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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
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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}
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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 temperaturerelated 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}
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Chapter 10 Key Economic Sectors and Services

10

10.1. Introduction and Context

Thischapter discussesthe implications ofclimate change on key economic

sectors and services,for example, economic activity.Otherchapters discuss

impacts 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 ofclimate 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

showsthe list ofsectors 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 discussindividualsectorsin isolation.Markets

are connected, however. Section 10.9 therefore assesses the implications

ofchanges 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.

Previous 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

Studiesconducted sinceAR4 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 ThirdAssessment Report (TAR; Scott et al., 2001) andAR4 (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

technologicalconditions.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 (CO2) concentrations as CO2 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.
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Key Economic Sectors and Services Chapter 10

historical 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 climatedependent energy sources, such as hydropower (Hamlet et al., 2010).

Sorting the assessed studies according to the presentclimate (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 airconditioning. 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 changesin 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.Gradualclimate change will progressively affect the operation

of 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 climaticconditions, from the coldArcticto 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 (CO2) 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 lossin 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
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Chapter 10 Key Economic Sectors and Services

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Technology Changes in climatic 

or related attributes Possible impacts Adaptation options

Thermal

and nuclear 

power plants

Increasing air temperature Reduces efficiency of thermal conversion by 0.1–0.2% in

the USA; by 0.1– 0.5% in Europe, where the capacity loss

is estimated in the range of 1– 2% per 1°C temperature 

increase, accounting for decreasing cooling efficiency and

reduced operation level /shutdown

Siting at locations with cooler local climates where possible

Changing (lower) precipitation and 

increasing air temperature increases 

temperature 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 fields, coal mines and treatment, treated sewage); re-use

of process water from flue gases (can cover 25–37% of the

power plant’s cooling needs), coal drying, condensers (drier coal 

has higher heating value, cooler water enters cooling tower),

flue-gas desulfurization; using ice to cool air before entering the

gas turbine increases efficiency and output, melted ice used in

cooling tower; condenser mounted at the outlet of cooling tower 

to reduce evaporation losses (by up to 20%). Alternative cooling

technologies: dry cooling towers, regenerative cooling, heat

pipe exchangers; costs of retrofitting cooling options depend on

features of existing systems, distance to water, required additional

equipment, estimated at US$250,000 –500,000 per megawatt

Increasing frequency of extreme hot 

temperatures

Exacerbating impacts of warmer conditions: reduced thermal 

and cooling efficiency; limited cooling water discharge;

overheating buildings; self-ignition of coal stockpiles

Cooling of buildings (air conditioning) and of coal stockpiles 

(water spraying)

Drought: reduced water availability Exacerbating impacts of warmer conditions, reduced

operation and output, shutdown

Same as reduced water availability under gradual climate change

Hydropower Increase /decrease in average water 

availability

Increased /reduced power output Schedule release to optimize income

Changes in seasonal and inter-annual 

variation in inflows (water availability)

Shifts in seasonal and annual power output; floods and lost

output in the case of higher peak flows

Soft: adjust water management

Hard: build additional storage capacity, improve turbine runner 

capacity

Extreme precipitation causing floods Direct and indirect (by debris carried from flooded 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 efficiency of PV and CSP with water cooling; PV

efficiency drops by ~0.5% per 1°C temperature increase

for crystalline silicon and thin-film modules as well, but

performance varies across types of modules, with thin film

modules performing better; long-term exposure to heat 

causes faster aging.

Changing cloudiness Increasing unfavorable (reduced output), decreasing 

beneficial (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 fixed 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 efficiency 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 flows or actively by

forced air or liquid coolants

Hail Material damage to TH: evacuated tube collectors are more 

vulnerable than flat 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); Schaefli 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

discharge 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).

Focusing 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 undercloudy 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 weatherconditions 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 depositsand and dust on the collector’ssurface,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
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Chapter 10 Key Economic Sectors and Services

10

weather 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 weatherconditions.

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 flyash, 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.

Climate- 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 (CO2,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 CO2 capture and storage (CCS) technology, another important

function will be to deliver CO2 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

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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 impactsinclude 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-renewalcycle than asindependent

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 economicresearch related to climate change impacts on the energy

sector has focused on mitigation rather than the economic implications

ofclimate 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

maintenance and repair

Adjust design code and planning criteria, install 

disaster mitigation plans

Increasing high wind, storms, hurricanes Damage to offshore and onshore pipelines and 

related equipment, spills; lift and blow heavy 

objects against pipelines, damage equipment

Enhance design criteria, update disaster preparedness

Flooding caused by heavy rain, storm surge, or sea level rise Damage to pipelines, spills Siting (exclude flood plains), waterproofing

Electricity grid Increasing average temperature Increased transmission line losses Include increasing temperature in the design 

calculation for maximum temperature /rating

Increasing high wind, storms, hurricanes Direct mechanical damage to overhead lines,

towers, poles, substations, flashover caused by

live cables galloping and thus touching or getting 

too close to each other; indirect mechanical 

damage and short circuit by trees blown over or 

debris blown against overhead lines

Adjust wind loading standards, reroute lines alongside

roads or across open fields; manage vegetation;

improve storm and hurricane forecasting

Extreme high temperatures Lines and transformers may overheat and trip off;

flashover to trees underneath expanding cable

Increase system capacity, increase tension in the line to 

reduce 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 

open fields; 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).
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Chapter 10 Key Economic Sectors and Services

10

Study Model 

type Climate impacts modeled Energy /economic impacts Regions Sectorsstudied

Bosello et al.

(2009)

IAM Rising temperatures /changing demand for energy; impacts from 

four other sectors /events (Global, 2001–2050)

Change 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) 

14 4

Jorgenson et al.

(2004)

CGE Rising temperatures /changing demand for energy; climate 

impacts from three other sectors (USA, 2000 –2100)

Optimistic adaptation: 4 –6.7% higher energy productivity per

year (2000 –2100)

Output from electricity: – 6% in 2050; GDP is +0.7% (aggregate 

all sectors, average annual 2000–2100)

Pessimistic adaptation: 0.5–2.2% lower energy productivity

per year

Output from electricity: +2% in 2050; GDP is – 0.6% (aggregate 

impact all sectors)

1 35

Bosello et al.

(2007a)

CGE Rising temperatures /changing demand for energy (Global, 2050) Change in GDP in 2050 (perfect competition): – 0.297% to 

0.027% 

Change 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; 

rising 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% 

to – 0.25%

GDP in warmer regions: – 3% to – 0.5%

Adaptation can mitigate 80 –85% of economic impact

8 11

Boyd and 

Ibarraran 

(2009)

CGE Drought scenario affecting hydroelectric plus three other sectors 

(Mexico, 2005–2026)

• Generation output in 2026: – 2.1%

• Refining output: –10.1%

• Coal output: – 7.8%

• NG output: – 2%

• Crude oil output: +1.7%

• GDP: – 3%

With adaptation: 

• Generation output in 2026: 0.24%

• Refining 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 efficiency; 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 efficiency; change in water

inflow (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 

efficiency; 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%).

4 1

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

1 1

Gabrielsen et al.

(2005)

Econometric Rising temperatures /changing demand for energy; change 

in water inflow; 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%

4 1

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

studiesthat 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 ofAnnex 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 ofclimate 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 Sectorsstudied

UNDP (2011) PE Damage Case 1 (DC1): hotter in both winter and summer—

decreased demand for heating and increased demand for 

cooling;

Damage Case 2 (DC2): colder in both winter and summer—

increased demand for heating and decreased demand for 

cooling;

Damage Case 3 (DC3): colder in the winter and hotter in the 

summer—increased demand for heating and increased demand 

for cooling (Macedonia, 2009 –2030)

Change in electricity demand in residential and commercial 

sectors:

• DC1: 3.5%

• DC2: 0.3%

• DC3: 8%

Change in electricity system cost: 

• DC1: 0.8%

• DC2: 0.06%

• DC3: 1.74%

9 5

DOE (2009) PE Drought scenario (Western Electric Coordinating Council, USA,

2010 –2020)

In 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

electricity 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-specific. 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

10

inflow, 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 fullcost

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 aswell 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.

Adaptation 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 costeffective 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, aswell as about the effectiveness of adaptation options.

10.3. Water Services

This section focuses on economic aspects of climate change in waterintensive 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 nonclimatic 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
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variability 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

supply 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

SouthAfrica 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 SRESA2 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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supplies 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 ofclimate change-induced changesin 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-widewelfare 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 ofclimate 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 newconstruction,

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 ofcritical 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 benefitsfrom adaptation on long-term maintenance.
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Chapter 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 ofservice (Chinowsky andArndt, 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 CO2 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

andWarren, 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

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prone (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 andWarren, 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

Thissection assessesthe impact ofclimate 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 ofclimate 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 changewill accelerate

tree growth.Thiswill reduce pricesto the benefit ofconsumers everywhere.

Low to mid latitude producers will benefit too as they switch to shortrotation 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 forestbased industries (Moiseyev et al., 2011).

10.5.1.3. Fisheries and Aquaculture

Chapter 4 assesses impacts ofclimate 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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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 conditionsthat 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 allowfor proper airflowin a facility face significant issueswith 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

and 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 largestsectors 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.Whereastourists,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

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SLR. 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 forski 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 toWaterton

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 ofclimate

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

China. 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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Key Economic Sectors and Services Chapter 10

hotter 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), andwaterscarcity and the costs ofsnowmakingwill 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 environmentalchange,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

wild 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, unlessthey 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 economicimpacts 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;

theAtlantic 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 inwinter but increased opportunitiesin 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

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10.7. Insurance and Financial Services

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

More intense or frequent weather-related disaster would affect property

insurance, of which coverage is expanding with economic growth

(WGIIAR4 Section 7.4.2.2.4). Insurability can be preserved through riskreducing 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 economicimpact 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,weatherrelated 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 catastrophiclosses 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

ongoing 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 weatherrelated 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

reflect 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 hasrisen 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

to very likely increased heavy precipitation events in central North

America (WGI AR5 Section 2.6.2.1), while the evidence for climatedriven 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 (WGIAR5 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 specified

(aggregation mode) Reference

World All weather-related 1990–2008 No trend (annual aggregates) 1

Australia Aggregate of bushfire, flood, 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

flash flood, 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 flood (“flood only” and floods specifically 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 significant at the 10% level are indicated as a trend).

Sources: 1Barthel and Neumayer (2012); 2Barredo et al. (2012); 3Changnon (2007); 4Changnon (2008); 5Changnon (2009a); 6Changnon (2009b); 7Crompton and McAneney 

(2008); 8

Kunz et al. (2009); 9Sander 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 NorthAtlanticresponse 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 longterm 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 lossesin 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’ 

insurance

Europe Projected increases in mean annual loss ratio lie in a range from one- to two-digit percentages in time slices before 

and around 2050 for regions such as France, Belgium/Netherlands, UK/Ireland, Germany, and Poland, with larger

increases at the end of the century. Southern European regions expect decreases, such as Portugal/Spain (SRES A1B,

A2).4,5,8,13–15,19 

Currently 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

roughly 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

rise by 5 –41% in 2041–2070 (SRES A1B).

8,10

River flood, maritime

flood, flash flood

from rainfall, melting

snow

Property and 

business interruption 

insurances

Europe, North America Germany: projected increases in mean annual insured flood 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 flood 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 five-member

ensemble 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

of the century.9

The Netherlands: expected annual property loss caused by increasing river discharge and sea level with an assumed

flood 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

Tropical cyclone Foremost property

insurance lines

North America, Asia USA: three of four GCMs driving a specific 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.

17

Analogously, a wide range of model projections is reflected 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).

12,18 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, fifth GCM

generation; MRI = Meteorological Research Institute of Japan Meteorological Agency (JMA); SRES = Special Report on Emission Scenarios.

Sources: 1Aerts and Botzen (2011); 2Botzen et al. (2010b); 3Cheng et al. (2012); 4Dailey et al. (2009); 5Donat et al. (2011); 6Emanuel (2011); 7German Insurance Association 

(Gesamtverband der Deutschen Versicherungswirtschaft) (2011); 8Gerstengarbe et al. (2013); 9Haug et al. (2011); 10Held et al. (2013); 11Iizumi et al. (2008); 12Kunreuther et al.

(2012); 13Leckebusch et al. (2007); 14Pinto et al. (2007); 15Pinto et al. (2009); 16Pinto et al. (2012); 17Raible et al. (2012); 18Ranger and Niehoerster (2012); 19Schwierz et al. (2010).
683

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Key Economic Sectors and Services Chapter 10

Hailstorm 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. Increasesin large weather-related losses may corrode an insurer’s

solvency 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 lowincome 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

Example /explanation

Failure to reflect 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 flood 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 flooding

events.11,22 In addition, the large majority of older properties situated within flood 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 flood risk have been missing.

Policyholders residing in flood plains where flood cover was made precondition for mortgage drop the cover after only 2–4 years, accounting for missing

insurance penetration and insufficient 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 deficit, reaching more than US$20 billion in 2006, after the big hurricanes.

14

Non-quantifiable 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 CO2 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

 CO2 emissions were declared pollution (US Supreme 

Court /EPA). Existing and future regulation on limits for CO2

 emissions could continue to displace liability claims for CO2 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 CO2

 emissions and defense costs depends on the specific occurrence-definition underlying the contract (e.g., if the cover pertains to

accident, warming due to CO2

 emissions and resulting damage does not match this definition).

19

Share of insurance in national 

risk financing

In the years following weather-related disasters countries with high insurance penetration show almost no impact on sovereign deficit and increasing

economic output (GDP), whereas low-penetration countries experience substantially rising government deficit 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: 1Burby (2006); 2Charpentier (2008); 3Collier et al. (2009); 4Ebert (2010); 5Faure and Peeters (2011); 6GAO (2010); 7GAO (2011); 8Gerrard (2007); 9Grace and Klein (2009); 10

Hecht (2008); 11Kousky and Kunreuther (2010); 12Kunreuther et al. (2009); 13Melecky and Raddatz (2011); 14Michel-Kerjan (2010); 15Michel-Kerjan and Kunreuther (2011); 16Mills 

(2009); 17

Patton (2011); 18Stewart and Willard (2010); 19Supreme Court of Virginia USA (2012); 20Taylor and Tollin (2009); 21von Peter et al. (2012); 22Zahran et al. (2009).
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Chapter 10 Key Economic Sectors and Services

10

The 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 riskbased 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

governments 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; riskscan be further reduced, and premium discounts

contingent on building standard are offered (Kunreuther et al., 2009,

2012). However,risk-based premiumsrequired 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

changesin 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 regulationsin 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.,windbased) 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 andWeiss, 2009;

see Table 10-7).
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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

Risk-adjusted premiums convey the risk 

to the insured, encouraging them to 

pursue adaptive measures.

Flood hazard insurance zoning systems, e.g., HORA (Austria), SIGRA (Italy), and ZÜRS (Germany), hamper development in high-risk zones by

allocating adequately high premiums.26

 Prior to Germany’s disastrous River Elbe flood in 2002, 48.5% of insured households had obtained

information on flood 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 flood insurance uptake range from risk

awareness9 to pure peer group expectation32—the latter might blur the role of the risk-premiums-nexus in some societal contexts.

Conditions of insurance policies 

incentivizing vulnerability reduction

Premium discounts for compliance with local building codes or other prevention options27,45; share of the insured in claims payment by deductibles

or upper coverage limits, and exclusion of systematically affected property1,7,8,10,11,15,21; long-term natural-hazard insurance tied to the property

and 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 flexible for the insurance market.

34

Amplifying factors in large disaster 

losses included in risk models

Evacuation and systemic economic catastrophe impacts, adversely affecting regional workforce and repair capacity, or knock-on catastrophes 

following initial catastrophes, e.g., long-term flooding following hurricane landfall.

38

Diversifying large disaster risk across 

securitization markets

Following 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 financial market risks (e.g., currency risks) and the well defined loss-per-index structure. The higher

transparency relative to other asset-backed securities, such as mortgage-backed securities, contributed to the better performance of catastrophe

bonds following the financial crisis of 2007/2008.

16,18 As bonds typically cover large losses, the basis risk, i.e., suffering damage without parametric

triggering, is reduced44; 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

bond 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 

products

Agricultural insurances predominantly cover crops, but also livestock, forestry, aquaculture, and greenhouses. Main products are indemnity-based

crop 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.

23,33,40,46 Risk-based price signals may 

better foster adaptation if schemes are coupled with access to advanced technology, e.g., drought-resistant seed.

4,15,23,33 Various index definitions

(cumulative rainfall, area-yield, etc.) and applications exist or have been proposed.

4,29,30,31 Adjusting to uncertain regional changes in temporal hazard

condition 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 identification and yield estimation and loss assessment.

22

 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 diversification, 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 insignificant and

(2) that disaster loss is seen small in comparison with a government’s portfolio of diversified 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, Pacific Catastrophe Risk Insurance Pilot).

2,39 Pooling natural catastrophe risks across an array of megacities has also been

proposed.25

Table 10-7 | Products and systems responding to changes in weather risks.

Sources: 1Aakre et al. (2010); 2Wilcox et al. (2010); 3Arrow and Lind (1970); 4Barnett et al. (2008); 5Barrieu and Loubergé (2009); 6Biener and Eling (2012); 7Botzen and van den 

Bergh (2008); 8Botzen and van den Bergh (2009); 9Botzen and van den Bergh (2012); 10Botzen et al. (2009); 11Botzen et al. (2010a); 12Candel (2007); 13Chantarat et al. (2013); 14

Clarke and Grenham (2012); 15Collier et al. (2009); 16Cummins (2012); 17Cummins and Mahul (2009); 18Cummins and Weiss (2009); 19Ghesquiere and Mahul (2007); 20Guy 

Carpenter (2011); 21Hecht (2008); 22Herbold (2013b); 23Hess and Hazell (2009); 24Hochrainer et al. (2010); 25Hochrainer and Mechler (2011); 26Kron (2009); 27Kunreuther et al.

(2009); 28Kunreuther and Michel-Kerjan (2009); 29Leblois and Quirion (2011); 30Leiva and Skees (2008); 31Linnerooth-Bayer and Mechler (2009); 32Lo (2013); 33Mahul and Stutley 

(2010); 34Maynard and Ranger (2012); 35Meze-Hausken et al. (2009); 36Michel-Kerjan and Kunreuther (2011); 37Michel-Kerjan and Morlaye (2008); 38Muir-Wood and Grossi

(2008); 39The World Bank (2013); 40Prabhakar et al. (2013); 41Swiss Re (2013a); 42Thieken et al. (2006); 43Trærup (2012); 44Van Nostrand and Nevius (2011); 45Ward et al. (2008); 46

Zhu (2011).
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Chapter 10 Key Economic Sectors and Services

10

IPCC, 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

10.7.6.1. 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 highincome 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 postdisaster 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,

by 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 systemsin developing markets,challengesinclude adequate

public-private partnership framing, improved risk assessment with

sufficient detail and appropriate dynamics, development of markets and

regulation, and scaling-up ofsuccessfulschemes. 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 slowonset 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 nondiversifiable disaster risk portion

Systems with government intervention range from ex ante risk financing 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 financing 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: 1Aakre et al. (2010); 2Barnett et al. (2008); 3Botzen and van den Bergh (2008); 4Bruggeman et al. (2010); 5Duus-Otterström and Jagers (2011); 6Kunreuther et al. (2009); 7

Linnerooth-Bayer et al (2009); 8Linnerooth-Bayer et al. (2011); 9Mahul and Stutley (2010); 10Monti (2012); 11Paudel (2012); 12Schwarze and Wagner (2007); 13Schwarze et al.

(2011); 14Van den Berg and Faure (2006); 15Warner and Spiegel (2009); 16Warner et al (2012).
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Key Economic Sectors and Services Chapter 10

against 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(OliverWyman, 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

Principlesfor 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 onand off-balance-sheet exposures (Stenek et al., 2010).

10.7.8. Summary

More frequent or more severe extreme weather events, and increased

uncertainty aboutsuch 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

theirshareholders. Improvementsin 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,

and 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

CO2-eq in 2210, and currentcosts 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 costswere 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 ofclimate change

in Europe in 2071–2100 using physical and monetary metrics, taking

socioeconomicchange 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 costsforsalmonellosis 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?

Economic 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

of 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

Estimated 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) additionalcost 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 countries1 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 breaksin 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,sectorscompete

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 ofsimplifying

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 21stcentury, finding thatchanges 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

electricity), 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

SinceAR4, 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

agree 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 iscomplete;most expertsspeculate that excluded

impacts are on balance negative. Estimates across the studies reflect

different assumptions about inter-sectoral, inter-regional, and intertemporal 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 CO2 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

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 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).

Quantitative 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 0 200 100 300 400 500 600 700 800 900 1000

0.0025

0.002

0.0015

0.001

0.000

0.0005

2010 US$ per tonne of carbon

–200 –100 0 200 100 300 400 500 600 700 800 900 1000

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 0 200 100 300 400 500 600 700 800 900 1000

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

standard 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% inAustralia 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 airconditioning) to –0.7% for natural gas(as demand forspace 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

causes 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 ofclimate (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 climatehealth-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 diseasessuch as

malaria and diarrhea impairchildren’scognitive 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 CO2 emitted vary by two orders of
693

10

Key Economic Sectors and Services Chapter 10

magnitude, 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

actor), 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 economicimpact assessments of adaptation both on existing

and future infrastructure, but also the costs and benefitsfor 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

• Snow

Negative • Population

• Lifestyle

• 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 findings.

Severity
694

Chapter 10 Key Economic Sectors and Services

10

climate 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 ofclimate 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 isfar more extensive. Currentstudies 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 ofchanging 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 underinvestigated.

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

dominant 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 analysisis warranted to improve confidence

in the conclusions and investigation of a broader suite of Representative

Concentration Pathways (RCPs).

References

Aaheim, A., H. Amundsen, T. Dokken, T. Ericson, and T.Wei, 2009: A Macroeconomic

Assessment of Impacts and Adaptation of Climate Change in Europe. CICERO

Report 2009:06, Center for International Climate and Environmental Research

(CICERO), Oslo, Norway, 50 pp.

Aaheim, A., T. Dokken, S. Hochrainer, A. Hof, E. Jochem, R. Mechler, and D.P. van

Vuuren, 2010: National responsibilities for adaptation strategies: lessons from

four modelling framework. In: Making Climate Change Work for Us: European

Perspectives on Adaptation and Mitigation Strategies [Hulme, M. and H.

Neufeldt (eds.)]. Cambridge University Press, Cambridge, UK, pp. 87-112.

Aaheim, A., R.K. Chaturvedi, and A.D. Sagadevan, 2011a: Integrated modelling

approaches to analysis of climate change impacts on forests and forest

management. Mitigation and Adaptation Strategies for Global Change, 16(2),

247-266.

Aaheim,A., R. Gopalakrishnan, R.K. Chaturvedi, N.H. Ravindranath,A.D. Sagadevan,

N. Sharma, and T.Wei, 2011b:A macroeconomic analysis of adaptation to climate

change impacts on forests in India. Mitigation and Adaptation Strategies for

Global Change, 16(2), 229-245.

Aakre, S., I. Banaszak, R. Mechler, D. Rübbelke, A. Wreford, and H. Kalirai, 2010:

Financial adaptation to disaster risk in the European Union. Mitigation and

Adaptation Strategies for Global Change, 15, 721-736.

ABI, 2005: Financial Risks of Climate Change. Association of British Insurers (ABI)

Report based on research by Climate Change Management in collaboration

with Metroeconomica and in partnership with AIRWorldwide Corporation,ABI,

London, UK, 39 pp.

Acemoglu, D., S.Johnson, and J.A. Robinson, 2001:The colonial origins ofcomparative

development: an empirical investigation. American Economic Review, 91(4),

1369-1401.

Acemoglu, D., S. Johnson, and J.A. Robinson, 2002: Reversal of fortune: geography

and institutions in the making of the modern world income distribution.

Quarterly Journal of Economics, 117(4), 1231-1294.

Ackerman, F. and E.A. Stanton, 2012: Climate risks and climate prices: revisiting the

social cost of carbon. Economics: The Open-Access,Open-Assessment E-Journal,

6(10), doi:10.5018/economics-ejournal.ja.2012-10.

Acosta-Moreno, R., J. Skea, A. Gacuhi, D.L. Greene, W. Moomaw, T. Okita, A.

Riedacker, and T.V. Lien, 1995: Industry, energy, and transportation: impacts

and adaptation. In: Climate Change 1995: Impacts,Adaptations and Mitigation

of Climate Change: Scientific-TechnicalAnalyses. Contribution ofWorking Group

II to the Second Assessment Report of the Intergovernmental Panel on Climate

Change [Watson, R.T., M.C. Zinyowera, and R.H. Moss (eds.)]. Cambridge

University Press, Cambridge, UK, NewYork, NY, USA, and Melbourne,Australia,

pp. 365-398.

Adger,W.N., S.Agrawala, M.M.Q. Mirza, C. Conde, K. O’Brien, J. Pulhin, R. Pulwarty,

B. Smit, and K. Takahashi, 2007: Assessment of adaptation practices, options,

constraints and capacity. In: Climate Change 2007: Impacts, Adaptation and

Vulnerability. Contribution ofWorking Group II to the FourthAssessment Report

of the Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani,

J.P. Palutikof, P.J. van der Linden, and C.E. Hanson (eds.)]. Cambridge University

Press, Cambridge, UK and New York, NY, USA, pp. 717-743.

Aerts, J.C.J.H. and W.J.W. Botzen, 2011: Climate change impacts on pricing longterm flood insurance: a comprehensive study for the Netherlands. Global

Environmental Change, 21, 1045-1060.

AgroClima, 2013: AgroClima InformáticaAvanzada SA de CV.AgroClima Informática

Avanzada SA de CV (AgroClima), a ProAgro subsidiary specializing in climate

information, Mexico, DF, Mexico, www.agroclima.com.mx/.

Allard, M., A. Fortier, D. Sarrazin, F. Calmels, D. Fortier, D. Chaumont, J.P. Savard, and

Tarussov A., 2007: L’Impact du Réchauffement Climatique sur les Aéroports du

Nunavik: Caractéristiques du Pergélisol et Caractérisation des Processus de
695

10

Key Economic Sectors and Services Chapter 10

Dégradation des Pistes. Ressources naturelles Canada (Natural Resources

Canada), Ottawa, ON, Canada, 184 pp.

Allison, E.H., A.L. Perry, M.-C. Badjeck,W.N. Adger, K. Brown, D. Conway, A.S. Halls,

G.M. Pilling, J.D. Reynolds, N.L. Andrew, and N.K. Dulvy, 2009: Vulnerability of

national economies to the impacts of climate change on fisheries. Fish and

Fisheries, 10(2), 173-196.

Álvarez-Díaz, M. and J. Rosselló-Nadal, 2010: Forecasting British tourist arrivals in

the Balearic Islands using meteorological variables. Tourism Economics, 16(1),

153-168.

Amelung, B. and A. Moreno, 2012: Costing the impact of climate change on tourism

in Europe: results of the PESETA project. Climatic Change, 112(1), 83-100.

Amelung, B., S. Nicholls, and D. Viner, 2007: Implications of global climate change

for tourism flows and seasonality. Journal of Travel Research, 45(3), 285-296.

Amengual,A.,V. Homar, R. Romero, S.Alonso, and C. Ramis, 2012: Projections of the

climate potential for tourism at local scales: application to Platja de Palma,

Spain. International Journal of Climatology, 32(14), 2095-2107.

Anthoff, D. and R.S.J. Tol, 2013a: Climate policy under fat-tailed risk: an application

of FUND. Annals of Operations Research, doi:10.1007/s10479-013-1343-2.

Anthoff, D. and R.S.J. Tol, 2013b: The uncertainty about the social cost of carbon: a

decomposition analysis using FUND. Climatic Change, 117(3), 515-530.

Antonioni, G., S. Bonvicini, G. Spadoni, and V. Cozzani, 2009: Development of a

framework for the risk assessment of Na-Tech accidental events. Reliability

Engineering and System Safety, 94(9), 1442-1450.

Apipattanavis, S., K. Sabol, K.R. Molenaar, B. Rajagopalan, Y. Xi, B. Blackard, and S.

Patil, 2010: Integrated framework for quantifying and predicting weatherrelated highway construction delays. Journal of Construction Engineering and

Management, 136(11), 1160-1168.

Arkell, B.P. and G.J.C. Darch, 2006: Impact of climate change on London’s transport

network. Municipal Engineer, 159(ME4), 231-237.

Arndt, C. and M. Bacou, 2000: Economy-wide effects ofclimate variability and climate

prediction in Mozambique. American Journal of Agricultural Economics, 82(3),

750-754.

Arndt, C.,J.Thurlow, S. Kenneth, P. Chinowsky,A. Kuriakose,J.Neumann, and R.Nicholls,

2012: Impacts of climate change on development paths for Mozambique.

Review of International Economics.

Arrow, K.J. and R.C. Lind, 1970: Uncertainty and the evaluation of public investment

decisions. The American Economic Review, 60(3), 364-378.

Astrom, C., H. Orru, J. Rocklöv, G. Strandberg, K.L. Ebi, and B. Forsberg, 2013: Heatrelated respiratory hospital admissions in Europe in a changing climate: a

health impact assessment. BMJ Open, 3(1), e001842, doi:10.1136/bmjopen2012-001842.

Banks, L.L., M.B. Shah, and M.E. Richards, 2007: Effective healthcare system response

to consecutive Florida hurricanes. American Journal of Disaster Medicine, 2(6),

285-295.

Barlas, T.K. and G.A.M. Van Kuik, 2010: Review of state of the art in smart rotor

control research for wind turbines. Progress in Aerospace Sciences, 46, 1-27.

Barnett, B.J., C.B. Barrett, and J.R. Skees, 2008: Poverty traps and index-based risk

transfer products. World Development, 36(10), 1766-1785.

Barredo, J.I., D. Sauri, and M.C. Llasat, 2012:Assessing trends in insured losses from

floods in Spain 1971-2008. Natural Hazards and Earth System Sciences, 12(5),

1723-1729, doi:10.5194/nhess-12-1723-2012.

Barrieu, P. and H. Loubergé, 2009: Hybrid cat bonds. The Journal of Risk and Insurance,

76(3), 547-578.

Barrios, S., L. Bertinelli, and E. Strobl, 2010: Trends in rainfall and economic growth

inAfrica: a neglected cause of theAfrican growth tragedy. Review of Economics

and Statistics, 92(2), 350-366.

Barthel, F. and E. Neumayer, 2012: A trend analysis of normalized insured damage

from natural disasters. Climatic Change, 113, 215-237, doi:10.1007/s10584-

011-0331-2.

Barthelmie, R.J., F. Murray, and S.C. and Pryor, 2008: The economic benefit of shortterm forecasting for wind energy in the UK electricity market. Energy Policy,

36, 1687-1696.

Becken, S., 2005: Harmonising climate change adaptation and mitigation: the case

of tourist resorts in Fiji. Global Environmental Change, 15(4), 381-393.

Becken, S. and J. Hay, 2007: Tourism and Climate Change: Risks and Opportunities.

Channel View Publications, Bristol, UK, 352 pp.

Becker, A., S. Inoue, M. Fischer, and B. Schwegler, 2011: Climate change impacts on

international seaports: knowledge, perceptions, and planning efforts among

port adminstrators. Climatic Change, 10(1-2), 5-29.

Belle, N. and B. Bramwell, 2005: Climate change and small island tourism: policy

maker and industry perspectives in Barbados. Journal of Travel Research, 44(1),

32-41.

Bengtsson, L., K. Hodges, and E. Roeckner, 2006: Storm tracks and climate change.

Journal of Climate, 19, 3518-3543.

Berrittella, M., A. Bigano, R. Roson, and R.S.J. Tol, 2006: A general equilibrium

analysis of climate change impacts on tourism. Tourism Management, 27(5),

913-924.

Bicknell, S. and P. McManus, 2006: The canary in the coalmine:Australian ski resorts

and their response to climate change. Geographical Research, 44(4), 386-400.

Biener, C. and M. Eling, 2012: Insurability in microinsurance markets: an analysis of

problems and potential solutions. The Geneva Papers, 37(1), 77-107,

doi:10.1057/gpp.2011.29.

Bigano, A., J.M. Hamilton, and R.S.J. Tol, 2006: The impact of climate on holiday

destination choice. Climatic Change, 76(3-4), 389-406.

Bigano, A., J.M. Hamilton, and R.S.J. Tol, 2007: The impact of climate change on

domestic and international tourism: a simulation study. IntegratedAssessment,

7(1), 25-49.

Bigano,A., F. Bosello, R. Roson, and R.S.J.Tol, 2008: Economy-wide impacts of climate

change: a joint analysis for sea level rise and tourism.Mitigation andAdaptation

Strategies for Global Change, 13(8), 765-791.

Block, P. and K.M. Strzepek, 2012: Power ahead: meeting Ethiopia’s energy needs

under a changing climate. Review of International Economics, 6(3), 476-488.

Bloom, A., V. Kotroni, and K. Lagouvardos, 2008: Climate change impact of wind

energy availability in the Eastern Mediterranean using the regional climate

model PRECIS. Natural Hazards and Earth System Sciences, 8, 1249-1257.

Bloom, D.E., D. Canning, and J. Sevilla, 2003: Geography and poverty traps. Journal

of Economic Growth, 8(4), 355-378.

Bonds, M.H., D.C. Keenan, P. Rohani, and J.D. Sachs, 2010: Poverty trap formed by

the ecology of infectious diseases. Proceedings of the Royal Society B,

277(1685), 1185-1192.

Borch, K., 1962: Equilibrium in a reinsurance market. Econometrica, 30(3), 424-444.

Bosello, F., R. Roson, and R.S.J.Tol, 2006: Economy-wide estimates of the implications

of climate change: human health. Ecological Economics, 58(3), 579-591.

Bosello, F., E. De Cian, and R. Roson, 2007a: Climate Change, Energy Demand and

Market Power in a General Equilibrium Model of the World Economy. CMCC

Research Paper No. 10, Centro Euro-Mediterraneo per i Cambiamenti Climatici,

Climate Impacts and Policy Division (CMCC), Lecce, Italy,16 pp.

Bosello, F., R. Roson, and R.S.J.Tol, 2007b: Economy-wide estimates of the implications

of climate change: sea level rise. Environmental and Resource Economics,

37(3), 549-571.

Bosello, F., C. Carraro, and E. De Cian, 2009: AnAnalysis ofAdaptation as a Response

to Climate Change.Working Paper No. 26 Department of Economics, Ca’ Foscari

University of Venice, Venice, Italy, 60 pp.

Bosello, F., F. Eboli, and R. Pierfederici, 2012: Assessing the economic impacts of

climate change. Review of Energy Environment and Economics, Re3,

doi:10.7711/feemre3.2012.02.002.

Botzen,W.J.W. and J.C.J.M. van den Bergh, 2008: Insurance against climate change

and flooding in the Netherlands: present, future, and comparison with other

countries. Risk Analysis, 28(2), 413-426.

Botzen,W.J.W. and J.C.J.M. van den Bergh, 2009: Bounded rationality,climate risks,

and insurance: is there a market for natural disasters? Land Economics, 85(2),

265-278.

Botzen, W.J.W. and J.C.J.M. van den Bergh, 2012: Monetary valuation of insurance

against flood risk under climate change. International Economic Review, 53(3),

1005-1025.

Botzen, W.J.W., J.C.J.H. Aerts, and J.C.J.M. van den Bergh, 2009: Willingness of

homeowners to mitigate climate risk through insurance. Ecological Economics,

68, 2265-2277.

Botzen,W.J.W., J.C.J.M. van den Bergh, and L.M. Bouwer, 2010a: Climate change and

increased risk for the insurance sector: a global perspective and an assessment

for the Netherlands. Natural Hazards, 52, 577-598.

Botzen, W.J.W., L.M. Bouwer, and J.C.J.M. van den Bergh, 2010b: Climate change

and hailstorm damage: empirical evidence and implications for agriculture and

insurance. Resource and Energy Economics, 32, 341-362.

Bougheas, S., P.O. Demetriades, and E.L.W. Morgenroth, 1999: Infrastructure,

transport costs and trade. Journal of International Economics, 47(1), 169-189.

Bougheas, S., P.O.Demetriades, and T.P.Mamuneas, 2000: Infrastructure,specialization,

and economic growth. Canadian Journal of Economics, 33(2), 506-522.
696

Chapter 10 Key Economic Sectors and Services

10

Bourdeau, P., 2009: From après-ski to après-tourism: the Alps in transition?

Reflections based on the French situation. Revue de Geographie Alpine, 97(3),

rga.revues.org/1049.

Bouwer, L.M., 2011: Have disaster losses increased due to anthropogenic climate

change? Bulletin of the American Meteorological Society, 92, 39-46.

Bouwer, L.M., R.P. Crompton, E. Faust, P. Höppe, and R.A. Pielke Jr., 2007: Confronting

disaster losses. Science, 318(5851), 753.

Boyd, R. and M.E. Ibarraran, 2009: Extreme climate events and adaptation: an

exploratory analysis of drought in Mexico. Environment and Development

Economics, 14, 371-395.

Brander, L.M., K. Rehdanz, R.S.J. Tol, and P.H.J. van Beukering, 2012: The economic

impact of ocean acidification on coral reefs. Climate Change Economics, 3(1),

1-29.

Breisinger, C.,T. Zhu, P.Al Riffai, G. Nelson, R. Robertson,J. Funes, and D.Verner, 2013:

Global and local economic impacts of climate change in Syria and options for

adaptation. Climate Change Economics, 4(1), doi:10.1142/S2010007813500024.

Bretschger, L. and S. Valente, 2011: Climate change and uneven development.

Scandinavian Journal of Economics, 113(4), 825-845.

Brimble, M. and J. Stewart, 2009: The financial sector and climate change risks,

opportunities and overall preparedness. In: APABIS 2009: Proceedings of the

Asia Pacific Academy of Business in Society Conference: Finding Solutions to

Global Problems through Stakeholder Engagement, New Social Partnerships

and Strategic Alliances for a Sustainable Enterprise Economy, 5 – 6 November

2009, Brisbane,Australia.Asia Pacific Academy of Business in Society (APABIS),

Hamilton, New Zealand, doi:10.2139/ssrn.1460676.

Brown, C., R. Meeks, K. Hunu, andW.Yu, 2011: Hydroclimate risk to economic growth

in sub-Saharan Africa. Climatic Change, 106(4), 621-647.

Bruggeman, V., M.G. Faure, and K. Fiore, 2010: The government as reinsurer of

catastrophe risks. The Geneva Papers, 35, 369-390.

Buchholz, W. and M. Schymura, 2012: Expected utility theory and the tyranny of

catastrophic risks. Ecological Economics, 77, 234-239.

Buckley, L.B. and M.S. Foushee, 2012: Footprints of climate change in US national

park visitation. International Journal of Biometeorology, 56(6), 1173-1177.

Bujosa,A. and J. Rosselló, 2012: Climate change and summer mass tourism: the case

of Spanish domestic tourism. Climatic Change, 117(1-2), 363-375.

Burby, J.R., 2006: Hurricane Katrina and the paradoxes of government disaster policy:

bringing about wise governmental decisions for hazardous areas. The ANNALS

of the American Academy of Political and Social Science, 604, 171-191.

Buzinde, C.N., D. Manuel-Navarrete, D. Kerstetter, and M. Redclift, 2010a:

Representations and adaptation to climate change. Annals of Tourism Research,

37(3), 581-603.

Buzinde, C.N., D. Manuel-Navarrete, E.E. Yoo, and D. Morais, 2010b: Tourists’

perceptions in a climate of change: eroding destinations. Annals of Tourism

Research, 37(2), 333-354.

Byass, P., d.C. Maximilian, J.G.Wendy, L. Lucie, M.Affette,A.S. Osman, M.T. Stephen,

and Z. Basia, 2013: Reflections on the global burden of disease 2010 estimates.

PLoS Med, 10(7), e1001477, doi:10.1371/journal.pmed.1001477.

Bye, T., A. Bruvoll, and F.R. Aune, 2008: Inflow shortages in deregulated power

markets – reasons for concern? Energy Economics, 30(4), 1693-1711.

Callaway, J.M., D.B. Louw, and M. Hellmuth, 2012: Benefits and costs of measures

for coping with water and climate change: Berg River Basin, South Africa. In:

Climate Change Adaptation in the Water Sector [Ludwig, F., P. Kabat, H. van

Schaik, and M. van der Valk (eds.)]. Earthscan, London, UK and Sterling, VA,

USA, pp. 205-226.

Calzadilla, A., K. Rehdanz, and R.S.J. Tol, 2011: Trade liberalization and climate

change: a computable general equilibrium analysis of the impacts on global

agriculture. Water, 3(2), 526-550.

Candel, F.M., 2007: Climate change and the global insurance industry: impacts and

problems in Latin America. The Geneva Papers, 32, 29-34.

Carter, M.R., P.D. Little, T. Mogues, and W. Negatu, 2007: Poverty traps and natural

disasters in Ethiopia and Honduras. World Development, 35(5), 835-856.

Carthey, J.,V. Chandra, and M. Loosemore, 2009:AdaptingAustralian health facilities

to cope with climate-related extreme weather events. Journal of Facilities

Management, 7, 36-51.

Cavallo, E., E. Galiani, I. Noy, and J. Pantano, 2013: Catastrophic natural disasters

and economic growth. Review of Economics and Statistics, 95(5), 1549-1561.

Cavan, G., J.F. Handley, J.Aylen, K.Albertson, J. McMorrow, S. Lindley, and D. McEvoy,

2006: Climate change and the visitor economy in the uplands. International

Journal of Biodiversity Science and Management, 2(3), 170-173.

CCSP, 2007: Effects of Climate Change on Energy Production and Use in the United

States [Wilbanks, T.J. (coordinating author), V. Bhatt, D.E. Bilello, S.R. Bull, J.

Ekmann,W.C. Horak,Y.J. Huang, M.D. Levine, M.J. Sale, D.K. Schmalzer, and M.J.

Scott (authors)]. U.S. Climate Change Science Program Synthesis andAssessment

Product 4.5, Report by the U.S. Climate Change Science Program (CCSP) and

the Subcommittee on Global Change Research, U.S. Department of Energy,

Office of Biological & Environmental Research,Washington, DC, USA, 144 pp.

CCSP, 2008: Impacts of Climate Change and Variability on Transportation Systems

and Infrastructure: Gulf Coast Study, Phase I [Potter, J., V. Burkett, M. Savonis,

and D. Dokken (eds.)]. U.S. Climate Change Science Program Synthesis and

Assessment Product 4.7, Report by the U.S. Climate Change Science Program

and the Subcommittee on Global Change Research, U.S. Department of

Transportation,Washington, DC, USA, 445 pp.

City of Chicago, 2008: Chicago Climate Action Plan: Our City, Our Future. City of

Chicago, Chicago Climate Task Force (CCTF), Chicago, IL, USA, 56 pp.

Ceron, J.-P. and G. Dubois, 2005: The potential impacts of climate change on French

tourism. Current Issues in Tourism, 8(2-3), 125-139.

Changnon, S.A., 2005: Economic impacts of climate conditions in the United States:

past, present, and future – an editorial essay. Climatic Change, 68(1-2), 1-9.

Changnon, S.A., 2007: Catastrophic winter storms: an escalating problem. Climatic

Change, 84, 131-139.

Changnon, S.A., 2008: Assessment of flood losses in the United States. Journal of

Contemporary Water Research & Education, 138, 38-44.

Changnon, S.A., 2009a: Characteristics of severe Atlantic hurricanes in the United

States: 1949-2006. Natural Hazards, 48, 329-337.

Changnon, S.A., 2009b: Increasing major hail losses in the U.S. Climatic Change,

96(1-2), 161-166.

Channing, A., P Chinowsky, S Robinson, K. Strzepek, F. Tarp, and J. Thurlow, 2012:

Economic development under climate change. Review of International

Economics, 16(3), 369-377, DOI: 10.1111/j.1467-9361.2012.00668.x

Chantarat, S.,A.G. Mude, C.B. Barrett, and M.R. Carter, 2013: Designing index-based

livestock insurance for managing asset risk in northern Kenya. The Journal of

Risk and Insurance, 80(1), 205-237.

Chapman, L., 2007: Transport and climate change: a review. Journal of Transport

Geography, 15, 354-367.

Charpentier, A., 2008: Insurability of climate risks. The Geneva Papers, 33, 91-109.

Chen, C.-C., D. Gillig, and B.A. McCarl, 2001: Effects of climatic change on a water

dependent regional economy: a study of the Texas Edwards Aquifer. Climatic

Change, 49(4), 397-409.

Cheng, C.S., Q. Li, G. Li, and H.Auld, 2012: Climate change and heavy rainfall-related

water damage insurance claims and losses in Ontario, Canada. Journal ofWater

Resource and Protection, 4(2), 49-62, doi:10.4236/jwarp.2012.42007.

Chinowsky, P. and C. Arndt, 2012: Climate change and roads: a dynamic stressor–

response model. Review of Development Economics, 16(3), 448-462.

Chinowsky, P., K.M. Strzepek, P. Larsen, and A. Opdahl, 2010: Adaptive climate

response cost models for infrastructure. ASCE Journal of Infrastructure Systems,

16, 173-181.

Chinowsky, P., C. Hayles, A. Schwwikert, N. Strzepek, K.M. Strzepek, and C.A.

Schlosser, 2011: Climate change: comparative impact on developing and

developed countries. The Engineering Project Organization Journal, 1(1), 67-

80.

Chinowsky, P.S.,A.E. Schweikert,N.L. Strzepek, and K.M. Strzepek, 2013: Infrastructure

and Climate Change: Impacts and Adaptations for the Zambezi River Valley.

UNU-WIDER Working Paper, WP/2013/041, United Nations University-World

Institute for Development Economics Research (UNU-WIDER), Helsinki, Finland,

14 pp.

Choi, O. and A. Fisher, 2003: The impacts of socioeconomic development and climate

change on severe weather catastrophe losses: Mid-Atlantic Region (MAR) and

the U.S. Climatic Change, 58(1-2), 149-170.

Ciscar, J.-C.,A. Iglesias, L. Feyen, L. Szabó, D.Van Regemorter, B.Amelung, R. Nicholls,

P. Watkiss, O.B. Christensen, R. Dankers, L. Garrote, C.M. Goodess, A. Hunt, A.

Moreno, J. Richards, and A. Soria, 2011: Physical and economic consequences

of climate change in Europe. Proceedings of the National Academy of Sciences

of the United States of America, 108(7), 2678-2683.

Clarke, D.J. and D. Grenham, 2012: Microinsurance and natural disasters:challenges

and options. Environmental Science & Policy, 27(Suppl. 1), S89-S98.

Cogan, D.G., M. Good and E. McAteer, 2008: Corporate Governance and Climate

Change: The Banking Sector. Ceres Report produced by the RiskMetrics Group,

Ceres, Boston, MA, USA, 55 pp.
697

10

Key Economic Sectors and Services Chapter 10

Collier, B., J. Skees, and B. Barnett, 2009: Weather index insurance and climate

change: opportunities and challenges in lower income countries. The Geneva

Papers, 34, 401-424.

Cooley, S.R. and S.C. Doney, 2009: Anticipating ocean acidification’s economic

consequences for commercial fisheries. Environmental Research Letters, 4(2),

doi:10.1088/1748-9326/4/2/024007.

Coombes, E.G., A.P. Jones, and W.J. Sutherland, 2009: The implications of climate

change on coastal visitor numbers: a regional analysis. Journal of Coastal

Research, 25(4), 981-990.

Corti,T.,V. Muccione, P. Köllner-Heck, D. Bresch, and S.I. Seneviratne, 2009: Simulating

past droughts and associated building damages in France. Hydrology and Earth

System Sciences, 13, 1739-1747.

Courbage, C., M. Orie, and W.R. Stahel, 2012: 2011 Thai floods and insurance. In:

The Geneva Reports – Risk and Insurance Research No. 5. Extreme Events and

Insurance: 2011 annus horribilis [Courbage, C. and W.R. Stahel (eds.)]. The

Geneva Association, The International Association for the Study of Insurance

Economics, Geneva, Switzerland, pp. 121-132.

Crompton, R.P. and K.J. McAneney, 2008: Normalised Australian insured losses from

meteorological hazards: 1967-2006. Environmental Science & Policy, 11, 371-

378.

Crompton, R.P., R.A.J. Pielke, and K.J. McAneney, 2011: Emergence timescales for

detection of anthropogenic climate change in US tropical cyclone loss data.

Environmental Research Letters, 6(1), 014003, doi:10.1088/1748-9326/6/1/

014003.

Cruz,A.M. and E. Krausmann, 2013:Vulnerability of the oil and gas sector to climate

change and extreme weather events. Climatic Change, 121(1), 41-53.

Cummins, J.D., 2012: CAT bonds and other risk-linked securities: product design and

evolution of the market. In: The Geneva Reports – Risk and Insurance Research

No. 5. Extreme Events and Insurance: 2011 annus horribilis [Courbage, C. and

W.R. Stahel (eds.)]. The Geneva Association, The International Association for

the Study of Insurance Economics, Geneva, Switzerland, pp. 39-61.

Cummins,J.D. and O.Mahul, 2009: Catastrophe Risk Financing in Developing Countries:

Principles for Public Intervention. The International Bank for Reconstruction

and Development / The World Bank,Washington, DC, USA, 268 pp.

Cummins,J.D. and M.A.Weiss, 2009: Convergence of insurance and financial markets:

hybrid and securitized risk-transfer solutions. The Journal of Risk and Insurance,

76(3), 493-545.

Dailey, P., M. Huddleston, S. Brown, and D. Fasking, 2009: The Financial Risks of

Climate Change: Examining the Financial Implications of Climate Change using

Climate Models and Insurance Catastrophe Risk Models. ABI Research Paper

No. 19, Report byAIRWorldwide Corporation and the Met Office,TheAssociation

of British Insurers, London, UK, 107 pp.

Damigos, D., 2012: Monetizing the impacts of climate change on the Greek mining

sector. Mitigation and Adaptation Strategies for Global Change, 17(8), 865-

878.

Darwin, R.F., 2004: Effects of greenhouse gas emissions on world agriculture, food

consumption, and economic welfare. Climatic Change, 66(1-2), 191-238.

Darwin, R.F. and D. Kennedy, 2000: Economic effects of CO2 fertilization of crops:

transforming changes in yield into changes in supply. Environmental Modeling

and Assessment, 5, 157-168.

Darwin, R.F. and R.S.J. Tol, 2001: Estimates of the economic effects of sea level rise.

Environmental and Resource Economics, 19(2), 113-129.

Dasgupta, S., M. Huq, H. Khan, Z. Ahmed, N. Mukherjee, M. Khan, and K. Pandey,

2010: Vulnerability of Bangladesh to Cyclones in a Changing Climate: Potential

Damages and Adaptation Cost. Policy Research Working Paper No. 5280, The

World Bank, Development Research Group, Environment and Energy Team,

Washington, DC, USA, 54 pp.

Daugherty, D.J., D.L. Buckmeier, and P.K. Kokkanti, 2011: Sensitivity of recreational

access to reservoir water level variation: an approach to identify future access

needs in reservoirs. North American Journal of Fisheries Management, 31(1),

63-69.

Dawson, J. and D. Scott, 2010: Climate change and tourism in the great lakes region:

a summary of risks and opportunities. Tourism in Marine Environments, 6(2-3),

119-132.

Dawson, J. and D. Scott, 2013: Managing for climate change in the alpine ski sector.

Tourism Management, 35, 244-254.

Dawson, J., D. Scott, and G. McBoyle, 2009: Climate change analogue analysis of ski

tourism in the northeastern USA. Climate Research, 39(1), 1-9, doi:10.3354/

cr00793.

Day, J., N. Chin, S. Sydnor, and K. Cherkauer, 2013: Weather, climate, and tourism

performance: a quantitative analysis. Tourism Management Perspectives, 5,

51-56.

De Bruin, K., R.B. Dellink, A. Ruijs, L. Bolwidt, A. Van Buuren, J. Graveland, R.S. De

Groot, P.J. Kuikman, S. Reinhard, and R.P. Roetter, 2009: Adapting to climate

change in The Netherlands: an inventory of climate adaptation options and

ranking of alternatives. Climatic Change, 95(1-2), 23-45.

de Cian, E., E. Lanzi, and R. Roson, 2013: Seasonal temperature variations and energy

demand: a panel cointegration analysis for climate change impact assessment.

Climatic Change, 116(3-4), 805-825.

De Freitas, C.R., D. Scott, and G. McBoyle, 2008:A second generation climate index

for tourism (CIT): specification and verification. International Journal of

Biometeorology, 52(5), 399-407.

de Lucena, A.F.P., R. Schaeffer, and A.S. Szklo, 2010: Least-cost adaptation options

for global climate change impacts on the Brazilian electric power system.

Global Environmental Change, 20(2), 342-350.

de Nooij, M., R. Lieshout, and C. Koopmans, 2009: Optimal blackouts: empirical

results on reducing the social cost of electricity outages through efficient

regional rationing. Energy Economics, 31(3), 342-347.

Dell, J.J. and P. Pasteris, 2010: Adaptation in the oil and gas industry to projected

impacts of climate change. In: SPE International Conference on Health, Safety

and Environment in Oil and Gas Exploration and Production, 2010, 12-14 April,

Rio de Janeiro, Brazil.Vol. 1, Society of Petroleum Engineers (SPE), Richardson,

TX, USA, pp. 333-348.

Dell, M., B.F. Jones, and B.A. Olken, 2009: Temperature and income: reconciling new

cross-sectional and panel estimates.American Economic Review, 99(2), 198-204.

Dell, M., B.F. Jones, and B.A. Olken, 2012:Temperature shocks and economic growth:

evidence from the last halfcentury.American EconomicJournal:Macroeconomics,

4(3), 66-95.

Denstadli, J.M., J.K.S. Jacobsen, and M. Lohmann, 2011: Tourist perceptions of

summer weather in Scandinavia. Annals of Tourism Research, 38(3), 920-940.

Devitt, C. and R.S.J.Tol, 2012: Civil war,climate change, and development: a scenario

study for sub-Saharan Africa. Journal of Peace Research, 49(1), 129-145.

DOE, 2007: Concentrating Solar Power Commercial Application Study: Reducing

Water Consumption of Concentrating Solar Power Electricity Generation. U.S.

Department of Energy (DOE) Report to Congress, DOE, Washington, DC, USA,

24 pp.

DOE, 2009: An Analysis of the Effects of Drought Conditions on Electric Power

Generation in theUnited States. DOE/NETL-2009/1365,U.S.Department of Energy

(DOE) and National Energy Technology Laboratory (NREL), DOE,Washington, DC,

USA, 32 pp.

DOE, 2013: U.S. Energy Sector Vulnerabilities to Climate Change and ExtremeWeather.

DOE/PI-0013, U.S. Department of Energy, Office of Policy and International

Affairs (DOE-PI) and the National Energy Technology Laboratory (NREL), DOE,

Washington, DC, USA, 81 pp.

Donat, M.G., G.C. Leckebusch, S. Wild, and U. Ulbrich, 2011: Future changes in

European winter storm losses and extreme wind speeds detected from GCM

and RCM multi-model simulations. Natural Hazards and Earth System Sciences,

11, 1351-1370.

Doney, S.C., V.J. Fabry, R.A. Feely, and J.A. Kleypas, 2009: Ocean acidification: the

other CO2 problem. Annual Review of Marine Science, 1, 169-192.

Dore, M. and I. Burton, 2001: The Costs of Adaptation to Climate Change in Canada:

A Stratified Estimate by Sectors and Regions – Social Infrastructure. Climate

Change Laboratory, Brock University, St Catharines, ON, Canada, 338 pp.

DOT, 2002: The Potential Impacts of Climate Change on Transportation. Federal Research

Partnership Workshop October 1-2, 2002 held at the Brookings Institution,

Washington, DC, USA, Summary and Discussion Papers, U.S. Department of

Transportation Center for Climate Change and Environmental Forecasting

(DOT),Washington, DC, USA, 263 pp.

Droogers, P., 2009: Climate Change and Hydropower, Impact and Adaptation Costs:

Case Study Kenya. FutureWater Report 85, FutureWater,Wageningen,Netherlands,

27 pp.

Dunne, J., S. R., and J. J., 2013: Reductions in labour capacity from heat stress under

climate warming. Nature Climate Change, 3(3), 1-4.

Duus-Otterström, G. and S.C. Jagers, 2011:Why (most) climate insurance schemes

are a bad idea. Environmental Politics, 20(3), 322-339, doi:10.1080/09644016.

2011.573354.

Easterly,W. and R. Levine, 2003:Tropics, germs, and crops: how endowmentsinfluence

economic development. Journal of Monetary Economics, 50(1), 3-39.
698

Chapter 10 Key Economic Sectors and Services

10

Ebert, I., 2010: Legal aspects of U.S. claims based on greenhouse gas emissions. In:

Liability for Climate Change? Experts’ Views on a Potential Emerging Risk.

Munich Re, Munich, Germany, pp. 14-15.

Ebi, K.L., 2008: Adaptation costs for climate change-related cases of diarrhoeal

disease, malnutrition, and malaria in 2030. Globalization and Health, 4(9),

doi:10.1186/1744-8603-4-9.

Ebinger, J. andW.Vergara, 2011: Climate Impacts on Energy Systems: Key Issues for

Energy Sector Adaptation. The International Bank for Reconstruction and

Development / The World Bank and Energy Sector Management Assistance

Program (ESMAP), The World Bank,Washington, DC, USA, 178 pp.

Eboli, F., R. Parrado, and R. Roson, 2010: Climate-change feedback on economic

growth: explorations with a dynamic general equilibrium model. Environment

and Development Economics, 15(5), 515-533.

Eide,A., 2008:An integrated study of economic effects of and vulnerabilities to global

warming on the Barents Sea cod fisheries. Climatic Change, 87(1-2), 251-262.

Eide, A. and K. Heen, 2002: Economic impacts of global warming. A study of the

fishing industry in North Norway. Fisheries Research, 56(3), 261-274.

Eijgelaar, E., C. Thaper, and P. Peeters, 2010:Antarctic cruise tourism: the paradoxes

of ambassadorship, “last chance tourism” and greenhouse gas emissions.

Journal of Sustainable Tourism, 18(3), 337-354.

Eisenack, K. and R. Stecker, 2012: A framework for analyzing climate change

adaptations as actions. Mitigation and Adaptation Strategies for Global

Change, 17(3), 243-260.

Eisenack, K., R. Stecker, D. Reckien, and E. Hoffmann, 2012: Adaptation to climate

change in the transport sector: a review of actions and actors. Mitigation and

Adaptation Strategies for Global Change, 17, 451-469.

Ekman, J., 2013: Climate change impacts on coal from resource assessment through

to environmental remediation. Climatic Change, 21(1), 27-39.

Elsasser, H. and R. Bürki, 2002: Climate change as a threat to tourism in the Alps.

Climate Research, 20(3), 253-257.

Emanuel, K., 2011: Global warming effects on U.S. hurricane damage. Weather,

Climate and Society, 3(4), 261-268.

Endler, C. andA. Matzarakis, 2010a: Climatic potential for tourism in the Black Forest,

Germany – winter season. International Journal of Biometeorology, 55(3), 339-

351.

Endler, C. and A. Matzarakis, 2010b: Analysis of high-resolution simulations for the

Black Forest region from a point of view of tourism climatology – a comparison

between two regional climate models (REMO and CLM). Theoretical and

Applied Climatology, 103(3-4), 427-440.

Endler, C. and A. Matzarakis, 2011: Climate and tourism in the Black Forest during

the warm season. International Journal of Biometeorology, 55(2), 173-186.

Endler, C., K. Oehler, and A. Matzarakis, 2010: Vertical gradient of climate change

and climate tourism conditions in the Black Forest. International Journal of

Biometeorology, 54(1), 45-61.

EPA, 2001: Technical Development Document for the Final Regulations Addressing

Cooling Water Intake Structures for the New Facilities., EPA-821-R-01-036,

Office of Water, U.S. Environmental Protection Agency (EPA), Washington, DC,

USA, 269 pp.

Eskeland, G.S. and T.K. Mideksa, 2010: Electricity demand in a changing climate.

Mitigation and Adaptation Strategies for Global Change, 15(8), 877-897.

Falk, M., 2013: Impact of long-term weather on domestic and foreign winter tourism

demand. International Journal of Tourism Research, 15(1), 1-17, doi:10.1002/

jtr.865.

Fankhauser, S. and R.S.J. Tol, 2005: On climate change and economic growth.

Resource and Energy Economics, 27(1), 1-17.

Fant, C.,Y. Gebretsadik, and K.M. Strzepek, 2013: Impact of climate change on crops,

irrigation, and hydropower in the Zambezi River Basin. UNU-WIDER Working

Paper No. 2013/039, Helsinki, Finland, 26 pp. ISBN 978-92-9230-61-616-8.

Farbotko, C., 2010: ‘The global warming clock is ticking so see these places while

you can’: voyeuristic tourism and model environmental citizens on Tuvalu’s

disappearing islands. Singapore Journal of Tropical Geography, 31(2), 224-

238.

Faure, M.G. and M. Peeters (eds.), 2011: Climate Change Liability. Edward Elgar

Publishing, Cheltenham, UK, 304 pp.

Feeley III, T.J., T.J. Skone, G.J. Stiegel Jr, A. McNemar, M. Nemeth, B. Schimmoller, J.T.

Murphy, and L. Manfredo, 2008:Water: a critical resource in the thermoelectric

power industry. Energy, 33(1), 1-11.

Feyen, L., R. Dankers, K. Bódis, P. Salamon, and J.I. Barredo, 2012: Fluvial flood risk

in Europe in present and future climates. Climatic Change, 112(1), 47-62.

FHWA, 2006: Long-Term Pavement Performance (LTPP) Data Analysis Support:

National Pooled Fund Study TPF-5(013) – Effects of Multiple Freeze Cycles and

Deep Frost Penetration on Pavement Performance and Cost. Publication No.

FHWA-HRT-06-121, U.S. Department of Transportation, Federal Highway

Administration (FHWA),Washington, DC, USA, 244 pp.

Fischer, G., T. Francesco, V. Harrij van, and W. David, 2007: Climate change impacts

on irrigation water requirements: effects of mitigation, 1990-2080. 74(7), 1083-

1107.

Ford, J.D., T. Pearce, J. Prno, F. Duerden, L.B. Ford, M. Beaumier, and T. Smith, 2010:

Perceptions of climate change risks in primary resource use industries: a survey

of the Canadian mining sector. Regional Environmental Change, 10(1), 65-

81.

Ford, J.D., T. Pearce, J. Prno, F. Duerden, L. Berrang Ford, T.R. Smith, and M. Beaumier,

2011: Canary in a coal mine: perceptions of climate change risks and response

options among Canadian mine operations. Climatic Change, 109(3-4), 399-

415.

Førland, E.J., J.K. Steen Jacobsen, J.M. Denstadli, M. Lohmann, I. Hanssen-Bauer, H.O.

Hygen, and H. Tømmervik, 2012: Cool weather tourism under global warming:

comparing Arctic summer tourists’ weather preferences with regional climate

statistics and projections. Tourism Management, 36, 567-579.

Förster, H. and J. Lilliestam, 2009: Modeling thermoelectric power generation in view

of climate change. Regional Environmental Change, 10(4), 327-338.

Forster,J., P.W. Schuhmann, I.R. Lake,A.R.Watkinson, and J.A.Gill, 2012:The influence

of hurricane risk on tourist destination choice in the Caribbean. Climatic

Change, 114(3-4), 745-768.

Furrer, B., J. Hamprecht, and V.H. Hoffmann, 2012: Much Ado about Nothing? How

Banks Respond to Climate Change. Business & Society, 51(1), 62-88.

Füssel, H.-M., 2010: Modeling impacts and adaptation in global IAMs. Wiley

Interdisciplinary Reviews: Climate Change, 1(2), 288-303.

Gabrielsen, K., T. Bye, and F.R. Aune, 2005: Climate Change-Lower Electricity Prices

and Increasing Demand: An Application to the Nordic Countries. Discussion

Paper No. 430, Statistics Norway, Research Department, Kongsvinger, Norway,

34 pp.

Gallup, J.L., J.D. Sachs, and A.D. Mellinger, 1999: Geography and economic

development. International Regional Science Review, 22(2), 179-232.

Galor, O. and D.N. Weil, 1996: The gender gap, fertility, and growth. American

Economic Review, 86(3), 374-387.

GAO, 2010: National Flood Insurance Program: Continued Actions needed to address

Financial and Operational Issues. GAO-10-1063T, Testimony before the

Subcommittee on Housing and Community Opportunity, Committee on Financial

Services, House of Representatives, United States Government Accountability

Office (GAO),Washington, DC, USA, 22 pp.

GAO, 2011: Flood Insurance: Public Policy Goals Provide a Framework for Reform.

GAO-11-429T, Testimony before the Subcommittee on Insurance, Housing, and

Community Opportunity, Committee on Financial Services, House of

Representatives, United States Government Accountability Office (GAO),

Washington, DC, USA, 15 pp.

Garnaut, R., 2008: The Garnaut Climate Change Review: Final Report. Cambridge

University Press, Port Melbourne, Australia, 634 pp.

Garvey, S.D., 2010: Structural capacity and the 20 MW wind turbine. Proceedings of

the Institution of Mechanical Engineers, Part A: Journal of Power and Energy,

224(8), 1083-1115.

Garza-Gil, M.D., J. Torralba-Cano, and M.M. Varela-Lafuente, 2011: Evaluating the

economic effects of climate change on the European sardine fishery. Regional

Environmental Change, 11(1), 87-95.

German Insurance Association, 2011: Auswirkungen des Klimawandels auf die

Schadensituation in der deutschen Versicherungswirtschaft. Kurzfassung

Hochwasser. Studie im Auftrag des Gesamtverbandes der Deutschen

Versicherungswirtschaft e.V., Produced by Potsdam Institute for Climate Impact

Research (PIK), Free University of Berlin (FUB), University of Cologne (Germany),

Institute forAppliedWater, and Geoinformatics(IAWG) for the German Insurance

Association (Gesamtverband der Deutschen Versicherungswirtschaft), GDV,

Berlin, Germany, 29 pp.

Gerrard, B.G. (ed.), 2007: Global Climate Change and U.S. Law. American Bar

Association, Chicago, IL, USA, 784 pp.

Gerstengarbe, F.-W., P.C. Werner, H. Österle, and O. Burghoff, 2013: Winter stormand summer thunderstorm- related loss events with regard to climate change

in Germany. Theoretical and Applied Climatology, 114(3-4), 715-724,

doi:10.1007/s00704-013-0843-y.
699

10

Key Economic Sectors and Services Chapter 10

Ghesquiere, F. and O.Mahul, 2007: Sovereign Natural Disaster Insurance for Developing

Countries: A Paradigm Shift in Catastrophe Risk Financing. Policy Research

Working Paper, 4345, The World Bank Hazard Risk Management Unit Latin

America and Caribbean Region and the Financial Markets for Social Safety Net

Department, Financial and Private Sector Development Vice Presidency, The

World Bank,Washington, DC, USA, 24 pp.

Giannakopoulos, C., E. Kostopoulou, K.V. Varotsos, K. Tziotziou, and A. Plitharas,

2011: An integrated assessment of climate change impacts for Greece in the

near future. Regional Environmental Change, 11(4), 829-843.

Gibbons, J.H., 2012: Technology and innovation. In: Energy for Development:

Resources, Technologies, Environment [Toth, F.L. (ed.)]. Kluwer, Dordrecht,

Netherlands, pp. 141-148.

Ginsburgh, V. and M.A. Keyzer, 1997: The Structure of Applied General Equilibrium

Models. The MIT Press, Cambridge, MA, USA, 555 pp.

Glauber, J.W., 2004: Crop insurance reconsidered. American Journal of Agricultural

Economics, 86(5), 1179-1195.

Gollin, D. and C. Zimmermann, 2008: Malaria: Disease Impacts and Long-Run Income

Differences. IZA Discussion Paper No. 2997, Forschungsinstitut zur Zukunft der

Arbeit Institute for the Study of Labor (IZA), IZA, Bonn, Germany, 25 pp.

Gollin, D. and C. Zimmermann, 2012: Global climate change and the resurgence of

tropical disease: an economic approach. Mathematical Population Studies,

19(1), 51-62.

Golombek, R., S.A.C. Kittelsen, and I. Haddeland, 2011: Climate change: impacts on

electricity markets in Western Europe. Climatic Change, 113(2), 357-370.

Gómez-Martín, M.B., 2006: Climate potential and tourist demand in Catalonia

(Spain) during the summer season. Climate Research, 32(1), 75-87.

Gössling, S. and C.M. Hall, 2006: Uncertainties in predicting tourist flows under

scenarios of climate change. Climatic Change, 79(3-4), 163-173.

Gössling, S., M. Bredberg, A. Randow, E. Sandström, and P. Svensson, 2006: Tourist

perceptions of climate change: a study of international tourists in Zanzibar.

Current Issues in Tourism, 9(4-5), 419-435.

Gössling, S., D. Scott, C.M. Hall, J.-P. Ceron, and G. Dubois, 2012: Consumer behaviour

and demand response of tourists to climate change. Annals of Tourism

Research, 39(1), 36-58.

Grace, M.F. and R.W. Klein, 2009: The perfect storm: hurricanes, insurance, and

regulation. Risk Management and Insurance Review, 12(1), 81-124.

Graff Zivin, J. and M.J. Neidell, 2010: Temperature and the Allocation of Time:

Implications for Climate Change. NBERWorking Paper 15717, National Bureau

of Economic Research (NBER),Washington, DC, USA, 40 pp.

Grey, D. and C.W. Sadoff, 2007: Sink or swim? Water security for growth and

development. Water Policy, 9(6), 545-571.

Guha-Sapir, D., F. Vos, R. Below, and S. Ponserre, 2012: Annual Disaster Statistical

Review 2011:The Numbers and Trends. Centre for Research on the Epidemiology

of Disasters (CRED), Institute of Health and Society (IRSS), Université catholique

de Louvain – Brussels, Belgium, 42 pp.

Gusmao, D., 2010: Case-study 2: European airports and sea level rise. In:“Challenges

of Growth” – Environmental Update Study: Climate Adaptation Case Studies

[Thomas, C. and A.J. Drew (eds.)]. European Organisation for the Safety of Air

Navigation (EUROCONTROL), Brussels, Belgium, pp. 71-91.

Guy Carpenter, 2011: Global Reinsurance Outlook, Points of Inflection – Positioning

for Change in a Challenging Market. Guy Carpenter & Company, LLC, NewYork,

NY, USA, 72 pp.

Hall, C.M., 2006: New Zealand tourism entrepreneur attitudes and behaviours with

respect to climate change adaptation and mitigation. International Journal of

Innovation and Sustainable Development, 1(3), 229-237.

Hall, J.W., P.B. Sayers, and R.J. Dawson, 2005: National-scale assessment of current

and future flood risk in England andWales. Natural Hazards, 36(1-2), 147-164.

Hallegatte, S., 2005: The long time scales of the climate-economy feedback and the

climatic cost of growth. Environmental Modeling and Assessment, 10(4), 277-

289.

Hallegatte, S. and P. Dumas, 2009: Can natural disasters have positive consequences?

Investigating the role of embodied technical change. Ecological Economics,

68(3), 777-786.

Hallegatte, S. and M. Ghil, 2008: Natural disasters impacting a macroeconomic

model with endogenous dynamics. Ecological Economics, 68(1-2), 582-592.

Hallegatte, S. and D. Thery, 2007: Are the economic impacts of climate change

underestimated? Revue d’Economie Politique, 117(4), 507-522.

Hamilton, J.M., 2007: Coastal landscape and the hedonic price of accommodation.

Ecological Economics, 62(3-4), 594-602.

Hamilton, J.M. and R.S.J. Tol, 2007: The impact of climate change on tourism in

Germany, the UK and Ireland: a simulation study. Regional Environmental

Change, 7(3), 161-172.

Hamilton, J.M., D.J. Maddison, and R.S.J. Tol, 2005a: Effects of climate change on

international tourism. Climate Research, 29(3), 245-254.

Hamilton, J.M., D.J. Maddison, and R.S.J.Tol, 2005b: Climate change and international

tourism: a simulation study. Global Environmental Change, 15(3), 253-266.

Hamilton, L.C., C. Brown, and B.D. Keim, 2007: Ski areas, weather and climate: time

series modelsfor New England case studies. International Journal of Climatology,

27(15), 2113-2124.

Hamlet, A.F., S.Y. Lee, K.E.B. Mickelson, and M.M. Elsner, 2010: Effects of projected

climate change on energy supply and demand in the Pacific Northwest and

Washington State. Climatic Change, 102(1-2), 103-128.

Hamzah, J., A. Habibah, A. Buang, K. Jusoff, M.E. Toriman, M.J. Mohd. Fuad, A.C. Er,

and A.M. Azima, 2012: Flood disaster, impacts and the tourism providers’

responses: the Kota Tinggi experience. Advances in Natural and Applied

Sciences, 6(1), 26-32.

Hanewinkel,M., D.A. Cullmann,M.-J. Schelhaas,G.-J.Nabuurs, and N.E. Zimmermann,

2013: Climate change may cause severe loss in the economic value of European

forest land. Nature Climate Change, 3(3), 203-207.

Harrison, S.J., S.J. Winterbottom, and C. Sheppard, 1999: The potential effects of

climate change on the Scottish tourist industry. Tourism Management, 20(2),

203-211.

Haug, O., X.K. Dimakos, J.F. Vardal, M. Aldrin, and E. Meze-Hausken, 2011: Future

building water loss projections posed by climate change. ScandinavianActuarial

Journal, 2011(1), 1-20, doi:10.1080/03461230903266533.

Haugen, J.E. and T. Iversen, 2008: Response in extremes of daily precipitation and

wind from a downscaled multi- model ensemble of anthropogenic global

climate change scenarios. Tellus A, 60(3), 411-426.

Hecht, S.B., 2008: Climate change and the transformation of risk: insurance matters.

UCLA Law Review, 55, 1559-1620.

Hein, L., M.J. Metzger, and A. Moreno, 2009: Potential impacts of climate change on

tourism: a case study for Spain. Current Opinion in Environmental Sustainability,

1(2), 170-178.

Heintz, J.E., M.H. Kanemitsu, and E. Scanlan, 2009: Insurance coverage for climate

change suits: the battle has begun. Environmental Claims Journal, 21(1), 29-

51.

Held, H., F.-W. Gerstengarbe, T. Pardowitz, J.F. Pinto, U. Ulbrich, K. Born, M.G. Donat,

M.K. Karremann, G.C. Leckebusch, P. Ludwig, K.M. Nissen, H. Österle, B.F. Prahl,

P.C.Werner, D.J. Befort, and O. Burghoff, 2013: Projections of global warminginduced impacts on winter storm losses in the German private household sector.

Climatic Change, 121(2), 195-207.

Henderson, J., C. Rodgers, R. Jones, J. Smith, K. Strzepek, and J. Martinich, 2013:

Economic impacts of climate change on water resources in the coterminous

United States. Mitigation and Adaptation Strategies for Global Change,

doi:10.1007/s11027-013-9483-x.

Hendrikx, J. and E.Ö. Hreinsson, 2012: The potential impact of climate change on

seasonal snow in New Zealand: part II – industry vulnerability and future snowmaking potential. Theoretical and Applied Climatology, 110(4), 619-630.

Hendrikx, J., E.Ö. Hreinsson, M.P. Clark, and A.B. Mullan, 2012: The potential impact

of climate change on seasonal snow in New Zealand: part I – an analysis using

12 GCMs. Theoretical and Applied Climatology, 110(4), 607-618.

Hendrikx,J., C. Zammit, E.Ö.Hreinsson, and S. Becken, 2013:A comparative assessment

of the potential impact of climate change on the ski industry in New Zealand

and Australia. Climatic Change, 119(3-4), 965-978.

Herbold, J., 2013a: New approaches to agricultural insurance in developing

economies. In: Finance for Food: Towards New Agricultural and Rural Finance

[Köhn, D. (ed.)]. Kreditanstalt für Wiederaufbau (KfW), Springer, Heidelberg,

Gemany (in press).

Herbold, J., 2013b: Index insurance in agriculture – the (re)insurer’s perspective. In:

The Challenges of Index-Based Insurance for Food Security in Developing Countries

[Gommes, R. and F. Kayitakire (eds.)]. Proceedings of a technical workshop

organised by the EC Joint Research Centre (JRC) and the International Research

Institute for Climate and Society (IRI, Earth Institute, Columbia University), JRC

Ispra, Italy, 2 and 3 May 2012, European Union Publication Office, Luxembourg,

Luxembourg, pp. 47-54.

Hertin, J., F. Berkhout, D.M. Gann, and J. Barlow, 2003: Climate change and the UK

house building sector: perceptions, impacts and adaptive capacity. Building

Research and Information, 31(3-4), 278-290.
700

Chapter 10 Key Economic Sectors and Services

10

Herweijer, C., N. Ranger, and R.E.T. Ward, 2009: Adaptation to climate change:

threats and opportunities for the insurance industry. The Geneva Papers, 34,

360-380.

Hess, U. and P. Hazell, 2009: Sustainability and scalability of index-based insurance

for agriculture. In: Innovations in Insuring the Poor [Vargas Hill, R. and M.Torrero

(eds.)]. The International Food Policy Research Institute (IFPRI), Washington,

DC, USA, pp. 13-14.

Hess,J.J., K.L.Heilpern,T.E.Davis, and H. Frumkin, 2009: Climate change and emergency

medicine: impacts and opportunities. Academic Emergency Medicine, 16(8),

782-794.

Hill, M.,A.Wallner, and J. Furtado, 2010: Reducing vulnerability to climate change in

the Swiss Alps: a study of adaptive planning. Climate Policy, 10(1), 70-86.

Hilmi, N., D. Allemand, S. Dupont, A. Safa, G. Haraldsson, P.A.L.D. Nunes, C. Moore,

C. Hattam, S. Reynaud, J.M. Hall-Spencer, M. Fine, C. Turley, R. Jeffree, J. Orr, P.L.

Munday, and S.R. Cooley, 2013:Towards improved socio-economic assessments

of ocean acidification’s impacts. Marine Biology, 160(8), 1773-1787.

Hines, P., J. Apt, and S. Talukdar, 2009: Large blackouts in North America: historical

trends and policy implications. Energy Policy, 37(12), 5249-5259.

Hochrainer, S., R. Mechler, and D. Kull, 2010: Micro-insurance against drought risk

in changing climate.Assessing demand and supply considerations. International

Journal of Climate Strategies and Management, 2(2), 148-166.

Hochrainer, S. and R. Mechler, 2011: Natural disaster risk in Asian megacities: a case

for risk pooling? Cities, 28, 53-61.

Hoffmann, B.,U.Müller, S. Häfele, and U. Karl, 2010:Analysis of the impact ofchanging

hydro-meteorological parameters on the electricity production of once-through

cooled thermal power plants in Germany – a System Dynamics modelling

approach. In: Proceedings of the International Conference on Energy, Environment

and Health – Optimisation of Future Energy Systems [Siggaard-Andersen, M.-

L. and E. Kaas (eds.)]. CEEH Scientific Report No. 9, July, 2011, Proceedings of

the conference at Carlsberg Academy, Copenhagen, Denmark, May 31 -June

2, 2010, organized by the Centre for Energy Environment and Health (CEEH),

the REBECa project (Renewable Energy in the transport sector - using Biofuels

as Energy Carriers), and the CEESA project (Coherent Energy and Environmental

SystemAnalysis), CEEH, Earth and Planetary Science, University of Copenhagen,

Copenhagen, Denmark, pp. 36-42.

Hoffmann,V.H., D.C. Sprengel,A. Ziegler, M. Kolb, and B.Abegg, 2009: Determinants

of corporate adaptation to climate change in winter tourism: an econometric

analysis. Global Environmental Change, 19(2), 256-264.

Honeyborne, R., 2009: Flat Plate versus Evacuated Tube Solar Collectors. International

Technology Sourcing (ITS), Solar Division, ITS Solar, Cape Town, South Africa.

Hope, C.W., 2013: Critical issues for the calculation of the social cost of CO2: why

the estimates from PAGE09 are higher than those from PAGE2002. Climatic

Change, 117, 531-543.

Hope, C.W. and M. Hope, 2013:The social cost of CO2 in a low-growth world. Nature

Climate Change, 3, 722-724.

Hopkins, D., J.E.S. Higham, and S. Becken, 2012: Climate change in a regional context:

relative vulnerability in the Australasian skier market. Regional Environmental

Change, 13(2), 449-458.

Horowitz, J.K., 2009: The income-temperature relationship in a cross-section of

countries and its implications for predicting the effects of global warming.

Environmental and Resource Economics, 44(4), 475-493.

Hsiang, S.M., 2010: Temperatures and cyclones strongly associated with economic

production in the Caribbean and Central America. Proceedings of the National

Academy of Sciences of the United States of America, 107(35), 15367-15372.

Hübler, M., G. Klepper, and S. Peterson, 2008: Costs of climate change. The effects of

rising temperatures on health and productivity in Germany. Ecological Economics,

68(1-2), 381-393.

Huebner,A., 2012: Public perceptions of destination vulnerability to climate change

and implications for long-haul travel decisions to small island states. Journal

of Sustainable Tourism, 20(7), 939-951.

Hughes, G., P. Chinowsky, and K. Strzepek, 2010: The costs of adaptation to climate

change for water infrastructure in OECD countries.Utilities Policy, 18(3), 142-153.

Hunt, A. and P. Watkiss, 2010: Climatic change impacts and adaptation in cities: a

review of the literature. Climatic Change, 104(1), 13-49.

Hurd, B. and M. Rouhi-Rad, 2013: Estimating economic effects of changes in climate

and water availability. Climatic Change, 117(3), 575-584.

Hurd, B.H., M. Callaway, J. Smith, and P. Kirshen, 2004: Climatic change and U.S.

water resources: from modeled watershed impacts to national estimates.

Journal of the American Water Resource Association, 40(1), 129-148.

Hutton, G., 2011: The economics of health and climate change: key evidence

for decision making. Globalization and Health, 7(18), doi:10.1186/1744-8603-

7-18.

Hwang, I.C., F. Reynès, and R.S.J. Tol, 2013: Climate policy under fat-tailed risk: an

application of Dice. Environmental and Resource Economics, 56, 415-436,

doi:10.1007/s10640-013-9654-y .

IAEA, 2011: Core Knowledge on Instrumentation and Control Systems in Nuclear

Power Plants. IAEA Nuclear Energy Series No. NP-T-3.12, International Atomic

Energy Agency (IAEA), Vienna, Austria, 141 pp.

Ibarra, E.M., 2011:The use of webcam images to determine tourist-climate aptitude:

favourable weather types for sun and beach tourism on the Alicante coast

(Spain). International Journal of Biometeorology, 55(3), 373-385.

IEA, 2008: World Energy Outlook 2008. Organization for Economic Cooperation and

Development (OECD) and International EnergyAgency (IEA), Paris, France, 569 pp.

IEA, 2009: World Energy Outlook 2009. Organization for Economic Cooperation and

Development (OECD) and International EnergyAgency (IEA), Paris, France, 691 pp.

IEA, 2010a: World Energy Outlook 2010. Organization for Economic Cooperation and

Development (OECD) and International EnergyAgency (IEA), Paris, France, 731 pp.

IEA, 2010b: Energy Technology Perspectives 2010. Organization for Economic

Cooperation and Development (OECD) and International Energy Agency (IEA),

Paris, France, 706 pp.

IEA, 2012: World Energy Outlook 2012. Organization for Economic Cooperation and

Development (OECD) and International EnergyAgency (IEA), Paris, France, 668 pp.

Iizumi, T., M. Yokozawa, Y. Hayashi, and F. Kimura, 2008: Climate change impact on

rice insurance payoutsin Japan.Journal ofApplied Meteorology and Climatology,

47, 2265-2278.

Ikefuji, M., J. Magnus, and H. Sakamoto, 2010: Climate Change, Economic Growth,

and Health. Discussion Paper No. 2010-86, Tilburg University Center for

Economic Research, Amsterdam, Netherland, 39 pp.

Ikefuji, M. and R. Horii, 2012: Natural disasters in a two-sector model of endogenous

growth. Journal of Public Economics, 96(9-10), 784-796.

Interagency Working Group on the Social Cost of Carbon, 2010: Social Cost of

Carbon for Regulatory Impact Analysis under Executive Order 12866. February,

2010,The InteragencyWorking Group (IAW) on the Social Cost of Carbon (SCC),

United States Government,Washington, DC, USA, 51 pp.

Interagency Working Group on the Social Cost of Carbon, 2013: Technical

Support Document – Technical Update of the Social Cost of Carbon for

Regulatory Impact Analysis – under Executive Order 12866. The Interagency

Working Group (IAW) on the Social Cost of Carbon (SCC), United States

Government,Washington, DC, USA, 21 pp.

IPCC, 2005: IPCC Special Report on Carbon Dioxide Capture and Storage. Prepared

byWorking Group III of the Intergovernmental Panel on Climate Change [Metz,

B., O. Davidson, H.C. de Coninck, M. Loos, and L.A. Meyer (eds.)]. Cambridge

University Press, Cambridge, UK and New York, NY, USA, 442 pp.

IPCC, 2011: IPCC Special Report on Renewable Energy Sources and Climate Change

Mitigation. Prepared by Working Group III of the Intergovernmental Panel on

Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, K. Seyboth, P.

Matschoss, S. Kadner, T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, and C.

von Stechow (eds.)]. Cambridge University Press, Cambridge, UK and NewYork,

NY, USA, 1075 pp.

IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate

Change Adaptation. A Special Report of Working Groups I and II of the

Intergovernmental Panel on Climate Change [Field, C.B., V. Barros, T.F. Stocker,

D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K.

Allen,M.Tignor, and P.M.Midgley (eds.)]. Cambridge University Press, Cambridge,

UK and New York, NY, USA, 582 pp.

Isaac, M. and D. Van Vuuren, 2009: Modelling global residential sector energy

demand for heating and air conditioning in the context of climate change.

Energy Policy, 37, 507-521.

Jiang, M., E. Wong, L.M. Klint, T. DeLacy, and D. Dominey-Howes, 2012: Tourism

adaptation to climate change –analysing the policy environment of Fiji.

International Journal of Tourism Policy, 4(3), 238-260.

Jochem, E.S., T. Barker, G. Catenazzi, W. Eichhammer, T. Fleiter, A. Held, N. Helfrich,

M. Jakob, P. Criqui, S. Mima, L. Quandt,A. Peters , M. Ragwitz, U. Reiter, F. Reitze,

M. Schelhaas, S. Scrieciu, and H. Turton, 2009: Report of the Reference and 2°C

Scenario for Europe. Project No. 018476-GOCE, Deliverable D-M1.2 of the

ADAM project (Adaptation and Mitigation Strategies: Supporting European

Climate Policy), Fraunhofer Institute for Systems and Innovation Research (ISI),

Karlsruhe, Germany, 231 pp.
701

10

Key Economic Sectors and Services Chapter 10

Jones, B.F. and B.A. Olken, 2010: Climate shocks and exports. American Economic

Review, 100(2), 454-459.

Jones, B., D. Scott, and H.A. Khaled, 2006: Implications of climate change for outdoor

event planning: a case study of three special events in Canada’s National

Capital Region. Event Management, 10(1), 63-76.

Jopp, R., T. DeLacy, J. Mair, and M. Fluker, 2013: Using a regional tourism adaptation

framework to determine climate change adaptation options for Victoria’s Surf

Coast. Asia Pacific Journal of Tourism Research, 18(1-2), 144-164.

Jorgenson, D., R. Goettle, B. Hurd, and J. and Smith, 2004: U.S. Market Consequences

of Global Climate Change. Pew Center on Global Climate Change, Arlington,

VA, USA, 44 pp.

Kabat, P., H. van Schaik, and B. Appleton, 2003: Climate Changes the Water Rules:

HowWater Managers can cope with Today’s Climate Variability and Tomorrow’s

Climate Change. Dialogue on Water and Climate (DWC), International

Secretariat of DWC,Wageningen, Netherlands, 105 pp.

Kane, S., J.M. Reilly, and J. Tobey, 1992: An empirical study of the economic effects

of climate change on world agriculture. Climatic Change, 21(1), 17-35.

Karl,T.R., J.M. Melillo, and T.C. Peterson (eds.), 2009: Global Climate Change Impacts

in the United States. State of the Knowledge Report by the U.S. Global Change

Research Program, Cambridge University Press, New York, NY, USA, 188 pp.

Kemfert, C., 2002: An integrated assessment model of economy-energy-climate –

the Model WIAGEM. Integrated Assessment, 3(4), 281-298.

Khosla, V., 2008: Scalable Electric Power from Solar Energy. Breaking the Climate

Deadlock Briefing Paper, The Climate Group, Brussels, Belgium, 19 pp.

Kingwell, R. and I. Farré, 2009: Climate change impacts on investment in crop sowing

machinery. Australian Journal of Agricultural and Resource Economics, 53(2),

265-284.

Kirkinen, J.,A. Martikainen, H. Holttinen, I. Savolainen, O.Auvinen, and S. Syri, 2005:

Impacts on the Energy Sector andAdaptation of the Electricity Network Business

under a Changing Climate in Finland. Finnish Environment Institute (SKYE)

Mimeographs No. 340, FINADAPT Working Paper 10, FINADAPT Consortium

co-ordinated at SYKE as a part of the Finnish Environmental Cluster Research

Programme,co-ordinated by the Finnish Ministry of the Environment, Helsinki,

Finland, 36 pp.

Kirshen, P., M. Ruth, and W. Anderson, 2006: Climate’s long-term impacts on urban

infrastructures and services: the case of Metro Boston. In: Regional Climate

Change and Variability: Impacts and Responses [Ruth, M., K. Donaghy, and P.

Kirshen (eds.)]. New Horizons in Regional Science Series, Edward Elgar

Publishers, Northampton, MA, USA, pp. 190-252.

Kirshen, P., K. Knee, and M. Ruth, 2008a: Climate change and coastal flooding in Metro

Boston: impacts and adaptation strategies. Climatic Change, 90(4), 453-473.

Kirshen, P., M. Ruth, and W. Anderson, 2008b: Interdependencies of urban climate

change impacts and adaptation strategies: a case study of Metropolitan Boston

USA. Climatic Change, 86(1-2), 105-122.

Kjellstrom, T., R.S. Kovats, S.J. Lloyd, T. Holt, and R.S. Tol, 2009: The direct impact of

climate change on regional labor productivity. Archives of Environmental &

Occupational Health, 64(4), 217-227.

Kjellstrom, T., B. Lemke, and M. Otto, 2013: Mapping occupational heat exposure

and effects in South-East Asia: ongoing time trends 1980-2009 and future

estimates to 2050. Industrial Health, 51, 56-67.

Klint, L.M., M. Jiang, A. Law, T. DeLacy, S. Filep, E. Calgaro, D. Dominey-Howes, and

D. Harrison, 2012: Dive tourism in Luganville, Vanuatu: shocks, stressors, and

vulnerability to climate change. Tourism in Marine Environments, 8(1-2), 91-109.

Knowlton, K., M. Rotkin-Ellman, L. Geballe,W. Max, and M.S. Gina, 2011: Six climate

change-related events in the United States accounted for about $14 billion in

lost lives and health costs. Health Affairs, 30(11), 2167-2176.

Koch, H., S.Vögele, M. Kaltofen, and U. Grünewald, 2012:Trends inWater availability

for power plants-scenario analyses for the German Capital Berlin. Climatic

Change, 110(3-4), 879-899.

Koetse, M. and P. Rietveld, 2009: The impact of climate change and weather on

transport: an overview of empirical findings. Transportation Research Part D:

Transport and Environment, 14(3), 205-221.

Kong, C., J. Bang, and Y. Sugiyama, 2005: Structural investigation of composite wind

turbine blade considering various load cases and fatigue life. Energy, 30, 2101-

2114.

Kopp, R.E.,A.Golub,N.O. Keohane, and C.Onda, 2012:The influence of the specification

of climate change damages on the social cost of carbon. Economics: The OpenAccess, Open-Assessment E-Journal, 6, 2012-13, doi:10.5018/economicsejournal.ja.2012-13.

Kousky, C. and R. Cooke, 2012: Explaining the failure to insure catastrophic risks.

The Geneva Papers, 37(2), 206-227, doi:10.1057/gpp.2012.14.

Kousky, C. and H. Kunreuther, 2010: Improving flood insurance and flood-risk

management: insights from St. Louis, Missouri. Natural Hazards Review, 11(4),

162-172.

Krausmann, E. and F. Mushtaq, 2008: A qualitative Natech damage scale for the

impact of floods on selected industrial facilities. Natural Hazards, 46(2), 179-

197.

Krekt,A.H., T.J. van der Laan, R.A.E. van der Meer, B. Turpin, O.E. Jonkeren,A. van der

Toorn, E. Mosselman, J. van Meijeren, and T. Groen, 2011: Climate Change and

Inland Waterway Transport: Impacts on the Sector, the Port of Rotterdam and

Potential Solutions. Research Project HSRR08, National Research Programme

Knowledge for Climate, Secretariat of the Knowledge for Climate Programme,

University of Utrecht, Utrecht, Netherlands, 74 pp.

Kron,W., 2009: Flood insurance: from clients to global financial markets. Journal of

Flood Risk Management, 2, 68-75, doi:10.1111/j.1753-318X.2008.01015.x.

Kron,W., 2012: Changing flood risk – a re-insurer’s viewpoint. In: Changes in Flood

Risk in Europe [Kundzewicz, Z.W. (ed.)]. InternationalAssociation of Hydrological

Sciences,Wallingford, UK, pp. 459-490.

Kron,W. and G. Berz, 2007: Flood disasters and climate change: trends and options

– a (re-)insurer’s view. In: Global Change: EnoughWater for All? [Lozán, J.L., H.

Graßl, P. Hupfer, L. Menzel, and C.-D. Schönwiese (eds.). Wissenschaftliche

Auswertungen, Hamburg, Germany, pp. 268-273.

Kron,W., M. Steuer, P. Loew, and A.Wirtz, 2012: How to deal properly with a natural

catastrophe database – analysis of flood losses. Natural Hazards and Earth

System Sciences, 12, 535-550.

Kulshreshtha, S.N., 2011: Climate change, prairie agriculture, and prairie economy:

the new normal. Canadian Journal of Agricultural Economics, 59(1), 19-44.

Kundzewicz, Z.W., L.J. Mata, N.W. Arnell, P. Döll, P. Kabat, B. Jiménez, K.A. Miller, T.

Oki, Z. Sen and I.A. Shiklomanov , 2007: Freshwater resources and their

management. In: Climate Change 2007: Mitigation of Climate Change.

Contribution of Working Group III to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change [Metz, B., O.R. Davidson, P.R.

Bosch, R. Dave, and L.A. Meyer (eds.)]. Cambridge University Press, Cambridge,

UK and New York, NY, USA, pp. 173-210.

Kunreuther, H.C. and E.O. Michel-Kerjan, 2009:The development of new catastrophe

risk markets. Annual Review of Resource Economics, 1, 119-139.

Kunreuther, H.C., E.O. Michel-Kerjan, N.A. Doherty, M.R. Grace, R.W. Klein, and M.V.

Pauly, 2009: At War with the Weather: Managing Large-Scale Risks in a New

Era of Catastrophes. The Massachusetts Institute of Technology (MIT) Press,

Cambridge, MA, USA and London, UK, pp. 416.

Kunreuther, H.C., E.O. Michel-Kerjan, and N. Ranger, 2012: Insuring future climate

catastrophes. Climatic Change, 118(2), 339-354.

Kunz, K., J. Sander, and C. Kottmeier, 2009: Recent trends of thunderstorm and

hailstorm frequency and their relation to atmospheric characteristics in

southwest Germany. International Journal of Climatology, 29, 2283-2297.

Kurtz, S., J. Granata, and M. Quintana, 2009: Photovoltaic reliability R&D toward a

solar-powered world. In: Proceedings of SPIE: Reliability of Photovoltaic Cells,

Modules, Components, and Systems II [Dhere, N.G., J.H.Wohlgemuth, and D.T.

Ton (eds.)]. Proceedings of the International Society for Optics and Photonics

(SPIE),Vol. 7412, SPIE, Bellingham,WA, USA,Article No. 7412 0Z, doi:10.1117/

12.825649.

Kurtz, S., K.Whitfield, G. TamizhMani, M. Koehl, D. Miller, J. Joyce, J.Wohlgemuth, N.

Bosco, M. Kempe, and T. Zgonena, 2011: Evaluation of high temperature

exposure of photovoltaic modules. Progress in Photovoltaics: Research and

Applications, 19(8), 954-965.

Landauer, M., U. Pröbstl, and W. Haider, 2012: Managing cross-country skiing

destinations under the conditions ofclimate change – scenarios for destinations

in Austria and Finland. Tourism Management, 33(4), 741-751.

Larsen, P., S. Goldsmith, O. Smith, M.Wilson, K. Strezpek, P. Chinowsky, and B. Saylor,

2008: Estimating future costs for Alaska public infrastructure at risk from

climate change. Global Environmental Change, 18, 442-457.

Lavin, P.G., 2003: Asphalt Pavements: A Practical Guide to Design, Production, and

Maintenance for Architects and Engineers. Spon Press, London, UK and New

York, NY, USA, 444 pp.

Le Floc’h, P., J.-C. Poulard, O. Thébaud, F. Blanchard, J. Bihel, and F. Steinmetz, 2008:

Analyzing the market position of fish species subject to the impact of longterm changes: a case study of French fisheries in the Bay of Biscay. Aquatic

Living Resources, 21(3), 307-316.
702

Chapter 10 Key Economic Sectors and Services

10

Leblois, A. and P. Quirion, 2013: Agricultural insurances based on meteorological

indices: realizations, methods and research challenges. Meteorological

Applications, 20(1), 1-9, doi:10.1002/met.303.

Leckebusch, G.C., U. Ulbrich, L. Fröhlich, and J.G. Pinto, 2007: Property loss potentials

for European midlatitude storms in a changing climate. Geophysical Research

Letters, 34(5), doi:10.1029/2006GL027663.

Leckebusch, G.C.,A.Weimer, J.G. Pinto, M. Reyers, and P. Speth, 2008: Extreme wind

storms over Europe in present and future climate: a cluster analysis approach.

Meteorologische Zeitschrift, 17(1), 67-82.

Lee, D.M. and K.S. Lyon, 2004:A dynamic analysis of the global timber market under

global warming: an integrated modeling approach. Southern Economic Journal,

70(3), 467-489.

Lee, H.-L., 2009: The impact of climate change on global food supply and demand,

food prices, and land use. Paddy and Water Environment, 7(4), 321-331.

Leiva, A.J. and J.R. Skees, 2008: Using irrigation insurance to improve water usage

of the Rio Mayo irrigation system in Northwestern Mexico.World Development,

36(12), 2663-2678.

Lemmen, D. and F. Warren, 2010: Climate Change Impacts and Adaptation: A

Canadian Perspective [Lemmen, D. and F. Warren (eds.)]. Government of

Canada, Climate Change Impacts andAdaptation Directorate,Natural Resources

Canada, Ottawa, ON, Canada, 174 pp.

Lin, S., H. Wan-Hsiang, V.Z. Alissa, S. Shubhayu, L. George, and H. Syni-An, 2012:

Excessive heat and respiratory hospitalizations in New York State: estimating

current and future public health burden related to climate change.

Environmental Health Perspectives, 120(12), 1571-1577, doi: 10.1289/

ehp.1104728.

Lin, T.-P. and A. Matzarakis, 2008: Tourism climate and thermal comfort in Sun Moon

Lake, Taiwan. International Journal of Biometeorology, 52(4), 281-290.

Lin, T.-P. and A. Matzarakis, 2011: Tourism climate information based on human

thermal perception in Taiwan and Eastern China. Tourism Management, 32(3),

492-500.

Link, P.M. and R.S.J. Tol, 2009: Economic impacts on key Barents Sea fisheries arising

from changes in the strength of the Atlantic thermohaline circulation. Global

Environmental Change, 19(4), 422-433.

Linnerooth-Bayer, J. and R. Mechler, 2009: Insurance against Losses from Natural

Disasters in Developing Countries. DESA Working Paper No. 85, ST/ESA/2009/

DWP/85, United Nations Department of Economic and SocialAffairs (UN DESA),

New York, NY, USA, 35 pp.

Linnerooth-Bayer, J., K. Warner, C. Bals, P. Höppe, I. Burton, T. Loster, and A. Haas,

2009: Insurance, developing countries and climate change. The Geneva Papers,

34, 381-400.

Linnerooth-Bayer, J., R. Mechler, and S. Hochrainer-Stigler, 2011: Insurance against

losses from natural disasters in developing countries – evidence, gaps and the

way forward. Journal of Integrated Disaster Risk Management, 1(1), 59-81,

doi:10.5595/idrim.2011.0013.

Linnerud, K., T.K. Mideksa, and G.S. Eskeland, 2011: The impact of climate change

on nuclear power supply. Energy Journal, 32(1), 149-168.

Lise,W. and R.S.J. Tol, 2002: Impact of climate on tourist demand. Climatic Change,

55(4), 429-449.

Lo, A.J., 2013: The likelihood of having flood insurance increases with social

expectations. Area, 45(1), 70-76.

Lu, C., M.T. Schneider, P. Gubbins, K. Leach-Kemon, D. Jamison, and C.J. Murray, 2010:

Public financing of health in developing countries: a cross-national systematic

analysis. The Lancet, 375(9723), 1375-1387.

Macknick, J., R. Newmark, G. Heath, and K.C. Hallett, 2011: A Review of Operational

Water Consumption and Withdrawal Factors for Electricity Generating

Technologies. Technical Report NREL/TP-6A20-50900. National Renewable Energy Laboratory, Golden, CO, USA, 21 pp. http://www.nrel.gov/docs/fy11osti/

50900.pdf.

Maddison, D., 2001: In search of warmer climates? The impact of climate change

on flows of British tourists. Climatic Change, 49(1-2), 193-208.

Maddison, D. and K. Rehdanz, 2011: The impact of climate on life satisfaction.

Ecological Economics, 70(12), 2437-2445.

Mahul, O. and C.J. Stutley, 2010: Government Support to Agricultural Insurance:

Challenges and Options for Developing Countries. The International Bank for

Reconstruction and Development / The World Bank, Washington, DC, USA,

219 pp.

Makki, S.S. and A. Somwaru, 2001: Evidence of adverse selection in crop insurance

markets. The Journal of Risk and Insurance, 68(4), 685-708.

Mansur, E.T., R.O. Mendelsohn, andW. Morrison, 2005: A Discrete-Continuous Choice

Model of Climate Change Impacts on Energy. Yale SOM Working Paper ES-43,

Yale School of Management and School of Forestry and Environmental Studies,

New Haven, CT, USA, 45 pp.

Markandya, A. and A. Chaibai, 2009: Valuing climate change impacts on human

health: empirical evidence from the literature. International Journal of

Environmental Resources and Public Health, 6, 759-786.

Markoff, M.S. and A.C. Cullen, 2008: Impact of climate change on Pacific northwest

hydropower. Climatic Change, 87, 451-469.

Masters, W.A. and M.S. McMillan, 2001: Climate and scale in economic growth.

Journal of Economic Growth, 6(3), 167-186.

Matzarakis,A. and C. Endler, 2010: Climate change and thermal bioclimate in cities:

impacts and options for adaptation in Freiburg, Germany. International Journal

of Biometeorology, 54(4), 479-483.

Matzarakis, A., T. Georgiadis, and F. Rossi, 2007: Thermal bioclimate analysis for

Europe and Italy. Il Nuovo Cimento, 30(6), 623-632, doi:10.1393/ncc/i2007-

10268-0.

Matzarakis,A., E. Rudel, M. Zygmuntowski, and E. Koch, 2010: Bioclimatic maps for

tourism purposes. Physics and Chemistry of the Earth, 35(1-2), 57-62.

Matzarakis, A., M. Hämmerle, C. Endler, S. Muthers, and E. Koch, 2012: Assessment

of tourism and recreation destinations under climate change conditions in

Austria. Meteorologische Zeitschrift, 21(2), 157-165.

Maynard,T. and N. Ranger, 2012:What role for “long-term insurance” in adaptation?

An analysis of the prospects for and pricing of multi-year insurance contracts.

The Geneva Papers, 37(2), 318-339.

McBoyle, G., D. Scott, and B. Jones, 2007: Climate change and the future of

snowmobiling in non-mountainous regions of Canada. Managing Leisure,

12(4), 237-250.

McColl, L., 2012: Assessing the Potential Impact of Climate Change on the UK’s

Electricity Network. Met Office, Exeter, UK, 2 pp.

McDermott, G. and O. Nilsen, 2013: Electricity Prices, River Temperatures and Cooling

Water Scarcity. IZA Discussion Paper No. 6482, Institute for the Study of Labour

(IZA), Bonn, Germany, 38 pp.

McGoodwin, J.R., 2007: Effects of climatic variability on three fishing economies in

high-latitude regions: implications for fisheries policies. Marine Policy, 31(1),

40-55.

McIlgorm, A., 2010: Economic impacts of climate change on sustainable tuna

and billfish management: insights from the Western Pacific. Progress in

Oceanography, 86(1-2), 187-191.

Mechler, R., J. Linnerooth-Bayer, and D. Peppiatt, 2006: Disaster Insurance for the

Poor? A Review of Microinsurance for Natural Disaster Risks in Developing

Countries. ProVention Consortium and International Institute for Applied

Systems Analysis (IIASA) Study, ProVention, Geneva, Switzerland and IIASA,

Laxenburg, Austria, 31 pp.

Melecky,M. and C. Raddatz, 2011:How Do Governments Respond after Catastrophes?

Natural-Disaster Shocks and the Fiscal Stance. Policy Research Working Paper

No. 5564,TheWorld Bank, Private & Financial Sectors Development Sector Unit,

Europe and Central Asia Region,Washington, DC, USA, 25 pp.

Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012: The impact

of climate change on global tropical cyclone damage. Nature Climate Change,

2, 205-209.

Mendoza, R.U., 2009: A proposal for an Asian rice insurance mechanism. Global

Economy Journal, 9(1), 6, doi:10.2202/1524-5861.1454.

Mercer, 2011: Climate Change Scenarios – Implications for StrategicAssetAllocation.

Mercer, LLC, Carbon Trust, UK, and International Finance Corporation (IFC),

Mercer, LLC, New York, NY, USA, 124 pp.

Merino, G., M. Barange, and C. Mullon, 2010: Climate variability and change

scenarios for a marine commodity: modelling small pelagic fish, fisheries and

fishmeal in a globalized market. Journal of Marine Systems, 81(1-2), 196-205.

Meze-Hausken, E.,A. Patt, and S. Fritz, 2009: Reducing climate risk for micro-insurance

providers in Africa: a case study of Ethiopia. Global Environmental Change, 19,

66-73.

Michel-Kerjan, E.O., 2010: Catastrophe economics: the national flood insurance

program. Journal of Economic Perspectives, 24(4), 165-186.

Michel-Kerjan, E. and H. Kunreuther, 2011: Redesigning flood insurance. Science,

333, 408-409.

Michel-Kerjan, E. and F. Morlaye, 2008: Extreme events, global warming, and

insurance-linked securities: how to trigger the tipping point. The Geneva Papers,

33, 153-176.
703

10

Key Economic Sectors and Services Chapter 10

Michel-Kerjan, E., S. Hochrainer-Stigler, H. Kunreuther, H. Kunreuther, J. LinneroothBayer, R. Mechler, R. Muir-Wood, N. Ranger, P. Vaziri, and M. Young, 2013:

Catastrophe risk models for evaluating disaster risk reduction investments in

developing countries. Risk Analysis, 33(6), 984-999.

Middelkoop, H., K. Daamen, D. Gellens, W. Grabs, J.C.J. Kwadijk, H. Lang, B.W.A.H.

Parmet, B. Schädler, J. Schulla, and K.Wilke, 2001: Impact of climate change on

hydrological regimes and water resources management in the Rhine basin.

Climatic Change, 49(1-2), 105-128.

Mideksa,T.K. and S. Kallbekken, 2010:The impact of climate change on the electricity

market: a review. Energy Policy, 38(7), 3579-3585.

Mika, J., 2013: Changes in weather and climate extremes: phenomenology and

empirical approaches. Climatic Change, 121(1), 15-26.

Millerd, F., 2010:The potential impacts ofclimate change on Great Lakes international

shipping. Climatic Change, 104(3-4), 629-652.

Millner, A., 2013: On welfare frameworks and catastrophic climate risks. Journal of

Environmental Economics and Management, 65(2), 310-325.

Mills, B.N. and J. Andrey, 2002: Climate change and transportation: potential

interactions and impacts. In: The Potential Impacts of Climate Change on

Transportation. Federal Research PartnershipWorkshop October 1-2, 2002, held

at the Brookings Institution, Washington, DC, USA, Summary and Discussion

Papers, U.S. Department of Transportation Center for Climate Change and

Environmental Forecasting (DOT),Washington, DC, USA, pp. 77-88.

Mills, E., 2009: A global review of insurance industry responses to climate change.

The Geneva Papers, 34, 323-359.

Mills, E., 2012: The Greening of insurance. Science, 338, 1424-1425.

Moen, J. and P. Fredman, 2007: Effects of climate change on alpine skiing in Sweden.

Journal of Sustainable Tourism, 15(4), 418-437.

Mogaka, H., S. Gichere, and R. Davis, 2006: Climate Variability and Water Resources

Degradation in Kenya: ImprovingWater Resources Development and Management.

World Bank Working Paper No. 69, The International Bank for Reconstruction

and Development / The World Bank,Washington, DC, USA, 105 pp.

Moiseyev, A., B. Solberg, A.M.I. Kallio, and M. Lindner, 2011: An economic analysis

of the potential contribution of forest biomass to the EU RES target and its

implications for the EU forest industries. Journal of Forest Economics, 17(2),

197-213.

Monti, A., 2012: Public-private initiatives to cover extreme events. In: The Geneva

Reports – Risk and Insurance Research No.5. Extreme Events and Insurance:

2011 annus horribilis [Courbage, C. and W.R. Stahel (eds.)]. The Geneva

Association,The InternationalAssociation for the Study of Insurance Economics,

Geneva, Switzerland, pp. 27-38.

Moreno, A., 2010: Mediterranean tourism and climate (change): a survey-based

study. Tourism and Hospitality, Planning and Development, 7(3), 253-265.

Moreno, A., B. Amelung, and L. Santamarta, 2008: Linking beach recreation to

weather conditions: a case study in Zandvoort, Netherlands. Tourism in Marine

Environments, 5(2-3), 111-119.

Moreno, A. and B. Amelung, 2009: Climate change and tourist comfort on Europe’s

beaches in summer: a reassessment. Coastal Management, 37(6), 550-568.

Morrison, C. and C. Pickering, 2012: Limits to climate change adaptation:case study

of the Australian Alps. Geographical Research, 51(1), 11-25.

Muir-Wood, R. and P. Grossi, 2008: The catastrophe modeling response to Hurricane

Katrina. In: Climate Extremes and Society [Diaz, H.F. and R.J. Murnane (eds.)].

Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 296-

319.

Mukheibir, P., 2013: Potential consequences of projected climate change impacts

on hydroelectricity generation. Climatic Change, 121(1), 67-78.

Muller, M., 2007: Adapting to climate change: water management for urban

resilience. Environment and Urbanization, 19(1), 99-113.

Munich Re, 2005: Weather Catastrophes and Climate Change: Is There Still Hope for

Us? Münchener Rückversicherungs-Gesellschaft, Munich Reinsurance Company

(Munich Re), Munich, Germany, 264 pp.

Munich Re, 2011: Topics Geo: Natural Catastrophes 2010: Analyses, Assessments,

Positions. Muenchener Rückversicherungs-Gesellschaft, Munich Reinsurance

Company (Munich Re), Munich, Germany, 40 pp.

Munich Re, 2013: Topics Geo: Natural Catastrophes 2012: Analyses, Assessments,

Positions. Muenchener Rückversicherungs-Gesellschaft, Munich Reinsurance

Company (Munich Re), Munich, Germany, 60 pp.

Narita, D., R.S.J. Tol, and D. Anthoff, 2009: Damage costs of climate change through

intensification of tropical cyclone activities: an application of FUND. Climate

Research, 39(2), 87-97.

Narita, D., R.S.J. Tol, and D. Anthoff, 2010: Economic costs of extratropical storms

under climate change: an application of FUND. Journal of Environmental

Planning and Management, 53(3), 371-384.

Narita, D., K. Rehdanz, and R.S.J. Tol, 2012: Economic costs of ocean acidification: a

look into the impacts on global shellfish production. Climatic Change, 113(3-4),

1049-1063.

Nassopoulos, H., P. Dumas, and S. Hallegatte, 2012: Adaptation to an uncertain

climate change: cost benefit analysis and robust decision making for dam

dimensioning. Climatic Change, 114(3-4), 497-508.

National Research Council, 2008: Potential Impacts of Climate Change on U.S.

Transportation. Transportation Research Board Special Report 290. Committee

on Climate Change and U.S. Transportation, Transportation Research Board

(TRB) and Division on Earth and Life Studies, National Research Council of the

National Academies, TRB,Washington, DC, USA, 280 pp.

Nelson, G., M. Rosegrant, J. Koo, R. Robertson, T. Sulser, R. Zhu, C. Ringler, S. Msangi,

A. Palazzo, M. Batka, M. Magalhaes, R.Valmonte-Santos, M. Ewing, and D. Lee,

2009: Climate Change Impact on Agriculture and Costs of Adaptation. IFPRI

Food Policy Report, International Food Policy Research Institute (IFPRI),

Washington, DC, USA, 30 pp.

Nemry, F. and H. Demirel, 2012: Impacts of Climate Change on Transport:A Focus on

Road and Rail Transport Infrastructures. JRC Scientific and Policy Reports,

European Commission, Joint Research Centre, and Institute for Prospective

Technological Studies, Publications Office of the European Union, Luxembourg,

Luxembourg, 89 pp.

NETL, 2007: Potential Impacts of Climate Change on the Energy Sector. DOE/NETL403/101807, U.S. Government, National Energy Technology Laboratory (NETL),

Pittsburgh, PA, USA, 51 pp.

Neumann, J., J. Price, P. Chinowsky, L.Wright, L. Ludwig, R. Streeter, R. Jones, J. Smith,

B. Perkins, L. Jantarasami, and J. Martinich, 2013: Climate change risks to United

States infrastructure: impacts on coastal development, roads, bridges, and

urban drainage. Climatic Change, doi:10.1007/s10584-013-1037-4.

Niesing, B., 2004:WIND provides storm warnings. Fraunhofer Magazine, 1, 20-21.

Nordhaus,W.D., 2010:The economics of hurricanes and implications of globalwarming.

Climate Change Economics, 1(1), 1-20, DOI: 10.1142/S2010007810000054.

Nordhaus, W.D., 2011: The economics of tail events with an application to climate

change. Review of Environmental Economics and Policy, 5(2), 240-257.

Norton, B., 2006:Anatomy of a solarcollector: developmentsin materials,components

and efficiency improvements in solar thermal collector systems. Refocus, 7(3),

32-35.

Noy, I., 2009:The macroeconomic consequences of disasters. Journal of Development

Economics, 88(2), 221-231.

Ntelekos,A.A.,M.Oppenheimer,J.A. Smith, andA.J.Miller, 2010:Urbanization,climate

change and flood policy in the United States. Climatic Change, 103(3), 597-616.

Nursey-Bray, M. and T. Miller, 2012: Ports and climate change: building skills in

climate change adaptation, Australia. In: Climate Change and the Sustainable

Use of Water Resources [Filho, W.L. (ed.)]. Springer-Verlag, Berlin and

Heidelberg, Germany, pp. 273-282.

Nyaupane, G.P. and N. Chhetri, 2009:Vulnerability to climate change of nature-based

tourism in the Nepalese Himalayas. Tourism Geographies, 11(1), 95-119.

Oliver Wyman, 2007: Climate Change: Risks and Opportunities for Global Financial

Services. Oliver Wyman, Ltd., New York, NY, USA, 58 pp.

Olonscheck, M., A. Holsten, and J.P. Kropp, 2011: Heating and cooling energy

demand and related emissions of the German residential building stock under

climate change. Energy Policy, 39(9), 4795-4806.

Ott, H.E. and C. Richter, 2008: Anpassung an den Klimawandel – Risiken und Chancen

für deutsche Unternehmen. Wuppertal Papers No. 171, Brief analysis for the

Federal Ministry for the Environment, Nature Conservation and Nuclear Safety,

Project: Economic Opportunities Of International Climate Policy (FKZ

90511504), Wuppertal Institit für Klima, Umwelt, Energie GmbH, Wuppertal,

Germany, 26 pp.

Ou-Yang, C., H. Kunreuther, and E. Michel-Kerjan, 2013: An economic analysis of

climate adaptations to hurricane risk in St. Lucia. The Geneva Papers, 38, 521-

546.

Pandey, K., 2010: Costs ofAdapting to Climate Change for Human Health in Developing

Countries. World Bank Development and Climate Change Discussion Paper

No.11,The International Bank for Reconstruction and Development / TheWorld

Bank, Washington, DC, USA, 19 pp.

Pang, S.F.H., B. McKercher, and B. Prideaux, 2013: Climate change and tourism: an

overview. Asia Pacific Journal of Tourism Research, 18(1-2), 4-20.
704

Chapter 10 Key Economic Sectors and Services

10

Parkpoom, S., G.P. Harrison, and J.W. Bialek, 2005: Climate and weather uncertainty

in the electricity industry. International Energy Journal, 6(1), 455-464.

Patt, A., P. Suarez, and U. Hess, 2010: How do small-holder farmers understand

insurance, and how much do they want it? Evidence from Africa. Global

Environmental Change, 20, 153-161.

Patt,A., Pfenninger S., and J. Lilliestam, 2013:Vulnerability ofsolar energy infrastructure

and output to climate change. Climatic Change, 121(1), 93-102.

Patton, L., 2011: Why insurers should focus on climate risk issues. In: Risk

Management, (SC5), June 2011. The Geneva Association, The International

Association for the Study of Insurance Economics, Geneva, Switzerland, pp. 5-

15, www.genevaassociation.org/media/185056/ga2011-rmsc5.pdf.

Paudel, Y., 2012: A comparative study of public – private catastrophe insurance

systems: lessons from current practices. The Geneva Papers, 37(2), 257-285,

doi:10.1057/gpp.2012.16.

Pearce, T.D., J.D. Ford, J. Prno, F. Duerden, J. Pittman, M. Beaumier, L. Berrang-Ford,

and B. Smit, 2011: Climate change and mining in Canada. Mitigation and

Adaptation Strategies for Global Change, 16(3), 347-368.

Pechan, A. and K. Eisenack, 2013: The Impact of Heat Waves on Electricity Spot

Markets. Oldenburg Economic Discussion PaperV-357-13,Oldenburg University,

Oldenburg, Germany, 31 pp.

Peduzzi, P., B. Chatenoux, H. Dao,A. De Bono, C. Herold, J. Kossin, F. Mouton, and O.

Nordbeck, 2012: Global trends in tropical cyclone risk. Nature Climate Change,

2, 289-294, doi:10.1038/NCLIMATE1410.

Pejovic,T.,V.Williams, R. Noland, and R.Toumi, 2009: Factors affecting the frequency

and severity of airport weather delays and the implications of climate change

for future delays. Journal of the Transportation Research Board, 2139, 97-106.

Perch-Nielsen, S.L., 2010: The vulnerability of beach tourism to climate change – an

index approach. Climatic Change, 100(3), 579-606.

Perch-Nielsen, S.L., B. Amelung, and R. Knutti, 2009: Future climate resources for

tourism in Europe based on the daily Tourism Climatic Index. Climatic Change,

103(3-4), 363-381.

Perez-Garcia, J., L.A. Joyce, A.D. Mcguire, and X. Xiao, 2002: Impacts of climate

change on the global forest sector. Climatic Change, 54(4), 439-461.

Phelan, L., R. Taplin,A. Henderson-Sellers, and G.Albrecht, 2011: Ecological viability

or liability? Insurance system responses to climate risk. Environmental Policy

and Governance, 21, 112-130.

Pickering, C.M. and R.C. Buckley, 2010: Climate response by the ski industry: the

shortcomings of snowmaking for Australian resorts. Ambio, 39(5-6), 430-438.

Pickering, C.M., J.G. Castley, and M. Burtt, 2010: Skiing less often in a warmer world:

attitudes of tourists to climate change in an Australian ski resort. Geographical

Research, 48(2), 137-147.

Pielke, R.A.J. and M.W. Downton, 2000: Precipitation and damaging floods: trends

in the United States, 1932-97. Journal of Climate, 13(20), 3625-3637.

Pielke, R.A.J. and C.W. Landsea, 1999: La Nina, El Nino, and Atlantic hurricane

damages in the United States. Bulletin of the American Meteorological Society,

80, 2027-2033.

Pinto, J.G., E.L. Fröhlich, G.C. Leckebusch, and U. Ulbrich, 2007: Changing European

storm loss potentials under modified climate conditions according to ensemble

simulations of the ECHAM5/MPI-OM1 GCM. Natural Hazards and Earth System

Sciences, 7, 165-175.

Pinto, J.G., C.P. Neuhaus, G.C. Leckebusch, M. Reyers, and M. Kerschgens, 2009:

Estimation of wind storm impacts overWestern Germany under future climate

conditions using a statistical-dynamical downscaling approach. Tellus A, 62,

188-201.

Pinto, J.G., M.K. Karremann, K. Born, P.M. Della-Marta, and M. Klawa, 2012: Loss

potentials associated with European windstorms under future climate conditions.

Climate Research, 54(1), 1-20, doi:10.3354/cr01111.

Pons-Pons, M., P.A. Johnson, M. Rosas-Casals, B. Sureda, and E. Jover, 2012: Modeling

climate change effects on winter ski tourism in Andorra. Climate Research,

54(3), 197-207.

Potocka, I. and A. Zajadacz, 2009: Weather and climate as elements of tourism

space controlling the demand for and forms of tourist activity. Quaestiones

Geographicae, (28 A1), 53-64.

Pouta, E., M. Neuvonen, and T. Sievänen, 2009: Participation in cross-country skiing

in Finland under climate change: application of multiple hierarchy stratification

perspective. Journal of Leisure Research, 41(1), 91-108.

Prabhakar, S.V.R.K., G.S. Rao, K. Fukuda, and S. Hayashi, 2013: Promoting risk

insurance in theAsia-Pacificregion lessonsfrom the ground for the future climate

regime under UNFCCC. In: Climate Change Adaption in Practice: From Strategy

Development to Implementation [Schmidt-Thome, T. and J. Klein (eds.)]. John

Wiley & Sons, Ltd., Hoboken, NJ, USA, pp. 303-323.

Prideaux, B. and K.E. Mcnamara, 2012: Turning a global crisis into a tourism

opportunity: the perspective from Tuvalu. International Journal of Tourism

Research, 15(6), 583-594.

Pryor, S.C. and R.J. Barthelmie, 2010: Climate change impacts on wind energy: a

review. Renewable and Sustainable Energy Reviews, 14(1), 430-437.

Pryor, S.C. and R.J. Barthelmie, 2011:Assessing climate change impacts on the nearterm stability of the wind energy resource over the USA. Proceedings of the

National Academy of Sciences of the United States of America, 108, 8167-

8171.

Pryor, S.C. and R.J. Barthelmie, 2013: Assessing the vulnerability of wind energy to

climate change and extreme events. Climatic Change, 121(1), 79-91.

Pryor, S.C. and J.T. Schoof, 2010: Importance of the SRES in projections of climate

change impacts on near-surface wind regimes. Meteorologische Zeitschrift,

19(3), 267-274.

Pryor, S.C., J.T. Schoof, and R.J. Barthelmie, 2006: Winds of change? Projections of

near-surface winds under climate change scenarios. Geophysical Research

Letters, 33(11), L11702, doi:10.1029/2006GL026000.

Pryor, S.C., R.J. Barthelmie, N.E. Clausen, M. Drews, N. MacKellar, and E. Kjellström,

2011: Analyses of possible changes in intense and extreme wind speeds over

northern Europe under climate change scenarios. Climate Dynamics, 38, 189-

208.

Qin, D., C.Yiyu, and L. Xueyong, 2005: Evolution of Climate and Environment in China

(in Chinese). Science Press, 1, 562 pp.

Qiu, L. andW.A. Nixon, 2008: Effects of adverse weather on traffic crashes:systematic

review and meta-analysis. Journal of the Transportation Research Board,

2055(1), 139-146.

Quinto, C., 2011: Insurance Systems in Times of Climate Change: Insurance of

Buildings against Natural Hazards. Springer, Berlin, Germany, 180 pp.

Qureshi, Z. and D. Reinhard (eds.), 2011: Making Insurance Work for the Poor.

Proceedings of the 6th International Microinsurance Conference 2010,November

9-11, 2010, Manila, Philippines, Munich Re Foundation in cooperation with the

Microinsurance Network, Munich Re Foundation, Munich, Germany, 89 pp.

Raddatz, C., 2009: The Wrath of God – Macroeconomic Costs of Natural Disasters.

Policy Research Working Paper 5039, The Word Bank Development Research

Group, Macroeconomics and Growth Team, The World Bank, Washington, DC,

USA, 35 pp.

Raible, C.C., S. Kleppek, M.Wüest, D.N. Bresch,A. Kitoh, H. Murakami, and T.F. Stocker,

2012: Atlantic hurricanes and associated insurance loss potentials in future

climate scenarios: limitations of high-resolution AGCM simulations. Tellus A,

64, 15672, doi:10.3402/tellusa.v64i0.15672.

Ramakrishnan, S.K., 2011:Adaptation cost of diarrhea and malaria in 2030 for India.

Indian Journal of Occupational and Environmental Medicine, 15(2), 64-67.

Ranger, N. and F. Niehoerster, 2012: Deep uncertainty in long-term hurricane risk:

scenario generation and implications for future climate experiments. Global

Environmental Change, 22(3), 703-712.

Raschky, P.A.,R. Schwarze,M. Schwindt, and F. Zahn, 2013:Uncertainty of governmental

relief and the crowding out of flood insurance. Environmental and Resource

Economics, 54, 179-200.

Ravishankar, N., P. Gubbins, R.J. Cooley, K. Leach-Kemon, C.M. Michaud, T.J. Dean,

and J.M. Christopher, 2009: Financing of global health: tracking development

assistance for health from 1990 to 2007. Lancet, 373(9681), 2113-2124.

Raymond, C.M. and G. Brown, 2011: Assessing spatial associations between

perceptions of landscape value and climate change risk for use in climate

change planning. Climatic Change, 104(3-4), 653-678.

Reed, D.A., 2008: Electric utility distribution analysis for extreme winds. Journal of

Wind Engineering and Industrial Aerodynamics, 96(1), 123-140.

Reilly, J.M., N. Hohmann, and S. Kane, 1994: Climate change and agricultural trade:

who benefits, who loses? Global Environmental Change, 4(1), 24-36.

Reilly, J.M., S. Paltsev, B. Felzer, X.Wang, D.W. Kicklighter, J.M. Melillo, R.G. Prinn, M.

Sarofim, A.P. Sokolov, and C.Wang, 2007: Global economic effects of changes

in crops, pasture, and forests due to changing climate, carbon dioxide, and

ozone. Energy Policy, 35, 5370-5383.

Reinhold, T., B. O’Connor, and C. Iskowitz, 2012: Reducing vulnerabilities through

science. In: Severe Weather in North America. Perils, Risks, Insurance [Kron,W.

and F.Wöst (eds.)]. Munich Re, Munich, Germany, pp. 197-225.

Guoyu, G.Y., 1996: Global climate changes and the tourism of China. Journal of

Chinese Geography, 6(2), 97-102.
705

10

Key Economic Sectors and Services Chapter 10

Richardson, R.B. and J.B. Loomis, 2005: Climate change and recreation benefits in

an alpine national park. Journal of Leisure Research, 37(3), 307-320.

Richter, C., S. Teske, and R. Short, 2009: Concentrating Solar Power: Global Outlook

2009 – Why Renewable Energy is Hot. Greenpeace International, Amsterdam,

Netherlands, ESTALA, Brussels, Belgium, and IEA SolarPaces, Tabernas, Spain,

87 pp.

Rising,W.R., J.A. O’Daniel, and C.S. Roberts, 2006: Correlating weather and trauma

admissions at a level I trauma center. Journal of Trauma – Injury, Infection and

Critical Care, 60(5), 1096-1110.

Robinson, S., K. Strzepek, and R. Cervigni, 2013: The Cost of Adapting to Climate

Change in Ethiopia. Ethiopia Strategy Support Program II Working Paper 53,

IFPRI ESSP WP 53, 2013.

Ronneberger, K., M. Berrittella, F. Bosello, and R.S.J. Tol, 2009: KLUM@GTAP:

introducing biophysical aspects of land-use decisions into a computable general

equilibrium model, a coupling experiment. Environmental Modeling and

Assessment, 14(2), 149-168.

Roson, R. and D. van der Mensbrugghe, 2012: Climate change and economic growth:

impacts and interactions. International Journal of Sustainable Economy, 4(3),

270-285.

Rossello, J., 2011: North Atlantic Oscillation influences on European airline traffic.

Transportation Research Part D: Transport and Environment, 16(2), 183-187.

Rosselló-Nadal, J., A. Riera-Font, and V. Cárdenas, 2010: The impact of weather

variability on British outbound flows. Climatic Change, 105(1-2), 281-292.

Rübbelke, D.T.G. and S.Vögele, 2010: Impacts of Climate Change on European Critical

Infrastructures: The Case of the Power Sector. Basque Centre for Climate

Change (BC3) Working Paper 2010-08, BC3, Bilbao, Spain, 20 pp.

Rübbelke, D. and S.Vögele, 2013: Short-term distributional consequences of climate

change impacts on the power sector: who gains and who loses? Climatic

Change, 116(2), 191-206.

Rummukainen, M., 2013: Climate change:changing means and changing extremes.

Climatic Change, 121(1), 3-13.

Rutty, M. and D. Scott, 2010:Will the Mediterranean become “too hot” for tourism?

A reassessment. Tourism and Hospitality, Planning and Development, 7(3),

267-281.

Ryley, T. and L. Chapman (eds.), 2012: Transport and Sustainability, Vol. 2: Transport

and Climate Change. Emerald Group Publishing, Ltd., Bingley, UK, 396 pp.

Saarinen, J. and K. Tervo, 2006: Perceptions and adaptation strategies of the

tourism industry to climate change: the case of Finnish nature-based tourism

entrepreneurs. International Journal of Innovation and Sustainable Development,

1(3), 214-228.

Saarinen, J., W.L. Hambira, J. Atlhopheng, and H. Manwa, 2012: Tourism industry

reaction to climate change in Kgalagadi South District, Botswana. Development

Southern Africa, 29(2), 273-285.

Sabbioni, C., M. Cassar, P. Brimblecombe, and R.A. Lefevre, 2009: Vulnerability of

cultural heritage to climate change. Pollution Atmospherique, (202), 157-169.

Sailor, D.J., M. Smith, and M. Hart, 2008: Climate change implications for wind power

resources in the Northwest United States. Renewable Energy, 33(11), 2393-

2406.

Sander,J.,J.F. Eichner, E. Faust, and M. Steuer, 2013: Rising variability in thunderstormrelated U.S. losses as a reflection of changes in large-scale thunderstorm forcing. Weather, Climate, and Society, 5, 317-331, doi:10.1175/WCAS-D-12-

00023.1.

Schaefli, B., D. Balin Talamba, and A. Musy, 2007: Quantifying hydrological modeling

errors through a mixture of normal distributions. Journal of Hydrology, 332,

303-315.

Schmidt, P., R. Steiger, and A. Matzarakis, 2012: Artificial snowmaking possibilities

and climate change based on regional climate modeling in the Southern Black

Forest. Meteorologische Zeitschrift, 21(2), 167-172.

Schreider, S.Y., D.I. Smith, and A.J. Jakeman, 2000: Climate change impacts on urban

flooding. Climatic Change, 47(1-2), 91-115.

Schwarze, R. and G.G. Wagner, 2007: The political economy of natural disaster

insurance: lessons from the failure of a proposed compulsory insurance scheme

in Germany. European Environment, 17(6), 403-415.

Schwarze, R., H. Schwindt, H.Weck-Hannemann, P. Raschky, F. Zahn, and G.G.Wagner,

2011: Natural hazard insurance in Europe: tailored responses to climate change

are needed. Environmental Policy and Governance, 21, 14-30.

Schwierz, C., P. Köllner-Heck, E. Zenklusen-Mutter, D.N. Bresch, P.-L. Vidale, M.Wild,

and C. Schär, 2010: Modelling European winter wind storm losses in current

and future climate. Climatic Change, 101(3-4), 485-514.

Scott, D. and B. Jones, 2006: The impact of climate change on golf participation in

the Greater Toronto Area (GTA): a case study. Journal of Leisure Research,

38(3), 363-380.

Scott, D. and B. Jones, 2007: A regional comparison of the implications of climate

change for the golf industry in Canada. Canadian Geographer, 51(2), 219-232.

Scott, D. and G. McBoyle, 2007: Climate change adaptation in the ski industry.

Mitigation and Adaptation Strategies for Global Change, 12(8), 1411-1431.

Scott, D., G. McBoyle, and B. Mills, 2003: Climate change and the skiing industry in

southern Ontario (Canada): exploring the importance of snowmaking as a

technical adaptation. Climate Research, 23(2), 171-181.

Scott, D., G. McBoyle, A. Minogue, and B. Mills, 2006: Climate change and the

sustainability of ski-based tourism in eastern North America: a reassessment.

Journal of Sustainable Tourism, 14(4), 376-398.

Scott, D., B. Jones, and J. Konopek, 2007: Implications of climate and environmental

change for nature-based tourism in the Canadian Rocky Mountains: a case

study ofWaterton Lakes National Park. Tourism Management, 28(2), 570-579.

Scott, D., G. McBoyle, and A. Minogue, 2007: Climate change and Quebec’s ski

industry. Global Environmental Change, 17(2), 181-190.

Scott, D., J. Dawson, and B. Jones, 2008a: Climate change vulnerability of the US

Northeast winter recreation- tourism sector.Mitigation andAdaptation Strategies

for Global Change, 13(5-6), 577-596.

Scott, D., S. Gössling, and C.R. De Freitas, 2008b: Preferred climates for tourism: case

studiesfrom Canada,New Zealand and Sweden. Climate Research, 38(1), 61-73.

Scott, D., S. Gössling, and C.M. Hall, 2012a: International tourism and climate change.

Wiley Interdisciplinary Reviews: Climate Change, 3(3), 213-232.

Scott, D., M.C. Simpson, and R. Sim, 2012b: The vulnerability of Caribbean coastal

tourism to scenarios of climate change related sea level rise. Journal of

Sustainable Tourism, 20(6), 883-898.

Scott, M., S. Gupta, E. Jáuregui, J. Nwafor, D. Satterthwaite, W. Wanasinghe, and M.

Yoshino, 2001: Human settlements, energy and industry. In: Climate Change

2001: Impacts, Adaptation, and Vulnerability. Contribution of Working Group

II to the Third Assessment Report of the Intergovernmental Panel on Climate

Change [McCarthy, J.J., O.F. Canziani, N.A. Leary, D.J. Dokken, and K.S. White

(eds.)]. Cambridge University Press, Cambridge, UK and NewYork, NY, USA, pp.

381-416.

Seo, J. and O. Mahul, 2009: The Impact of Climate Change on Catastrophe Risk Model.

Implications for Catastrophe Risk Markets in Developing Countries. Policy

ResearchWorking Paper 4959,A joint product of theWorld Bank Global Facility

for Disaster Reduction and Recovery Unit, Sustainable Development Network

Vice Presidency, and the Global Capital Markets Development Department,

Financial and Private Sector Development Vice Presidency, The World Bank,

Washington, DC, USA, 16 pp.

Seppala, R., A. Buck, and P. Katila (eds.), 2009: Adaptation of Forests and People to

Climate Change – A Global Assessment Report. IUFRO World Series Vol. 22,

International Union of Forest Research Organizations (IUFRO), IUFRO, Helsinki,

Finland, 224 pp.

Serquet, G. and M. Rebetez, 2011: Relationship between tourism demand in the

Swiss Alps and hot summer air temperatures associated with climate change.

Climatic Change, 108(1-2), 291-300.

Shaw, W.D. and J.B. Loomis, 2008: Frameworks for analyzing the economic effects

of climate change on outdoor recreation. Climate Research, 36(3), 259-269.

Sherement, O. and A. Lucas, 2009: Global loss diversification in the insurance sector.

Insurance: Mathematics and Economics, 44(3), 415-425, doi:10.1016/j.

insmatheco.2008.12.001.

Shih, C., S. Nicholls, and D.F. Holecek, 2009: Impact of weather on downhill ski lift

ticket sales. Journal of Travel Research, 47(3), 359-372.

Sieber, J., 2013: Impacts of, and adaptation options to, extreme weather events and

climate change concerning thermal power plants. Climatic Change, 121(1), 55-66.

Skidmore, M. and H. Toya, 2002: Do natural disasters promote long-term growth?

Economic Inquiry, 40(4), 664-687.

Smith, A.B. and R.W. Katz, 2013: US billion-dollar weather and climate disasters:

data sources, trends, accuracy and biases. Natural Hazards, 67(2), 387-410,

doi:10.1007/s11069-013-0566-5.

Sohngen, B. and R. Mendelsohn, 1997:A dynamic model of forest carbon storage in

the United States during climatic change. Critical Reviews in Environmental

Science and Technology, 27(SI), S309-S321.

Sohngen, B. and R. Mendelsohn, 1998: Valuing the impact of large-scale ecological

change in a market: the effect of climate change on U.S. timber. American

Economic Review, 88(4), 686-710.
706

Chapter 10 Key Economic Sectors and Services

10

Sohngen, B., R. Mendelsohn, and R. Sedjo, 2001: A global model of climate change

impacts on timber markets. Journal of Agricultural and Resource Economics,

26(2), 326-343.

Steger, C., S. Kotlarski, T. Jonas, and C. Schär, 2012:Alpine snow cover in a changing

climate: a regional climate model perspective. Climate Dynamics, 41(3-4), 735-

754.

Steiger, R., 2010:The impact ofclimate change on ski season length and snowmaking

requirements in Tyrol, Austria. Climate Research, 43(3), 251-262.

Steiger, R., 2012: Scenariosforskiing tourism inAustria: integrating demographics with

an analysis of climate change. Journal of Sustainable Tourism, 20(6), 867-882.

Steiger, R. and M. Mayer, 2008: Snowmaking and climate change: future options for

snow production in Tyrolean ski resorts. Mountain Research and Development,

28(3-4), 292-298.

Stenek,V., J.C.Amado, and R. Connell, 2010: Climate Risk and Financial Institutions:

Challenges and Opportunities [Kamins, R.,A. Hidalgo,V. Stenek, and R. Connell

(eds.)]. International Finance Corporation (IFC),World Bank Group,Washington,

DC, USA, 120 pp.

Stewart, E.J., S.E.L. Howell, D. Draper, J.Yackel, and A. Tivy, 2007: Sea ice in Canada’s

Arctic: implications for cruise tourism. Arctic, 60(4), 370-380.

Stewart, W. and D. Willard, 2010: Kivalina v. ExxonMobil dismissed by Federal trial

court. In: Liability for Climate Change? Experts’ Views on a Potential Emerging

Risk. Munich Re, Munich, Germany, pp. 11-13.

Strulik, H., 2008: Geography, health, and the pace of demo-economic development.

Journal of Development Economics, 86(1), 61-75.

Strzepek, K.,Y. Gary, N. James, and B. Brent, 2010: Characterizing changes in drought

risk for the United States from climate change. Environmental Research Letters,

5, 044012, doi:10.1088/1748-9326/5/4/044012.

Strzepek, K., B. Boehlert,A. McCluskey,W. Farmer, J. Neumann, and M. Fuchs, 2011:

Assessment of the Impacts of Climate Change on Multi-Sector Investment

Opportunities in the Zambezi River Basin. IEC Report to TheWorld Bank,World

Bank,Washington, DC, USA, pp.

Strzepek, K., J. Smith, J. Martinich, J. Neumann, B. Boehlert, M. Hejazi, J. Henderson,

C.Wobus, K. Calvin, D. Johnson, J. R., E. Monier, J. Strzepek, and J. Yoon, 2013:

Climate change impacts on water resources in the United States. Climatic

Change, (under review).

Subak, S., J.P. Palutikof, M.D. Agnew, S.J. Watson, C.G. Bentham, M.G.R. Cannell, M.

Hulme, S. McNally, J.E. Thornes, D.Waughray, and J.C.Woods, 2000: The impact

of the anomalous weather of 1995 on the U.K. economy. Climatic Change,

44(1-2), 1-26.

Supreme Court of Virginia, USA, 2012: Declaratory judgment following rehearing.

In: The AES Corporation v. Steadfast Insurance Company, Record No.100764:

Opinion by Justice S. Bernard Goodwyn, April 20, 2012. Supreme Court of

Virginia, USA, Richmond,VA, USA, www.courts.state.va.us/opinions/opnscwp/

1100764r.pdf.

Sweeney, M., A. Gasca, M. Garcia-Lopez, and A.C. Palmer, 2005: Pipelines and

landslides in rugged terrain: a database, historic risks and pipeline vulnerability.

In: International Conference on Terrain and Geohazard Challenges Facing

Onshore Oil and Gas Pipelines: Evaluation, Routing, Design, Construction,

Operation [Sweeney, M. (ed.)]. Institution of Civil Engineers in coordination

with BP Exploration, Thomas Telford, Ltd., London, UK, pp. 641-659.

Swiss Re, 2013a: SIGMA No. 1/2013: Partnering for Food Security in Emerging

Markets. Economic Research & Consulting, Swiss Reinsurance Company, Ltd.

(Swiss Re), Zürich, Switzerland, 40 pp.

Swiss Re, 2013b: SIGMA No. 3/2013: World Insurance in 2012: Progressing on the

Long and Winding Road to Recovery. Economic Research & Consulting, Swiss

Reinsurance Company, Ltd. (Swiss Re), Zürich, Switzerland, 42 pp.

Swiss Re, 2013c: SIGMA No. 2/2012: Natural Catastrophes and Man-Made Disasters

in 2012: A Year of Extreme Weather Events in the US. Economic Research &

Consulting, Swiss Reinsurance Company, Ltd. (Swiss Re), Zürich, Switzerland,

44 pp.

Tamiotti, L., R. Teh, V. Kalucoglu, A. Olhoff, B. Simmons, and H. Abaza, 2009: Trade

and Climate Change. World Trade Organization (WTO) and the United Nations

Environment Programme (UNEP),WTO, Geneva, Switzerland, 166 pp.

Tang, K.K., D. Petrie, and D.S.P. Rao, 2009: The income-climate trap of health

development: a comparative analysis of African and Non-African countries.

Social Science and Medicine, 69(7), 1099-1106.

Taylor, R.J. and H.M. Tollin, 2009: Insurance market for global warming heats up: old

products and new policies respond to climate change risks. Environmental

Claims Journal, 21(3), 247-261.

Taylor,T. and R.A. Ortiz, 2009: Impacts of climate change on domestic tourism in the

UK: a panel data estimation. Tourism Economics, 15(4), 803-812.

Tervo, K., 2008: The operational and regional vulnerability of winter tourism to

climate variability and change: the case of the Finnish nature-based tourism

entrepreneurs. Scandinavian Journal of Hospitality and Tourism, 8(4), 317-332.

Thieken,A.H., T. Petrow, H. Kreiblich, and B. Merz, 2006: Insurability and mitigation

of flood losses in private households in Germany. Risk Analysis, 26, 383-395.

Tol, R.S.J., 2008:Why worry about climate change?A research agenda. Environmental

Values, 17(4), 437-470.

Tol, R.S.J., 2011: The social cost of carbon. Annual Review of Resource Economics, 3,

419-443.

Tol, R.S.J., 2013: Targets for global climate policy: an overview. Journal of Economic

Dynamics and Control, 37(5), 911-928.

Towler, E., B. Raucher, B. Rajagopalan,A. Rodriguez, D.Yates, and R. Summers, 2011:

Incorporating climate uncertainty in a cost assessment for a new municipal

source water. Journal of Water Resources Planning and Management, 138(5),

396-402.

Trærup, S.L.M., 2012: Informal networks and resilience to climate change impacts:

a collective approach to index insurance. Global Environmental Change, 22,

255-267.

Trærup, S.L.M., R.A. Ortiz, and A. Markandya, 2011: The costs of climate change: a

study of cholera in Tanzania. International Journal of Environmental Research

and Public Health, 8(12), 4386-4405.

Travers, J. and V.G. Payne, 1998:The impact of climate change on traditional seasonal

ranges for High Street women’s wear in England. Journal of Fashion Marketing

and Management, 2(4), 370-382.

Troccoli, A. (ed.), 2010: Management of Weather and Climate Risk in the Energy

Industry. Springer, Dordrecht, Netherlands, 330 pp.

Tsai, H.-T., C.-J.Tseng, S.-Y. Tzeng,T.-J.Wu, and J.-d. Day, 2012:The impacts of natural

hazards on Taiwan’s tourism industry. Natural Hazards, 62(1), 83-91.

Turton, S., T. Dickson, W. Hadwen, B. Jorgensen, T. Pham, D. Simmons, P. Tremblay,

and R. Wilson, 2010: Developing an approach for tourism climate change

assessment: evidence from four contrasting Australian case studies. Journal of

Sustainable Tourism, 18(3), 429-447.

UNCTAD, 2009: Multi-Year Expert Meeting on Transport and Trade Facilitation:

Maritime Transport and the Climate Change Challenge, 16 – 18 February 2009,

Geneva, Summary of Proceedings. UNCTAD/DTL/TLB/2009/1 United Nations

Conference on Trade and Development (UNCTAD), Geneva, Switzerland, 47 pp.

UNDP, 2011: The economic impacts of climate change on energy demand for space

heating and cooling. In: Assessing the Economic Impact of Climate Change –

National Case Studies. United Nations Development Program (UNDP), New

York, NY, USA, pp. 37-56.

UNECE and UNCTAD, 2010: Note by the United Nations Economic Commission for

Europe (UNECE) and United Nations Conference on Trade and Development

(UNCTAD) Secretariats. Proceedings of Joint Workshop on Climate Change

Impacts on International Transport Networks, 7 – 8 September 2010, Geneva,

ECE/TRANS/WP.5/2010/3, UNECE and UNCTAD, Geneva, Switzerland, 14 pp.

UNFCCC, 2007: Investment and Financial Flows to Address Climate Change. United

Nations Framework Convention on Climate Change (UNFCCC), Bonn, Germany,

272 pp.

URS, 2010: Adapting Energy, Transport, and Water Infrastructure to the Long-Term

Impacts of Climate Change, Summary Report. Reference No. RMP/5456, Report

by URS Corporation, Ltd. for the UK Government’s cross-departmental

Infrastructure and Adaptation Project, URS Corporation, Ltd., London, UK,

16 pp.

Uyarra, M.C., I.M. Côté, J.A. Gill, R.R.T. Tinch, D. Viner, and A.R. Watkinson, 2005:

Island-specific preferences of tourists for environmental features: implications

of climate change for tourism-dependent states. Environmental Conservation,

32(1), 11-19.

van den Berg, M., 2010: Household income strategies and natural disasters: dynamic

livelihoods in rural Nicaragua. Ecological Economics, 69(3), 592-602.

Van den Bergh, R. and M. Faure, 2006: Compulsory insurance of loss to property

caused by natural disasters: competition or solidarity? World Competition,

29(1), 25-54.

Van der Vliert, E., 2008: Climate,Affluence, and Culture. Cambridge University Press,

New York, NY, USA, 253 pp.

Van Nostrand, J.M. and J.G. Nevius, 2011: Parametric insurance: using objective

measures to address the impacts of natural disasters and climate change.

Environmental Claims Journal, 23(3-4), 227-237.
707

10

Key Economic Sectors and Services Chapter 10

van Rensburg, J.J.J. and J.N. Blignaut, 2002: The economic impact of an increasing

health risk due to global warming. In: Forum for Economics and Environment

– First Conference Proceedings. Economics for Environment Forum (Econ4Env),

Cape Town, South Africa, pp. 117-141, citeseerx.ist.psu.edu/viewdoc/

download?doi=10.1.1.199.630&rep=rep1&type=pdf.

Van Vliet, M.T.H., J.R. Yearsley, F. Ludwig, S. Vögele, D.P. Lettenmaier, and P. Kabat,

2012: Vulnerability of US and European electricity supply to climate change.

Nature Climate Change, 2(9), 676-681.

Vergara,W.,A. Deeb,A.Valencia, R. Bradley, B. Francou,A. Zarzar,A. Grunwaldt, and

S. Haeussling, 2007: Economic impacts of rapid glacier retreat in the Andes.

Eos, Transactions American Geophysical Union, 88(25), 261-268.

Vlasova, L.V. and G.S. Rakitina, 2010: Natural risks management in the Gas

Transmission System (GTS) of Russia and the contribution of climate services

under global climate change. In: Management of Weather and Climate Risk in

the Energy Industry [Troccoli, A. (ed.)]. Springer, Bilthoven, Netherlands, pp.

315-325.

von Peter, G., S. von Dahlen, and S. Saxena, 2012: Unmitigated Disasters? New

Evidence on the Macroeconomic Cost of Natural Catastrophes. BIS Working

Paper No. 394, Monetary and Economic Department of the Bank for

International Settlements, Basel, Switzerland, 38 pp.

Walter,A., K. Keuler, D. Jacob, R. Knoche,A. Block, S. Kotlarski, G. Muller-Westermeier,

D. Rechid, andW.Ahrens, 2006:A high resolution reference data set of German

wind velocity 1951-2001 and comparison with regional climate model results.

Meteorologische Zeitschrift, 15, 585-596.

Wang, S., Y. He, and X. Song, 2010: Impacts of climate warming on Alpine glacier

tourism and adaptive measures: a case study of Baishui Glacier No. 1 in Yulong

Snow Mountain, Southwestern China.Journal of Earth Science, 21(2), 166-178.

Ward, A.M., 2013: The effect of weather on grid systems and the reliability of

electricity supply. Climatic Change, 121(1), 103-113.

Ward, R.E.T., C. Herweijer, N. Patmore, and R. Muir-Wood, 2008: The role of insurers

in promoting adaptation to the impacts of climate change. The Geneva Papers,

33, 133-139.

Ward, P.J., K.M. Strzepek, W.P. Pauw, L.M. Brander, G.A. Hughes, and J.C.J.H. Aerts,

2010: Partial costs of global climate change adaptation for the supply of raw

industrial and municipal water: a methodology and application. Environmental

Research Letters, 5(4), 044011, doi:10.1088/1748-9326/5/4/044011.

Warner, K. and A. Spiegel, 2009: Climate change and emerging markets: the role of

the insurance industry in climate change management. In: The Geneva Reports

– Risk and Insurance Research No. 2. The Insurance Industry and Climate

Change – Contribution to the Global Debate. The Geneva Association, The

International Association for the Study of Insurance Economics, Geneva,

Switzerland, pp. 83-95.

Warner, K., S. Kreft, M. Zissner, P. Höppe, C. Bals, T. Loster, J. Linnerooth-Bayer, S.

Tschudi, E. Gurenko, A. Haas, S. Young, P. Kovacs, A. Dlugolecki, and A. Oxley,

2012: Insurance Solutions in the Context of Climate Change-Related Loss and

Damage: Needs, Gaps, and Roles of the Convention in addressing Loss and

Damage. UNU-EHS Publication Series Policy Brief No. 6,Munich Climate Insurance

Initiative (MCII) hosted by United Nations University-Institute for Environment

and Human Security (UNU-EHS), Bonn, Germany, 47 pp.

Watkiss, P. and A. Hunt, 2012: Projection of economic impacts of climate change in

sectors of Europe based on bottom up analysis: human health. Climatic Change,

112(1), 101-126.

Weitzman, M.L., 2009: On modeling and interpreting the economics of catastrophic

climate change. Review of Economics and Statistics, 91(1), 1-19.

Welker, C. and E. Faust, 2013: Tropical cyclone-related socio-economic losses in the

western North Pacific region. Natural Hazards and Earth System Sciences, 13,

115-124, doi:10.5194/nhess-13-115-2013.

Whalley, J. and Y.Yuan, 2009: Global financial structure and climate change. Journal

of Financial Transformation, 25, 161-168.

Whitehead, J.C., B. Poulter, C.F. Dumas, and O. Bin, 2009: Measuring the economic

effects of sea level rise on shore fishing. Mitigation and Adaptation Strategies

for Global Change, 14(8), 777-792.

WHO, 2004: Evaluation of the Costs and Benefits ofWater and Sanitation Improvements

at the Global Level. WHO/SDE/WSH/04.04, Water, Sanitation and Health

Protection of the Human Environment, World Health Organization (WHO),

Geneva, Switzerland, 87 pp.

Wilbanks, T.J., P. Romero Lankao, M. Bao, F. Berkhout, S. Cairncross, J.-P. Ceron, M.

Kapshe, R. Muir-Wood, and R. Zapata-Marti, 2007: Industry, settlement and

society. In: Climate Change 2007: Impacts, Adaptation and Vulnerability.

Contribution of Working Group II to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani, J.P.

Palutikof, P.J. van der Linden, and C.E. Hanson (ed.)]. Cambridge University

Press, Cambridge, UK and New York, NY, USA, pp. 357-390.

Wilcox, R., J. Syroka, F. Kassam, and S. Mapfumo, 2010: African Risk Capacity (ARC)

Briefing Book. The African Risk Capacity (ARC) Project initiated by the African

Union Commission, Department of Rural Economy and Agriculture, and the

United Nations World Food Programme (WFP), ARC, Johannesburg, South

Africa, 48 pp.

Williams, E., 2013: Economy-wide and sectoral implications of climate change and

extreme weather impacts on the energy sector. In: Climate Change Impacts on

the Energy Sector. Interim Report, International Atomic Energy Agency (IAEA),

Vienna, Austria, pp. 18-33.

Williams, E. and F.L. Toth, 2013: Nuclear power: impacts of climate change and

extreme weather. In: Climate Change Impacts on the Energy Sector. Interim

Report, International Atomic Energy Agency (IAEA), Vienna, Austria, pp. 1-17.

Williams, P.D. and M.M. Joshi, 2013: Intensification of winter transatlantic aviation

turbulence in response to climate change. Nature Climate Change, 3, 644-648.

Wilson, K.J.,J. Falkingham,H.Melling, and R.DeAbreu, 2004: Shipping in the Canadian

Arctic: other possible climate change scenarios. In: IGARSS, 2004. Proceedings

of the 2004 IEEE International Geoscience and Remote Sensing Symposium:

Science for Society, Exploring and Managing a Changing Planet, Anchorage,

Alaska, 20 – 24 September 2004. Institute of Electrical and Electronics Engineers

(IEEE), New York, NY, USA, pp. 1853-1856.

Winkler, J., L. Dueñas-Osorio, R. Stein, and D. Subramanian, 2010: Performance

assessment of topologically diverse power systems subjected to hurricane

events. Reliability Engineering and System Safety, 95(4), 323-336.

Winters, P., R. Murgai, E. Sadoulet,A. De Janvry, and G. Frisvold, 1998: Economic and

welfare impacts of climate change on developing countries. Environmental and

Resource Economics, 12(1), 1-24.

Wirtz, A., P. Löw, T. Mahl, and S. Yildirim, 2013: Hitting the poor – public private

partnerships as an option: impact of natural catastrophes in economies at

various stages of development. In: Extreme Natural Hazards, Disaster Risks and

Societal Implications [Ismail-Zadeh,A., J. Fucugauchi,A. Kijko, K. Takeuchi, and

I. Zaliapin (eds.)]. Cambridge University Press, Cambridge, UK (in press).

Wobus, C., M. Lawson, R. Jones, J. Smith, and J. Martinich, 2013: Estimating monetary

damages from flooding in the United States under a changing climate. Journal

of Flood Risk Management (in press), doi:10.1111/jfr3.12043.

Wolfsegger, C., S. Gössling, and D. Scott, 2008: Climate change risk appraisal in the

Austrian ski industry. Tourism Review International, 12(1), 13-23.

World Bank, 2006: Ethiopia: Managing Water Resources to Maximize Sustainable

Growth. CountryWater ResourcesAssistance Strategy,The International Bank for

Reconstruction and Development / TheWorld Bank,Washington, DC,USA, 91 pp.

World Bank, 2013: Pacific Catastrophe Risk Insurance Pilot. Regional Financial

ProtectionAgainst Natural Disasters for PacificIsland States. Pacific Catastrophe

Risk Assessment and Financing Initiative (PCRAFI), 4 pp. http://siteresources.

worldbank.org/EXTDISASTER/Resources/8308420-1361321776050/PacificCatastrophe-Risk-Insurance-Pilot_4pager_12Feb13.pdf.

Wright, L., P. Chinowsky, K. Strzepek, R. Jones, R. Streeter, J.B. Smith, J.-M. Mayotte,

A. Powell, L. Jantarasami, and W. Perkins, 2012: Estimated effects of climate

change on flood vulnerability of U.S. bridges.Mitigation andAdaptation Strategies

for Global Change, 17(8), 939-955.

WTO and UNEP, 2008: Climate Change and Tourism – Responding to Global Challenges.

World Tourism Organization (WTO) and United Nations Environment Programme

(UNEP),WTO, Madrid, Spain, 256 pp.

WTTC, 2011: Travel & Tourism 2011. World Travel and Tourism Council (WTTC),

London, UK, 40 pp.

Yates, D.N. and K.M. Strzepek, 1998: An assessment of integrated climate change

impacts on the agricultural economy of Egypt. Climatic Change, 38(3), 261-287.

Yazdi, S.K. andA. Fashandi, 2010:The economic effects of climate change on fisheries

and aquaculture. In: 2010 International Conference on Environmental

Engineering and Applications (ICEEA 2010). Proceedings of a meeting held 10

– 12 September 2010, Singapore, Institute of Electrical and Electronics Engineers

(IEEE), New York, NY, USA, pp. 335-340.

Yohe, G.W., 2008:A research agenda to improve economic estimates of the benefits

of climate change policies. Integrated Assessment, 8(1), 1-17.

Yu, G., Z. Schwartz, and J.E. Walsh, 2009a: A weather-resolving index for assessing

the impact of climate change on tourism related climate resources. Climatic

Change, 95(3-4), 551-573.
708

Chapter 10 Key Economic Sectors and Services

10

Yu, G., S. Zvi, and J.E. Walsh, 2009b: Effects of climate change on the seasonality of

weather for tourism in Alaska. Arctic, 62(4), 443-457.

Zachariadis, T., 2010: Forecast of electricity consumption in Cyprus up to the year

2030: The potential impact of climate change. Energy Policy, 38(2), 744-750.

Zahran, S., S. Weiler, S.D. Brody, M.K. Lindell, and W.E. Highfield, 2009: Modeling

national flood insurance policy holding at the county scale in Florida, 1999-

2005. Ecological Economics, 68(10), 2627-2636.

Zaninović, K. and A. Matzarakis, 2009: The bioclimatological leaflet as a means

conveying climatological information to tourists and the tourism industry.

International Journal of Biometeorology, 53(4), 369-374.

Zhu, J., 2011: Evaluation of an insurance scheme based on the weather index – a

case study of Anhui Province. The Chinese Economy, 44(6), 56-72.

Zimmerman, F.J. and M.R. Carter, 2003: Asset smoothing, consumption smoothing

and the reproduction of inequality under risk and subsistence constraints.

Journal of Development Economics, 71(2), 233-260.