-
onset changes and to more frequent and/or intensive weather-related
disasters. Equity investors potentially face a higher exposure than debt
investors, due to exit conditions and a focus on longer-term returns in
equity markets, but ultimately the impact on debt investors depends on
the exposure of credit collateral to climate change (Stenek et al., 2010).
In the short- to medium-term, the financial sector is better sheltered
from climate change due to high capital mobility, an ability to hedge
Structural element Example /explanation
Public–private arrangements involving
government intervention on the non-
diversifi able disaster risk portion
Systems with government intervention range from ex ante risk fi nancing design, such as public monopoly natural hazard insurance (e.g.,
Switzerland, with inter-cantonal pool) or compulsory forms of coverage to maximize the pool of insureds (e.g., Spain, France, with unlimited
state guarantee on top), to ex post fi nancing design, such as taxation-based governmental relief funds (e.g., Austria, Netherlands). In
between these boundaries rank predominantly private insurance markets, in several countries combined with governmental post-disaster ad
hoc relief (e.g., Germany, Italy, UK, Poland, USA)
13
; see also
1,3,4,10,11,12,14
.
Care for people who cannot afford insurance Either by funds outside the insurance system, e.g., insurance vouchers, or by premium subsidies (particularly for the catastrophic risk
portion).
1,6,14
Public-private partnership to expedite
agricultural development
Insurance improves the farmers’ creditworthiness, which in turn strengthens their adaptive capacity. For instance, by means of loans farmers
can step from low-yield to higher-yield cropping systems.
2,8,9
Concepts for adaptation-oriented climate
change risk management frameworks linked
to United Nations Framework Convention on
Climate Change (UNFCCC)
Risk prevention and risk reduction often are the starting points that can absorb many of the smaller weather risks, and various forms of
insurance, including international coordination, are meant to cover all of the remaining risks.
7,15,16
A global framework, where the wealthy
agree to pool risks with the most vulnerable, equals social insurance that is different from a risk-based share in insurance funds.
5
Table 10-8 | Governance, public–private partnerships, and insurance market regulation.
Sources:
1
Aakre et al. (2010);
2
Barnett et al. (2008);
3
Botzen and van den Bergh (2008);
4
Bruggeman et al. (2010);
5
Duus-Otterström and Jagers (2011);
6
Kunreuther et al. (2009);
7
Linnerooth-Bayer et al (2009);
8
Linnerooth-Bayer et al. (2011);
9
Mahul and Stutley (2010);
10
Monti (2012);
11
Paudel (2012);
12
Schwarze and Wagner (2007);
13
Schwarze et al.
(2011);
14
Van den Berg and Faure (2006);
15
Warner and Spiegel (2009);
16
Warner et al (2012).
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10
Key Economic Sectors and Services Chapter 10
a
gainst a range of business risks, and an aptitude for the development
of new products to cater for changing demand in particular with respect
to risk transfers and investment in growing markets (Oliver Wyman, 2007;
Whalley and Yuan, 2009). In the longer-term, some risks associated with
climate change will be more difficult to diversify in particular for financial
institutions with local reach.
There are few papers on the impact of climate change on the financial
sector (other than insurance). Surveys agree with earlier views (WGII
AR3 Section 8.4) that climate change is perceived as a material threat
by few bankers and asset managers. There is growing awareness of
climate change impacts, as illustrated by increasing membership of
sector initiatives—such as the Carbon Disclosure Project, the UN
Principles for Responsible Investment, or the Global Reporting Initiative—
potentially influencing the responsiveness of the sector to climate
change (Brimble and Stewart, 2009). However, only a few financial
institutions have systematically factored in climate change into their
risk management and analytical framework (Cogan et al., 2008; Furrer
et al., 2012).
While direct physical impact (i.e., damage to financial infrastructure) is
not seen to be a material issue, this may change in the future in light of
the exposure of major financial centers to rising sea levels and the
reliance on complex IT infrastructure. Moreover, there is an increasing
share of equity allocated to infrastructure and real estate that is more
long-term oriented and could face higher maintenance and adaptation
requirements (Stenek et al., 2010; Mercer, 2011).
Indirect impacts may become material over the next few decades, for
example, value losses of assets/loan portfolios as a result of physical
damage. Regulatory and reputational effects, together with liability and
litigation risks linked to climate change are of concern too (Cogan et
al., 2008; Mercer, 2011; Furrer et al., 2012). However, legitimacy concerns
linked to climate change (as reflected by clients) are insufficient,
overshadowed by the financial crisis, or mitigated by the size and
influence of the financial sector (Brimble and Stewart, 2009).
It is difficult to quantify how significant the impact of climate change
will be for the industry. While it is not probable that climate change
alone will affect the liquidity or financial capacity of an institution, the
financial performance of both equity and debt markets could be
weakened by a variety of factors including changes in market conditions
through climate-driven price variations, higher capital and operating
expenditure, or aggravation of country risk but also regulatory drivers,
for example, higher capital reserve requirements to cover higher on-
and off-balance-sheet exposures (Stenek et al., 2010).
10.7.8. Summary
More frequent or more severe extreme weather events, and increased
uncertainty about such hazards, would lead to higher insurance premiums
and reduced cover in several regions, to the detriment of the insured,
and perhaps to reduced profitability of insurers, and to the detriment of
their shareholders. Improvements in risk management, product innovation,
financial innovation, and better regulation would partially alleviate
these impacts.
10.8. Services Other than Tourism and Insurance
Other service sectors of the economy include waste management,
wholesale and retail trade, engineering, government, education, defense,
a
nd health. Contributions to the economy vary substantially by country;
however, overall worldwide economic activity related to government
accounts for approximately 30% of global expenditures.
10.8.1. Sectors Other than Health
The literature on the impact of climate change on other sectors of the
economy is sparse (see Section SM10.1 of the on-line supplementary
material). Few studies have evaluated the possible impacts of climate
change, and particularly the economic impacts, on these sectors.
Tamiotti et al. (2009) conducted a qualitative assessment of climate and
trade. Travers and Payne (1998) and Subak et al. (2000) find that weather
affects retail, mostly through transfers in the economy. Sabbioni et al.
(2009) note that climate change may require a greater effort to protect
cultural heritage. Chapter 12 discusses the impact of climate change on
violent conflict, which has implications for military expenditures.
10.8.2. Health
Climate change-related alterations in weather patterns, particularly
extreme weather and climate events, have the potential to affect the
health sector through impacts on infrastructure and the delivery of
health care services from changing demand. Increased demands for
services put additional burdens on public health and health care
personnel and supplies, with potential economic consequences. For
example, hydrologic disasters (floods and wet mass movements) in
2011 were associated with 20% of all reported disaster deaths and
19% of total damages (Guha-Sapir et al., 2012).
Health care facilities are priority infrastructure that can be damaged by
weather and climate events, compromising critical resources required
for patient treatment; physical damage and destruction of equipment
and buildings; and possibly requiring evacuation of critical care patients,
with attendant risks for the patients (Carthey et al., 2009). Adverse
impacts on transportation (such as flooded roads) can further affect
access and evacuation. The ability of health care facilities to properly
care for the affected and for those with ongoing health issues requiring
medication or treatment may be compromised by very large events that
affect multiple health care facilities. Areas projected to experience
increases in extreme events could consider additional “surge capacity”
to manage such events without interruption of service (Banks et al.,
2007; Hess et al., 2009).
Although the proportion of individuals seeking medical treatment during
a disaster is typically a small subset of the total number of those affected,
the additional burden on health care facilities can be significant (Hess
et al., 2009). Six weather and climate events that struck the USA
between 2000 and 2009 were estimated to have increased health care
costs by US$740 million, reflecting more than 760,000 encounters with
the health care system (Knowlton et al., 2011). Hospitalizations, with
attendant costs, can increase from cases of heat stress, heat stroke, and
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Chapter 10 Key Economic Sectors and Services
10
acerbations of cardiorespiratory diseases and other health conditions
during heat waves (e.g., Lin et al., 2012; Astrom et al., 2013), and from
the adverse health impacts of other extreme events (Sections 11.4.1-2).
For example, one trauma center in the USA found a 5% increase in
hourly admissions for each approximately 5°C increase in temperature
(Rising et al., 2006). Individuals looking for an air-conditioned location
during high ambient temperatures can further increase hospital visits
(Carthey et al., 2009).
Climate change is projected to increase the burden of major worldwide
causes of childhood mortality, including malnutrition, diarrheal diseases,
and malaria (Sections 11.5.1-2, 11.6.1). Any increase in health burdens
or risks would increase the demands for public health services (e.g.,
surveillance and control programs) and the demands for health care
and relevant supplies (e.g., antimalarials, insecticide-treated bednets,
oral rehydration). Studies estimating the costs of additional cases of
climate-sensitive health outcomes focus on the costs of treatment,
typically omitting the costs of providing additional health services,
implementing new policies, and health actions in other sectors (Hutton,
2011). Because most climate change-related cases of adverse health
outcomes are projected to occur in low-income countries, treatment
costs will primarily be borne by families where governments provide
limited health care (WHO, 2004). Time off from work to care for sick
children could affect productivity.
Public and private health expenditures account for approximately 10%
of global GDP (http://data.worldbank.org/indicator/SH.XPD.TOTL.ZS). A
systematic analysis of developing country government expenditures on
health from domestic sources estimated that from 1995 to 2006, public
financing of health in constant US$ increased nearly 100%; this was a
product of rising GDP, slight decreases in the share of GDP spent by
government, and increases in the share of government spending on
health (Lu et al., 2010). The results varied by region, with shares of
government expenditures on health increasing in many regions but
decreasing in many sub-Saharan African countries. Development
assistance for health rose from about US$8 billion (in constant US$
2007
)
in 1995 to nearly US$19 billion in 2005 (Ravishankar et al., 2009).
Domestic government spending on health was negatively affected by
development assistance to governments and positively affected when
assistance was to the non-governmental sector (Lu et al., 2010).
Estimates of the costs of treating future cases of adverse health
outcomes from climate change are in the range of billions of US$
annually (Ebi, 2008; Pandey, 2010). An estimate of the worldwide costs
in 2030 of additional cases of malnutrition, diarrheal disease, and
malaria due to climate change—assuming no population or economic
growth, emissions reductions resulting in stabilization at 750 ppm
CO
2
-eq in 2210, and current costs of treatment in developing countries—
estimated treatment costs without adaptation could be US$4 to 12
billion worldwide, depending on assumptions of the sensitivity of these
health outcomes to climate change (Ebi, 2008). The costs for additional
infrastructure and health care workers were not estimated, nor were
the costs of additional public health services, such as surveillance and
monitoring. The costs were estimated to be unevenly distributed, with most
of the costs borne by developing countries, particularly in Southeast
Asia and Africa, to address the projected approximately 3 to 5%
increase in the number of cases of diarrheal disease and malaria from
the 2002 baseline (Markandya and Chaibai, 2009). The prevalence of
these diseases have since declined (http://apps.who.int/gho/data/node.
main.14?lang=en; Section 11.1.1), although there is considerable
uncertainty in mortality data from many low-income countries because
of the low proportion of deaths covered by vital registration programs
(Byass et al., 2013).
A second global estimate assumed UN population projections, strong
economic growth, updated projections of the current health burden of
diarrheal diseases and malaria, two climate scenarios, and updated
estimates of the costs of malaria treatment (Pandey, 2010). In 2010,
the average annual adaptation costs for treating diarrheal disease and
malaria were estimated to be US$3 to 5 billion, with the costs expected
to decline over time with improvement in basic health services. Over
the period 2010–2050, the average annual costs were estimated to be
around US$2 billion, with most of the costs related to treating diarrheal
disease; the largest burden is expected to be in sub-Saharan Africa. The
differences in costs from Ebi (2008) are due primarily to a reduction in
the baseline burden of disease and lower costs for malaria treatment.
Watkiss and Hunt (2012) estimated the health impacts of climate change
in Europe in 2071–2100 using physical and monetary metrics, taking
socioeconomic change into consideration. Temperature-related mortality
during winter and summer due to climate change included positive and
negative effects, with welfare costs (and benefits) of up to US$130
billion annually, with impacts unevenly distributed across countries.
Assumptions about acclimatization influenced the size of the health
impacts. The welfare costs for salmonellosis were estimated at potentially
several hundred million euro annually, and those for the mental health
impacts associated with coastal flooding due to climate change were
up to approximately US$2 billion annually.
Frequently Asked Questions
FAQ 10.3 | Are other economic sectors vulnerable to climate change too?
E
conomic activities such as agriculture, forestry, fisheries, and mining are exposed to the weather and thus vulnerable
to climate change. Other economic activities, such as manufacturing and services, largely take place in controlled
environments and are not really exposed to climate change. However, markets connect sectors so that the impacts
o
f climate change spill over from one activity to all others. The impact of climate change on economic development
and growth also affects all sectors.
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Key Economic Sectors and Services Chapter 10
E
stimated additional health care costs for climate change-related cases
of malaria are similar in southern Africa (van Rensburg and Blignaut,
2002). Ranges for (low-high) additional cost scenarios for the prevention
and treatment of malaria in South Africa in 2025 were estimated to be
approximately US$280 to 3764 million. Estimates for Botswana and
Namibia are US$9 to 124 million and US$13 to 177 million, respectively.
The high cost scenario for Namibia is about 4.6% of GDP. The climate
change-related malaria inpatient and outpatient treatments costs at
the end of the century (2080–2100) in 25 African countries
1
indicated
that even marginal changes in temperature and precipitation could
affect the number of malaria cases, with increases in most countries
and decreases in others (Egbendewe-Mondzozo et al., 2011). The end
of century treatment costs as a proportion of annual 2000 health
expenditures per 1000 people would increase in the vast majority of
countries, with increases of more than 20% in inpatient treatment costs
for Burundi, Côte D’Ivoire, Malawi, Rwanda, and Sudan.
The costs of treating cases of cholera in Tanzania due to climate change
in 2030 were estimated to be in the range of 0.32 to 1.4% of GDP
(Trærup et al., 2011), and there would be costs for treating additional
cases of diarrhea and malaria in India in 2030, depending on the emission
scenario (Ramakrishnan, 2011).
Bosello et al. (2006) used a computable general equilibrium model to
study the economic impacts of climate-change-induced changes in
mortality and morbidity due to cardiovascular and respiratory diseases,
malaria, diarrhea, schistosomiasis, and dengue fever. They considered
the effects on labor productivity and demand for health care, and found
that health and welfare impacts have the same sign. The economy-wide
health impacts were greater than simple aggregation of the costs of
the individual health outcomes. Increased health problems were
associated with an expansion of the public sector at the expense of the
private sector.
Estimates of the impacts of climate change on worker productivity,
assuming current work practices, primarily through heat stress, indicate
that productivity has already declined during the hottest and wettest
seasons in parts of Africa and Asia, with more than half of afternoon
hours projected to be lost to the need for rest breaks in 2050 in Southeast
Asia and up to a 20% loss in global productivity in 2100 under RCP4.5
(Kjellstrom et al., 2009, 2013; Dunne et al., 2013; see also Section
11.6.2). Alternate work practices may offer some relief from a health
perspective, but would likely lead to significantly decreased productivity
(Chapter 11).
10.9. Impacts on Markets and Development
Prior sections of this chapter present the direct impacts of climate
change on the economy sector by sector. There are, however, also
indirect impacts, from the one sector on the rest of the economy (Section
10.9.1) and on economic growth and development (Section 10.9.2).
10.9.1. Effects of Markets
There are three channels through which economic impact diffuse. First,
outputs of one sector are used as inputs to other sectors. For example,
a change in crop yields would affect the food-processing industry. Second,
products compete for the consumers’ finite budget. If, for example, food
becomes more expensive, a consumer would shift to cheaper food but
also spend less money on other goods and services. Third, sectors compete
for the primary factors of production (labor, capital, land, water). If,
besides more fertilizers and irrigation, more labor is needed in agriculture
to offset a drop in crop yields, less labor is available to produce other
goods and services. Firms and households react to changes in relative
prices, domestically and internationally. Ignoring these effects would
lead to biased estimates of the impacts of climate change.
General equilibrium analysis describes how climate change impacts in
one sector propagate to the rest of the economy, how impacts in one
country influence other countries, and how macroeconomic conditions
affect each impact (Ginsburgh and Keyzer, 1997). General equilibrium
models can provide a comprehensive and internally consistent analysis
of the medium-term impact of climate change on economic activity and
welfare. However, these models necessarily make a number of simplifying
assumptions, particularly with regard to the rationality of consumers
and producers and the absence of market imperfections. Other types of
economic models have yet to be applied to the estimation of indirect
economic effects of climate change.
Computable general equilibrium models have long been used to study
the wider economic implications of changes in crop yields. Yates and
Strzepek (1998) show, for instance, that the impact of a reduced flow
of the Nile on the economy of Egypt is much more severe without
international trade than with, because trade would allow Egypt to focus
on water-extensive production for export and import its food.
Older studies focused on the impact of climate change on patterns of
specialization and trade, food prices, food security, and welfare (Kane
et al., 1992; Reilly et al., 1994; Winters et al., 1998; Yates and Strzepek,
1998; Darwin and Kennedy, 2000; Darwin, 2004). This has been extended
to land use (Lee, 2009; Ronneberger et al., 2009), water use (Kane et
al., 1992; Calzadilla et al., 2011), and multiple stresses (Reilly et al.,
2007). General equilibrium models have also been used to estimate the
value of improved weather forecasts (Arndt and Bacou, 2000), a form of
adaptation to climate change. Computable general equilibrium analysis
has also been used to study selected impacts other than agriculture,
notably SLR (Darwin and Tol, 2001; Bosello et al., 2007b), tourism
(Berrittella et al., 2006; Bigano et al., 2008), human health (Bosello et
al., 2006), and energy (see Section 10.2).
Bigano et al. (2008) study the joint, global impact on tourism and coasts
in the 21st century, finding that changes in tourist demand dominate the
welfare impacts of SLR. Kemfert (2002) and Eboli et al. (2010) estimate
the joint, global effect on the world economy of a range of climate
change impacts in the 21st century, but conflate general equilibrium
and growth effects. Aaheim et al. (2010) analyze the economic effects
of impacts of climate change on agriculture, forestry, fishery, energy
demand, hydropower production, and tourism on the Iberian Peninsula.
They find positive impacts on output in some sectors (agriculture,
1
Algeria, Benin, Botswana, Burkina, Burundi, Central African Republic, Chad, Côte
D’Ivoire, Djibouti, Egypt, Ethiopia, Ghana, Guinea, Malawi, Mali, Mauritania,
Morocco, Niger, Rwanda, South Africa, Sudan, Togo, Uganda, Tanzania, Zimbabwe.
690
Chapter 10 Key Economic Sectors and Services
10
e
lectricity), negative impacts in other sectors (forestry, transport), and
negligible ones in others (manufacturing, services). Ciscar et al. (2011)
study the combined impact on agriculture, coasts, river floods, and
tourism in the current European economy. They find an average welfare
loss of 0.2 to 1.0% (depending on the SRES scenario) but there are large
regional differences with losses in southern Europe and gains in northern
Europe.
The following initial conclusions emerge. First, markets matter. Impacts
are transmitted across locations—with local, regional, and global
impacts—and across multiple sectors of the economy. For instance,
landlocked countries are affected by SLR because their agricultural land
increases in value as other countries face erosion and floods. Second,
consumers and producers are often affected differently. The price
increases induced by a reduction in production may leave producers
better off while hurting consumers. Third, the distribution of the direct
impacts can be very different than the distribution of the indirect effects.
For instance, a loss of production may be advantageous to an individual
company or country if the competition loses more. Fourth, a loss of
productivity or productive assets in one sector leads to further losses in
the rest of the economy. Fifth, markets offer options for adaptation,
particularly possibilities for substitution. This changes the size, and
sometimes the sign, of the impact estimate.
10.9.2. Aggregate Impacts
Since AR4, four new estimates of the global aggregate impact on human
welfare of moderate climate change were published (Maddison and
Rehdanz, 2011; Bosello et al., 2012; Roson and van der Mensbrugghe,
2012), including two estimates for warming greater than 3°C. Estimates
a
gree on the size of the impact (small relative to economic growth),
and 17 of the 20 impact estimates shown in Figure 10-1 are negative.
Losses accelerate with greater warming, and estimates diverge. The new
estimates have slightly widened the uncertainty about the economic
impacts of climate.
Welfare impacts have been estimated with different methods, ranging
from expert elicitation to econometric studies and simulation models.
Different studies include different aspects of the impacts of climate
change, but no estimate is complete; most experts speculate that excluded
impacts are on balance negative. Estimates across the studies reflect
different assumptions about inter-sectoral, inter-regional, and inter-
temporal interactions, about adaptation, and about the monetary values
of impacts. Aggregate estimates of costs mask significant differences
in impacts across sectors, regions, countries, and populations. Relative
to their income, economic impacts are higher for poorer people.
10.9.3. Social Cost of Carbon
The social cost of carbon (SCC) monetizes the expected welfare impacts
of a marginal increase in carbon dioxide emissions in a given year (i.e.,
the welfare loss associated with an additional tonne of CO
2
emitted),
aggregated across space, time, and probability (Tol, 2011). Figure 10-2
shows estimates published before AR4 and since, using the kernel
density estimator by Tol (2013), extending the data with new estimates
by Anthoff and Tol (2013b), Hope and Hope (2013), Hope (2013), and
the Interagency Working Group on the Social Cost of Carbon (2013).
Central estimates of the social cost of carbon have fallen slightly for
all pure rates of time preference and the uncertainty has tightened,
particularly for studies that use a pure rate of time preference of zero.
Temperature (ºC)
Impact on welfare (equivalent income change, %)
Studies published before IPCC AR4
Studies published after IPCC AR4
Figure 10-1 | Estimates of the total impact of climate change plotted against the assumed climate change (proxied by the increase in the global mean surface air temperature);
studies published since IPCC AR4 are highlighted as diamonds; see Table SM10-1.
3
0
–3
–6
–9
–12
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
0.0 5.5
691
10
Key Economic Sectors and Services Chapter 10
See Table 10-9. For comparison, the EU ETS price in July 2013 was about
US$21/tC.
Uncertainty in SCC estimates is high due to the uncertainty in underlying
total damage estimates (see Section 10.9.2), uncertainty about future
emissions, future climate change, future vulnerability and future valuation.
The spread in estimates is also high due to disagreement regarding the
appropriate framework for aggregating impacts over time (discounting),
regions (equity weighing), and states of the world (risk aversion).
Q
uantitative analyses have shown that SCC estimates can vary by at
least approximately two times depending on assumptions about future
demographic conditions (Interagency Working Group on the Social
Cost of Carbon, 2010), at least approximately three times owing to
the incorporation of uncertainty (Kopp et al., 2012), and at least
approximately four times owing to differences in discounting (Tol, 2011)
or alternative damage functions (Ackerman and Stanton, 2012).
Concerns have been raised that the uncertainty about climate change is
so large that the SCC would be unbounded (Weitzman, 2009), but this
result is sensitive to assumptions about the utility function (Nordhaus,
2011; Buchholz and Schymura, 2012; Millner, 2013) and disappears
when climate policy is formulated as balancing the risks of climate
change against those of mitigation policy (Anthoff and Tol, 2013a;
Hwang et al., 2013).
10.9.4. Effects on Growth
10.9.4.1. The Rate of Economic Growth
Climate change will also affect economic growth and development, but
our understanding is limited. Fankhauser and Tol (2005) investigate four
PRTP Post-AR4 Pre-AR4 All studies
Avg SD N Avg SD N Avg SD N
0% 270 233 97 745 774 89 585 655 142
1% 181 260 88 231 300 49 209 284 137
3% 33 29 35 45 39 42 40 36 186
All 241 233 462
(35)
565 822 323
(49)
428 665 785
(84)
Table 10-9 | Selected statistical characteristics of the social cost of carbon: average
(Avg) and standard deviation (SD), both in dollar per tonne of carbon, and number of
estimates ( N; number of studies in brackets).
Sources: See Section SM10.2 of the on-line supplementary material.
PRTP = pure rate of time preference.
0.018
0.014
0.010
0.006
0.000
0.002
2010 US$ per tonne of carbon
Pure rates of time preferences (PRTP)
0.016
0.012
0.008
0.004
–200 –100 0 100 300 400 500 600 700 800 900 1000200
0.0025
0.002
0.0015
0.001
0.000
0.0005
2010 US$ per tonne of carbon
–200 –100 0 100 300 400 500 600 700 800 900 1000200
Pure rates of time preferences (PRTP)
0.0025
0.002
0.0015
0.001
0.000
0.0005
2010 US$ per tonne of carbon
–200 –100 0 100 300 400 500 600 700 800 900 1000200
Pure rates of time preferences (PRTP)
0.018
0.014
0.010
0.006
0.000
0.002
2010 US$ per tonne of carbon
–25 0 25 50 75 100 125
Pure rates of time preferences (PRTP)
0.016
0.012
0.008
0.004
Post AR4
All studies
AR4
3% PRTP
1% PRTP
0% PRTP
0% PRTP
1% PRTP
3% PRTP Post AR4
Post AR4
All studies
AR4
Post AR4
All studies
AR4
Figure 10-2 | Kernel densities of the social cost of carbon for all studies and studies before or after AR4 for three alternative pure rates of time preference (PRTP).
692
Chapter 10 Key Economic Sectors and Services
10
s
tandard models of economic growth and three transmission mechanisms:
economic production, capital depreciation, and the labor force. They
find that, in three models, the fall in economic output is slightly larger
than the direct impact on markets while in the fourth model (which
emphasizes human capital accumulation) indirect impacts are 1.5 times
as large. The difference can be understood as follows. In the three
models, the impacts of climate change crowd out consumption and
investment in physical capital, while in the fourth model investment in
human capital is also crowded out; lower investment implies slower
growth. Hallegatte (2005) reaches a similar conclusion. Hallegatte and
Thery (2007), Hallegatte and Ghil (2008), and Hallegatte and Dumas
(2009) highlight that the impact of climate change through natural
hazards on economic growth can be amplified by market imperfections
and the business cycle. In addition, Eboli et al. (2010) use a multi-sector,
multi-region growth model, and find that the impact of climate change
would lead to a 0.3% reduction of global GDP in 2050. Regional impacts
are more pronounced, ranging from –1.0% in developing countries to
+0.4% in Australia and Canada. In contrast, Garnaut (2008) finds –2.1%
for Australia; the difference is due mainly to impacts on infrastructure
(cf. Section 10.4). Sectoral results are varied too, with output changes
ranging from +0.5% for power generation (to meet increased demand
to air conditioning) to –0.7% for natural gas (as demand for space heating
falls).
Using a biophysical model of the human body’s ability to do work,
Kjellstrom et al. (2009) find that by the end of the century climate change
may reduce labor productivity by 11 to 27% in the humid (sub)tropics
(depending on the SRES scenario; see Chapter 11 for further discussion).
Assuming an output elasticity of labor of 0.8, this would reduce economic
output in the affected sectors (involving heavy manual labor without
air conditioning) by 8 to 22%. Although structural changes in the
economy may well reduce the dependence on manual labor and air
conditioning would be an effective adaptation, even the ameliorated
impact would have a substantial, but as yet unquantified, impact on
economic growth.
There are also statistical analyses of the relationship between climate
and economic growth. Barrios et al. (2010) find that the decline in rainfall
in the 20th century partly explains the economies of sub-Saharan Africa
have grown more slowly than those of other developing regions. Brown
et al. (2011) corroborate this. Dell et al. (2012) find that, in the second
half of the 20th century, anomalously hot weather slowed down
economic growth in poor countries, in both the agricultural and the
industrial sectors. Dell et al. (2009) find that 1°C of warming would
reduce income by 1.1% in the short run, and by 0.5% in the long run.
The difference is due to adaptation. Horowitz (2009) finds a much larger
effect: a 3.8% drop in income in the long run for one degree of warming.
The impact of natural disasters on economic growth in the long-term is
disputed, with studies reporting positive effects (Skidmore and Toya,
2002), negative effects (Raddatz, 2009), and no discernible effects
(Cavallo et al., 2013).
10.9.4.2. Poverty Traps
Poverty is concentrated in the tropics and subtropics. This has led some
analysts to the conclusion that a tropical climate is one in a complex of
c
auses of poverty (which itself is a cause of poverty). We here focus on
national economies, while Chapter 13 discusses groups of people in
poverty. Gallup et al. (1999) emphasize the link between climate,
disease, and poverty while Masters and McMillan (2001) focus on
climate, agricultural pests, and poverty. Other studies (Acemoglu et al.,
2001, 2002; Easterly and Levine, 2003) argue that climatic influence on
development disappears if differences in human institutions (the rule
of law, education, etc.) are accounted for. However, Van der Vliert (2008)
demonstrates that climate affects human culture and thus institutions,
but this has yet to be explored in the economic growth literature. Brown
et al. (2011) find that weather affects economic growth in sub-Saharan
Africa—particularly, drought decelerates growth. Jones and Olken (2010)
find that exports from poor countries fall during hot years. Bloom et al.
(2003) find limited support for an impact of climate (rather than weather)
on past growth in a single-equilibrium model, but strong support in a
multiple-equilibrium model: hot and wet conditions and large variability
in rainfall reduce long-term growth in poor countries (but not in hot
ones) and increase the probability of being poor.
Galor and Weil (1996) speculate about the existence of a climate-
health-poverty trap. Strulik (2008), Bonds et al. (2010), Bretschger and
Valente (2011), Gollin and Zimmermann (2012), and Ikefuji and Horii
(2012) posit theoretical models and offer limited empirical support,
while Tang et al. (2009) offers more rigorous empirical evidence. This
is further supported by yet-to-be-published analyses (Gollin and
Zimmermann, 2008; Ikefuji et al., 2010). Climate-related diseases such as
malaria and diarrhea impair children’s cognitive and physical development.
This contributes to poverty in their later life so that there are limited
means to protect their own children against these diseases. Furthermore,
high infant mortality may induce parents to have many children so that
the investment in education is spread thin. An increase in infant and
child mortality and morbidity due to climate change could thus trap
more people in poverty.
Zimmerman and Carter (2003) build a model in which the risk of natural
disasters causes a poverty trap: at higher risk levels, households prefer
assets with a safe but low return. Carter et al. (2007) find empirical
support for this model at the household level, but van den Berg (2010)
concludes the natural disaster itself has no discernible impact on
investment choices. At the macroeconomic level, natural disasters
disproportionally affect the growth rate of poor countries (Noy, 2009).
Devitt and Tol (2012) construct a model with a conflict-poverty trap,
and show that climate change may exacerbate this. Bougheas et al.
(1999, 2000) show that more expensive infrastructure, for example,
because of frequent repairs after natural disasters, slows down economic
growth and that there is a threshold infrastructure cost above which
trade and specialization do not occur, suggesting another mechanism
through which climate could cause a poverty trap. The implications of
climate change have yet to be assessed.
10.9.5. Summary
In sum, estimates of the aggregate economic impact of climate change
are relatively small but with a large downside risk. Estimates of the
incremental damage per tonne of CO
2
emitted vary by two orders of
693
10
Key Economic Sectors and Services Chapter 10
m
agnitude, with the assumed discount rate the main driver of the
differences between estimates. The literature on the impact of climate
and climate change on economic growth and development has yet to
reach firm conclusions. There is agreement that climate change would
slow economic growth, by a little according to some studies and by a
lot according to other studies. Different economies will be affected
differently. Some studies suggest that climate change may trap more
people in poverty.
10.10. Summary; Research Needs and Priorities
Table 10-10 summarizes the main findings. For each of the sectors
discussed above, it gives the main climate drivers, the relationship
between climate and impact (limited to less than linear, linear, and more
than linear), the sign of the impacts (where needed split by economic
a
ctor), drivers other than climate change, and the relative importance
of climate change.
Evaluating the economic aspects of the impacts has emerged as an active
research area. Initial work has developed in a few key economic sectors
and through economy-wide economic assessments. Data, tools, and
methods continue to evolve to address additional sectors and more
complex interactions among the sectors in the economic systems and
a changing climate.
Based on a comprehensive assessment across economic sectors, few
key sectors have been subject to detailed research. Multiple aspects of
energy impacts have been assessed, but others remain to be evaluated,
particularly economic impact assessments of adaptation both on existing
and future infrastructure, but also the costs and benefits for future systems
under differing climatic conditions. Studies focused on the impacts of
Magnitude of
climate change
Sector
Climate change
drivers
Sensitivity to
climate change
Sign Other drivers
Relative impact of climate
change to other drivers
Winter tourism • Temperature
•
S
now
Negative • Population
•
L
ifestyle
• Income
• Aging
Much less
Summer tourism
• Temperature
• Rainfall
• Cloudiness
Negative for suppliers in low altitudes and latitudes
Positive for suppliers in high altitudes and latitudes
Neutral for tourists
• Population
• Income
• Lifestyle
• Aging
Much less
Cooling demand
• Temperature
• Humidity
• Hot spells
Positive for suppliers
Negative for consumers
• Population
• Income
• Energy prices
• Technology change
Less
Heating demand
• Temperature
• Humidity
• Cold spells
Negative for suppliers
Positive for consumers
• Population
• Income
• Energy prices
• Technology change
Less
Health services
• Temperature
• Precipitation
Positive for suppliers
Negative for consumers
• Aging
• Income
• Diet/lifestyle
Less
Water infrastructure
and services
• Temperature
• Precipitation
• Storm Intensity
• Seasonal Variability
Negative for water users
Positive for suppliers
Spatially heterogeneous
• Population
• Income
• Urbanization
• Regulation
Less in developing countries
Equal in developed countries
Transportation
• Temperature
• Precipitation
• Storm intensity
• Seasonal variability
• Freeze/thaw cycles
Negative for all users
Positive for transport construction industry
• Population
• Income
• Urbanization
• Regulation
• Mode shifting
• Consumer and
commuter behavior
Much less in developing
countries
Less in developed countries
Insurance
• Temperature
• Precipitation
• Storm intensity
• Seasonal variability
• Freeze/thaw cycles
Negative for consumers
Neutral for suppliers
• Population
• Income
• Regulation
• Product innovation
Less or equal in developing
countries
Equal or more in developed
countries
Table 10-10 | Summary of fi ndings.
Severity
694
Chapter 10 Key Economic Sectors and Services
10
c
limate change on the energy sector indicate both potential benefits
and detrimental impacts across developed and developing countries. In
energy supply, the deployment of extraction, transport and processing
infrastructure, power plants, and other installations are expected to
proceed rapidly in developing countries in the coming decades to satisfy
fast growing demand for energy. Designing newly deployed facilities
with a view to projected changes in climate attributes and extreme
weather patterns would require targeted inquiries into the impacts of
climate change on the energy-related resource base, conversion, and
transport technologies.
The economics of climate change impacts on transportation systems and
their role in overall economic activity have yet to be well understood.
For water related sectors, improved estimation of flood damages to
economic sectors, research on economic impacts of ecosystems, rivers,
lakes and wetlands, ecosystems service, and tourism and recreation are
needed. Economic assessments of adaptation strategies such as water
savings technologies, particularly for semiarid and arid developing
countries, are also needed. Further, detailed studies are needed of the
integrated impact of climate change on all water-dependent economic
sectors, as existing studies do not examine competitiveness between
water uses among sectors and economic productivity.
Although both tourism and recreation are sensitive to climate change,
the literature on tourism is far more extensive. Current studies either have
a rudimentary representation of the effect of weather and climate but
a detailed representation of substitution between holiday destination
and activities, or a detailed representation of the immediate impact of
climate change but a rudimentary representation of alternatives to the
affected destinations or activities.
Considerable research has been developed related to climate change
impacts on insurance; however, only limited research is available on
observed and projected changes in insured climate-related losses. To
advance such research, climate science and risk research communities
need to be better integrated. In addition, only few quantitative projection
studies exist on regional markets including scenarios of changing hazard
properties, exposure, vulnerability and adaption status, regulation, and
availability of risk-based capital to indemnify disaster losses. Little
research is available on the implications of climate change for banking/
investment activities, in particular regarding the direct exposure of
financial infrastructure. But also indirect effects through value losses in
loan portfolios and assets as a result of physical damage and regulatory/
reputational effects, together with liability and litigation risks, are under-
investigated.
Little literature exists on potential climate impacts on other economic
sectors, such as mining, manufacturing, and services (apart from health,
insurance, and tourism), in particular assessments of whether these
sectors are indeed sensitive to climate and climate change.
The spillover effects of the impacts of climate change in one sector on
other markets are understood in principle, but the number of quantitative
studies is too few to place much confidence in the numerical results.
Similarly, the impact of climate and climate change on economic growth
and development is not well understood, with some studies pointing
to a small or negligible effect and other studies arguing for a large or
d
ominant effect. A limited set of studies have evaluated the aggregate
economic impact of climate change up to 3°C annual mean temperature
rise, while only one study has evaluated larger temperature scenarios,
suggesting considerable new analysis is warranted to improve confidence
in the conclusions and investigation of a broader suite of Representative
Concentration Pathways (RCPs).
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