SPOTLIGHTING IMPACTS FUNCTIONS

IN INTEGRATED ASSESSMENT

Research Report Prepared for the Stern Review

on the Economics of Climate Change

Rachel Warren, Chris Hope, Michael Mastrandrea, 

Richard Tol, Neil Adger and Irene Lorenzoni

September 2006

Tyndall Centre for Climate Change Research Working Paper 91
SPOTLIGHTING IMPACTS 

FUNCTIONS IN INTEGRATED 

ASSESSMENT 

Research Report Prepared for the Stern Review 

on the Economics of Climate Change 

 September 2006 

Rachel Warren1

, Chris Hope2, Michael Mastrandrea3, Richard Tol4, 

Neil Adger1

 and Irene Lorenzoni1 

1 Tyndall Centre, School of Environmental Sciences, University of East Anglia, UK 

2 Judge School of Management, University of Cambridge, UK 

3 Stanford University, CA, USA 

4 University of Hamburg, Germany 
 - 2 - 

CONTENTS 

Executive Summary ........................................... 3 

Introduction................................................................. 6 

1. DICE/RICE......................................... 7 

 2. MERGE............................................. 21 

 3. PAGE................................................ 30 

4. FUND.................................................. 44 

5. Comparison between FUND and RICE............66 

6. Critique of treatment of adaptation costs ......... 74 

References.................................................. 76 
3

EXECUTIVE SUMMARY 

This project reviews how the integrated assessment models DICE/RICE, MERGE, 

PAGE, and FUND simulate climate impacts under different socioeconomic scenarios, 

and compares this with the results of a parallel study, Understanding the Regional 

Impacts of Climate Change. These models are commonly used in studies to 

determine the social cost of carbon, and in cost-benefit analyses to determine 

“optimal” climate change policy. 

RICE and FUND simulate regionally specific impacts in a number of sectors (either 

in the aggregate or sector-specific), whilst PAGE, DICE and MERGE simulate 

aggregate global market and non-market damages, as well as damage due to rapid or 

catastrophic climate change. Only FUND shows how damages expressed as %loss of 

GDP for a given temperature rise vary with socioeconomic scenario1

. The versions of 

DICE/RICE, MERGE and FUND studied for this report are deterministic models. 

Only the PAGE model is probabilistic in nature2

. 

Non-market damages are normally estimated through a willingness-to-pay approach. 

All damages in the DICE/RICE, MERGE, PAGE and FUND models are output from 

the models in terms of % loss of global or regional GDP or $$. No physical metrics 

are used. Only FUND includes sector-specific equations based on simple physical 

processes, but it also does not produce outputs in physical metrics3

. Adaptation is 

usually modelled implicitly through the calibration procedure, such that the models 

reflect the assumptions of the underlying literature. Only PAGE can explicitly 

simulate adaptation. 

The study found that all the models are based on literature from 2000 and earlier. 

Since this time, some predictions of climate impacts have become more pessimistic, 

for example predictions of the potential for rapid or catastrophic climate change. The 

implications of these predictions has, however, recently been studied using PAGE4

. 

Most of the models are calibrated using studies of the USA which are then scaled for 

application to other world regions. Thus the models’ representation of impacts in 

non-US regions is questionable. Specifically, market and non-market damages in 

DICE/RICE and MERGE are calibrated using an impacts study for the USA only, 

which is then scaled to represent impacts in other regions5

. PAGE in its default mode 

 

1

 Note that although PAGE and DICE/RICE simulate identical impacts as %GDP for identical temperatures 

but different socioeconomic scenarios, that the social costs of carbon will differ between socioeconomic 

scenarios since these are also influenced by discounting and equity weighting factors 

2 It is of course, possible to carry out uncertainty analysis on the three deterministic models, but this is 

beyond the scope of this study. 

3

 Examples of physical metrics are: numbers of people at risk from hunger, disease, coastal flooding, or 

crop production changes, or ecosystem areas or species lost. 

4

 This work is reported in a parallel study which forms part of the Stern Review 

5 That study found a 0.25% GDP loss for doubled CO2 in the USA, and a willingness to pay for nonmarket damages of 2% of GDP for a 2.5C temperature rise. 
4

is calibrated to estimates quoted in IPCC (2001) but many of these are also based on 

an extrapolation of studies of the USA. Only FUND uses regionally and sectorspecific calibration points at CO2 doubling. However, in some sectors the estimates 

also originate in one country, or may be dominated by estimates from one region - for 

example in the energy sector, (the sector which accounts for most of the economic 

damage in FUND) estimates for the UK are scaled across the world. 

Models have various ways of simulating damage due to rapid or catastrophic climate 

change, but all are necessarily simplistic. The impact of climate-change induced 

catastrophic change such as breakdown of ocean currents, melting ice sheets or 

release of methane from permafrost is represented as a calibrated power function in 

DICE/RICE, and through a probabilistic method in PAGE. It is not included in the 

FUND model. Since this time, impact estimates have been revised upwards for 

discontinuities, which are now considered more likely at lower temperatures. In 

PAGE it is easy for the user to change the representation of catastrophic change, 

whilst in other models small or larger alterations would need to be made to the model 

code. Hence the results from these models will not yet reflect these latest estimates 

of the likelihood of melting ice sheets, changing ocean currents or release of methane 

from permafrost. In PAGE default-mode probabilistic analysis shows that damage 

due to discontinuities is of a similar magnitude to damages from market and nonmarket impacts, whilst in DICE/RICE impacts due to catastrophic change actually 

dominate the estimates of damages. 

In all models the discount rate has a very strong influence on any assessment of social 

cost of carbon, over and above any variations in climate impacts themselves, because 

at a discount rate of 5%, impacts >50 years in the future are much smaller than they 

would be with lower discount rates. Since this is the period when climate damages 

accrue strongly, a high discount rate will inevitably result in lower values of the social 

cost of carbon. 

The representation of the carbon cycle in models will also very strongly affect 

assessments of the social cost of carbon, since this will determine the ratio between 

carbon emitted and carbon remaining in the atmosphere, i.e. the relationship between 

emissions and concentrations of greenhouse gases in the atmosphere. The same is 

also true of climate sensitivity. 

Model outputs are also affected by shape of damage function, which is at best 

theoretical in both global and regionally specific models, and is often based on an 

arbitrary choice of function. Specifically, market damages are quadratic in 

DICE/RICE and MERGE, between linear and cubic in a probabilistic fashion in 

PAGE, and take a variety of theoretical forms in FUND. Only FUND’s damage 

functions take into account the rate of temperature change as well as its magnitude, 

and this only for the agricultural and ecosystem sectors. 

Owing to the uncertainties in functional form and calibration points of models, users 

may want to perform uncertainty analyses. Whilst this is possible with all models, it 
5

often requires changes to the model code. Only PAGE allows a user-friendly 

probabilistic analysis. Thus PAGE has been used to explore the full range of 

optimistic and pessimistic views concerning climate impacts. 

A comparison of the outputs of RICE and FUND with those of the parallel project 

Understanding Regional Impacts of Climate Change finds that the RICE and FUND 

models present a more optimistic picture of climate impacts (and in some cases a 

much more optimistic one) than the literature-based impacts study. This is because 

the literature upon which the models are based is very different. However, the RICE, 

MERGE and FUND models all use a limited set of literature which does not span the 

full range of potential negative climate impact outcomes found in the literature, and 

thus the models tend towards an optimistic picture of how climate damages accrue 

with impacts6

. Examples may be found in the agriculture and ecosystems sectors: 

- For agriculture the regionally specific integrated assessment models use literature 

which assumes strong adaptation (RICE, FUND) and/or strong CO2 fertilisation 

(FUND) and hence smaller impacts that the overall combined literature would 

suggest. 

- For ecosystems, the use of a WTP approach and a zero lifetime for impacts (FUND) 

in the models does not reflect the true value of ecosystems in, for example, preserving 

watersheds, combating soil erosion, buffering sea level rise, pollinating crops, 

providing new medicines, etc. It also does not reflect the notion of permanent loss of 

species, since such damages are regarded as transient. 

Some models omit impacts in important sectors or regions, for example FUND omits 

catastrophic change, and RICE omits impacts in forestry, energy systems, water 

systems, construction, fisheries, and outdoor recreation in temperate regions. Models 

do not take account of interactions between sectors, in other words, regional problems 

which may occur due to the coincidence of climate impacts in different sectors in the 

same region. Treatment of adaptation in the models is considered inadequate by 

experts in the field, being generally modelled on large scale and long-term 

assumptions, not reflecting key aspects of societal development or local factors. 

In FUND, most of the climate change damages accrue in the energy sector due to 

increased need for air conditioning. 

Most models do not take into account the influences of extreme weather events which 

are likely to contribute very strongly to economic impacts; however the top end of the 

PAGE input ranges do include them as far as the literature allows, albeit that literature 

on potential changes in the frequency and intensity extreme events is in its infancy. 

 

6 In some cases this is because the models need estimates of aggregate global damage, and such literature 

may itself be based on a non-representative or out of date assessment of impacts.
6

INTRODUCTION 

IA models have frequently been used to calculate social costs of carbon and a very wide 

range of estimates for this quantity exists in the literature. Hence this project details the 

assumptions and data lying behind the assessment of climate change damage in four well 

known IA models: RICE/DICE, MERGE, PAGE and FUND. Graphics showing how 

monetized and non-monetized damages vary with temperature in the various models are 

also provided. The project also assesses how functions vary with socioeconomic 

scenario and how they handle adaptation. Finally, a comparison is made with the outputs 

of the parallel report Understanding the Regional Impacts of Climate Change. 
7

1. DICE/RICE 

Treatment of Climate Damages in DICE/RICE 

The Dynamic Integrated Climate and Economy (DICE) model is a simple integrated 

model of the climate and global economy. It is designed to calculate the optimal balance 

between greenhouse gas abatement and economic damages from climate change, based 

on a suite of assumptions. It uses an aggregate damage function, consisting of impact 

estimates for agriculture, other market sectors, coastal vulnerability, health, non-market 

impacts, human settlements and ecosystems, and catastrophic climate change. The 

Regional Integrated Climate and Economy (RICE) model is a regionalized version of this 

model, split into 8 regions: USA, OECD Europe, Other High Income (e.g., Japan, 

Canada, and Australia), China, Russia and Eastern Europe, Middle Income (e.g., Brazil, 

South Korea, Argentina, Taiwan, Malaysia, and high-income OPEC countries), LowerMiddle Income (e.g., Mexico, Turkey, Thailand, South Africa, much of South America, 

and several populous oil exporters such as Iran), and Low Income (South Asia, most of 

India and Southeast Asia, much of the Asian part of the former Soviet Union, subSaharan Africa, and a few countries in Latin America). In RICE, each region is assigned 

a different climate damage function, based on the same impact categories. 

The global (DICE) and regional (RICE) aggregate damage functions are derived from a 

climate impact analysis most completely described by Nordhaus and Boyer (2000), 

Chapter 4. The assessment relies on a willingness to pay (WTP) approach to estimating 

the value of preventing future climate change. These sources describe the derivation of 

the damage functions, but, unfortunately, not in enough detail to fully separate the 

contribution of each impact category to the aggregate functions. We outline here the 

aggregate representation of climate damages in the DICE and RICE models as a function 

of temperature increase, and, as much as possible, the breakdown of these aggregate 

functions into impact indices relating damages and temperature for each impact category 

and each region. 

Aggregate Damage Functions 

All DICE/RICE aggregate damage functions assume a quadratic relationship between 

temperature increase since 1900 and economic damages, of the form: 

D(t) = α1T(t) + α 2T(t)2

where D(t) is climate damages measured in % loss of GDP for the globe or region, T(t) is 

global mean temperature increase above preindustrial levels in oC, and α 1 and α 2 are 

constants specific to each function. Table 1.1 gives the values for these constants for the 

global function and each regional function. 
8

Table 1.1: Coefficients for Aggregate Damage Functions 

Function: α1 α 2 % Loss GDP for 2oC increase 

Globala -0.0045 0.0035 0.5 

United Statesb -0.0026 0.0017 0.16 

Europeb -0.001 0.0049 1.76 

Other High Incomeb -0.007 0.003 -0.2 

Chinab -0.0041 0.002 -0.02 

Russia and Eastern Europeb -0.0076 0.0025 -0.52 

Middle Incomeb 0.0039 0.0013 1.3 

Lower-Middle Incomeb 0.0022 0.0026 1.48 

Low Incomeb 0.01 0.0027 3.08 a

DICE-99 Excel Spreadsheet version 

b

RICE-99 Excel Spreadsheet version 

Figure 1.1 shows the relationship between global aggregate damages and temperature 

increase for DICE-99 and RICE-99. The DICE-99 output matches that of the DICE 

model running with its optimal carbon tax and emissions control rates set to match the 

projections of these variables in the optimal run of RICE-99. The RICE-99 outputs may 

therefore be directly compared with the DICE-99 outputs. RICE-99 has only regional 

damage functions, not aggregate damage functions, and thus global aggregate output 

depends on how those regional estimates of damages are aggregated. Results are shown 

for two different weighting schemes used by Nordhaus, global output weights and 

population weights, which are given numerically in Table 1.2. 

Figure 1.1 
9

Figure 1.2 shows the corresponding regional aggregate damages for RICE-99. 

Figure 1.2 
10

Impact Sector Breakdown 

Table 1.2 shows the distribution of impacts across sectors assumed in DICE/RICE. 

Table 1.2 Impact of 2.5 degree warming above 1900 on different sectors (source Table 10, 

Nordhaus 1992): (units: % loss of regional total GDP from damages in each sector; except for 

TOTAL which shows % loss of regional GDP in all sectors combined. A negative number implies a 

benefit).

 

Region TOTAL Agriculture Other 

Vulnerable 

Market 

Coastal Health NonMarket 

Time Use 

Catastrophic Settlements 

USA 0.45 0.06 0 0.11 0.02 -0.28 0.44 0.1 

China 0.22 -0.37 0.13 0.07 0.09 -0.26 0.52 0.05 

Japan 0.5 -0.46 0 0.56 0.02 -0.31 0.45 0.25 

EU 2.83 0.49 0 0.6 0.02 -0.43 1.91 0.25 

Russia -0.65 -0.69 -0.37 0.09 0.02 -0.75 0.99 0.05 

India 4.93 1.08 0.4 0.09 0.69 0.3 2.27 0.1 

Other high 

income 

-0.39 -0.95 -0.31 0.16 0.02 -0.35 0.94 0.1 

Highincome 

OPEC 

1.95 0 0.91 0.06 0.23 0.24 0.46 0.05 

Eastern 

Europe 

0.71 0.46 0 0.01 0.02 -0.36 0.47 0.1 

Middle 

income 

2.44 1.13 0.41 0.04 0.32 -0.04 0.47 0.1 

Lowermiddle 

income 

1.81 0.04 0.29 0.09 0.32 -0.04 1.01 0.1 

Africa 3.91 0.05 0.09 0.02 3 0.25 0.39 0.1 

Low 

income 

2.64 0.04 0.46 0.09 0.66 0.2 1.09 0.1 

 

Global 

Outputweighted 1.5 0.13 0.05 0.32 0.1 -0.29 0.17 1.02 

Populationweighted 2.19 0.17 0.23 0.12 0.56 -0.03 0.1 1.05 
11

Impact Indices and Damage Function Calibration 

In the impact assessment by Nordhaus and Boyer, the 8 RICE regions are divided into 13 

sub-regions: USA, China, Japan, Western Europe, Russia, India, Other High Income, 

High-Income OPEC, Eastern Europe, Middle Income, Lower-Middle Income, Africa, and 

Low Income. This assessment constructed functional relationships between economic 

damages (% loss GDP) and regional or global temperature for each impact category and 

sub-region. These damage functions are based on sectoral impact assessments in the 

literature, or the expert judgment of the authors. Adaptation is considered implicitly in 

the derivation of these sectoral damage functions. 

All damages in the DICE/RICE models are expressed in terms of % loss of global or 

regional GDP. To make this conversion, the authors define a “future impact index” for 

each sector i and each region j, using a baseline year of 1995, of the form: 

θi,j(T, yj) = Qi,j(T) x [yj(t)/yj(1995)]ηi

where θi,j(T, yj) is the “impact index” for a global mean temperature increase of T and a 

per capita income of yj, expressed as current (1995) WTP to avoid a temperature increase 

of T (Q i,j(T)), multiplied by an income adjustment. The income adjustment is the ratio of 

future per capita GDP in year t, yj(t), to present (1995) per capita GDP, raised to the 

power ηi, the income elasticity of the impact index. The authors benchmark these indices 

for a 2.5oC warming, constructing estimates for Qi,j(2.5). To calculate θi,j(2.5, yj), the 

authors use a “base run” of RICE-99 to determine that a 2.5oC temperature increase 

occurs around 2100, and report that “a run” of RICE-98 is used to generate values for 

yj(2100) (Nordhaus and Boyer, 2000). 

Finally, to convert these impact indices into the damage functions used in the models, the 

authors calculate θi,j(2.5, yj(2100)) and θi,j(6, yj(2100)), sum across sectors to get regional 

aggregate estimates, and then define quadratic damages functions of the form given 

above that intersect these aggregate damage estimates for 2.5oC and 6oC temperature 

increase and zero damages for 0oC warming. Where the RICE-99 regions include 

multiple sub-regions, they are weighted by GDP. The authors provide some details about 

the calculation of impact indices for 2.5oC, but with the exception of catastrophic climate 

change, they provide no information about the calculation of impact indices for 6oC. An 

overview of this information follows. 

Agriculture 

For each region j, the WTP function for agriculture is of the form: 

Qag,j(T) = {αag,0 + αag,1(T + T0

j) + αag,2(T + T0j)

2

} – {αag,0 + αag,1(T0

j) + αag,2(T0j)

2

} + εag,j
12

where Qag,j(T) is the damage to the agricultural sector as a fraction of GDP, αag,i are 

coefficients, T0

j is the sub-regional mean temperature without climate change, T is the 

global mean temperature increase above preindustrial levels, and εag,j is the error in the 

estimate. The income elasticity is set to -0.1. 

These authors combine sub-regional agricultural impact estimates in most cases from 

Darwin et al. (1995) but also from Dinar et al. (1998) with estimates of sub-regional 

temperature to produce a relationship between agricultural damage and climate displayed 

in Figure 1.3. The regional temperature at which impacts shift from benefits to losses is 

11.5oC. 

Specifically, Nordhaus makes particular use of Darwin et al. (1995) Appendix Table B6, 

which reports changes in GDP by region as a result of climate impacts upon agriculture 

for various climate scenarios. This table lists the outputs of 4 GCMs considering the 

cases where land use is restricted to cultivation on existing land and where land use is 

unrestricted. Nordhaus has selected the second most unfavourable GCM for the case 

where land use is unrestricted. 

These damage estimates reflect the adaptation incorporated in the Darwin et al. (1995) 

study, which in its summary, states, “Farmer adaptations are the main mechanisms for 

keeping up world food production under global climate change.” This includes farmers 

selecting the most profitable mix of inputs and outputs on existing cropland, adjustments 

in domestic markets and international trade, and increases in the amount of land under 

cultivation. Incorporating all of these adaptations leads to a slight increase (0.2-1.2%) in 

world cereals production under the climate change scenarios Darwin et al. consider. It 

should be noted that CO2 fertilization is excluded in this study. Thus, Nordhaus uses 

damage estimates that allow transition of land between cropland, pasture and other uses, 

but which assume no CO2 fertilization.
13

Figure 1.3 

Other Market Sectors 

The authors consider forestry, energy systems, water systems, construction, fisheries, and 

outdoor recreation as sectors moderately vulnerable to climate change. However, citing 

estimates by Cline (1992), Nordhaus (1991), and Mendelsohn and Neumann (1999) of 

small losses, zero impacts, and small benefits in these sectors for the US, the authors state 

a lack of impact in this category for temperate climates. They also state an estimate of a 

decline of 5% in energy expenditures in cold climates, and an increase of 8% in tropical 

and semi-tropical climates for a 2.5oC warming. The income elasticity is set to 0.2. 

Although details are not given, the authors produce sub-regional estimates for damages in 

this category linked to sub-regional temperature as in the agricultural sector (Figure 1.3). 

These are used to derive WTP functions of the same form as for agriculture. Here, the 

temperature at which impacts shift from benefits to losses is 12.3oC. 
14

The authors’ assessment of zero damages in the forestry, energy systems, water systems, 

construction, fisheries, and outdoor recreation sectors in temperate climates implies 

costless adaptation in these sectors to climate change. The nonzero estimates for other 

climates are due to benefits and costs in the energy sector, specifically increased cooling 

needs or decreased heating needs. 

Figure 1.4 
15

Coastal Vulnerability 

For each sub-region j, the WTP function for coastal vulnerability is: 

Qcoastal,j(T) = αcoastal,j(T/2.5)1.5

where αcoastal,j are constants, and T is global mean temperature increase above 

preindustrial. The authors cite the work of Yohe and Schlesinger (1998) on impacts from 

sea-level rise and a consideration of 1987-1995 storm damages from the Statistical 

Abstract (1997), page 240, based on which they estimate 0.1% of income as a reasonable 

WTP for preventing a 2.5oC warming for the US. For other sub-regions, they scale this 

estimate based on the ratio of “coastal area” (within 10 km of the coast) to total land area, 

divided by the same ratio for the US. The income elasticity is set to 0.2. 

Since the coastal vulnerability estimates are not directly based on one specific study, it is 

difficult to fully assess what forms of adaptation are implicit. Estimates made by Yohe 

and Schlesinger (1998) are prominently mentioned, and they calculate “the economic 

damage that might be attributed to future sea-level rise in the absence of any decision to 

protect threatened property in terms of the value of that property at the (future) time of 

inundation, taking into account any adaptation that might have occurred naturally and 

efficiently prior to flooding and abandonment.” They designate adaptation as “any sort 

of cost-reducing activity undertaken in response to the threat of future (or immediate) 

inundation. Some adaptation occurs ‘naturally’ as, for example, markets’ incorporating 

the threat. Other adaptation requires specific action by specific individuals – such as 

deciding to protect a particular piece of property.” Protection is explained as involving 

“building dikes, building seawalls, nourishing beaches, raising houses – any activity that 

prevents (or reduces the likelihood of) damage from rising seas.” The authors do note 

that human resettlement and its associated costs are incorporated in the human 

settlements and ecosystems sector.

Health

For each sub-region j, the WTP function for climate impacts on health is: 

Qhealth,j(T) = αhealth,j(Tj)

0.2243

where αhealth,j are constants, and Tj is the sub-regional mean temperature increase. The 

authors base their impact estimates on work by Murray and Lopez (1996). The impacts 

for each sub-region are determined based on the results for the region from Murray and 

Lopez that it most overlaps. The income elasticity is not reported. 

Adaptation is taken into account through the use of the data of Murray and Lopez (1996), 

which represents baseline improvements in health care, such as antimicrobials and 
16

vaccines, by using time as a proxy, based on the rate of improvement over the 20th

century. The authors specifically reduce the estimated damages for Africa from 4.6% to 

3.0% GDP loss for a 2.5oC warming to account for expected additional improvements in 

public health in the region that reduce the incidence of climate-related diseases. 

Non-Market Impacts 

The authors’ non-market impacts sector focuses on climate-related time use such as 

outdoor recreation. Details are not provided, but the authors state that they represent 

impacts using the same methodology as in the agricultural sector. Citing a study by 

Nordhaus (1998) of the value of climate-related time use in the US, the authors estimate a 

benefit of 0.3% GDP for a 2.5oC warming and a quadratic relationship between subregional mean temperature and time use impacts. These estimates are based on increased 

outdoor activity, and therefore assume that people will take advantage of warmer weather 

and engage in more outdoor activities. The estimate is extended to other countries using 

this quadratic relationship and an adjustment for differences in per capita GDP and 

average hourly earnings. The authors set the income elasticity to 0. 

Human Settlements and Ecosystems 

The authors use an impact index function similar to that for coastal impacts, based on 

global temperature changes (the formula is not given). The authors cite unpublished 

estimates (of their own) of the capital value of climate-sensitive human settlements and 

natural ecosystems in each sub-region, and estimate that each sub-region has an annual 

WTP of 1% of the capital value of the vulnerable system, for a 2.5oC increase. The 

income elasticity is set to 0.1. The authors acknowledge the difficulty of adaptation for 

coastal cities, islands, and natural ecosystems, but give no specific guidance on how this 

was incorporated into their “rough estimates.” 

Catastrophic Climate Change 

The authors consider abrupt climate changes such as rapid sea-level rise from ice sheet 

collapse, shifting monsoons, and changing ocean currents as potentially catastrophic 

impacts of climate change. The authors assume a linear damage function up to 3oC, and 

an unspecified power function for temperatures above 3oC. To calibrate this damage 

function, they base estimates of the probability of catastrophic change on the expert 

survey by Nordhaus (1994), which reported results for scenarios of 3oC warming by 2090 

and 6oC warming by 2175. Citing growing concerns over abrupt changes such as 

shutdown of the thermohaline circulation since the survey was completed, they estimate 

the probability of catastrophic change for 2.5oC as double the probability estimate for 3oC 

warming (0.012), and the probability for 6oC as double the estimate for 6oC (0.068). The 

authors also assume sub-regions such as India and OECD Europe have a higher 

vulnerability than others. To calculate WTP, the authors assume a rate of relative risk 

aversion of 4, and a loss of 30% of global GDP for a catastrophic event, distributed 
17

between sub-regions based on their (author-defined) relative vulnerability. Given these 

assumptions and the probabilities of occurrence, the authors calculate WTP for each subregion to avoid both temperature levels. The income elasticity is set to 0.1. The 

difficulty of adapting to such impacts is a central criterion for inclusion in this sector. 

Comparison with the “Understanding Regional Impacts of Climate Change” Project 

In order to compare directly the results from RICE simulations with those of the 

Understanding Regional Impacts of Climate Change Project (URICC), the RICE model 

was run for the 4 SRES scenarios A1F1, B1, A2 and B2. These calculations were 

initiated using radiative forcing time series from 1905 to the present day provided by the 

Hadley Centre, matching the scenarios shown in Figure 6 of Johns et al. 2003 which are 

also those underlying the primary impacts studies (Parry et al. 2001) reported in URICC. 

The accumulation of damage with respect to temperature for each SRES scenario (Figure 

1.5, shown for completeness) lies on a curve identical to that shown in Figure 1.1 

(DICE99 curve). The accumulation of damage with respect to time in each SRES 

scenario therefore reflects only the temperature changes in the respective SRES scenarios 

(Figure 1.5) since damage in RICE does not depend on socioeconomic factors or on the 

rate of temperature change. Figures 1.6a-c shows the evolution of damage with time in 

the SRES scenarios using Nordhaus’s three weighting schemes. There is most damage 

under the A1F1 scenario and least under B1, but the differences are entirely due to the 

influence of temperature (Note that other literature utilizing the DICE/RICE model is 

based on different climate scenarios and, for example, simulate a different temperatures 

for 1995). 

Figure 1.5. RICE damage function derived from SRES scenario runs 

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

01234

Temperature (above 1985)

Damages (% GDP)

B1

B2

A2

A1FI
18

Figure 1.6a. 

Global Damages (DICE-99)

-0.01

0

0.01

0.02

0.03

0.04

1980 2000 2020 2040 2060 2080 2100

Time

Damages (% GDP)

B1

B2

A2

A1FI

Figure 1.6b 

Global Damages (Output-Weighted)

0

0.01

0.02

0.03

0.04

0.05

1980 2000 2020 2040 2060 2080 2100

Time

Damages (% GDP)

B1

B2

A2

A1FI

Figure 1.6c 

Global Damages (Population-Weighted)

0

0.01

0.02

0.03

0.04

0.05

1980 2000 2020 2040 2060 2080 2100

Time

Damages (% GDP)

B1

B2

A2

A1FI
19

The distribution of impacts across regions evolves with temperature as shown in Figure 

1.2. Damages accrue most rapidly with temperature in low income countries, and also in 

Europe and China. In middle income countries damages increase more slowly. In 

contrast in the USA, other high income countries, Russia, E Europe and lower-middle 

income countries, there are small benefits until temperatures reach 2.5 to 3 degrees above 

1900, or 2 to 2.5 degrees above 1990, after which point damages do accrue. Once 

temperature rise exceeds 5C above 1900 regional aggregate damages are a higher fraction 

of the economy in Europe than in any other region. 

The monetization process makes direct comparisons of outputs from RICE with the 

URICC project impossible, but it is clear that a monetized approach gives a very different 

picture of the distribution of impacts than does the URICC project which uses physical 

metrics. 

A key point is that the RICE model is showing benefits up till a temperature rise of 2 to 

2.5 degrees C above 1990 in many regions, whilst the URICC project finds potentially 

high impacts in many world regions for these temperature rises. URICC’s main study 

finds the possibility of benefits only in Australasia, Europe and N America in the 

agriculture sector when the full CO2 fertilisation of crops is assumed. 

Table 1.2 above shows that for a temperature rise of 2.5C above 1900 (2C above 1990) 

the distribution of impacts across sectors is highly non-uniform with effects in the 

agriculture and non-market-time-use sectors and consideration of the potential impact of 

catastrophic change dominating the overall picture. Regionally, damage to agriculture 

coastal settlements and catastrophic climate impacts are most important in the EU, offset 

somewhat by benefits from non-market time use; in the US, these benefits offset the low 

damage estimates in all sectors beyond the consideration of catastrophic change; damage 

by catastrophic change, in agriculture and health are particularly strong in India; in 

Africa, catastrophic change is the strongest driver of damage. The lack of influence of 

other market sectors in RICE is due to the treatment of these sectors referred to above 

under “Other Market Sectors”. 

Hence, whilst DICE/RICE is progressive in its recognition of the importance of 

catastrophic change, the literature upon which its assessments of other market sectors is 

based is rather limited and dates back to the 1990s. The authors themselves point out the 

lack of treatment of impacts on “other market sectors” in temperate climates (this chapter 

has already highlighted the implication of costless adaptation in the forestry, energy 

systems, water systems, construction, fisheries, and outdoor recreation sectors in 

temperate climates). This chapter has already detailed the treatment of agriculture in 

DICE/RICE, and how this is based on a study which, whilst it does not include CO2 

fertilisation, assumes unrestricted cultivation on new land and farmer adaptations (see 

above). For this reason, the impacts on agriculture appear much smaller than those 

found for the URICC project, the main study of which assumes no cultivation on new 

land and a specified set of farmer adaptations. Hence overall, the treatment of impacts in 
20

RICE is completely different from that of the URICC project and estimated impacts from 

RICE are much lower in all sectors and regions. 

Nordhaus (2006) pioneers a new method of damage estimation based not on the DICE 

model but on an entirely new, statistical method of estimating the relationship between 

economic and geographic conditions, including latitude, elevation, climate, distance from 

the coast, and climate. The climatic data used includes annual mean temperature and 

precipitation and the model as a whole has a resolution of 1 degree of latitude and 1 

degree of longitude. The resultant aggregate estimates of global output lost due to a 

2.5C warming are dependent on the method of aggregation and vary between -0.72% 

(area weighted aggregation) -0.93% (output weighted) and -1.73% (population 

weighted). If potential changes in precipitation are allowed for the figures increase to -

1.05%, -1.41% and -2.95% respectively. Integrated modeling inevitably does not yet 

represent this work. 
21

2. MERGE 

MERGE builds on Nordhaus (1991) in the sense that, like DICE/RICE, it is a simple 

integrated model of the climate and global economy, and designed to calculate the 

optimal balance between greenhouse gas abatement and economic damages from climate 

change, based on a suite of assumptions. It uses two sets of regionalized damage 

functions, one for market impacts and one for non-market impacts. Unlike DICE/RICE 

it simulates out to 2200. The utility discount rate in MERGE is chosen so that the net 

real rates of return on capital (ie interest rates) are identical at 5%/year in all regions, to 

avoid the model dealing with the rate of change of each market exchange rate with the 

US dollar. Two discount rates are relevant, R and p: 

R = p + g (1) 

Where R is the net real rate of return on capital, p is the pure rate of time preference and g 

is the net rate of growth of GDP in 1 year. 

MERGE regions are US, other OECD (W Europe, Japan, Canada, Australia & NZ), 

China, Former Soviet Union, and Rest of World. 

This study reports on two model versions : firstly that described by Manne, Mendelsohn 

& Richels 1995, and secondly the freely downloadable version documented by Manne 

(2004). 

2.1 Manne, Mendelsohn & Richels 1995 MERGE version 

Treatment of Market Impacts 

 Here it is assumed that damages rise quadratically with T in the equation below 

d2,r = 2 (the value of 2 is said to be taken from Nordhaus, 1992). The quadratic form 

assumed means that each damage function needs only 1 calibration point. In some 

applications of the model it is calibrated using Nordhaus (1992) whilst in other 

applications an uncertainty analysis is carried out on this calibration. 

In this version market damages, Dt,r , for any time period t in the region r, are 

defined by the following relationship: 

Dt,r = d1,r * [∆AT t,r ]d2,r * GDPt,r (2)

where d1,r and d2,r are constants. This can be shown graphically as the solid or dashed 

curves in Figure 2.1, which illustrates the specific case for calibration such that a 2.5C 

increase in T above 1990 levels would result in 0.25% loss of GDP in line with Nordhaus 

(1992) early estimates of damage to the USA for doubled CO2 (solid line) whilst losses 

were twice as large if the country is a developing one (dashed line). 
22

Figure 2.1 

 Example MERGE market damage 

curve

0

2

4

6

8

10

12

14

0123456

Annual global mean temperature rise (C)

GDP loss (%)

 

Treatment of non-market damages 

Non market damages are modelled with a willingness-to-pay (WTP approach). It is 

assumed to have an S shaped dependence with GDP based on a WTP formulation 

calibrated so that it does not exceed 100% of GDP. Consumers in a particular region 

place the same value on losses whether they occur inside or outside their region. The 

model explores the options of different valuations that society might place on these nonmarket damages rather than advocating any particular set of numbers. 

WTPt,r = d3,r * [∆AT t,r ]d4,r / [ 1+ (100* exp (-0.23*GDPt,r /POPt,r)) ] (3)

where d3,r and d4,r are constants. 

Figure 2.2 shows the willingness to pay to avoid non-market damages associated with a 

2.5C change in temperature, with d3,r set to 0.0032 and d4,r set to 2. This means that when 

per capita income approaches $40,000 US 1990, consumers are willing to pay 2% of 

GDP to avoid a 2.5C increase in temperature ( and 8% to avoid 5C) as shown. In 1995 
23

US expenditure on environmental protection was 2% GDP. Thus the WTP in a 

developed country, i.e. the non-market damages as modelled in MERGE, may be very 

much larger than the market impacts (Table 2.1) 

Figure 2.2 (taken from Manne, Mendelsohn & Richels 1995). 

 

Table 2.1 Damages in the MERGE model calibrated according to the method 

explained in Manne, Mendelsohn & Richels, 1995. 

Increase in global 

average temperature 

from 1990 

Market damage 

(% loss GDP, 

developed) 

Market damage 

(% loss GDP, 

developing) 

Non-market damage 

(% loss GDP, for 

developed country 

with $40,000 per 

capita income) 

0 0.00 0.00 0.00 

0.5 0.01 0.02 0.08 

1 0.04 0.08 0.32 

1.5 0.09 0.18 0.72 

2.0 0.16 0.32 1.28 

2.5 0.25 0.5 2.0 

3.0 0.36 0.72 2.88 

3.5 0.49 0.98 3.92 

4.0 0.64 1.28 5.12 

4.5 0.81 1.62 6.48 

5.0 1.0 2.0 8.0 
24

2.2 MERGE freely downloadable version (Manne, 2004) 

Treatment of market-damages 

Manne (2004) explains that in this version market damages are proportional to T rise ie 

d2,r is assumed to be 1. The calibration point is the same as in the original version. 

The following “market damage multipliers” are used to determine the regional 

distribution of market impacts (Table 2.2) 

Table 2.2 Market damage multipliers for GDP loss at a reference 

 temperature in MERGE (Manne 2004) 

 Region Multiplier 

 USA 0.0025 

 W Europe 0.0025 

 Japan 0.0025 

 Canada/New Zealand 0.0025 

 Eastern Europe/FSU 0.0025 

 China 0.005 

 India 0.005 

 OPEC 0.005 

 Rest of World 0.005 

Treatment of non-market damages 

Manne (2004) explains that the freely downloadable version of the model uses the same 

calibration point of a 2% loss in GDP for a 2.5C temperature rise in a developed country, 

but a different formulation of WTP than the earlier version. 

In this version, instead of equation (3), two new parameters hsx and catt are used to 

define the WTP. An economic loss function (ELF) is calculated which represents the 

amount of consumption that society is willing to invest in avoiding non-market damages 

of climate change. In developed countries: 

ELF(x) = [1 – (x/catt)2

] hsx (4) 

where hsx =1 in developed countries. ELF is the remaining consumption such that 

ELF(2.5)=0.98. 

Where x is the increase in temperature rise since 2000, catt is a catastrophic temperature 

chosen such that the entire regional product is wiped out at this level, and hsx modifies 

the quadratic loss function so as to allow for a hockey-stick shape. 
25

For consistency with a willingness to pay of 2% of GDP, such that ELF(2.5) = 0.98, a 

catt value of 17.7C is implied. 

In low income countries hsx << 1 and is chosen such that for a per capita income of 

$25,000 a region would be willing to spend 1% of its GDP so as to avoid a global 

temperature rise of 2.5C. If per capita income reached $50,000 or above, a value of 2% 

GDP is considered appropriate. 

Figure 2.3 shows the resultant ELFs for the USA and India. 

Manne (2004) emphasizes that the values of catt and hsx are highly speculative, and 

that with different numerical values one can attain different estimates of the WTP to 

avoid non-market damages. The general principle that WTP is higher in high income 

countries however, seems defensible whilst a specific calculation is questionable. 

In both versions when damages across regions are combined the resultant economic 

welfare figures are combined by calculating their discounted logarithms and using 

Negishi weights which are an instrument to account for disparities in economic 

development (Nordhaus and Yang, 1996). 
26

Figure 2.3 Economic Loss Functions for the USA and India 
27
28

Table 2.3 Damages in the MERGE model calibrated according to the method 

explained in Manne, 2004. 

Increase in global 

average temperature 

from 1990 

Market damage (% 

loss GDP, developed) 

Market damage 

(% loss GDP, 

developing) 

Non-market 

damage (% loss 

GDP, for 

developed 

country ) 

0 0.00 0.00 0.00 

0.5 0.01 0.02 0.08 

1 0.04 0.08 0.32 

1.5 0.09 0.18 0.72 

2.0 0.16 0.32 1.28 

2.5 0.25 0.5 1.99 

3.0 0.36 0.72 2.87 

3.5 0.49 0.98 3.91 

4.0 0.64 1.28 5.11 

4.5 0.81 1.62 6.46 

5.0 1.0 2.0 7.98 

6.0 11.49 

7.0 15.64 

10.0 31.92 

17.0 92.25 

17.7 100.00 

Outputs 

Tables 2.1 and 2.3 show global damages simulated in the two versions of the MERGE 

model described here, and the proportions from market and non-market impacts. It has 

already been observed that non-market impacts may be larger than market impacts. 

When the model is used for cost-benefit analysis, in the 1995 version the default 

assumptions show that for stabilisation at 415 ppmv CO2 the costs outweight benefits by 

8:1 with the 5% discount rate applied. If consumers were willing to pay 17% GDP to 

avoid non-market damages the model shows benefits equalling costs. If more optimistic 

assumptions are made about costs or the discount rate is lowered to 2% then the break 

even WTP falls to to 2.5%. 

Such results show the importance of the discount rate: if one adopts a real annual 

discount rate of 5% of more the present value is virtually negligible for impacts that lie at 

least 50 years into the future, so within a cost-benefit framework, even if one allows a 

gradual rise in the relative value of environmental goods it becomes exceedingly difficult 

to justify any near term mitigation action. 

Manne & Stephan (2005) make the interesting observation that their derivation of optimal 

emission reduction (which they call the Pareto-efficient stock of atmospheric carbon) is 

independent of the initial distribution of emissions rights (i.e. different ideas about initial 

allocation of permits for emissions trading) when market damages are considered in 
29

MERGE. However when non-market damages are considered, if there is an income 

effect on the willingness to pay to avoid non-market damages, the different values that 

different regions place on avoiding damage can interact with the initial distribution of 

emission rights, so that different optima would be arrived at for different initial 

distributions. It is explained that in the Pareto-optimal case, nonmarket damages enter 

the Negishi welfare definition, and market damages represent one of the competing 

claims on the allocation of total production resources. In all other cases, non-market and 

market damages are excluded from the determination of the equilibrium, but are 

determined in a post-optimization step. In this way, a representation of the possibility for 

non-cooperation or of non-optimal international control agreements is allowed for. 
30

3. PAGE 

PAGE, Policy Analysis of the Greenhouse Effect, uses relatively simple equations to 

simulate the effects of different policies for mitigation of and adaptation to climate 

change. The main outputs are equity-weighted impacts, calculated as $million, which can 

then be divided by regional or global GDP outside the model if desired. These 

calculations approximate the results of more complex simulations through calibration of 

key model parameters to the outputs in the literature. Most parameter values are taken 

from the IPCC Third Assessment Report. PAGE is set up in a probabilistic spreadsheet 

format which enables the user to carry out extensive sensitivity analysis of outputs such 

as the social cost of carbon to the input assumptions. The model simulates (i) emissions 

of the primary greenhouse gases (ii) the resultant increase in radiative forcing (iii) cooling 

from sulphate aerosols (iv) regional temperature changes (v) damage caused by warming 

to market and non-market sectors, taking account of non-linearity (vi) the potential for a 

discontinuity in the climate damages caused by catastrophic rapid change (vii) regional 

economic growth, allowing impacts to be evaluated in terms of an annual % loss of GDP 

in each region (viii) the potential for adaptation to climate change. Detailed model 

equations may be found in Hope (2006). Regional temperature rise is calculated by a 

very simple formulation of radiative forcing, sulphate aerosol forcing and carbon cycle 

feedback which has uncertain parameters that are varied strongly in the model to allow 

for uncertainties. To derive social costs of carbon from the model the user must compare 

two very similar policies or emissions scenarios in which the second has 1 billion tons 

less of C than the first. The spreadsheet model then automatically computes the 

differences in damages for the two policies to generate the marginal benefits of reduced 

carbon emissions. 

Damage functions in Page 

Market and non-market impacts in each region are not calculated physically but in terms 

of GDP losses, and are an uncertain power function of temperature rise. The uncertain 

functional form is then calibrated with a benchmark estimate of impacts from the 

literature when mean temperature rises by 2.5C over pre-industrial. Each regional impact 

is then multiplied by a weighting factor. Impact parameters in PAGE 2002, the most 

recent version of PAGE, are given in Table 3.1a below. 

The following sectors only are considered in PAGE: 

Sector 1: Economic 

Sector 2: Non-economic 

Sector 3: Discontinuity impacts 
31

More specifically, the impact functions (for sectors 1 and 2 in all regions) take the form 

of a polynomial 

 I(r) = A(r)Tn(r) 

where n is an uncertain variable whose minimum, most likely and maximum values are 

taken as 1, 1.3 and 3 respectively, and r is an index specifying region. Plambeck and 

Hope (1997) explain (section 2.6) that the mean value taken from the expert survey of 

Fankhauser (1994), who in turn takes it from Cline (1992), which is a study based on 

estimates of climate impacts in the USA for CO2 doubling and recommends that further 

studies should include detail of other countries. Cline derived the exponent’s value of 1.3 

from estimates of the annual damage from warming to the US 1990 economy. 

Aggregate damage was estimated at $335.7 billion for a 10C warming, compared to 61.6 

billion for 2.5C warming (above 1900). Cline recommended that future studies should 

take into account explicit studies of the situation in other countries. The range of 1 to 3 

for the exponent is taken from Peck & Teisberg (1992) in their simulation of damage in 

the CETA integrated model. Cline is now of the opinion that the exponent 1.3 needs to 

be revised upwards (pers. comm.) 

The sum of economic and non-economic impacts is modeled to lie between a 2% 

reduction in GDP and a 0.1% increase in GDP for a 2.5C temperature rise, consistent 

with IPCC TAR Table 19.4, page 940 figures for world impacts, reproduced below as 

Table 3.1b. 

Discontinuity impacts represent damages due to rapid climate change events such as 

collapse of the THC, or other very large earth system changes. These have been 

estimated to be an order of magnitude greater than the impacts from continuous change, 

an estimation broadly consistent with IPCC TAR WGII p947. The chance of a largescale discontinuity is estimated to be zero until temperatures reach between 2 and 8C 

(most likely 5C) above pre-industrial (hence 1.5 to 7.5, mean 4.5, above 1990 

temperatures since PAGE models a 0.5C difference between pre-industrial temperatures 

and those of 2000). Beyond this the risks increase by 1 to 20% (most likely 10%) for 

each subsequent 1C rise in temperature. Damages from discontinuities comprise about 

15% of today’s social cost of carbon, a percentage that will rise in the future as we get 

closer to the time that discontinuities are likely to occur . 

Weights are used to monetize the above impacts to allow for comparison and aggregation 

across economic and non-economic sectors. The weights used are the % of GDP lost for 

benchmark warming of 2.5C in each impact sector in the focus region (the EU); 

multiplicative weights are used to express the % of GDP lost for benchmark warming of 

2.5C in other regions. They are chosen to be consistent with IPCC TAR WGII, page 940 

and involved a large amount of expert judgement to encompass the various studies 

covered by IPCC (see Tables 3.1a, 3.1b). 
32

PAGE considers 8 world regions: European Union, Latin America, Eastern Europe & 

former Soviet Union; USA; China & CP Asia; India & SE Asia; Africa & Middle East; 

and Other OECD (which includes Canada, Japan and Australia). 

Table 3.1a. Impact parameters in PAGE 2002, version 1.4 

PAGE 2002 Impact 

Parameter 

Mean Min Mode Max Metric (where 

relevant) 

Impact function exponent 1.77 1 1.3 3 

Economic impact 0.5 -0.1 0.6 1 %GDP loss 

for 2.5 degC 

Non-econ impact 0.73 0 0.7 1.5 %GDP loss 

for 2.5 degC 

Loss if discontinuity 

occurs 

11.67 5 10 20 %GDP 

Eastern Europe and 

former Soviet Union 

weights factor 

-0.35 -1 -0.25 0.2 

USA weights factor 0.25 0 0.25 0.5 

China and E Asia 

weights factor 

0.2 0 0.1 0.5 

India and S Asia 

weights factor 

2.5 1.5 2 4 

Africa weights factor 1.83 1 1.5 3 

Latin America weights 

factor 

1.83 1 1.5 3 

Other OECD weights 

factor Canada Aus Japan 

NZ 

0.25 0 0.25 0.5 
33

Table 3.1b (taken from Table 19.4 IPCC TAR): Indicative world impacts, by region (% of 

current GDP). Estimates are incomplete, and confidence in individual numbers is very 

low. There is a considerable range of uncertainty around estimates. Tol's (1999) estimated 

standard deviations are lower bounds to real uncertainty. Figures are expressed as impacts 

on a society with today's economic structure, population, laws, etc. Mendelsohn et al. 

(2000) estimates denote impact on a future economy. Positive numbers denote benefits; 

negative numbers denote costs (Pearce et al., 1996; Tol, 1999a; Mendelsohn et al., 2000; 

Nordhaus & Boyer, 2000). 

 IPCC 

SAR 

Mendelsohn et al. Nordhaus 

and Boyer 

Tol 

 2.5°C 

Warming 

1.5°C 

Warming 

2.5°C 

Warming 

2.5°C 

Warming 

1°C 

Warminga

North America 3.4 (1.2) 

United States 0.3 -0.5 

OECD Europe 3.7 (2.2) 

- EU -2.8 

OECD Pacific 1.0 (1.1) 

- Japan -0.1 -0.5 

E. Europe/FSU 2.0 (3.8) 

- Eastern Europe -0.7 

- Russia 11.1 0.7 

Middle East -2.0b 1.1 (2.2) 

Latin America -0.1 (0.6) 

- Brazil -1.4 

S & SE Asia -1.7 (1.1) 

- India -2.0 -4.9 

China 1.8 -0.2 2.1 (5.0)c

Africa -3.9 -4.1 (2.2) 

Developed 

countries 

-1.0 to -

1.5 

0.12 0.03 

Developing 

countries 

-2.0 to -

9.0 

0.05 -0.17 

 

World 

- Output weighted -1.5 to -

2.0 

0.09 0.1 -1.5 2.3 (1.0) 

- Population 

weighted 

 -1.9 

- At world average 

prices 

 -2.7 (0.8) 

- Equity weighted 0.2 (1.3) 

a

 Figures in brackets denote standard deviations. b

 High-income countries in Organization of Petroleum Exporting countries (OPEC). 

c

 China, Laos, North Korea, Vietnam
34

Treatment of Adaptation 

 Impacts are modelled to occur for each of the three sectors (market, non-market and 

discontinuity) and regions, above some time-dependant profile of tolerable regionspecific and sector-specific temperature rise. In the absence of adaptation the tolerable 

temperature rise is assumed to be zero at all times in the market and non-market sectors. 

 

 The user can “buy” different amounts of adaptation in different sectors and regions in 

different time periods. Figure 3.1 shows a conceptual PAGE damage function (curve 

Actual). The user can influence (a) the tolerable level of temperature rise (b) the 

tolerable rate of temperature rise or (c) the reduction in damage if the temperature does 

rise beyond what is tolerable. The model can thus be tuned for different assumptions 

about the dates and amount of adaptation in each year, region and sector. Thus 

adaptation is modeled both to affect the date/temperature changes at which an onset of 

impacts begins, and also to reduce the severity of impacts once they begin. 

Figure 3.1 shows how (a) and (b) are modeled within PAGE. The figure shows 

temperature-time space, and the red line shows the actual temperature rise from an 

emissions scenario. The blue curve shows the situation if the user of the model purchases 

adaptation of types (a) and (b). In the example shown adaptation is purchased 

increasingly between 2000 and 2020, which is sufficient to stave off climate impacts in 

this particular sector and region until 2050, after which impacts begin to accrue. Figure 

3.2 shows how the user can simulate the purchase of adaptation to reduce the impacts 

which still eventually accrue after adaptation of types (a) and (b) has been purchased. In 

the example shown the additional purchase reduces impacts by a percentage that 

increases linearly between 2010 and 2040. There are, however, limits to adaptation and 

hence not more than 60% of the impacts can be avoided, in this example. 

In version 1.4 of PAGE2002, adaptation is a policy which may be selected, so there is no 

probabilistic treatment. The user may purchase different amounts of adaptation in 

different regions, but there is a default set of parameters which were derived from 

CRU/ERL (1992), and in PAGE the assessment therein of adaptive capacity in the EU 

was extrapolated to the OECD and used as the default case in version 1.4. At this time 

adaptation was considered economically advantageous and hence this large amount of 

adaptation was used as a default. In other developing regions adaptive capacity is 

considered to be smaller. The default set of parameters (Table 3.2) offsets 33% of the 

climate impacts without adaptation in scenario A2. 
35

Table 3.2: Parameters for the default adaptation case with 25% cut in non-economic impacts. 

“Slope” and “plateau” may be found in Figure 3.1, “impact” as % reduction in Figure 3.2. 

Adaptation 2001 2002 2010 2020 2040 2060 2080 2100 2150 2200 

EU Economic slope 1 1 1 1 1 1 1 1 1 1 °C/dec 

EE Economic slope 1 1 1 1 1 1 1 1 1 1 °C/dec 

US Economic slope 1 1 1 1 1 1 1 1 1 1 °C/dec 

CA Economic slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

IA Economic slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

AF Economic slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

LA Economic slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

OT Economic slope 1 1 1 1 1 1 1 1 1 1 °C/dec 

EU Economic plateau 2 2 2 2 2 2 2 2 2 2 °C 

EE Economic plateau 2 2 2 2 2 2 2 2 2 2 °C 

US Economic plateau 2 2 2 2 2 2 2 2 2 2 °C 

CAEconomic plateau 0 0 0 0 0 0 0 0 0 0 °C 

IA Economic plateau 0 0 0 0 0 0 0 0 0 0 °C 

AF Economic plateau 0 0 0 0 0 0 0 0 0 0 °C 

LA Economic plateau 0 0 0 0 0 0 0 0 0 0 °C 

OT Economic plateau 2 2 2 2 2 2 2 2 2 2 °C 

EU Economic impact 18 54 90 90 90 90 90 90 90 90 % 

EE Economic impact 18 54 90 90 90 90 90 90 90 90 % 

US Economic impact 18 54 90 90 90 90 90 90 90 90 % 

CA Economic impact 10 30 50 50 50 50 50 50 50 50 % 

IA Economic impact 10 30 50 50 50 50 50 50 50 50 % 

AF Economic impact 10 30 50 50 50 50 50 50 50 50 % 

LA Economic impact 10 30 50 50 50 50 50 50 50 50 % 

OT Economic impact 18 54 90 90 90 90 90 90 90 90 % 

EU Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

EE Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

US Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

CA Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

IA Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

AF Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

LA Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

OT Non-econ slope 0 0 0 0 0 0 0 0 0 0 °C/dec 

EU Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

EE Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

US Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

CA Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

IA Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

AF Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

LA Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

OT Non-econ plateau 0 0 0 0 0 0 0 0 0 0 °C 

EU Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

EE Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

US Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

CA Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

IA Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

AF Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

LA Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 

OT Non-econ impact 25 25 25 25 25 25 25 25 25 25 % 
36

Treatment of adaptation costs 

Adaptation costs do not affect the computed value of the social cost of carbon in PAGE, 

as a marginal reduction in emissions is assumed not to affect the amount of adaptation 

performed. The model estimates adaptation costs using a set of uncertain parameters 

CS(d,0), CP(d,0), and CI(d,0) in sector d and region 0, reflecting the cost per unit 

increment of the slope, plateau and impact options described above. Costs in other 

regions are obtained through multiplication by a multiplicative cost factor for each 

region, CF(r) which is identical for slope, plateau, and impact whether economic or noneconomic sectors are considered. 

The total adaptive costs AC(i,d,r) for sector d in region r for time period i are then 

obtained by: 

AC(i,d,r) = CS(d,r)*SLOPE(i,d,r) + CP(d,r)*PLAT(i,d,r) + CI(d,r)*IMP(i,d,r) 

for i=timesclice or analysis period, d= sector (there are two), r=region (there are 8). 

The accumulated adaptive costs AAC(i,d,r) are aggegated using: 

AAC(i.d.r)=AC(i,d,r)*(Yhi(i)-Ylo(i)) 

where Yhi(i) and Tlo(i) are the start and end of each analysis period i. 

and then discounted over time using the same discount rate as the model uses for costs. 

Default adaptation costs (Table 3.3) are calibrated to CRU/ERL (1992). 

Table 3.3. Default adaptation cost parameters. 

PAGE2002 version 1.4epu 

Adaptive 

cost 

parameters 

Economic 

slope 

Economic 

plateau 

Economic 

impacts 

Nonecon 

slope 

Nonecon 

plateau 

Nonecon 

impacts 

 CS1 CP1 CI1 CS2 CP2 CI2 

EU 0.01 13333 173 0.01 0.01 0.01 

EE 0.001 1333 17 0.001 0.001 0.001 

US 0.005 6667 87 0.005 0.005 0.005 

CA 0.0052 6933 90 0.0052 0.0052 0.0052 

IA 0.0052 6933 90 0.0052 0.0052 0.0052 

AF 0.0052 6933 90 0.0052 0.0052 0.0052 

LA 0.0052 6933 90 0.0052 0.0052 0.0052 

OT 0.002 2667 35 0.002 0.002 0.002 

 $million $million $million $million $million $million 

 /°C/dec per °C per % /°C/dec per °C per % 

2000 2001 2002 2010 2020 2040 2060 2080 2100 2150 2200

Analysis 

period 1 1 8 10 20 20 20 20 50 50

Yhi 2002 2006 2015 2030 2050 2070 2090 2125 2175 2200

Ylo 2000 2002 2006 2015 2030 2050 2070 2090 2125 2175

Aggregation 

period 2 5 9 15 20 20 20 35 50 25
37

Figure 3.1. How adaptation shapes damage functions in PAGE. 

Figure 3.2. 

Discount rate 

The discount rate in PAGE, which is applied to all financially valued quantities, and 

is therefore key in influencing how damages are treated in the model is derived from 

d = p + e * g

Actual 

Tolerable 

Start date 

0

1

2

3

4

2000 2020 2040 2060 2080 2100

Year

DegC

Plateau 

Slope 

Impacts 

Start date 

0

10

20

30

40

50

60

70

80

90

100

2000 2020 2040 2060 2080 2100

Year

%

Plateau 

Slope 

Reduction 
38

where p is the pure rate of time preference, e is the negative of the elasticity of marginal 

utility with respect to income, and g is the per capita GDP growth rate. Thus in the year 

2000 the discount rate is close to 4% and by 2100 it is close to 5%, because the 

population growth rate slows more than the GDP growth rate does. This discount rate 

formula is applied to all regions, so different regions can have different values of d. 

Input parameters used in this study 

The version of PAGE used in this study is v1.4, which utilizes a probabilistic pure time 

preference rate and equity weight parameters7

. The inputs detailed here are those which 

gave a mean SCC of $43 per tC, and a 5 to 95% range of $7 to $128, including a pure 

time preference rate in the range of 1% to 3% per year, and an equity weight parameter 

(the negative of the elasticity of marginal utility with respect to income) in the range of 

0.5 to 1.5, as reported in Chris Hope’s evidence to the Stern review. Apart from the PTP 

rate and the equity weight parameters, for both of which triangular probability 

distribution functions are assumed, the input values are identical to those documented in 

tables 1 to 6 of Hope, 2006. The GDP growth rates are those of SRES Scenario A2, 

converted to PPP exchange rates (in Hope, 2006, the GDP growth rates were expressed in 

market exchange rates). Population and emissions are also input to the model to 

represent the scenario A2. (There is no economic module: emissions are an input). 

 

Outputs from PAGE 

The main outputs are equity-weighted impacts, calculated as $million, which can then be 

divided by regional or global GDP outside the model. Figure 3.3 shows simulated global 

damages as a function of temperature increase for the three sectors for the globe as a 

whole, expressed in terms of both %GDP and in $$million (year 2000). 

 

7 This model version thus does not match exactly the version used in Hope (2002) where PPP is not used 

consistently throughout the model. The only other version of PAGE which is used currently is the Green 

book which has declining discount rates taken from the Treasury Green Book which has single value 

declining discount rates. 
39

Figure 3.3 

Economic impacts by global mean temperature rise

-10

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14

global mean temperatur

% of GDP
40

Non-economic impacts by global mean temperature rise

-10

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14

global mean temperatur

% of GDP
41

Source: PAGE2002 model runs 

Source: PAGE2002 model runs 

Discontinuity impacts by global mean temperature rise

-10

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14

global mean temperatur

% of GDP

All impacts by global mean temperature rise

-10

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16

global mean temperature

% of GDP

Economic

Non-economic

Discontinuity
42

Source: PAGE2002 model runs 

Source: PAGE2002 model runs 

Economic impacts by global mean temperature rise

-200000000

0

200000000

400000000

600000000

800000000

1000000000

1200000000

1400000000

1600000000

1800000000

2000000000

0 2 4 6 8 10 12 14 16

global mean temperature

$million

Non-economic impacts by global mean temperature rise

0

200000000

400000000

600000000

800000000

1000000000

1200000000

1400000000

1600000000

1800000000

2000000000

0 2 4 6 8 10 12 14 16

global mean temperature

$million
43

Source: PAGE2002 model runs 

Source: PAGE2002 model runs 

Discontinuity impacts by global mean temperature rise

0

200000000

400000000

600000000

800000000

1000000000

1200000000

1400000000

1600000000

1800000000

2000000000

0 2 4 6 8 10 12 14 16

global mean temperature

$million

All impacts by global mean temperature rise

-200000000

0

200000000

400000000

600000000

800000000

1000000000

1200000000

1400000000

1600000000

1800000000

2000000000

0 2 4 6 8 10 12 14 16

global mean temperature

$million

Economic

Non-economic

Discontinuity
44

Tables A3.1a to A3.8b of Annex 1 show corresponding regional information for different 

time periods. In each table ‘min’ is the 5% value and ‘max’ the 95% value on the output 

probability distribution. 

Since the outputs are equity-weighted, very occasionally (eg the 95% value for total 

impacts in India in 2150 and 2200), the impacts can come out as more than 100% of 

GDP; the equity weights in India are still around 3 in 2150 and 2200, so an actual impact 

of, say, 40% of GDP would be recorded as 120% of GDP after equity weighting. 

In one or two cases (eg EU in 2060), both the 5% and 95% values for the discontinuity 

are 0, but the mean value is not zero ($870million for the EU in 2060). This is because 

the threshold temperature for a discontinuity to be possible has very occasionally been 

reached, but only in fewer than 5% of the runs. 

The 5% and mean values are negative (ie benefits) in the EE region for all three of the 

impact categories. This is a consequence of only having a single weights factor for each 

region, which is applied to all three of the impacts. For EE, this factor has a negative 

mean value. 

The top of the y axis for each of the graphs is $2000 trillion. Global GDP is under $50 

trillion in 2000, but under scenario A2 rises to $340 trillion by 2100, and over $2100 

trillion by 2200 (in $US 2000). 

Temperature is an output of the PAGE2002 model, and is probabilistic like all other 

outputs. Therefore it is noted in the tables as a probability distribution for each year in 

each region. In the graphs, output from 100 runs of the model was post-processed to 

match up 1000 values for each of the three impacts with 1000 values for the temperatures 

(100 values for each of 10 analysis years).
45

4. FUND 

THE CLIMATE FRAMEWORK FOR UNCERTAINTY, NEGOTIATION AND 

DISTRIBUTION, TECHNICAL DESCRIPTION, VERSION 2.9 

Resolution 

FUND2.9 is defined for 16 regions, specified in Table A4.1 of the Annex 2. The model runs 

from 1950 to 2300 in time-steps of a year. 

Population and income 

Population and per capita income follow exogenous scenarios. There are five standard 

scenarios (FUND, A1B, A2, B1 and B2), specified in Tables A4.2a-e and A4.3a-e. 

Emission, abatement and costs 

Carbon dioxide (CO2) 

Carbon dioxide emissions are calculated on the basis of the Kaya identity: 

(CO2.1) , ,,

, , , ,,

, ,,

: rt rt rt

rt rt rt rt rt

rt rt rt

M EY

M P Y

EYP = = ψ φ

where M denotes emissions, E denote energy use, Y denotes GPD and P denotes population; t

is the index for time, r for region. The carbon intensity of energy use, and the energy intensity 

of production follow from: 

(CO2.2) , ,1 ,1 ,1

ψ ψ ψ ψ ατ rt rt rt rt = − g −− −

and 

(CO2.3) , ,1 ,1 ,1

φ φ φ φ ατ rt rt rt rt = − g −− −

where τ is policy intervention and α is a parameter. The exogenous growth rates g are referred 

to as the Autonomous Energy Efficiency Improvement (AEEI) and the Autonomous Carbon 

Efficiency Improvement (ACEI); see Tables A4.4a-e and A4.5a-e for each scenario. Policy 

also affects emissions via 

(CO2.1’) , , , , ,, ( )( ) ψ φ Mrt rt rt rt rt rt =− − ψ χφχ Y

(CO2.4) , ,1 ,1 (1 ) ψ ψ χ rt rt rt = +− κ χ ατ ψ − −

and 

(CO2.5) , ,1 ,1 (1 ) φ φ χ rt rt rt = +− κ χ ατ φ − −

Thus, the parameter 0<α <1 governs which part of emission reduction is permanent (reducing 

carbon and energy intensities at all future times) and which part of emission reduction is 

temporary (reducing current energy consumptions and carbon emissions), fading at a rate of 
46

0<κ<1. In the base case, α=0.5, κψ=κφ=0.9. Alternatively, one may interpret the difference 

between permanent and temporary emission reduction as affecting commercial technologies 

and capital stocks, respectively. The emission reduction module is a reduced form way of 

modelling that part of the emission reduction fades away after the policy intervention is 

reversed, but that another part remains through technological lock-in. Learning effects are 

described below. 

The costs of emission reduction C are given by 

(CO2.6) 

2

, ,,

, ,

β τ = rt rt rt

g

rt rt t

C

Y HH

H denotes the stock of knowledge. Equation (CO2.6) gives the costs of emission reduction in 

a particular year for emission reduction in that year. In combination with Equations (CO2.2)-

(CO2.5), emission reduction is cheaper if smeared out over a longer time period. The 

parameter β follows from 

(CO2.7) , ,

,

, ,

β =− − 1.57 0.17 min rt st

r t s rt st

M M

Y Y

That is, emission reduction is relatively expensive for the region that has the lowest emission 

intensity. The calibration is such that a 10% emission reduction cut in 2003 would cost 1.57% 

(1.38%) of GDP of the least (most) carbon-intensive region, and a 80% (85%) emission 

reduction would completely ruin its economy; later emission reductions are cheaper by 

Equations (CO2.6) and (CO2.7). Emission reduction is relatively cheap for regions with high 

emission intensities. The thought is that emission reduction is cheap in countries that use a lot 

of energy and rely heavily on fossil fuels, while other countries use less energy and less fossil 

fuels and are therefore closer to the technological frontier of emission abatement. For 

relatively small emission reduction, the costs in FUND correspond closely to those reported 

by other top-down models, but for higher emission reduction, FUND finds higher costs, 

because FUND does not include backstop technologies, that is, a carbon-free energy supply 

that is available in unlimited quantities at fixed average costs. 

The regional and global knowledge stocks follow from 

(CO2.8) , ,1 ,1 1 H H rt rt R rt γ τ = + − −

and 

(CO2.9) 1 , 1 G G H H t t G rt γ τ = + −

Knowledge accumulates with emission abatement. More knowledge implies lower emission 

reduction costs. Equations (CO2.6) and (CO2.8) together constitute learning by doing. The 

parameters γ determines which part of the knowledge is kept within the region, and which part 

spills over to other regions as well. In the base case, γR=0.9 and γG=0.1. Note that, although 

there is learning by doing – Equations (CO2.8) and (CO2.9) – technology diffusion – Equation 

(CO2.7) – as well as permanent effects of emission reduction on the growth path of the 

economy – Equations (CO2.2) and (CO2.3) – the model does assume that policy interventions 

are always costly, and that larger interventions are more costly. 

Emissions from land use change and deforestation are exogenous, and cannot be mitigated. 

Numbers are found in Tables A4.6a-e. 
47

Methane (CH4) 

Methane emissions are exogenous, specified in Table A4.7a. Note that there is no distinction 

between scenarios. Parameters for the emission reduction of methane are given in Table 

A4.7b. 

Nitrous oxide (N2O) 

Nitrous oxide emissions are exogenous, specified in Table A4.8. Note that there is no 

distinction between scenarios. Parameters for the emission reduction of methane are given in 

Table A4.8b. 

Sulfurhexafluoride (SF6) 

SF6 emissions are linear in GDP and GDP per capita. Table A4.9 gives the parameters. The 

numbers for 1990 and 1995 are estimated from IEA data. 

Sulphur dioxide (SO2) 

Sulphur dioxide emissions follow grow with population (elasticity 0.33), fall with per capita 

income (elasticity 0.45), and fall with the sum of energy efficiency improvements and 

decarbonisation (elasticity 1.02). The parameters are estimated on the IMAGE scenarios. 

Atmosphere and climate

Concentrations 

Methane, nitrous oxide and sulphur hexafluoride are taken up in the atmosphere, and then 

geometrically depleted: 

(C.1) Ct t-1 t t-1 pre = C +α β E - (C -C )

where C denotes concentration, E emissions, t year, and pre pre-industrial. Table A4.10 

displays the parameters α and β for all gases. 

The atmospheric concentration of carbon dioxide follows from a five-box model: 

(C.2a) i,t i it Box i t = Box 0.000471 ρ , + α E

with 

(C.2b) t

i=1

5

C i i,t = ∑α Box

where αi denotes the fraction of emissions E (in million metric tonnes of carbon) that is 

allocated to Box i (0.13, 0.20, 0.32, 0.25 and 0.10, respectively) and ρ the decay-rate of the 

boxes (ρ = exp(-1/lifetime), with life-times infinity, 363, 74, 17 and 2 years, respectively). The 

model is due to Maier-Reimer and Hasselmann (1987), its parameters are due to Hammitt et 

al. (1992). Thus, 13% of total emissions remains forever in the atmosphere, while 10% is—on 

average—removed in two years. Carbon dioxide concentrations are measured in parts per 

million by volume. 
48

For sulphur, emissions are used rather than concentrations. 

Radiative forcing 

Radiative forcing is specified as follows: 

(C.3) ( )

( )

( )

2

4 2

5 0.75 0.75 15 2.52 1.52

42 42

2

6

6.3ln 0.036 790 0.14 285

275

2 0.47 ln 1 2.01 10 5.31 10

ln 1 34.4 0.00052( 0.04) 0.03 0.08 14.6 14.6 ln 1 34.4

t

CO RF CH N O

CH N O CH N O

S SO SF

− −

⎛ ⎞ = + −+ −− ⎜ ⎟ ⎝ ⎠

⋅ +⋅ +⋅ +

+ −− − +

Temperature and sea level rise

The global mean temperature T is governed by a geometric build-up to its equilibrium 

(determined by radiative forcing RF), with a half-time of 50 years. In the base case, global 

mean temperature T rises in equilibrium by 2.5°C for a doubling of carbon dioxide 

equivalents, so: 

(C.4) Tt t-1 t = 1-

1

50 T +

1

50

2.5

6.3 (2) RF ⎛

⎝

⎜ ⎞

⎠

⎟ ln

Global mean sea level is also geometric, with its equilibrium level determined by the 

temperature and a life-time of 50 years. Temperature and sea level are calibrated to the best 

guess temperature and sea level for the IS92a scenario of Kattenberg et al. (1996). 

Impacts 

There follows a detailed description of how FUND simulates climate damages in key sectors, 

specifying the mathematical form assumed in each sector, and the parameters used to calibrate 

these. The accompanying Table 4.1 summarises the sources of some of these key parameters. 

Agriculture 

For the impact of the rate of climate change on agriculture, the assumed model is: 

(A.1) , 1, 0.04

r r t

tr r t r

T A A

β

α ρ −

⎛ ⎞ ∆

= + ⎜ ⎟ ⎝ ⎠

ar

 denotes the change in agricultural production due the rate of climate change (see Table 

A4.11); t denotes time; r denotes region; ∆T denotes the change in the global mean 

temperature; α is a parameter, denoting the benchmark change in agricultural production (cf. 

Table 1); β = 2.0 (1.5-2.5) is a parameter, denoting the non-linearity of the reaction to 

temperature; ρ =10 (5-15) is a parameter, denoting the speed of adaptation. 

The model for the impact due to the level of climate change is: 
49

(A.2) 2

,

2

12 12

B opt B

l rr r

tr t t opt opt

r r

AT A A T T

T T

− = + − −

Al

 denotes the change in agricultural production due to the level of climate change; t denotes 

time; r denotes region; T denotes the change in global mean temperature relative to 1990; AB

 is 

a parameter, denoting the benchmark change in agricultural production (cf. Table A4.11); Topt

is a parameter, denoting the optimal temperature (cf. Table A4.11). It is derived from Darwin 

et al 1995, Reilly et al 1994, and Rosenzweig and Parry (1994) by regressing the change in 

agricultural production after adaptation upon the change in global mean temperature and its 

square, as described in Tol (2002b). 

CO2 fertilisation has a positive, but saturating effect on agriculture, specified by 

(A.3) 2,

, ln

275

f t

tr r

CO

A γ

⎛ ⎞ = ⎜ ⎟ ⎝ ⎠

Af

 denotes the change in agricultural production due to the CO2 fertilisation; t denotes time; r

denotes region; T denotes the atmospheric concentration of carbon dioxide; 275 ppm is the 

pre-industrial concentration; γ is a parameter, see Table A4.11. 

The share of agricultural production in total income falls with per capita income. The 

elasticity across the nine regions is -0.31. So, 

(A.4) 

ε

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

t r

r

r

r

t r

t r

y

y

Y

GAP

Y

GAP

,

1990,

1990,

1990,

,

,

GAP denotes gross agricultural product; Y denotes gross domestic product; y denotes gross 

domestic product per capita; t denotes times; r denotes regions; ε = 0.31 (0.15-0.45) is a 

parameter. 

Forestry 

The model is: 

(F.1) , 2,

,

1990,

0.5 0.5 ln

275

tr t

tr r t

r

y CO F T

y

ε

β α γ

⎛ ⎞ ⎛ ⎞ ⎛ ⎞

= + ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ ⎝ ⎠ ⎝ ⎠

where F denotes the change in forestry consumer and producer surplus (as a share of total 

income); t denotes time; r denotes region; y denotes per capita income; T denotes the global 

mean temperature; α is a parameter; see Table A4.12; ε = 0.31 (0.11-0.51) is a parameter; β = 

1 (0.5-1.5) is a parameter; γ = 0.44 (0.29-0.87) is a parameter; γ is such that a doubling of the 

atmospheric concentration of carbon dioxide would lead to a change of forest value of 15%. 

Water resources 

Earlier versions of FUND incorrectly assumed that water technologies are constant. FUND’s 

impact estimates are based on Downing et al. (1995, 1996), and they also assume that there is 

no technological change in water. In reality, however, there are considerable improvements, 

both for water supply (e.g., desalinisation) and water demand (e.g., drip irrigation). In the 

revised impacts module, we assume that water technology progress by 0.5% a year, with a 
50

standard deviation of 0.5%. This manifests itself in that the sensitivity of the water sector to 

climate change falls by 0.5% a year. 

The impact of climate change on water resources follows: 

(W.1) 1990 ,

,

,1990

(1 )t r t

rt r t

r

Y

W T

Y

β

γ α τ − ⎛ ⎞ = − ⎜ ⎟

⎝ ⎠

where W denotes the change in water resources, expressed in billion dollars; t denotes time; r

denotes region; Y denotes income; T denotes the global mean temperature; α is a parameter, 

the benchmark estimate; see Table A4.12; β = 0.85 (0.70 - 1.00) is a parameter; γ = 1 (0.5-

1.5) is a parameter; τ = 0.005 (0.000-0.010) is a parameter. 

Energy consumption 

FUND’s impact estimates for energy consumption are based on Downing et al. (1995, 1996). 

As Downing et al. do not provide details on the functional form, earlier versions of FUND 

assume that the demand for heating decreases linearly with temperature, while the demand for 

cooling increases linearly. There is obviously a limit to the savings on heating, whereas the 

additional demand for cooling may actually rise faster than linearly. Therefore, cooling energy 

demand is assumed to rise with temperature to the power 1.5, and heating energy demand is 

assumed to fall with the square root of temperature. 

Downing et al. is itself based on a UK model relating energy used for heating or cooling to 

degree days, per capita income, and energy efficiency. Downing et al. derived the economic 

impacts from energy price scenarios and extrapolated these to the rest of the world. 

For space heating, the model is: 

(E.1) s r

t

s t

t r

t

t r

t r r t AEEI

P

P

y

y SH a T ,

,1990 1990

,

,1990

,

, Π= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

ε

β

SH denotes the amount of money spent less on space heating; t denotes time; r denotes region; 

T denotes the change in the global mean temperature relative to 1990; y denotes per capita 

income; P denotes population size; α is a parameter; cf. Table A4.12; β is a parameter; β = 1 

(0.5-1.5); ε is a parameter; ε = 0.8 (0.6-1.0); AEEI is a parameter (Tables A4.4a-e); it is about 

1% per year in 1990, converging to 0.2% in 2200; its standard deviation is set at a quarter of 

the mean. 

For space cooling, the model is: 

(E.2) s r

t

s t

t r

t

t r

t r r t AEEI

P

P

y

y SC a T ,

,1990 1990

,

,1990

,

, Π= ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

ε

β

SC denotes the amount of money spent additionally on space cooling; t denotes time; r

denotes region; T denotes the change in the global mean temperature relative to 1990; y

denotes per capita income; P denotes population size; α is a parameter; cf. Table A4.12; β is a 

parameter; β = 1 (0.5-1.5); ε is a parameter; ε = 0.8 (0.6-1.0); AEEI is a parameter (Tables 

A4.4a-e). 
51

Sea level rise 

Dry- and wetlands become inundated by rising waters, and are lost unless protected. Costs, 

then, are the costs of the degree of protection selected, plus the value of lost drylands and 

wetlands. Protection of the drylands also results in additional losses to wetlands. Figure A4.1 

illustrates these relationships. The total land lost to sea level rise is provided by wetlands lost, 

dryland not protected, and additional wetlands lost due to protection efforts for drylands. 

As populated drylands are lost, the people living there must emigrate, incurring costs to them, 

plus costs to the region to which the displaced people immigrate. 

Table A4.13 shows the accumulated loss of drylands and wetlands for a one metre rise in sea 

level. Land loss is assumed to be a linear function of sea level rise. The value of dryland is 

assumed to be linear in income density ($/km2

), with an average value of $4 million per 

square kilometre for the OECD. Wetland value follows: is assumed to be logistic in per capita 

income, with an: 

(SLR.1) 

,

max,

,

, max, ,

30,000 max 2,1

1 30,000

t r

r

t r

t r r tr

y

L

V

y L L

α σσ

⎛ ⎞

= −+ ⎜ ⎟ − + ⎝ ⎠

where V is wetland value; y is per capita income; L is the wetland lost to date; Lmax is a 

parameter, given the maximum amount of wetland that can be lost to sea level rise; α is a 

parameter such that the average value for the OECD is $5 million per square kilometre; and 

σ=0.05 is a parameter. 

If dryland gets lost, the people living there are forced to move. The number of forced migrants 

follows from the amount of land lost and the average population density in the region. The 

value of this is set at three times the regional per capita income per migrant. In the receiving 

country, costs equal 40% of per capita income per migrant. The costs of emigration for the 

first year of emigration are set to an arbitrary 0.3 times the per capita income but these costs 

continue over time, declining at a rate of 7% annually. Immigration costs are set to 40% of 

the income per capita in the region and decline by 33% annually. 

Immigration due to sea level rise is estimated by applying the immigration ratios from Table 

A4.14 of the Annex. The majority of the people stay within their regions but some interregional migration is estimated with the trend being emigration to developed economies. 

Table A4.14 displays the estimates of migration from one region to another, a small fraction 

of the total number of people estimated to be displaced. Inter-regional emigration estimates 

follow from multiplying the number of people who emigrate when one person is displaced by 

SLR (a fraction) times the average population density times the regional dryland loss. 

Table A4.13 displays the annual costs of fully protecting all coasts against a one metre sea 

level rise in a hundred years time. If sea level would rise slower, annual costs are assumed to 

be proportionally lower. 

The level of protection, that is, the share of the coastline protected, depends on the annualized 

capital costs of shoreline protection, and is based on a cost-benefit analysis: 

(SLR.2) ⎭

⎬

⎫

⎩

⎨

⎧ ⎟

⎠

⎞ ⎜

⎝

⎛ + = − DL

PC WL L

2

1 min 0,1
52

L is the fraction of the coastline to be protected and is selected so that (1 minus the fraction) is 

proportional to (protection costs plus the value of secondary loss of adjacent wetlands) divided 

by (the value of the dryland potentially lost with half the dryland protected where this 

proportion is one) after Fankhauser (1994b). If a piece of dryland is protected fully, no 

dryland services will be forgone but the costs of protection will be high and the adjacent 

wetland may be inundated. This means that FUND is selecting an adaptive policy which in 

turn determines the impacts of sea level rise in the output. 

PC is the net present value of the protection if the whole coast is protected. 

Table A4.13 reports average costs per year over the next century. PC is calculated assuming 

annual costs to be constant. This is based on the following. Firstly, the coastal protection 

decision makers anticipate a linear sea level rise. Secondly, coastal protection entails large 

infrastructural works which last for decades. Thirdly, the considered costs are direct 

investments only, and technologies for coastal protection are mature. Throughout the analysis, 

a pure rate of time preference, ρ, of 1% per year is used. The actual discount rate lies thus 1% 

above the growth rate of the economy, g. The net present costs of protection PC thus equal 

(SLR.3) a a

t

t

PC

g

g PC

g

PC

+

+ + = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ + = Σ

∞

= ρ

ρ

ρ

1

1

1

1

where PCa is the average annual costs of protection. 

WL is the net present value of the wetlands lost due to full coastal protection. Land values are 

assumed constant, reflecting how much current decision makers care about the non-marketed 

services and goods that get lost. The amount of wetland lost is assumed to increase linearly 

over time. The net present costs of wetland loss WL follow from 

(SLR.4) ( ) 0 2 0

0

1

1

1 WL

g

g WL

g

WL t

t

t +

+ + = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ + = Σ

∞

= ρ

ρ

ρ

where WL0 denotes the value of wetland loss in the first year. 

DL denotes the net present value of the dryland lost if no protection takes place. Land values 

are assumed to rise at the same pace as the economy grows. The amount of dryland lost is 

assumed to increase linearly over time. The net present costs of dryland loss DL are 

(SLR.5) ( )( ) 0 2 0

0

1 1

1

1 DL g g DL

g

DL t

t

t ρ

ρ

ρ

+ + + = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ + = Σ

∞

=

where DL0 is the value of dryland loss in the first year. 

. 
53

Ecosystems 

Tol (1999) assesses the impact of climate change on ecosystems, biodiversity, species, 

landscape etcetera based on the "warm-glow" effect. Essentially, the value people are 

assumed to place on such impacts are independent of any real change in ecosystems etcetera. 

This values is specified as 

(E.1) 

, ,

, 0

,

, , ,

1

1 1

t r t r

t r b

t r

tr tr tr t

b

y T

E y B P

Y B y T

y

τ α σσ

τ

∆ ⎛ ⎞

= −+ ⎜ ⎟ ∆

+ + ⎝ ⎠

where E denotes the value of the loss of ecosystems; t denotes time; r denotes region; Y 

denotes GDP; y denotes per capita income; P denotes population size; ∆T denotes the change 

in regional temperature; B is the number of species; α is a parameter such that E equals $50 

per person if per capita income equals the OECD average in 1990; yb = $30,000 is a 

parameter; τ=0.025 is a parameter; σ=0.05 is a parameter; and B0=14,000,000 is a parameter. 

The number of species follows 

(E.2) ( ) 2

1 1 1 Btt t B T ρ γ = − −∆ − −

where ρ = 0.003 is a parameter; and γ = 0.625 is a parameter. 

Human health: Diarrhoea 

The number of additional diarrhoea deaths Dd

 is given by 

(HD) ( ) ,

, , , ,0

,0

d d r t

rt r rt rt r

r

y D P TT

y

ε

η η μ

⎛ ⎞ = − ⎜ ⎟ ⎜ ⎟ ⎝ ⎠

where P denotes population, y per capita income, and T regional temperature; μ is the 

mortality in 1995, ε=-1.58 (0.23) and η=1.14 (with a standard deviation of 0.51) are 

parameters; r indexes region, and t time. Equation (HD) was estimated based on the WHO 

Global Burden of Diseases data.8

 Diarrhoea morbidity has the same equation as mortality, but 

with ε=-0.42 (0.12) and η=0.70 (0.26). Table A4.15 gives impact estimates, ignoring 

economic and population growth. 

Human health: Vector-borne diseases 

The model for vector-borne diseases is: 

(HV) ( ) ,

, , ,1990, , 1990

1990,

t r

rtd r d rd t

r

y m m TT

y

γ

β

α

⎛ ⎞ = − ⎜ ⎟

⎝ ⎠

m denotes mortality (see Table A4.16); t denotes time; r denotes regions; d denotes disease; α

is a parameter, the benchmark impact of climate change on vector-borne diseases; see Table 

A4.16; y denotes per capita income; T denotes the change in the global mean temperature 

relative to 1990; β=1 (0.5) and γ=-2.65 (0.69) are parameters. 

 

8

 http://www.who.int/health_topics/global_burden_of_disease/en/ 
54

Mortality is valued at 200 times the per capita income, with a standard deviation of 100. 

Morbidity is proportional to mortality, using the factor specified in Table A4.21. Morbidity is 

valued at 80% of per capita income per year of illness, with a standard deviation of 1. 

Cardiovascular and respiratory mortality 

Cardiovascular and respiratory disorders are worsened by both extreme cold and extreme hot 

weather. Martens (1998) assesses the increase in mortality for 17 countries. Tol (1999) 

extrapolates these findings to all other countries, based on formulae of the shape: 

(HC.1) ∆ =+ Md d dB α β T

where ∆M denotes the change in mortality due to a one degree global warming; TB is the 

current temperature in the country; and α and β are parameters, given in Table A4.17. 

Equation (HC.1) is specified for populations above and below 65 years of age for 

cardiovascular disorders. Cardiovascular mortality is affected by both heat and cold. In the 

case of heat, TB denotes the average temperature of the warmest month. In the case of cold, TB

denotes the average temperature of the coldest month. Respiratory mortality is not agespecific. 

Equation (HC.1) is readily extrapolated. If warming exceeds one degree, the baseline 

temperature TB changes. If this change is proportional to the change in the global mean 

temperature, the equation becomes quadratic. Summing country-specific quadratic functions 

results in quadratic functions for the regions: 

(HC.2) 2 ∆= + Mrd rd rd ,, , α β T T

where T denotes the change in global mean temperature; α and β are parameters, specified in 

Tables A4.18-A4.20. 

One problem with (HC.2) is that it is a non-linear extrapolation based on a data-set that is 

limited to 17 countries and, more importantly, a single climate change scenario. A global 

warming of 1°C leads to changes in cardiovascular and respiratory mortality in the order of 

magnitude of 1% of baseline mortality due to such disorders. Per cause, the total change in 

mortality is restricted to a maximum of 5% of baseline mortality. (This restriction is binding.) 

Baseline cardiovascular and respiratory mortality derives from the share of the population 

above 65 in the total population. 

If the fraction of people over 65 increases by 1%, cardiovascular mortality increases by 

0.0259% (0.0096%). For respiratory mortality, the change is 0.0016% (0.0005%). These 

parameters are estimated from the variation in population above 65 and cardiovascular and 

respiratory mortality over the nine regions in 1990. 

Mortality as in equations (HC.1) and (HC.2) is expressed as a fraction of population size. 

Cardiovascular mortality, however, is separately specified for younger and older people. In 

1990, the per capita income elasticity of the share of the population over 65 is 0.25 (0.08). 

Heat-related mortality is assumed to be limited to urban populations. Urbanisation is a 

function of per capita income and population density: 

(HC.3) 1

α β

α β

+ = + +

t t

t

t t

y PD U

y PD
55

where U is the fraction of people living in cities, y is per capita income, PD is population 

density and t is time; α and β are parameters, estimated from national data for the year 1995; 

α=0.031 (0.002) and β=-0.011 (0.005); R2

=0.66. 

Mortality is valued at 200 times the per capita income, with a standard deviation of 100. 

Morbidity is proportional to mortality, using the factor specified in Table A4.21. Morbidity is 

valued at 80% of per capita income per year of illness, with a standard deviation of 1. 
56

Table 4.1 Sources of parameters used in FUND. 

Sector Formula and 

its source 

Parameter Value Description 

in 

words 

Source 

parameter 

Wher

e 

withi

n 

sourc

e 

Key assumptions 

Agriculture Rate of 

change (A.1) 

αr Table A columns 2 

and 3 give regional 

breakdown 

Rate of 

change 

of ag. 

prod. 

due to 

rate of 

cc 

Based on 

assumptions 

about how 

the results 

underlying 

Darwin et al 

(1995), 

Reilly et al 

(1994), 

Rosenzweig 

and Parry 

(1994) 

would 

change with 

and without 

adaptation 

(Tol 2002b) 

The 

effects of 

adaptation

: these are 

not in the 

underlying 

literature, 

which 

itself 

includes 

adaptation 

assumptions 

 β An estimate (no 

reference) and is not 

regionally specific 

Nonlinearity 

of 

reaction 

to 

tempera

ture 

 

 ρ 1/p =10 (range 5 – 

15) 

Also estimated, and 

not regionally 

specific 

Speed 

of 

adaptati

on 

 

 Level of 

climate 

change (A.2) 

Ar

B Table A cols 4 and 5 

give regional 

breakdown 

Benchm

ark 

change 

in 

agricult

ural 

producti

on 

Regression 

analysis of 

Darwin et al 

(1995), 

Reilly et al 

(1994), 

Rosenzweig 

and Parry 

(1994) 

Quadratic 

equation 

assumed 

to which 

literature 

fitted; 

 Topt Table A gives 

regional breakdown 

Optimal 

tempera

ture 

Tol 2002a, b 

 Carbon γ Table A gives Carbon Tol 2002a, b 
57

dioxide 

fertilization 

(A.3) 

regional breakdown dioxide 

fertilizat

ion 

factor 

 Agricultural 

production 

(A.4) 

GAP1990

,r 

Y1990,r 

 Gross 

ag. 

prod. 

and 

GDP in 

1990 

 

 ε 0.31 (0.15-0.45) Elasticit

y 

 

 

Forestr

y 

Forestry 

consumer 

and 

producer 

surplus (F1) 

αr Table EFW cols 2 

and 3 

Benchm

ark 

estimate 

 

 β 

 γ 0.44 (0.29-0.87) Set so that a 

doubling of 

[CO2] leads 

to a change 

in forest 

value of 

15% 

Water Change in 

water 

resources 

(W.1) 

αr Table EFW cols Benchm

ark 

estimate 

Downing 

1995, 1996, 

assume 

water tech 

progress 

by 

0.5%/year 

(s.d. 0.5%) 

hencese5n

s of water 

to cc falls 

by 

0.5%/year 

(previous 

vs 

assumed 

no tech 

change) 

 β 0.85 (0.11-0.51) 

 γ 1.0 (0.5-1.5) 

 τ 0.005 (0.000-0.010) 

Energy Space 

heating/cool

ing (E.1 and 

E.2) 

αr Table EFW cols 6-9 Benchm

ark 

estimate

s 

Downing et 

al. 1995, 

1996 

 β 1 (0.5-1.5) 

 ε 0.8(0.6-1.0) 

 ΑΕΕΙs,r Table AEEI.FUND 

Tables AEE1.A1B, 

AEEI.A2, 

AEEI.B1 

AEEI.B2 

Autono

mous 

energy 

efficien

cy 

improve

ment in 

years s 

and 

region r 

 
58

Sea 

Level 

Rise 

Dryland 

value 

proportion

al to GDP 

per square 

kilometre 

$4 million/km2 in 

1990 (average across 

globe) 

Dryland 

value 

Fankauser 

(1994) 

 

 Wetland 

value 

(SLR.1) 

α Calibrated such that 

average OECD 

wetland value is $2 

million /km2 in 1990 

with logistic relation 

to per capita income 

Wetland 

value 

Source of 

OECD $2 

m/sq km in 

1990 is 

Fankhauser 

1994c 

 σ 0.05 

 Yt,r Available from 

SRES? 

 

 Lmax,r Database of wetland 

area in each country? 

Maximu

m 

amount 

of 

wetland 

that can 

be lost 

to sea 

level 

rise 

Source is 

Hoozemans 

et al (1993) 

 Share of 

coastline 

protected 

(SLR.2) 

Cost-benefit 

calculation 

following 

Fankhauser 

1994c 

 Cost-benefit in each 

region separately 

 Costs from 

Tol 1999 

(same as Tol 

2002a which 

follows 

partly from 

Bijlsma et al 

1996, 

Hoozemans 

et al. 1993 

and 

Fankhauser 

1994c) 

Wetland 

and 

dryland 

loss are 

assumed 

to be 

linear with 

sea level 

rise in 

absence of 

protection 

 NPV of 

protection 

cost (SLR.3) 

PCa Calculated within 

model for each year 

or 1990 value taken 

from current data? 

Average 

annual 

protecti

on costs 

 Unit dike 

costs from 

Hoozemans 

et al. 1993 

Costs are 

linear with 

dike 

height 

 ρ 1%/year Pure 

rate of 

time 

preferen

ce 

 

 g Different in regions 

or not? And by 

SRES? 

Growth 

rate of 

econom

y 

 

 NPV 

wetland and 

dryland loss 

(SLR.4 and 

5) 

WL0

and DL0

 Value 

of 

wetland 

lost in 

first 

year 

Derived 

from losses 

for 1m sea 

level rise 

reported in 

Hoozemand 

et al (1993) 

Ecosys Value of lost α Calibrated such that Tol 1999, 

Comment [rjn1]: Fankhauser, 

S. (1994), 'Protection vs. Retreat -- 

The Economic Costs of Sea Level 

Rise', Environment and Planning 

A, 27, 299-319. 
59

tem ecosystem 

(E1) 

value E is $50/per 

person if pc income = 

OCED 1990 mean. 

2002a. 

 σ 0.05 

 τ 0.025 

 Yb $30,000 

 Number of 

species (E2) 

ρ 0.003 

 γ 0.625 

Human 

health 

Diarrhoea 

mortality/m

orbidity 

(H1) 

ε -1.5/-0.42 (s.d. 

0.23,0.12) 

 WHO 

burden 

disease data 

(ref 3 1

http://www.

who.int/heal

th_topics/gl

obal_burden

_of_disease/

en/ 

) 

 η 1.14/0.7 

(s.d.0.51,0.26) 

 As above 

 Vector 

borne 

disease 

mortality/m

orbidity(HV

) 

αr,d How derived? From 

bnchmrks? 

 

 β 1 (s.d. 0.5) 

 γ -2.65 (s.d. 0.65) 

 Valuation of 

life 

200 times per capita 

income (s.d. 100) 

v.o.s.l. 

 Valuation of 

morbidity 

80% of p.c. income 

annually (s.d. 1) 

 

 Cardiovascu

lar mortality 

(HC.1) 

α Table 

HC1 

Tol 1999 

 β Table 

HC1 

 

 Cardiovascu

lar mortality 

regional 

(HC.2) 

αr Tables 

HC2-4 

17 country 

data-set and 

1 climate 

scenario 

Cannot 

exceed 5% 

baseline 

mortality 

derived 

from 

popn>65 

 βr Tables 

HC2-4 

 

 Heat-related 

mortality 

(HC.3) 

U(t) Fraction 

living in 

cities. 

 Assumes 

limited to 

urban 

pops 

 PD(t) Table 

** 

 

 α 0.031 (s.d. 0.002) Estimated 

from 

national data 

from 1995 

 β -0.011 s.d. 0.005) 

 
60

Damage Functions Extracted From FUND 

The FUND spreadsheet model described above was used to simulate the 4 SRES scenarios, 

A1, A2, B1 and B2. For each SRES scenario a graph was produced showing how global 

impacts accrue with temperature. Damages in the FUND model are provided as outputs in 

monetized form only for the years 2000-2100, so graphs may show damages in US$ or in 

%GDP (Figures A4.2a-A4.3b). These graphs were disaggregated (using FUND) to show how 

damage accrues globally and in each of the main sectors FUND includes (agriculture & 

forestry, sea level rise, ecosystems, water, energy and health) (Figures A4.2a,b) and in each of 

the regions considered in the parallel study “Understanding the Regional Impacts of Climate 

Change” (Figures A4.3a,b). Further disaggregation showing the impacts in each sector and 

region is shown in Figures A4.4a to A4.9b. Note that these graphs show impacts of climate 

change, so a positive value on the “damage” axis implies a benefit, and a negative value on the 

“damage” axis implies a disbenefit. 

A global damage function extracted from the FUND model 

Globally the FUND results show an initial small benefit to GDP from climate change, up to a 

temperature increase of approximately 0.5C above 1990 for all SRES scenarios, after which 

damages steadily accrue, reaching to between 1.2 and 2.7% global GDP for a 3 degree rise in 

temperature (Figure A4.2a, b), between 0.5-1.0% for a 2 degree rise above 1990, and 0-0.5% 

for a 2 degree rise above pre-industrial. These FUND damage estimates are broadly similar to 

those of DICE, although for the A1 scenario there are no damages for a temperature rise of 

1.5C above 1990; whilst DICE shows that damages begin to accrue globally for a 1C rise 

above 1990. 

Distribution of monetised impacts across sectors 

Figure A4.2a shows that the energy sector almost totally dominates the calculation of impacts 

of climate change on global GDP out to 2100, and is responsible for most of the negative 

impacts, with the water sector playing a lesser role. This results from the assumed high 

growth rates of per capita income, the assumed high income elasticity of cooling energy, and 

the high temperatures in Africa, where in FUND air conditioning is simulated to be common 

by 2100. 

In contrast, in FUND health shows a net benefit for all SRES scenarios until 2100, with the 

benefit slowly tailing off. Agriculture shows a small benefit to global GDP, again positive for 

all SRES scenarios out to 2100, slowly tailing off, and ecosystems a small disbenefit to global 

GDP for all SRES scenarios out to 2100. 

The parallel project “Understanding the Regional Impacts of Climate Change” (URICC 

project, Warren et al. 2006) which describes climate impacts in each sector in different 

metrics, finds that ecosystems are the sector which experiences a very, if not the most, 

dramatic damage due to climate change. The difference between the impression given by this 

project and that of FUND is largely because the valuation process produces a different 

distribution of impacts than an examination of the distribution of physical changes resulting 

from climate change. 
61

Distribution of impacts across SRES scenarios 

Figures A4.2a-b and A4.3a-b show that for all sectors, the effects of given temperature rise 

may be very different depending on the socioeconomic scenario. These differences are not 

apparent in the RICE model because RICE’s damage functions depend only on temperature 

and not upon socioeconomic data or rate of temperature change. The following gives the order 

of impact magnitude in each sector, starting with the scenario which has the lowest damage or 

highest benefit: 

For a given temperature rise: 

All sectors and regions combined: A1<B1<B2<A2 

Energy: A1<B1<B2<A2 

Health: A1<A2<B2<B1 

Water: scenarios very close B2/B1/A1<A2 

Agriculture/forestry: A2<B2<A1<B1 

Coasts: scenarios very close 

Ecosystems: A2<B2<B1<A1 

It seems counter-intuitive that the high population scenario A2 shows smaller climate impacts 

on ecosystems for a given temperature rise than the low population scenario A1, but this is 

because the rate of change of temperature is faster in the FUND A1 scenario than in the 

FUND A2 scenario, and the FUND model for ecosystems does not take into account the 

effects of increased human populations upon land use and other resources. 

Distribution of impacts (in terms of losses in regional GDP) across regions 

Figures A4.3a-b show that the greatest impacts of climate change in all sectors combined, 

according to FUND, for a given temperature rise, will be in Africa, with the high population 

A2 and B2 scenarios having the worst effects at any given temperature. Here FUND and the 

URICC project are in agreement. 

However, West Asia, North America and Europe are all modeled to benefit from climate 

change until temperature rises reach 3C above 1990 when impacts become negative in N 

America. South America, South Asia, Central America and Australasia all show negative 

impacts of climate change out to 2100 for all SRES scenarios. In E Asia the picture is more 

complex with benefits under scenarios A1 and B2 for smaller temperature increases and 

disbenefits for scenarios A2 and B1. In contrast, the URICC project finds many disbenefits in 

these regions between now and 2100. 
62

Distribution of impacts across regions in each sector and comparison with URICC project 

results 

(a) Water: In the water sector FUND shows disbenefits in all regions for all SRES scenarios 

through 2100 except East Asia where there are small benefits. The largest impacts in GDP 

terms are in Central Asia and N Africa. In the URICC project the largest impacts are in 

Africa, Asia, Europe and S America. In FUND there are small impacts only in W Africa, S 

Asia, Europe and S America. The two projects agree that N Africa is a badly affected area.

(b) Agriculture & Forestry: benefits occur through 2100 for all SRES scenarios in all regions 

except Australasia; the greatest benefits in GDP or monetized terms are in E Asia and these 

peak between 1 and 2C above 1990. The smallest benefits are in S America and C Asia. 

In the URICC project there are in contrast strong disbenefits in the agriculture sector in many 

regions. Specifically, for wheat there are strong disbenefits in Africa and W Asia and smaller 

dis-benefits in C America, C Asia and E Asia. There are benefits in only Australasia, Europe 

and N America under the assumption of CO2 fertilisation. For maize URICC finds large 

disbenefits in North and Southern Africa; next affected are Central Asia, West Africa, South 

Asia and West Asia with significant losses still occurring under the assumption of CO2 

fertilisation. Since maize is the dominant grain crop in most of Southern and West Africa, and 

there are losses even if CO2 fertilisation does occur, the anticipated yield losses here are a 

serious problem. Maize is also a dominant grain crop in much of Central America, South and 

North America. The situation in Southern and West Africa is also of concern, even under 

conditions of CO2 fertilisation. For rice yields URICC finds disbenefits in North and 

Southern Africa where reductions of 30-40% in yield could occur for temperature rises of 3-

4C in the absence of CO2 fertilisation. The next most affected countries are Central Asia, 

West Africa and S America. However if CO2 fertilisation does occur, URICC finds global 

increases in yield of 1-2% occur between temperature rises of 2-3C for many regions, except 

Africa and Central Asia where losses still occur. At higher temperatures of 3-4C global 

reductions of around 3% in yield still occur as temperature effects dominate CO2 fertilisation. 

Rice is the dominant grain crop in S and E Asia, where the 10-20% potential losses in the 

absence of CO2 fertilisation are significant. It is also a dominant grain crop in much of 

Central and South America, and West Africa.

Hence the pictures painted for agriculture in URICC and FUND contrast markedly. This is 

because the underlying literature upon which the two simulations are based is very different: 

(i) URICC uses the Parry et al. results which examine he consequences of CO2 fertilisation 

being fully realized or not realized at all whilst FUND assumes a single level of quite strong 

CO2 fertilisation derived from an extrapolation from values given in Darwin et al 1995, Reilly 

et al 1994, and Rosenzweig and Parry (1994). (ii) The Parry et al. results assume that 

marginal land only is brought under cultivation, whilst Darwin et al. 1995 simulate successful 

cultivation of new land previously climatically unsuitable for cultivation as the world 

warms(i.e .a high level of adaptation). Darwin (2004) explored scenarios with and without 

this process included. (iii) Although Darwin et al. 1995 did not include CO2 fertilisation, Tol 

(2002a) use the outputs of this study through factoring in the effects of CO2 fertilisation in 

line with the other literature which included it. Similarly FUND is based on a calculation of 

what the results of the three studies would be both with and without adaptation (Tol 2002a) , 

and uses for FUND the case where, in the long term, adaptation is included. (iv) In FUND 
63

regionally optimal temperatures for agriculture are used, which are derived through regression 

of the results of the three studies assuming the formula given above. This means that FUND 

presents a full adaptation, strong CO2 fertilisation analysis of the impacts of climate change 

upon agriculture, whereas URICC’s tables are describing literature which examines a range of 

outcomes for CO2 fertilisation and only the agricultural potential on existing land. Parry et al. 

do include a significant amount of adaptation on existing land, specifically that “level 0” 

adaptation is applied at zero cost at the farm level, by shifting planting dates and available 

crop varieties; “level 1” low cost adaptation is applied at the farm level by methods such as 

choice of crop, variety, planting date, and irrigation : this is assumed applied 100% in 

developed countries and 75% in developing countries; and “level 2” adaptation is applied in 

developed countries, involving some regional or national policy change resulting in major 

changes in planting dates, availability of new cultivars, extensive expansion of irrigation and 

increased fertilizer application (Parry 2005). These imply economic adjustments and are 

applied in developed countries only, based on current GDP. These large differences in model 

assumptions explain the discrepancy between the respective results. 

(c ) Coasts: Sea level rise causes disbenefits to all regions through 2100 for all SRES 

scenarios. The largest impacts in GDP terms are in W Africa, then S Asia and Australasia 

whilst in monetized terms the largest impacts are in the Americas. The URICC project finds 

the largest impacts in S and E Asia. 

(d) Energy: In the energy sector the greatest impact (in million $$) is seen in North Africa. 

In fact the impacts in the energy sector are dominated by changes in developing countries. 

This follows from assumed high growth rates of per capita income, assumed high income 

elasticity, and the high temperatures predicted for Africa, implying that air conditioning will 

be common in Africa well before 2100. This means that the largest contribution to global 

climate change damages in FUND is the installation of air conditioning in developing 

countries, particularly Africa. 

(e) Health In FUND Central and E Asia, North America and Europe show health benefits 

through 2100 for all SRES scenarios. In all other regions there are health disbenefits through 

2100 for all SRES scenarios. The reason for the discrepancy is largely that in the richer parts 

of the world, cardiovascular and respiratory illness dominate infectious disease, whereas in the 

developing world the reverse is the case. The greatest benefits in GDP terms in FUND are in 

Central Asia, whilst the largest damages in GDP terms are in S and W Africa. 

URICC also finds particular problems in Africa, but finds it less clear whether there are 

benefits or disbenefits in N America and Europe, since literature variously suggests that 

benefits due to reduced cold waves might, or might not, more than offset the disbenefits due to 

heat waves. The FUND analysis posits that reduced cold stress dominates increased heat 

stress. Also FUND does not include the large estimates of increased flood deaths predicted by 

WHO and included in the URICC report. Patz (2005) highlights also that temperate latitudes 

are particularly vulnerable in human health terms, due to heat stress caused by 

disproportionate warming compared to tropical regions. FUND also does not include indirect 

impacts of higher temperatures on health such as exacerbation of episodes of tropospheric 

ozone pollution or increased sensitivity of tissue to UV light. These differences in the 

literature underlying FUND compared to URICC account for the differences in the findings. 

(f) Ecosystems In FUND ecosystems are negatively impacted by climate change through 2100 

for all SRES scenarios in all regions. Impacts accrue strongly with both increase in 
64

temperature and increase in rate of temperature change, and in GDP terms are fairly evenly 

distributed across regions. In monetized terms impacts are small in Australasia, Central 

America, N Africa, Central Asia, W Africa and W Asia. URICC shows strong negative 

ecosystem impacts around the globe, and in particular that ecosystem impacts are particularly 

large in the Arctic ( which is not modeled explicitly in FUND), Australasia, and in 

biodiversity hotspots. 

Comments on shapes and magnitudes of damage functions in FUND 

(a) Health sector: Health impacts in FUND are dominated by cardiovascular and respiratory 

disease in the rich parts of the world and by infectious disease in the poorer parts. The 

damage functions for Europe and N America show benefits due to the dominating effect of 

reduced cold stress over increased heat stress. However, the effect of reduced cold stress 

saturates whilst heat stress continues to increase, so at some point during the next century 

there is a turnover point and benefits decrease. Also, in Australasia, there is no cold stress 

initially so disbenefits due to heat stress occur right away as climate warms. In FUND the 

health disbenefits in Africa decrease over the coming century with economic growth, and heat 

stress takes over as the main health problem. 

(b) Coastal sector: Sea level rise impacts in FUND are dominated by protection costs. 

Because dike building has low labour costs, its costs rise much more slowly than the economy 

grows, and hence annual impacts decrease as temperature increases even though more 

protection is being constructed. Wetland loss stops increasing after a small amount of 

temperature rise because there is no wetland left. These factors explain the shapes of the 

curves in Figures A4.7a,b. In Australasia there are marked “oscillations” in the damage 

function, reflecting the “myopic” behaviour of decision makers who respond to accelerating 

sea level rise or economic growth by building more protection, and if economic growth then 

slows again little additional protection is bought. 

There are several different estimates of the costs of sea level rise. For the USA, for instance, 

costs range from $7 to $12 billion a year, compared to $2 billion, here. Fankhauser’s (1995) 

estimate for the OECD amounts to $25 billion (for a 50 cm sea level rise) compared to $7 

billion here (for a 100 cm rise). Fankhauser’s estimate for the world as a whole of $47 billion 

compares to $13 ($19 B) billion here. This is largely the result of the optimal protection 

strategy used here as opposed to the arbitrary protection strategy used in earlier studies. 

Fankhauser finds that a 1 metre sea level rise would cost the OECD some $9 billion per year if 

protection is optimized, compared to $25 billion , his estimate for a non-optimized strategy for 

a 50 cm rise. CCRAF, in turn, estimates median (10th- 90th) protection costs of about $5 B ($3-

7 B). Whilst OECD estimated a value of (1990)$5 million/km2 for wetlands following 

Fankhauser (1994b), a recent trade in the US Wetland Banking and Trading System implicitly 

valued wetlands at (2005)$365,000 per hectare i.e. $36,500,000 per km2. 

(c)Ecosystem sector: The outputs of FUND show ecosystems suffer increasing damages in all 

regions as temperature increases, as the URIC literature also shows. However in the B1 

scenario (and to a lesser extent the A1 scenario) FUND annual damages decrease above a 

certain temperature rise, because the ecosystem damage model is strongly influenced by the 

rate of temperature change. Figure 5.2 shows that the rate of temperature change increases 

steadily in FUND A2, increases to a plateau (which slowly falls) in FUND B2; reaches a peak 
65

in 2040 and falls dramatically in FUND B1, and to a lesser extent in FUND A1 in 2050. 

Hence annual ecosystem impacts decrease once the rate of temperature change starts to 

decrease in scenarios FUND B1 and FUND A1. 

Losses due to impacts in FUND have an assumed zero lifetime, so that for example permanent 

changes, such as loss of species, are only valued as losses during the time period during which 

they disappear. Whilst it is reasonable to assume that some impacts are transient, in that 

society eventually adapts, a policy maker may feel that permanent change, such as loss of 

species, loss of land area to the sea, and even arguably loss of human life (which is 

permanent) has an infinite lifetime. The FUND ecosystem damage model assumes a loss of 

31% species associated with a temperature rise of 3C above 1990, compared to Thomas et al 

(2004) who predict a 35% species loss associated with a temperature rise of only 2C above 

1990. The use of a zero lifetime for impacts in FUND implies that ecosystem loss is costed 

transiently, in other words that extinction damage is not valued beyond the year in which it 

occurs. 
66

5. COMPARISON BETWEEN FUND AND RICE OUTPUTS 

5.1 Simulation of temperature profiles of SRES scenarios 

The temperature profiles that FUND simulates for the SRES scenarios are reproduced in 

Figure 5.1, whilst Figure 5.2 shows how the rate of temperature change varies. Those which 

RICE simulates are shown in Figure 5.3. 

Figure 5.1 Increase in global annual average temperature relative to 1990 in 4 SRES 

scenarios according to the FUND model 

2000 2020 2040 2060 2080 2100

Year

0

1

2

3

4

Global Temperature Increase Relative to 1990

Scenario

A1

A2

B1

B2

Increase in global annual average temperature relative to 1990 in 4 SRES 

scenarios according to the FUND model
67

Figure 5.2 Rate of change of global annual average temperature in 4 SRES scenarios 

according to the FUND model 

2000 2020 2040 2060 2080 2100

Year

0.012

0.016

0.02

0.024

0.028

0.032

0.036

Rate of change of global average temperature (deg C / year)

Scenario

A1

A2

B1

B2

Rate of change of global annual average temperature in 4 SRES scenarios according to the FUND model

Figure 5.3 Increase in global annual average temperature 

relative to 1985 in 4 SRES scenarios according to the RICE model 

forced with HadCM3 radiative forcing series 

0

1

2

3

4

1995

2015

2035

2055

2075

2095

Year

Global Temperature 

Increase Relative to 1985

A1F1RICE

A2 RICE

B2 RICE

B1 RICE
68

Figure 5.1 shows that the temperature profiles for the 4 SRES scenarios lie very close together 

in FUND. For example, in 2080 the FUND simulations give annual global mean temperature 

rises of 2.2C (B1), 2.3C (B2), 2.4C (A2) and 2.6C (A1B) above 1990, whereas Arnell et al 

(2004) for 2080 use 1.7C (B1), 2.1C (B2), 3.0C (A2) and 3.7C (A1F1) above 1990 . The 

IPCC TAR shows ranges of temperature rise relative to 1990 of 1.4 to 2.5C for B1 and 1.7 to 

5.8C for A1 (2.1 to 3.8C for A1B and 3.2 to 5.8C for A1F1) in 2100. Whilst the FUND 

simulations are not individually outside the IPCC range, the IPCC marker scenarios (as shown 

in Figure 3.1 of the IPCC 2001 Synthesis Report) show a much greater temperature difference 

between SRES scenarios than does the FUND simulation. IPCC gives 2100 marker 

temperature rises relative to 1990 of close to 1.9C (B1) 2.6C (B2) 3.7C (A2) 3C (A1B) and 

4.4C (A1F1), and 2080 rises of approximately 1.8C (B1), 2.1C (B2), 2.8C (A2), 2.5C (A1B) 

and 4.0C (A1F1). 

This means that the temperature (and thus impact) outputs of the FUND model, and to a lesser 

extent the RICE model, are responding less strongly to the choice of SRES scenario than are 

other models underpinning the IPCC TAR climate simulations. The CO2 concentrations in 

2085 in FUND are 720 ppm (A2), 751 ppm (A1B), 573 ppm (B1) and 673 ppm (B2). In the 

URICC project the scenarios (Arnell et al. 2004) from HadCM3 simulations give 

concentrations in the 2080s of 709 ppm (A2), 810 ppm (A1F1), 527 ppm (B1) and 561 ppm 

(B2). Thus the range of concentrations which the SRES scenarios spread in 2080 is rather 

similar between FUND and Arnell et al. 2004 (HadCM3), but the temperature range spanned 

by the 4 scenarios in 2080 is half as great. Figures 5.4-5.6 show the relationships between 

CO2 concentration and temperature change in FUND, RICE and those used in Arnell et al. 

2004. The RICE model shows relationships intermediate between the other two studies. 
69

Figure 5.4 FUND SRES 2085

500

550

600

650

700

750

800

850

1 1.5 2 2.5 3 3.5 4

Global mean T increase above 1990

CO2 ppm

Figure 5.5 RICE SRES 2085

500

550

600

650

700

750

800

850

1 1.5 2 2.5 3 3.5 4

Global mean T increase above 1990

CO2 ppm

Figure 5.6 HadCM3 (as in Parry et al. 2004) SRES 2080s

500

550

600

650

700

750

800

850

1 1.5 2 2.5 3 3.5 4

Global mean T increase above 1990

CO2 ppm

Hence high emission scenarios in FUND may not result in such high temperatures as in other 

models, and the damage avoided by moving from a high emissions trajectory such as A1 to a 

lower one similar to B1, will be much smaller than in other models, because the resultant 

avoided temperature increments will be smaller. In particular, for these FUND simulations, 

the A1 scenario always has the highest temperatures, whereas in Arnell et al. 2004 and in the 

IPCC marker scenarios, this is only true up to around 2065, after which greater temperature 

increases result in the A2 scenario than the A1B scenario. Hence, FUND is likely to simulate 
70

the greatest damages for the A1B scenario, and this is likely to contrast with other models 

which simulate greater temperature rises in the period 2080-2100 for A2 than for A1B. The 

RICE model temperature simulations are more similar to those used by Arnell et al. 2004 

(HadCM3) although the high emission scenarios produce a smaller increase in temperature 

than the HadCM3 scenarios. 

5.2 Simulation of climate change damages 

Table 5.1 provides a comparison of global damages in %GDP as simulated by FUND and 

RICE at matching temperatures and matching dates for several SRES scenarios. (Note that 

A1B was simulated in FUND and A1F1 in RICE, but the other three SRES scenarios match). 

Unsurpisingly estimates differ significantly, and in spite of RICE’s omission of impacts in 

temperate regions in many sectors, damages are higher in Europe and the USA in RICE than 

they are in FUND, owing to its prediction of strong benefits in Europe and the USA in the 

agriculture & forestry, and health sectors. 
71

Table 5.1. Comparison of % GDP losses in selected regions simulated by FUND and 

RICE for SRES scenarios in 2095 

SRES 

scenario 

Year FUND 

Global 

Temperature 

rise above 

1990 

RICE Global 

Temperature 

rise above 

1985 

Region FUND 

damages

RICE 

damages 

 

A2 2095 3.3 2.9 Globe 2.18 2.1496 

A2 2080 2.9 Globe 1.44 

B2 2095 2.7 2.1 Globe 1.59 0.9295 

B2 2075 2.2 Globe 0.98 

B1 2095 2.5 1.8 Globe 1.18 0.5679 

B1 2060 1.8 Globe 0.53 

A2 2095 3.3 2.9 USA 0.14 0.9114 

A2 2080 2.9 USA 0.21 

B2 2095 2.7 2.1 USA 0.15 0.3523 

B2 2075 2.2 USA 0.42 

B1 2095 2.5 1.8 USA 0.003 0.1901 

B1 2060 1.8 USA 0.59 

A2 2095 3.3 Europe 0.742 4.707 

A2 2080 2.9 2.9 Europe 0.838 

B2 2095 2.7 Europe 0.3 2.5705 

B2 2075 2.2 2.1 Europe 0.48 

B1 2095 2.5 Europe 0.19 1.8919 

B1 2060 1.8 1.8 Europe 0.63 

A2 2095 3.3 2.9 S Africa/Low 

Income 

 7.39 5.9733 

A2 2080 2.9 S Africa/Low 

Income 

 6.89 

B2 2095 2.7 2.1 S Africa/Low 

Income 

7.47 3.9431 

B2 2075 2.2 S Africa/Low 

Income 

6.51 

B1 2095 2.5 1.8 S Africa/Low 

Income 

3.47 3.2262 

B1 2060 1.8 S Africa/Low 

Income 

3.68 
72

6. CRITIQUE OF TREATMENT OF ADAPTATION COSTS IN 

PAGE AND FUND MODELS 

 Irene Lorenzoni and W. Neil Adger 

Both the PAGE and FUND models conceptualise adaptation as a means of reducing (or 

avoiding) the impacts of climate change. In both models adaptation costs are calculated based 

on parameters denoting change, often linked to technological development or innovation. The 

PAGE model derives adaptation costs mainly a function of protecting against the first 50cm or 

so of sea level rise (costs would be higher in Europe with its long coastline and low-lying 

areas). 

More accurate estimates of the costs of adaptation should take into account the complexity 

through which adaptation occurs. From observations of adaptation to climate variability 

several lessons emerge. Adaptation is context-specific, often does occur as step changes, and 

most often evolves in anticipation to an event or series of events9

. The costs of anticipatory 

adaptation may be lower than those from reactive adaptation (because some of the 

consequences of climate change impacts will be prevented by anticipating change)10 (Leary, 

1999). For anticipation, however, one should also allow for the costs of misplaced foresight 

(in other words, getting it wrong). In the latter case, costs may also be incurred in the process 

towards adaptation (e.g. adjustment costs). 

It is unclear whether the PAGE and FUND IA models include considerations about 

adjustment costs. These are defined as “the extra costs incurred relative to the (counterfactual) 

case of instantly adapting to changed circumstances” (Kelly et al., 2005:469). In other words, 

these are costs that accrue to agents before they modify their behaviour (i.e. adapt). Some 

would argue these adjustment costs could be seen as part of the costs of adaptation, rather than 

as a category per se. In any case, adjustment costs can significantly influence the net costs / 

benefits of adaptation. Kelly and colleagues’ 2005 theoretical and empirical study on 

adjustment costs - whilst learning about a climate change shock (imperfect information) - 

estimated adjustment costs at 1.4% of annual profits per acre from climate change to 

agricultural land in the US mid-west. 

Furthermore, the PAGE and FUND models assume that markets are efficient and therefore 

that adaptation is efficient. This approach, however, does not consider the more complex 

relationships between socio-economic systems and the way costs and benefits of adaptation 

are diffused and distributed. 

 

9

 In contrast, the PAGE model assumes that adaptation is possible (to different degrees) in various locations 

around the world (dictated by different assumptions and ‘purchase’ of adaptation types) and that this occurs 

linearly until it reaches some plateau. 

10 Not all adaptation to climate change is planned. Recent studies have indicated that there are only few 

examples in the UK (e.g. Tompkins et al., 2005) and in the EU (EEA, 2006) where adaptation is specifically 

planned for weather-related events (and climate change). In addition, not all adaptation reduces the negative 

effect of climate change impacts. Some adaptive responses may become unsuitable over time due to changing 

circumstances, resulting in maladaptation. Therefore, models may want to consider that adaptation may serve not 

only as a means to decrease the impacts of climate change or take advantage of its opportunities, but may operate 

by reducing the ability to address these impacts. 
73

Measuring the impacts of climate change on agriculture has provided most extensive 

discussions of how much cost reduction can be expected from adaptation by economic agents. 

Two approaches are the most used: 

- a Ricardian approach, whereby farmland values are statistically linked to climatic, soil 

and economic variables. The fitted relationship is then used to predict changes in 

value (e.g. rent or price, usually of farmland) as a function of climate change. 

- agronomic analysis to predict the impacts of climate change on crop yields and 

subsequently the economic effects of changes in yields on agriculture. These estimates 

are often extrapolated to other environments and situations with altered climates. 

Mendelsohn’s (2000) work, following a Ricardian approach, suggests that -providing there are 

no externalities or collective action - adaptation will be efficient and therefore reduce the costs 

of climate change. However, there is much evidence suggesting that efficiency is not always 

maximised, in every situation and in very case. Hanemann (2000) argues that adaptation 

cannot always be considered as efficient, simply because externalities and policy changes 

invariably affect the price of the land and other related assets; in addition, land markets are 

non existent or operate very differently in other non-market economies (e.g. subsistence 

farming in developing nations), which can be overcome by considering net farm revenues as a 

measure of the value of agricultural activities. 

It has been argued that the Ricardian approach estimates the full range of adaptation 

possibilities (e.g. adaptation by farmers, market adaptations reflected in shifts of land use into 

or out of agriculture), whereas the agronomic approach understates adaptation (overestimating 

the economic costs and underestimating the benefits of climate change by focussing 

exclusively on crops). Theoretical economic studies in the literature have often considered 

that the difference between the former and the latter yields reliable measures of the economic 

effects of adaptation. Hanemann (2000:576) revisits such claims, pointing out that both 

approaches disregard their inherent measurement errors (often from empirical observational 

data). In other words, the difference in estimates between the two, considering such 

measurement errors, should be more appropriately considered the “difference in adaptation + 

difference in measurement error”. These limitations should be considered in relation to 

developing a more accurate representation of adaptation in the two models. 
74

Some broader general considerations 

Discounting is always a complex and thorny issue. Caution should be exercised on how it is 

applied in the models. The default case in the PAGE model is that impacts will be reduced in 

total by 33%, with adaptation, over 200 years (applying the default discount rates). Rates can 

also be set by the user. However, discounting over 200 years is unrealistic. Changes in socioeconomic systems cannot be projected semi-realistically for more than 5 -10 years at a time 

(e.g., Berkhout and Hertin, 2000). The utility of calculating costs and benefits of adaptation 

over these timescales may be debatable (this same reasoning can also be applied to 

calculations relating to mitigation measures also). Therefore it is important to take into 

account the outputs of various different models which utilise different methodologies and 

assumptions, and to use outputs of models which explore the implications of various future 

socioeconomic scenarios and possible decisions about adaptation. 

 

Beyond the metricised approaches to costing of adaptation, wider ranging considerations 

about adaptation in practice (often not reducible to economic metrics) should be taken into 

consideration, where feasible, in the construction, interpretation and use of the models. This 

approach can be limited by the context-specificity of adaptation, an area of research in which 

reconciling some of the discrepancies between adaptation modelling and findings from the 

field may prove challenging, lengthy and a source of much debate. 

For instance, modelling based on standardised approaches to countries worldwide masks the 

diversity that exists between countries - as demonstrated in the CRU / ERAL 1992 report and 

which Nicholls and Klein (2003) seem to suggest in their paper (in EEA 2006:23, Table2). 

Adaptive capacity may not be highest, under all circumstances, in the EU and OECD. The 

response to the 2003 heat wave in France lends itself as an interesting case. It has also been 

shown that the capacity to adapt among resource-dependent societies in Southern Africa is 

high if based on adaptations to previous changes (Thomas et al., 2005). 

Research has also drawn attention to the importance of actors at different levels (institutions, 

communities, individuals) in promoting / constraining adaptation (e.g. through knowledge 

transfer, support for measures and practices) (e.g. Tompkins et al., 2005; Thomas et al., 2005). 

Some acknowledgement of their role would be helpful (see EEA, 2006). 

The models clearly do not (and perhaps cannot) make reference to considerations of equity 

and justice in climate change management. They do not address issues such as responsibility 

for human-induced climate change; beneficiaries and victims (or winners and losers) from the 

changes that have, and will take, place; any form of aid or compensation for the damages 

already incurred. This is where the realms of modelling and policy-making intersect. Costing 

adaptation provides estimates for guiding policy. This in turn should eventually give due 

account to the wider underlying circumstances and implications of actions, as these are 

inevitably entwined with any policy decision on climate change. 
75

Acknowledgements 

The lead author is grateful to Rita Yu for assistance in preparing graphics for this report. This 

work was made possible by a grant from the UK Treasury. 

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Impacts of Climate Change. Tyndall Working Paper no .....www .tyndall.ac.uk 

Yohe, G., and Schlesinger, M. (1988) Sea Level Change: the Expected Economic Cost of 

Protection or Abandonment in the United States. Climatic Change 38, 447-472 
80

ANNEX 

Table A3.1a. Regional impacts of different levels of temperature change for the EU between 2020 and 2200 ($$ million) Global mean temperature rise above 1990 

Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ 

million 

Total im

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 0 0 0 2674 290 7146 0 0 0 2674 2040 0.96 0.34 1.82 11 0 9 9941 1188 25975 0 0 0 9952 2060 1.73 0.78 3.06 190 0 1060 26630 4076 66279 871 0 0 27691 2080 2.65 1.32 4.52 1166 0 5001 66217 10331 164699 9485 0 64270 76868 2100 3.65 1.90 6.03 4291 0 15292 155177 23671 410357 54113 0 320820 213581 2150 5.90 3.30 9.30 40079 2324 132445 906480 125877 2642860 655784 0 2384995 1602343 2200 7.39 4.22 11.56 181465 13240 607975 3522563 441963 10597250 2840952 0 8732939 6544980 Table A3.1b. Regional impacts of different levels of temperature change for the EU between 2020 and 2200 (%GDP) Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Me2020 0.38 0.10 0.80 0.00 0.00 0.00 0.02 0.00 0.06 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.00 0.00 0.00 0.06 0.01 0.16 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.00 0.00 0.00 0.12 0.02 0.30 0.00 0.00 0.00 2080 2.65 1.32 4.52 0.00 0.00 0.02 0.22 0.03 0.56 0.03 0.00 0.22 2100 3.65 1.90 6.03 0.01 0.00 0.04 0.39 0.06 1.04 0.14 0.00 0.81 2150 5.90 3.30 9.30 0.04 0.00 0.13 0.91 0.13 2.66 0.66 0.00 2.40 2200 7.39 4.22 11.56 0.07 0.01 0.24 1.42 0.18 4.27 1.14 0.00 3.52 
81

Table A3.2a. Regional impacts of different levels of temperature change for the Eastern Europe and Former Soviet Union between 2020 and 2200 

Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Disco

Year Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 0 0 0 -2781 -8716 175 0 2040 0.96 0.34 1.82 -22 -119 0 -8483 -26855 564 0 2060 1.73 0.78 3.06 -273 -1342 1 -22463 -69475 1204 -537 2080 2.65 1.32 4.52 -1450 -6044 53 -58822 -186630 2858 -5612 2100 3.65 1.90 6.03 -5237 -21149 274 -148155 -477248 6823 -34652150 5.90 3.30 9.30 -41841 -167428 2529 -836187 -2775385 34933 -41662200 7.39 4.22 11.56 -176146 -692159 9455 -3139917 -11042240 116070 -1806

Table A3.2b. Regional impacts of different levels of temperature change for Eastern Europe & Former Soviet Union between 2020 and 2200 (%G

Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Tota

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mea2020 0.38 0.10 0.80 0.00 0.00 0.00 -0.07 -0.20 0.00 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.00 0.00 0.00 -0.12 -0.39 0.01 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.00 -0.01 0.00 -0.21 -0.65 0.01 0.00 0.00 0.00 2080 2.65 1.32 4.52 -0.01 -0.04 0.00 -0.35 -1.12 0.02 -0.03 -0.23 0.00 2100 3.65 1.90 6.03 -0.02 -0.08 0.00 -0.58 -1.85 0.03 -0.13 -0.90 0.00 2150 5.90 3.30 9.30 -0.06 -0.26 0.00 -1.30 -4.30 0.05 -0.65 -2.88 0.00 2200 7.39 4.22 11.56 -1.94 -6.83 0.07 -1.12 -4.38 0.06 
82

Table A3.3a. Regional impacts of different levels of temperature change for the USA between 2020 and 2200 ($$ million) Global mean temperature rise above 1990 

Economic impacts $$ million 

Non-economic impacts $ million Discontinuity impacts $$ 

million 

Total im

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 0 0 0 765 74 2159 0 0 0 765 2040 0.96 0.34 1.82 6 0 32 3110 327 8820 0 0 0 3116 2060 1.73 0.78 3.06 93 0 461 8863 969 23880 230 0 0 9187 2080 2.65 1.32 4.52 518 0 2107 23353 2409 65422 2562 0 15040 26433 2100 3.65 1.90 6.03 1878 31 6886 58495 5852 170537 16158 0 95173 76531 2150 5.90 3.30 9.30 15593 746 53042 327696 31295 960327 200333 0 788173 543622 2200 7.39 4.22 11.56 67801 3323 225733 1247334 110233 3578630 869367 0 2839733 2184501 Table A3.3bRegional impacts of different levels of temperature change for the USA between 2020 and 2200 (%GDP) Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Me2020 0.38 0.10 0.80 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.00 0.00 0.00 0.02 0.00 0.05 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.00 0.00 0.00 0.04 0.00 0.10 0.00 0.00 0.00 2080 2.65 1.32 4.52 0.00 0.00 0.01 0.07 0.01 0.20 0.01 0.00 0.05 2100 3.65 1.90 6.03 0.00 0.00 0.02 0.13 0.01 0.38 0.04 0.00 0.21 2150 5.90 3.30 9.30 0.01 0.00 0.05 0.29 0.03 0.86 0.18 0.00 0.71 2200 7.39 4.22 11.56 0.02 0.00 0.08 0.45 0.04 1.28 0.31 0.00 1.02 
83

Table A3.4a Regional impacts of different levels of temperature change for China between 2020 and 2200 ($$ million) 

Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million 

Year Mean Min Max Mean Min Max Mean Min Max 2020 0.38 0.10 0.80 83 0 474 174 0 1082 2040 0.96 0.34 1.82 445 0 2645 971 0 5642 2060 1.73 0.78 3.06 2636 0 11671 5836 0 25178 2080 2.65 1.32 4.52 13306 182 46483 29365 739 102038 2100 3.65 1.90 6.03 47708 2347 156270 104976 7876 326579 2150 5.90 3.30 9.30 369971 23112 1249852 807984 72885 2482477 2200 7.39 4.22 11.56 1531516 89850 5445028 3329953 284605 10732760 

Table A3.4b Regional impacts of different levels of temperature change for China between 2020 and 2200 (%GDP) Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Me2020 0.38 0.10 0.80 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.00 0.00 0.02 0.01 0.00 0.03 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.01 0.00 0.05 0.03 0.00 0.11 0.00 0.00 0.00 2080 2.65 1.32 4.52 0.04 0.00 0.15 0.09 0.00 0.32 0.03 0.00 0.18 2100 3.65 1.90 6.03 0.11 0.01 0.35 0.23 0.02 0.73 0.13 0.00 0.76 2150 5.90 3.30 9.30 0.33 0.02 1.11 0.72 0.06 2.21 0.62 0.00 2.30 2200 7.39 4.22 11.56 0.54 0.03 1.93 1.18 0.10 3.81 1.08 0.00 3.51 
84

Table A3.5a Regional impacts of different levels of temperature change for India between 2020 and 2200 ($$ million) 

Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Year Mean Min Max Mean Min Max Mean Min Max 2020 0.38 0.10 0.80 19777 1326 56259 42996 4732 132036 2040 0.96 0.34 1.82 82360 7185 234479 179458 23127 516074 2060 1.73 0.78 3.06 260965 26447 722604 569844 90160 1551985 2080 2.65 1.32 4.52 712667 86386 2051023 1558000 264875 4185445 2100 3.65 1.90 6.03 1686336 212198 4942781 3688221 607283 10049840 2150 5.90 3.30 9.30 10130400 1183751 33536480 22125990 3319951 60399210 2200 7.39 4.22 11.56 38988210 4196394 138374200 85010580 11735550 249796300 

Table A3.5b Regional impacts of different levels of temperature change for India between 2020 and 2200 (%GDP) Global mean temperature rise above 1990 

Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Total 

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 0.25 0.02 0.72 0.55 0.06 1.69 0.00 0.00 0.00 02040 0.96 0.34 1.82 0.59 0.05 1.69 1.29 0.17 3.71 0.00 0.00 0.00 12060 1.73 0.78 3.06 1.32 0.13 3.67 2.89 0.46 7.87 0.09 0.00 0.00 42080 2.65 1.32 4.52 2.55 0.31 7.34 5.58 0.95 14.99 0.73 0.00 4.55 82100 3.65 1.90 6.03 4.26 0.54 12.49 9.32 1.53 25.39 2.80 0.00 15.52 162150 5.90 3.30 9.30 10.22 1.19 33.83 22.32 3.35 60.93 13.49 0.00 47.76 462200 7.39 4.22 11.56 15.70 1.69 55.73 34.24 4.73 100.60 23.14 0.00 68.49 73
85

Table A3.6a Regional impacts of different levels of temperature change for the Africa between 2020 and 2200 ($$ million) 

Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million 

Year Mean Min Max Mean Min Max Mean Min Max 2020 0.38 0.10 0.80 10450 1005 28512 22757 3780 58018 2040 0.96 0.34 1.82 47808 5151 122232 104156 16934 257328 2060 1.73 0.78 3.06 151150 18217 384231 329670 57845 843444 2080 2.65 1.32 4.52 401747 50160 1059862 876927 159302 2253393 2100 3.65 1.90 6.03 927032 115034 2462154 2023839 359925 5271611 2150 5.90 3.30 9.30 5246023 543667 15133850 11443390 1811905 31295700 2200 7.39 4.22 11.56 19882320 1856552 60662740 43310900 6127983 123160000 

Table A3.6b Regional impacts of different levels of temperature change for Africa between 2020 and 2200 (%GDP) Global mean temperature rise above 1990 

Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Me2020 0.38 0.10 0.80 0.16 0.02 0.44 0.35 0.06 0.90 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.35 0.04 0.89 0.76 0.12 1.88 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.70 0.08 1.78 1.53 0.27 3.92 0.04 0.00 0.00 2080 2.65 1.32 4.52 1.19 0.15 3.13 2.59 0.47 6.65 0.30 0.00 1.97 2100 3.65 1.90 6.03 1.74 0.22 4.62 3.80 0.68 9.90 1.05 0.00 5.92 2150 5.90 3.30 9.30 3.93 0.41 11.35 8.58 1.36 23.46 4.99 0.00 17.43 2200 7.39 4.22 11.56 5.95 0.56 18.16 12.96 1.83 36.87 8.61 0.00 25.29 
86

Table A3.7a. Regional impacts of different levels of temperature change for Latin America between 2020 and 2200 ($$ million) Global mean temperature rise above 1990 

Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ million Total imp

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 5106 450 13233 11190 1593 27256 0 0 0 16296 2040 0.96 0.34 1.82 21453 2307 54283 47086 7026 119427 0 0 0 68540 2060 1.73 0.78 3.06 66581 7898 175284 146388 24013 376762 3720 0 0 216689 2080 2.65 1.32 4.52 188179 22874 526714 414243 69624 1079439 48357 0 335859 650779 2100 3.65 1.90 6.03 488178 58668 1452686 1075451 185613 2984154 300765 0 1752892 1864395 2150 5.90 3.30 9.30 2810353 292444 9324687 6188381 881942 18605640 3626043 0 13275550 12624780 2200 7.39 4.22 11.56 10723110 1026594 36637150 23586440 3181729 70261750 15631410 0 48940560 49940960 Table A3.7b. Regional impacts of different levels of temperature change for Latin America between 2020 and 2200 (% GDP) 

Global mean temperature rise above 1990 

Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mea2020 0.38 0.10 0.80 0.07 0.01 0.18 0.15 0.02 0.37 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.14 0.01 0.35 0.30 0.04 0.76 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.27 0.03 0.71 0.59 0.10 1.53 0.02 0.00 0.00 2080 2.65 1.32 4.52 0.49 0.06 1.36 1.07 0.18 2.79 0.12 0.00 0.87 2100 3.65 1.90 6.03 0.80 0.10 2.39 1.77 0.30 4.90 0.49 0.00 2.88 2150 5.90 3.30 9.30 1.84 0.19 6.12 4.06 0.58 12.21 2.38 0.00 8.71 2200 7.39 4.22 11.56 2.81 0.27 9.60 6.18 0.83 18.40 4.09 0.00 12.82 
87

Table A3.8a Regional impacts of different levels of temperature change for Rest of OECD between 2020 and 2200 ($$ million) Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Discontinuity Year Mean Min Max Mean Min Max Mean Min Max Mean 2020 0.38 0.10 0.80 0 0 0 983 123 2519 0 2040 0.96 0.34 1.82 16 0 96 2858 355 7883 0 2060 1.73 0.78 3.06 112 0 479 6376 754 18077 94 2080 2.65 1.32 4.52 397 7 1458 13828 1556 41480 1012 2100 3.65 1.90 6.03 1120 47 3977 29878 3036 88973 6111 2150 5.90 3.30 9.30 7949 459 26400 156547 13545 469995 75926 2200 7.39 4.22 11.56 32764 1987 114340 578683 46411 1815734 330163 Table A3.8b Regional impacts of different levels of temperature change for Rest of OECD between 2020 and 2200 (% GDP) 

Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts % 

GDP 

Discontinuity impacts % GDP 

Tot

Year Mean Min Max Mean Min Max Mean Min Max Mean Min Max Me2020 0.38 0.10 0.80 0.00 0.00 0.00 0.01 0.00 0.03 0.00 0.00 0.00 2040 0.96 0.34 1.82 0.00 0.00 0.00 0.02 0.00 0.06 0.00 0.00 0.00 2060 1.73 0.78 3.06 0.00 0.00 0.00 0.04 0.00 0.11 0.00 0.00 0.00 2080 2.65 1.32 4.52 0.00 0.00 0.01 0.06 0.01 0.19 0.00 0.00 0.03 2100 3.65 1.90 6.03 0.00 0.00 0.01 0.10 0.01 0.30 0.02 0.00 0.11 2150 5.90 3.30 9.30 0.01 0.00 0.04 0.21 0.02 0.63 0.10 0.00 0.40 2200 7.39 4.22 11.56 0.02 0.00 0.06 0.31 0.03 0.98 0.18 0.00 0.60 
88

Table A4.1. The regions in FUND. Acronym Name Countries USA USA United States of America CAN Canada Canada WEU Western 

Europe 

Andorra, Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, United Kingdom 

JPK Japan and 

South Korea 

Japan, South Korea 

ANZ Australia and 

New Zealand 

Australia, New Zealand 

CEE Central and 

Eastern Europe 

Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary, FYR Macedonia, Poland, Romania, Slovakia, Slovenia, Yugoslavia 

FSU Former 

Soviet Union 

Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan 

MDE Middle East Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, 

Saudi Arabia, Syria, Turkey, United Arab Emirates, West Bank and 

Gaza, Yemen 

CAM Central 

America 

Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama 

SAM South 

America 

Argentina, Bolivia, Brazil, Chile, French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela 

SAS South Asia Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka SEA Southeast 

Asia 

Brunei, Cambodia, East Timor, Indonesia, Laos, Malaysia, Myanmar, Papua New Guinea, Philippines, Singapore, Taiwan, Thailand, Vietnam 

CHI China plus China, Hong Kong, North Korea, Macau, Mongolia NAF North Africa Algeria, Egypt, Libya, Morocco, Tunisia, Western Sahara SSA Sub-Saharan Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape 
89

Acronym Name Countries 

Africa Verde, Central African Republic, Chad, Congo-Brazzaville, CongoKinshasa, Cote d’Ivoire, Djibouti, Equatorial Guinea, Eritrea, 

Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea- Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe 

SIS Small Island 

States 

Antigua and Barbuda, Aruba, Bahamas, Barbados, Bermuda, Comoros, Cuba, Dominica, Dominican Republic, Fiji, French Polynesia, Grenada, Guadeloupe, Haiti, Jamaica, Kiribati, Maldives, Marshall Islands, Martinique, Mauritius, Micronesia, Nauru, Netherlands Antilles, New Caledonia, Palau, Puerto Rico, Reunion, Samoa, Sao Tome and Principe, Seychelles, Solomon Islands, St Kitts and Nevis, St Lucia, St Vincent and Grenadines, Tonga, Trinidad and Tobago, Tuvalu, Vanuatu, Virgin Islands 
90

Table A4.2a. FUND Population in standard Scenario: 2000 = 100.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.25 0.27 0.33 0.34 0.35 0.43 1960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.32 0.36 0.43 0.42 0.43 0.51 1970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.43 0.50 0.56 0.53 0.55 0.65 1980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.58 0.66 0.70 0.67 0.69 0.78 1990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.80 0.82 0.85 0.83 0.85 0.90 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.06 1.06 1.01 1.07 1.11 1.01 1.00 1.23 1.15 1.13 1.16 1.15 1.09 2020 1.07 1.08 1.02 1.16 1.19 1.01 1.01 1.47 1.26 1.24 1.30 1.29 1.16 2030 1.08 1.09 1.03 1.20 1.24 1.02 1.01 1.67 1.34 1.32 1.42 1.41 1.20 2040 1.08 1.09 1.03 1.23 1.27 1.02 1.01 1.82 1.41 1.39 1.53 1.51 1.24 2050 1.07 1.08 1.02 1.25 1.30 1.01 1.01 1.94 1.47 1.44 1.64 1.61 1.26 2060 1.07 1.08 1.02 1.27 1.31 1.01 1.01 2.04 1.50 1.48 1.72 1.70 1.27 2070 1.06 1.07 1.01 1.28 1.32 1.00 1.00 2.15 1.54 1.52 1.81 1.78 1.28 2080 1.06 1.07 1.01 1.29 1.33 1.00 1.00 2.23 1.57 1.54 1.88 1.85 1.29 2090 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.27 1.57 1.55 1.91 1.89 1.30 2100 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.29 1.58 1.55 1.93 1.90 1.30 2110 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2120 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2130 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2140 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2150 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2160 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2170 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2180 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2190 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2200 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2210 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2220 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2230 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2240 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2250 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 
91

2260 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2270 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2280 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2290 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 2300 1.06 1.07 1.01 1.30 1.34 1.00 1.00 2.30 1.58 1.55 1.93 1.91 1.30 
92

Table A4.2b. FUND Population in A1B Scenario: 2000 = 100.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.25 0.27 0.33 0.34 0.35 0.43 1960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.32 0.36 0.43 0.42 0.43 0.51 1970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.43 0.50 0.56 0.53 0.55 0.65 1980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.58 0.66 0.70 0.67 0.69 0.78 1990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.80 0.82 0.85 0.83 0.85 0.90 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.06 1.07 1.03 1.04 1.07 1.01 1.01 1.16 1.19 1.18 1.14 1.12 1.09 2020 1.11 1.12 1.07 1.08 1.12 1.02 1.02 1.26 1.40 1.38 1.23 1.21 1.18 2030 1.14 1.15 1.11 1.12 1.15 1.02 1.02 1.31 1.57 1.54 1.29 1.27 1.23 2040 1.16 1.18 1.13 1.14 1.18 1.01 1.01 1.33 1.69 1.67 1.30 1.28 1.24 2050 1.18 1.19 1.14 1.15 1.19 0.98 0.98 1.30 1.78 1.75 1.28 1.26 1.22 2060 1.19 1.20 1.15 1.16 1.20 0.95 0.94 1.24 1.80 1.78 1.21 1.20 1.16 2070 1.19 1.20 1.15 1.16 1.20 0.91 0.90 1.17 1.80 1.77 1.14 1.13 1.09 2080 1.20 1.21 1.16 1.17 1.21 0.87 0.86 1.07 1.75 1.72 1.05 1.03 1.00 2090 1.20 1.22 1.17 1.18 1.22 0.82 0.82 0.96 1.66 1.63 0.94 0.93 0.90 2100 1.21 1.22 1.17 1.18 1.22 0.78 0.78 0.87 1.58 1.55 0.85 0.84 0.82 2110 1.22 1.23 1.18 1.19 1.23 0.75 0.75 0.79 1.51 1.48 0.77 0.76 0.74 2120 1.23 1.24 1.19 1.20 1.24 0.72 0.72 0.72 1.44 1.42 0.71 0.70 0.68 2130 1.23 1.24 1.19 1.20 1.24 0.69 0.69 0.67 1.39 1.37 0.66 0.65 0.63 2140 1.24 1.25 1.20 1.21 1.25 0.67 0.67 0.63 1.34 1.32 0.61 0.60 0.59 2150 1.24 1.25 1.20 1.21 1.25 0.65 0.65 0.59 1.31 1.29 0.58 0.57 0.55 2160 1.24 1.25 1.20 1.21 1.25 0.64 0.64 0.56 1.28 1.26 0.55 0.55 0.53 2170 1.25 1.26 1.21 1.22 1.26 0.63 0.63 0.54 1.25 1.24 0.53 0.53 0.51 2180 1.25 1.26 1.21 1.22 1.26 0.62 0.62 0.53 1.24 1.22 0.52 0.51 0.50 2190 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2200 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2210 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2220 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2230 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2240 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2250 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 
93

2260 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2270 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2280 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2290 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 2300 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.52 1.23 1.21 0.51 0.50 0.49 
94

Table A4.2c. FUND Population in A2 Scenario: 2000 = 100  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.25 0.27 0.33 0.34 0.35 0.43 1960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.32 0.36 0.43 0.42 0.43 0.51 1970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.43 0.50 0.56 0.53 0.55 0.65 1980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.58 0.66 0.70 0.67 0.69 0.78 1990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.80 0.82 0.85 0.83 0.85 0.90 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.08 1.04 1.05 1.08 1.02 1.02 1.19 1.21 1.19 1.16 1.15 1.12 2020 1.12 1.13 1.09 1.10 1.13 1.06 1.06 1.34 1.46 1.44 1.31 1.29 1.25 2030 1.18 1.19 1.14 1.15 1.19 1.12 1.12 1.51 1.77 1.74 1.47 1.45 1.41 2040 1.22 1.23 1.18 1.19 1.23 1.17 1.17 1.66 2.08 2.05 1.63 1.60 1.56 2050 1.26 1.27 1.22 1.23 1.27 1.22 1.22 1.75 2.26 2.23 1.72 1.69 1.64 2060 1.30 1.31 1.26 1.27 1.31 1.28 1.28 1.84 2.44 2.40 1.80 1.78 1.73 2070 1.36 1.37 1.32 1.33 1.37 1.36 1.35 1.94 2.64 2.60 1.90 1.88 1.82 2080 1.48 1.49 1.43 1.45 1.49 1.49 1.49 2.08 2.91 2.87 2.03 2.01 1.95 2090 1.58 1.59 1.53 1.54 1.59 1.59 1.59 2.15 3.06 3.01 2.11 2.08 2.02 2100 1.63 1.64 1.58 1.59 1.64 1.64 1.64 2.19 3.13 3.09 2.14 2.11 2.05 2110 1.68 1.69 1.63 1.64 1.69 1.69 1.69 2.22 3.21 3.16 2.18 2.15 2.09 2120 1.72 1.74 1.67 1.68 1.74 1.74 1.74 2.26 3.27 3.22 2.21 2.18 2.12 2130 1.76 1.78 1.71 1.72 1.78 1.78 1.78 2.29 3.33 3.28 2.24 2.21 2.14 2140 1.80 1.82 1.74 1.76 1.82 1.82 1.82 2.31 3.39 3.33 2.26 2.23 2.17 2150 1.83 1.85 1.77 1.79 1.85 1.85 1.85 2.33 3.43 3.38 2.28 2.25 2.19 2160 1.86 1.87 1.80 1.81 1.87 1.88 1.88 2.35 3.47 3.41 2.30 2.27 2.20 2170 1.88 1.89 1.82 1.83 1.89 1.90 1.90 2.37 3.50 3.44 2.32 2.29 2.22 2180 1.89 1.91 1.83 1.85 1.91 1.92 1.91 2.38 3.52 3.46 2.33 2.29 2.23 2190 1.90 1.92 1.84 1.86 1.92 1.93 1.92 2.38 3.53 3.48 2.33 2.30 2.23 2200 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2210 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2220 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2230 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2240 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2250 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 
95

2260 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2270 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2280 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2290 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 2300 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.38 3.54 3.48 2.33 2.30 2.24 
96

Table A4.2d. FUND Population in B1 Scenario: 2000 = 100  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.25 0.27 0.33 0.34 0.35 0.43 1960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.32 0.36 0.43 0.42 0.43 0.51 1970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.43 0.50 0.56 0.53 0.55 0.65 1980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.58 0.66 0.70 0.67 0.69 0.78 1990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.80 0.82 0.85 0.83 0.85 0.90 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.08 1.03 1.04 1.08 1.01 1.01 1.17 1.19 1.17 1.14 1.13 1.09 2020 1.11 1.12 1.08 1.09 1.12 1.02 1.02 1.25 1.39 1.37 1.22 1.21 1.17 2030 1.15 1.16 1.11 1.12 1.16 1.02 1.02 1.30 1.55 1.53 1.27 1.26 1.22 2040 1.17 1.18 1.13 1.14 1.18 1.01 1.00 1.31 1.67 1.65 1.29 1.27 1.23 2050 1.18 1.19 1.14 1.15 1.19 0.98 0.98 1.29 1.76 1.73 1.27 1.25 1.21 2060 1.18 1.19 1.15 1.16 1.19 0.94 0.94 1.24 1.80 1.77 1.21 1.20 1.16 2070 1.19 1.20 1.15 1.16 1.20 0.90 0.90 1.16 1.80 1.77 1.14 1.12 1.09 2080 1.20 1.21 1.17 1.18 1.21 0.86 0.86 1.07 1.75 1.72 1.05 1.03 1.00 2090 1.21 1.22 1.17 1.18 1.22 0.82 0.82 0.96 1.65 1.62 0.94 0.93 0.90 2100 1.21 1.22 1.18 1.19 1.23 0.78 0.78 0.85 1.52 1.50 0.83 0.82 0.80 2110 1.22 1.23 1.18 1.19 1.23 0.74 0.74 0.75 1.41 1.39 0.74 0.73 0.71 2120 1.22 1.23 1.18 1.19 1.23 0.71 0.71 0.68 1.32 1.30 0.67 0.66 0.64 2130 1.22 1.23 1.19 1.20 1.23 0.68 0.68 0.62 1.25 1.23 0.61 0.60 0.58 2140 1.23 1.24 1.19 1.20 1.24 0.66 0.66 0.57 1.19 1.17 0.56 0.55 0.54 2150 1.23 1.24 1.19 1.20 1.24 0.64 0.64 0.53 1.14 1.12 0.52 0.52 0.50 2160 1.23 1.24 1.19 1.20 1.24 0.63 0.63 0.51 1.10 1.08 0.49 0.49 0.47 2170 1.23 1.24 1.19 1.20 1.24 0.62 0.62 0.48 1.07 1.05 0.47 0.47 0.45 2180 1.23 1.24 1.19 1.20 1.24 0.61 0.61 0.47 1.05 1.03 0.46 0.45 0.44 2190 1.23 1.24 1.19 1.20 1.24 0.61 0.60 0.46 1.04 1.02 0.45 0.44 0.43 2200 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2210 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2220 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2230 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2240 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2250 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 
97

2260 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2270 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2280 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2290 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 2300 1.23 1.24 1.19 1.20 1.24 0.60 0.60 0.46 1.03 1.01 0.45 0.44 0.43 
98

Table A4.2e. FUND Population in B2 Scenario: 2000 = 100 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.25 0.27 0.33 0.34 0.35 0.43 1960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.32 0.36 0.43 0.42 0.43 0.51 1970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.43 0.50 0.56 0.53 0.55 0.65 1980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.58 0.66 0.70 0.67 0.69 0.78 1990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.80 0.82 0.85 0.83 0.85 0.90 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.06 1.07 1.02 1.03 1.07 1.00 1.00 1.17 1.21 1.19 1.15 1.13 1.10 2020 1.08 1.09 1.05 1.06 1.09 1.00 1.00 1.27 1.44 1.41 1.25 1.23 1.19 2030 1.09 1.10 1.05 1.06 1.10 0.99 0.99 1.36 1.65 1.63 1.33 1.31 1.27 2040 1.08 1.09 1.04 1.05 1.09 0.98 0.98 1.42 1.85 1.82 1.39 1.37 1.33 2050 1.06 1.07 1.03 1.04 1.07 0.96 0.96 1.46 2.01 1.98 1.43 1.41 1.37 2060 1.05 1.06 1.02 1.03 1.06 0.94 0.94 1.48 2.16 2.12 1.45 1.43 1.39 2070 1.04 1.05 1.00 1.01 1.05 0.93 0.93 1.50 2.27 2.24 1.47 1.45 1.41 2080 1.03 1.04 0.99 1.00 1.04 0.92 0.92 1.51 2.36 2.32 1.48 1.46 1.42 2090 1.02 1.03 0.99 1.00 1.03 0.91 0.91 1.52 2.42 2.39 1.49 1.47 1.43 2100 1.01 1.02 0.98 0.99 1.02 0.91 0.91 1.53 2.47 2.44 1.50 1.48 1.43 2110 1.01 1.02 0.98 0.99 1.02 0.90 0.90 1.54 2.52 2.49 1.51 1.49 1.44 2120 1.00 1.01 0.97 0.98 1.01 0.90 0.90 1.55 2.57 2.53 1.51 1.49 1.45 2130 1.00 1.01 0.97 0.98 1.01 0.90 0.89 1.55 2.61 2.57 1.52 1.50 1.46 2140 1.00 1.00 0.96 0.97 1.00 0.89 0.89 1.56 2.65 2.61 1.53 1.51 1.46 2150 0.99 1.00 0.96 0.97 1.00 0.89 0.89 1.57 2.68 2.64 1.53 1.51 1.47 2160 0.99 1.00 0.96 0.97 1.00 0.89 0.89 1.57 2.70 2.66 1.54 1.52 1.47 2170 0.99 1.00 0.96 0.96 1.00 0.89 0.89 1.57 2.72 2.68 1.54 1.52 1.47 2180 0.99 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.74 2.70 1.54 1.52 1.48 2190 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2200 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2210 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2220 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2230 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2240 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2250 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2260 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 
99

2270 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2280 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2290 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 2300 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.58 2.75 2.71 1.54 1.52 1.48 
100

Table A4.3a. FUND per capita income in standard Scenario; 2000=1.00.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS 

1950 0.33 0.28 0.24 0.17 0.48 0.33 0.53 0.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.30 1960 0.39 0.37 0.32 0.23 0.54 0.44 0.69 0.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.40 1970 0.47 0.48 0.41 0.32 0.61 0.59 0.91 0.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.53 1980 0.57 0.63 0.54 0.44 0.68 0.78 1.20 0.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.71 1990 0.78 0.86 0.85 0.88 0.83 0.90 1.79 0.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.22 1.22 1.22 1.22 1.23 1.33 1.38 1.14 1.17 1.18 1.34 1.41 1.45 1.18 1.16 1.22 2020 1.45 1.46 1.46 1.46 1.47 1.88 1.96 1.46 1.49 1.50 1.71 1.80 1.91 1.51 1.49 1.56 2030 1.70 1.71 1.71 1.71 1.72 2.61 2.71 1.85 1.89 1.91 2.17 2.28 2.54 1.92 1.89 1.98 2040 1.96 1.98 1.97 1.96 1.98 3.43 3.56 2.35 2.40 2.42 2.75 2.89 3.39 2.43 2.40 2.52 2050 2.22 2.24 2.23 2.22 2.24 4.27 4.43 2.98 3.04 3.07 3.48 3.66 4.51 3.08 3.03 3.19 2060 2.49 2.51 2.50 2.48 2.50 5.15 5.35 3.78 3.87 3.89 4.43 4.65 6.00 3.91 3.85 4.05 2070 2.79 2.80 2.79 2.78 2.80 6.23 6.46 4.84 4.95 4.99 5.67 5.96 7.95 5.01 4.94 5.18 2080 3.11 3.13 3.11 3.11 3.13 7.51 7.79 6.24 6.38 6.42 7.30 7.68 10.50 6.45 6.36 6.68 2090 3.43 3.46 3.44 3.43 3.46 8.78 9.11 7.89 8.06 8.12 9.23 9.71 13.48 8.16 8.04 8.44 2100 3.75 3.77 3.75 3.75 3.78 9.90 10.27 9.74 9.96 10.03 11.40 11.99 16.73 10.07 9.93 10.42 2110 4.05 4.07 4.05 4.05 4.08 10.92 11.33 11.77 12.03 12.12 13.77 14.48 20.22 12.17 11.99 12.59 2120 4.36 4.39 4.36 4.36 4.39 11.99 12.44 13.94 14.25 14.36 16.32 17.16 23.95 14.42 14.21 14.92 2130 4.68 4.70 4.68 4.68 4.71 13.12 13.61 16.20 16.56 16.68 18.95 19.93 27.83 16.75 16.51 17.33 2140 4.99 5.03 5.00 4.99 5.03 14.29 14.83 18.45 18.86 19.00 21.59 22.70 31.70 19.08 18.80 19.74 2150 5.31 5.35 5.31 5.31 5.36 15.51 16.09 20.60 21.06 21.21 24.11 25.35 35.40 21.31 21.00 22.05 2160 5.64 5.68 5.64 5.64 5.69 16.80 17.43 22.76 23.26 23.43 26.63 28.00 39.10 23.54 23.19 24.35 2170 5.99 6.03 5.99 5.99 6.04 18.19 18.87 25.14 25.69 25.88 29.42 30.93 43.19 26.00 25.62 26.90 2180 6.36 6.40 6.36 6.36 6.41 19.70 20.44 27.77 28.38 28.59 32.49 34.17 47.71 28.72 28.30 29.72 2190 6.75 6.79 6.75 6.75 6.80 21.34 22.14 30.68 31.35 31.58 35.89 37.74 52.70 31.73 31.26 32.83 2200 7.17 7.21 7.17 7.17 7.22 23.10 23.97 33.89 34.63 34.89 39.65 41.69 58.21 35.05 34.53 36.26 2210 7.61 7.66 7.61 7.61 7.67 24.98 25.91 37.36 38.19 38.47 43.72 45.97 64.19 38.65 38.07 39.98 2220 8.08 8.13 8.08 8.08 8.14 26.89 27.90 41.04 41.94 42.25 48.02 50.49 70.50 42.44 41.82 43.91 2230 8.58 8.63 8.58 8.57 8.64 28.84 29.92 44.89 45.88 46.22 52.53 55.24 77.12 46.43 45.75 48.04 
101

2240 9.10 9.16 9.11 9.10 9.18 30.81 31.96 48.92 49.99 50.36 57.23 60.19 84.03 50.59 49.85 52.34 2250 9.67 9.73 9.67 9.66 9.74 32.78 34.01 53.09 54.26 54.66 62.12 65.32 91.20 54.91 54.10 56.81 2260 10.26 10.33 10.26 10.26 10.34 34.80 36.11 57.39 58.65 59.09 67.15 70.61 98.59 59.36 58.48 61.41 2270 10.89 10.96 10.89 10.89 10.98 36.95 38.33 61.79 63.15 63.62 72.30 76.03 106.15 63.91 62.97 66.12 2280 11.57 11.64 11.57 11.56 11.66 39.22 40.69 66.27 67.73 68.23 77.54 81.54 113.85 68.54 67.53 70.91 2290 12.28 12.36 12.28 12.28 12.38 41.64 43.20 70.79 72.35 72.88 82.83 87.10 121.61 73.22 72.14 75.75 2300 13.04 13.12 13.04 13.03 13.14 44.21 45.87 75.32 76.98 77.54 88.13 92.67 129.39 77.90 76.75 80.60 
102

Table A4.3b. FUND per capita income in A1B Scenario; 2000=1.00.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS 

1950 0.33 0.28 0.24 0.17 0.48 0.33 0.53 0.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.30 1960 0.39 0.37 0.32 0.23 0.54 0.44 0.69 0.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.40 1970 0.47 0.48 0.41 0.32 0.61 0.59 0.91 0.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.53 1980 0.57 0.63 0.54 0.44 0.68 0.78 1.20 0.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.71 1990 0.78 0.86 0.85 0.88 0.83 0.90 1.79 0.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.20 1.21 1.20 1.20 1.21 1.09 1.13 1.53 1.40 1.39 1.79 1.84 1.90 1.40 1.38 1.44 2020 1.42 1.42 1.41 1.41 1.42 2.00 2.07 3.02 2.29 2.28 3.52 3.63 3.73 2.29 2.25 2.37 2030 1.66 1.67 1.66 1.66 1.67 3.40 3.52 5.33 3.62 3.60 6.20 6.40 6.59 3.62 3.57 3.75 2040 1.95 1.95 1.94 1.94 1.96 5.27 5.47 8.17 5.48 5.45 9.52 9.82 10.10 5.47 5.39 5.66 2050 2.29 2.30 2.28 2.29 2.30 7.57 7.85 12.14 7.78 7.74 14.14 14.60 15.02 7.77 7.66 8.04 2060 2.70 2.71 2.69 2.69 2.71 9.90 10.27 17.36 10.22 10.17 20.22 20.87 21.47 10.21 10.06 10.57 2070 3.17 3.18 3.16 3.16 3.18 12.82 13.31 24.34 13.29 13.22 28.36 29.28 30.11 13.28 13.09 13.74 2080 3.69 3.70 3.68 3.68 3.70 16.27 16.88 32.72 16.88 16.80 38.12 39.35 40.48 16.88 16.63 17.46 2090 4.28 4.28 4.26 4.26 4.29 20.40 21.16 42.96 21.17 21.07 50.05 51.67 53.15 21.16 20.85 21.90 2100 4.96 4.97 4.94 4.94 4.97 25.26 26.21 55.77 26.22 26.08 64.97 67.07 68.99 26.20 25.82 27.11 2110 5.74 5.75 5.72 5.73 5.76 30.65 31.80 70.92 31.82 31.66 82.62 85.29 87.73 31.80 31.33 32.90 2120 6.61 6.63 6.60 6.60 6.64 36.84 38.22 88.87 38.24 38.04 103.53 106.88 109.94 38.22 37.65 39.54 2130 7.58 7.60 7.56 7.56 7.61 43.84 45.48 109.75 45.51 45.27 127.86 131.99 135.77 45.48 44.81 47.06 2140 8.65 8.67 8.63 8.63 8.68 51.66 53.60 133.56 53.62 53.35 155.59 160.62 165.22 53.59 52.80 55.45 2150 9.82 9.84 9.80 9.80 9.86 60.28 62.54 160.15 62.57 62.25 186.57 192.60 198.11 62.54 61.61 64.70 2160 11.10 11.12 11.07 11.07 11.14 69.65 72.26 189.23 72.29 71.93 220.45 227.57 234.09 72.26 71.19 74.76 2170 12.48 12.50 12.44 12.44 12.52 79.68 82.67 220.32 82.71 82.29 256.66 264.95 272.54 82.67 81.44 85.53 2180 13.96 13.98 13.92 13.92 14.00 90.26 93.65 252.74 93.70 93.22 294.43 303.95 312.65 93.65 92.26 96.89 2190 15.54 15.57 15.49 15.50 15.59 101.25 105.05 285.68 105.10 104.56 332.80 343.56 353.39 105.04 103.49 108.68 2200 17.21 17.24 17.16 17.16 17.27 112.45 116.67 318.15 116.73 116.13 370.63 382.61 393.57 116.66 114.94 120.70 2210 18.97 19.01 18.92 18.93 19.04 123.99 128.65 350.81 128.71 128.05 408.68 421.89 433.97 128.64 126.74 133.09 2220 20.84 20.88 20.78 20.79 20.91 136.18 141.29 385.30 141.36 140.64 448.85 463.36 476.62 141.29 139.20 146.18 2230 22.80 22.84 22.73 22.74 22.87 148.98 154.57 421.50 154.64 153.85 491.02 506.89 521.40 154.56 152.28 159.91 
103

2240 24.84 24.89 24.77 24.78 24.92 162.33 168.42 459.27 168.50 167.64 535.03 552.32 568.13 168.41 165.93 174.24 2250 26.96 27.01 26.88 26.89 27.05 176.18 182.79 498.45 182.88 181.94 580.67 599.44 616.60 182.78 180.08 189.10 2260 29.14 29.20 29.06 29.07 29.24 190.45 197.59 538.83 197.69 196.68 627.71 648.00 666.55 197.58 194.67 204.42 2270 31.38 31.44 31.29 31.30 31.49 205.06 212.75 580.17 212.86 211.77 675.87 697.71 717.69 212.75 209.60 220.11 2280 33.65 33.72 33.56 33.57 33.77 219.92 228.17 622.21 228.28 227.12 724.84 748.27 769.69 228.16 224.79 236.06 2290 35.95 36.02 35.85 35.86 36.07 234.92 243.73 664.65 243.85 242.61 774.28 799.31 822.19 243.72 240.12 252.16 2300 38.25 38.32 38.14 38.15 38.38 249.95 259.32 707.17 259.45 258.13 823.81 850.44 874.79 259.31 255.49 268.29 
104

Table A4.3c. FUND per capita income in A2 Scenario; 2000=1.00  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS 

1950 0.33 0.28 0.24 0.17 0.48 0.33 0.53 0.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.30 1960 0.39 0.37 0.32 0.23 0.54 0.44 0.69 0.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.40 1970 0.47 0.48 0.41 0.32 0.61 0.59 0.91 0.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.53 1980 0.57 0.63 0.54 0.44 0.68 0.78 1.20 0.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.71 1990 0.78 0.86 0.85 0.88 0.83 0.90 1.79 0.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.15 1.15 1.15 1.16 1.16 0.92 0.96 1.17 1.15 1.16 1.37 1.42 1.46 1.17 1.15 1.21 2020 1.27 1.28 1.27 1.28 1.28 1.23 1.27 1.54 1.45 1.46 1.80 1.86 1.91 1.47 1.45 1.52 2030 1.45 1.45 1.44 1.45 1.45 1.73 1.80 2.09 1.87 1.88 2.44 2.52 2.59 1.89 1.86 1.96 2040 1.62 1.62 1.62 1.62 1.63 2.25 2.33 2.70 2.29 2.31 3.15 3.26 3.35 2.32 2.29 2.40 2050 1.75 1.75 1.75 1.76 1.76 2.66 2.76 3.19 2.64 2.66 3.73 3.86 3.97 2.67 2.63 2.76 2060 1.90 1.91 1.90 1.91 1.91 3.16 3.27 3.80 3.06 3.08 4.45 4.60 4.73 3.10 3.05 3.21 2070 2.10 2.11 2.10 2.11 2.11 3.87 4.02 4.71 3.66 3.69 5.51 5.69 5.85 3.71 3.65 3.84 2080 2.46 2.47 2.45 2.47 2.47 5.25 5.45 6.53 4.88 4.92 7.64 7.89 8.12 4.94 4.87 5.11 2090 2.74 2.75 2.73 2.75 2.75 6.44 6.68 8.17 5.97 6.02 9.56 9.87 10.15 6.05 5.96 6.26 2100 2.95 2.95 2.94 2.96 2.96 7.11 7.38 9.06 6.57 6.62 10.60 10.95 11.26 6.65 6.56 6.88 2110 3.26 3.26 3.25 3.27 3.27 7.85 8.15 10.01 7.26 7.32 11.70 12.09 12.44 7.35 7.24 7.60 2120 3.60 3.60 3.59 3.61 3.61 8.67 9.00 11.05 8.02 8.08 12.93 13.36 13.74 8.12 8.00 8.40 2130 3.97 3.98 3.96 3.99 3.99 9.58 9.94 12.21 8.86 8.93 14.28 14.75 15.18 8.97 8.84 9.28 2140 4.39 4.40 4.38 4.40 4.40 10.58 10.98 13.49 9.78 9.86 15.78 16.30 16.76 9.91 9.76 10.25 2150 4.85 4.86 4.83 4.86 4.86 11.69 12.13 14.90 10.81 10.89 17.43 18.00 18.52 10.94 10.78 11.32 2160 5.35 5.37 5.34 5.37 5.37 12.92 13.40 16.46 11.94 12.03 19.25 19.89 20.46 12.09 11.91 12.51 2170 5.92 5.93 5.90 5.93 5.94 14.27 14.80 18.18 13.18 13.29 21.26 21.97 22.60 13.35 13.16 13.82 2180 6.53 6.55 6.52 6.55 6.56 15.76 16.35 20.08 14.56 14.68 23.49 24.27 24.96 14.75 14.53 15.26 2190 7.22 7.23 7.20 7.24 7.24 17.41 18.06 22.18 16.09 16.22 25.95 26.80 27.57 16.29 16.05 16.86 2200 7.97 7.99 7.95 8.00 8.00 19.23 19.95 24.50 17.77 17.92 28.66 29.61 30.46 18.00 17.73 18.62 2210 8.79 8.81 8.77 8.82 8.82 21.20 22.00 27.02 19.60 19.76 31.60 32.65 33.58 19.85 19.55 20.53 2220 9.66 9.67 9.63 9.69 9.69 23.29 24.16 29.68 21.52 21.70 34.71 35.86 36.88 21.80 21.48 22.55 2230 10.56 10.58 10.53 10.60 10.60 25.48 26.43 32.46 23.54 23.74 37.97 39.23 40.35 23.84 23.49 24.67 
105

2240 11.51 11.53 11.48 11.55 11.55 27.76 28.80 35.37 25.65 25.86 41.37 42.74 43.97 25.98 25.60 26.88 2250 12.49 12.52 12.46 12.53 12.53 30.13 31.26 38.39 27.84 28.07 44.90 46.39 47.72 28.20 27.78 29.17 2260 13.50 13.53 13.47 13.55 13.55 32.57 33.79 41.50 30.10 30.34 48.54 50.15 51.58 30.48 30.03 31.54 2270 14.54 14.57 14.50 14.59 14.59 35.07 36.38 44.69 32.41 32.67 52.26 53.99 55.54 32.82 32.34 33.96 2280 15.59 15.62 15.55 15.64 15.65 37.61 39.02 47.92 34.75 35.04 56.05 57.91 59.56 35.20 34.68 36.42 2290 16.66 16.69 16.61 16.71 16.71 40.17 41.68 51.19 37.12 37.43 59.87 61.86 63.63 37.60 37.05 38.90 2300 17.72 17.76 17.67 17.78 17.78 42.74 44.35 54.47 39.50 39.82 63.70 65.81 67.70 40.01 39.42 41.39 
106

Table A4.3d. FUND per capita income in B1 Scenario; 2000=1.00  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS 

1950 0.33 0.28 0.24 0.17 0.48 0.33 0.53 0.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.30 1960 0.39 0.37 0.32 0.23 0.54 0.44 0.69 0.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.40 1970 0.47 0.48 0.41 0.32 0.61 0.59 0.91 0.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.53 1980 0.57 0.63 0.54 0.44 0.68 0.78 1.20 0.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.71 1990 0.78 0.86 0.85 0.88 0.83 0.90 1.79 0.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.21 1.21 1.21 1.21 1.22 1.04 1.08 1.36 1.31 1.31 1.59 1.64 1.69 1.31 1.29 1.36 2020 1.42 1.42 1.41 1.41 1.42 1.71 1.78 2.25 1.96 1.96 2.63 2.71 2.79 1.97 1.94 2.04 2030 1.60 1.61 1.60 1.60 1.61 2.72 2.82 3.68 2.95 2.95 4.29 4.43 4.56 2.96 2.92 3.06 2040 1.80 1.80 1.79 1.79 1.80 4.12 4.27 5.79 4.31 4.31 6.75 6.97 7.17 4.33 4.27 4.48 2050 2.00 2.01 2.00 2.00 2.01 5.86 6.08 8.49 5.93 5.93 9.91 10.24 10.53 5.95 5.87 6.16 2060 2.19 2.19 2.18 2.18 2.20 7.85 8.15 11.62 7.71 7.71 13.56 14.01 14.41 7.74 7.63 8.01 2070 2.38 2.38 2.37 2.37 2.38 10.26 10.65 15.35 9.84 9.84 17.91 18.50 19.03 9.88 9.74 10.22 2080 2.61 2.62 2.61 2.60 2.62 13.16 13.65 19.92 12.38 12.38 23.24 24.00 24.69 12.43 12.25 12.86 2090 2.89 2.90 2.89 2.88 2.90 16.50 17.12 25.46 15.34 15.34 29.71 30.68 31.56 15.41 15.18 15.94 2100 3.21 3.21 3.20 3.20 3.22 20.31 21.07 31.74 18.85 18.84 37.05 38.26 39.35 18.92 18.64 19.58 2110 3.54 3.55 3.53 3.53 3.56 24.65 25.57 38.53 22.87 22.86 44.96 46.43 47.76 22.96 22.63 23.76 2120 3.91 3.92 3.90 3.90 3.93 29.62 30.73 46.30 27.49 27.47 54.03 55.80 57.39 27.60 27.19 28.55 2130 4.32 4.33 4.31 4.31 4.34 35.25 36.57 55.10 32.71 32.69 64.30 66.40 68.30 32.84 32.36 33.98 2140 4.78 4.78 4.76 4.76 4.79 41.54 43.10 64.93 38.55 38.52 75.77 78.24 80.48 38.70 38.13 40.04 2150 5.28 5.29 5.26 5.26 5.29 48.47 50.29 75.76 44.98 44.95 88.41 91.30 93.91 45.16 44.49 46.72 2160 5.83 5.84 5.81 5.81 5.85 56.00 58.10 87.53 51.97 51.94 102.15 105.49 108.51 52.18 51.41 53.98 2170 6.44 6.45 6.42 6.41 6.46 64.07 66.47 100.14 59.45 59.42 116.87 120.68 124.14 59.69 58.81 61.76 2180 7.11 7.12 7.09 7.09 7.13 72.58 75.30 113.44 67.35 67.31 132.39 136.71 140.63 67.62 66.62 69.96 2190 7.85 7.87 7.83 7.83 7.88 81.41 84.47 127.25 75.55 75.50 148.50 153.35 157.74 75.85 74.73 78.48 2200 8.68 8.69 8.65 8.65 8.71 90.42 93.81 141.33 83.90 83.86 164.93 170.32 175.19 84.24 83.00 87.16 2210 9.57 9.59 9.54 9.53 9.60 99.70 103.44 155.84 92.52 92.47 181.86 187.80 193.18 92.89 91.52 96.11 2220 10.51 10.53 10.48 10.47 10.54 109.50 113.61 171.16 101.61 101.56 199.74 206.26 212.17 102.02 100.52 105.55 2230 11.49 11.52 11.46 11.45 11.53 119.79 124.29 187.24 111.16 111.10 218.50 225.64 232.10 111.61 109.96 115.47 
107

2240 12.52 12.55 12.49 12.48 12.57 130.53 135.42 204.02 121.12 121.05 238.09 245.86 252.90 121.61 119.82 125.82 2250 13.59 13.62 13.55 13.54 13.64 141.66 146.98 221.42 131.45 131.38 258.40 266.84 274.48 131.98 130.04 136.55 2260 14.69 14.72 14.65 14.64 14.74 153.14 158.88 239.36 142.10 142.02 279.33 288.45 296.71 142.68 140.57 147.62 2270 15.82 15.85 15.78 15.76 15.87 164.89 171.07 257.73 153.01 152.92 300.76 310.59 319.48 153.62 151.36 158.94 2280 16.97 17.00 16.92 16.91 17.02 176.84 183.47 276.40 164.09 164.00 322.56 333.09 342.63 164.76 162.32 170.46 2290 18.12 18.16 18.07 18.06 18.19 188.90 195.98 295.25 175.28 175.19 344.56 355.81 366.00 175.99 173.39 182.08 2300 19.28 19.32 19.23 19.22 19.35 200.98 208.52 314.14 186.50 186.39 366.60 378.57 389.41 187.25 184.49 193.73 
108

Table A4.3e. FUND per capita income in B2 Scenario; 2000=1.00 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS 

1950 0.33 0.28 0.24 0.17 0.48 0.33 0.53 0.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.30 1960 0.39 0.37 0.32 0.23 0.54 0.44 0.69 0.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.40 1970 0.47 0.48 0.41 0.32 0.61 0.59 0.91 0.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.53 1980 0.57 0.63 0.54 0.44 0.68 0.78 1.20 0.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.71 1990 0.78 0.86 0.85 0.88 0.83 0.90 1.79 0.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.16 1.16 1.15 1.16 1.16 0.99 1.03 1.37 1.13 1.14 1.60 1.65 1.70 1.15 1.13 1.19 2020 1.27 1.27 1.26 1.27 1.27 1.52 1.57 2.18 1.46 1.48 2.55 2.63 2.70 1.48 1.46 1.53 2030 1.37 1.38 1.37 1.38 1.38 2.41 2.50 3.14 2.06 2.08 3.67 3.78 3.89 2.09 2.06 2.17 2040 1.49 1.49 1.49 1.50 1.50 3.77 3.91 4.22 2.99 3.02 4.93 5.09 5.24 3.03 2.98 3.13 2050 1.61 1.62 1.61 1.62 1.62 5.34 5.54 5.40 4.11 4.15 6.30 6.51 6.70 4.17 4.11 4.31 2060 1.76 1.76 1.75 1.76 1.76 6.90 7.16 6.59 5.27 5.32 7.69 7.94 8.17 5.34 5.26 5.53 2070 1.93 1.93 1.92 1.93 1.93 8.28 8.59 7.79 6.31 6.37 9.08 9.38 9.65 6.40 6.30 6.62 2080 2.10 2.11 2.10 2.11 2.11 9.39 9.75 8.94 7.18 7.25 10.43 10.77 11.08 7.28 7.18 7.54 2090 2.30 2.31 2.30 2.31 2.31 10.44 10.84 10.11 7.99 8.06 11.80 12.18 12.53 8.10 7.98 8.38 2100 2.53 2.54 2.53 2.54 2.54 11.54 11.97 11.28 8.82 8.90 13.16 13.59 13.98 8.94 8.81 9.25 2110 2.80 2.80 2.79 2.80 2.81 12.74 13.22 12.46 9.74 9.83 14.54 15.01 15.44 9.88 9.73 10.22 2120 3.09 3.10 3.08 3.10 3.10 14.08 14.61 13.76 10.76 10.86 16.06 16.58 17.06 10.91 10.75 11.29 2130 3.41 3.42 3.40 3.42 3.42 15.55 16.13 15.20 11.88 12.00 17.74 18.32 18.84 12.05 11.87 12.47 2140 3.77 3.78 3.76 3.78 3.78 17.18 17.82 16.79 13.13 13.25 19.59 20.23 20.81 13.31 13.12 13.77 2150 4.16 4.17 4.15 4.18 4.18 18.97 19.69 18.55 14.50 14.64 21.64 22.35 22.99 14.71 14.49 15.21 2160 4.60 4.61 4.59 4.61 4.62 20.96 21.75 20.49 16.02 16.17 23.91 24.69 25.39 16.24 16.00 16.81 2170 5.08 5.09 5.07 5.10 5.10 23.15 24.02 22.63 17.69 17.86 26.41 27.27 28.05 17.94 17.68 18.56 2180 5.61 5.62 5.60 5.63 5.63 25.57 26.53 25.00 19.55 19.73 29.17 30.12 30.99 19.82 19.53 20.51 2190 6.20 6.21 6.18 6.22 6.22 28.25 29.31 27.61 21.59 21.79 32.22 33.27 34.23 21.89 21.57 22.65 2200 6.85 6.86 6.83 6.87 6.87 31.21 32.38 30.50 23.85 24.07 35.59 36.76 37.81 24.19 23.83 25.02 2210 7.55 7.57 7.53 7.57 7.58 34.41 35.70 33.64 26.30 26.55 39.25 40.53 41.69 26.67 26.27 27.59 2220 8.29 8.31 8.27 8.32 8.32 37.79 39.21 36.94 28.88 29.16 43.11 44.51 45.79 29.29 28.86 30.30 2230 9.07 9.09 9.05 9.10 9.11 41.34 42.89 40.41 31.60 31.89 47.16 48.69 50.09 32.04 31.57 33.15 
109

2240 9.89 9.91 9.86 9.91 9.92 45.05 46.74 44.03 34.43 34.75 51.38 53.06 54.58 34.91 34.40 36.12 2250 10.73 10.75 10.70 10.76 10.77 48.89 50.72 47.79 37.37 37.72 55.77 57.59 59.23 37.89 37.33 39.20 2260 11.60 11.62 11.57 11.63 11.64 52.85 54.83 51.66 40.39 40.77 60.28 62.25 64.03 40.96 40.36 42.38 2270 12.49 12.51 12.45 12.52 12.53 56.91 59.04 55.63 43.49 43.90 64.91 67.03 68.95 44.10 43.45 45.63 2280 13.39 13.42 13.36 13.43 13.44 61.03 63.32 59.66 46.64 47.08 69.61 71.88 73.94 47.30 46.60 48.94 2290 14.31 14.34 14.27 14.35 14.36 65.19 67.64 63.73 49.82 50.29 74.36 76.79 78.98 50.53 49.78 52.27 2300 15.22 15.25 15.18 15.26 15.28 69.36 71.96 67.80 53.01 53.51 79.12 81.70 84.04 53.76 52.96 55.62 
110

Table A4.4a. Autonomous energy efficiency (AEEI) in FUND standard scenario 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA

1950 0.66 0.60 0.60 1.42 1.05 0.80 1.21 2.75 1.12 0.97 1.76 1.65 1.23 1960 0.74 0.70 0.59 1.16 1.06 0.70 1.03 1.91 1.08 0.98 1.51 1.33 0.46 1970 0.71 0.68 0.59 0.79 1.11 0.65 1.00 1.41 1.15 1.01 1.39 1.03 0.62 1980 0.79 0.73 0.65 0.86 1.11 0.66 0.99 1.34 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.09 1.01 1.04 1.02 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.07 1.09 1.05 1.07 1.09 0.99 1.00 1.06 1.05 1.06 1.02 1.16 2020 1.17 1.16 1.16 1.14 1.16 1.16 1.09 1.06 1.16 1.15 1.16 1.12 1.27 2030 1.23 1.22 1.21 1.19 1.22 1.25 1.18 1.14 1.26 1.25 1.26 1.22 1.39 2040 1.27 1.26 1.26 1.24 1.26 1.34 1.27 1.23 1.36 1.35 1.36 1.32 1.52 2050 1.31 1.30 1.30 1.28 1.30 1.42 1.34 1.32 1.46 1.45 1.46 1.41 1.65 2060 1.35 1.34 1.34 1.31 1.34 1.49 1.41 1.42 1.57 1.55 1.56 1.51 1.78 2070 1.39 1.38 1.37 1.35 1.38 1.55 1.47 1.51 1.67 1.65 1.66 1.61 1.92 2080 1.42 1.41 1.41 1.38 1.41 1.62 1.53 1.60 1.77 1.75 1.77 1.71 2.06 2090 1.46 1.45 1.44 1.42 1.45 1.68 1.59 1.70 1.87 1.85 1.87 1.81 2.20 2100 1.49 1.48 1.47 1.45 1.48 1.73 1.64 1.79 1.97 1.95 1.97 1.91 2.34 2110 1.52 1.51 1.50 1.48 1.51 1.78 1.69 1.88 2.07 2.05 2.06 2.00 2.48 2120 1.55 1.54 1.53 1.50 1.54 1.82 1.73 1.96 2.16 2.14 2.16 2.09 2.61 2130 1.58 1.57 1.56 1.54 1.57 1.86 1.76 2.04 2.25 2.23 2.25 2.18 2.75 2140 1.61 1.60 1.60 1.57 1.60 1.90 1.80 2.12 2.34 2.31 2.33 2.26 2.88 2150 1.65 1.63 1.63 1.60 1.63 1.94 1.83 2.19 2.41 2.39 2.40 2.33 3.00 2160 1.68 1.67 1.66 1.63 1.67 1.98 1.87 2.26 2.49 2.46 2.48 2.41 3.13 2170 1.71 1.70 1.69 1.66 1.70 2.02 1.91 2.34 2.58 2.55 2.57 2.50 3.27 2180 1.75 1.73 1.73 1.70 1.73 2.06 1.95 2.41 2.66 2.63 2.65 2.57 3.40 2190 1.78 1.77 1.76 1.73 1.77 2.10 1.99 2.46 2.71 2.68 2.70 2.62 3.49 2200 1.82 1.80 1.80 1.77 1.80 2.14 2.03 2.51 2.77 2.74 2.76 2.67 3.57 2210 1.86 1.84 1.84 1.80 1.84 2.18 2.07 2.56 2.82 2.79 2.81 2.73 3.64 2220 1.89 1.88 1.87 1.84 1.88 2.23 2.11 2.61 2.88 2.85 2.87 2.78 3.72 2230 1.93 1.92 1.91 1.87 1.92 2.27 2.15 2.66 2.94 2.90 2.93 2.84 3.79 2240 1.97 1.95 1.95 1.91 1.95 2.32 2.20 2.71 3.00 2.96 2.99 2.90 3.87 2250 2.01 1.99 1.99 1.95 1.99 2.37 2.24 2.77 3.06 3.02 3.05 2.96 3.95 
111

2260 2.05 2.03 2.03 1.99 2.03 2.41 2.28 2.82 3.12 3.08 3.11 3.01 4.03 2270 2.09 2.08 2.07 2.03 2.08 2.46 2.33 2.88 3.18 3.15 3.17 3.08 4.11 2280 2.13 2.12 2.11 2.07 2.12 2.51 2.38 2.94 3.25 3.21 3.23 3.14 4.19 2290 2.18 2.16 2.15 2.11 2.16 2.56 2.43 3.00 3.31 3.27 3.30 3.20 4.28 2300 2.22 2.20 2.20 2.16 2.20 2.61 2.47 3.06 3.38 3.34 3.37 3.27 4.36 
112

Table A4.4b Autonomous energy efficiency (AEEI) in FUND A1B 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA1950 0.66 0.60 0.60 1.42 1.05 0.80 1.21 2.75 1.12 0.97 1.76 1.65 1.23 1960 0.74 0.70 0.59 1.16 1.06 0.70 1.03 1.91 1.08 0.98 1.51 1.33 0.46 1970 0.71 0.68 0.59 0.79 1.11 0.65 1.00 1.41 1.15 1.01 1.39 1.03 0.62 1980 0.79 0.73 0.65 0.86 1.11 0.66 0.99 1.34 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.09 1.01 1.04 1.02 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.07 1.09 1.05 1.07 1.09 0.99 1.00 1.06 1.05 1.06 1.02 1.16 2020 1.21 1.20 1.20 1.17 1.20 1.26 1.19 1.14 1.22 1.20 1.25 1.21 1.37 2030 1.37 1.36 1.35 1.33 1.36 1.70 1.61 1.51 1.51 1.49 1.66 1.61 1.82 2040 1.54 1.53 1.53 1.50 1.53 2.16 2.05 1.88 1.82 1.80 2.06 2.00 2.26 2050 1.71 1.69 1.69 1.66 1.69 2.63 2.49 2.23 2.14 2.12 2.45 2.37 2.68 2060 1.84 1.83 1.82 1.79 1.83 3.04 2.88 2.50 2.41 2.39 2.75 2.66 3.01 2070 1.99 1.98 1.97 1.93 1.98 3.50 3.32 2.79 2.74 2.71 3.07 2.98 3.36 2080 2.16 2.15 2.14 2.10 2.15 4.03 3.82 3.11 3.18 3.14 3.41 3.31 3.74 2090 2.36 2.34 2.33 2.29 2.34 4.64 4.39 3.44 3.71 3.67 3.78 3.67 4.14 2100 2.56 2.54 2.53 2.49 2.54 5.24 4.96 3.76 4.23 4.18 4.14 4.02 4.53 2110 2.76 2.73 2.73 2.68 2.73 5.78 5.47 4.05 4.66 4.61 4.46 4.32 4.88 2120 2.96 2.94 2.93 2.87 2.94 6.34 6.00 4.35 5.11 5.05 4.78 4.64 5.24 2130 3.17 3.14 3.13 3.08 3.14 6.91 6.54 4.66 5.58 5.52 5.12 4.97 5.61 2140 3.39 3.36 3.35 3.29 3.36 7.51 7.11 4.98 6.06 5.99 5.47 5.31 5.99 2150 3.61 3.58 3.57 3.50 3.58 8.11 7.68 5.31 6.54 6.47 5.83 5.66 6.39 2160 3.84 3.81 3.79 3.72 3.81 8.72 8.26 5.64 7.04 6.96 6.20 6.02 6.79 2170 4.07 4.04 4.02 3.95 4.04 9.33 8.83 5.98 7.53 7.44 6.58 6.38 7.20 2180 4.30 4.27 4.26 4.18 4.27 9.93 9.40 6.33 8.01 7.92 6.96 6.75 7.62 2190 4.54 4.51 4.49 4.41 4.50 10.52 9.96 6.68 8.49 8.39 7.34 7.12 8.04 2200 4.78 4.74 4.73 4.64 4.74 11.09 10.50 7.03 8.95 8.85 7.73 7.50 8.46 2210 5.02 4.98 4.97 4.88 4.98 11.66 11.03 7.39 9.40 9.30 8.12 7.88 8.89 2220 5.28 5.24 5.22 5.13 5.24 12.26 11.60 7.76 9.89 9.78 8.54 8.29 9.35 2230 5.55 5.51 5.49 5.39 5.51 12.88 12.19 8.16 10.39 10.28 8.98 8.71 9.82 2240 5.83 5.79 5.77 5.66 5.79 13.54 12.82 8.58 10.92 10.80 9.43 9.15 10.33 2250 6.13 6.09 6.07 5.95 6.08 14.23 13.47 9.02 11.48 11.35 9.92 9.62 10.86 
113

2260 6.45 6.40 6.38 6.26 6.40 14.96 14.16 9.48 12.07 11.93 10.42 10.11 11.41 2270 6.78 6.72 6.70 6.58 6.72 15.73 14.88 9.96 12.69 12.54 10.96 10.63 11.99 2280 7.12 7.07 7.04 6.91 7.07 16.53 15.64 10.47 13.33 13.19 11.52 11.18 12.61 2290 7.49 7.43 7.41 7.27 7.43 17.38 16.44 11.01 14.02 13.86 12.11 11.75 13.25 2300 7.87 7.81 7.78 7.64 7.81 18.26 17.29 11.57 14.73 14.57 12.73 12.35 13.93 
114

Table A4.4c Autonomous energy efficiency (AEEI) in FUND A2 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA1950 0.66 0.60 0.60 1.42 1.05 0.80 1.21 2.75 1.12 0.97 1.76 1.65 1.23 1960 0.74 0.70 0.59 1.16 1.06 0.70 1.03 1.91 1.08 0.98 1.51 1.33 0.46 1970 0.71 0.68 0.59 0.79 1.11 0.65 1.00 1.41 1.15 1.01 1.39 1.03 0.62 1980 0.79 0.73 0.65 0.86 1.11 0.66 0.99 1.34 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.09 1.01 1.04 1.02 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.07 1.09 1.05 1.07 1.09 0.99 1.00 1.06 1.05 1.06 1.02 1.16 2020 1.20 1.19 1.19 1.16 1.19 1.23 1.15 1.09 1.20 1.19 1.20 1.16 1.31 2030 1.35 1.34 1.33 1.31 1.34 1.58 1.49 1.33 1.47 1.45 1.46 1.42 1.60 2040 1.47 1.46 1.45 1.43 1.46 1.90 1.80 1.55 1.71 1.69 1.71 1.66 1.87 2050 1.55 1.53 1.53 1.50 1.53 2.11 2.00 1.67 1.79 1.77 1.84 1.78 2.01 2060 1.64 1.63 1.63 1.60 1.63 2.40 2.27 1.86 1.92 1.90 2.05 1.98 2.24 2070 1.77 1.75 1.75 1.72 1.75 2.83 2.68 2.17 2.16 2.13 2.38 2.31 2.61 2080 2.00 1.99 1.98 1.94 1.99 3.74 3.54 2.85 2.72 2.69 3.13 3.04 3.43 2090 2.10 2.08 2.08 2.04 2.08 4.35 4.12 3.31 3.03 2.99 3.64 3.53 3.98 2100 2.11 2.09 2.08 2.05 2.09 4.50 4.26 3.43 3.09 3.05 3.77 3.66 4.13 2110 2.22 2.20 2.19 2.15 2.20 4.73 4.48 3.60 3.24 3.21 3.96 3.84 4.34 2120 2.33 2.31 2.30 2.26 2.31 4.97 4.71 3.79 3.41 3.37 4.16 4.04 4.56 2130 2.45 2.43 2.42 2.38 2.43 5.23 4.95 3.98 3.58 3.54 4.38 4.25 4.79 2140 2.57 2.55 2.54 2.50 2.55 5.50 5.20 4.18 3.77 3.72 4.60 4.46 5.04 2150 2.70 2.68 2.67 2.63 2.68 5.78 5.47 4.40 3.96 3.91 4.84 4.69 5.29 2160 2.84 2.82 2.81 2.76 2.82 6.07 5.75 4.62 4.16 4.11 5.08 4.93 5.56 2170 2.99 2.96 2.96 2.90 2.96 6.38 6.04 4.86 4.37 4.33 5.34 5.18 5.85 2180 3.14 3.12 3.11 3.05 3.12 6.71 6.35 5.11 4.60 4.55 5.62 5.45 6.15 2190 3.30 3.28 3.27 3.20 3.28 7.05 6.68 5.37 4.83 4.78 5.90 5.73 6.46 2200 3.47 3.44 3.43 3.37 3.44 7.41 7.02 5.64 5.08 5.02 6.21 6.02 6.79 2210 3.65 3.62 3.61 3.54 3.62 7.79 7.38 5.93 5.34 5.28 6.52 6.33 7.14 2220 3.83 3.80 3.79 3.72 3.80 8.19 7.75 6.23 5.61 5.55 6.86 6.65 7.51 2230 4.03 4.00 3.99 3.91 4.00 8.61 8.15 6.55 5.90 5.83 7.21 6.99 7.89 2240 4.24 4.20 4.19 4.11 4.20 9.05 8.57 6.89 6.20 6.13 7.58 7.35 8.29 2250 4.45 4.42 4.40 4.32 4.42 9.51 9.00 7.24 6.52 6.45 7.96 7.73 8.72 
115

2260 4.68 4.64 4.63 4.54 4.64 10.00 9.46 7.61 6.85 6.78 8.37 8.12 9.16 2270 4.92 4.88 4.87 4.78 4.88 10.51 9.95 8.00 7.20 7.12 8.80 8.54 9.63 2280 5.17 5.13 5.11 5.02 5.13 11.05 10.46 8.41 7.57 7.49 9.25 8.97 10.12 2290 5.44 5.39 5.38 5.28 5.39 11.61 10.99 8.84 7.96 7.87 9.72 9.43 10.64 2300 5.71 5.67 5.65 5.55 5.67 12.21 11.55 9.29 8.37 8.27 10.22 9.92 11.19 
116

Table A4.4d Autonomous energy efficiency (AEEI) in FUND B1. 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA1950 0.66 0.60 0.60 1.42 1.05 0.80 1.21 2.75 1.12 0.97 1.76 1.65 1.23 1960 0.74 0.70 0.59 1.16 1.06 0.70 1.03 1.91 1.08 0.98 1.51 1.33 0.46 1970 0.71 0.68 0.59 0.79 1.11 0.65 1.00 1.41 1.15 1.01 1.39 1.03 0.62 1980 0.79 0.73 0.65 0.86 1.11 0.66 0.99 1.34 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.09 1.01 1.04 1.02 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.07 1.09 1.05 1.07 1.09 0.99 1.00 1.06 1.05 1.06 1.02 1.16 2020 1.23 1.22 1.22 1.20 1.22 1.26 1.19 1.15 1.22 1.21 1.26 1.22 1.38 2030 1.49 1.48 1.48 1.45 1.48 1.72 1.63 1.59 1.54 1.52 1.74 1.69 1.91 2040 1.84 1.82 1.82 1.78 1.82 2.38 2.25 2.34 1.99 1.97 2.57 2.50 2.82 2050 2.21 2.20 2.19 2.15 2.20 3.34 3.16 3.46 2.64 2.61 3.80 3.69 4.16 2060 2.60 2.58 2.58 2.53 2.58 4.62 4.38 4.89 3.60 3.56 5.38 5.22 5.89 2070 3.02 3.00 2.99 2.93 3.00 6.38 6.04 6.71 4.94 4.88 7.38 7.16 8.08 2080 3.52 3.49 3.48 3.42 3.49 8.64 8.18 8.96 6.66 6.58 9.85 9.56 10.79 2090 4.07 4.04 4.03 3.95 4.04 11.41 10.80 11.62 8.94 8.85 12.78 12.40 13.99 2100 4.61 4.58 4.56 4.48 4.57 14.43 13.66 14.60 11.52 11.39 16.05 15.57 17.57 2110 5.08 5.04 5.03 4.93 5.04 17.47 16.54 17.67 13.95 13.79 19.44 18.86 21.28 2120 5.57 5.53 5.51 5.41 5.53 20.85 19.74 21.09 16.64 16.46 23.19 22.50 25.39 2130 6.08 6.04 6.02 5.90 6.03 24.52 23.20 24.80 19.57 19.35 27.27 26.46 29.85 2140 6.60 6.55 6.53 6.41 6.55 28.40 26.88 28.73 22.67 22.42 31.59 30.65 34.58 2150 7.14 7.08 7.06 6.93 7.08 32.42 30.69 32.80 25.88 25.59 36.06 34.99 39.48 2160 7.67 7.61 7.59 7.45 7.61 36.47 34.52 36.89 29.11 28.78 40.56 39.36 44.40 2170 8.21 8.15 8.12 7.97 8.14 40.41 38.25 40.88 32.26 31.90 44.95 43.61 49.20 2180 8.74 8.67 8.64 8.48 8.67 44.13 41.76 44.63 35.22 34.83 49.08 47.62 53.72 2190 9.26 9.19 9.16 8.99 9.18 47.47 44.92 48.01 37.89 37.46 52.79 51.23 57.79 2200 9.76 9.68 9.65 9.47 9.68 50.31 47.61 50.88 40.15 39.70 55.95 54.29 61.25 2210 10.26 10.18 10.14 9.96 10.18 52.88 50.05 53.48 42.20 41.73 58.81 57.06 64.38 2220 10.78 10.70 10.66 10.47 10.70 55.58 52.60 56.22 44.36 43.87 61.82 59.98 67.67 2230 11.33 11.24 11.21 11.00 11.24 58.42 55.30 59.09 46.63 46.11 64.98 63.05 71.13 2240 11.91 11.82 11.78 11.56 11.82 61.41 58.12 62.11 49.02 48.47 68.30 66.28 74.77 2250 12.52 12.42 12.38 12.16 12.42 64.55 61.10 65.29 51.52 50.95 71.80 69.66 78.59 
117

2260 13.16 13.06 13.02 12.78 13.06 67.85 64.22 68.63 54.16 53.55 75.47 73.23 82.61 2270 13.83 13.73 13.68 13.43 13.73 71.32 67.50 72.14 56.93 56.29 79.33 76.97 86.84 2280 14.54 14.43 14.38 14.12 14.43 74.97 70.96 75.83 59.84 59.17 83.39 80.91 91.28 2290 15.28 15.17 15.12 14.84 15.17 78.81 74.58 79.71 62.90 62.20 87.65 85.05 95.95 2300 16.07 15.94 15.89 15.60 15.94 82.84 78.40 83.78 66.12 65.38 92.13 89.40 100.85 
118

Table A4.4e Autonomous energy efficiency (AEEI) in FUND B2 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA

1950 0.66 0.60 0.60 1.42 1.05 0.80 1.21 2.75 1.12 0.97 1.76 1.65 1.23 1960 0.74 0.70 0.59 1.16 1.06 0.70 1.03 1.91 1.08 0.98 1.51 1.33 0.46 1970 0.71 0.68 0.59 0.79 1.11 0.65 1.00 1.41 1.15 1.01 1.39 1.03 0.62 1980 0.79 0.73 0.65 0.86 1.11 0.66 0.99 1.34 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.09 1.01 1.04 1.02 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.07 1.09 1.05 1.07 1.09 0.99 1.00 1.06 1.05 1.06 1.02 1.16 2020 1.18 1.17 1.17 1.14 1.17 1.23 1.16 1.10 1.21 1.20 1.20 1.17 1.32 2030 1.24 1.24 1.23 1.21 1.24 1.61 1.52 1.33 1.48 1.47 1.46 1.42 1.60 2040 1.32 1.31 1.31 1.28 1.31 2.12 2.01 1.58 1.85 1.83 1.74 1.69 1.91 2050 1.37 1.36 1.36 1.33 1.36 2.68 2.54 1.84 2.21 2.19 2.02 1.96 2.21 2060 1.43 1.42 1.41 1.39 1.42 3.16 2.99 2.04 2.52 2.49 2.25 2.18 2.46 2070 1.50 1.48 1.48 1.45 1.48 3.48 3.29 2.21 2.72 2.69 2.43 2.36 2.66 2080 1.56 1.54 1.54 1.51 1.54 3.77 3.57 2.32 2.80 2.77 2.55 2.47 2.79 2090 1.66 1.65 1.64 1.61 1.65 4.04 3.82 2.42 2.91 2.87 2.66 2.58 2.91 2100 1.77 1.76 1.76 1.72 1.76 4.24 4.01 2.54 3.06 3.03 2.79 2.71 3.05 2110 1.87 1.85 1.84 1.81 1.85 4.45 4.21 2.67 3.22 3.18 2.93 2.84 3.21 2120 1.96 1.95 1.94 1.90 1.95 4.68 4.43 2.80 3.39 3.35 3.08 2.99 3.37 2130 2.06 2.04 2.04 2.00 2.04 4.92 4.66 2.95 3.56 3.52 3.24 3.14 3.55 2140 2.17 2.15 2.14 2.10 2.15 5.17 4.90 3.10 3.74 3.70 3.41 3.30 3.73 2150 2.28 2.26 2.25 2.21 2.26 5.44 5.15 3.25 3.93 3.89 3.58 3.47 3.92 2160 2.39 2.38 2.37 2.32 2.37 5.71 5.41 3.42 4.13 4.09 3.76 3.65 4.12 2170 2.52 2.50 2.49 2.44 2.50 6.01 5.69 3.60 4.34 4.30 3.95 3.84 4.33 2180 2.64 2.62 2.62 2.57 2.62 6.31 5.98 3.78 4.57 4.52 4.16 4.03 4.55 2190 2.78 2.76 2.75 2.70 2.76 6.64 6.28 3.97 4.80 4.75 4.37 4.24 4.78 2200 2.92 2.90 2.89 2.84 2.90 6.98 6.60 4.18 5.04 4.99 4.59 4.46 5.03 2210 3.07 3.05 3.04 2.98 3.05 7.33 6.94 4.39 5.30 5.24 4.83 4.68 5.28 2220 3.23 3.20 3.19 3.13 3.20 7.71 7.30 4.61 5.57 5.51 5.07 4.92 5.56 2230 3.39 3.37 3.36 3.29 3.37 8.10 7.67 4.85 5.86 5.79 5.33 5.18 5.84 2240 3.57 3.54 3.53 3.46 3.54 8.52 8.06 5.10 6.16 6.09 5.61 5.44 6.14 2250 3.75 3.72 3.71 3.64 3.72 8.95 8.47 5.36 6.47 6.40 5.89 5.72 6.45 
119

2260 3.94 3.91 3.90 3.83 3.91 9.41 8.91 5.63 6.80 6.73 6.20 6.01 6.78 2270 4.14 4.11 4.10 4.02 4.11 9.89 9.36 5.92 7.15 7.07 6.51 6.32 7.13 2280 4.35 4.32 4.31 4.23 4.32 10.40 9.84 6.22 7.52 7.43 6.85 6.64 7.49 2290 4.58 4.54 4.53 4.44 4.54 10.93 10.34 6.54 7.90 7.82 7.20 6.98 7.88 2300 4.81 4.77 4.76 4.67 4.77 11.49 10.87 6.88 8.31 8.21 7.56 7.34 8.28 
120

Table A4.5a Autonomous carbon efficiency improvement (ACEI) FUND standard scenario 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.67 0.60 0.60 1.42 1.05 0.80 1.21 2.72 1.12 0.97 1.76 1.65 1.24 1960 0.74 0.70 0.60 1.16 1.06 0.70 1.03 1.90 1.08 0.98 1.51 1.33 0.46 1970 0.72 0.68 0.59 0.79 1.11 0.65 1.00 1.40 1.15 1.01 1.38 1.03 0.62 1980 0.79 0.73 0.66 0.86 1.11 0.66 0.99 1.33 1.04 1.09 1.14 1.08 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.08 1.01 1.04 1.02 1.10 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.07 1.07 1.05 1.07 1.09 0.99 1.07 1.06 1.05 1.07 1.04 1.12 2020 1.14 1.15 1.11 1.14 1.16 1.13 1.07 1.23 1.16 1.14 1.20 1.17 1.17 2030 1.15 1.16 1.12 1.19 1.21 1.14 1.08 1.39 1.23 1.22 1.31 1.27 1.21 2040 1.15 1.16 1.12 1.22 1.24 1.14 1.08 1.51 1.30 1.28 1.41 1.37 1.24 2050 1.15 1.16 1.12 1.24 1.26 1.14 1.08 1.61 1.35 1.33 1.51 1.46 1.27 2060 1.15 1.16 1.12 1.25 1.28 1.14 1.08 1.70 1.38 1.37 1.58 1.54 1.28 2070 1.15 1.16 1.12 1.26 1.29 1.14 1.08 1.78 1.42 1.40 1.66 1.62 1.29 2080 1.15 1.16 1.12 1.27 1.30 1.14 1.08 1.85 1.44 1.42 1.73 1.68 1.30 2090 1.15 1.17 1.12 1.28 1.31 1.15 1.08 1.90 1.45 1.43 1.77 1.72 1.31 2100 1.17 1.19 1.14 1.30 1.33 1.16 1.10 1.94 1.47 1.46 1.81 1.76 1.34 2110 1.19 1.21 1.16 1.33 1.36 1.19 1.12 1.98 1.50 1.49 1.85 1.79 1.36 2120 1.22 1.23 1.19 1.36 1.39 1.21 1.14 2.02 1.53 1.52 1.88 1.83 1.39 2130 1.24 1.26 1.21 1.38 1.41 1.23 1.17 2.06 1.57 1.55 1.92 1.86 1.42 2140 1.27 1.29 1.24 1.41 1.44 1.26 1.19 2.10 1.60 1.58 1.96 1.90 1.45 2150 1.29 1.31 1.26 1.44 1.47 1.28 1.22 2.14 1.63 1.61 2.00 1.94 1.48 2160 1.32 1.34 1.29 1.47 1.50 1.31 1.24 2.19 1.66 1.64 2.04 1.98 1.51 2170 1.35 1.36 1.31 1.50 1.53 1.34 1.27 2.23 1.70 1.68 2.08 2.02 1.54 2180 1.37 1.39 1.34 1.53 1.56 1.36 1.29 2.28 1.73 1.71 2.12 2.06 1.57 2190 1.40 1.42 1.37 1.56 1.59 1.39 1.32 2.32 1.76 1.74 2.17 2.10 1.60 2200 1.43 1.45 1.39 1.59 1.63 1.42 1.34 2.37 1.80 1.78 2.21 2.14 1.63 2210 1.46 1.48 1.42 1.62 1.66 1.45 1.37 2.42 1.84 1.82 2.25 2.19 1.66 2220 1.49 1.51 1.45 1.66 1.69 1.48 1.40 2.46 1.87 1.85 2.30 2.23 1.70 2230 1.52 1.54 1.48 1.69 1.73 1.51 1.43 2.51 1.91 1.89 2.35 2.28 1.73 2240 1.55 1.57 1.51 1.72 1.76 1.54 1.46 2.56 1.95 1.93 2.39 2.32 1.77 2250 1.58 1.60 1.54 1.76 1.80 1.57 1.48 2.62 1.99 1.97 2.44 2.37 1.80 
121

2260 1.61 1.63 1.57 1.79 1.83 1.60 1.51 2.67 2.03 2.01 2.49 2.42 1.84 2270 1.64 1.67 1.60 1.83 1.87 1.63 1.55 2.72 2.07 2.05 2.54 2.47 1.88 2280 1.68 1.70 1.63 1.87 1.91 1.66 1.58 2.78 2.11 2.09 2.59 2.52 1.91 2290 1.71 1.73 1.67 1.91 1.95 1.70 1.61 2.83 2.15 2.13 2.65 2.57 1.95 2300 1.74 1.77 1.70 1.94 1.99 1.73 1.64 2.89 2.20 2.17 2.70 2.62 1.99 
122

Table A4.5b Autonomous carbon efficicency improvement (ACEI) in FUND A1B 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NA1950 0.67 0.60 0.60 1.43 1.05 0.80 1.21 2.72 1.14 0.98 1.77 1.66 1.24 1960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.90 1.09 0.99 1.52 1.34 0.46 1970 0.72 0.68 0.59 0.80 1.11 0.65 1.00 1.40 1.16 1.02 1.40 1.04 0.62 1980 0.79 0.73 0.66 0.87 1.11 0.66 0.99 1.33 1.05 1.10 1.15 1.09 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.08 1.02 1.06 1.03 1.12 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.07 1.07 1.01 1.04 1.09 0.99 1.07 0.97 0.96 1.00 0.97 1.12 2020 1.16 1.17 1.13 1.07 1.10 1.18 1.11 1.22 0.99 0.98 1.05 1.02 1.20 2030 1.24 1.26 1.21 1.15 1.17 1.33 1.26 1.38 1.12 1.10 1.18 1.14 1.34 2040 1.33 1.35 1.30 1.23 1.26 1.56 1.47 1.63 1.28 1.27 1.39 1.35 1.58 2050 1.51 1.53 1.47 1.39 1.42 1.83 1.73 1.93 1.49 1.47 1.64 1.59 1.87 2060 1.81 1.84 1.77 1.67 1.71 2.18 2.06 2.28 1.76 1.74 1.94 1.88 2.21 2070 2.15 2.18 2.10 1.98 2.03 2.55 2.41 2.67 2.05 2.02 2.27 2.20 2.59 2080 2.49 2.52 2.43 2.29 2.35 2.90 2.74 3.05 2.28 2.25 2.59 2.52 2.95 2090 2.84 2.88 2.77 2.62 2.67 3.24 3.06 3.44 2.47 2.44 2.93 2.84 3.33 2100 3.19 3.24 3.11 2.94 3.01 3.56 3.37 3.84 2.64 2.61 3.27 3.17 3.72 2110 3.52 3.57 3.43 3.24 3.32 3.83 3.62 4.23 2.78 2.74 3.60 3.50 4.10 2120 3.86 3.91 3.76 3.56 3.64 4.11 3.89 4.64 2.92 2.88 3.95 3.83 4.50 2130 4.21 4.27 4.11 3.88 3.97 4.40 4.17 5.06 3.07 3.03 4.31 4.18 4.91 2140 4.58 4.64 4.46 4.22 4.31 4.70 4.45 5.50 3.22 3.19 4.68 4.54 5.33 2150 4.94 5.01 4.82 4.56 4.66 5.01 4.74 5.94 3.39 3.35 5.06 4.91 5.76 2160 5.32 5.39 5.18 4.90 5.01 5.33 5.04 6.39 3.56 3.52 5.44 5.28 6.19 2170 5.69 5.77 5.54 5.24 5.36 5.65 5.35 6.84 3.74 3.70 5.82 5.65 6.63 2180 6.05 6.14 5.90 5.58 5.70 5.98 5.66 7.28 3.94 3.89 6.20 6.01 7.06 2190 6.41 6.50 6.25 5.91 6.04 6.31 5.97 7.71 4.14 4.09 6.56 6.37 7.47 2200 6.76 6.85 6.59 6.23 6.37 6.64 6.28 8.12 4.35 4.30 6.92 6.71 7.88 2210 7.10 7.20 6.93 6.55 6.69 6.98 6.61 8.54 4.57 4.52 7.27 7.05 8.28 2220 7.47 7.57 7.28 6.88 7.03 7.33 6.94 8.98 4.80 4.75 7.64 7.42 8.70 2230 7.85 7.96 7.65 7.23 7.39 7.71 7.30 9.44 5.05 4.99 8.03 7.79 9.15 2240 8.25 8.37 8.04 7.60 7.77 8.10 7.67 9.92 5.31 5.25 8.45 8.19 9.62 2250 8.67 8.79 8.46 7.99 8.17 8.52 8.06 10.43 5.58 5.52 8.88 8.61 10.11 
123

2260 9.12 9.24 8.89 8.40 8.59 8.95 8.48 10.96 5.87 5.80 9.33 9.05 10.62 2270 9.58 9.72 9.34 8.83 9.03 9.41 8.91 11.52 6.17 6.10 9.81 9.52 11.17 2280 10.07 10.21 9.82 9.28 9.49 9.89 9.37 12.11 6.48 6.41 10.31 10.00 11.74 2290 10.59 10.74 10.32 9.76 9.97 10.40 9.84 12.73 6.81 6.74 10.84 10.51 12.34 2300 11.13 11.29 10.85 10.26 10.48 10.93 10.35 13.38 7.16 7.08 11.39 11.05 12.97 
124

Table A4.5c Autonomous carbon efficicency improvement (ACEI) in FUND A2 2000=1.0  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.67 0.60 0.60 1.44 1.06 0.80 1.21 2.72 1.13 0.97 1.77 1.66 1.24 1960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.90 1.09 0.98 1.52 1.34 0.46 1970 0.72 0.68 0.59 0.80 1.12 0.65 1.00 1.40 1.16 1.02 1.40 1.04 0.62 1980 0.79 0.73 0.66 0.87 1.12 0.66 0.99 1.33 1.04 1.09 1.15 1.09 0.60 1990 0.96 0.98 0.90 1.05 0.99 0.86 1.20 1.08 1.02 1.05 1.03 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.07 1.07 0.98 1.01 1.09 0.99 1.07 1.00 0.99 1.01 0.98 1.12 2020 1.15 1.16 1.12 1.00 1.02 1.15 1.08 1.19 1.03 1.02 1.03 1.00 1.17 2030 1.19 1.21 1.16 1.03 1.05 1.20 1.14 1.24 1.09 1.08 1.06 1.03 1.20 2040 1.22 1.24 1.19 1.05 1.08 1.30 1.23 1.29 1.18 1.17 1.11 1.08 1.25 2050 1.25 1.27 1.22 1.08 1.10 1.36 1.28 1.32 1.20 1.19 1.13 1.10 1.28 2060 1.27 1.29 1.24 1.09 1.12 1.38 1.31 1.31 1.17 1.15 1.13 1.09 1.27 2070 1.29 1.31 1.26 1.11 1.13 1.40 1.33 1.31 1.14 1.13 1.13 1.10 1.27 2080 1.31 1.33 1.28 1.13 1.15 1.41 1.33 1.32 1.13 1.11 1.14 1.10 1.28 2090 1.33 1.35 1.29 1.14 1.17 1.41 1.33 1.33 1.12 1.10 1.14 1.11 1.29 2100 1.37 1.39 1.33 1.18 1.20 1.44 1.37 1.37 1.14 1.12 1.17 1.14 1.33 2110 1.44 1.46 1.40 1.24 1.27 1.52 1.44 1.44 1.19 1.18 1.23 1.20 1.39 2120 1.51 1.53 1.47 1.30 1.33 1.59 1.51 1.51 1.25 1.24 1.30 1.26 1.47 2130 1.59 1.61 1.55 1.37 1.40 1.68 1.59 1.59 1.32 1.30 1.36 1.32 1.54 2140 1.67 1.69 1.63 1.44 1.47 1.76 1.67 1.67 1.39 1.37 1.43 1.39 1.62 2150 1.75 1.78 1.71 1.51 1.55 1.85 1.75 1.76 1.46 1.44 1.51 1.46 1.70 2160 1.84 1.87 1.80 1.59 1.62 1.95 1.84 1.85 1.53 1.51 1.58 1.54 1.79 2170 1.94 1.97 1.89 1.67 1.71 2.05 1.94 1.94 1.61 1.59 1.67 1.62 1.88 2180 2.04 2.07 1.99 1.76 1.79 2.15 2.04 2.04 1.69 1.67 1.75 1.70 1.98 2190 2.14 2.17 2.09 1.85 1.89 2.26 2.14 2.14 1.78 1.76 1.84 1.79 2.08 2200 2.25 2.28 2.19 1.94 1.98 2.38 2.25 2.25 1.87 1.85 1.93 1.88 2.18 2210 2.37 2.40 2.31 2.04 2.08 2.50 2.36 2.37 1.97 1.94 2.03 1.97 2.30 2220 2.49 2.52 2.42 2.14 2.19 2.62 2.48 2.49 2.07 2.04 2.14 2.07 2.41 2230 2.61 2.65 2.55 2.25 2.30 2.76 2.61 2.62 2.17 2.15 2.25 2.18 2.54 2240 2.75 2.79 2.68 2.37 2.42 2.90 2.75 2.75 2.28 2.26 2.36 2.29 2.67 2250 2.89 2.93 2.82 2.49 2.54 3.05 2.89 2.89 2.40 2.37 2.48 2.41 2.80 
125

2260 3.04 3.08 2.96 2.62 2.67 3.20 3.03 3.04 2.52 2.49 2.61 2.53 2.95 2270 3.19 3.24 3.11 2.75 2.81 3.37 3.19 3.19 2.65 2.62 2.74 2.66 3.10 2280 3.35 3.40 3.27 2.89 2.95 3.54 3.35 3.36 2.79 2.76 2.88 2.80 3.25 2290 3.53 3.58 3.44 3.04 3.11 3.72 3.52 3.53 2.93 2.90 3.03 2.94 3.42 2300 3.71 3.76 3.61 3.19 3.26 3.91 3.70 3.71 3.08 3.04 3.19 3.09 3.60 
126

Table A4.5d Autonomous carbon efficicency improvement (ACEI) in FUND B1 2000 = 1.00.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.67 0.60 0.60 1.43 1.05 0.80 1.21 2.72 1.14 0.98 1.77 1.66 1.24 1960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.90 1.09 0.99 1.52 1.34 0.46 1970 0.72 0.68 0.59 0.80 1.11 0.65 1.00 1.40 1.16 1.03 1.39 1.04 0.62 1980 0.79 0.73 0.66 0.87 1.11 0.66 0.99 1.33 1.05 1.10 1.15 1.09 0.60 1990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.08 1.03 1.06 1.03 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.07 1.07 1.01 1.04 1.09 0.99 1.07 0.96 0.95 1.01 0.98 1.12 2020 1.17 1.18 1.14 1.08 1.10 1.16 1.10 1.20 0.93 0.92 1.05 1.02 1.17 2030 1.27 1.29 1.24 1.17 1.19 1.28 1.21 1.26 0.91 0.90 1.10 1.06 1.22 2040 1.39 1.41 1.36 1.28 1.31 1.36 1.29 1.35 1.02 1.01 1.17 1.13 1.30 2050 1.58 1.60 1.54 1.45 1.48 1.40 1.33 1.48 1.11 1.09 1.29 1.25 1.44 2060 1.76 1.78 1.72 1.62 1.66 1.56 1.48 1.65 1.19 1.18 1.43 1.39 1.60 2070 1.84 1.87 1.79 1.69 1.73 1.78 1.68 1.84 1.27 1.25 1.59 1.55 1.78 2080 1.89 1.92 1.84 1.74 1.78 1.94 1.83 2.05 1.35 1.33 1.78 1.72 1.98 2090 1.97 2.00 1.93 1.82 1.86 1.95 1.85 2.25 1.40 1.38 1.95 1.89 2.18 2100 2.07 2.10 2.02 1.91 1.95 1.84 1.74 2.39 1.45 1.43 2.07 2.01 2.31 2110 2.18 2.21 2.12 2.01 2.05 1.84 1.75 2.51 1.52 1.50 2.18 2.11 2.43 2120 2.29 2.32 2.23 2.11 2.15 1.86 1.76 2.64 1.60 1.58 2.29 2.22 2.56 2130 2.41 2.44 2.35 2.22 2.26 1.88 1.78 2.77 1.68 1.66 2.41 2.33 2.69 2140 2.53 2.57 2.47 2.33 2.38 1.91 1.81 2.91 1.76 1.74 2.53 2.45 2.83 2150 2.66 2.70 2.59 2.45 2.50 1.96 1.85 3.06 1.86 1.83 2.66 2.58 2.97 2160 2.80 2.84 2.73 2.57 2.63 2.01 1.90 3.22 1.95 1.93 2.79 2.71 3.12 2170 2.94 2.98 2.87 2.71 2.77 2.08 1.97 3.39 2.05 2.03 2.94 2.85 3.28 2180 3.09 3.13 3.01 2.84 2.91 2.15 2.04 3.56 2.15 2.13 3.09 2.99 3.45 2190 3.25 3.29 3.17 2.99 3.06 2.25 2.13 3.74 2.26 2.24 3.24 3.15 3.63 2200 3.41 3.46 3.33 3.14 3.21 2.36 2.23 3.93 2.38 2.35 3.41 3.31 3.81 2210 3.59 3.64 3.50 3.30 3.38 2.48 2.34 4.13 2.50 2.47 3.59 3.48 4.01 2220 3.77 3.83 3.68 3.47 3.55 2.60 2.46 4.34 2.63 2.60 3.77 3.66 4.21 2230 3.97 4.02 3.87 3.65 3.73 2.74 2.59 4.57 2.76 2.73 3.96 3.84 4.43 2240 4.17 4.23 4.06 3.84 3.92 2.88 2.72 4.80 2.91 2.87 4.16 4.04 4.65 2250 4.38 4.44 4.27 4.03 4.12 3.02 2.86 5.04 3.05 3.02 4.38 4.25 4.89 
127

2260 4.61 4.67 4.49 4.24 4.33 3.18 3.01 5.30 3.21 3.17 4.60 4.46 5.14 2270 4.84 4.91 4.72 4.45 4.55 3.34 3.16 5.57 3.37 3.34 4.84 4.69 5.40 2280 5.09 5.16 4.96 4.68 4.79 3.51 3.32 5.86 3.55 3.51 5.08 4.93 5.68 2290 5.35 5.42 5.21 4.92 5.03 3.69 3.49 6.16 3.73 3.69 5.34 5.18 5.97 2300 5.62 5.70 5.48 5.17 5.29 3.88 3.67 6.47 3.92 3.88 5.62 5.45 6.28 
128

Table A4.5e Autonomous carbon efficicency improvement (ACEI) in FUND B2 2000 = 1.00.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.67 0.60 0.60 1.43 1.05 0.80 1.21 2.72 1.13 0.97 1.77 1.66 1.24 1960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.90 1.08 0.98 1.52 1.33 0.46 1970 0.72 0.68 0.59 0.80 1.11 0.65 1.00 1.40 1.15 1.02 1.39 1.03 0.62 1980 0.79 0.73 0.66 0.87 1.11 0.66 0.99 1.33 1.04 1.09 1.15 1.09 0.60 1990 0.96 0.98 0.90 1.04 0.99 0.86 1.20 1.08 1.02 1.05 1.03 1.11 0.81 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.07 1.07 1.07 1.01 1.03 1.09 0.99 1.07 1.02 1.01 1.02 0.99 1.12 2020 1.16 1.17 1.13 1.05 1.07 1.13 1.07 1.20 1.08 1.07 1.08 1.05 1.18 2030 1.23 1.25 1.20 1.12 1.14 1.15 1.09 1.29 1.15 1.14 1.15 1.11 1.25 2040 1.33 1.35 1.30 1.20 1.23 1.16 1.10 1.41 1.27 1.25 1.25 1.21 1.37 2050 1.42 1.44 1.39 1.29 1.32 1.21 1.15 1.56 1.36 1.34 1.38 1.34 1.51 2060 1.51 1.53 1.48 1.37 1.40 1.31 1.24 1.70 1.45 1.44 1.51 1.47 1.65 2070 1.60 1.63 1.56 1.45 1.48 1.38 1.30 1.82 1.57 1.55 1.59 1.54 1.74 2080 1.65 1.68 1.61 1.49 1.53 1.43 1.35 1.86 1.67 1.65 1.59 1.54 1.74 2090 1.68 1.70 1.64 1.52 1.55 1.51 1.43 1.81 1.76 1.74 1.55 1.51 1.70 2100 1.73 1.76 1.69 1.57 1.60 1.60 1.51 1.82 1.85 1.83 1.55 1.51 1.70 2110 1.82 1.85 1.78 1.65 1.68 1.68 1.59 1.91 1.94 1.92 1.63 1.58 1.79 2120 1.91 1.94 1.87 1.73 1.77 1.77 1.67 2.01 2.04 2.02 1.72 1.67 1.88 2130 2.01 2.04 1.96 1.82 1.86 1.86 1.76 2.11 2.15 2.12 1.80 1.75 1.97 2140 2.12 2.15 2.06 1.91 1.96 1.95 1.85 2.22 2.26 2.23 1.90 1.84 2.07 2150 2.22 2.25 2.17 2.01 2.06 2.05 1.94 2.33 2.37 2.35 1.99 1.93 2.18 2160 2.34 2.37 2.28 2.11 2.16 2.16 2.04 2.45 2.49 2.47 2.10 2.03 2.29 2170 2.46 2.49 2.40 2.22 2.27 2.27 2.14 2.58 2.62 2.59 2.20 2.14 2.41 2180 2.58 2.62 2.52 2.34 2.39 2.38 2.25 2.71 2.76 2.72 2.32 2.25 2.53 2190 2.71 2.75 2.65 2.45 2.51 2.50 2.37 2.85 2.90 2.86 2.43 2.36 2.66 2200 2.85 2.89 2.78 2.58 2.64 2.63 2.49 2.99 3.04 3.01 2.56 2.48 2.80 2210 3.00 3.04 2.92 2.71 2.77 2.77 2.62 3.14 3.20 3.16 2.69 2.61 2.94 2220 3.15 3.20 3.07 2.85 2.91 2.91 2.75 3.31 3.36 3.33 2.83 2.74 3.09 2230 3.31 3.36 3.23 3.00 3.06 3.06 2.89 3.47 3.54 3.50 2.97 2.88 3.25 2240 3.48 3.53 3.40 3.15 3.22 3.21 3.04 3.65 3.72 3.68 3.12 3.03 3.42 2250 3.66 3.71 3.57 3.31 3.38 3.38 3.20 3.84 3.91 3.86 3.28 3.19 3.59 
129

2260 3.85 3.90 3.75 3.48 3.56 3.55 3.36 4.04 4.11 4.06 3.45 3.35 3.77 2270 4.05 4.10 3.94 3.66 3.74 3.73 3.53 4.24 4.32 4.27 3.63 3.52 3.97 2280 4.25 4.31 4.15 3.85 3.93 3.92 3.71 4.46 4.54 4.49 3.81 3.70 4.17 2290 4.47 4.53 4.36 4.04 4.13 4.12 3.90 4.69 4.77 4.72 4.01 3.89 4.38 2300 4.70 4.76 4.58 4.25 4.34 4.33 4.10 4.93 5.01 4.96 4.21 4.09 4.61 
130

Table A4.6a.FUND standard scenario: Carbon dioxide emissions from land use; million metric tones carbon.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 3 3 0 0 0 0 0 0 47 426 60 180 19 1960 3 3 0 0 0 0 0 0 47 426 60 180 19 1970 3 3 0 0 0 0 0 0 47 426 60 180 19 1980 3 3 0 0 0 0 0 0 47 426 60 180 19 1990 3 3 0 0 0 0 0 0 47 426 60 180 19 2000 3 3 0 0 0 0 0 0 47 426 60 180 19 2010 3 3 0 0 0 0 0 0 44 400 56 169 18 2020 3 3 0 0 0 0 0 0 41 373 53 158 17 2030 2 2 0 0 0 0 0 0 38 341 48 144 15 2040 2 2 0 0 0 0 0 0 33 301 42 127 13 2050 2 2 0 0 0 0 0 0 29 262 37 111 12 2060 1 1 0 0 0 0 0 0 23 203 29 86 9 2070 1 1 0 0 0 0 0 0 16 144 20 61 6 2080 1 1 0 0 0 0 0 0 9 85 12 36 4 2090 0 0 0 0 0 0 0 0 3 26 4 11 1 2100 0 0 0 0 0 0 0 0 -4 -33 -5 -14 -1 2110 0 0 0 0 0 0 0 0 0 0 0 0 0 2120 0 0 0 0 0 0 0 0 0 0 0 0 0 2130 0 0 0 0 0 0 0 0 0 0 0 0 0 2140 0 0 0 0 0 0 0 0 0 0 0 0 0 2150 0 0 0 0 0 0 0 0 0 0 0 0 0 2160 0 0 0 0 0 0 0 0 0 0 0 0 0 2170 0 0 0 0 0 0 0 0 0 0 0 0 0 2180 0 0 0 0 0 0 0 0 0 0 0 0 0 2190 0 0 0 0 0 0 0 0 0 0 0 0 0 2200 0 0 0 0 0 0 0 0 0 0 0 0 0 2210 0 0 0 0 0 0 0 0 0 0 0 0 0 2220 0 0 0 0 0 0 0 0 0 0 0 0 0 2230 0 0 0 0 0 0 0 0 0 0 0 0 0 2240 0 0 0 0 0 0 0 0 0 0 0 0 0 2250 0 0 0 0 0 0 0 0 0 0 0 0 0 2260 0 0 0 0 0 0 0 0 0 0 0 0 0 
131

2270 0 0 0 0 0 0 0 0 0 0 0 0 0 2280 0 0 0 0 0 0 0 0 0 0 0 0 0 2290 0 0 0 0 0 0 0 0 0 0 0 0 0 2300 0 0 0 0 0 0 0 0 0 0 0 0 0 
132

Table A4.6b FUND A1B scenario Carbon dioxide emissions from land use; million metric tonnes of carbon.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 3 3 0 0 0 0 0 0 47 426 60 180 19 1960 3 3 0 0 0 0 0 0 47 426 60 180 19 1970 3 3 0 0 0 0 0 0 47 426 60 180 19 1980 3 3 0 0 0 0 0 0 47 426 60 180 19 1990 3 3 0 0 0 0 0 0 47 426 60 180 19 2000 8 8 6 0 6 0 100 0 39 355 51 152 92 2010 13 13 13 0 13 0 200 0 32 284 41 124 165 2020 8 8 8 0 8 0 30 0 20 180 6 19 25 2030 5 5 5 0 5 0 10 0 16 144 15 45 60 2040 0 0 0 0 0 0 -60 0 15 131 21 64 85 2050 -3 -3 -3 0 -3 0 -130 0 13 117 31 94 125 2060 3 3 3 0 3 0 -110 0 13 113 20 60 80 2070 15 15 15 0 15 0 -90 0 12 104 14 41 55 2080 28 28 28 0 28 0 -70 0 11 95 13 38 50 2090 23 23 23 0 23 0 -50 0 10 86 18 53 70 2100 18 18 18 0 18 0 -30 0 8 72 24 71 95 2110 16 16 16 0 16 0 -27 0 7 65 21 64 86 2120 14 14 14 0 14 0 -24 0 6 58 19 57 76 2130 12 12 12 0 12 0 -21 0 6 50 17 50 67 2140 11 11 11 0 11 0 -18 0 5 43 14 43 57 2150 9 9 9 0 9 0 -15 0 4 36 12 36 48 2160 7 7 7 0 7 0 -12 0 3 29 10 29 38 2170 5 5 5 0 5 0 -9 0 2 22 7 21 29 2180 4 4 4 0 4 0 -6 0 2 14 5 14 19 2190 2 2 2 0 2 0 -3 0 1 7 2 7 10 2200 0 0 0 0 0 0 0 0 0 0 0 0 0 2210 0 0 0 0 0 0 0 0 0 0 0 0 0 2220 0 0 0 0 0 0 0 0 0 0 0 0 0 2230 0 0 0 0 0 0 0 0 0 0 0 0 0 2240 0 0 0 0 0 0 0 0 0 0 0 0 0 2250 0 0 0 0 0 0 0 0 0 0 0 0 0 2260 0 0 0 0 0 0 0 0 0 0 0 0 0 
133

2270 0 0 0 0 0 0 0 0 0 0 0 0 0 2280 0 0 0 0 0 0 0 0 0 0 0 0 0 2290 0 0 0 0 0 0 0 0 0 0 0 0 0 2300 0 0 0 0 0 0 0 0 0 0 0 0 0 
134

Table A4.6c FUND A2 secnario. Carbon dioxide emissions from land use; million metric tonnes of carbon. 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 3 3 0 0 0 0 0 0 47 426 60 180 19 1960 3 3 0 0 0 0 0 0 47 426 60 180 19 1970 3 3 0 0 0 0 0 0 47 426 60 180 19 1980 3 3 0 0 0 0 0 0 47 426 60 180 19 1990 3 3 0 0 0 0 0 0 47 426 60 180 19 2000 2 2 0 0 0 0 0 0 43 386 51 154 94 2010 0 0 0 0 0 0 0 0 39 347 43 128 170 2020 0 0 0 0 0 0 0 0 43 383 49 146 195 2030 0 0 0 0 0 0 0 0 42 378 44 131 175 2040 0 0 0 0 0 0 0 0 39 351 35 105 140 2050 0 0 0 0 0 0 0 0 36 320 28 83 110 2060 0 0 0 0 0 0 0 0 26 234 19 56 75 2070 0 0 0 0 0 0 0 0 16 144 10 30 40 2080 0 0 0 0 0 0 0 0 11 95 5 15 20 2090 0 0 0 0 0 0 0 0 9 81 4 11 15 2100 0 0 0 0 0 0 0 0 8 72 3 8 10 2110 0 0 0 0 0 0 0 0 7 65 2 7 9 2120 0 0 0 0 0 0 0 0 6 58 2 6 8 2130 0 0 0 0 0 0 0 0 6 50 2 5 7 2140 0 0 0 0 0 0 0 0 5 43 2 5 6 2150 0 0 0 0 0 0 0 0 4 36 1 4 5 2160 0 0 0 0 0 0 0 0 3 29 1 3 4 2170 0 0 0 0 0 0 0 0 2 22 1 2 3 2180 0 0 0 0 0 0 0 0 2 14 1 2 2 2190 0 0 0 0 0 0 0 0 1 7 0 1 1 2200 0 0 0 0 0 0 0 0 0 0 0 0 0 2210 0 0 0 0 0 0 0 0 0 0 0 0 0 2220 0 0 0 0 0 0 0 0 0 0 0 0 0 2230 0 0 0 0 0 0 0 0 0 0 0 0 0 2240 0 0 0 0 0 0 0 0 0 0 0 0 0 2250 0 0 0 0 0 0 0 0 0 0 0 0 0 2260 0 0 0 0 0 0 0 0 0 0 0 0 0 
135

2270 0 0 0 0 0 0 0 0 0 0 0 0 0 2280 0 0 0 0 0 0 0 0 0 0 0 0 0 2290 0 0 0 0 0 0 0 0 0 0 0 0 0 2300 0 0 0 0 0 0 0 0 0 0 0 0 0 
136

Table A4.6d FUND B1 secnario. Carbon dioxide emissions from land use; million metric tonnes of carbon.  USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 3 3 0 0 0 0 0 0 47 426 60 180 19 1960 3 3 0 0 0 0 0 0 47 426 60 180 19 1970 3 3 0 0 0 0 0 0 47 426 60 180 19 1980 3 3 0 0 0 0 0 0 47 426 60 180 19 1990 3 3 0 0 0 0 0 0 47 426 60 180 19 2000 14 14 13 0 13 0 -5 0 35 314 45 135 69 2010 25 25 25 0 25 0 -10 0 23 203 30 90 120 2020 15 15 15 0 15 0 -100 0 23 203 28 83 110 2030 -5 -5 -5 0 -5 0 -310 0 2 18 26 79 105 2040 -18 -18 -18 0 -18 0 -350 0 -13 -113 24 71 95 2050 -23 -23 -23 0 -23 0 -360 0 -7 -59 23 68 90 2060 -20 -20 -20 0 -20 0 -380 0 -8 -68 20 60 80 2070 -15 -15 -15 0 -15 0 -410 0 -5 -45 19 56 75 2080 -35 -35 -35 0 -35 0 -360 0 -11 -95 14 41 55 2090 -30 -30 -30 0 -30 0 -340 0 -11 -99 -11 -34 -45 2100 -28 -28 -28 0 -28 0 -290 0 -11 -99 -44 -131 -175 2110 -25 -25 -25 0 -25 0 -261 0 -10 -89 -39 -118 -158 2120 -22 -22 -22 0 -22 0 -232 0 -9 -79 -35 -105 -140 2130 -19 -19 -19 0 -19 0 -203 0 -8 -69 -31 -92 -123 2140 -17 -17 -17 0 -17 0 -174 0 -7 -59 -26 -79 -105 2150 -14 -14 -14 0 -14 0 -145 0 -6 -50 -22 -66 -88 2160 -11 -11 -11 0 -11 0 -116 0 -4 -40 -18 -53 -70 2170 -8 -8 -8 0 -8 0 -87 0 -3 -30 -13 -39 -53 2180 -6 -6 -6 0 -6 0 -58 0 -2 -20 -9 -26 -35 2190 -3 -3 -3 0 -3 0 -29 0 -1 -10 -4 -13 -18 2200 0 0 0 0 0 0 0 0 0 0 0 0 0 2210 0 0 0 0 0 0 0 0 0 0 0 0 0 2220 0 0 0 0 0 0 0 0 0 0 0 0 0 2230 0 0 0 0 0 0 0 0 0 0 0 0 0 2240 0 0 0 0 0 0 0 0 0 0 0 0 0 2250 0 0 0 0 0 0 0 0 0 0 0 0 0 2260 0 0 0 0 0 0 0 0 0 0 0 0 0 
137

2270 0 0 0 0 0 0 0 0 0 0 0 0 0 2280 0 0 0 0 0 0 0 0 0 0 0 0 0 2290 0 0 0 0 0 0 0 0 0 0 0 0 0 2300 0 0 0 0 0 0 0 0 0 0 0 0 0 
138

Table A4.6e FUND B2 scenario. Carbon dioxide emissions from land use; million metric tonnes of carbon. 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 3 3 0 0 0 0 0 0 47 426 60 180 19 1960 3 3 0 0 0 0 0 0 47 426 60 180 19 1970 3 3 0 0 0 0 0 0 47 426 60 180 19 1980 3 3 0 0 0 0 0 0 47 426 60 180 19 1990 3 3 0 0 0 0 0 0 47 426 60 180 19 2000 -1 -1 -3 0 -3 0 -5 0 39 355 42 127 59 2010 -5 -5 -5 0 -5 0 -10 0 32 284 25 75 100 2020 -15 -15 -15 0 -15 0 -180 0 21 189 -19 -56 -75 2030 -20 -20 -20 0 -20 0 -140 0 6 54 -20 -60 -80 2040 -15 -15 -15 0 -15 0 -90 0 1 5 -11 -34 -45 2050 -13 -13 -13 0 -13 0 -40 0 -6 -50 -4 -11 -15 2060 -15 -15 -15 0 -15 0 -40 0 -6 -50 -4 -11 -15 2070 -20 -20 -20 0 -20 0 -30 0 -5 -45 -5 -15 -20 2080 -28 -28 -28 0 -28 0 -30 0 -6 -54 -6 -19 -25 2090 -38 -38 -38 0 -38 0 -40 0 -8 -72 -6 -19 -25 2100 -48 -48 -48 0 -48 0 -40 0 -10 -90 -8 -23 -30 2110 -43 -43 -43 0 -43 0 -36 0 -9 -81 -7 -20 -27 2120 -38 -38 -38 0 -38 0 -32 0 -8 -72 -6 -18 -24 2130 -33 -33 -33 0 -33 0 -28 0 -7 -63 -5 -16 -21 2140 -29 -29 -29 0 -29 0 -24 0 -6 -54 -5 -14 -18 2150 -24 -24 -24 0 -24 0 -20 0 -5 -45 -4 -11 -15 2160 -19 -19 -19 0 -19 0 -16 0 -4 -36 -3 -9 -12 2170 -14 -14 -14 0 -14 0 -12 0 -3 -27 -2 -7 -9 2180 -10 -10 -10 0 -10 0 -8 0 -2 -18 -2 -5 -6 2190 -5 -5 -5 0 -5 0 -4 0 -1 -9 -1 -2 -3 2200 0 0 0 0 0 0 0 0 0 0 0 0 0 2210 0 0 0 0 0 0 0 0 0 0 0 0 0 2220 0 0 0 0 0 0 0 0 0 0 0 0 0 2230 0 0 0 0 0 0 0 0 0 0 0 0 0 2240 0 0 0 0 0 0 0 0 0 0 0 0 0 2250 0 0 0 0 0 0 0 0 0 0 0 0 0 2260 0 0 0 0 0 0 0 0 0 0 0 0 0 
139

2270 0 0 0 0 0 0 0 0 0 0 0 0 0 2280 0 0 0 0 0 0 0 0 0 0 0 0 0 2290 0 0 0 0 0 0 0 0 0 0 0 0 0 2300 0 0 0 0 0 0 0 0 0 0 0 0 0 
140

Table A4.7a. Methane emissions for all FUND scenarios; 2000 = 100. 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.59 0.59 0.59 0.61 0.59 0.60 0.59 0.59 0.60 0.59 0.60 0.60 0.60 1960 0.76 0.77 0.76 0.79 0.77 0.75 0.76 0.75 0.75 0.76 0.76 0.76 0.76 1970 0.91 0.92 0.91 0.93 0.91 0.91 0.91 0.91 0.90 0.91 0.91 0.91 0.91 1980 1.06 1.08 1.07 1.07 1.07 1.06 1.07 1.07 1.06 1.06 1.06 1.06 1.07 1990 1.22 1.23 1.21 1.21 1.22 1.21 1.21 1.21 1.21 1.21 1.21 1.21 1.21 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.08 1.28 1.08 1.11 0.74 1.00 1.17 1.41 1.37 1.18 1.19 1.33 1.14 2020 1.14 1.64 1.09 1.11 0.74 1.39 1.47 1.92 1.77 1.46 1.36 1.64 1.27 2030 1.27 1.54 1.09 1.14 0.77 1.31 1.65 2.58 2.13 1.79 1.57 1.85 1.49 2040 1.34 1.38 1.08 1.18 0.78 1.22 1.69 3.51 2.40 1.96 1.77 2.01 1.77 2050 1.34 1.64 1.06 1.21 0.77 1.17 1.67 4.54 2.58 2.16 2.00 2.14 2.08 2060 1.39 1.79 1.04 1.21 0.90 1.16 1.92 5.63 2.77 2.37 2.14 2.32 2.42 2070 1.42 1.87 1.05 1.21 1.07 1.23 2.25 6.30 3.00 2.59 2.02 2.68 2.76 2080 1.52 2.03 1.10 1.25 1.29 1.36 2.52 5.88 3.29 2.77 1.91 2.77 3.12 2090 1.70 2.28 1.19 1.32 1.51 1.47 2.66 5.11 3.56 3.27 1.88 2.74 3.41 2100 1.90 2.56 1.30 1.32 1.96 1.44 2.79 4.67 3.73 3.59 1.89 2.70 3.61 2110 1.99 2.69 1.35 1.39 2.03 1.51 2.91 4.87 3.88 3.74 1.97 2.81 3.76 2120 2.06 2.79 1.41 1.43 2.12 1.56 3.03 5.07 4.04 3.89 2.05 2.93 3.91 2130 2.15 2.90 1.46 1.50 2.20 1.62 3.14 5.26 4.21 4.05 2.13 3.04 4.07 2140 2.23 3.00 1.52 1.54 2.29 1.69 3.26 5.46 4.37 4.20 2.21 3.15 4.22 2150 2.31 3.13 1.57 1.61 2.36 1.75 3.38 5.66 4.52 4.35 2.29 3.27 4.37 2160 2.39 3.23 1.62 1.64 2.45 1.81 3.50 5.86 4.67 4.50 2.37 3.38 4.52 2170 2.47 3.33 1.68 1.71 2.54 1.87 3.62 6.05 4.83 4.66 2.45 3.50 4.68 2180 2.55 3.44 1.73 1.75 2.61 1.94 3.73 6.25 5.00 4.81 2.53 3.61 4.83 2190 2.63 3.56 1.79 1.82 2.70 1.99 3.85 6.45 5.15 4.96 2.61 3.73 4.98 2200 2.71 3.67 1.84 1.89 2.78 2.05 3.97 6.64 5.31 5.11 2.69 3.84 5.13 2210 2.83 3.82 1.92 1.96 2.90 2.14 4.14 6.92 5.54 5.33 2.80 4.00 5.35 2220 2.94 3.97 1.99 2.07 3.01 2.22 4.31 7.21 5.75 5.55 2.92 4.17 5.57 2230 3.05 4.13 2.07 2.14 3.13 2.31 4.47 7.49 5.98 5.76 3.04 4.33 5.79 2240 3.17 4.31 2.15 2.21 3.25 2.40 4.64 7.76 6.21 5.98 3.15 4.49 6.01 2250 3.28 4.46 2.23 2.29 3.38 2.48 4.81 8.05 6.42 6.20 3.26 4.65 6.22 2260 3.40 4.62 2.30 2.39 3.49 2.57 4.98 8.33 6.65 6.41 3.38 4.81 6.44 
141

2270 3.51 4.77 2.38 2.46 3.61 2.66 5.14 8.61 6.88 6.63 3.49 4.98 6.66 2280 3.63 4.92 2.46 2.54 3.72 2.75 5.31 8.89 7.10 6.84 3.60 5.14 6.87 2290 3.74 5.08 2.53 2.64 3.84 2.83 5.48 9.17 7.33 7.06 3.72 5.30 7.09 2300 3.86 5.23 2.62 2.71 3.96 2.92 5.65 9.45 7.56 7.27 3.83 5.47 7.31 
142

Table A4.7b Parameters of the methane emission reduction cost curve; the 67% confidence interval is given in bracket Quadratic Exponential - constant Exponential - exponent USA 5.74E04 (4.15E- 04 7.90E- 04) 5.43E- 06 (4.44E- 06 6.64E- 06) 10.28 (9.66 10.90) CAN 1.20E03 (8.70E- 04 1.64E- 03) 7.69E- 06 (6.30E- 06 9.37E- 06) 12.49 (11.75 13.23) WEU 3.71E04 (2.34E- 04 5.80E- 04) 1.82E- 06 (1.37E- 06 2.43E- 06) 14.27 (13.10 15.45) JPK 1.27E04 (8.75E- 05 1.84E- 04) 4.19E- 07 (3.32E- 07 5.29E- 07) 17.43 (16.23 18.63) ANZ 4.12E03 (3.03E- 03 5.57E- 03) 1.25E- 05 (1.03E- 05 1.51E- 05) 18.18 (17.14 19.21) EEU 3.90E03 (2.81E- 03 5.38E- 03) 3.13E- 05 (2.56E- 05 3.83E- 05) 11.17 (10.49 11.85) FSU 8.87E03 (7.49E- 03 1.05E- 02) 8.51E- 05 (7.65E- 05 9.46E- 05) 10.21 (9.89 10.52) MDE 6.32E03 (4.86E- 03 8.19E- 03) 1.26E- 05 (1.07E- 05 1.49E- 05) 22.38 (21.29 23.47) CAM 3.65E03 (2.87E- 03 4.62E- 03) 1.30E- 05 (1.12E- 05 1.51E- 05) 16.77 (16.03 17.52) SAM 2.75E02 (1.81E- 02 4.14E- 02) 4.07E- 06 (3.14E- 06 5.27E- 06) 82.24 (75.89 88.58) SAS 3.16E02 (2.43E- 02 4.08E- 02) 2.51E- 05 (2.13E- 05 2.95E- 05) 35.45 (33.74 37.16) SEA 1.43E02 (1.06E- 02 1.91E- 02) 1.94E- 05 (1.62E- 05 2.33E- 05) 27.15 (25.66 28.65) CHI 1.26E02 (9.50E- 03 1.67E- 02) 3.18E- 05 (2.67E- 05 3.80E- 05) 19.93 (18.88 20.97) MAF 1.43E02 (1.06E- 02 1.91E- 02) 1.94E- 05 (1.62E- 05 2.33E- 05) 27.15 (25.66 28.65) SSA 1.43E02 (1.06E- 02 1.91E- 02) 1.94E- 05 (1.62E- 05 2.33E- 05) 27.15 (25.66 28.65) SIS 1.43E02 (1.06E- 02 1.91E- 02) 1.94E- 05 (1.62E- 05 2.33E- 05) 27.15 (25.66 28.65) 
143
144

Table A4.8a Nitrous oxide emissions in all FUND scenarios; 2000 = 100. 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI N

1950 0.16 0.17 0.17 0.11 0.13 0.16 0.18 0.15 0.14 0.17 0.16 0.16 0.17 1960 0.29 0.25 0.29 0.33 0.25 0.26 0.27 0.31 0.29 0.29 0.29 0.28 0.29 1970 0.44 0.42 0.43 0.44 0.38 0.42 0.45 0.46 0.43 0.44 0.44 0.44 0.43 1980 0.74 0.75 0.74 0.78 0.75 0.74 0.73 0.77 0.71 0.74 0.73 0.75 0.74 1990 0.78 0.75 0.78 0.78 0.75 0.79 0.82 0.85 0.79 0.78 0.79 0.78 0.80 2000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 2010 1.06 1.08 1.00 1.11 1.00 1.11 1.18 1.38 1.14 1.06 1.11 1.16 1.03 2020 1.09 1.08 0.97 1.11 1.13 1.16 1.36 1.62 1.36 1.16 1.21 1.25 1.03 2030 1.09 1.08 0.93 1.22 1.25 1.16 1.55 2.00 1.50 1.26 1.30 1.34 1.03 2040 1.08 1.17 0.90 1.22 1.38 1.11 1.73 2.15 1.57 1.32 1.38 1.44 1.03 2050 1.15 1.25 0.92 1.33 1.38 1.05 1.91 2.31 1.57 1.40 1.46 1.50 1.06 2060 1.21 1.33 0.94 1.33 1.50 1.05 2.09 2.38 1.64 1.52 1.53 1.56 1.11 2070 1.30 1.42 0.99 1.33 1.50 1.05 2.27 2.46 1.71 1.61 1.55 1.72 1.09 2080 1.40 1.58 1.03 1.33 1.63 1.05 2.45 2.54 1.71 1.69 1.58 1.75 1.14 2090 1.53 1.67 1.10 1.44 1.63 1.05 2.73 2.62 1.86 1.87 1.62 1.81 1.17 2100 1.67 1.83 1.17 1.44 1.63 1.05 2.91 2.62 1.86 1.99 1.63 1.91 1.20 2110 1.68 1.83 1.17 1.44 1.63 1.05 2.91 2.62 1.86 2.00 1.64 1.91 1.20 2120 1.69 1.83 1.17 1.44 1.63 1.05 2.91 2.69 1.86 2.01 1.65 1.91 1.20 2130 1.70 1.83 1.18 1.44 1.63 1.05 2.91 2.69 1.93 2.03 1.66 1.94 1.20 2140 1.70 1.83 1.18 1.44 1.63 1.11 2.91 2.69 1.93 2.04 1.67 1.94 1.23 2150 1.71 1.92 1.19 1.56 1.63 1.11 3.00 2.69 1.93 2.05 1.68 1.94 1.23 2160 1.72 1.92 1.19 1.56 1.75 1.11 3.00 2.69 1.93 2.06 1.69 1.97 1.23 2170 1.74 1.92 1.21 1.56 1.75 1.11 3.00 2.77 1.93 2.06 1.70 1.97 1.23 2180 1.75 1.92 1.21 1.56 1.75 1.11 3.00 2.77 1.93 2.08 1.71 1.97 1.26 2190 1.76 1.92 1.22 1.56 1.75 1.11 3.00 2.77 2.00 2.09 1.71 2.00 1.26 2200 1.76 1.92 1.22 1.56 1.75 1.11 3.00 2.77 2.00 2.10 1.72 2.00 1.26 2210 1.77 1.92 1.22 1.56 1.75 1.11 3.00 2.77 2.00 2.12 1.73 2.00 1.26 2220 1.78 1.92 1.24 1.56 1.75 1.11 3.00 2.77 2.00 2.13 1.74 2.03 1.26 2230 1.79 1.92 1.24 1.56 1.75 1.11 3.00 2.85 2.07 2.14 1.75 2.03 1.29 2240 1.79 1.92 1.25 1.56 1.75 1.11 3.00 2.85 2.07 2.16 1.76 2.03 1.29 2250 1.80 2.00 1.25 1.67 1.88 1.16 3.09 2.85 2.07 2.17 1.77 2.06 1.29 2260 1.82 2.00 1.26 1.67 1.88 1.16 3.09 2.85 2.07 2.18 1.78 2.06 1.29 
145

2270 1.83 2.00 1.26 1.67 1.88 1.16 3.09 2.85 2.14 2.19 1.79 2.06 1.29 2280 1.84 2.00 1.26 1.67 1.88 1.16 3.09 2.92 2.14 2.21 1.79 2.09 1.31 2290 1.85 2.00 1.28 1.67 1.88 1.16 3.09 2.92 2.14 2.22 1.80 2.09 1.31 2300 1.85 2.00 1.28 1.67 1.88 1.16 3.09 2.92 2.14 2.23 1.82 2.09 1.31 
146

Table A4.8b Parameters of the nitrous oxide emission reduction cost curve; the 67% confidence interval is given in bracke Quadratic Exponential - constant Exponential - exponent USA 2.14E05 (1.91E- 05 2.39E- 05) 1.36E- 08 (1.29E- 08 1.45E- 08) 39.61 (38.56 40.65) CAN 6.92E05 (6.29E- 05 7.60E- 05) 1.62E- 08 (1.54E- 08 1.70E- 08) 65.33 (63.88 66.78) WEU 7.26E06 (6.60E- 06 7.98E- 06) 1.97E- 08 (1.88E- 08 2.08E- 08) 19.18 (18.75 19.60) JPK 5.32E07 (3.21E- 07 8.57E- 07) 9.54E- 09 (7.38E- 09 1.23E- 08) 7.46 (6.60 8.33) ANZ 2.08E04 (1.89E- 04 2.29E- 04) 4.62E- 09 (4.39E- 09 4.86E- 09) 212.40 (207.68 217.11) EEU 9.39E05 (8.89E- 05 9.93E- 05) 8.35E- 08 (7.91E- 08 8.83E- 08) 33.53 (33.53 33.53) FSU 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) MDE 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) CAM 2.35E04 (2.19E- 04 2.53E- 04) 2.00E- 08 (1.89E- 08 2.13E- 08) 108.39 (107.83 108.95) SAM 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) SAS 5.64E04 (5.29E- 04 6.01E- 04) 1.71E- 07 (1.62E- 07 1.80E- 07) 57.44 (57.14 57.74) SEA 2.55E15 (2.16E- 15 3.01E- 15) 4.72E- 18 (4.12E- 18 5.40E- 18) 23.25 (22.91 23.60) CHI 2.16E05 (2.02E- 05 2.30E- 05) 1.42E- 07 (1.35E- 07 1.50E- 07) 12.32 (12.26 12.39) MAF 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) SSA 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) SIS 1.05E05 (1.00E- 05 1.10E- 05) 1.94E- 08 (1.91E- 08 1.98E- 08) 23.25 (22.91 23.60) 
147

Table A4.9. Determinants of SF6 emissions in FUND. 

 C GDP GDP/cap 

1990 1.6722E-01 5.0931E-06 -5.7537E05  (1.9297E01) (2.3482E- 07) (1.8505E- 05) 1995 1.6255E-01 5.7234E-06 -6.0384E05  (2.1143E01) (2.3082E- 07) (1.8727E- 05) Used 1.6489E-01 5.4083E-06 -5.8961E05  (1.4312E01) (1.6464E- 07) (1.3164E- 05) SF6 emissions are in million metric tonnes of carbon dioxide equivalent. GDP is in million dollar (1995, MEX). GDP/capit

MEX) 
148

Table A4.10 Parameters of equation (C.1). Gas αa βb pre-industrial concentrationMethane (CH4) 0.3597 1/8.6 790 ppb Nitrous oxide (N2O) 0.2079 1/120 285 ppb Sulphur hexafluoride (SF6) 0.0398 1/3200 0.04 ppt a The parameter α translates emissions (in million metric tonnes) into concentrations (in parts per billion or trillion by vob The parameter β determines how fast concentrations return to their pre-industrial (and assumedly equilibrium) concen

atmospheric life-time (in years) of the gases. Source: Schimel, D., Alves, D., Enting, I., et al (1996) Radiative Forcing of Climate Change. In: Houghton, J.T., Meiro FB.A., Harris, N., Kattenberg, A. and Maskell, K., (Eds.) Climate Change 1995: The Science of Climate Change -- ContribGroup I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, 1 edn. pp. 65-131. CambridUniversity Press. 
149

Table A4.11 Impacts of climate change on agriculture  Rate of change Benchmark impact Optimal temperature CO2 fertilisation (% Ag. Prod/0.04C) (% Ag Prod /0.04 oC) (oC) (% Ag. Prod) USA -0.00021 (0.00176) -0.0389 (0.0750) 1.09 (4.14) 0.0890 (0.1484)CAN -0.00029 (0.00073) 0.1336 (0.0319) 2.92 (7.64) 0.0402 (0.0650)WEU -0.00039 (0.00138) -0.0718 (0.0588) 0.79 (3.29) 0.1541 (0.1183)JPK -0.00033 (0.00432) -0.0936 (0.1834) 0.98 (6.61) 0.2319 (0.3660)ANZ -0.00015 (0.00142) 0.0824 (0.0613) 2.00 (8.00) 0.1048 (0.0850)EEU -0.00027 (0.00062) -0.0337 (0.0280) 1.31 (2.73) 0.0952 (0.0514)FSU -0.00018 (0.00066) -0.0184 (0.0294) 1.46 (2.44) 0.0671 (0.0548)MDE -0.00022 (0.00032) -0.0465 (0.0140) 1.32 (2.03) 0.0943 (0.0266)CAM -0.00034 (0.00061) -0.1070 (0.0260) 1.05 (3.60) 0.1641 (0.0538)SAM -0.00009 (0.00060) -0.0348 (0.0258) 0.35 (8.82) 0.0596 (0.0504)SAS -0.00014 (0.00021) -0.0339 (0.0093) 1.13 (2.41) 0.0580 (0.0164)SEA -0.00009 (0.00482) -0.0556 (0.2045) 0.70 (5.12) 0.0845 (0.4181)CHI -0.00013 (0.00075) -0.0794 (0.0319) 1.43 (2.49) 0.1921 (0.0613)NAF -0.00016 (0.00023) -0.0357 (0.0099) 1.20 (2.74) 0.0727 (0.0190)SSA -0.00011 (0.00026) -0.0244 (0.0114) 1.22 (2.76) 0.0505 (0.0220)SIS -0.00050 (0.00103) -0.0689 (0.0442) 1.51 (2.92) 0.2377 (0.0864)Standard deviations are given in brackets. 
150

Table A4.12. Impact of a 1°C warming on forestry, water, heating, and cooling, in fraction of GDP.  Forestry Water Heating  (% GDP) (% GDP) (% GDP) USA 0.000053 (0.000014) -0.00065 (0.00065) 0.00429 (0.00429) CAN 0.000011 (0.000072) -0.00057 (0.00057) 0.00378 (0.00378) WEU 0.000025 (0.000006) -0.00027 (0.00027) 0.00241 (0.00241) JPK 0.000042 (0.000012) 0.00000 (0.00000) 0.00207 (0.00207) ANZ -0.000121 (0.000033) 0.00000 (0.00000) 0.00151 (0.00151) EEU 0.000055 (0.000025) -0.00697 (0.00697) 0.00456 (0.00456) FSU -0.000023 (0.000053) -0.02754 (0.02754) 0.01663 (0.01663) MDE 0.000000 (0.000034) -0.00133 (0.00133) 0.02074 (0.02074) CAM 0.000018 (0.000034) -0.00130 (0.00130) 0.00366 (0.00366) SAM 0.000024 (0.000012) -0.00140 (0.00140) 0.00395 (0.00395) SAS 0.000062 (0.000023) -0.00156 (0.00156) 0.00361 (0.00361) SEA 0.000067 (0.000028) -0.00314 (0.00314) 0.00695 (0.00695) CHI 0.000087 (0.000032) 0.00569 (0.00569) 0.03971 (0.03971) NAF 0.000000 (0.000034) -0.00902 (0.00902) 0.00015 (0.00015) SSA 0.000011 (0.000035) -0.00360 (0.00360) 0.00006 (0.00006) SIS 0.000000 (0.000034) -0.00130 (0.00130) 0.00366 (0.00366) Standard deviations are given in brackets.
151

Table A4.13. Impact of a one metre sea level rise.  level 

prot. 

dryland loss dryland value wetland loss wetland value protection 

costs 

em

 % 103 km2 106$/km2 103 km2 106$/km2 109$ 106OECD-A 0.77 4.8 (2.4) 1.3 (0.6) 12.0 (8.6) 5.4 (2.7) 83 (74) 0.OECD-E 0.86 0.7 (0.4) 13.1 (6.6) 4.0 (2.3) 4.3 (2.2) 136 (45) 0.OECD-P 0.95 0.3 (0.4) 13.7 (6.7) 1.0 (1.1) 5.9 (2.9) 63 (38) 0.CEE&fSU 0.93 1.2 (2.7) 0.9 (0.5) 0.0 (0.0) 2.9 (1.5) 53 (50) 0.ME 0.30 0.6 (1.2) 0.5 (0.3) 0.0 (0.0) 1.3 (0.7) 5 (3) 0.LA 0.86 7.8 (7.1) 0.3 (0.2) 50.2 (36.4) 0.9 (0.5) 147 (74) 0.S&SEA 0.93 9.3 (9.6) 0.5 (0.3) 54.9 (48.0) 0.3 (0.2) 305 (158) 2.CPA 0.93 8.4 (15.1) 0.3 (0.2) 15.6 (17.1) 0.2 (0.1) 171 (126) 2.AFR 0.89 15.4 (18.4) 0.4 (0.2) 30.8 (14.8) 0.4 (0.2) 92 (35) 2.Standard deviations in brackets.
Table A4.14. Migration from row to column. 

 USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS USA 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.040 0.100 0.100 0.040 0.040 0.040 0.010 0.030 0.150  (0.100) (0.050) (0.050) (0.050) (0.050) (0.050) (0.050) (0.040) (0.100) (0.100) (0.040) (0.040) (0.040) (0.010) (0.030) (0.150) CAN 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.010 0.000 0.000 0.010 0.010 0.010 0.000 0.005 0.100  (0.050) (0.100) (0.020) (0.020) (0.020) (0.020) (0.020) (0.010) (0.050) (0.050) (0.010) (0.010) (0.010) (0.005) (0.005) (0.100) WEU 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.040 0.000 0.000 0.040 0.020 0.020 0.090 0.060 0.150  (0.020) (0.020) (0.100) (0.020) (0.020) (0.020) (0.020) (0.040) (0.050) (0.050) (0.040) (0.020) (0.020) (0.090) (0.060) (0.150) JPK 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.010 0.010 0.000 0.000 0.050  (0.010) (0.010) (0.010) (0.100) (0.010) (0.001) (0.001) (0.005) (0.010) (0.010) (0.005) (0.010) (0.010) (0.001) (0.001) (0.050) ANZ 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.010 0.000 0.000 0.005 0.020 0.020 0.000 0.005 0.150  (0.020) (0.020) (0.020) (0.020) (0.100) (0.010) (0.010) (0.010) (0.010) (0.010) (0.005) (0.020) (0.020) (0.005) (0.005) (0.150) EEU 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.100) (0.050) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) FSU 0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.100) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) MDE 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) CAM 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000 0.000 0.000 0.000 0.100  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.005) (0.001) (0.001) (0.001) (0.001) (0.001) (0.100) SAM 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000 0.000 0.000 0.100  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.005) (0.900) (0.001) (0.001) (0.001) (0.001) (0.001) (0.100) SAS 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000 0.000 0.100  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.005) (0.001) (0.001) (0.001) (0.100) SEA 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000 0.100  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.005) (0.900) (0.005) (0.001) (0.001) (0.100) CHI 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.001) (0.001) (0.001) NAF 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.005) (0.001) SIS 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.900 0.000  (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.900) (0.001) SIS 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 
 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) 
Table A4.15 Diarrhoea mortality and morbidity due to a 2.5 C global warming. 

Region Populationa Mortalityb Morbidityc ∆

Td Additional Mortalitye Additional Morbidityf

USA 278357 0.041 1.704 3.0 40 (23 70) 1019 (767 1354) CAN 31147 0.041 1.704 3.7 6 (3 11) 132 (94 185) WEU 388581 0.015 0.632 2.8 18 (11 31) 506 (387 662) JPK 173558 0.009 0.166 2.6 5 (3 8) 57 (44 73) ANZ 22748 0.001 0.083 2.4 0 (0 0) 3 (3 4) EEU 121191 0.018 0.847 2.9 7 (4 13) 217 (164 287) FSU 291538 0.122 6.735 3.2 135 (74 244) 4443 (3279 6020) MDE 237590 0.030 0.166 2.9 24 (14 41) 83 (63 109) CAM 135222 0.162 0.643 2.2 54 (36 81) 151 (123 185) LAM 345779 0.168 0.650 2.1 138 (94 202) 381 (313 463) SAS 1366902 0.229 0.896 2.3 798 (526 1212) 2171 (1755 2687) SEA 522462 0.135 0.631 1.8 136 (102 182) 492 (424 571) CHI 1311659 0.033 0.401 3.0 150 (86 261) 1122 (846 1488) MAF 143482 0.415 0.990 2.9 197 (116 337) 296 (225 389) SSA 637887 3.167 5.707 2.2 4958 (3321 7404) 6306 (5141 7737) SIS 44002 0.252 1.092 1.9 23 (17 31) 75 (63 88) a Thousands of people, 2000. b Deaths per thousand people. c Years of life diseased per thousand people. d Regional temperature change for a 2.5 C global warming. e Additional deaths, thousands of people (67% confidence interval in brackets). f Additional years of life diseased, thousands (67% confidence interval in brackets).
Table A4.16 . Parameters for vector-borne mortality. 

 Malaria Dengue fever Schistosomiasis  Basea Impactb Basea Impactb Basea Impactb USA 0.023 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.007 -0.1149 (0.0614) CAN 0.023 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.007 -0.1149 (0.0614) WEU 0.240 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.020 -0.1149 (0.0614) JPK 2.358 0.0794 (0.0575) 0.125 0.3534 (0.0614) 0.423 -0.1149 (0.0614) ANZ 0.069 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.037 -0.1149 (0.0614) EEU 0.377 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.012 -0.1149 (0.0614) FSU 0.133 0.0794 (0.0575) 0.000 0.3534 (0.0614) 0.003 -0.1149 (0.0614) MDE 24.113 0.0794 (0.0575) 0.286 0.3534 (0.0614) 4.229 -0.1149 (0.0614) CAM 2.913 0.0794 (0.0575) 0.508 0.3534 (0.0614) 1.235 -0.1149 (0.0614) SAM 3.090 0.0794 (0.0575) 0.541 0.3534 (0.0614) 1.217 -0.1149 (0.0614) SAS 48.413 0.0794 (0.0575) 6.896 0.3534 (0.0614) 0.898 -0.1149 (0.0614) SEA 22.129 0.0794 (0.0575) 2.072 0.3534 (0.0614) 0.629 -0.1149 (0.0614) CHI 8.987 0.0794 (0.0575) 0.593 0.3534 (0.0614) 1.430 -0.1149 (0.0614) NAF 458.133 0.0794 (0.0575) 1.089 0.3534 (0.0614) 7.474 -0.1149 (0.0614) SSA 1414.284 0.0794 (0.0575) 0.351 0.3534 (0.0614) 8.275 -0.1149 (0.0614) SIS 116.586 0.0794 (0.0575) 1.010 0.3534 (0.0614) 1.296 -0.1149 (0.0614) a Mortality (deaths per million people) in 1990. b The change in mortality due to a one-degree global warming. 
Table A4.17. Parameters of Equation (HC.1). 

 Constant Temperature Cardiovascular Cold 65- -2.9787 (0.5914) 0.0946 (0.0464) 65+ -162.6459 (18.3041) 5.6628 (1.4367) Heat 65- -1.4610 (0.9599) 0.0941 (0.0406) 65+ -40.9953 (3.4570) 3.4570 (1.6218)Respiratory -17.9222 (6.0196) 0.8683 (0.2545)
Table A4.18. Parameters of Equation (HC.2) for cold-related cardiovascular mortality. 

 65- 65+  linear Quadratic linear Quadratic USA 151.6768 (3.4583) -155.1251 (2.8292) -161.4521 (62.3397) 2.8314 (62.3080)CAN 195.6424 (3.4583) -199.0906 (2.8292) -205.4176 (62.3397) 2.8314 (62.3080)WEU 19.2327 (1.2716) -21.7191 (1.0403) -145.9539 (23.8362) 2.8279 (23.8241)JPK 65.5934 (3.5211) -67.1850 (2.8805) -33.6830 (24.9641) 1.2018 (24.9514)ANZ 67.1775 (2.9403) -68.9576 (2.4054) -91.0606 (53.2451) 2.8314 (53.2180)EEU 61.4840 (1.5395) -65.2217 (1.2594) -201.8789 (27.0842) 2.8314 (27.0704)FSU -3.4422 (3.4583) 0.0473 (2.8292) -190.3936 (62.3397) 2.8314 (62.3080)MDE -2.4508 (1.5732) 0.0457 (1.2870) -136.8033 (30.2768) 2.7443 (30.2614)CAM -0.6855 (2.6117) -0.4840 (2.1366) -54.1635 (45.5739) 2.7085 (45.5507)SAM 16.6942 (1.8829) -18.2021 (1.5404) -78.4126 (32.7397) 2.8094 (32.7230)SAS -1.6072 (2.6242) 0.0473 (2.1468) -80.2320 (51.2055) 2.8314 (51.1794)SEA -0.6838 (1.4722) 0.0413 (1.2044) 12.0899 (12.0535) -1.1081 (12.0474)CHI 81.1077 (3.4522) -84.8815 (2.8242) -66.6796 (43.8249) 2.0193 (43.8025)NAF -1.9826 (1.9196) 0.0473 (1.5704) -102.4339 (35.4522) 2.8314 (35.4341)SSA -1.0407 (0.9609) 0.0448 (0.7861) -49.9700 (16.5999) 2.6771 (16.5915)SIS 1.6035 (1.1897) -2.3428 (0.9733) -10.4503 (7.4943) 0.5138 (7.4905)
Table A4.19. Parameters of Equation (HC.2) for heat-related cardiovascular mortality. 

 65- 65+  linear quadratic linear Quadratic USA 1.0988 (1.0738) 0.0471 (0.8815) 34.9374 (42.9155) 1.7285 (35.2319)CAN 1.0705 (1.0738) 0.0471 (0.8815) 27.3280 (42.9155) 1.7285 (35.2319)WEU 0.4022 (0.4226) 0.0467 (0.3469) 25.7570 (17.8447) 1.7966 (14.6498)JPK 1.0356 (1.1234) 0.0559 (0.9223) 8.2986 (17.7713) 0.7493 (14.5895)ANZ 0.4493 (0.9147) 0.0470 (0.7509) 18.8372 (36.7267) 1.7286 (30.1512)EEU 0.6119 (0.4767) 0.0470 (0.3914) 29.6249 (18.8672) 1.7531 (15.4893)FSU 0.6468 (1.0738) 0.0471 (0.8815) 36.4415 (42.9155) 1.7285 (35.2319)MDE 1.0931 (0.4791) 0.0452 (0.3933) 50.5493 (20.6547) 1.7011 (16.9568)CAM 0.9144 (0.8887) 0.0471 (0.7296) 44.7697 (34.4286) 1.6620 (28.2646)SAM 0.5893 (0.5874) 0.0470 (0.4823) 33.7621 (23.0347) 1.7535 (18.9106)SAS 1.6317 (0.8373) 0.0470 (0.6874) 74.5092 (36.2131) 1.7378 (29.7296)SEA 0.8545 (0.4641) 0.0411 (0.3810) -18.7223 (8.1867) -0.6683 (6.7210)CHI 0.7565 (1.0335) 0.0474 (0.8485) 82.0355 (29.0776) 1.2095 (23.8716)NAF 1.0409 (0.5662) 0.0471 (0.4648) 50.4842 (23.0206) 1.7096 (18.8991)SSA 0.8682 (0.3408) 0.0440 (0.2798) 43.4397 (13.5145) 1.6578 (11.0949)SIS 1.0227 (0.4957) 0.0324 (0.4070) 16.9938 (8.0489) 0.4223 (6.6079)
Table A4.20 Parameters of Equation (HC.2) for (heat-related) respiratory mortality.

 linear Quadratic 

USA 0.9452 (6.7337) 0.4342 (5.5281) CAN -1.9284 (6.7337) 0.4342 (5.5281) WEU -0.7650 (2.4863) 0.4341 (2.0412) JPK 0.4185 (5.8130) 0.4342 (4.7723) ANZ 0.2579 (5.7279) 0.4342 (4.7024) EEU -1.2946 (2.9883) 0.4342 (2.4533) FSU 1.5277 (6.7337) 0.4342 (5.5281) MDE 5.6711 (3.0690) 0.4194 (2.5196) CAM 3.8894 (5.0789) 0.4342 (4.1696) SAM 1.0893 (3.6563) 0.4335 (3.0017) SAS 10.2485 (5.1264) 0.4342 (4.2086) SEA 4.8562 (3.2809) 0.4339 (2.6935) CHI 4.4083 (6.5634) 0.4319 (5.3883) NAF 5.1980 (3.7408) 0.4341 (3.0711) SSA 3.6196 (1.8681) 0.411 (1.5337) SIS 4.1354 (2.0330) 0.2522 (1.6690) 
Table A4.21 Ratio of morbidity impacts (measured in years of life disabled) to mortality impacts (measured in number of c

 Malaria Schistosomiasis Dengue fever Cardiovascular RespiratoryUSA 0.0000 0.0000 0.0000 0.9609 8.7638CAN 0.0000 0.0000 0.0000 0.9609 8.7638WEU 0.0000 0.0000 0.0000 0.9609 8.7638JPK 0.0000 0.0000 0.0000 0.9609 8.7638ANZ 0.0000 0.0000 0.0000 0.9609 8.7638EEU 0.0000 0.0000 0.0000 0.8986 11.8101FSU 0.0000 0.0000 0.0000 0.8986 11.8101MDE 24.8571 51.5000 0.0000 1.3459 21.8098CAM 4.5714 69.0000 0.0000 1.2548 22.1552SAM 4.5714 69.0000 0.0000 1.2548 22.1552SAS 16.3462 0.0000 0.2500 1.3879 16.5094SEA 3.2727 6.0000 0.4286 1.3729 20.0541CHI 0.0000 11.0000 0.0000 1.2399 8.3072NAF 24.8571 51.5000 0.0000 1.3459 21.8098SSA 3.6940 293.7500 0.0000 1.3301 21.5857SIS 4.5714 69.0000 0.0000 1.2548 22.1552
Table A4.22 Parameters of the Monte Carlo analysis (μ: expected value; σ: standard deviation; M: mode; L: lower bound; U: upper 

Parameter Distribution Methane emissions Normal μ = Table CH4 σ = 6.83/yr Nitrous oxide emissions Normal μ = Table N2O σ = 0.0059/yr Climate sensitivity Gamma M = 2.85 σ = 1.00 Sea level sensitivity Gamma M = 0.31 σ = 0.15 Life time methane Triangular L = 8.00 M = 8.60 Life time nitrous oxide Triangular L = 100 M = 120 Response time temperature Triangular L = 25 M = 50 Response time sea level Triangular L = 25 M = 50 Life time carbon dioxide Trunc. normal μ = 363.00 σ = 90.75 Life time carbon dioxide Trunc. normal μ = 74.00 σ = 18.50 Life time carbon dioxide Trunc. normal μ = 17.00 σ = 4.25 Life time carbon dioxide Trunc. normal μ = 2.00 σ = 0.50 Baseline loss biodiversity Trunc. normal μ = 0.003 σ = 0.002 Sensitivity biodiversity Trunc. normal μ = 0.001 σ = 0.001 Share biodiversity Triangular L = 0.00 M = 0.05 Water technology rate Trunc. normal μ = 0.005 σ = 0.005 Population growth Normal μ = Table P σ = 0.0048/yr Income growth Normal μ = Table Y σ = 0.0026/yr Energy efficiency Normal μ = Table AEEI σ = 0.0005/yr Decarbonisation Normal μ = Table ACEI σ = 0.0009/yr Land use emissions Normal μ = Table CO2F σ = 0.20/yr Ecosystem value Trunc. normal μ = 50 σ = 50 Anchor income Trunc. normal μ = 30,000 σ = 10,000 Value of a statistical life Trunc. normal μ = 200 σ = 100 Value of a year diseased Trunc. normal μ = 0.8 σ = 1.2 Sensitivity malaria Trunc. normal μ = 0.0794 σ = 0.0575 Non-linearity malaria Trunc. normal μ = 1.0 σ = 0.5 Sensitivity dengue fever Trunc. normal μ = 0.3534 σ = 0.0614 Non-linearity dengue fever Trunc. normal μ = 1.0 σ = 0.5 Sensitivity schistosomiasis Trunc. normal μ = -0.1149 σ = 0.0614 Non-linearity schistosomiasis Trunc. normal μ = 1.0 σ = 0.5 Income elasticity vector-borne diseases Trunc. normal μ = -2.65 σ = 0.69 Income elasticity diarrhoea mortality Trunc. normal μ = -1.58 σ = 0.23 Income elasticity diarrhoea morbidity Trunc. normal μ = -0.42 σ = 0.12 Non-linearity diarrhoea mortality Trunc. normal μ = 1.14 σ = 0.51 
Non-linearity diarrhoea morbidity Trunc. normal μ = 0.70 σ = 0.26 

Cardiovascular and respiratory mortality Normal Table HC Table HC Change in baseline cardiovascular disease Trunc. normal μ = 0.0259 σ = 0.0096 Change in baseline respiratory disease Trunc. normal μ = 0.0016 σ = 0.0005 Change in population above 65 Trunc. normal μ = 0.25 σ = 0.08 Maximum increase cardiovascular & respiratory disease Trunc. normal μ = 0.05 σ = 0.02 Sensitivity water Normal Table EFW Table EFW Income elasticity water Trunc. normal μ = 0.85 σ = 0.15 Non-linearity water Trunc. normal μ = 1.00 σ = 0.50 Sensitivity forestry Normal Table EFW Table EFW Income elasticity forestry Trunc. normal μ = 0.31 σ = 0.20 Non-linearity forestry Trunc. normal μ = 1.00 σ = 0.50 Sensitivity heating Trunc. normal Table EFW Table EFW Non-linearity heating Trunc. normal μ = 1.00 σ = 0.50 Sensitivity cooling Trunc. normal Table EFW Table EFW Non-linearity cooling Trunc. normal μ = 1.00 σ = 0.50 Agriculture, rate Trunc. normal μ =Table A σ = Table A Adaptation time Trunc. normal μ = 10.0 σ = 5.0 Non-linearity Trunc. normal μ = 2.0 σ = 0.5 Agriculture, level Normal μ =Table A σ = Table A Agriculture, optimum Normal μ =Table A σ = Table A Agriculture, CO2 Trunc. normal μ =Table A σ = Table A Income elasticity agriculture Trunc. normal μ = 0.31 σ = 0.15 Dryland value Trunc. normal μ = 4.0 σ = 2.0 Adaptation time Exponential σ = 0.1 Wetland value Trunc. normal μ = 5.0 σ = 2.5 Adaptation time Exponential σ = 0.1 Dryland loss Trunc. normal μ = Table SLR σ = Table SLR Protection cost Trunc. normal μ = Table SLR σ = Table SLR Dryland value Trunc. normal μ = Table SLR σ = Table SLR Wetland value Trunc. normal μ = Table SLR σ = Table SLR Immigration Trunc. normal μ = Table I σ = Table I Immigration cost Trunc. normal μ = 0.4 σ = 0.2 Adaptation time Trunc. normal μ = 3.0 σ = 1.0 Emigration cost Trunc. normal μ = 3.0 σ = 1.5 Adaptation time Exponential σ = 0.1 
Sea Level 

Rise 

Drylands potentially lost 

Drylands 

Protected

Drylands Not 

Protected 

Value of 

Drylands Lost 

Total Costs of 

Protection 

Drylands Value 

fade rate 

Dryland Protection 

Wetlands lost 

Additional 

Wetlands Lost due 

to Protection of 

Drylands

Value of 

Wetlands Lost 

Total Wetlands 

lost 

Base Wetland 

Value 

Wetlands Value 

(Annual and 

NPV)

Scarcity Value 

Protection Cost 

(annual and 

NPV)

GDPc 

Population Density 

Drylands Value 

(Annual and 

NPV) 

Base Protection 

Cost 

Base Dryland 

Value 

Wetlands Value 

fade rate 

Figure A4.1

Impacts Module: Loss of Drylands and Wetlands and Protection Costs due to 

Sea Level Rise
Figure A4.2a FUND-simulated global and sectoral damage costs of climate change impacts (% GDP) as a function of glo

rise above 1990 for the major FUND impact sectors for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Total global damage due to climate impacts as % GDP

01234

Global Temperature Increase Relative to 1990

-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the water sector in all the regions combined

01234

Global Temperature Increase Relative to 1990

-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the energy sector in all the regions combined
01234

Global Temperature Increase Relative to 1990

-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the health sector in all the regions combined

01234

Global Temperature Increase Relative to 1990

-0.16-0.14-0.12

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the sea level rise sector in all the regions combined

Figure A4.2a cont’d. FUND-simulated global and sectoral damage costs of climate change impacts (% GDP) as a function temperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-0.16-0.14-0.12

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the agriculture and forestry sector in all the regions combined

01234

Global Temperature Increase Relative to 1990

-0.16-0.14-0.12

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as % GDP in the ecosystem sector in all the regions combined

Figure A4.2b FUND-simulated global and sectoral damage costs of climate change impacts (million $$) as a function of 

temperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-8000000-6000000-4000000-2000000

0

2000000

Damage, million $$

Scenario

A1

A2B1B2

Total global damage due to climate impacts in million $$

01234

Global Temperature Increase Relative to 1990

-8000000-6000000-4000000-2000000

0

2000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in all regions combined

01234

Global Temperature Increase Relative to 1990

-8000000-6000000-4000000-2000000

0

2000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in all regions combined
01234

Global Temperature Increase Relative to 1990

-8000000-6000000-4000000-2000000

0

2000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in all regions combined

01234

Global Temperature Increase Relative to 1990

-8000000-6000000-4000000-2000000

0

2000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the water sector in all regions combined

Figure A4.2b cont’d FUND-simulated global and sectoral damage costs of climate change impacts (million $$) as a functemperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-60000-40000-20000

0

2000040000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in all regions combined

01234

Global Temperature Increase Relative to 1990

-60000-40000-20000

0

2000040000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in all regions combined

Figure A4.3a FUND-simulated regional damage costs of climate change impacts 

(% GDP) as a function of global mean temperature rise above 1990 for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in North Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in Southern Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in West Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in West Asia
01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in South America

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in South Asia
Figure A4..3a cont’d FUND-simulated regional damage costs of climate change impacts 

(% GDP) as a function of global mean temperature rise above 1990 for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in Australasia

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in Central America
01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in Central Asia

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in East Asia

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in Europe

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in all sectors combined in North America

Figure A4.3b FUND-simulated regional damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990 for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in East Asia

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in Europe

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in North Africa

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in North America
01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in South Africa

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in South America
Figure A4.3b cont’d. FUND-simulated regional damage costs of climate change impacts 

(million $$) as a function of global mean temperature rise above 1990 for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in South Asia

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in West Africa

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in West Asia

01234

Global Temperature Increase Relative to 1990

-500000-400000-300000-200000-100000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in Central Asia
01234

Global Temperature Increase Relative to 1990

-500000-400000-300000-200000-100000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in Australasia

01234

Global Temperature Increase Relative to 1990

-500000-400000-300000-200000-100000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in all sectors combined in Central America
Figure A4.4a FUND-simulated damage costs of climate change impacts (% GDP) 

as a function of global mean temperature rise above 1990 in the energy sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in North Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in West Africa

01234

Global Temperature Increase Relative to 1990

-20-15-10

-5

05

Damage, % GDP

Scenario

A1A2B1B2

Damage due to climate impacts as %GDP in the energy sector in West Asia
01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in Australasia

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in Central America

Figure A4.4a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean tem1990 in the energy sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in East Asia
01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in Europe

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in North America

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in South America

01234

Global Temperature Increase Relative to 1990

-4-3-2-1

012

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the energy sector in South Asia

Figure A4.4b FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above 1990 in the energy sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in East Asia

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1A2B1B2

Damage due to climate impacts in million $$ in the energy sector in Europe

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in North Africa

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in North America
01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in South America

Figure A4.4b cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean tem1990 in the energy sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in South Asia

01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1A2B1B2

Damage due to climate impacts in million $$ in the energy sector in West Africa
01234

Global Temperature Increase Relative to 1990

-3000000-2000000-1000000

0

1000000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in West Asia

01234

Global Temperature Increase Relative to 1990

-200000-150000-100000

-50000

0

50000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-200000-150000-100000

-50000

0

50000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in Australasia

01234

Global Temperature Increase Relative to 1990

-200000-150000-100000

-50000

0

50000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the energy sector in Central America

Figure A4.5a FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above 1990 in the agriculture and forestry sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in Australasia

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in Central America

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in East Asia
01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in Europe

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in North Africa

Figure A4.5a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean tem1990 in the agriculture and forestry sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in North America
01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in South America

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in South Asia

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in West Africa

01234

Global Temperature Increase Relative to 1990

-0.02-0.01

0

0.010.020.03

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the agriculture and forestry sector in West Asia

Figure A4.5b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperin the agriculture and forestry sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-4000

0

40008000

1200016000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in East Asia

01234

Global Temperature Increase Relative to 1990

-4000

0

40008000

1200016000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in Europe

01234

Global Temperature Increase Relative to 1990

-4000

0

40008000

1200016000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in North America

01234

Global Temperature Increase Relative to 1990

-4000

0

40008000

1200016000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in South America
01234

Global Temperature Increase Relative to 1990

-4000

0

40008000

1200016000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in South Asia

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in West Asia

Figure A4.5b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean above 1990 in the agriculture and forestry sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in Australasia

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in Central America
01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in North Africa

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-800-400

0

400800

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the agriculture and forestry sector in West Africa

Figure A4.6a FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above 1990 in the ecosystems sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in Australasia

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in Central America

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in East Asia
01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in Europe

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in North Africa

Figure A4.6a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean te1990 in the ecosystems sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in North America

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in Southern Africa
01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in South America

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in South Asia

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in West Africa

01234

Global Temperature Increase Relative to 1990

-0.2

-0.16-0.12-0.08-0.04

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the ecosystem sector in West Asia

Figure A4.6b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperin the ecosystems sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in Australasia

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in Central America

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in East Asia
01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in Europe

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in North Africa

Figure A4.6b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean above 1990 in the ecosystems sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in North America

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in Southern Africa
01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in South America

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in South Asia

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in West Africa

01234

Global Temperature Increase Relative to 1990

-200000-160000-120000

-80000-40000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the ecosystem sector in West Asia

Figure A4.7a FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above 1990 in the sea level rise sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in Australasia

01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in Central America

01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in North Africa

01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in Southern Africa
01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in South America

01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in South Asia

Figure A4.7a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean te1990 in the sea level rise sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-0.1

-0.08-0.06-0.04-0.02

0

0.02

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in West Africa

01234

Global Temperature Increase Relative to 1990

-0.02

-0.015

-0.01

-0.005

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in West Asia
01234

Global Temperature Increase Relative to 1990

-0.02

-0.015

-0.01

-0.005

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-0.02

-0.015

-0.01

-0.005

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in East Asia

01234

Global Temperature Increase Relative to 1990

-0.02

-0.015

-0.01

-0.005

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in Europe

01234

Global Temperature Increase Relative to 1990

-0.02

-0.015

-0.01

-0.005

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the sea level rise sector in North America

Figure A4.7b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperin the sea level rise sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in Central America

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in East Asia

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in Europe

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in North Africa
01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in North America

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in South America

Figure A4.7b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean above 1990 in the sea level rise sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in South Asia

01234

Global Temperature Increase Relative to 1990

-12000

-8000-4000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in west Africa
01234

Global Temperature Increase Relative to 1990

-1200

-800-400

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in Australasia

01234

Global Temperature Increase Relative to 1990

-1200

-800-400

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-1200

-800-400

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in southern Africa

01234

Global Temperature Increase Relative to 1990

-1200

-800-400

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the sea level rise sector in West Asia

Figure A4.8a FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above 1990 in the water sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-5-4-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-5-4-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in East Asia

01234

Global Temperature Increase Relative to 1990

-5-4-3-2-1

01

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in North Africa

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in West Asia
01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in Australasia

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in Central America

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in Europe

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in North America
01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in South America

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in South Asia

01234

Global Temperature Increase Relative to 1990

-0.8-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the water sector in West Africa

Figure A4.9a FUND-simulated damage costs of climate change impacts (% GDP) 
as a function of global mean temperature rise above 1990 in the health sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in East Asia

01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in Europe

01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in North America
01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-3-2-1

01234

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in West Africa

Figure A4.9a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean te1990 in the health sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in Australasia

01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in Central America
01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in North Africa

01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in South America

01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in South Asia

01234

Global Temperature Increase Relative to 1990

-0.6-0.4-0.2

0

Damage, % GDP

Scenario

A1

A2B1B2

Damage due to climate impacts as %GDP in the health sector in West Asia

Figure A4.9b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperin the health sector for 4 SRES scenarios 
01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in Central Asia

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in East Asia

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in Europe

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in North America
01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in Southern Africa

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in South America

Figure A4.9b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperin the health sector for 4 SRES scenarios 

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in South Asia

01234

Global Temperature Increase Relative to 1990

-800000-400000

0

400000800000

1200000

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in West Africa
01234

Global Temperature Increase Relative to 1990

-100000

-80000-60000-40000-20000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in Australasia

01234

Global Temperature Increase Relative to 1990

-100000

-80000-60000-40000-20000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in Central America

01234

Global Temperature Increase Relative to 1990

-100000

-80000-60000-40000-20000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in North Africa

01234

Global Temperature Increase Relative to 1990

-100000

-80000-60000-40000-20000

0

Damage, million $$

Scenario

A1

A2B1B2

Damage due to climate impacts in million $$ in the health sector in West Asia
The trans-disciplinary Tyndall Centre for Climate Change Research undertakes integrated research into the 

long-term consequences of climate change for society and into the development of sustainable responses 

that governments, business-leaders and decision-makers can evaluate and implement. Achieving these 

objectives brings together UK climate scientists, social scientists, engineers and economists in a unique 

collaborative research effort. 

Research at the Tyndall Centre is organised into seven research programmes that collectively contribute to all 

aspects of the climate change issue: International Climate Policy; Energy Futures; Adaptation and Resilience;

International Development; Coasts, Cities and Integrated Modelling. All programmes address a clear question 

posed to society by climate change, and will generate results to guide the strategic development of climate 

change policies at local, national and global scales.

The Tyndall Centre is named after the 19th century UK scientist John Tyndall, who was the first to prove the 

Earth’s natural greenhouse effect and suggested that slight changes in atmospheric composition could bring 

about climate variations. In addition, he was committed to improving the quality of science education and 

knowledge. 

The Tyndall Centre is a partnership of the following institutions: 

University of East Anglia

Manchester University

SPRU – Science and Technology Policy Research (University of Sussex) 

Southampton Oceanography Centre 

University of Southampton 

University of Cambridge

University of Newcastle

University of Oxford

Institute for Transport Studies at the University of Leeds

The Centre is core funded by the UK Research Councils: 

 Natural Environmental Research Council (NERC) 

 Economic and Social Research Council (ESRC) 

 Engineering and Physical Sciences Research Council (EPSRC) 

For more information, visit the Tyndall Centre Web site (www.tyndall.ac.uk) or contact: 

Communications Manager 

Tyndall Centre for Climate Change Research 

University of East Anglia, Norwich NR4 7TJ, UK 

Phone: +44 (0) 1603 59 3900; Fax: +44 (0) 1603 59 3901 

Email: tyndall@uea.ac.uk 
Tyndall Working Papers are available online at 

http://www.tyndall.ac.uk/publications/working_papers/working_papers.shtml 

Mitchell, T. and Hulme, M. (2000). A Country-byCountry Analysis of Past and Future Warming 

Rates, Tyndall Centre Working Paper 1. 

Hulme, M. (2001). Integrated Assessment 

Models, Tyndall Centre Working Paper 2. 

Berkhout, F, Hertin, J. and Jordan, A. J. (2001). 

Socio-economic futures in climate change 

impact assessment: using scenarios as 

'learning machines', Tyndall Centre Working 

Paper 3. 

Barker, T. and Ekins, P. (2001). How High are 

the Costs of Kyoto for the US Economy?, 

Tyndall Centre Working Paper 4. 

Barnett, J. (2001). The issue of 'Adverse Effects 

and the Impacts of Response Measures' in the 

UNFCCC, Tyndall Centre Working Paper 5. 

Goodess, C.M., Hulme, M. and Osborn, T. (2001). 

The identification and evaluation of suitable 

scenario development methods for the 

estimation of future probabilities of extreme 

weather events, Tyndall Centre Working Paper 6. 

Barnett, J. (2001). Security and Climate 

Change, Tyndall Centre Working Paper 7. 

Adger, W. N. (2001). Social Capital and Climate 

Change, Tyndall Centre Working Paper 8. 

Barnett, J. and Adger, W. N. (2001). Climate 

Dangers and Atoll Countries, Tyndall Centre 

Working Paper 9. 

Gough, C., Taylor, I. and Shackley, S. (2001). 

Burying Carbon under the Sea: An Initial 

Exploration of Public Opinions, Tyndall Centre 

Working Paper 10. 

Barker, T. (2001). Representing the Integrated 

Assessment of Climate Change, Adaptation 

and Mitigation, Tyndall Centre Working Paper 11. 

Dessai, S., (2001). The climate regime from 

The Hague to Marrakech: Saving or sinking 

the Kyoto Protocol?, Tyndall Centre Working 

Paper 12. 

Dewick, P., Green K., Miozzo, M., (2002). 

Technological Change, Industry Structure and 

the Environment, Tyndall Centre Working Paper 

13. 

Shackley, S. and Gough, C., (2002). The Use of 

Integrated Assessment: An Institutional 

Analysis Perspective, Tyndall Centre Working 

Paper 14. 

Köhler, J.H., (2002). Long run technical change 

in an energy-environment-economy (E3) 

model for an IA system: A model of 

Kondratiev waves, Tyndall Centre Working Paper 

15. 

Adger, W.N., Huq, S., Brown, K., Conway, D. and 

Hulme, M. (2002). Adaptation to climate 

change: Setting the Agenda for Development 

Policy and Research, Tyndall Centre Working 

Paper 16. 

Dutton, G., (2002). Hydrogen Energy 

Technology, Tyndall Centre Working Paper 17. 

Watson, J. (2002). The development of large 

technical systems: implications for hydrogen, 

Tyndall Centre Working Paper 18. 

Pridmore, A. and Bristow, A., (2002). The role of 

hydrogen in powering road transport, Tyndall 

Centre Working Paper 19. 

Turnpenny, J. (2002). Reviewing organisational 

use of scenarios: Case study - evaluating UK 

energy policy options, Tyndall Centre Working 

Paper 20. 

Watson, W. J. (2002). Renewables and CHP 

Deployment in the UK to 2020, Tyndall Centre 

Working Paper 21. 

Watson, W.J., Hertin, J., Randall, T., Gough, C. 

(2002). Renewable Energy and Combined Heat 

and Power Resources in the UK, Tyndall Centre 

Working Paper 22. 

Paavola, J. and Adger, W.N. (2002). Justice and 

adaptation to climate change, Tyndall Centre 

Working Paper 23. 

Xueguang Wu, Jenkins, N. and Strbac, G. (2002). 

Impact of Integrating Renewables and CHP 

into the UK Transmission Network, Tyndall 

Centre Working Paper 24 

Xueguang Wu, Mutale, J., Jenkins, N. and Strbac, 

G. (2003). An investigation of Network 

Splitting for Fault Level Reduction, Tyndall 

Centre Working Paper 25 

Brooks, N. and Adger W.N. (2003). Country level 

risk measures of climate-related natural 

disasters and implications for adaptation to 

climate change, Tyndall Centre Working Paper 26 

Tompkins, E.L. and Adger, W.N. (2003). Building 

resilience to climate change through adaptive 

management of natural resources, Tyndall 

Centre Working Paper 27 
Dessai, S., Adger, W.N., Hulme, M., Köhler, J.H., 

Turnpenny, J. and Warren, R. (2003). Defining 

and experiencing dangerous climate change, 

Tyndall Centre Working Paper 28 

Brown, K. and Corbera, E. (2003). A MultiCriteria Assessment Framework for CarbonMitigation Projects: Putting “development” in 

the centre of decision-making, Tyndall Centre 

Working Paper 29 

Hulme, M. (2003). Abrupt climate change: can 

society cope?, Tyndall Centre Working Paper 30 

Turnpenny, J., Haxeltine A. and O’Riordan, T. 

(2003). A scoping study of UK user needs for 

managing climate futures. Part 1 of the pilotphase interactive integrated assessment 

process (Aurion Project), Tyndall Centre 

Working Paper 31 

Xueguang Wu, Jenkins, N. and Strbac, G. (2003). 

Integrating Renewables and CHP into the UK 

Electricity System: Investigation of the impact 

of network faults on the stability of large 

offshore wind farms, Tyndall Centre Working 

Paper 32 

Pridmore, A., Bristow, A.L., May, A. D. and Tight, 

M.R. (2003). Climate Change, Impacts, Future 

Scenarios and the Role of Transport, Tyndall 

Centre Working Paper 33 

Dessai, S., Hulme, M (2003). Does climate policy 

need probabilities?, Tyndall Centre Working Paper 

34 

Tompkins, E. L. and Hurlston, L. (2003). Report to 

the Cayman Islands’ Government. Adaptation 

lessons learned from responding to tropical 

cyclones by the Cayman Islands’ Government, 

1988 – 2002, Tyndall Centre Working Paper 35 

Kröger, K. Fergusson, M. and Skinner, I. (2003).

Critical Issues in Decarbonising Transport: The 

Role of Technologies, Tyndall Centre Working 

Paper 36 

Ingham, A. and Ulph, A. (2003) Uncertainty, 

Irreversibility, Precaution and the Social Cost 

of Carbon, Tyndall Centre Working Paper 37 

Brooks, N. (2003). Vulnerability, risk and 

adaptation: a conceptual framework, Tyndall 

Centre Working Paper 38 

Tompkins, E.L. and Adger, W.N. (2003). 

Defining response capacity to enhance climate 

change policy, Tyndall Centre Working Paper 39 

Klein, R.J.T., Lisa Schipper, E. and Dessai, S. 

(2003), Integrating mitigation and adaptation 

into climate and development policy: three 

research questions, Tyndall Centre Working Paper 

40 

Watson, J. (2003), UK Electricity Scenarios for 

2050, Tyndall Centre Working Paper 41 

Kim, J. A. (2003), Sustainable Development and 

the CDM: A South African Case Study, Tyndall 

Centre Working Paper 42 

Anderson, D. and Winne, S. (2003), 

Innovation and Threshold Effects in 

Technology Responses to Climate Change, 

Tyndall Centre Working Paper 43 

Shackley, S., McLachlan, C. and Gough, C. (2004) 

The Public Perceptions of Carbon Capture and 

Storage, Tyndall Centre Working Paper 44 

Purdy, R. and Macrory, R. (2004) Geological 

carbon sequestration: critical legal issues, 

Tyndall Centre Working Paper 45 

Watson, J., Tetteh, A., Dutton, G., Bristow, A., 

Kelly, C., Page, M. and Pridmore, A., (2004) UK 

Hydrogen Futures to 2050, Tyndall Centre 

Working Paper 46 

Berkhout, F., Hertin, J. and Gann, D. M., (2004) 

Learning to adapt: Organisational adaptation 

to climate change impacts, Tyndall Centre 

Working Paper 47 

Pan, H. (2004) The evolution of economic 

structure under technological development, 

Tyndall Centre Working Paper 48 

Awerbuch, S. (2004) Restructuring our 

electricity networks to promote 

decarbonisation, Tyndall Centre Working Paper 49 

Powell, J.C., Peters, M.D., Ruddell, A. & Halliday, J. 

(2004) Fuel Cells for a Sustainable Future?

Tyndall Centre Working Paper 50 

Agnolucci, P., Barker, T. & Ekins, P. (2004) 

Hysteresis and energy demand: the 

Announcement Effects and the effects of the 

UK climate change levy, Tyndall Centre Working 

Paper 51 

Agnolucci, P. (2004) Ex post evaluations of CO2 

–Based Taxes: A Survey, Tyndall Centre Working 

Paper 52 
Agnolucci, P. & Ekins, P. (2004) The 

Announcement Effect and environmental 

taxation, Tyndall Centre Working Paper 53 

Turnpenny, J., Carney, S., Haxeltine, A., & 

O’Riordan, T. (2004) Developing regional and 

local scenarios for climate change mitigation 

and adaptation, Part 1: A framing of the East 

of England, Tyndall Centre Working Paper 54 

Mitchell, T.D. Carter, T.R., Jones, .P.D, Hulme, M. 

and New, M. (2004) A comprehensive set of 

high-resolution grids of monthly climate for 

Europe and the globe: the observed record 

(1901-2000) and 16 scenarios (2001-2100), 

Tyndall Centre Working Paper 55 

Vincent, K. (2004) Creating an index of social 

vulnerability to climate change for Africa, 

Tyndall Centre Working Paper 56 

Shackley, S., Reiche, A. and Mander, S (2004) The 

Public Perceptions of Underground Coal 

Gasification (UCG): A Pilot Study, Tyndall Centre 

Working Paper 57 

Bray, D and Shackley, S. (2004) The Social 

Simulation of The Public Perceptions of 

Weather Events and their Effect upon the 

Development of Belief in Anthropogenic 

Climate Change, Tyndall Centre Working Paper 58 

Anderson, D and Winne, S. (2004) Modelling 

Innovation and Threshold Effects 

In Climate Change Mitigation, Tyndall Centre 

Working Paper 59 

Few, R., Brown, K. and Tompkins, E.L. (2004) 

Scaling adaptation: climate change response 

and coastal management in the UK, Tyndall 

Centre Working Paper 60 

Brooks, N. (2004) Drought in the African Sahel: 

Long term perspectives and future prospects, 

Tyndall Centre Working Paper 61 

Barker, T. (2004) The transition to 

sustainability: a comparison of economics 

approaches, Tyndall Centre Working Paper 62 

Few, R., Ahern, M., Matthies, F. and Kovats, S. 

(2004) Floods, health and climate change: a 

strategic review, Tyndall Centre Working Paper 63 

Peters, M.D. and Powell, J.C. (2004) Fuel Cells for 

a Sustainable Future II, Tyndall Centre Working 

Paper 64 

Adger, W. N., Brown, K. and Tompkins, E. L. 

(2004) The political economy of cross-scale 

networks in resource co-management, Tyndall 

Centre Working Paper 65 

Turnpenny, J., Haxeltine, A., Lorenzoni, I., 

O’Riordan, T., and Jones, M., (2005) Mapping 

actors involved in climate change policy 

networks in the UK, Tyndall Centre Working 

Paper 66 

Turnpenny, J., Haxeltine, A. and O’Riordan, T., 

(2005) Developing regional and local scenarios 

for climate change mitigation and adaptation: 

Part 2: Scenario creation, Tyndall Centre 

Working Paper 67 

Bleda, M. and Shackley, S. (2005) The formation 

of belief in climate change in business 

organisations: a dynamic simulation model, 

Tyndall Centre Working Paper 68 

Tompkins, E. L. and Hurlston, L. A. (2005) Natural 

hazards and climate change: what knowledge 

is transferable?, Tyndall Centre Working Paper 69 

Abu-Sharkh, S., Li, R., Markvart, T., Ross, N., 

Wilson, P., Yao, R., Steemers, K., Kohler, J. and 

Arnold, R. (2005) Can Migrogrids Make a Major 

Contribution to UK Energy Supply?, Tyndall 

Centre Working Paper 70 

Boyd, E. Gutierrez, M. and Chang, M. (2005) 

Adapting small-scale CDM sinks projects to 

low-income communities, Tyndall Centre 

Working Paper 71 

Lowe, T., Brown, K., Suraje Dessai, S., Doria, M., 

Haynes, K. and Vincent., K (2005) Does tomorrow 

ever come? Disaster narrative and public 

perceptions of climate change, Tyndall Centre 

Working Paper 72 

Walkden, M. (2005) Coastal process simulator 

scoping study, Tyndall Centre Working Paper 73 

Ingham, I., Ma, J., and Ulph, A. M. (2005) How do 

the costs of adaptation affect optimal 

mitigation when there is uncertainty, 

irreversibility and learning?, Tyndall Centre 

Working Paper 74 

Fu, G., Hall, J. W. and Lawry, J. (2005) Beyond 

probability: new methods for representing 

uncertainty in projections of future climate, 

Tyndall Centre Working Paper 75 
Agnolucci,. P (2005) The role of political 

uncertainty in the Danish renewable energy 

market, Tyndall Centre Working Paper 76 

Barker, T., Pan, H., Köhler, J., Warren., R and 

Winne, S. (2005) Avoiding dangerous climate 

change by inducing technological progress: 

scenarios using a large-scale econometric 

model, Tyndall Centre Working Paper 77 

Agnolucci,. P (2005) Opportunism and 

competition in the non-fossil fuel obligation 

market, Tyndall Centre Working Paper 78 

Ingham, I., Ma, J., and Ulph, A. M. (2005) Can 

adaptation and mitigation be 

complements?, Tyndall Centre Working Paper 79 

Wittneben, B., Haxeltine, A., Kjellen, B., Köhler, J., 

Turnpenny, J., and Warren, R., (2005) A 

framework for assessing the political economy 

of post-2012 global climate regime, Tyndall 

Centre Working Paper 80 

Sorrell, S., (2005) The economics of energy 

service contracts, Tyndall Centre Working Paper 

81 

Bows, A., and Anderson, K. (2005) An analysis of 

a post-Kyoto climate policy model, Tyndall 

Centre Working Paper 82 

Williamson, M. Lenton, T. Shepherd, J. and 

Edwards, N. (2006) An efficient numerical 

terrestrial scheme (ENTS) for fast earth 

system modelling Tyndall Centre Working Paper 

83 

Kevin Anderson, Alice Bows and Paul Upham (2006) 

Growth scenarios for EU & UK aviation: 

contradictions with climate policy, Tyndall 

Centre Working Paper 84 

Michelle Bentham, (2006) An assessment of 

carbon sequestration potential in the UK – 

Southern North Sea case study Tyndall Centre 

Working Paper 85 

Peter Stansby, Cui-Ping Kuang, Dominique 

Laurence and Brian Launder, (2006) Sandbanks 

for coastal protection: implications of sealevel rise - Part 1: Application to East Anglia

Tyndall Centre Working Paper 86 

Peter Stansby and Cui-Ping Kuang, (2006) 

Sandbanks for coastal protection: implications 

of sea-level rise – Part 2: current and 

morphological modelling Tyndall Centre Working 

Paper 87 

Peter Stansby and Cui-Ping Kuang, (2006) 

Sandbanks for coastal protection: implications 

of sea-level rise – part 3: wave modelling

Tyndall Centre Working Paper 88