AdaptCost
Briefing Paper 1: Integrated Assessment Models – Africa results
Key Messages
1.
Estimates of the economic costs of adaptation require investigation of several lines of evidence. These range
from case studies of projects and plans through to global scale assessment. Each of these evidence lines
brings insight into a complex area, where we have relatively little information.
2.
Many of the cited estimates of the economics of climate change are derived from global integrated
assessment models (IAMs). These provide aggregated estimates, assessing costs in a single iterative
framework. However, to make analysis manageable, they involve assumptions and simplifications, and they
have been the subject of considerable debate. The AdaptCost project has commissioned two leading global
IAMs, the FUND and PAGE models, to provide results for Africa.
3.
The first important conclusion from the model runs is that the economic costs of climate change in Africa are
likely to be significantly higher in relative terms than in other world regions and that they could be significant
even in the short-term. The models indicate that the central net economic costs of climate change could be
equivalent to 1.5 - 3% of GDP each year by 2030 in Africa. These costs include market and non-market
sectors and are subject to assumptions and uncertainties.
4.
The FUND model estimates that under a business as usual scenario, net economic costs could be equivalent
to 2.7% of GDP each year in Africa by 2025 (around $40 billion/year [undiscounted]). The model reports
large costs from water resources, health impacts, and energy costs for cooling, but some potential benefits
from agriculture. Note that positives and negatives are combined in the results. It shows relatively similar
levels of economic costs in future years through to 2100. A separate analysis with the FUND national model
has provided net estimates for each African country. This shows strong distributional patterns of economic
costs by country (and region) and strong increases in economic costs in all regions over future years.
5.
The PAGE model estimates that economic costs could be equivalent to around 2% of GDP each year in
Africa by 2040 (central value, market and non-market sectors, A2 scenario), with a 5-95% range of 0.4% to
4%. The model shows economic costs rises rapidly in future years, so that by 2100, they could be equivalent
to 10% of GDP each year, i.e. too high for a sustainable economy.
6.
The PAGE model has been run with adaptation included, which reduces costs significantly. Under the A2
scenario, the (mean) economic costs of climate change drop from 0.8% of GDP in 2020 and 1.7% in 2040 (no
adaptation) to 0.5% and 1.1% respectively (with adaptation included). The benefits of adaptation are far in
excess of estimated costs, with mean benefits of 17 billion in Africa in 2020 compared to mean costs of $4.5
billion and increase significantly in future years. However, the model shows high residual costs in Africa even
with adaptation, estimating that these are around 50% for market sectors and around 70% of total impacts.
7.
Finally, the PAGE model has been run with a mitigation scenario for a 2 degrees (450 ppm) scenario. This
shows very reduced costs from climate change in later years (post 2040), such that annual economic costs of
climate change to Africa are limited to just over 2% of GDP by 2100 – this is dramatically below the business
as usual scenario. Adaptation reduces these economic costs further, leaving low residual impacts.
8.
The runs shows that in the absence of mitigation, economic costs from climate change in Africa could be
extremely large, even in the short-term. Impacts will be unevenly distributed across countries, and between
sectors. Moreover, high economic costs are likely even with adaptation. Mitigation reduces longer-term
impacts significantly, but there will still be significant adaptation needs and residual economic costs.
Adaptation needs are similar in early years in all scenarios, due to the change already locked into the system.
9.
All these estimates are indicative only. They provide some insights on signs, orders of magnitude, and
patterns of effects. They can provide information on the potential economic costs, which are useful in the
context of adaptation financing requirements. However, the models reflect only a partial coverage of the
effects of climate change, and do not capture extreme events (including flooding and droughts), crosssectoral links and socially contingent effects, or the cumulative effects on adaptive capacity, all of which may
be important for Africa.
1
Background
Discussion of the Models
There are relatively few estimates of the
economic costs of climate change, and the costs
and benefits of adaptation. Of the estimates that
do exist, many derive from a set of aggregated
economic
models
known
as
Integrated
Assessment Models, or IAMs.
What are Integrated Assessment Models?
Integrated assessment is a generic term used to
describe the integration of different models or
methods within a single analysis. However, the
term is often used in a specific sense for the
integration of a number of climate change impact
sectors within a single analytical model. There
are a large number of IAMs (see Dickinson,
2007iv). These are used for different applications,
investigating different aspects, and work at
different geographical scales, but as the focus in
the AdaptCost is at the African (continental) scale,
the most relevant are the global integrated
assessment models.
These models provide a way to value the
economic costs of climate change over time, at a
global and continental scale (e.g. Africa), in a
single iterative framework, and to look at the
potential costs and benefits of adaptation, all
within a consistent economic framework. They are
therefore an extremely important part of the
overall evidence base on the economics of
climate change in Africa.
Most of the recent policy estimates have focused
on a few key models.
The results of these models from a large part of
the economic evidence base on climate change,
as reported in the AR4 2007 chapter 20 WGIIi.
They were used to provide the headline economic
costs in the Stern review (2006) ii, i.e. that ‘BAU
climate change will reduce welfare by an amount
equivalent to a reduction in consumption per head
of between 5 and 20%’.

The FUND model (Tolv);

The PAGE model (Hopevi);

The DICE/RICE/AD-RICE models
(Nordhausvii).

The MERGE model (viii).
There are other models which use the functional
impact forms in the models above. This includes
a new generation of global IAMs that are based
on computable general equilibrium models
(CGEMs), as well as more detailed energy /
mitigation models.
They were also used in the IIED/Grantham study
(2009iii) to estimate the global costs and benefits
of adaptation, which reported that adaptation
measures had large (mean) benefit:cost ratios of
about 60 under a business-as-usual A2 scenario,
and about 20 in the aggressive abatement
‘450ppm’ scenario. However, their widespread
use has also led to considerable debate about the
models and results.
How do these models work?
The global IAMs combine the scientific and
economic aspects of climate change within a
single, iterative analytical framework. They
typically include an energy / economy / emissions
module, a climate module and an impact/valuation
module. The models have an additional element
where climate impacts feed back to the socioeconomic module thereby linking emissions,
climate modelling, climate change impacts and
the economy.
This briefing note reviews the methods and
estimates in this area. It is one of a series of
briefing notes from the UNEP sponsored
AdaptCost project1, which is investigating the
economic costs of adaptation in Africa. It provides
results from a number of IAM modelling runs
which have been commissioned by the project to
provide specific output for Africa. The briefing
note does not necessarily represent the official
views of the sponsors or the host organisations.
To make analysis of economic costs manageable,
they use simplified analysis of climate projections
- rather than full-scale climate models - and
simplified impact relationships. The models do
1
The AdaptCost project benefits from related projects
funded by DFID and DANIDA (Economics of Climate
Change in East Africa) and the EC (ClimateCost).
2
not undertake physical impact assessment per se,
but instead link changes in climate to economic
values, using relationships between climate
change and economic damage. Some models
(such as the PAGE model) use generic categories
of damages to do this, working with the broad
categories of market and non-market sectors.
Other models (such as the FUND model) use
reduced form equations that work at a sectoral
level, so that economic costs are estimated for
agriculture, human health, sea level rise, etc.
Some of the models can also look at the
economic costs of climate change with and
without adaptation, estimating the costs and
benefits of adaptation (PAGE) – or a mix of
mitigation and adaptation policies (AD-RICE).
The models have a very strong focus on
uncertainty analysis, and a number of them
(PAGE, FUND) include Monte Carlo analysis, a
risk modelling technique that presents both the
range, as well as the expected value, of collective
risks: designed for when there are many variables
with significant uncertainties, or when the
implications of uncertainty (and the impact on a
decision) cannot be adequately captured through
sensitivity analysis.
The models also have mitigation modules, with
some based on top-down cost curve information,
and others including some bottom-up detail. Note
that the wider list of IAM models includes a much
greater diversity on the mitigation side (including
the CGEM models above). Some also consider
adaptation, discussed below.
A key benefit of these models is that they provide
information and outputs that cannot be generated
by other approaches, such as the SCC, or the PV
of future economic costs. They also produce
overall context and ‘headline’ values, putting
climate policy in an economic context.
What uses do these models have?
The global IAM can be used to address a number
of policy questions. In relation to the potential
economic costs of GHG emissions and climate
change this includes:

Aggregate economic costs, e.g. at the global
or continental scale, presented as $ values for
any future year and as the equivalent % costs
of gross domestic product (GDP) in that year.
This can be undertaken for a business as
usual scenario, or for a specific scenario such
as stabilisation of CO2 equivalents at 450 CO2
ppmv. Note the models also estimate
economic costs with temperature changes.

The total economic costs over time – for each
year - aggregated and expressed in present
day prices (present values).

The marginal global economic costs caused
by one tonne of carbon emitted, often known
as the Social Cost of Carbon (SCC), defined
as the net present value of climate change
impacts over the next 100 years (or longer) of
one additional tonne of carbon emitted to the
atmosphere today.
The models can be extremely useful in providing
information on the drivers of economic costs,
including the influence of different parameters on
the overall numbers. They can often provide
important underlying information that would not be
derived by other means. As an example, the
FUND model has identified that the economic
costs of air conditioning to address increased
cooling demand could be one of the most
important sectoral effects in global costs (see
Downing et al, 2005ix) – yet this area is often
omitted in national bottom-up assessments.
Similar, the PAGE model has demonstrated the
economic importance of major discontinuities,
even for events post 2100 when discounted.
These models are generally applied at the global
scale, though they work by aggregating outputs
from a continental / large-scale regional level.
While their use has predominantly been to look at
global estimates, they have also been used for
regional and national assessments, with
examples in the US and Caribbean (SEI, 2008x)
and in South East Asia (AdB, 2008xi).
The models can be used to look at costs and
benefits of a baseline vs. mitigation scenarios,
and can determine the optimized level of
mitigation using cost-benefit analysis, though
such assessments have been controversial.
3
What are the advantages and
disadvantages of these models?
The integrated models
important advantages.
have a number of

They represent multi-sectoral and/or multiimpact inter-linkages in a quantitative way.

They allow analysis of a very large number of
possible scenarios very quickly.

They provide high flexibility and can work with
a variety of metrics, including the social cost
of carbon, estimates in future years, GDP %
losses, present values, and even cost-benefit
analysis, which cannot be derived from
bottom-up impact assessments.


assumptions about how steeply damages
increase as temperatures rise. However, these
studies may also have overlooked positive
impacts of climate change and not adequately
accounted for how development could reduce
impacts of climate change.
Key
Strengths


Key
Weaknesses
They provide estimates over very long timescale and post 2100 – for example PAGE
extends to 2200 and FUND to 2300.



They factor in uncertainty, with several
models working with Monte Carlo analysis.

The main disadvantages of these integrated
assessment models is that they are technically
complex to construct, that they often cover a
limited number of impacts and linkages, and they
are often considered “black boxes” in the sense
that model parameterisation and structure may
not be entirely transparent, documented or
independently validated.


These models – and their strengths and
weaknesses - have been the subject of
considerable debate, not least due to their high
profile use in the Stern Review. Their strengths
and weaknesses are outlined below.
Useful headline values. Total
damages (in given time period,
including GDP loss) and cumulative
present value as well as Social Cost
of Carbon values.
Detailed uncertainty analysis (e.g.
Monte Carlo assessment)
Extend to longer time periods (>2100)
No representation of physical impacts
and partial coverage of effects due to
use of reduced form impact functions.
Coarse spatial resolution and high
level of aggregation.
Do not capture adaptation, or only in a
highly theoretical and aggregated
form.
Partial coverage of potential effects,
e.g. basing analysis on average
temperature but not the effects of
extreme weather events or major
discontinuities. Omission of many
categories of effects.
Poor coverage of existing vulnerability
to climate variability and short-term
analysis.
Based on existing literature, thus
limited in terms of transferability at a
regional or national level, and not
validated at a local scale.
The review concluded that the current generation
of aggregate estimates may understate the true
cost of climate change because they tend to fully
capture extreme weather events, major climate
discontinuities and socially contingent effects (see
also next section); to underestimate the
compounding effect of multiple stresses; and to
ignore the costs of transition, learning and
institutional development
The models have been the subject of several
reviews and inter-comparisons. Warren et al
(2006xii) reviewed the main four global IAMs listed
earlier, and concluded that most were based on
literature from 2000 and earlier, and thus that they
may omit some predictions of climate impacts
(e.g. from AR4) that have become more
pessimistic. The review also identified issues in
relation to the calibration of the models based on
US literature, the higher possibility of
discontinuities at lower temperatures, and the
treatment of adaptation. It also highlighted that
many of the IAMs rely on aggregated damage
functions with a smooth functional form, and make
The issue of the damage functions has reemerged more recently (see Ackerman et al,
2009xiii), with concerns that the functions may not
perform well at higher temperature levels.
Note, however, that since this time all the models
have been updated, with the emergence of a
4
newer version of DICE, the forthcoming
PAGE2009 model, and multiple updates to the
FUND model. Moreover, many of the criticisms of
the models reflect underlying gaps in the evidence
base: there is simply a lack of information on the
economics of climate change, and as this
improves, so will integrated assessment.
weighting has a significantly bearing on the
results. Use of an approach that includes
equity weights increases the values, but there
have been concerns raised over their use
(e.g. see Anthoff et al, 2009xiv). This is a key
issue for Africa.
 The consideration of uncertainty and risk
aversion, and related to this, whether best
guess / central values are used (and whether
mean or median values are cited as the
central estimate). Both the mean and the
median have been used as a measure of
central tendency. However, when uncertainty
is considered, the models show right skewed
distributions, thus the mean is greater than
the median and studies which consider
uncertainty often derive higher values. A key
issue here regards climate sensitivity, i.e. the
equilibrium warming expected with a doubling
of CO2 concentrations, and the risk of low
probability high consequence events.
However, a key point for Africa is that these IAMs
are global models. None have been developed in
Africa to capture particular development futures or
specific impacts. They have not been validated in
Africa with detailed case studies, noting that very
few such case studies exist in Africa.
Key issues of debate with the models
The outputs from these models have been the
subject of considerable debate, particularly
following their use in the Stern review (2006) and
the critiques that followed.
This focuses on a number of specific factors
(Watkiss and Downing, 2008), notably;
Each of these factors can significantly influence
the values from the models, leading to a range
that approaches several orders of magnitude. For
instance the IPCC noted plausible estimates of
the social cost of carbon from $1 to 1000/tC
(Yohe et al, 2007). There is no consensus on
which parameter values or ranges to use, though
there are many alternative views expressed.
 The discount rate. The choice of discount rate
has a very large effect on any values,
because most impacts of climate change
occur in the future. Higher discount rates lead
to lower values. The choice of discount rate
was the main controversy in the Stern review,
though as noted above, this is an issue with
the economics of climate change, not just
IAMs – though the longer time horizons in
these models make this issue more important.
The choice of specific parameters can explain the
difference between recent Government Social
Cost of Carbon estimates in the UK (Stern, 2006)
and the wider literature (such as Tol, 2007xv;
Yohe, 2007xvi, Nordhaus, 2006xvii), even though
both use much the same models and damage
functions. The Stern Review adopted a global
rather than a national perspective, with substantial
aversion to risk, and consideration of intertemporal equity. This translated to the use of a
very low discount rate (0.1% PRTP), use of mean
values and higher climate sensitivity based on
more recent evidence.
 The study time period and time horizon.
Aggregate models suggest that net impacts of
climate change may be modest in the short
term, but strongly negative for more severe
climate change in the future. The time horizon
chosen, even with discounting, can therefore
substantially affect the economic costs of
climate change. The models can address this
through the long time-scales, but this
increases uncertainty.
 The approach to weighting impacts in different
regions (equity weighting), when aggregating
global values. Most adverse impacts in the
medium term occur in developing countries,
and so the decision on whether to use
compensation values or adjust estimates
using a form of distributional or equity
There is also an issue with the coverage of the
models. Climate change includes many types of
climatic parameters, which in turn affect many
sectors (market and non-market) in different
ways. A key issue is therefore over the coverage
(or completeness) of any valuation of climate
change. It is clear that different estimates of the
5
valuation of greenhouse gas emissions are based
on different types of climate effects, and include
different impacts across varying sectors. Watkiss
and Downing (2008xviii) framed this issue of
coverage in a matrix, see figure below.
Market
Projection
e.g. temperature
and sea level rise
Bounded
e.g. precipitation
and extremes
Major change
e.g. major
tipping points
All models
include
Model coverage
is partial or
missing
PAGE/DICE
include
Non-Market
All models
include
in deriving long-term global
omissions are more important.
None
None
None
None
these
It is also stressed that the missing categories are
likely to include both positive and negative effects.
However, there is a general view that the missing
effects are likely to have net damages, which
could be potentially very large. A clear research
priority is to investigate the missing elements of
the risk matrix – to fill the evidence gaps. An initial
inventory of adaptation estimates indicates a
similar early concentration on categories that are
relatively easy to quantify.
Socially
contingent
Some initial
coverage
values,
A final issue here centres on the use of the
models in cost-benefit analysis. CBA for climate
change relies critically on the assumption that
marginal costs and benefits, as well as absolute
costs and benefits, are known and finite. This is
not necessarily the case and has been highlighted
in work by Weitzman (2009xx), which concludes
that including plausible, if unknown, probabilities
of catastrophic climate change (and so called fat
tails), leads to radically different conclusions for
policy from the conventional advice coming out of
a standard economic analysis and formalised
CBA, as the conventional approach essentially
ignores this kind of potential for disasters.
Figure 1. Coverage of the global IAMs
Mapping the IAMs onto a matrix of coverage of
climate parameters, and coverage of categories of
impacts/valuation shows that most of the model
outputs focus on the top left area reflecting market
damages from predictable events such as
temperature and SLR. There is some coverage of
non-market damages, but the models do not
generally include consideration of all factors
associated with ecosystem services, nor do they
capture all the effects associated with bounded
elements such as extreme events.
Consideration of adaptation
Within the global IAMs, adaptation is also
aggregated and partial, though this reflects the
underlying information available on the costs and
benefits of adaptation. There is far less explicit
coverage in the models, and also wide differences
between models. Where adaptation is included, it
is based on an aggregated approach without any
technological, institutional or spatial detail.
Some models do include some potential to
consider major catastrophic events, i.e. so called
tipping points (Lenton et al, 2008 xix), but tend to
focus on economic considerations, i.e. omitting
the full implications of ecosystem services and in
particular socially contingent effects, i.e. large
scale dynamics related to human values and
equity that are poorly represented in cost values,
e.g. from regional conflict, famine, poverty.
The main IAMs adopt different approaches. In
FUND, adaptation happens optimally in the
damage function specification, such that damage
functions are residual damage and adaptation
costs, though this is more explicit in some sectors,
such as coasts. In PAGE, the adaptation analysis
is run separately, using parameterized functions
to represent how much adaptation can reduce
impacts. Adaptation affects the rate and level of
temperature change at which an onset of impacts
begins, and can reduce the severity of these
impacts once they begin. It can also assess the
It is highlighted that these gaps also exist for all
other approaches, e.g. bottom-up assessments;
however, because of the uses of these IAMs, e.g.
6
costs and benefits, though this is not run as an
optimisation calculation. Within the AD-DICE
model, the analysis disaggregates the damage
function into adaptation costs and residual
damages. While adaptation and mitigation are not
modelled as substitutes, the model can select a
preferred
combination
of
mitigation
and
adaptation in response to climate impacts.
standard monetary valuation methods, particularly
benefit transfer. It has a time period through to
2300, and has 16 world regions.
The FUND model runs commissioned for
AdaptCostxxii estimate that climate change could
lead to economic costs equivalent to 2.7% of
GDP each year in Africa by 2025 (central value,
including market and non-market sectors). The
values are shown below, along with the splits for
north and sub Saharan Africa.
These approaches highlight an essential
difference in the assessment of adaptation. The
IAMs assume adaptation reduces projected
impacts, that is, they adopt a simple predict-andprovide rational decision model. Conversely, most
adaptation case studies (but not all) evaluate
adaptation as a socio-institutional process, based
on decision models such as act-learn-then act
again (as portrayed in Klein et al. 2007xxi).
Economic costs of climate change as % of GDP/year in 2025
2025
The assessment of ‘actual’ adaptation includes
costs that anticipate risk but might not reduce
actual impacts (akin to risk aversion),
maladaptation, where actions increase impacts
(e.g., displaced flooding risk) and costs not
directly associated with additional climate change
but essential to build competence and capacity to
adapt. These processes are the foundation of the
adaptation signature approach (see separate
briefing note).
Running the Models for Africa
As part of the AdaptCost project, the project has
commissioned two of the leading IAM runs for
Africa, the FUND and PAGE models. The results
are summarised below.
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
North Africa
Sub Saharan
Africa
All Africa
of GDP.
as a Faction
of Climate
Costs
2. Annual
Figure 2.
Figure
Annual
costs
as a Change
% of GDP
in 2025
Source FUND model
Source FUND model
The FUND Model: Africa Results
In absolute terms, the costs predicted are
extremely high, with estimated net costs in 2025
(undiscounted) of $10 billion for North Africa and
almost $30 billion for Sub-Saharan Africa, and
thus a total of $40 billion overall (values are
presented as estimated model outputs, and are
not discounted).
FUND (The Climate Framework for Uncertainty,
Negotiation, and Distribution model), is an IAM
which
couples
demographics,
economy,
technology, carbon cycle, climate, and climate
change impacts.
It includes: sea level rise (including adaptation),
energy consumption, agriculture, forestry, water
resources,
cardiovascular
and
respiratory
diseases, malaria, dengue fever, schistosomiasis,
diarrhoea and ecosystems, at varying levels of
detail. The model includes reduced forms of more
complex models.
It values impacts using
These economic costs include both market and
non-market sectors. The economic costs are
dominated by energy (cooling of buildings), water
resources and health (as negative effects) but
also include agriculture, where there may be
positive effects (assuming adaptation).
7
An additional more disaggregated version of the
model has also been run, the FUND national
model, for Africa, with the results shown in the
figure belowxxiii – with strong orange and red
colours showing higher levels of equivalent GDP
loss in 2030 – and through to 2050 and 2100.
This shows that the economic costs of climate
change have strong differences across countries
and regions in Africa.
Note this contrasts with the main FUND model
shows much more modest future changes post
2030, with economic costs rising in North Africa
(equivalent to 1.6% of GDP each year in 2025, to
3.4% in 2100), but with very little change in SubSaharan Africa, partly due to the assumed
economic growth in the region, and the greater
adaptive capacity.
The national model shows that the economic
costs could potentially increase very significant in
2050, and especially towards 2100 (noting the
very high uncertainty in such long-term
predictions) with very strong increases in some
countries that would have major effects for the
economies of these countries.
2050
Key
0 – 1% GDP loss
1 – 2% GDP loss
2 – 3% GDP loss
3 – 5% GDP loss
5 – 10% GDP loss
>10% GDP loss
2030
2100
Figure 3. Annual Costs from Climate Change as a Faction of GDP in Africa.
Source FUND national model
8
The PAGE Model: Africa Results
In absolute terms the model estimates total
economic costs (mean, market and non market)
at $49 billion in 2020.
PAGE2002 is a leading IAM.
It classifies
economic costs into three categories: market,
non-market, and future large-scale discontinuities.
Parameter values are taken from the IPCC Third
Assessment Report. It is a multi-gas model with 8
world regions. Rather than only give single
estimates,
PAGE
builds
up
probability
distributions of results of key inputs. The PAGE
model was used in the Stern review, and
generated the main headline values that climate
change could lead to 5 to 20% equivalent losses.
The model is then run with adaptation included,
shown over the page, with adaptation cost levels
broadly similar to the Stern review analysis (see
the investment flow briefing note). Adaptation is
found to have significant economic benefits.
Under a BAU A2 scenario, the economic costs of
climate change drop from an annual cost
equivalent of 0.8% of GDP in 2020 and 1.7% in
2040 (no adaptation) down to 0.5% and 1.1%
respectively, with adaptation (mean values).
The PAGE model runs commissioned by the
AdaptCost project use the Stern parameter
assumptionsxxiv shown in the figure below. Under
an unmitigated A2 scenario, it estimates that
change could lead to economic costs which would
be equivalent to just under 2% per year GDP
by 2040 in Africa (central mean value, including
market and non-market sectors, with no
adaptation). The lower value (5% of costs are less
than this) from the model is 0.4% of GDP in 2040
and the upper value (95% of costs are less) from
the model is 4.1% of GDP by 2040.
Adaptation reduces the total economic costs in
Africa from $49 billion to $32 billion in 2020 (a
benefit of $17 billion. The benefits in 2040 are
even larger. The costs of adaptation xxv to achieve
these benefits are based on input assumptions,
and are equivalent to mean costs for Africa of
$4.5 billion per year from 2020 on, with a 5 to
95% range of $1.9 to 7.8 billion per year.
However, there are still high residual impacts
even with adaptation.
25.6%
Business as usual scenario
15.6%
Annual Cost, as a % of GDP
12
10
Major (catastrophic)
Non-Market
Market
9.6%
8.2%
8
6.1%
6
4.1%
4
3.4%
1.9%
2
1.7%
0.8%
0
2020
2040
2060
2080
2100
Equivalent Annual Cost of Climate Change in Africa, as a % of GDP
PAGE model, A2 scenario (BAU) mean, 5 and 95% range, no adaptation
9
25.6%
18.2%
With Adaptation
15.6%
12
11.2%
Annual cost, as % of GDP
Baseline
10
With adaptation
9.6%
8.2%
8
6.6%
6.1%
6
5.2%
4.1%
4.1%
4
3.4%
2.7%
1.9%
2.2%
1.7%
2
1.1%
0.8%
0.5%
0
2020
2040
2060
2080
2100
Equivalent Annual Cost of Climate Change in Africa, as a % of GDP
With and without adaptation
PAGE model, A2 scenario (BAU) mean, 5 and 95% range
A PAGE model run also been undertaken for a
450 ppm type scenario. This would give a mean
expectation of limiting average temperature
change to around 2 degrees by 2100.
This dramatic reduction is due to the reduction in
damages in the economic and non-economic
sectors, but also due to the reduced probability of
large-scale discontinuities (major events).
The results are shown below, comparing the
baseline run against the 450 scenario – in both
cases with no adaptation included.
When adaptation is included, the mean annual
costs are reduced even further. The PAGE model
estimates that under the 450 ppm scenario,
adaptation reduces down the economic costs so
that residual impacts are limited to a maximum
(mean) annual cost equivalent to 1.5% of GDP by
2100. Note that adaptation needs are similar in
early years irrespective of the scenario, due to the
climate change already locked into the system.
The real benefit of the 450 ppm scenario is in
limiting the potentially much higher costs in the
longer term through to 2100.
The PAGE model estimates that under the
business as usual scenario the mean economic
annual costs around 10% of GDP by 2100.
However, under the 450 ppm scenario, the annual
economic costs of climate change are projected to
fall to annual costs equivalent to 2.3% of GDP by
2100. Note that these results are for one IAM
only.
There is therefore a need for global mitigation, as
well as adaptation.
10
450 ppm/2 Degrees scenario
Annual cost, expressed as equivalent % of GDP
Annual cost,
as % of GDP
(no adaptation)
10
Baseline
8
450 ppm scenario
6
4
2
0
2020
2040
2060
2080
2100
Equivalent Annual Cost of Climate Change in Africa, as a % of GDP
A2 business as usual vs. 450 ppm 2 degrees scenario
PAGE model, mean value
Key Findings and Conclusions
2. The economic costs of climate change in
Africa are large, even in early periods.
The IAMs have an important role in providing key
messages and insight to the debate on the
economics of climate change. They provide
aggregated estimates and important policy
metrics, though there are also considerable
sources of uncertainty. The key findings of the
IAM model runs for Africa are summarised below.
The models provide an indication of the potential
scale of the economic costs of climate change in
Africa. While caution must be taken in using the
values, reflecting the issues outlined above, they
provide aggregated assessment of the potential
economic costs.
1. The economic costs of climate change in
Africa relative to GDP are much greater
than for other world regions.
The results indicate that the potential scale of
economic costs could be equivalent to 1.5-3% of
GDP each year in Africa by 2030. Note that these
include market and non-market sectors, so they
are not the loss in annual GDP, i.e. they give the
full social costs of climate change. Nonetheless,
even in early periods, these costs are very
significant. Moreover, it is possible that these
values are underestimates, because they exclude
existing climate variability, extreme events
(including flooding), cross-sectoral links and
socially contingent effects and the cumulative
effects on adaptive capacity.
In the absence of mitigation, the economic costs
of climate change for Africa could potentially be
very large.
The models show much higher
economic costs in Africa than for other regions,
reflecting the consensus that damages will be
higher because of greater vulnerability and lower
adaptive capacity. As an example, the PAGE
model shows economic costs that are equivalent
to almost 10% of GDP each year by 2100 for
Africa. These are far higher than the world’s at
2.6%. Global mitigation does reduce these
significantly.
11
This implies that adaptation financing needs will
also need to be high, i.e. $billions of annual
investment, even in early years, and could
increase potentially very significantly in the future.
It also shows that there are potentially high
residual economic costs in Africa, even with
adaptation.
The AdaptCost Project
The AdaptCost Africa project, funded by United
Nations Environment Programme (UNEP) under
the Climate Change – Norway Partnership, is
producing a range of estimates of the financial
needs for climate adaptation in Africa using
different evidence lines. The study aims:
 To help African policymakers and the
international climate change community to
establish a collective target for financing
adaptation in Africa.
3. Impacts will be unevenly distributed
across countries, and between sectors.
The models show a range of values according to
the sector and location, even for some models,
with winners and losers (net benefits and net
gains between sectors).
While the exact
distribution will vary with the model, it is clear that
there will be a very strong location-specific nature
to future climate change impacts.
 To investigate estimates to adapt to climate
change and improve understanding of
adaptation processes. This will provide useful
information
for
planning
adaptation
programmes and support decision-making by
national governments and multi- and bilateral
donors by allowing them to better compare
projects and policies on their economic
grounds. In the process, countries will also
gain a better understanding of their adaptation
investment requirements, and build a stronger
basis for articulating their financing priorities
and attracting capital.
Just as there are distributional effects at the
aggregate level, there will also be differences
between countries, and between socio-economic
groups within countries.
The distributional
consequences are therefore extremely important.
These effects may also mask further inequalities
between the formal and non-formal economy, and
particularly on groups that depend on ecosystems
services, because of the potentially higher effects
on these.
4. The values are very dependent
assumed growth and development.
This briefing note was prepared by Paul Watkiss,
based on model runs commissioned from Chris
Hope and David Anthoff. It is stressed that the
views expressed in this note do not necessarily
represent the views of these contributors. The
paper also benefited from comments from several
reviewers.
on
The future damages from the models are
determined by input parameters, notably the
assumed rate of growth. This is particularly the
case when expressing values as % GDP
equivalents.
Footnotes and References
i
Yohe, G.W., R.D. Lasco, Q.K. Ahmad, N.W. Arnell, S.J.
Cohen, C. Hope, A.C. Janetos and R.T. Perez, 2007:
Perspectives on climate change and sustainability. Climate
Change 2007: Impacts, Adaptation and Vulnerability.
Contribution of Working Group II to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change,
M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden
and C.E. Hanson, Eds., Cambridge University Press,
Cambridge, UK, 811-841.
ii
Stern . N., Peters, S., Bakhshi, V., Bowen, A., Cameron, C.,
Catovsky, S., Crane, D., Cruickshank, S., Dietz, S.,
Edmondson, N., Garbett, S., Hamid, L., Hoffman, G., Ingram,
D., Jones, B., Patmore, N., Radcliffe, H., Sathiyarajah, R.,
Stock, M., Taylor, C., Vernon, T., Wanjie, H., and Zenghelis, D.
(2006). The Economics of Climate Change. Cabinet Office –
HM Treasury. Cambridge University Press.
Different models give different results according to
the assumed level of growth and development,
and the changes that this may have on future
vulnerability, reflecting the fact that development
should increase adaptive capacity.
However, our current understanding of (future)
adaptive capacity, particularly in developing
countries, is still too limited to allow firm
conclusions about the direction of the estimation.
12
iii
xiv
Martin Parry, Nigel Arnell, Pam Berry, David Dodman,
Samuel Fankhauser, Chris Hope, Sari Kovats, Robert Nicholls,
David Satterthwaite, Richard Tiffin, Tim Wheeler (2009)
Assessing the Costs of Adaptation to Climate Change: A
Review of the UNFCCC and Other Recent Estimates,
International Institute for Environment and Development and
Grantham Institute for Climate Change, London.
iv
Dickinson, T., 2007, The Compendium of Adaptation Models
for Climate Change:First Edition, Adaptation and Impacts
Research Division, Environment Canada, 52 pgs.
http://www.preventionweb.net/files/2287_CompendiumofAdapt
ationModelsforCC.pdf
v
Tol, R.S.J. (2002a) New estimates of the damage costs of
climate change, Part I: Benchmark estimates. Environmental
and Resource Economics 21 (1): 47-73.
Tol, R.S.J. (2002b) New estimates of the damage costs of
climate change, Part II: Dynamic estimates. Environmental and
Resource Economics 21 (1): 135-160.
vi
Hope C, 2004, The marginal impact of CO2 from PAGE2002:
An integrated assessment model incorporating the IPCC’s five
reasons for concern, submitted to Integrated Assessment.
vii
Nordhaus, W.D. and Boyer, J.G. (2000) Warming the World:
Economic Models of Global Warming, Cambridge: The MIT
Press.
Nordhaus, W. D. (2007) The challenge of global warming:
Economic models and environmental policy. New Haven,
Connecticut: Yale University.
viii
Manne, A.S., R. Mendelsohn, and R. Richels. 1995.
"MERGE -- A Model for Evaluating Regional and Global
Effects of GHG Reduction Policies." Energy Policy, 23(1):1734.
Manne, A.S., Richels, R.G., 2004. MERGE: An integrated
assessment model for global climate change. Stanford
University, Stanford, CA, USA.
ix
Downing, T. Downing, David Anthoff, Ruth Butterfield,
Megan Ceronsky, Michael Grubb, Jiehan Guo, Cameron
Hepburn, Chris Hope, Alistair Hunt, Ada Li, Anil Markandya,
Scott Moss, Anthony Nyong, Richard Tol, Paul Watkiss (2005).
Scoping uncertainty in the social cost of carbon. Final project
report. Social Cost of Carbon: A Closer Look at Uncertainty
(SCCU). July 2005. Report to Defra.
http://www.defra.gov.uk/environment/climatechange/carboncos
t/aeat-scc.htm
x
SEI (2008). The Cost of Climate Change. What We’ll Pay if
Global Warming Continues Unchecked. Frank Ackerman and
Elizabeth A. Stanton Global Development and Environment
Institute and Stockholm Environment Institute-US Center, Tufts
University. May 2008.
xi
ADB (2009). The Economics of Climate Change in Southeast
Asia: A Regional Review, Asian Development Bank, May 2009
xii
Warren, R, Hope, C., Mastrandrea, M., Tol, R., Adger, N and
Lorenzoni, I (2006). Spotlighting Impacts Functions In
Integrated Assessment Research Report Prepared for the
Stern Review on the Economics of Climate Change.
September 2006.
xiii
Ackerman, F., Stephen J. DeCanio,S. J., Howarth, R., B.,
Sheeran, K. (2009). Limitations of Integrated Assessment
Models of Climate Change. Forthcoming in Climatic Change.
Stanton, E. A., F. Ackerman and S. Kartha (2009). "Inside the
Integrated Assessment Models: Four Issues in Climate
Economics." Climate and Development 1.2.
Anthoff, D., C.J. Hepburn and R.S.J. Tol (2009) "Equity
weighting and the marginal damage costs of climate change."
Ecological Economics, 68(3): 836-849.
xv
Tol, R.S.J. (2006). The Stern Review Of The Economics Of
Climate Change: A Comment. Economic and Social Research
Institute/Hamburg, Vrije and Carnegie Mellon Universities.
Octobe, 2006.
xvi
Yohe, G. (2006). Some thoughts on the damage estimates
presented in the Stern Review—An Editorial Integrated
Assessment Journal. Vol. 6, Iss. 3 (2006), Pp. 65–72.
xvii
Nordhaus, W.D. (2006) The Stern Review on the
Economics of Climate Change. Journal of Economic Literature
(forthcoming). Yale University,
http://nordhaus.econ.yale.edu/stern_050307.pdf.
xviii
Watkiss, P and Downing, T. E (2008). The Social Cost of
Carbon: Valuation Estimates and their Use in UK Policy. The
Integrated Assessment Journal, Vol 8, Issue 1 (2008),
xix
Lenton, T. M., H. Held, E. Kriegler, J. W. Hall, W. Lucht, S.
Rahmstorf and H. J. Schellnhuber (2008) Tipping elements in
the Earth’s climate system, Proceedings of the National
Academy of Sciences USA 105(6), 1786–1793.
xx
Weitzman, M. L. (2009). "On Modeling and Interpreting the
Economics of Catastrophic Climate Change." The Review of
Economics and Statistics 91(1): 1-19.
xxi
Klein, R.J.T., S. Huq, F. Denton, T.E. Downing, R.G.
Richels, J.B. Robinson, F.L. Toth, 2007: Inter-relationships
between adaptation and mitigation. Climate Change 2007:
Impacts, Adaptation and Vulnerability. Contribution of Working
Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, M.L. Parry, O.F.
Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson,
Eds., Cambridge University Press, Cambridge, UK, 745-777.
xxii
Run commissioned from the FUND model by David Anthoff,
funded by the DFID / Danida project Economic Impacts of
Climate Change in East Africa / UNEP funded AdaptCost
project/ EC ClimateCost study. The run was for the business
as usual scenario with the main FUND model and ‘best
estimates’ and reporting of median value. The model
estimates effects on agriculture, energy demand, health,
forests, storms, coastal protection, water, and ecosystems. It
does not include the effects of extreme events (including
inland flooding), major events, or socially contingent events.
The estimates include adaptation in some sectors. The main
model reports Africa in two main regions, Sub-Saharan Africa
and North Africa.
xxiii
This map was produced by Paul Watkiss, based on a
FUND national model run commissioned by David Antoff and
outelined above.
xxiv
Run commissioned from Chris Hope. The run was for an A2
scenario with 'Stern review' assumptions, without adaptation.
Note the values and graph show mean estimates. The lower
and upper values (and upper line on the later graph) show the
5% and 95% value. The model includes three aggregate
categories of damage: market, non-market and major events
(discontinuities) based on the available literature.
xxv
Note that for adaptation in developing countries, for market
sectors, it is assumed that 50% of economic damages are
eliminated by low-cost adaptation. For non-economic impacts,
adaptation is assumed to remove 25% of the impact. No
adaptation is assumed for discontinuity impacts.
13