# SPOTLIGHTING IMPACTS FUNCTIONS

IN INTEGRATED ASSESSMENT

Research Report Prepared for the Stern Review
on the Economics of Climate Chang e

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 Warren 1 

# , Chris Hope 2 

# , Michael Mastrandrea 3 

# , Richard Tol 4



# , Neil Adger 1 

# and Irene Lorenzoni 1



# 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 un der 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 scenario 1 

# . The versions of DICE/RICE, MERGE and FUND studied for this report are deterministic models. Only the PAGE model is probabilistic in nature 2



# .

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 metrics 3 

# . 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 PAGE 4



# .

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 regions 5 

# . 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 damagedue 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 fo rm 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 impacts 6 

# . 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, F UND) 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 c limate 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 calc ulate 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, PA GE 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 Econom y (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 econ omic 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, Thaila nd, 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 Asia n part of the former Soviet Union, subSaharan Africa, and a few countri es 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 will ingness 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) = α 1 

# T(t) + α 2 

# T(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: α 1 

# α 2 

# % Loss GDP for 2oC increase

Global a 

# -0.0045 0.0035 0.5

United States b 

# -0.0026 0.0017 0.16
Europe b 

# -0.001 0.0049 1.76 Other High Income b 

# -0.007 0.003 -0.2

China b 

# -0.0041 0.002 -0.02 Russia and Eastern Europe b 

# -0.0076 0.0025 -0.52

Middle Income b 

# 0.0039 0.0013 1.3

Lower-Middle Income b 

# 0.0022 0.0026 1.48

Low Income b 

# 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 base d on sectoral impact assessments in the literature, or the expert judgment of the author s. 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:

θ i,j 

# (T, y j 

# ) = Q i,j 

# (T) x [y j 

# (t)/y j 

# (1995)] η i



# where θ i,j 

# (T, y j 

# ) is the “impact index” for a global mean temperature increase of T and a per capita income of y j 

# , expressed as current (1995) WT P 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, y j 

# (t), to present (1995) per capita GDP, raised to the

power η i 

# , the income elasticity of the impact index. The authors benchmark these indices for a 2.5oC warming, constructing estimates for Q i,j 

# (2.5). To calculate θ i,j 

# (2.5, y j 

# ), 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

y

j 

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

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

authors calculate θ i,j 

# (2.5, y j 

# (2100)) and θ i,j 

# (6, y j 

# (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:

Q ag,j 

# (T) = { α ag,0 

# + α ag,1 

# (T + T 0 j 

# ) + α ag,2 

# (T + T 0 j 

# ) 2 

# } – { α ag,0 

# + α ag,1 

# (T 0 j 

# ) + α ag,2 

# (T 0 j 

# ) 2 

# } + ε ag,j

# 12 where Q ag,j 

# (T) is the damage to the agricultural sector as a fraction of GDP, α ag,i 

# are coefficients, T 0 j 

# is the sub-regional mean temperature without climate change, T is the global mean temperature increase above preindustrial levels, and ε ag,j 

# is the error in the estimate. The income elasticity is set to -0.1.

These authors combine sub-regi onal 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 chan ge scenarios Darwin et al. consider. It

should be noted that CO 2 

# fertilization is excluded in this study. Thus, Nordhaus uses damage estimates that allow transition of la nd between cropland, pasture and other uses,

but which assume no CO 2 

# 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 temperat e 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 th e forestry, energy systems, water systems, construction, fisheries, and outdoor recreat ion sectors in temperate climates implies costless adaptation in these sectors to climat e 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 func tion for coastal vulnerability is:

Q coastal,j 

# (T) = α coastal,j 

# (T/2.5) 1.5



# where α coastal,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 ment ioned, 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 abandonmen t.” They designate adaptation as “any sort of cost-reducing activity undertaken in respon se 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 b eaches, 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:

Q health,j 

# (T) = α health,j 

# (T j 

# ) 0.2243



# where α health,j 

# are constants, and T j 

# 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 20 th



# 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 th e 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 th e agricultural sector. Citing a study by Nordhaus (1998) of the value of climate-related time use in the US, the authors estimate abenefit of 0.3% GDP for a 2.5oC warming and a quadratic relationship between subregional mean temperature and time use impact s. 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 es timate 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 es timate 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 author s 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 sin ce the survey was completed, they estimate the probability of catastrophic change for 2.5o C 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 occurrenc e, 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 Region al 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 l iterature 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

TimeDamages (% 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

TimeDamages (% 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

TimeDamages (% 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 co untries 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 temperatur e 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 a nd 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 unrestri cted 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 fo r 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 capita l, 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

d 2,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 calibrate d using Nordhaus (1992) whilst in other applications an uncertainty analysis is carried out on this calibration.

In this version market damages, D t,r 

# , for any time period t in the region r, are defined by the following relationship:

D t,r 

# = d 1,r 

# * [ ∆ AT t,r 

# ] d2,r 

# * GDP t,r 

# (2)

where d 1,r 

# and d 2,r 

# are constants. This can be shown graphically as the solid or dashed curves in Figure 2.1, which illustrates the sp ecific 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 dama g e

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 valuat ions that society might place on these nonmarket damages rather than advocating any particular set of numbers.

WTP t,r 

# = d 3,r 

# * [ ∆ AT t,r 

# ] d4,r 

# / [ 1+ (100* exp (-0.23*GDP t,r 

# /POP t,r 

# )) ] (3)

where d 3,r 

# and d 4,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 d 3,r 

# set to 0.0032 and d 4,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 downloadab le version (Manne, 2004)

Treatment of market-damages

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

d 2,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 di scount 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 th e 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 th is way, a representation of the possibility for non-cooperation or of non-optimal internat ional 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 li terature. 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). Re gional 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 s ectors 1 and 2 in all regions) take the form of a polynomial I(r) = A(r)T n(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 m ean 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 situat ion 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 GD P 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 great er 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 risk s 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 percenta ge 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. Th e 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 As ia; 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 uncerta inty 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., 19 96; 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 Warming a



# 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 -2.0 b 

# 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 anddiscontinuity) 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. Th ere 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 eachregion, 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

CS 1 

# CP 1 

# CI 1 

# CS 2 

# CP 2 

# CI 2 

# 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



# g e p d * + = 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 parameters 7 

# . 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 rate s (in Hope, 2006, the GDP grow th 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 increas e for the three sectors for the globe as a whole, expressed in terms of both %G DP 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 temperatu r % 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 temperatu r % 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 temperatu r % 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 im pacts 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 imp acts 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 (CO 2 

# )

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 

# ψ χφχ =− − rt rt rt rt rt rt 

# M Y

(CO2.4) , ,1 ,1 

# (1 ) ψ ψ

ψ 

# χ κ χ ατ

− − 

# = +−
rt rt rt



# and

(CO2.5) , ,1 ,1 

# (1 ) φ φ

φ 

# χ κ χ ατ

− − 

# = +−
rt rt rt



# Thus, the parameter 0< 

# α <1 governs which part of emission reduction is 

# permanent (reducing carbon and energy intensities at all future time s) 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, 

# κ ψ 

# = 

# κ φ 

# =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 thro ugh 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) , ,

,

, , 

# 1.57 0.17 min β
=− − 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 reductio n 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
rt rt R rt 

# H H γ τ

− − 

# = +



# and

(CO2.9) 1 , 

# 1 G G

t t G rt 

# H H 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, γ R 

# =0.9 and γ G 

# =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 deforest ation are exogenous, and cannot be mitigated.

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

# 47 Methane (CH 4 

# )



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

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

A4.7b.

Nitrous oxide (N 2 

# O)



# 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 (SF 6 

# )



# SF 6 

# emissions are linear in GDP and GDP per ca pita. Table A4.9 gives the parameters. The

numbers for 1990 and 1995 are estimated from IEA data.

Sulphur dioxide (SO 2 

# )



# 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) 

# ( )

t t-1 t t-1 pre 

# C = C + E

- C

- C α β



# 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 i t 

# Box = Box 0.000471 E ρ α

, 

# +



# with

(C.2b)

t

i=1 5

i i,t 

# C = Box 

# ∑ α



# where α i 

# 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 fore ver 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) t t-1 t 

# 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

− 

# ∆ ⎛ ⎞

= + ⎜ ⎟ ⎝ ⎠

a r



# 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 − = +

− −

A l 

# 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; A B 

# is a parameter, denoting the benchmark change in agricultural production (cf. Table A4.11); T opt


# is a parameter, denoting the optimal temperature (c f. 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 t f

tr r 

# CO A γ ⎛ ⎞

= ⎜ ⎟ ⎝ ⎠

A

f 

# denotes the change in agricultural pr oduction 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 tota l income falls with per capita income. The elasticity across the nine regions is -0.31. So,

(A.4) ε 

# ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

r t r

r r

r t r

t



# 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 wate r 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 ) r t t

rt r t

r 

# Y W T Y β

γ 

# α τ − 

# ⎛ ⎞

= − ⎜ ⎟ ⎝ ⎠

where W denotes the change in water reso urces, 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 en ergy 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)

r s t

s t r

t

t r

t

t r r t 

# AEEI P

P

y

y T a SH

, 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)

r s t

s t r

t

t r

t

t r r t 

# AEEI P

P

y

y T a SC

, 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 ($/km 2 

# ), 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; L

max 

# 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 TableA4.14 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 WL PC L 2 1 1

, 0 min

# 52 L

is the fraction of the coastline to be protected an d 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 protec tion 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 PC

a 

# 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 t WL t

t 

# + + +

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ + = 

# Σ ∞

= 

# ρ ρ

ρ

where WL 0 

# 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 t DL t

t 

# ρ ρ

ρ + + +

= ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

+ + = 

# Σ ∞

=



# where DL 0 

# 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 T Y B

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; y b 

# = $30,000 is a

parameter; τ =0.025 is a parameter; σ =0.05 is a parameter; and B 0 

# =14,000,000 is a parameter.

The number of species follows

(E.2) 

# ( ) 2

1 1



# 1

tt t 

# B 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 D d 

# is given by

(HD) 

# ( )

, , , , ,0

,0 r t d d

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)
d d dB 

# M T α β ∆ =+



# where ∆ M



# denotes the change in mortality due to a one degree global warming; T B 

# 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, T B 

# denotes the average temperature of the warmest month. In the case of cold, T B

# 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 T B 

# 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

,, , rd rd rd 

# M T T α β ∆= +



# 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 importantl y, 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 su ch 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 separate ly 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 lim ited 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); R 2 

# =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) α r 

# 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) A

r 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;



# T opt 

# 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) GAP 1990

,r 

# Y 1990,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) α r 

# 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) α r 

# 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) α r 

# 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)

ΑΕΕΙ s,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

Y t,r 

# Available from

SRES?

L max,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) PC

a 

# 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) WL 0 

# and DL 0 

# 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 [r j n1]: 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

Y b 

# $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 ) α r,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) α r 

# Tables

HC2-4 17 country

data-set and

1 climate

scenario Cannot

exceed 5%

baseline mortality

derived

from popn>65 

# β

r 

# 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 smal l 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 FU ND 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. Th ere 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 o ccur, 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 whil st 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). Darw in (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 de scribing 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 plan ting 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 Amer ica 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 Eur ope, 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 (10 th 

# - 90 th 

# ) 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 (whi ch 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 a ssume 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 4Global 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 0.036Rate of change of global average temperature (deg C / year)



# Scenario

A1

A2

B1

B2 Rate of change of global annual average temperatur e in 4 SRES scenarios acco rding 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
2 0

75
2 0

95



# YearGlobal TemperatureIncrease 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 climat e 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 inte rmediate 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 1990CO2 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 1990CO2 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 1990CO2 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 observ ations 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 events 9 

# . 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 cas e, 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 res ponses 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 environmen ts 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 ca se. Hanemann (2000) argues that adaptation cannot always be considered as efficient, si mply 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 st udies 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 (a pplying the default discount rates). Rates canalso 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 importa nce 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 assist ance in preparing graphics for this report. This work was made possible by a grant from the UK Treasury.

References



# Arnell, N.W., Cannell, M.G.R., Hulme, M., Kovats, R.S., Mitchell, J.F.B., Nicholls, Arnell, N.W., Livermore, M.J.J., Kovats, S., Levy, P.E., Nicholls, R., Parry, M.L., and Gaffin S.R. 2004. Climate and socioeconomic scenarios for global-scale climate change impacts assessments: characterising the SRES storylines. Global Environmental Change 14 pp 3-20.

Berkhout, F. and Hertin, J. (2000) Viewpoint : socio-economic scenarios for climate impact

assessment, Global Environmental Change , 10:165-168.

Bijlsma, L., Ehler, C.N., Klein, R.J.T., Kulshrestha, S.M., McLean, R.F., Mimura, N., Nicholls, R.J., Nurse, L.A., Nieto, P., Stakhiv, E.Z., Turner, R.K., Warrick, R.A. (1996) Coastal zones and small islands. In Watson, R.T., Zinyomera, M.C., Moss, R.H., Impacts, Adaptations and Mitigation of Climate Change: Scientific Technical Analyses. Cambridge University Press, Cambridge, pp. 289-324.

Climatic Research Unit and Environmental Resources Limited (CRU / ERL) (1992) Economic evaluation of impacts and adaptive measures in the European Community. Development of a Framework for the Evaluation of Policy Options to deal with The Greenhouse Effect . ERL,

CRU and EC DG XI.

Cline, W.R. (1992) The Economics of Global Warming, Institute for International Economics, Washington D.C.

CRU/ERL (1992) Economic evaluation of impacts and adaptive measures in the European Community, phase 3, final report, CEC, DG XI,

Brussels.

Darwin, R, Tsigas, M., Leqandrowski, J., and Raneses, A. (1995) World Agriculture and Climate Change: Economic Adaptations . Agricultural economic report number 703, US Dept of Agriculture.

Darwin, R. (2004) Effects of greenhouse gas emissions on world agriculture, food consumption, and economic welfare. Climatic Change 66, 191-238.

Dinar, A., R. Mendelsohn, R. Evenson, J. Parikh, A. Sanghi, K., Kumar, J. McKinsey, and S. Lonergan (1998). Measuring the Impact of Climate Change on Indian Agriculture, World Bank Technical Paper No. 402, 1998.

Downing, T.E., Greener, R.A., and Eyre, N. (1995) The Economic Impacts of Climate Change: Assessment of Fossil Fuel Cycles for ExternE Project. Oxford and Lonsdale, Environmental Change Unit and Eyre Energy Environment.

# 76 Downing, T.E., Eyre, N., Greener, R., and Blackwell, D. (1996) Projected Costs of Climate Change for Two Reference Scenarios and Fossil Fuel Cycles Oxford: Environmental Change

Unit.

European Environment Agency (EEA) (2006) Vulnerability and adaptati on to climate change in Europe . EEA Technical Report no. 7/2005, EEA Copenhagen and Office for Official Publication of the European Communities, Luxembourg.

Fankhauser, S. (1994a). The economic costs of global warming damage: a survey. Global Environmental Change 4, pp. 301-109.

Fankhauser, S (1994b) Protection versus retreat: The Economic Costs of Sea Level Rise Environmental and Planning A 27, 299-319

Fankhauser, S (1995) Valuing Climate Change: The Economics of the Greenhouse. London,

Earthscan.

Hanemann, W.M. (2000) Adaptation and its measurement; an editorial comment, Climatic Change , 45:571-581.

Hammitt, J.K., Lempert, R.J. and Schlesinger, M.E. (1992) A Sequential-Decision Strategy for Abating Climate Change. Nature 357 

# 315-318.

Hoozemans, F.M.J., Marchand, M., Pennkamp, H.A. (1993) A Global Vulnerability Analysis: Vulnerability Assessment for Population, Coas tal Wetlands, and Rice Production on a Global

Scale . 2 nd 

# Edition, Delft Hydraulics, The Netherlands.

Hope C (2006) The marginal impact of CO2 from PAGE2002: An integrated assessment model incorporating the IPCC's five reasons for concern, Integrated Assessment , 6, 1.

IPCC (2001) Intergovernmental Panel on Climate Change (IPCC).: 2001, ‘ Climate Change 2001: Volume II Impacts, Adaptation and Vulnerability ’, Cambridge University Press, 397

pp.

Johns, T.C., Gregory, J.M., Ingram, W.J., John son, C.E., Jones, A., Lowe, J.A., Mitchell, J.F.B., Roberts, D.L., Sexton, D.M.H., Steven son, D.S., Tett, S.F.B., and Woodage, M.J. (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics 20, 583-612.

Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G.A ., Mitchell, J.F.B., Stouffer, R.J., Tokioka, T., Weaver, A.J., and Wigley, T.M.L. (1996) Climate Models-Projections of Future Climate in Houghton, J.T., MeiroFilho, L.G., Callander, B.A., Harris, N., Kattenberg, A., and Maskell, K. (eds) Climate Change 1995: The Science of Climate Change-Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change Cambridge: Cambridge University Press, pp285-357

Kelly, D.L., Kolstad, C.D. and Mitchell, G.T. (2005) Adjustment costs from environmental change, Journal of Environmental Economics and Management , 50: 468-495.

# 77 Leary, N. A. (1999) A framework for benefit cost analysis of adaptation to climate change and climate variability. Mitigation and Adaptation Strategies for Global Change 4:307-318.

Maier-Reimer, E. and Hasselmann, K. (1987) Tr ansport and Storage of Carbon Dioxide in the Ocean: An Inorganic Ocean Circulation Carbon Cycle Model. Climate Dynamics 2 

# 63-90

Manne, A. (2004) Global Climate Change: An Opponent’s Notes. Copenhagen Consensus Opponent Note, Copenhagen Consensus.

Manne, A., Mendelsohn, R., and Richels, R. (1995) A model for evaluating regional and global effects of greenhouse policies. Energy Policy 23, 17-34.

Manne, A., and Richels, R. (1995) The Gr eenhouse Debate: Economic Efficiency, Burden Sharing and Hedging Strategies, Environmental Economics 16 

# , 1-38

Manne, A., and Stephan, G. (1999) Climate-ch ange policies and intern ational rate-if-return

differentials Energy Policy 27, 309-316

Mendelsohn, R. (2000) Efficient adaptation to climate change, Climatic Change , 45:583-600.

Mendelsohn, R. and Neumann, J. (eds) (1999) The Impact of Climate Change on the United States Economy Cambridge University Press, Cambridge UK.

Mendelsohn, R., Morrison, W., Schlesinger, M.E., and Andronova, N.G., (2000) Countryspecific market impacts of climate change, Climatic Change , 45, 553-569.

Murray, C.J.L., and Lopez, A.D. (eds) (1996) The Global Burden of Disease. Harvard School of Public Health. Cambridge, UK. Cambridge University Press.

Nicholls, R.J., and Leatherman, S.P. (1995a) Global Sea Level Rise in Strzepek, S., and Smith, J.B. (eds) When Climate Changes: Potential Impact and Implications. Cambridge, Cambridge University Press.

Nicholls, R.J., Leatherman, S.P., Dennis, K.C., and Volonte, C.R. (1995b) Impacts and Responses to Sea Level Rise: Qualit ative and Quantitative Assessments Journal of Coastal

Research 14, 26-43

Nicholls, Robert J. and Richard J.T. Klein (2003) Climate change and coastal management on Europe's Coast. EVA Working Paper No.3. PIK.

Nordhaus, W.D. (1991) To Slow or Not to Slow: The Economics of the Greenhouse Effect

The Economic Journal 101, 920-937

Nordhaus, W.D. (1992) An Optimal Transition Path for Controlling Greenhouse Gases,”
Science, 258, 1315-1319

Nordhaus, W.D. (1994) Expert Opinion on Climate Change. American Scientist 82, 45-51.

# 78 Nordhaus, W.D. (1998) New Estimates of the Economic Impacts of Climate Change, available at http://www.econ.yale.edu/%7Enordhaus/homepage/impact%20text%20122998a.PDF

Nordhaus, W.D. (2006) Geography and macroeconomics: new data and new findings. Proceedings of the National Academy of Sciences

Nordhaus, W.D., and Boyer, M. (2000). Warming the World: Economic Models of Global Warming. Cambridge, Massachusets, MIT Press.

Pearce, D.W., Cline, W.R., Achanta, A., Fankha user, S., Pachauri, R., Tol. R., and Vellinga, P. , 1996. The social costs of climate change: greenhouse damage and the benefits of control, in Bruce, J., Lee. H., and Haites, E. (1995) (eds) Climate Change 1995: Economics and Social Dimensions of Climate Change, Cambridge University Press, pp. 183-224.

Peck , S.C. and Teisberg, T.J. (1992). CETA: A model for carbon emissions trajectory

assessment. The Energy Journal 13, issue 1, pp. 55-67.

Plambeck, E., and Hope, C. (1997). The PAGE model: integrating the science ad economics of global warming. Energy Economics 19, pp. 77-101

Reilly, J.M., Hohmann, N., and Kane, S. (1994 ) Climate Change and Agricultural Trade: Who Benefits, Who Loses? Global Environmental Change 4, 24-36

Rosenzweig, C., and Parry, M.L. (1994). Potential impacts of climate change on world food supply. Nature 367, 133-138.

Thomas, C.D., A. Cameron, R.E. Green, M. Bakkenes, L.J. Beaumont, Y.C. Collingham, B.F.N. Erasmus, M.F. de Siqueira, A. Grainger , L. Hannah, L. Hughes, B. Huntley, A.S. van Jaarsveld, G.F. Midgley, L. Miles, M.A. Ortega-H uerta, A.T. Peterson, O.L. Phillips, and S.E. Williams, 2004: Extinction risk from climate change. Nature (London), 427(6970), pp. 145-

148.

Thomas, D., Osbahr, H., Twyman, C., Adger, W.N. and Hewitson, B. (2005) ADAPTIVE: adaptation to climate change amongst natural resource-dependent societies in the developing world: across the Southern African climate gradient. Technical report 35, Tyndall Centre for Climate Change Research, UEA, Norwich.

Tol, R.S.J. 1999 Spatial and Temporal Efficien cy in Climate Policy: Applications of FUND.

Environmental and Resource Economics 14, 33-49

Tol, R.S.J., 2002a: Estimates of the damage costs of climate change. Part 1: Benchmark

estimates. Environmental and Resource Economics , 21(1), pp. 47-73.

Tol, R.S.J., 2002b: Estimates of the damage costs of climate change. Part 2: Dynamic

estimates. Environmental and Resource Economics , 21(1), pp. 135-160.

# 79 Tompkins, E.L., Boyd, E., Nicholson-Cole, S.A., Weatherhead, K., Arnell, N.W. and Adger, W.N. (2005) Linking adaptation research and practice. Tyndall Centre for Climate Change Research, UEA, Norwich.

Warren, R., Arnell, N.W., Nicholls. R.J., Levy, P., and Price, J. Understanding the Regional 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, 447-47280ANNEXTable A3.1a. Regional impacts of different levels of temperature change for the EU between 2020 and 2200 ($$ million)Global mean temperature riseabove 1990Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$millionTotal impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0 0 0 2674 290 7146 0 0 0 2674 290 71462040 0.96 0.34 1.82 11 0 9 9941 1188 25975 0 0 0 9952 1188 259772060 1.73 0.78 3.06 190 0 1060 26630 4076 66279 871 0 0 27691 4099 718472080 2.65 1.32 4.52 1166 0 5001 66217 10331 164699 9485 0 64270 76868 11066 2386852100 3.65 1.90 6.03 4291 0 15292 155177 23671 410357 54113 0 320820 213581 26358 7168752150 5.90 3.30 9.30 40079 2324 132445 906480 125877 2642860 655784 0 2384995 1602343 175849 46250442200 7.39 4.22 11.56 181465 13240 607975 3522563 441963 10597250 2840952 0 8732939 6544980 831555 18073590Table 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 %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.00 0.00 0.00 0.02 0.00 0.06 0.00 0.00 0.00 0.02 0.00 0.062040 0.96 0.34 1.82 0.00 0.00 0.00 0.06 0.01 0.16 0.00 0.00 0.00 0.06 0.01 0.162060 1.73 0.78 3.06 0.00 0.00 0.00 0.12 0.02 0.30 0.00 0.00 0.00 0.12 0.02 0.322080 2.65 1.32 4.52 0.00 0.00 0.02 0.22 0.03 0.56 0.03 0.00 0.22 0.26 0.04 0.802100 3.65 1.90 6.03 0.01 0.00 0.04 0.39 0.06 1.04 0.14 0.00 0.81 0.54 0.07 1.812150 5.90 3.30 9.30 0.04 0.00 0.13 0.91 0.13 2.66 0.66 0.00 2.40 1.62 0.18 4.662200 7.39 4.22 11.56 0.07 0.01 0.24 1.420.18 4.27 1.14 0.00 3.52 2.63 0.33 7.2881Table A3.2a. Regional impacts of different levels of temperature change for the Eastern Europeand Former Soviet Union between2020 and 2200 ($$ million)Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ million ToYear Mean Min Max Mean Min Max Mean Min Max Mean Min MaxM2020 0.38 0.10 0.80 0 0 0 -2781 -8716 175 0 0 0 -22040 0.96 0.34 1.82 -22 -119 0 -8483 -26855 564 0 0 0 -82060 1.73 0.78 3.06 -273 -1342 1 -22463 -69475 1204 -537 0 0 -22080 2.65 1.32 4.52 -1450 -6044 53 -58822 -186630 2858 -5612 -38934 0 -62100 3.65 1.90 6.03 -5237 -21149 274 -148155 -477248 6823 -34658 -232335 0 -12150 5.90 3.30 9.30 -41841 -167428 2529 -836187 -2775385 34933 -416692 -1856134 1714 -12200 7.39 4.22 11.56 -176146 -692159 9455 -3139917 -11042240 116070 -1806448 -7074760 90184 -5Table A3.2b. Regional impacts of different levels of temperature change for Eastern Europe & Former Soviet Union between 2020and 2200 (%GDP)Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.00 0.00 0.00 -0.07 -0.20 0.00 0.00 0.00 0.00 -0.07 -0.20 0.002040 0.96 0.34 1.82 0.00 0.00 0.00 -0.12 -0.39 0.01 0.00 0.00 0.00 -0.12 -0.39 0.012060 1.73 0.78 3.06 0.00 -0.01 0.00 -0.21 -0.65 0.01 0.00 0.00 0.00 -0.22 -0.69 0.012080 2.65 1.32 4.52 -0.01 -0.04 0.00 -0.35 -1.12 0.02 -0.03 -0.23 0.00 -0.40 -1.33 0.022100 3.65 1.90 6.03 -0.02 -0.08 0.00 -0.58 -1.85 0.03 -0.13 -0.90 0.00 -0.73 -2.52 0.032150 5.90 3.30 9.30 -0.06 -0.26 0.00 -1.30 -4.30 0.05 -0.65 -2.88 0.00 -2.01 -7.05 0.092200 7.39 4.22 11.56 -1.94 -6.83 0.07 -1.12 -4.38 0.06 -3.17 -10.73 0.1482Table A3.3a. Regional impacts of different levels of temperature change for the USA between 2020 and 2200 ($$ million)Global mean temperature riseabove 1990Economic impacts $$millionNon-economic impacts $ million Discontinuity impacts $$millionTotal impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0 0 0 765 74 2159 0 0 0 765 74 21592040 0.96 0.34 1.82 6 0 32 3110 327 8820 0 0 0 3116 327 88222060 1.73 0.78 3.06 93 0 461 8863 969 23880 230 0 0 9187 969 251932080 2.65 1.32 4.52 518 0 2107 23353 2409 65422 2562 0 15040 26433 2494 800002100 3.65 1.90 6.03 1878 31 6886 58495 5852 170537 16158 0 95173 76531 6219 2588542150 5.90 3.30 9.30 15593 746 53042 327696 31295 960327 200333 0 788173 543622 44172 16304562200 7.39 4.22 11.56 67801 3323 225733 1247334 110233 3578630 869367 0 2839733 2184501 183498 6302201Table A3.3bRegional impacts of different levels of temperature change for the USA between 2020 and 2200 (%GDP)Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.00 0.00 0.00 0.01 0.00 0.02 0.00 0.00 0.00 0.01 0.00 0.022040 0.96 0.34 1.82 0.00 0.00 0.00 0.02 0.00 0.05 0.00 0.00 0.00 0.02 0.00 0.052060 1.73 0.78 3.06 0.00 0.00 0.00 0.04 0.00 0.10 0.00 0.00 0.00 0.04 0.00 0.102080 2.65 1.32 4.52 0.00 0.00 0.01 0.07 0.01 0.20 0.01 0.00 0.05 0.08 0.01 0.242100 3.65 1.90 6.03 0.00 0.00 0.02 0.13 0.01 0.38 0.04 0.00 0.21 0.17 0.01 0.582150 5.90 3.30 9.30 0.01 0.00 0.05 0.29 0.03 0.86 0.18 0.00 0.71 0.49 0.04 1.462200 7.39 4.22 11.56 0.02 0.00 0.08 0.450.04 1.28 0.31 0.00 1.02 0.78 0.07 2.2583Table A3.4a Regional impacts of different levels of temperature change for China between 2020 and 2200 ($$ million)Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 83 0 474 174 0 1082 0 0 02040 0.96 0.34 1.82 445 0 2645 971 0 5642 0 0 02060 1.73 0.78 3.06 2636 0 11671 5836 0 25178 837 0 02080 2.65 1.32 4.52 13306 182 46483 29365 739 102038 9944 0 576762100 3.65 1.90 6.03 47708 2347 156270 104976 7876 326579 59192 0 3433562150 5.90 3.30 9.30 369971 23112 1249852 807984 72885 2482477 699311 0 25856192200 7.39 4.22 11.56 1531516 89850 5445028 3329953 284605 10732760 3035695 0 9903641Table 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 %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.00 0.00 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.022040 0.96 0.34 1.82 0.00 0.00 0.02 0.01 0.00 0.03 0.00 0.00 0.00 0.01 0.00 0.052060 1.73 0.78 3.06 0.01 0.00 0.05 0.03 0.00 0.11 0.00 0.00 0.00 0.04 0.00 0.182080 2.65 1.32 4.52 0.04 0.00 0.15 0.09 0.00 0.32 0.03 0.00 0.18 0.16 0.00 0.622100 3.65 1.90 6.03 0.11 0.01 0.35 0.23 0.02 0.73 0.13 0.00 0.76 0.47 0.03 1.712150 5.90 3.30 9.30 0.33 0.02 1.11 0.72 0.06 2.21 0.62 0.00 2.30 1.67 0.14 5.222200 7.39 4.22 11.56 0.54 0.03 1.93 1.180.10 3.81 1.08 0.00 3.51 2.80 0.26 8.5584Table 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 Discontinuity impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 19777 1326 56259 42996 4732 132036 0 0 02040 0.96 0.34 1.82 82360 7185 234479 179458 23127 516074 0 0 02060 1.73 0.78 3.06 260965 26447 722604 569844 90160 1551985 17093 0 02080 2.65 1.32 4.52 712667 86386 2051023 1558000 264875 4185445 203748 0 12695552100 3.65 1.90 6.03 1686336 212198 4942781 3688221 607283 10049840 1108327 0 61432242150 5.90 3.30 9.30 10130400 1183751 33536480 22125990 3319951 60399210 13369870 0 473485902200 7.39 4.22 11.56 38988210 4196394 138374200 85010580 11735550 249796300 57467810 0 170060800Table A3.5b Regional impacts of different levels oftemperature change for India between 2020 and 2200 (%GDP)Global mean temperature rise above1990Economic impacts % GDP Non-economic impacts %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.25 0.02 0.72 0.55 0.06 1.69 0.00 0.00 0.00 0.80 0.10 2.202040 0.96 0.34 1.82 0.59 0.05 1.69 1.29 0.17 3.71 0.00 0.00 0.00 1.88 0.30 5.082060 1.73 0.78 3.06 1.32 0.13 3.67 2.89 0.46 7.87 0.09 0.00 0.00 4.30 0.80 11.472080 2.65 1.32 4.52 2.55 0.31 7.34 5.58 0.95 14.99 0.73 0.00 4.55 8.86 1.70 25.072100 3.65 1.90 6.03 4.26 0.54 12.49 9.32 1.53 25.39 2.80 0.00 15.52 16.38 2.86 49.992150 5.90 3.30 9.30 10.22 1.19 33.83 22.32 3.3560.93 13.49 0.00 47.76 46.02 6.96 132.682200 7.39 4.22 11.56 15.70 1.69 55.73 34.24 4.73100.60 23.14 0.00 68.49 73.08 12.22 212.1585Table A3.6a Regional impacts of different levels of temperature change for the Africa between 2020 and 2200 ($$ million)Global mean temperature rise above 1990 Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 10450 1005 28512 22757 3780 58018 0 0 02040 0.96 0.34 1.82 47808 5151 122232 104156 16934 257328 0 0 02060 1.73 0.78 3.06 151150 18217 384231 329670 57845 843444 8680 0 02080 2.65 1.32 4.52 401747 50160 1059862 876927 159302 2253393 102004 0 6680532100 3.65 1.90 6.03 927032 115034 2462154 2023839 359925 5271611 557614 0 31523642150 5.90 3.30 9.30 5246023 543667 15133850 11443390 1811905 31295700 6651060 0 232482602200 7.39 4.22 11.56 19882320 1856552 60662740 43310900 6127983 123160000 28751300 0 84473740Table A3.6b Regional impacts of different levels of temperature change for Africa between 2020 and 2200 (%GDP)Global mean temperature rise above1990Economic impacts % GDP Non-economic impacts %GDPDiscontinuity impacts %GDP Total impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.16 0.02 0.44 0.350.06 0.90 0.00 0.00 0.00 0.51 0.10 1.262040 0.96 0.34 1.82 0.35 0.04 0.89 0.760.12 1.88 0.00 0.00 0.00 1.11 0.23 2.542060 1.73 0.78 3.06 0.70 0.08 1.78 1.530.27 3.92 0.04 0.00 0.00 2.27 0.51 5.502080 2.65 1.32 4.52 1.19 0.15 3.13 2.590.47 6.65 0.30 0.00 1.97 4.08 0.92 11.322100 3.65 1.90 6.03 1.74 0.22 4.62 3.800.68 9.90 1.05 0.00 5.92 6.59 1.24 19.862150 5.90 3.30 9.30 3.93 0.41 11.35 8.581.36 23.46 4.99 0.00 17.43 17.50 2.85 46.722200 7.39 4.22 11.56 5.95 0.56 18.16 12.961.83 36.87 8.61 0.00 25.29 27.52 4.53 69.2986Table A3.7a. Regional impacts of different levels of temperature change for Latin America between 2020 and 2200 ($$ million)Global mean temperature riseabove 1990Economic impacts $$ million Non-economic impacts $ million Discontinuity impacts $$ million Total impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 5106 450 13233 11190 1593 27256 0 0 0 16296 3105 372702040 0.96 0.34 1.82 21453 2307 54283 47086 7026 119427 0 0 0 68540 13189 1670842060 1.73 0.78 3.06 66581 7898 175284 146388 24013 376762 3720 0 0 216689 41788 5558842080 2.65 1.32 4.52 188179 22874 526714 414243 69624 1079439 48357 0 335859 650779 118079 18831322100 3.65 1.90 6.03 488178 58668 1452686 1075451 185613 2984154 300765 0 1752892 1864395 312806 54452912150 5.90 3.30 9.30 2810353 292444 9324687 6188381 881942 18605640 3626043 0 13275550 12624780 2019515 374978702200 7.39 4.22 11.56 10723110 1026594 36637150 23586440 3181729 70261750 15631410 0 48940560 49940960 7534222 142634100Table A3.7b. Regional impacts of different levels of temperature change for Latin Americabetween 2020 and 2200 (% GDP)Global mean temperature rise above1990Economic impacts % GDPNon-economic impacts %GDPDiscontinuity impacts % GDP Total impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.07 0.01 0.18 0.150.02 0.37 0.00 0.00 0.00 0.22 0.04 0.502040 0.96 0.34 1.82 0.14 0.01 0.35 0.300.04 0.76 0.00 0.00 0.00 0.44 0.08 1.072060 1.73 0.78 3.06 0.27 0.03 0.71 0.590.10 1.53 0.02 0.00 0.00 0.88 0.17 2.262080 2.65 1.32 4.52 0.49 0.06 1.36 1.070.18 2.79 0.12 0.00 0.87 1.68 0.31 4.862100 3.65 1.90 6.03 0.80 0.10 2.39 1.770.30 4.90 0.49 0.00 2.88 3.06 0.51 8.952150 5.90 3.30 9.30 1.84 0.19 6.12 4.060.58 12.21 2.38 0.00 8.71 8.28 1.32 24.602200 7.39 4.22 11.56 2.81 0.27 9.60 6.18 0.83 18.40 4.09 0.00 12.82 13.08 1.97 37.3687Table 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 impacts $$ millionYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0 0 0 983 123 2519 0 0 02040 0.96 0.34 1.82 16 0 96 2858 355 7883 0 0 02060 1.73 0.78 3.06 112 0 479 6376 754 18077 94 0 02080 2.65 1.32 4.52 397 7 1458 13828 1556 41480 1012 0 68652100 3.65 1.90 6.03 1120 47 3977 29878 3036 88973 6111 0 331292150 5.90 3.30 9.30 7949 459 26400 156547 13545 469995 75926 0 2968762200 7.39 4.22 11.56 32764 1987 114340 578683 46411 1815734 330163 0 1109101Table A3.8b Regional impacts of differentlevels of temperature change for Restof OECD between 2020 and 2200 (% GDP)Global mean temperature rise above 1990 Economic impacts % GDP Non-economic impacts %GDPDiscontinuity impacts %GDPTotal impacts % GDPYear Mean Min Max Mean Min Max Mean Min Max Mean Min Max Mean Min Max2020 0.38 0.10 0.80 0.00 0.00 0.00 0.01 0.00 0.03 0.00 0.00 0.00 0.01 0.00 0.032040 0.96 0.34 1.82 0.00 0.00 0.00 0.02 0.00 0.06 0.00 0.00 0.00 0.02 0.00 0.062060 1.73 0.78 3.06 0.00 0.00 0.00 0.04 0.00 0.11 0.00 0.00 0.00 0.04 0.00 0.112080 2.65 1.32 4.52 0.00 0.00 0.01 0.06 0.01 0.19 0.00 0.00 0.03 0.07 0.01 0.212100 3.65 1.90 6.03 0.00 0.00 0.01 0.10 0.01 0.30 0.02 0.00 0.11 0.13 0.01 0.422150 5.90 3.30 9.30 0.01 0.00 0.04 0.21 0.02 0.63 0.10 0.00 0.40 0.32 0.03 1.062200 7.39 4.22 11.56 0.02 0.00 0.06 0.310.03 0.98 0.18 0.00 0.60 0.51 0.05 1.6288Table A4.1. The regions in FUND.Acronym Name CountriesUSA USA United States of AmericaCAN Canada CanadaWEU WesternEuropeAndorra, Austria, Belgium, Cyprus, Denmark, Finland, France,Germany, Greece, Iceland, Ireland, Italy, Liechtenstein,Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, SanMarino, Spain, Sweden, Switzerland, United KingdomJPK Japan andSouth KoreaJapan, South KoreaANZ Australia andNew ZealandAustralia, New ZealandCEE Central andEasternEuropeAlbania, Bosnia and Herzegovina, Bulgaria, Croatia, CzechRepublic, Hungary, FYR Macedonia, Poland, Romania, Slovakia,Slovenia, YugoslaviaFSU FormerSoviet UnionArmenia, Azerbaijan, Belarus,Estonia, Georgia, Kazakhstan,Latvia, Lithuania, Moldova, Russia, Tajikistan, Turkmenistan,Ukraine, UzbekistanMDE Middle East Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar,Saudi Arabia, Syria, Turkey, United Arab Emirates, West Bank andGaza, YemenCAM CentralAmericaBelize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico,Nicaragua, PanamaSAM SouthAmericaArgentina, Bolivia, Brazil, Chile, French Guiana, Guyana,Paraguay, Peru, Suriname, Uruguay, VenezuelaSAS South Asia Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, Sri LankaSEA SoutheastAsiaBrunei, Cambodia, East Timor, Indonesia, Laos, Malaysia,Myanmar, Papua New Guinea, Philippines, Singapore, Taiwan,Thailand, VietnamCHI China plus China, Hong Kong, North Korea, Macau, MongoliaNAF North Africa Algeria, Egypt, Libya, Morocco, Tunisia, Western SaharaSSA Sub-Saharan Angola, Benin, Botswana, Burkina Faso,Burundi, Cameroon, Cape89Acronym Name CountriesAfrica 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,ZimbabweSIS Small IslandStatesAntigua and Barbuda, Aruba,Bahamas, Barbados, Bermuda,Comoros, Cuba, Dominica, Dominican Republic, Fiji, FrenchPolynesia, 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, SolomonIslands, St Kitts and Nevis, StLucia, St Vincent and Grenadines,Tonga, Trinidad and Tobago, Tuvalu, Vanuatu, Virgin Islands90Table A4.2a. FUND Population in standard Scenario: 2000 = 100.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 0.31 0.28 0.431960 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 0.39 0.34 0.521970 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 0.50 0.44 0.641980 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 0.64 0.58 0.751990 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 0.82 0.77 0.872000 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 1.002010 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 1.23 1.27 1.132020 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 1.50 1.55 1.232030 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 1.75 1.80 1.312040 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 1.96 2.01 1.382050 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 2.14 2.20 1.432060 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 2.31 2.37 1.472070 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 2.49 2.56 1.502080 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 2.65 2.72 1.532090 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 2.75 2.83 1.542100 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 2.81 2.88 1.542110 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 2.83 2.91 1.542120 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 2.83 2.91 1.542130 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 2.83 2.91 1.542140 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 2.83 2.91 1.542150 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 2.83 2.91 1.542160 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 2.83 2.91 1.542170 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 2.83 2.91 1.542180 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 2.83 2.91 1.542190 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 2.83 2.91 1.542200 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 2.83 2.91 1.542210 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 2.83 2.91 1.542220 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 2.83 2.91 1.542230 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 2.83 2.91 1.542240 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 2.83 2.91 1.542250 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 2.83 2.91 1.54912260 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 2.83 2.91 1.542270 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 2.83 2.91 1.542280 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 2.83 2.91 1.542290 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 2.83 2.91 1.542300 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 2.83 2.91 1.5492Table A4.2b. FUND Population in A1B Scenario: 2000 = 100.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.250.27 0.33 0.34 0.35 0.43 0.31 0.28 0.431960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.320.36 0.43 0.42 0.43 0.51 0.39 0.34 0.521970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.430.50 0.56 0.53 0.55 0.65 0.50 0.44 0.641980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.580.66 0.70 0.67 0.69 0.78 0.64 0.58 0.751990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.800.82 0.85 0.83 0.85 0.90 0.82 0.77 0.872000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.06 1.07 1.03 1.04 1.07 1.01 1.01 1.161.19 1.18 1.14 1.12 1.09 1.20 1.23 1.172020 1.11 1.12 1.07 1.08 1.12 1.02 1.02 1.261.40 1.38 1.23 1.21 1.18 1.41 1.45 1.372030 1.14 1.15 1.11 1.12 1.15 1.02 1.02 1.311.57 1.54 1.29 1.27 1.23 1.58 1.62 1.532040 1.16 1.18 1.13 1.14 1.18 1.01 1.01 1.331.69 1.67 1.30 1.28 1.24 1.70 1.75 1.652050 1.18 1.19 1.14 1.15 1.19 0.98 0.98 1.301.78 1.75 1.28 1.26 1.22 1.79 1.84 1.742060 1.19 1.20 1.15 1.16 1.20 0.95 0.94 1.241.80 1.78 1.21 1.20 1.16 1.81 1.86 1.762070 1.19 1.20 1.15 1.16 1.20 0.91 0.90 1.171.80 1.77 1.14 1.13 1.09 1.81 1.86 1.762080 1.20 1.21 1.16 1.17 1.21 0.87 0.86 1.071.75 1.72 1.05 1.03 1.00 1.75 1.80 1.702090 1.20 1.22 1.17 1.18 1.22 0.82 0.82 0.961.66 1.63 0.94 0.93 0.90 1.67 1.71 1.622100 1.21 1.22 1.17 1.18 1.22 0.78 0.78 0.871.58 1.55 0.85 0.84 0.82 1.59 1.63 1.542110 1.22 1.23 1.18 1.19 1.23 0.75 0.75 0.791.51 1.48 0.77 0.76 0.74 1.51 1.55 1.472120 1.23 1.24 1.19 1.20 1.24 0.72 0.72 0.721.44 1.42 0.71 0.70 0.68 1.45 1.49 1.412130 1.23 1.24 1.19 1.20 1.24 0.69 0.69 0.671.39 1.37 0.66 0.65 0.63 1.40 1.43 1.362140 1.24 1.25 1.20 1.21 1.25 0.67 0.67 0.631.34 1.32 0.61 0.60 0.59 1.35 1.39 1.312150 1.24 1.25 1.20 1.21 1.25 0.65 0.65 0.591.31 1.29 0.58 0.57 0.55 1.31 1.35 1.282160 1.24 1.25 1.20 1.21 1.25 0.64 0.64 0.561.28 1.26 0.55 0.55 0.53 1.28 1.32 1.252170 1.25 1.26 1.21 1.22 1.26 0.63 0.63 0.541.25 1.24 0.53 0.53 0.51 1.26 1.30 1.232180 1.25 1.26 1.21 1.22 1.26 0.62 0.62 0.531.24 1.22 0.52 0.51 0.50 1.25 1.28 1.212190 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.24 1.27 1.202200 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202210 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202220 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202230 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202240 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202250 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.20932260 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202270 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202280 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202290 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.202300 1.25 1.26 1.21 1.22 1.26 0.61 0.61 0.521.23 1.21 0.51 0.50 0.49 1.23 1.27 1.2094Table A4.2c. FUND Population in A2 Scenario: 2000 = 100USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.250.27 0.33 0.34 0.35 0.43 0.31 0.28 0.431960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.320.36 0.43 0.42 0.43 0.51 0.39 0.34 0.521970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.430.50 0.56 0.53 0.55 0.65 0.50 0.44 0.641980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.580.66 0.70 0.67 0.69 0.78 0.64 0.58 0.751990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.800.82 0.85 0.83 0.85 0.90 0.82 0.77 0.872000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.07 1.08 1.04 1.05 1.08 1.02 1.02 1.191.21 1.19 1.16 1.15 1.12 1.22 1.25 1.192020 1.12 1.13 1.09 1.10 1.13 1.06 1.06 1.341.46 1.44 1.31 1.29 1.25 1.47 1.51 1.432030 1.18 1.19 1.14 1.15 1.19 1.12 1.12 1.511.77 1.74 1.47 1.45 1.41 1.78 1.83 1.732040 1.22 1.23 1.18 1.19 1.23 1.17 1.17 1.662.08 2.05 1.63 1.60 1.56 2.09 2.15 2.032050 1.26 1.27 1.22 1.23 1.27 1.22 1.22 1.752.26 2.23 1.72 1.69 1.64 2.28 2.34 2.212060 1.30 1.31 1.26 1.27 1.31 1.28 1.28 1.842.44 2.40 1.80 1.78 1.73 2.45 2.52 2.382070 1.36 1.37 1.32 1.33 1.37 1.36 1.35 1.942.64 2.60 1.90 1.88 1.82 2.65 2.73 2.582080 1.48 1.49 1.43 1.45 1.49 1.49 1.49 2.082.91 2.87 2.03 2.01 1.95 2.93 3.01 2.842090 1.58 1.59 1.53 1.54 1.59 1.59 1.59 2.153.06 3.01 2.11 2.08 2.02 3.08 3.16 2.992100 1.63 1.64 1.58 1.59 1.64 1.64 1.64 2.193.13 3.09 2.14 2.11 2.05 3.15 3.24 3.062110 1.68 1.69 1.63 1.64 1.69 1.69 1.69 2.223.21 3.16 2.18 2.15 2.09 3.22 3.31 3.132120 1.72 1.74 1.67 1.68 1.74 1.74 1.74 2.263.27 3.22 2.21 2.18 2.12 3.29 3.38 3.202130 1.76 1.78 1.71 1.72 1.78 1.78 1.78 2.293.33 3.28 2.24 2.21 2.14 3.35 3.44 3.262140 1.80 1.82 1.74 1.76 1.82 1.82 1.82 2.313.39 3.33 2.26 2.23 2.17 3.40 3.50 3.312150 1.83 1.85 1.77 1.79 1.85 1.85 1.85 2.333.43 3.38 2.28 2.25 2.19 3.45 3.54 3.352160 1.86 1.87 1.80 1.81 1.87 1.88 1.88 2.353.47 3.41 2.30 2.27 2.20 3.49 3.58 3.392170 1.88 1.89 1.82 1.83 1.89 1.90 1.90 2.373.50 3.44 2.32 2.29 2.22 3.52 3.61 3.422180 1.89 1.91 1.83 1.85 1.91 1.92 1.91 2.383.52 3.46 2.33 2.29 2.23 3.54 3.64 3.442190 1.90 1.92 1.84 1.86 1.92 1.93 1.92 2.383.53 3.48 2.33 2.30 2.23 3.55 3.65 3.452200 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462210 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462220 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462230 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462240 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462250 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.46952260 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462270 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462280 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462290 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.462300 1.90 1.92 1.84 1.86 1.92 1.93 1.93 2.383.54 3.48 2.33 2.30 2.24 3.56 3.65 3.4696Table A4.2d. FUND Population in B1 Scenario: 2000 = 100USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 0.31 0.28 0.431960 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 0.39 0.34 0.521970 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 0.50 0.44 0.641980 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 0.64 0.58 0.751990 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 0.82 0.77 0.872000 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 1.002010 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 1.20 1.23 1.172020 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 1.40 1.44 1.362030 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 1.56 1.61 1.522040 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 1.68 1.73 1.632050 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 1.76 1.81 1.712060 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 1.81 1.86 1.762070 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 1.81 1.86 1.762080 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 1.75 1.80 1.712090 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 1.65 1.70 1.612100 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 1.53 1.57 1.492110 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 1.42 1.46 1.382120 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 1.33 1.37 1.292130 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 1.26 1.29 1.222140 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 1.19 1.23 1.162150 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 1.14 1.18 1.112160 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 1.10 1.13 1.072170 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 1.07 1.10 1.042180 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 1.05 1.08 1.022190 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 1.04 1.07 1.012200 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 1.04 1.07 1.012210 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 1.04 1.07 1.012220 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 1.04 1.07 1.012230 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 1.04 1.07 1.012240 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 1.04 1.07 1.012250 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 1.04 1.07 1.01972260 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 1.04 1.07 1.012270 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 1.04 1.07 1.012280 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 1.04 1.07 1.012290 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 1.04 1.07 1.012300 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 1.04 1.07 1.0198Table A4.2e. FUND Population in B2 Scenario: 2000 = 100USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.57 0.44 0.79 0.60 0.49 0.70 0.62 0.250.27 0.33 0.34 0.35 0.43 0.31 0.28 0.431960 0.67 0.57 0.84 0.69 0.61 0.79 0.74 0.320.36 0.43 0.42 0.43 0.51 0.39 0.34 0.521970 0.75 0.68 0.91 0.79 0.73 0.86 0.83 0.430.50 0.56 0.53 0.55 0.65 0.50 0.44 0.641980 0.83 0.79 0.94 0.89 0.81 0.94 0.91 0.580.66 0.70 0.67 0.69 0.78 0.64 0.58 0.751990 0.91 0.89 0.97 0.96 0.87 0.99 0.99 0.800.82 0.85 0.83 0.85 0.90 0.82 0.77 0.872000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.06 1.07 1.02 1.03 1.07 1.00 1.00 1.171.21 1.19 1.15 1.13 1.10 1.21 1.24 1.182020 1.08 1.09 1.05 1.06 1.09 1.00 1.00 1.271.44 1.41 1.25 1.23 1.19 1.44 1.48 1.402030 1.09 1.10 1.05 1.06 1.10 0.99 0.99 1.361.65 1.63 1.33 1.31 1.27 1.66 1.71 1.622040 1.08 1.09 1.04 1.05 1.09 0.98 0.98 1.421.85 1.82 1.39 1.37 1.33 1.86 1.91 1.802050 1.06 1.07 1.03 1.04 1.07 0.96 0.96 1.462.01 1.98 1.43 1.41 1.37 2.02 2.08 1.972060 1.05 1.06 1.02 1.03 1.06 0.94 0.94 1.482.16 2.12 1.45 1.43 1.39 2.17 2.23 2.112070 1.04 1.05 1.00 1.01 1.05 0.93 0.93 1.502.27 2.24 1.47 1.45 1.41 2.28 2.35 2.222080 1.03 1.04 0.99 1.00 1.04 0.92 0.92 1.512.36 2.32 1.48 1.46 1.42 2.37 2.44 2.312090 1.02 1.03 0.99 1.00 1.03 0.91 0.91 1.522.42 2.39 1.49 1.47 1.43 2.44 2.50 2.372100 1.01 1.02 0.98 0.99 1.02 0.91 0.91 1.532.47 2.44 1.50 1.48 1.43 2.49 2.56 2.422110 1.01 1.02 0.98 0.99 1.02 0.90 0.90 1.542.52 2.49 1.51 1.49 1.44 2.54 2.61 2.472120 1.00 1.01 0.97 0.98 1.01 0.90 0.90 1.552.57 2.53 1.51 1.49 1.45 2.58 2.66 2.512130 1.00 1.01 0.97 0.98 1.01 0.90 0.89 1.552.61 2.57 1.52 1.50 1.46 2.62 2.70 2.552140 1.00 1.00 0.96 0.97 1.00 0.89 0.89 1.562.65 2.61 1.53 1.51 1.46 2.66 2.73 2.592150 0.99 1.00 0.96 0.97 1.00 0.89 0.89 1.572.68 2.64 1.53 1.51 1.47 2.69 2.77 2.622160 0.99 1.00 0.96 0.97 1.00 0.89 0.89 1.572.70 2.66 1.54 1.52 1.47 2.72 2.79 2.642170 0.99 1.00 0.96 0.96 1.00 0.89 0.89 1.572.72 2.68 1.54 1.52 1.47 2.74 2.81 2.662180 0.99 0.99 0.95 0.96 0.99 0.89 0.88 1.582.74 2.70 1.54 1.52 1.48 2.75 2.83 2.682190 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.76 2.84 2.682200 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692210 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692220 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692230 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692240 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692250 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692260 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.69992270 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692280 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692290 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.692300 0.98 0.99 0.95 0.96 0.99 0.89 0.88 1.582.75 2.71 1.54 1.52 1.48 2.77 2.84 2.69100Table 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 SIS1950 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 0.301960 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 0.401970 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 0.531980 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 0.711990 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 0.812000 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 1.002010 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 1.222020 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 1.562030 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 1.982040 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 2.522050 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 3.192060 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 4.052070 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 5.182080 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 6.682090 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 8.442100 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 10.422110 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 12.592120 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 14.922130 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 17.332140 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 19.742150 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 22.052160 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 24.352170 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 26.902180 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 29.722190 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 32.832200 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 36.262210 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 39.982220 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 43.912230 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 48.041012240 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 52.342250 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 56.812260 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 61.412270 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 66.122280 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 70.912290 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 75.752300 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 80.60102Table 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 SIS1950 0.33 0.28 0.24 0.17 0.48 0.33 0.530.48 0.35 0.39 0.29 0.090.04 0.23 1.03 0.301960 0.39 0.37 0.32 0.23 0.54 0.44 0.690.60 0.50 0.54 0.31 0.150.05 0.33 1.12 0.401970 0.47 0.48 0.41 0.32 0.61 0.59 0.910.76 0.71 0.75 0.34 0.230.08 0.47 1.21 0.531980 0.57 0.63 0.54 0.44 0.68 0.78 1.200.96 1.01 1.04 0.36 0.350.12 0.67 1.30 0.711990 0.78 0.86 0.85 0.88 0.83 0.90 1.790.87 0.85 0.84 0.72 0.690.46 0.88 1.07 0.812000 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.002010 1.20 1.21 1.20 1.20 1.21 1.09 1.131.53 1.40 1.39 1.79 1.841.90 1.40 1.38 1.442020 1.42 1.42 1.41 1.41 1.42 2.00 2.073.02 2.29 2.28 3.52 3.633.73 2.29 2.25 2.372030 1.66 1.67 1.66 1.66 1.67 3.40 3.525.33 3.62 3.60 6.20 6.406.59 3.62 3.57 3.752040 1.95 1.95 1.94 1.94 1.96 5.27 5.478.17 5.48 5.45 9.52 9.82 10.10 5.47 5.39 5.662050 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 8.042060 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 10.572070 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 13.742080 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 17.462090 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 21.902100 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 27.112110 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 32.902120 6.61 6.63 6.60 6.60 6.64 36.84 38.22 88.8738.24 38.04 103.53 106.88 109.94 38.22 37.65 39.542130 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 47.062140 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 55.452150 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 64.702160 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 74.762170 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 85.532180 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 96.892190 15.54 15.57 15.49 15.50 15.59 101.25 105.05 285.68 105.10 104.56 332.80 343.56353.39 105.04 103.49 108.682200 17.21 17.24 17.16 17.16 17.27 112.45 116.67 318.15 116.73 116.13 370.63 382.61393.57 116.66 114.94 120.702210 18.97 19.01 18.92 18.93 19.04 123.99 128.65 350.81 128.71 128.05 408.68 421.89433.97 128.64 126.74 133.092220 20.84 20.88 20.78 20.79 20.91 136.18 141.29 385.30 141.36 140.64 448.85 463.36476.62 141.29 139.20 146.182230 22.80 22.84 22.73 22.74 22.87 148.98 154.57 421.50 154.64 153.85 491.02 506.89521.40 154.56 152.28 159.911032240 24.84 24.89 24.77 24.78 24.92 162.33 168.42 459.27 168.50 167.64 535.03 552.32568.13 168.41 165.93 174.242250 26.96 27.01 26.88 26.89 27.05 176.18 182.79 498.45 182.88 181.94 580.67 599.44616.60 182.78 180.08 189.102260 29.14 29.20 29.06 29.07 29.24 190.45 197.59 538.83 197.69 196.68 627.71 648.00666.55 197.58 194.67 204.422270 31.38 31.44 31.29 31.30 31.49 205.06 212.75 580.17 212.86 211.77 675.87 697.71717.69 212.75 209.60 220.112280 33.65 33.72 33.56 33.57 33.77 219.92 228.17 622.21 228.28 227.12 724.84 748.27769.69 228.16 224.79 236.062290 35.95 36.02 35.85 35.86 36.07 234.92 243.73 664.65 243.85 242.61 774.28 799.31822.19 243.72 240.12 252.162300 38.25 38.32 38.14 38.15 38.38 249.95 259.32 707.17 259.45 258.13 823.81 850.44874.79 259.31 255.49 268.29104Table A4.3c. FUND per capita income in A2 Scenario; 2000=1.00USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.33 0.28 0.24 0.17 0.48 0.33 0.530.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.301960 0.39 0.37 0.32 0.23 0.54 0.44 0.690.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.401970 0.47 0.48 0.41 0.32 0.61 0.59 0.910.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.531980 0.57 0.63 0.54 0.44 0.68 0.78 1.200.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.711990 0.78 0.86 0.85 0.88 0.83 0.90 1.790.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.812000 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.15 1.15 1.15 1.16 1.16 0.92 0.961.17 1.15 1.16 1.37 1.42 1.46 1.17 1.15 1.212020 1.27 1.28 1.27 1.28 1.28 1.23 1.271.54 1.45 1.46 1.80 1.86 1.91 1.47 1.45 1.522030 1.45 1.45 1.44 1.45 1.45 1.73 1.802.09 1.87 1.88 2.44 2.52 2.59 1.89 1.86 1.962040 1.62 1.62 1.62 1.62 1.63 2.25 2.332.70 2.29 2.31 3.15 3.26 3.35 2.32 2.29 2.402050 1.75 1.75 1.75 1.76 1.76 2.66 2.763.19 2.64 2.66 3.73 3.86 3.97 2.67 2.63 2.762060 1.90 1.91 1.90 1.91 1.91 3.16 3.273.80 3.06 3.08 4.45 4.60 4.73 3.10 3.05 3.212070 2.10 2.11 2.10 2.11 2.11 3.87 4.024.71 3.66 3.69 5.51 5.69 5.85 3.71 3.65 3.842080 2.46 2.47 2.45 2.47 2.47 5.25 5.456.53 4.88 4.92 7.64 7.89 8.12 4.94 4.87 5.112090 2.74 2.75 2.73 2.75 2.75 6.44 6.688.17 5.97 6.02 9.56 9.87 10.15 6.05 5.96 6.262100 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 6.882110 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 7.602120 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 8.402130 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 9.282140 4.39 4.40 4.38 4.40 4.40 10.58 10.9813.49 9.78 9.86 15.78 16.30 16.76 9.91 9.76 10.252150 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 11.322160 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 12.512170 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 13.822180 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 15.262190 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 16.862200 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 18.622210 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 20.532220 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 22.552230 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 24.671052240 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 26.882250 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 29.172260 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 31.542270 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 33.962280 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 36.422290 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 38.902300 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 41.39106Table A4.3d. FUND per capita income in B1 Scenario; 2000=1.00USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.33 0.28 0.24 0.17 0.48 0.33 0.530.48 0.35 0.39 0.29 0.090.04 0.23 1.03 0.301960 0.39 0.37 0.32 0.23 0.54 0.44 0.690.60 0.50 0.54 0.31 0.150.05 0.33 1.12 0.401970 0.47 0.48 0.41 0.32 0.61 0.59 0.910.76 0.71 0.75 0.34 0.230.08 0.47 1.21 0.531980 0.57 0.63 0.54 0.44 0.68 0.78 1.200.96 1.01 1.04 0.36 0.350.12 0.67 1.30 0.711990 0.78 0.86 0.85 0.88 0.83 0.90 1.790.87 0.85 0.84 0.72 0.690.46 0.88 1.07 0.812000 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.002010 1.21 1.21 1.21 1.21 1.22 1.04 1.081.36 1.31 1.31 1.59 1.641.69 1.31 1.29 1.362020 1.42 1.42 1.41 1.41 1.42 1.71 1.782.25 1.96 1.96 2.63 2.712.79 1.97 1.94 2.042030 1.60 1.61 1.60 1.60 1.61 2.72 2.823.68 2.95 2.95 4.29 4.434.56 2.96 2.92 3.062040 1.80 1.80 1.79 1.79 1.80 4.12 4.275.79 4.31 4.31 6.75 6.977.17 4.33 4.27 4.482050 2.00 2.01 2.00 2.00 2.01 5.86 6.088.49 5.93 5.93 9.91 10.24 10.53 5.95 5.87 6.162060 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 8.012070 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 10.222080 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 12.862090 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 15.942100 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 19.582110 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 23.762120 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 28.552130 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 33.982140 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 40.042150 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 46.722160 5.83 5.84 5.81 5.81 5.85 56.00 58.10 87.5351.97 51.94 102.15 105.49 108.51 52.18 51.41 53.982170 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 61.762180 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 69.962190 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 78.482200 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 87.162210 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 96.112220 10.51 10.53 10.48 10.47 10.54 109.50 113.61 171.16 101.61 101.56 199.74 206.26212.17 102.02 100.52 105.552230 11.49 11.52 11.46 11.45 11.53 119.79 124.29 187.24 111.16 111.10 218.50 225.64232.10 111.61 109.96 115.471072240 12.52 12.55 12.49 12.48 12.57 130.53 135.42 204.02 121.12 121.05 238.09 245.86252.90 121.61 119.82 125.822250 13.59 13.62 13.55 13.54 13.64 141.66 146.98 221.42 131.45 131.38 258.40 266.84274.48 131.98 130.04 136.552260 14.69 14.72 14.65 14.64 14.74 153.14 158.88 239.36 142.10 142.02 279.33 288.45296.71 142.68 140.57 147.622270 15.82 15.85 15.78 15.76 15.87 164.89 171.07 257.73 153.01 152.92 300.76 310.59319.48 153.62 151.36 158.942280 16.97 17.00 16.92 16.91 17.02 176.84 183.47 276.40 164.09 164.00 322.56 333.09342.63 164.76 162.32 170.462290 18.12 18.16 18.07 18.06 18.19 188.90 195.98 295.25 175.28 175.19 344.56 355.81366.00 175.99 173.39 182.082300 19.28 19.32 19.23 19.22 19.35 200.98 208.52 314.14 186.50 186.39 366.60 378.57389.41 187.25 184.49 193.73108Table A4.3e. FUND per capita income in B2 Scenario; 2000=1.00 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.33 0.28 0.24 0.17 0.48 0.33 0.530.48 0.35 0.39 0.29 0.09 0.04 0.23 1.03 0.301960 0.39 0.37 0.32 0.23 0.54 0.44 0.690.60 0.50 0.54 0.31 0.15 0.05 0.33 1.12 0.401970 0.47 0.48 0.41 0.32 0.61 0.59 0.910.76 0.71 0.75 0.34 0.23 0.08 0.47 1.21 0.531980 0.57 0.63 0.54 0.44 0.68 0.78 1.200.96 1.01 1.04 0.36 0.35 0.12 0.67 1.30 0.711990 0.78 0.86 0.85 0.88 0.83 0.90 1.790.87 0.85 0.84 0.72 0.69 0.46 0.88 1.07 0.812000 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.16 1.16 1.15 1.16 1.16 0.99 1.031.37 1.13 1.14 1.60 1.65 1.70 1.15 1.13 1.192020 1.27 1.27 1.26 1.27 1.27 1.52 1.572.18 1.46 1.48 2.55 2.63 2.70 1.48 1.46 1.532030 1.37 1.38 1.37 1.38 1.38 2.41 2.503.14 2.06 2.08 3.67 3.78 3.89 2.09 2.06 2.172040 1.49 1.49 1.49 1.50 1.50 3.77 3.914.22 2.99 3.02 4.93 5.09 5.24 3.03 2.98 3.132050 1.61 1.62 1.61 1.62 1.62 5.34 5.54 5.404.114.156.306.516.704.174.114.3120601.761.761.751.761.766.907.166.595.275.327.697.948.175.345.265.5320701.931.931.921.931.938.288.597.796.316.379.089.389.656.406.306.6220802.102.112.102.112.119.399.758.947.187.2510.4310.7711.087.287.187.5420902.302.312.302.312.3110.4410.8410.117.998.0611.8012.1812.538.107.988.3821002.532.542.532.542.5411.5411.9711.288.828.9013.1613.5913.988.948.819.2521102.802.802.792.802.8112.7413.2212.469.749.8314.5415.0115.449.889.7310.2221203.093.103.083.103.1014.0814.6113.7610.7610.8616.0616.5817.0610.9110.7511.2921303.413.423.403.423.4215.5516.1315.2011.8812.0017.7418.3218.8412.0511.8712.4721403.773.783.763.783.7817.1817.8216.7913.1313.2519.5920.2320.8113.3113.1213.7721504.164.174.154.184.1818.9719.6918.5514.5014.6421.6422.3522.9914.7114.4915.2121604.604.614.594.614.6220.9621.7520.4916.0216.1723.9124.6925.3916.2416.0016.8121705.085.095.075.105.1023.1524.0222.6317.6917.8626.4127.2728.0517.9417.6818.5621805.615.625.605.635.6325.5726.5325.0019.5519.7329.1730.1230.9919.8219.5320.5121906.206.216.186.226.2228.2529.3127.6121.5921.7932.2233.2734.2321.8921.5722.6522006.856.866.836.876.8731.2132.3830.5023.8524.0735.5936.7637.8124.1923.8325.0222107.557.577.537.577.5834.4135.7033.6426.3026.5539.2540.5341.6926.6726.2727.5922208.298.318.278.328.3237.7939.2136.9428.8829.1643.1144.5145.7929.2928.8630.3022309.079.099.059.109.1141.3442.8940.4131.6031.8947.1648.6950.0932.0431.5733.1510922409.899.919.869.919.9245.0546.7444.0334.4334.7551.3853.0654.5834.9134.4036.12225010.7310.7510.7010.7610.7748.8950.7247.7937.3737.7255.7757.5959.2337.8937.3339.20226011.6011.6211.5711.6311.6452.8554.8351.6640.3940.7760.2862.2564.0340.9640.3642.38227012.4912.5112.4512.5212.5356.9159.0455.6343.4943.9064.9167.0368.9544.1043.4545.63228013.3913.4213.3613.4313.4461.0363.3259.6646.6447.0869.6171.8873.9447.3046.6048.94229014.3114.3414.2714.3514.3665.1967.6463.7349.8250.2974.3676.7978.9850.5349.7852.27230015.2215.2515.1815.2615.2869.3671.9667.8053.0153.5179.1281.7084.0453.7652.9655.62110Table A4.4a. Autonomous energy efficiency (AEEI) in FUND standard scenario 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.23 1.53 1.251960 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 1.32 1.36 1.021970 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 1.06 1.17 0.871980 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 0.99 1.10 0.851990 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 1.04 0.99 1.062000 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 1.002010 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 1.05 1.07 1.032020 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 1.15 1.17 1.112030 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 1.25 1.27 1.212040 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 1.35 1.37 1.302050 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 1.45 1.47 1.402060 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 1.55 1.58 1.502070 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 1.65 1.68 1.602080 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 1.75 1.79 1.692090 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 1.85 1.89 1.792100 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 1.95 1.99 1.892110 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 2.05 2.09 1.982120 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 2.14 2.18 2.072130 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 2.23 2.27 2.152140 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 2.31 2.36 2.232150 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 2.39 2.43 2.312160 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 2.46 2.51 2.382170 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 2.55 2.60 2.472180 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 2.63 2.68 2.542190 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 2.68 2.73 2.592200 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 2.74 2.79 2.642210 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 2.79 2.84 2.702220 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 2.85 2.90 2.752230 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 2.90 2.96 2.812240 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 2.96 3.02 2.862250 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 3.02 3.08 2.921112260 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 3.08 3.14 2.982270 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 3.15 3.21 3.042280 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 3.21 3.27 3.102290 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 3.27 3.34 3.172300 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 3.34 3.41 3.23112Table A4.4b Autonomous energy efficiency (AEEI) in FUND A1B 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.23 1.53 1.251960 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 1.32 1.36 1.021970 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 1.06 1.17 0.871980 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 0.99 1.10 0.851990 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 1.04 0.99 1.062000 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 1.002010 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 1.05 1.07 1.032020 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 1.20 1.23 1.172030 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 1.49 1.52 1.442040 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 1.80 1.84 1.742050 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 2.12 2.16 2.052060 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 2.39 2.43 2.312070 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 2.71 2.77 2.622080 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 3.14 3.20 3.042090 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 3.67 3.74 3.552100 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 4.18 4.26 4.042110 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 4.61 4.70 4.452120 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 5.05 5.15 4.892130 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 5.52 5.62 5.332140 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 5.99 6.11 5.792150 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 6.47 6.60 6.262160 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 6.96 7.09 6.732170 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 7.44 7.59 7.202180 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 7.92 8.08 7.662190 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 8.39 8.56 8.112200 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 8.85 9.02 8.552210 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 9.30 9.48 8.992220 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 9.78 9.96 9.452230 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 10.28 10.47 9.932240 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 10.80 11.01 10.442250 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 11.35 11.57 10.981132260 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 11.93 12.17 11.542270 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 12.54 12.79 12.132280 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 13.19 13.44 12.752290 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 13.86 14.13 13.402300 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 14.57 14.85 14.08114Table A4.4c Autonomous energy efficiency (AEEI) in FUND A2 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.23 1.53 1.251960 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 1.32 1.36 1.021970 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 1.06 1.17 0.871980 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 0.99 1.10 0.851990 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 1.04 0.99 1.062000 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 1.002010 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 1.05 1.07 1.032020 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 1.19 1.21 1.152030 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 1.45 1.48 1.402040 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 1.69 1.72 1.632050 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 1.77 1.81 1.722060 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 1.90 1.94 1.842070 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 2.13 2.18 2.062080 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 2.69 2.74 2.602090 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 2.99 3.05 2.892100 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 3.05 3.11 2.952110 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 3.21 3.27 3.102120 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 3.37 3.44 3.262130 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 3.54 3.61 3.432140 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 3.72 3.80 3.602150 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 3.91 3.99 3.782160 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 4.12 4.19 3.982170 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 4.33 4.41 4.182180 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 4.55 4.63 4.402190 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 4.78 4.87 4.622200 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 5.02 5.12 4.862210 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 5.28 5.38 5.102220 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 5.55 5.66 5.372230 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 5.83 5.95 5.642240 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 6.13 6.25 5.932250 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 6.45 6.57 6.231152260 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 6.78 6.91 6.552270 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 7.12 7.26 6.892280 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 7.49 7.63 7.242290 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 7.87 8.02 7.612300 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 8.27 8.43 8.00116Table A4.4d Autonomous energy efficiency (AEEI) in FUND B1. 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.23 1.53 1.251960 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 1.32 1.36 1.021970 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 1.06 1.17 0.871980 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 0.99 1.10 0.851990 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 1.04 0.99 1.062000 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 1.002010 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 1.05 1.07 1.032020 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 1.21 1.23 1.172030 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 1.52 1.55 1.472040 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 1.97 2.01 1.902050 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 2.61 2.66 2.522060 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 3.56 3.63 3.442070 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 4.88 4.98 4.722080 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 6.58 6.71 6.362090 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 8.85 9.02 8.552100 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 11.39 11.61 11.012110 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 13.79 14.06 13.332120 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 16.46 16.78 15.912130 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 19.35 19.73 18.712140 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 22.42 22.85 21.672150 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 25.59 26.09 24.742160 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 28.78 29.34 27.832170 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 31.90 32.52 30.842180 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 34.83 35.50 33.672190 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 37.46 38.19 36.222200 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 39.71 40.47 38.392210 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 41.74 42.54 40.352220 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 43.87 44.72 42.412230 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 46.11 47.01 44.582240 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 48.47 49.41 46.862250 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 50.95 51.94 49.261172260 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 53.56 54.59 51.782270 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 56.30 57.39 54.422280 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 59.17 60.32 57.212290 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 62.20 63.40 60.132300 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 65.38 66.65 63.21118Table A4.4e Autonomous energy efficiency (AEEI) in FUND B2 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.23 1.53 1.251960 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 1.32 1.36 1.021970 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 1.06 1.17 0.871980 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 0.99 1.10 0.851990 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 1.04 0.99 1.062000 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 1.002010 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 1.05 1.07 1.032020 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 1.20 1.22 1.162030 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 1.47 1.49 1.422040 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 1.83 1.86 1.762050 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 2.19 2.23 2.122060 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 2.49 2.54 2.412070 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 2.69 2.74 2.602080 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 2.77 2.82 2.682090 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 2.87 2.93 2.782100 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 3.03 3.09 2.932110 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 3.18 3.25 3.082120 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 3.35 3.41 3.242130 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 3.52 3.59 3.402140 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 3.70 3.77 3.582150 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 3.89 3.96 3.762160 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 4.09 4.17 3.952170 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 4.30 4.38 4.152180 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 4.52 4.60 4.372190 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 4.75 4.84 4.592200 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 4.99 5.09 4.822210 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 5.24 5.35 5.072220 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 5.51 5.62 5.332230 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 5.79 5.91 5.602240 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 6.09 6.21 5.892250 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 6.40 6.53 6.191192260 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 6.73 6.86 6.512270 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 7.07 7.21 6.842280 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 7.44 7.58 7.192290 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 7.82 7.97 7.562300 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 8.22 8.37 7.94120Table A4.5a Autonomous carbon efficiency improvement (ACEI) FUND standard scenario 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.67 0.60 0.60 1.42 1.05 0.80 1.21 2.721.12 0.97 1.76 1.65 1.24 1.21 1.51 1.251960 0.74 0.70 0.60 1.16 1.06 0.70 1.03 1.901.08 0.98 1.51 1.33 0.46 1.30 1.34 1.021970 0.72 0.68 0.59 0.79 1.11 0.65 1.00 1.401.15 1.01 1.38 1.03 0.62 1.05 1.15 0.871980 0.79 0.73 0.66 0.86 1.11 0.66 0.99 1.331.04 1.09 1.14 1.08 0.60 0.98 1.08 0.841990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.081.01 1.04 1.02 1.10 0.81 1.03 0.98 1.062000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.07 1.07 1.07 1.05 1.07 1.09 0.99 1.071.06 1.05 1.07 1.04 1.12 1.14 1.16 1.042020 1.14 1.15 1.11 1.14 1.16 1.13 1.07 1.231.16 1.14 1.20 1.17 1.17 1.39 1.41 1.122030 1.15 1.16 1.12 1.19 1.21 1.14 1.08 1.391.23 1.22 1.31 1.27 1.21 1.61 1.65 1.202040 1.15 1.16 1.12 1.22 1.24 1.14 1.08 1.511.30 1.28 1.41 1.37 1.24 1.81 1.84 1.262050 1.15 1.16 1.12 1.24 1.26 1.14 1.08 1.611.35 1.33 1.51 1.46 1.27 1.98 2.01 1.312060 1.15 1.16 1.12 1.25 1.28 1.14 1.08 1.701.38 1.37 1.58 1.54 1.28 2.13 2.17 1.342070 1.15 1.16 1.12 1.26 1.29 1.14 1.08 1.781.42 1.40 1.66 1.62 1.29 2.29 2.34 1.372080 1.15 1.16 1.12 1.27 1.30 1.14 1.08 1.851.44 1.42 1.73 1.68 1.30 2.44 2.49 1.402090 1.15 1.17 1.12 1.28 1.31 1.15 1.08 1.901.45 1.43 1.77 1.72 1.31 2.55 2.60 1.412100 1.17 1.19 1.14 1.30 1.33 1.16 1.10 1.941.47 1.46 1.81 1.76 1.34 2.62 2.68 1.432110 1.19 1.21 1.16 1.33 1.36 1.19 1.12 1.981.50 1.49 1.85 1.79 1.36 2.68 2.73 1.462120 1.22 1.23 1.19 1.36 1.39 1.21 1.14 2.021.53 1.52 1.88 1.83 1.39 2.73 2.78 1.492130 1.24 1.26 1.21 1.38 1.41 1.23 1.17 2.061.57 1.55 1.92 1.86 1.42 2.79 2.84 1.522140 1.27 1.29 1.24 1.41 1.44 1.26 1.19 2.101.60 1.58 1.96 1.90 1.45 2.84 2.90 1.552150 1.29 1.31 1.26 1.44 1.47 1.28 1.22 2.141.63 1.61 2.00 1.94 1.48 2.90 2.96 1.582160 1.32 1.34 1.29 1.47 1.50 1.31 1.24 2.191.66 1.64 2.04 1.98 1.51 2.96 3.02 1.612170 1.35 1.36 1.31 1.50 1.53 1.34 1.27 2.231.70 1.68 2.08 2.02 1.54 3.02 3.08 1.642180 1.37 1.39 1.34 1.53 1.56 1.36 1.29 2.281.73 1.71 2.12 2.06 1.57 3.08 3.14 1.682190 1.40 1.42 1.37 1.56 1.59 1.39 1.32 2.321.76 1.74 2.17 2.10 1.60 3.14 3.20 1.712200 1.43 1.45 1.39 1.59 1.63 1.42 1.34 2.371.80 1.78 2.21 2.14 1.63 3.21 3.27 1.752210 1.46 1.48 1.42 1.62 1.66 1.45 1.37 2.421.84 1.82 2.25 2.19 1.66 3.27 3.33 1.782220 1.49 1.51 1.45 1.66 1.69 1.48 1.40 2.461.87 1.85 2.30 2.23 1.70 3.34 3.40 1.822230 1.52 1.54 1.48 1.69 1.73 1.51 1.43 2.511.91 1.89 2.35 2.28 1.73 3.40 3.47 1.852240 1.55 1.57 1.51 1.72 1.76 1.54 1.46 2.561.95 1.93 2.39 2.32 1.77 3.47 3.54 1.892250 1.58 1.60 1.54 1.76 1.80 1.57 1.48 2.621.99 1.97 2.44 2.37 1.80 3.54 3.61 1.931212260 1.61 1.63 1.57 1.79 1.83 1.60 1.51 2.672.03 2.01 2.49 2.42 1.84 3.61 3.68 1.972270 1.64 1.67 1.60 1.83 1.87 1.63 1.55 2.722.07 2.05 2.54 2.47 1.88 3.69 3.76 2.012280 1.68 1.70 1.63 1.87 1.91 1.66 1.58 2.782.11 2.09 2.59 2.52 1.91 3.76 3.83 2.052290 1.71 1.73 1.67 1.91 1.95 1.70 1.61 2.832.15 2.13 2.65 2.57 1.95 3.84 3.91 2.092300 1.74 1.77 1.70 1.94 1.99 1.73 1.64 2.892.20 2.17 2.70 2.62 1.99 3.91 3.99 2.13122Table A4.5b Autonomous carbon efficicency improvement (ACEI) in FUND A1B 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 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 1.24 1.55 1.251960 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 1.33 1.37 1.021970 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 1.07 1.18 0.871980 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 1.01 1.11 0.841990 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 1.06 1.00 1.062000 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 1.002010 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 0.96 0.98 1.042020 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 0.98 1.00 1.152030 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 1.10 1.13 1.322040 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 1.27 1.29 1.512050 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 1.47 1.50 1.752060 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 1.74 1.78 2.082070 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 2.02 2.06 2.412080 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 2.25 2.29 2.682090 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 2.44 2.49 2.912100 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 2.61 2.66 3.112110 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 2.74 2.80 3.272120 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 2.88 2.94 3.442130 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 3.03 3.09 3.612140 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 3.19 3.25 3.802150 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 3.35 3.42 3.992160 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 3.52 3.59 4.202170 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 3.70 3.77 4.412180 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 3.89 3.97 4.642190 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 4.09 4.17 4.872200 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 4.30 4.38 5.122210 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 4.52 4.61 5.382220 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 4.75 4.84 5.662230 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 4.99 5.09 5.952240 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 5.25 5.35 6.252250 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 5.52 5.62 6.571232260 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 5.80 5.91 6.912270 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 6.10 6.22 7.262280 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 6.41 6.53 7.632290 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 6.74 6.87 8.022300 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 7.08 7.22 8.43124Table A4.5c Autonomous carbon efficicency improvement (ACEI) in FUND A2 2000=1.0USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.67 0.60 0.60 1.44 1.06 0.80 1.21 2.721.13 0.97 1.77 1.66 1.24 1.24 1.54 1.251960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.901.09 0.98 1.52 1.34 0.46 1.33 1.37 1.021970 0.72 0.68 0.59 0.80 1.12 0.65 1.00 1.401.16 1.02 1.40 1.04 0.62 1.07 1.17 0.871980 0.79 0.73 0.66 0.87 1.12 0.66 0.99 1.331.04 1.09 1.15 1.09 0.60 1.00 1.11 0.841990 0.96 0.98 0.90 1.05 0.99 0.86 1.20 1.081.02 1.05 1.03 1.11 0.81 1.05 1.00 1.062000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.07 1.07 1.07 0.98 1.01 1.09 0.99 1.071.00 0.99 1.01 0.98 1.12 0.99 1.01 1.042020 1.15 1.16 1.12 1.00 1.02 1.15 1.08 1.191.03 1.02 1.03 1.00 1.17 1.02 1.04 1.122030 1.19 1.21 1.16 1.03 1.05 1.20 1.14 1.241.09 1.08 1.06 1.03 1.20 1.08 1.10 1.192040 1.22 1.24 1.19 1.05 1.08 1.30 1.23 1.291.18 1.17 1.11 1.08 1.25 1.17 1.19 1.292050 1.25 1.27 1.22 1.08 1.10 1.36 1.28 1.321.20 1.19 1.13 1.10 1.28 1.19 1.21 1.322060 1.27 1.29 1.24 1.09 1.12 1.38 1.31 1.311.17 1.15 1.13 1.09 1.27 1.15 1.18 1.282070 1.29 1.31 1.26 1.11 1.13 1.40 1.33 1.311.14 1.13 1.13 1.10 1.27 1.13 1.15 1.252080 1.31 1.33 1.28 1.13 1.15 1.41 1.33 1.321.13 1.11 1.14 1.10 1.28 1.11 1.13 1.232090 1.33 1.35 1.29 1.14 1.17 1.41 1.33 1.331.12 1.10 1.14 1.11 1.29 1.10 1.12 1.222100 1.37 1.39 1.33 1.18 1.20 1.44 1.37 1.371.14 1.12 1.17 1.14 1.33 1.12 1.14 1.242110 1.44 1.46 1.40 1.24 1.27 1.52 1.44 1.441.19 1.18 1.23 1.20 1.39 1.18 1.20 1.312120 1.51 1.53 1.47 1.30 1.33 1.59 1.51 1.511.25 1.24 1.30 1.26 1.47 1.24 1.26 1.372130 1.59 1.61 1.55 1.37 1.40 1.68 1.59 1.591.32 1.30 1.36 1.32 1.54 1.30 1.33 1.442140 1.67 1.69 1.63 1.44 1.47 1.76 1.67 1.671.39 1.37 1.43 1.39 1.62 1.37 1.40 1.522150 1.75 1.78 1.71 1.51 1.55 1.85 1.75 1.761.46 1.44 1.51 1.46 1.70 1.44 1.47 1.592160 1.84 1.87 1.80 1.59 1.62 1.95 1.84 1.851.53 1.51 1.58 1.54 1.79 1.51 1.54 1.682170 1.94 1.97 1.89 1.67 1.71 2.05 1.94 1.941.61 1.59 1.67 1.62 1.88 1.59 1.62 1.762180 2.04 2.07 1.99 1.76 1.79 2.15 2.04 2.041.69 1.67 1.75 1.70 1.98 1.67 1.71 1.852190 2.14 2.17 2.09 1.85 1.89 2.26 2.14 2.141.78 1.76 1.84 1.79 2.08 1.76 1.79 1.952200 2.25 2.28 2.19 1.94 1.98 2.38 2.25 2.251.87 1.85 1.93 1.88 2.18 1.85 1.89 2.052210 2.37 2.40 2.31 2.04 2.08 2.50 2.36 2.371.97 1.94 2.03 1.97 2.30 1.94 1.98 2.152220 2.49 2.52 2.42 2.14 2.19 2.62 2.48 2.492.07 2.04 2.14 2.07 2.41 2.04 2.08 2.262230 2.61 2.65 2.55 2.25 2.30 2.76 2.61 2.622.17 2.15 2.25 2.18 2.54 2.15 2.19 2.382240 2.75 2.79 2.68 2.37 2.42 2.90 2.75 2.752.28 2.26 2.36 2.29 2.67 2.26 2.30 2.502250 2.89 2.93 2.82 2.49 2.54 3.05 2.89 2.892.40 2.37 2.48 2.41 2.80 2.37 2.42 2.631252260 3.04 3.08 2.96 2.62 2.67 3.20 3.03 3.042.52 2.49 2.61 2.53 2.95 2.49 2.54 2.762270 3.19 3.24 3.11 2.75 2.81 3.37 3.19 3.192.65 2.62 2.74 2.66 3.10 2.62 2.67 2.902280 3.35 3.40 3.27 2.89 2.95 3.54 3.35 3.362.79 2.76 2.88 2.80 3.25 2.76 2.81 3.052290 3.53 3.58 3.44 3.04 3.11 3.72 3.52 3.532.93 2.90 3.03 2.94 3.42 2.90 2.95 3.202300 3.71 3.76 3.61 3.19 3.26 3.91 3.70 3.713.08 3.04 3.19 3.09 3.60 3.05 3.10 3.37126Table 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 NAF SSA SIS1950 0.67 0.60 0.60 1.43 1.05 0.80 1.21 2.721.14 0.98 1.77 1.66 1.24 1.24 1.55 1.251960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.901.09 0.99 1.52 1.34 0.46 1.33 1.38 1.021970 0.72 0.68 0.59 0.80 1.11 0.65 1.00 1.401.16 1.03 1.39 1.04 0.62 1.07 1.18 0.871980 0.79 0.73 0.66 0.87 1.11 0.66 0.99 1.331.05 1.10 1.15 1.09 0.60 1.01 1.11 0.841990 0.96 0.98 0.90 1.04 0.98 0.86 1.20 1.081.03 1.06 1.03 1.11 0.81 1.06 1.00 1.062000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.07 1.07 1.07 1.01 1.04 1.09 0.99 1.070.96 0.95 1.01 0.98 1.12 0.95 0.97 1.042020 1.17 1.18 1.14 1.08 1.10 1.16 1.10 1.200.93 0.92 1.05 1.02 1.17 0.92 0.94 1.102030 1.27 1.29 1.24 1.17 1.19 1.28 1.21 1.260.91 0.90 1.10 1.06 1.22 0.90 0.92 1.082040 1.39 1.41 1.36 1.28 1.31 1.36 1.29 1.351.02 1.01 1.17 1.13 1.30 1.01 1.03 1.222050 1.58 1.60 1.54 1.45 1.48 1.40 1.33 1.481.11 1.09 1.29 1.25 1.44 1.09 1.12 1.322060 1.76 1.78 1.72 1.62 1.66 1.56 1.48 1.651.19 1.18 1.43 1.39 1.60 1.18 1.20 1.422070 1.84 1.87 1.79 1.69 1.73 1.78 1.68 1.841.27 1.25 1.59 1.55 1.78 1.25 1.28 1.512080 1.89 1.92 1.84 1.74 1.78 1.94 1.83 2.051.35 1.33 1.78 1.72 1.98 1.33 1.36 1.612090 1.97 2.00 1.93 1.82 1.86 1.95 1.85 2.251.40 1.38 1.95 1.89 2.18 1.38 1.41 1.672100 2.07 2.10 2.02 1.91 1.95 1.84 1.74 2.391.45 1.43 2.07 2.01 2.31 1.43 1.46 1.732110 2.18 2.21 2.12 2.01 2.05 1.84 1.75 2.511.52 1.50 2.18 2.11 2.43 1.50 1.53 1.812120 2.29 2.32 2.23 2.11 2.15 1.86 1.76 2.641.60 1.58 2.29 2.22 2.56 1.58 1.61 1.912130 2.41 2.44 2.35 2.22 2.26 1.88 1.78 2.771.68 1.66 2.41 2.33 2.69 1.66 1.69 2.002140 2.53 2.57 2.47 2.33 2.38 1.91 1.81 2.911.76 1.74 2.53 2.45 2.83 1.75 1.78 2.112150 2.66 2.70 2.59 2.45 2.50 1.96 1.85 3.061.86 1.83 2.66 2.58 2.97 1.83 1.87 2.212160 2.80 2.84 2.73 2.57 2.63 2.01 1.90 3.221.95 1.93 2.79 2.71 3.12 1.93 1.97 2.332170 2.94 2.98 2.87 2.71 2.77 2.08 1.97 3.392.05 2.03 2.94 2.85 3.28 2.03 2.07 2.452180 3.09 3.13 3.01 2.84 2.91 2.15 2.04 3.562.15 2.13 3.09 2.99 3.45 2.13 2.17 2.572190 3.25 3.29 3.17 2.99 3.06 2.25 2.13 3.742.26 2.24 3.24 3.15 3.63 2.24 2.28 2.702200 3.41 3.46 3.33 3.14 3.21 2.36 2.23 3.932.38 2.35 3.41 3.31 3.81 2.35 2.40 2.842210 3.59 3.64 3.50 3.30 3.38 2.48 2.34 4.132.50 2.47 3.59 3.48 4.01 2.47 2.52 2.992220 3.77 3.83 3.68 3.47 3.55 2.60 2.46 4.342.63 2.60 3.77 3.66 4.21 2.60 2.65 3.142230 3.97 4.02 3.87 3.65 3.73 2.74 2.59 4.572.76 2.73 3.96 3.84 4.43 2.73 2.79 3.302240 4.17 4.23 4.06 3.84 3.92 2.88 2.72 4.802.91 2.87 4.16 4.04 4.65 2.87 2.93 3.472250 4.38 4.44 4.27 4.03 4.12 3.02 2.86 5.043.05 3.02 4.38 4.25 4.89 3.02 3.08 3.651272260 4.61 4.67 4.49 4.24 4.33 3.18 3.01 5.303.21 3.17 4.60 4.46 5.14 3.17 3.24 3.832270 4.84 4.91 4.72 4.45 4.55 3.34 3.16 5.573.37 3.34 4.84 4.69 5.40 3.34 3.40 4.032280 5.09 5.16 4.96 4.68 4.79 3.51 3.32 5.863.55 3.51 5.08 4.93 5.68 3.51 3.58 4.242290 5.35 5.42 5.21 4.92 5.03 3.69 3.49 6.163.73 3.69 5.34 5.18 5.97 3.69 3.76 4.452300 5.62 5.70 5.48 5.17 5.29 3.88 3.67 6.473.92 3.88 5.62 5.45 6.28 3.88 3.95 4.68128Table 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 NAF SSA SIS1950 0.67 0.60 0.60 1.43 1.05 0.80 1.21 2.721.13 0.97 1.77 1.66 1.24 1.23 1.54 1.251960 0.74 0.70 0.60 1.17 1.07 0.70 1.03 1.901.08 0.98 1.52 1.33 0.46 1.32 1.36 1.021970 0.72 0.68 0.59 0.80 1.11 0.65 1.00 1.401.15 1.02 1.39 1.03 0.62 1.06 1.17 0.871980 0.79 0.73 0.66 0.87 1.11 0.66 0.99 1.331.04 1.09 1.15 1.09 0.60 1.00 1.10 0.841990 0.96 0.98 0.90 1.04 0.99 0.86 1.20 1.081.02 1.05 1.03 1.11 0.81 1.05 1.00 1.062000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.07 1.07 1.07 1.01 1.03 1.09 0.99 1.071.02 1.01 1.02 0.99 1.12 1.01 1.03 1.042020 1.16 1.17 1.13 1.05 1.07 1.13 1.07 1.201.08 1.07 1.08 1.05 1.18 1.07 1.09 1.122030 1.23 1.25 1.20 1.12 1.14 1.15 1.09 1.291.15 1.14 1.15 1.11 1.25 1.14 1.16 1.212040 1.33 1.35 1.30 1.20 1.23 1.16 1.10 1.411.27 1.25 1.25 1.21 1.37 1.25 1.28 1.322050 1.42 1.44 1.39 1.29 1.32 1.21 1.15 1.561.36 1.34 1.38 1.34 1.51 1.34 1.37 1.422060 1.51 1.53 1.48 1.37 1.40 1.31 1.24 1.701.45 1.44 1.51 1.47 1.65 1.44 1.46 1.522070 1.60 1.63 1.56 1.45 1.48 1.38 1.30 1.821.57 1.55 1.59 1.54 1.74 1.55 1.58 1.642080 1.65 1.68 1.61 1.49 1.53 1.43 1.35 1.861.67 1.65 1.59 1.54 1.74 1.65 1.68 1.742090 1.68 1.70 1.64 1.52 1.55 1.51 1.43 1.811.76 1.74 1.55 1.51 1.70 1.74 1.77 1.842100 1.73 1.76 1.69 1.57 1.60 1.60 1.51 1.821.85 1.83 1.55 1.51 1.70 1.83 1.86 1.932110 1.82 1.85 1.78 1.65 1.68 1.68 1.59 1.911.94 1.92 1.63 1.58 1.79 1.92 1.96 2.032120 1.91 1.94 1.87 1.73 1.77 1.77 1.67 2.012.04 2.02 1.72 1.67 1.88 2.02 2.06 2.132130 2.01 2.04 1.96 1.82 1.86 1.86 1.76 2.112.15 2.12 1.80 1.75 1.97 2.12 2.16 2.242140 2.12 2.15 2.06 1.91 1.96 1.95 1.85 2.222.26 2.23 1.90 1.84 2.07 2.23 2.28 2.362150 2.22 2.25 2.17 2.01 2.06 2.05 1.94 2.332.37 2.35 1.99 1.93 2.18 2.35 2.39 2.482160 2.34 2.37 2.28 2.11 2.16 2.16 2.04 2.452.49 2.47 2.10 2.03 2.29 2.47 2.51 2.602170 2.46 2.49 2.40 2.22 2.27 2.27 2.14 2.582.62 2.59 2.20 2.14 2.41 2.59 2.64 2.742180 2.58 2.62 2.52 2.34 2.39 2.38 2.25 2.712.76 2.72 2.32 2.25 2.53 2.72 2.78 2.882190 2.71 2.75 2.65 2.45 2.51 2.50 2.37 2.852.90 2.86 2.43 2.36 2.66 2.86 2.92 3.022200 2.85 2.89 2.78 2.58 2.64 2.63 2.49 2.993.04 3.01 2.56 2.48 2.80 3.01 3.07 3.182210 3.00 3.04 2.92 2.71 2.77 2.77 2.62 3.143.20 3.16 2.69 2.61 2.94 3.16 3.23 3.342220 3.15 3.20 3.07 2.85 2.91 2.91 2.75 3.313.36 3.33 2.83 2.74 3.09 3.33 3.39 3.512230 3.31 3.36 3.23 3.00 3.06 3.06 2.89 3.473.54 3.50 2.97 2.88 3.25 3.50 3.56 3.692240 3.48 3.53 3.40 3.15 3.22 3.21 3.04 3.653.72 3.68 3.12 3.03 3.42 3.68 3.75 3.882250 3.66 3.71 3.57 3.31 3.38 3.38 3.20 3.843.91 3.86 3.28 3.19 3.59 3.86 3.94 4.081292260 3.85 3.90 3.75 3.48 3.56 3.55 3.36 4.044.11 4.06 3.45 3.35 3.77 4.06 4.14 4.292270 4.05 4.10 3.94 3.66 3.74 3.73 3.53 4.244.32 4.27 3.63 3.52 3.97 4.27 4.35 4.512280 4.25 4.31 4.15 3.85 3.93 3.92 3.71 4.464.54 4.49 3.81 3.70 4.17 4.49 4.57 4.742290 4.47 4.53 4.36 4.04 4.13 4.12 3.90 4.694.77 4.72 4.01 3.89 4.38 4.72 4.81 4.982300 4.70 4.76 4.58 4.25 4.34 4.33 4.10 4.935.01 4.96 4.21 4.09 4.61 4.96 5.05 5.23130Table 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 NAF SSA SIS1950 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01960 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01970 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01980 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01990 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02000 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02010 3 3 0 0 0 0 0 0 44 400 56 169 18 0 164 02020 3 3 0 0 0 0 0 0 41 373 53 158 17 0 153 02030 2 2 0 0 0 0 0 0 38 341 48 144 15 0 140 02040 2 2 0 0 0 0 0 0 33 301 42 127 13 0 124 02050 2 2 0 0 0 0 0 0 29 262 37 111 12 0 108 02060 1 1 0 0 0 0 0 0 23 203 29 86 9 0 83 02070 1 1 0 0 0 0 0 0 16 144 20 61 6 0 59 02080 1 1 0 0 0 0 0 0 9 85 12 36 4 0 35 02090 0 0 0 0 0 0 0 0 3 26 4 11 1 0 11 02100 0 0 0 0 0 0 0 0 -4 -33 -5 -14 -1 0 -13 02110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02120 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02130 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02140 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02160 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02170 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02190 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01312270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0132Table 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 NAF SSA SIS1950 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01960 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01970 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01980 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01990 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02000 8 8 6 0 6 0 100 0 39 355 51 152 92 0 245 02010 13 13 13 0 13 0 200 0 32 284 41 124 165 0 315 02020 8 8 8 0 8 0 30 0 20 180 6 19 25 0 200 02030 5 5 5 0 5 0 10 0 16 144 15 45 60 0 160 02040 0 0 0 0 0 0 -60 0 15 131 21 64 85 0 145 02050 -3 -3 -3 0 -3 0 -130 0 13 117 31 94 125 0 130 02060 3 3 3 0 3 0 -110 0 13 113 20 60 80 0 125 02070 15 15 15 0 15 0 -90 0 12 104 14 41 55 0 115 02080 28 28 28 0 28 0 -70 0 11 95 13 38 50 0 105 02090 23 23 23 0 23 0 -50 0 10 86 18 53 70 0 95 02100 18 18 18 0 18 0 -30 0 8 72 24 71 95 0 80 02110 16 16 16 0 16 0 -27 0 7 65 21 64 86 0 72 02120 14 14 14 0 14 0 -24 0 6 58 19 57 76 0 64 02130 12 12 12 0 12 0 -21 0 6 50 17 50 67 0 56 02140 11 11 11 0 11 0 -18 0 5 43 14 43 57 0 48 02150 9 9 9 0 9 0 -15 0 4 36 12 36 48 0 40 02160 7 7 7 0 7 0 -12 0 3 29 10 29 38 0 32 02170 5 5 5 0 5 0 -9 0 2 22 7 21 29 0 24 02180 4 4 4 0 4 0 -6 0 2 14 5 14 19 0 16 02190 2 2 2 0 2 0 -3 0 1 7 2 7 10 0 8 02200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01332270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0134Table A4.6c FUND A2 secnario. Carbon dioxide emissionsfrom land use; million metric tonnes of carbon.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01960 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01970 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01980 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01990 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02000 2 2 0 0 0 0 0 0 43 386 51 154 94 0 280 02010 0 0 0 0 0 0 0 0 39 347 43 128 170 0 385 02020 0 0 0 0 0 0 0 0 43 383 49 146 195 0 425 02030 0 0 0 0 0 0 0 0 42 378 44 131 175 0 420 02040 0 0 0 0 0 0 0 0 39 351 35 105 140 0 390 02050 0 0 0 0 0 0 0 0 36 320 28 83 110 0 355 02060 0 0 0 0 0 0 0 0 26 234 19 56 75 0 260 02070 0 0 0 0 0 0 0 0 16 144 10 30 40 0 160 02080 0 0 0 0 0 0 0 0 11 95 5 15 20 0 105 02090 0 0 0 0 0 0 0 0 9 81 4 11 15 0 90 02100 0 0 0 0 0 0 0 0 8 72 3 8 10 0 80 02110 0 0 0 0 0 0 0 0 7 65 2 7 9 0 72 02120 0 0 0 0 0 0 0 0 6 58 2 6 8 0 64 02130 0 0 0 0 0 0 0 0 6 50 2 5 7 0 56 02140 0 0 0 0 0 0 0 0 5 43 2 5 6 0 48 02150 0 0 0 0 0 0 0 0 4 36 1 4 5 0 40 02160 0 0 0 0 0 0 0 0 3 29 1 3 4 0 32 02170 0 0 0 0 0 0 0 0 2 22 1 2 3 0 24 02180 0 0 0 0 0 0 0 0 2 14 1 2 2 0 16 02190 0 0 0 0 0 0 0 0 1 7 0 1 1 0 8 02200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01352270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0136Table A4.6d FUND B1 secnario. Carbon dioxide emissionsfrom land use; million metric tonnes of carbon.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01960 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01970 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01980 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01990 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02000 14 14 13 0 13 0 -5 0 35 314 45 135 69 0 200 02010 25 25 25 0 25 0 -10 0 23 203 30 90 120 0 225 02020 15 15 15 0 15 0 -100 0 23 203 28 83 110 0 225 02030 -5 -5 -5 0 -5 0 -310 0 2 18 26 79 105 0 20 02040 -18 -18 -18 0 -18 0 -350 0 -13 -113 24 71 95 0 -125 02050 -23 -23 -23 0 -23 0 -360 0 -7 -59 23 68 90 0 -65 02060 -20 -20 -20 0 -20 0 -380 0 -8 -68 20 60 80 0 -75 02070 -15 -15 -15 0 -15 0 -410 0 -5 -45 19 56 75 0 -50 02080 -35 -35 -35 0 -35 0 -360 0 -11 -95 14 41 55 0 -105 02090 -30 -30 -30 0 -30 0 -340 0 -11 -99 -11 -34 -45 0 -110 02100 -28 -28 -28 0 -28 0 -290 0 -11 -99 -44 -131 -175 0 -110 02110 -25 -25 -25 0 -25 0 -261 0 -10 -89 -39 -118 -158 0 -99 02120 -22 -22 -22 0 -22 0 -232 0 -9 -79 -35 -105 -140 0 -88 02130 -19 -19 -19 0 -19 0 -203 0 -8 -69 -31 -92 -123 0 -77 02140 -17 -17 -17 0 -17 0 -174 0 -7 -59 -26 -79 -105 0 -66 02150 -14 -14 -14 0 -14 0 -145 0 -6 -50 -22 -66 -88 0 -55 02160 -11 -11 -11 0 -11 0 -116 0 -4 -40 -18 -53 -70 0 -44 02170 -8 -8 -8 0 -8 0 -87 0 -3 -30 -13 -39 -53 0 -33 02180 -6 -6 -6 0 -6 0 -58 0 -2 -20 -9 -26 -35 0 -22 02190 -3 -3 -3 0 -3 0 -29 0 -1 -10 -4 -13 -18 0 -11 02200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01372270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0138Table A4.6e FUND B2 scenario. Carbon dioxide emissionsfrom land use; million metric tonnes of carbon.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01960 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01970 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01980 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 01990 3 3 0 0 0 0 0 0 47 426 60 180 19 0 175 02000 -1 -1 -3 0 -3 0 -5 0 39 355 42 127 59 0 245 02010 -5 -5 -5 0 -5 0 -10 0 32 284 25 75 100 0 315 02020 -15 -15 -15 0 -15 0 -180 0 21 189 -19 -56 -75 0 210 02030 -20 -20 -20 0 -20 0 -140 0 6 54 -20 -60 -80 0 60 02040 -15 -15 -15 0 -15 0 -90 0 1 5 -11 -34 -45 0 5 02050 -13 -13 -13 0 -13 0 -40 0 -6 -50 -4 -11 -15 0 -55 02060 -15 -15 -15 0 -15 0 -40 0 -6 -50 -4 -11 -15 0 -55 02070 -20 -20 -20 0 -20 0 -30 0 -5 -45 -5 -15 -20 0 -50 02080 -28 -28 -28 0 -28 0 -30 0 -6 -54 -6 -19 -25 0 -60 02090 -38 -38 -38 0 -38 0 -40 0 -8 -72 -6 -19 -25 0 -80 02100 -48 -48 -48 0 -48 0 -40 0 -10 -90 -8 -23 -30 0 -100 02110 -43 -43 -43 0 -43 0 -36 0 -9 -81 -7 -20 -27 0 -90 02120 -38 -38 -38 0 -38 0 -32 0 -8 -72 -6 -18 -24 0 -80 02130 -33 -33 -33 0 -33 0 -28 0 -7 -63 -5 -16 -21 0 -70 02140 -29 -29 -29 0 -29 0 -24 0 -6 -54 -5 -14 -18 0 -60 02150 -24 -24 -24 0 -24 0 -20 0 -5 -45 -4 -11 -15 0 -50 02160 -19 -19 -19 0 -19 0 -16 0 -4 -36 -3 -9 -12 0 -40 02170 -14 -14 -14 0 -14 0 -12 0 -3 -27 -2 -7 -9 0 -30 02180 -10 -10 -10 0 -10 0 -8 0 -2 -18 -2 -5 -6 0 -20 02190 -5 -5 -5 0 -5 0 -4 0 -1 -9 -1 -2 -3 0 -10 02200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02230 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02240 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02250 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 01392270 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02280 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02290 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 02300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0140Table A4.7a. Methane emissions for all FUND scenarios; 2000 = 100.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.59 0.59 0.59 0.61 0.59 0.60 0.59 0.590.60 0.59 0.60 0.60 0.60 0.60 0.60 0.631960 0.76 0.77 0.76 0.79 0.77 0.75 0.76 0.750.75 0.76 0.76 0.76 0.76 0.77 0.76 0.751970 0.91 0.92 0.91 0.93 0.91 0.91 0.91 0.910.90 0.91 0.91 0.91 0.91 0.91 0.91 0.941980 1.06 1.08 1.07 1.07 1.07 1.06 1.07 1.071.06 1.06 1.06 1.06 1.07 1.06 1.07 1.061990 1.22 1.23 1.21 1.21 1.22 1.21 1.21 1.211.21 1.21 1.21 1.21 1.21 1.21 1.22 1.252000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.08 1.28 1.08 1.11 0.74 1.00 1.17 1.411.37 1.18 1.19 1.33 1.14 1.38 1.43 1.132020 1.14 1.64 1.09 1.11 0.74 1.39 1.47 1.921.77 1.46 1.36 1.64 1.27 1.84 1.75 1.252030 1.27 1.54 1.09 1.14 0.77 1.31 1.65 2.582.13 1.79 1.57 1.85 1.49 2.43 1.90 1.382040 1.34 1.38 1.08 1.18 0.78 1.22 1.69 3.512.40 1.96 1.77 2.01 1.77 3.25 2.34 1.502050 1.34 1.64 1.06 1.21 0.77 1.17 1.67 4.542.58 2.16 2.00 2.14 2.08 4.06 2.81 1.632060 1.39 1.79 1.04 1.21 0.90 1.16 1.92 5.632.77 2.37 2.14 2.32 2.42 4.25 3.44 1.752070 1.42 1.87 1.05 1.21 1.07 1.23 2.25 6.303.00 2.59 2.02 2.68 2.76 3.94 4.00 1.882080 1.52 2.03 1.10 1.25 1.29 1.36 2.52 5.883.29 2.77 1.91 2.77 3.12 4.38 4.23 1.942090 1.70 2.28 1.19 1.32 1.51 1.47 2.66 5.113.56 3.27 1.88 2.74 3.41 5.04 4.27 2.002100 1.90 2.56 1.30 1.32 1.96 1.44 2.79 4.673.73 3.59 1.89 2.70 3.61 5.40 4.29 2.062110 1.99 2.69 1.35 1.39 2.03 1.51 2.91 4.873.88 3.74 1.97 2.81 3.76 5.62 4.47 2.192120 2.06 2.79 1.41 1.43 2.12 1.56 3.03 5.074.04 3.89 2.05 2.93 3.91 5.86 4.66 2.252130 2.15 2.90 1.46 1.50 2.20 1.62 3.14 5.264.21 4.05 2.13 3.04 4.07 6.08 4.84 2.382140 2.23 3.00 1.52 1.54 2.29 1.69 3.26 5.464.37 4.20 2.21 3.15 4.22 6.31 5.02 2.442150 2.31 3.13 1.57 1.61 2.36 1.75 3.38 5.664.52 4.35 2.29 3.27 4.37 6.55 5.21 2.502160 2.39 3.23 1.62 1.64 2.45 1.81 3.50 5.864.67 4.50 2.37 3.38 4.52 6.77 5.38 2.632170 2.47 3.33 1.68 1.71 2.54 1.87 3.62 6.054.83 4.66 2.45 3.50 4.68 7.00 5.57 2.692180 2.55 3.44 1.73 1.75 2.61 1.94 3.73 6.255.00 4.81 2.53 3.61 4.83 7.22 5.75 2.812190 2.63 3.56 1.79 1.82 2.70 1.99 3.85 6.455.15 4.96 2.61 3.73 4.98 7.45 5.93 2.882200 2.71 3.67 1.84 1.89 2.78 2.05 3.97 6.645.31 5.11 2.69 3.84 5.13 7.68 6.11 3.002210 2.83 3.82 1.92 1.96 2.90 2.14 4.14 6.925.54 5.33 2.80 4.00 5.35 8.00 6.37 3.132220 2.94 3.97 1.99 2.07 3.01 2.22 4.31 7.215.75 5.55 2.92 4.17 5.57 8.32 6.62 3.252230 3.05 4.13 2.07 2.14 3.13 2.31 4.47 7.495.98 5.76 3.04 4.33 5.79 8.65 6.88 3.442240 3.17 4.31 2.15 2.21 3.25 2.40 4.64 7.766.21 5.98 3.15 4.49 6.01 8.96 7.14 3.562250 3.28 4.46 2.23 2.29 3.38 2.48 4.81 8.056.42 6.20 3.26 4.65 6.22 9.29 7.40 3.692260 3.40 4.62 2.30 2.39 3.49 2.57 4.98 8.336.65 6.41 3.38 4.81 6.44 9.61 7.66 3.811412270 3.51 4.77 2.38 2.46 3.61 2.66 5.14 8.616.88 6.63 3.49 4.98 6.66 9.94 7.92 3.942280 3.63 4.92 2.46 2.54 3.72 2.75 5.31 8.897.10 6.84 3.60 5.14 6.87 10.26 8.18 4.062290 3.74 5.08 2.53 2.64 3.84 2.83 5.48 9.177.33 7.06 3.72 5.30 7.09 10.58 8.43 4.252300 3.86 5.23 2.62 2.71 3.96 2.92 5.65 9.457.56 7.27 3.83 5.47 7.31 10.91 8.69 4.38142Table A4.7b Parameters of the methane emission reduction cost curve; the 67% confidence interval is given in brackets.Quadratic Exponential - constant Exponential - exponentUSA 5.74E04(4.15E-047.90E-04)5.43E-06(4.44E-066.64E-06)10.28 (9.66 10.90)CAN 1.20E03(8.70E-041.64E-03)7.69E-06(6.30E-069.37E-06)12.49 (11.75 13.23)WEU 3.71E04(2.34E-045.80E-04)1.82E-06(1.37E-062.43E-06)14.27 (13.10 15.45)JPK 1.27E04(8.75E-051.84E-04)4.19E-07(3.32E-075.29E-07)17.43 (16.23 18.63)ANZ 4.12E03(3.03E-035.57E-03)1.25E-05(1.03E-051.51E-05)18.18 (17.14 19.21)EEU 3.90E03(2.81E-035.38E-03)3.13E-05(2.56E-053.83E-05)11.17 (10.49 11.85)FSU 8.87E03(7.49E-031.05E-02)8.51E-05(7.65E-059.46E-05)10.21 (9.89 10.52)MDE 6.32E03(4.86E-038.19E-03)1.26E-05(1.07E-051.49E-05)22.38 (21.29 23.47)CAM 3.65E03(2.87E-034.62E-03)1.30E-05(1.12E-051.51E-05)16.77 (16.03 17.52)SAM 2.75E02(1.81E-024.14E-02)4.07E-06(3.14E-065.27E-06)82.24 (75.89 88.58)SAS 3.16E02(2.43E-024.08E-02)2.51E-05(2.13E-052.95E-05)35.45 (33.74 37.16)SEA 1.43E02(1.06E-021.91E-02)1.94E-05(1.62E-052.33E-05)27.15 (25.66 28.65)CHI 1.26E02(9.50E-031.67E-02)3.18E-05(2.67E-053.80E-05)19.93 (18.88 20.97)MAF 1.43E02(1.06E-021.91E-02)1.94E-05(1.62E-052.33E-05)27.15 (25.66 28.65)SSA 1.43E02(1.06E-021.91E-02)1.94E-05(1.62E-052.33E-05)27.15 (25.66 28.65)SIS 1.43E02(1.06E-021.91E-02)1.94E-05(1.62E-052.33E-05)27.15 (25.66 28.65)143144Table A4.8a Nitrous oxide emissions in all FUND scenarios; 2000 = 100.USA CAN WEU JPK ANZ EEU FSU MDE CAM SAM SAS SEA CHI NAF SSA SIS1950 0.16 0.17 0.17 0.11 0.13 0.16 0.18 0.150.14 0.17 0.16 0.16 0.17 0.17 0.16 0.001960 0.29 0.25 0.29 0.33 0.25 0.26 0.27 0.310.29 0.29 0.29 0.28 0.29 0.30 0.28 0.001970 0.44 0.42 0.43 0.44 0.38 0.42 0.45 0.460.43 0.44 0.44 0.44 0.43 0.43 0.44 0.001980 0.74 0.75 0.74 0.78 0.75 0.74 0.73 0.770.71 0.74 0.73 0.75 0.74 0.73 0.74 1.001990 0.78 0.75 0.78 0.78 0.75 0.79 0.82 0.850.79 0.78 0.79 0.78 0.80 0.77 0.79 1.002000 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.001.00 1.00 1.00 1.00 1.00 1.00 1.00 1.002010 1.06 1.08 1.00 1.11 1.00 1.11 1.18 1.381.14 1.06 1.11 1.16 1.03 1.33 1.21 1.002020 1.09 1.08 0.97 1.11 1.13 1.16 1.36 1.621.36 1.16 1.21 1.25 1.03 1.57 1.42 1.002030 1.09 1.08 0.93 1.22 1.25 1.16 1.55 2.001.50 1.26 1.30 1.34 1.03 1.73 1.58 1.002040 1.08 1.17 0.90 1.22 1.38 1.11 1.73 2.151.57 1.32 1.38 1.44 1.03 1.83 1.79 1.002050 1.15 1.25 0.92 1.33 1.38 1.05 1.91 2.311.57 1.40 1.46 1.50 1.06 1.97 1.95 1.002060 1.21 1.33 0.94 1.33 1.50 1.05 2.09 2.381.64 1.52 1.53 1.56 1.11 2.10 2.12 1.002070 1.30 1.42 0.99 1.33 1.50 1.05 2.27 2.461.71 1.61 1.55 1.72 1.09 2.20 2.23 1.002080 1.40 1.58 1.03 1.33 1.63 1.05 2.45 2.541.71 1.69 1.58 1.75 1.14 2.23 2.28 1.002090 1.53 1.67 1.10 1.44 1.63 1.05 2.73 2.621.86 1.87 1.62 1.81 1.17 2.30 2.30 1.002100 1.67 1.83 1.17 1.44 1.63 1.05 2.91 2.621.86 1.99 1.63 1.91 1.20 2.43 2.30 1.002110 1.68 1.83 1.17 1.44 1.63 1.05 2.91 2.621.86 2.00 1.64 1.91 1.20 2.47 2.32 1.002120 1.69 1.83 1.17 1.44 1.63 1.05 2.91 2.691.86 2.01 1.65 1.91 1.20 2.47 2.32 1.002130 1.70 1.83 1.18 1.44 1.63 1.05 2.91 2.691.93 2.03 1.66 1.94 1.20 2.47 2.33 1.002140 1.70 1.83 1.18 1.44 1.63 1.11 2.91 2.691.93 2.04 1.67 1.94 1.23 2.50 2.35 1.002150 1.71 1.92 1.19 1.56 1.63 1.11 3.00 2.691.93 2.05 1.68 1.94 1.23 2.50 2.37 1.002160 1.72 1.92 1.19 1.56 1.75 1.11 3.00 2.691.93 2.06 1.69 1.97 1.23 2.53 2.37 1.002170 1.74 1.92 1.21 1.56 1.75 1.11 3.00 2.771.93 2.06 1.70 1.97 1.23 2.53 2.39 1.002180 1.75 1.92 1.21 1.56 1.75 1.11 3.00 2.771.93 2.08 1.71 1.97 1.26 2.57 2.40 1.002190 1.76 1.92 1.22 1.56 1.75 1.11 3.00 2.772.00 2.09 1.71 2.00 1.26 2.57 2.42 1.002200 1.76 1.92 1.22 1.56 1.75 1.11 3.00 2.772.00 2.10 1.72 2.00 1.26 2.57 2.42 1.002210 1.77 1.92 1.22 1.56 1.75 1.11 3.00 2.772.00 2.12 1.73 2.00 1.26 2.57 2.44 1.002220 1.78 1.92 1.24 1.56 1.75 1.11 3.00 2.772.00 2.13 1.74 2.03 1.26 2.60 2.44 1.002230 1.79 1.92 1.24 1.56 1.75 1.11 3.00 2.852.07 2.14 1.75 2.03 1.29 2.60 2.46 1.002240 1.79 1.92 1.25 1.56 1.75 1.11 3.00 2.852.07 2.16 1.76 2.03 1.29 2.63 2.47 1.002250 1.80 2.00 1.25 1.67 1.88 1.16 3.09 2.852.07 2.17 1.77 2.06 1.29 2.63 2.49 1.002260 1.82 2.00 1.26 1.67 1.88 1.16 3.09 2.852.07 2.18 1.78 2.06 1.29 2.67 2.49 1.001452270 1.83 2.00 1.26 1.67 1.88 1.16 3.09 2.852.14 2.19 1.79 2.06 1.29 2.67 2.51 1.002280 1.84 2.00 1.26 1.67 1.88 1.16 3.09 2.922.14 2.21 1.79 2.09 1.31 2.67 2.53 1.002290 1.85 2.00 1.28 1.67 1.88 1.16 3.09 2.922.14 2.22 1.80 2.09 1.31 2.70 2.54 1.002300 1.85 2.00 1.28 1.67 1.88 1.16 3.09 2.922.14 2.23 1.82 2.09 1.31 2.70 2.54 1.00146Table A4.8bParameters of the nitrous oxide emission reduction cost curve; the 67% confidence interval is given in brackets.Quadratic Exponential - constant Exponential - exponentUSA 2.14E05(1.91E-052.39E-05)1.36E-08(1.29E-081.45E-08)39.61 (38.56 40.65)CAN 6.92E05(6.29E-057.60E-05)1.62E-08(1.54E-081.70E-08)65.33 (63.88 66.78)WEU 7.26E06(6.60E-067.98E-06)1.97E-08(1.88E-082.08E-08)19.18 (18.75 19.60)JPK 5.32E07(3.21E-078.57E-07)9.54E-09(7.38E-091.23E-08)7.46 (6.60 8.33)ANZ 2.08E04(1.89E-042.29E-04)4.62E-09(4.39E-094.86E-09)212.40 (207.68 217.11)EEU 9.39E05(8.89E-059.93E-05)8.35E-08(7.91E-088.83E-08)33.53 (33.53 33.53)FSU 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)MDE 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)CAM 2.35E04(2.19E-042.53E-04)2.00E-08(1.89E-082.13E-08)108.39 (107.83 108.95)SAM 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)SAS 5.64E04(5.29E-046.01E-04)1.71E-07(1.62E-071.80E-07)57.44 (57.14 57.74)SEA 2.55E15(2.16E-153.01E-15)4.72E-18(4.12E-185.40E-18)23.25 (22.91 23.60)CHI 2.16E05(2.02E-052.30E-05)1.42E-07(1.35E-071.50E-07)12.32 (12.26 12.39)MAF 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)SSA 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)SIS 1.05E05(1.00E-051.10E-05)1.94E-08(1.91E-081.98E-08)23.25 (22.91 23.60)147Table A4.9. Determinants of SF6emissions in FUND.C GDP GDP/cap1990 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)SF6emissions are in million metric tonnes of carbon dioxide equivalent. GDP is in million dollar (1995, MEX). GDP/capita is in dollar (1995,MEX)148Table A4.10 Parameters of equation (C.1).Gasαaβbpre-industrial concentrationMethane (CH4) 0.3597 1/8.6 790 ppbNitrous oxide (N2O) 0.2079 1/120 285 ppbSulphur hexafluoride (SF6) 0.0398 1/3200 0.04 pptaThe parameterαtranslates emissions (in million metric tonnes) into concentrations (in parts per billion or trillion by volume).bThe parameterβdetermines how fast concentrations return to their pre-industrial (and assumedly equilibrium) concentrations; 1/βis theatmospheric 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 Filho, L.G., Callander,B.A., Harris, N., Kattenberg, A. and Maskell, K., (Eds.)Climate Change 1995: The Science of Climate Change -- Contribution of WorkingGroup I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, 1 edn. pp. 65-131. Cambridge: CambridgeUniversity Press.149Table A4.11 Impacts of climate change on agricultureRate of change Benchmark impact Optimal temperature CO2fertilisation(% 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.150Table A4.12. Impact of a 1°C warming on forestry, water, heating, and cooling, in fraction of GDP.Forestry Water Heating Cooling(% GDP) (% GDP) (% GDP) (% GDP)USA 0.000053 (0.000014) -0.00065 (0.00065) 0.00429 (0.00429) -0.00212 (0.00212)CAN 0.000011 (0.000072) -0.00057 (0.00057) 0.00378 (0.00378) -0.00186 (0.00186)WEU 0.000025 (0.000006) -0.00027 (0.00027) 0.00241 (0.00241) -0.00372 (0.00372)JPK 0.000042 (0.000012) 0.00000 (0.00000) 0.00207 (0.00207) -0.00029 (0.00029)ANZ -0.000121 (0.000033) 0.00000 (0.00000) 0.00151 (0.00151) -0.00021 (0.00021)EEU 0.000055 (0.000025) -0.00697 (0.00697) 0.00456 (0.00456) -0.00185 (0.00185)FSU -0.000023 (0.000053) -0.02754 (0.02754) 0.01663 (0.01663) -0.00674 (0.00674)MDE 0.000000 (0.000034) -0.00133 (0.00133) 0.02074 (0.02074) -0.00233 (0.00233)CAM 0.000018 (0.000034) -0.00130 (0.00130) 0.00366 (0.00366) -0.00239 (0.00239)SAM 0.000024 (0.000012) -0.00140 (0.00140) 0.00395 (0.00395) -0.00259 (0.00259)SAS 0.000062 (0.000023) -0.00156 (0.00156) 0.00361 (0.00361) -0.00384 (0.00384)SEA 0.000067 (0.000028) -0.00314 (0.00314) 0.00695 (0.00695) -0.00740 (0.00740)CHI 0.000087 (0.000032) 0.00569 (0.00569) 0.03971 (0.03971) -0.02891 (0.02891)NAF 0.000000 (0.000034) -0.00902 (0.00902) 0.00015 (0.00015) -0.01892 (0.01892)SSA 0.000011 (0.000035) -0.00360 (0.00360) 0.00006 (0.00006) -0.00797 (0.00797)SIS 0.000000 (0.000034) -0.00130 (0.00130) 0.00366 (0.00366) -0.00239 (0.00239)Standard deviations are given in brackets.151Table A4.13. Impact of a one metre sea level rise.levelprot.dryland loss dryland value wetland loss wetland value protectioncostsemigrants% 103km2106$/km2103km2106$/km2109$ 106OECD-A 0.77 4.8 (2.4) 1.3 (0.6) 12.0 (8.6) 5.4 (2.7) 83 (74) 0.13 (0.07)OECD-E 0.86 0.7 (0.4) 13.1 (6.6) 4.0 (2.3) 4.3 (2.2) 136 (45) 0.22 (0.10)OECD-P 0.95 0.3 (0.4) 13.7 (6.7) 1.0 (1.1) 5.9 (2.9) 63 (38) 0.04 (0.02)CEE&fSU 0.93 1.2 (2.7) 0.9 (0.5) 0.0 (0.0) 2.9 (1.5) 53 (50) 0.03 (0.03)ME 0.30 0.6 (1.2) 0.5 (0.3) 0.0 (0.0) 1.3 (0.7) 5 (3) 0.05 (0.08)LA 0.86 7.8 (7.1) 0.3 (0.2) 50.2 (36.4) 0.9 (0.5) 147 (74) 0.71 (1.27)S&SEA 0.93 9.3 (9.6) 0.5 (0.3) 54.9 (48.0) 0.3 (0.2) 305 (158) 2.30 (1.40)CPA 0.93 8.4 (15.1) 0.3 (0.2) 15.6 (17.1) 0.2 (0.1) 171 (126) 2.39 (3.06)AFR 0.89 15.4 (18.4) 0.4 (0.2) 30.8 (14.8) 0.4 (0.2) 92 (35) 2.74 (2.85)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 SISUSA 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 2.5C global warming.Region PopulationaMortalitybMorbidityc∆TdAdditional MortalityeAdditional MorbidityfUSA 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)aThousands of people, 2000.bDeaths per thousand people.cYears of life diseased per thousand people.dRegional temperature change for a 2.5C global warming.eAdditional deaths, thousands of people(67% confidence interval in brackets).fAdditional years of life diseased, thousands (67% confidence interval in brackets).Table A4.16 . Parameters for vector-borne mortality.Malaria Dengue fever SchistosomiasisBaseaImpactbBaseaImpactbBaseaImpactbUSA 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)aMortality (deaths per million people) in 1990.bThe change in mortality due to a one-degree global warming.Table A4.17. Parameters of Equation (HC.1).Constant TemperatureCardiovascular 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 QuadraticUSA 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 QuadraticUSA 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 QuadraticUSA 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 cases).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 22.1552Table A4.22 Parameters of the Monte Carlo analysis (μ: expected value;σ: standard deviation; M: mode; L: lower bound; U: upper bound)Parameter DistributionMethane emissions Normal μ = Table CH4σ= 6.83/yrNitrous oxide emissions Normal μ = Table N2Oσ= 0.0059/yrClimate sensitivity Gamma M = 2.85σ= 1.00Sea level sensitivity Gamma M = 0.31σ= 0.15Life time methane Triangular L = 8.00 M = 8.60 U = 16.00Life time nitrous oxide Triangular L = 100 M = 120 U = 170Response time temperature Triangular L = 25 M = 50 U = 100Response time sea level Triangular L = 25 M = 50 U = 100Life time carbon dioxide Trunc. normal μ = 363.00σ= 90.75 L = 0.00Life time carbon dioxide Trunc. normal μ = 74.00σ= 18.50 L = 0.00Life time carbon dioxide Trunc. normal μ = 17.00σ= 4.25 L = 0.00Life time carbon dioxide Trunc. normal μ = 2.00σ= 0.50 L = 0.00Baseline loss biodiversity Trunc. normal μ = 0.003σ= 0.002 L = 0.000Sensitivity biodiversity Trunc. normal μ = 0.001σ= 0.001 L = 0.000Share biodiversity Triangular L = 0.00 M = 0.05 U = 1.00Water technology rate Trunc. normal μ = 0.005σ= 0.005 L = 0.000Population growth Normal μ = Table Pσ= 0.0048/yrIncome growth Normal μ = Table Yσ= 0.0026/yrEnergy efficiency Normal μ = Table AEEIσ= 0.0005/yrDecarbonisation Normal μ = Table ACEIσ= 0.0009/yrLand use emissions Normal μ = Table CO2Fσ= 0.20/yrEcosystem value Trunc. normal μ = 50σ= 50 L = 0Anchor income Trunc. normal μ = 30,000σ= 10,000 L = 0Value of a statistical life Trunc. normal μ = 200σ= 100 L = 0Value of a year diseased Trunc. normal μ = 0.8σ= 1.2 L = 0Sensitivity malaria Trunc. normal μ = 0.0794σ= 0.0575 L = 0.0000Non-linearity malaria Trunc. normal μ = 1.0σ= 0.5 L = 0.0Sensitivity dengue fever Trunc. normal μ = 0.3534σ= 0.0614 L = 0.0000Non-linearity dengue fever Trunc. normal μ = 1.0σ= 0.5 L = 0.0Sensitivity schistosomiasisTrunc. normal μ = -0.1149σ= 0.0614 U = 0.0000Non-linearity schistosomiasis Trunc. normal μ = 1.0σ= 0.5 L = 0.0Income elasticity vector-borne diseases Trunc. normal μ = -2.65σ= 0.69 U = 0.00Income elasticity diarrhoea mortality Trunc. normal μ = -1.58σ= 0.23 U = 0.00Income elasticity diarrhoea morbidity Trunc. normal μ = -0.42σ= 0.12 U = 0.00Non-linearity diarrhoea mortality Trunc. normal μ = 1.14σ= 0.51 L = 0.00Non-linearity diarrhoea morbidity Trunc. normal μ = 0.70σ= 0.26 L = 0.00Cardiovascular and respiratory mortality Normal Table HC Table HCChange in baseline cardiovasculardisease Trunc. normal μ = 0.0259σ= 0.0096 L = 0.0000Change in baseline respiratorydisease Trunc. normal μ = 0.0016σ= 0.0005 L = 0.0000Change in population above65 Trunc. normal μ = 0.25σ= 0.08 L = 0.00Maximum increase cardiovascular & respiratory disease Trunc. normal μ = 0.05σ= 0.02 L = 0.00Sensitivity water Normal Table EFW Table EFWIncome elasticity water Trunc. normal μ = 0.85σ= 0.15 U = 0.00Non-linearity water Trunc. normal μ = 1.00σ= 0.50 U = 0.00Sensitivity forestry Normal Table EFW Table EFWIncome elasticity forestry Trunc. normal μ = 0.31σ= 0.20 U = 0.00Non-linearity forestry Trunc. normal μ = 1.00σ= 0.50 U = 0.00Sensitivity heating Trunc. normal Table EFW Table EFWNon-linearity heating Trunc. normal μ = 1.00σ= 0.50 U = 0.00Sensitivity cooling Trunc. normal Table EFW Table EFWNon-linearity cooling Trunc. normal μ = 1.00σ= 0.50 U = 0.00Agriculture, rate Trunc. normal μ =Table Aσ= Table A U = 0.00Adaptation time Trunc. normal μ = 10.0σ= 5.0 U = 0.0Non-linearity Trunc. normal μ = 2.0σ= 0.5 U = 0.0Agriculture, level Normal μ =Table Aσ= Table AAgriculture, optimum Normal μ =Table Aσ= Table AAgriculture, CO2 Trunc. normal μ =Table Aσ= Table A U = 0.00Income elasticity agriculture Trunc. normal μ = 0.31σ= 0.15 U = 0.00Dryland value Trunc. normal μ = 4.0σ= 2.0 U = 0.0Adaptation time Exponentialσ= 0.1Wetland value Trunc. normal μ = 5.0σ= 2.5 U = 0.0Adaptation time Exponentialσ= 0.1Dryland loss Trunc. normal μ = Table SLRσ= Table SLR U = 0.0Protection cost Trunc. normal μ = Table SLRσ= Table SLR U = 0.0Dryland value Trunc. normal μ = Table SLRσ= Table SLR U = 0.0Wetland value Trunc. normal μ = Table SLRσ= Table SLR U = 0.0Immigration Trunc. normal μ = Table Iσ= Table I U = 0.0Immigration cost Trunc. normal μ = 0.4σ= 0.2 U = 0.0Adaptation time Trunc. normal μ = 3.0σ= 1.0 U = 0.0Emigration cost Trunc. normal μ = 3.0σ= 1.5 U = 0.0Adaptation time Exponentialσ= 0.1Sea LevelRiseDrylands potentiallylostDrylandsProtectedDrylands NotProtectedValue ofDrylands LostTotal Costs ofProtectionDrylands Valuefade rateDryland ProtectionWetlands lostAdditionalWetlands Lost dueto Protection ofDrylandsValue ofWetlands LostTotal WetlandslostBase WetlandValueWetlands Value(Annual andNPV)Scarcity ValueProtection Cost(annual andNPV)GDPcPopulation DensityDrylands Value(Annual andNPV)Base ProtectionCostBase DrylandValueWetlands Valuefade rateFigureA4.1Impacts Module: Loss of Drylands andWetlands and Protection Costs due toSea Level RiseDeleted:SLR___Figure A4.2a FUND-simulated global and sectoral damage costs of climate change impacts (% GDP) as a function of global meantemperaturerise above 1990 for the major FUND impact sectors for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-3-2-101 Damage, % GDPScenarioA1A2B1B2Total global damage due to climate impacts as % GDP01234Global Temperature Increase Relative to 1990-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the water sector in all the regions combined01234Global Temperature Increase Relative to 1990-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the energy sector in all the regions combined01234Global Temperature Increase Relative to 1990-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the health sector in all the regions combined01234Global Temperature Increase Relative to 1990-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the sea level rise sector in all the regions combinedFigure A4.2a cont’d. FUND-simulated global and sectoral damage costs of climate change impacts (% GDP) as a function of global meantemperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the agriculture and forestry sector in all the regions combined01234Global Temperature Increase Relative to 1990-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as % GDP in the ecosystem sector in all the regions combinedFigure A4.2b FUND-simulated global and sectoral damage costs of climate change impacts (million $$) as a function of globalmeantemperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-8000000-6000000-4000000-200000002000000

Damage, million $$ScenarioA1A2B1B2Total global damage due to climate impacts in million $$01234Global Temperature Increase Relative to 1990-8000000-6000000-4000000-200000002000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in all regions combined01234Global Temperature Increase Relative to 1990-8000000-6000000-4000000-200000002000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in all regions combined01234Global Temperature Increase Relative to 1990-8000000-6000000-4000000-200000002000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in all regions combined01234Global Temperature Increase Relative to 1990-8000000-6000000-4000000-200000002000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the water sector in all regions combinedFigure A4.2b cont’d FUND-simulated global and sectoral damage costs of climate change impacts (million $$) as a function ofglobal meantemperature rise above 1990 for the major FUND impact sectors for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-60000-40000-2000002000040000

Damage, m illion $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in all regions combined01234Global Temperature Increase Relative to 1990-60000-40000-2000002000040000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in all regions combinedFigure 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 scenarios01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in North Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in Southern Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in West Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in West Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in South America01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in South AsiaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in Australasia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in Central America01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in Central Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in East Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in Europe01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in all sectors combined in North AmericaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in East Asia01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in Europe01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in North Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in North America01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in South Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in South AmericaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in South Asia01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in West Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million$$ in all sectors combined in West Asia01234Global Temperature Increase Relative to 1990-500000-400000-300000-200000-1000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in Central Asia01234Global Temperature Increase Relative to 1990-500000-400000-300000-200000-1000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in Australasia01234Global Temperature Increase Relative to 1990-500000-400000-300000-200000-1000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in all sectors combined in Central AmericaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in North Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in Southern Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in West Africa01234Global Temperature Increase Relative to 1990-20-15-10-505

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in West Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in Australasia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in Central AmericaFigure A4.4a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above1990 in the energy sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in Central Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in East Asia01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in Europe01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in North America01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in South America01234Global Temperature Increase Relative to 1990-4-3-2-1012
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the energy sector in South AsiaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in East Asia01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in Europe01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$in the energy sector in North Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in North America01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in Southern Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in South AmericaFigure A4.4b cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above1990 in the energy sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in South Asia01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in West Africa01234Global Temperature Increase Relative to 1990-3000000-2000000-100000001000000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in West Asia01234Global Temperature Increase Relative to 1990-200000-150000-100000-50000050000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in Central Asia01234Global Temperature Increase Relative to 1990-200000-150000-100000-50000050000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in Australasia01234Global Temperature Increase Relative to 1990-200000-150000-100000-50000050000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the energy sector in Central AmericaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in Australasia01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in Central America01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in theagriculture and forestry sector in Central Asia01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in East Asia01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in Europe01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in North AfricaFigure A4.5a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperature rise above1990 in the agriculture and forestry sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in Southern Africa01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in North America01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in South America01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in theagriculture and forestry sector in South Asia01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in West Africa01234Global Temperature Increase Relative to 1990-0.02-0.0100.010.020.03

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the agriculture and forestry sector in West AsiaFigure A4.5b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990in the agriculture and forestry sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-40000400080001200016000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in East Asia01234Global Temperature Increase Relative to 1990-40000400080001200016000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ inthe agriculture and forestry sector in Europe01234Global Temperature Increase Relative to 1990-40000400080001200016000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in North America01234Global Temperature Increase Relative to 1990-40000400080001200016000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in South America01234Global Temperature Increase Relative to 1990-40000400080001200016000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in South Asia01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in theagriculture and forestry sector in West AsiaFigure A4.5b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature riseabove 1990 in the agriculture and forestry sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in Australasia01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in theagriculture and forestry sector in Central America01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in Central Asia01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in North Africa01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts inmillion $$ in the agriculture and forestry sector in Southern Africa01234Global Temperature Increase Relative to 1990-800-4000400800

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the agriculture and forestry sector in West AfricaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in Australasia01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in Central America01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in Central Asia01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in East Asia01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in Europe01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP inthe ecosystem sector in North AfricaFigure A4.6a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperaturerise above1990 in the ecosystems sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in North America01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in Southern Africa01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in South America01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in South Asia01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in West Africa01234Global Temperature Increase Relative to 1990-0.2-0.16-0.12-0.08-0.040

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the ecosystem sector in West AsiaFigure A4.6b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990in the ecosystems sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in Australasia01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in Central America01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$in the ecosystem sector in Central Asia01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in East Asia01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in Europe01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in North AfricaFigure A4.6b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature riseabove 1990 in the ecosystems sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in North America01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in Southern Africa01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in South America01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in South Asia01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in West Africa01234Global Temperature Increase Relative to 1990-200000-160000-120000-80000-400000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the ecosystem sector in West AsiaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in Australasia01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in Central America01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in North Africa01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in Southern Africa01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in South America01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP inthe sea level rise sector in South AsiaFigure A4.7a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperaturerise above1990 in the sea level rise sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-0.1-0.08-0.06-0.04-0.0200.02

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in West Africa01234Global Temperature Increase Relative to 1990-0.02-0.015-0.01-0.0050

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in West Asia01234Global Temperature Increase Relative to 1990-0.02-0.015-0.01-0.0050

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in Central Asia01234Global Temperature Increase Relative to 1990-0.02-0.015-0.01-0.0050

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in East Asia01234Global Temperature Increase Relative to 1990-0.02-0.015-0.01-0.0050

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in Europe01234Global Temperature Increase Relative to 1990-0.02-0.015-0.01-0.0050

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the sea level rise sector in North AmericaFigure A4.7b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990in the sea level rise sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ inthe sea level rise sector in Central America01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in East Asia01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in Europe01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in North Africa01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in North America01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ inthe sea level rise sector in South AmericaFigure A4.7b cont’d. FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature riseabove 1990 in the sea level rise sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in South Asia01234Global Temperature Increase Relative to 1990-12000-8000-40000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in west Africa01234Global Temperature Increase Relative to 1990-1200-800-4000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in Australasia01234Global Temperature Increase Relative to 1990-1200-800-4000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in Central Asia01234Global Temperature Increase Relative to 1990-1200-800-4000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in southern Africa01234Global Temperature Increase Relative to 1990-1200-800-4000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the sea level rise sector in West AsiaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-5-4-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in Central Asia01234Global Temperature Increase Relative to 1990-5-4-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in East Asia01234Global Temperature Increase Relative to 1990-5-4-3-2-101 Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in North Africa01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in West Asia01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in Australasia01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in Central America01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in Europe01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in North America01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in Southern Africa01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in South America01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in South Asia01234Global Temperature Increase Relative to 1990-0.8-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the water sector in West AfricaFigure 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 scenarios01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in Central Asia01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in East Asia01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in Europe01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in North America01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in Southern Africa01234Global Temperature Increase Relative to 1990-3-2-101234
Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in West AfricaFigure A4.9a cont’d. FUND-simulated damage costs of climate change impacts (% GDP) as a function of global mean temperaturerise above1990 in the health sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in Australasia01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in Central America01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in North Africa01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDPin the health sector in South America01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in South Asia01234Global Temperature Increase Relative to 1990-0.6-0.4-0.20

Damage, % GDPScenarioA1A2B1B2Damage due to climate impacts as %GDP in the health sector in West AsiaFigure A4.9b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990in the health sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in Central Asia01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in East Asia01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in Europe01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in North America01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in Southern Africa01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in South AmericaFigure A4.9b FUND-simulated damage costs of climate change impacts (million $$) as a function of global mean temperature rise above 1990in the health sector for 4 SRES scenarios01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in South Asia01234Global Temperature Increase Relative to 1990-800000-40000004000008000001200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in West Africa01234Global Temperature Increase Relative to 1990-100000-80000-60000-40000-200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in Australasia01234Global Temperature Increase Relative to 1990-100000-80000-60000-40000-200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in Central America01234Global Temperature Increase Relative to 1990-100000-80000-60000-40000-200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in North Africa01234Global Temperature Increase Relative to 1990-100000-80000-60000-40000-200000

Damage, million $$ScenarioA1A2B1B2Damage due to climate impacts in million $$ in the health sector in West Asia

# The trans-disciplinary Tyndall Centre for Climate Chan ge 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-mak ers 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 ; Adapt ation 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 Clim ate 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/w orking_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-environ ment-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 formanaging 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 resp onding 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 capaci ty 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 Belie f 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) Sandbanksfor 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