AdaptCost
Briefing Paper 6: Adaptation Costs for Agriculture in Africa
Key Messages
1.
Estimates of the economic costs of adaptation require investigation of several lines of evidence. These range
from detailed case studies of projects and plans through to the global scale of integrated assessments. Each
approach brings insight into a complex area, where we have relatively little experience. This note considers
the costs of adaptation for agriculture.
2.
Agriculture has been identified as one of the key sectors for adaptation in Africa. Most of the studies to date
have focused on the impacts of climate change on agriculture, rather than on the planned costs of adaptation.
They include continental scale studies as well as several national studies. These indicate a range of potential
effects could occur from climate change including production changes (positive and negative) for staple food
crops. Importantly they show that the distribution of economic impacts of climate change is unlikely to be
uniform across Africa.
3.
Highly aggregated, top-down methods have been the focus of work to date for impacts and adaptation costing
at regional and national levels.
4.
Existing studies show potentially large effects of climate change in Africa, though these include both positive
and negative effects. Dryland farming systems in semi-arid regions are likely to be impacted most negatively,
while irrigated systems in temperate regions seem less sensitive across the range of climate futures over this
century. Sub-Saharan Africa’s food security, and children’s health and wellbeing could be severely affected
by climate change.
5.
There are also a series of individual country studies, which generally show net negative climate impacts to
crop production and the economy, particularly from impacts on incomes of poor rural households and
unskilled labor.
6.
At the continental scale, a number of studies have estimated adaptation costs. The UNFCCC (2007)
estimated adaptation investments (above the business as usual case) at a total of $781 million by 2030 for
agricultural research, extension and capital formation.
7.
Work by the World Bank (2010) based on scenario based analysis, estimated that public investment
adaptation needs in Sub-Saharan Africa could account for about a third of global needs, at $3.2 to $3.3 billion
per year, mostly for rural roads investment. These do not account for overlaps in spending with baseline
(BAU) growth or of adaptation costs for other sectors.
8.
Other studies (Parry et al, 2009) highlight that the UNFCCC study, and other similar studies, have a number
of deficiencies, concluding that these previous numbers are potential under-estimates. In the case of
agriculture, there is also no analytical work examining the higher-order costs and inter-linkages between
adaptation in water, agriculture and energy sectors.
9.
Nonetheless, a general finding is that adaptation is frequently cost-effective, and can significantly reduce
potential impacts.
10. There are also a number of national studies that have assessed the impacts of climate change and the costs
of adaptation, including recent work in The Gambia, Mozambique, Ethiopia, and Ghana, among others.
11. These country studies highlight the possible adaptation benefits of improved agricultural productivity and
infrastructure investments, primarily irrigation. However, other studies (e.g. EACC, 2010) stress that the use
of aggregate modeling techniques to assess the costs and benefits of adaptation may mask residual
damages, or negative net benefits (e.g. The Gambia case) that occur at micro and sectoral levels.
12. Overall, this remains an area of early research and there is a need for further analysis on the costs, benefits
and effectiveness of adaptation at national and sub-national levels to validate economic prioritization of
adaptation options with additional social priorities at national and sub-national levels.
1
livestock management (i.e. stock increases)
under increased temperatures with a different
mix of more heat resistant species than today
and possible benefits to small farms (Seo and
Mendelsohn, 2006ix; Dinar et al., 2009).
Background: Climate Change
and Agriculture in Africa
Climate change is likely to affect the productivity
of Africa’s diverse agricultural and pastoral
systems over the coming century. As these
sectors underpin rural economic development,
employing over 60% of the labour force, this
poses a threat to rural livelihoods development
and food security across the continent (FAO,
2002).i
These changes will have economic consequences
and potentially large implications for the wellbeing
and sustainable development of rural populations.
Fundamental to this are a wide range of crosssectoral impacts affecting health, water and
energy resources, ecosystems, and land use.
Previous work, such as IPCC AR4ii and other
reviewsiii, has identified a potentially wide range
(positive and negative) of impacts on agriculture
at continental and some national levels. Studies
highlight Africa’s high level of vulnerability relative
to other world regions, due to a large dependence
on rainfed agriculture, low levels of development
and adaptive capacity to existing climate
variability.
Estimates of biophysical impacts of climate
change are highly uncertain, and their treatment
varied across different economics studies. Key
distinguishing features influencing outcomes of
different impact modelling approaches include:
Potentially large-scale impacts to agriculture over
the next 50 to 100 years may include:







Changing spatial and inter-temporal variability
in stream flows, onset of rain days, and dry
spells (Strzepek and McCluskey, 2006iv).
These parameters, among others, drive the
magnitude and direction (positive or negative) of
modeled agro-economic impacts.
More frequent floods and droughts, with
greater erosion rates from more intense
rainfall events and flooding (Agoumi, 2003).

Increased crop water requirements from
higher temperatures, reduced precipitation
and increased evaporation, with likely more
negative impacts on dryland than irrigated
agricultural systems (Dinar et al., 2009v
Fischer et al., 2005vi).

Positive and negative production and net yield
changes for key crops including maize, wheat,
and rice, among others, over different time
periods, resulting in changes in crop and
management choices (e.g. irrigation, crop
type) (Kurukulasuriya and Mendelsohn,
2006vii).

Potentially lengthened growing seasons and
production benefits to irrigated and dryland
systems under mild climate scenarios
(Thornton et al., 2006viii).

Increased heat and water stress on livestock,
with possible shifts from agriculture towards
Temporal, geographic and sectoral scope;
GCM model projections of temperature and
precipitation;
Emissions scenarios (e.g. with and without
mitigation);
CO2 fertilization effects;
Crop model structure and parameterization.
Furthermore, socio-economic development and
other factors will affect future agriculture over the
next century, potentially much more so than from
climate. The socio-economic factors affecting
agriculture include population growth, the
movement of people and goods, and land use and
management (e.g. irrigation, improved land
management) decisions. These considerations
might reduce (as well as increase) the burden of
potential impacts.
Existing Studies of the Impacts
of Climate Change on Agriculture
in Africa
Economic analysis of climate impacts and
adaptation is in its infancy in Africa. At regional
and national-levels, few studies have been carried
out to assess these costs. At the continental
level, two primary methods have been applied to
estimate economic impacts:
Ricardian and
2
agronomic models linked to computable general
equilibrium (CGE)) models.
A review and
summary and comparison of studies based on
these approaches has been carried out as part of
the AdaptCost study for continental level impacts.
Studies are summarised below.
increased losses to dryland to $38 billion and all
African cropland to $31 billion.
Under both uniform temperature scenarios, net
revenues in districts across the Sahara desert and
in southern Africa fall the most. Precipitation
reductions were estimated to lead to about the
same net revenue reductions in both dryland and
irrigated lands, but had a much more negative
effect on the wetter parts of Africa, namely the
central humid band.
Continental impacts with
Ricardian models
1) Kururkulasuriya and Mendelsohn
(2006)
The type of scenario, based on AOGCM results
from the Canadian Climate Centre (CCC), Centre
for Climate System Research (CCSR) and
Parallel Climate Model (PCM) assessed impacts
for 2020, 2060 and 2100. Scenario changes in
mean annual temperature and precipitation were
added to district-level baselines to assess net
revenue changes using the Ricardian model.
Ricardian economics represent one top-down
approach for calculating changes in farmers’
welfare under climate change. In 2006 work
carried out by the World Bank and Centre for
Environmental Economics and Policy in Africa
(CEEPA), using structural Ricardian methods,
modeled farmers’ reactions to climate change
under
two
types
of
climate
scenario
(uniform/synthetic and AOGCM).
Resulting
changes in net revenues per hectare were
calculated
across
11
countries,
against
unchanging demographic, technological and
policy conditions for 2020, 2060, and 2100.
Across the different models, the analysis found
that mean temperature steadily increased until
2100, ranging from 2.5C (PCM) to 6.7C (CCC)
by 2100 while precipitation changes differed
widely, ranging from 10% increase (PCM) to a
30% decrease (CCSR) by 2100.
Increased
rainfall and moderately higher temperatures under
the PCM model indicated sectoral gains of $97
billion per year, in contrast to hotter and drier
results under CCSR and CCC, showing
respective losses of $27 billion and $48 billion by
2100.
Net revenue considerations were based on a
traditional Ricardian approach of maximizing a
profit function operating over exogenous
variables, including climate (temperature and
precipitation changes), water flow, soil type, and
certain socio-economic variables.
Regression
analyses was used to explain how changes in
each variable affected net revenues per hectare
across different farming systems (e.g. irrigated
versus dryland) in different agroecological
contexts (e.g. semi-arid versus humid) across
representative countries.
Effects of CO 2
fertilization on crop production, price changes and
transaction costs of autonomous adaptation such
as crop switching or capital decommissioning
were not captured in the analysis.
Across all model futures, dryland farms were
either the most positively ($72 billion) or
negatively affected ($44 billion). Irrigated farms
showed the greatest resilience to change,
however, and in cases, even increased in value
due in part to their relatively cool temperate
locations.
The CCC and CCSR results suggested that areas
of high population density at present (West Africa
south of the Sahel, the Mediterranean coastline,
and a band across central Africa and a north to
south band in Eastern Africa) coincide with
regions likely to be most negatively affected.
Even under the PCM model’s more favorable
projections, populated regions along the
Mediterranean coastline, southern Africa and
central Africa were negatively affected. The
Under the uniform scenario, the results indicate
2.5C warming leads to losses of $23 billion for
dryland systems, a gain of $1 billion for irrigated
cropland, and losses of $16 billion for all African
cropland. Doubling warming to 5C increases
benefits to irrigation to $3.4 billion as the model
allows shifts between dryland and irrigation, and
3
analysis also indicated that impacts on rural
populations were likely to be significant across all
models.
importers experienced the largest production
losses (20.3%) and middle income calorie
exporters the least production losses (5.8%).
Total average losses across all groups were an
estimated 14.3%.
The analysis shows that the distribution of
economic impacts of climate change is unlikely to
be uniform across Africa, a key finding. Dryland
farming systems in semi-arid regions are likely to
be impacted most negatively, and irrigated
systems in temperate regions less sensitive
across a range of climate futures over this
century.
Moreover, the results indicate a large range for
the different approaches, with an unequal
geographic distribution of gains and losses.
However, these results are likely to be optimistic
given assumed water availability for irrigation
under circumstances in which surface waters will
be potentially reduced.
2) Cline et al. (2007)
Assessing the original CEEPA and World Bank
Ricardian model study (Kurukulasuriya and
Mendelsohn, 2006), Cline argued that the wide
range of impacts (losses of $48 billion to gains of
$97 billion), and particularly the average of $25
billion from their results “are likely to be
misleading”, suggesting that net negative impacts
are more likely. Across both studies, high levels
of uncertainty and a limited treatment of
autonomous adaptation and associated costs and
benefits, including transaction and residual costs,
which significantly influence net damage costs,
complicate these determinations.
In a 2007 bookx, William Cline published results
of an alternative approach to estimating the
economic impacts of climate change at global and
regional scale by applying both Ricardian and
crop modelling methods to arrive at a preferred
synthesis estimate. The Africa analysis employed
Ricardian model and data from the 2006 CEEPA
and World Bank study (discussed above), in
addition to crop model outputs of Rosenweig and
Iglesias, 2006.
Cline adapted the two original studies using what
is termed a “consensus” climate approach. The
approach averaged outputs of six GCM models
for the 2079- 2099 (labeled the 2080s) period to
arrive at a consensus climate future for assessing
climate impacts.1 Country-level results of both
models were used to derive two alternate impact
scenarios: one without CO2 fertilization effects,
and one with a uniform 15% production increase
in yields from CO2 fertilization. A synthesis
estimate was calculated, giving equal weight to
both Ricardian and crop model outputs, by
averaging country-level results, which were
summed to calculate Africa-wide percentage
production impacts.
Projected climate impacts from work carried out
by Kurukulasuriya and Mendelsohn (2006) and
Cline (2007) are summarized in the below table.
Time
period
Economic impact (US$
billion/year)
Study
2020
2060
-23 to +90.5 (GCM)
-23 to +87.4 (GCM)
2080
-28.06 (GCM w/o CO2 effects)
-17.04 (GCM w/ CO2 effects)
Uniform scenarios:
-16,000 (+2.5C)
-31,200 (+5C)
-5,9600 (-7% precipitation)
-12,100 (-14% precipitation)
GCM:
-48,400 to +96,700
Kurukulasuriya
and Mendelsohn
(2006)
Cline et al., 2007
2100
The result indicated that production impacts were
the most severe in Africa relative to other world
regions. Production losses for Africa were an
estimated $17 billion for the 2080s, and $28 billion
with no CO2 fertilization benefits. Crop modeling
results also indicated that low-income calorie
Kurukulasuriya
and Mendelsohn
(2006)
Note: GCM (Global Climate Model) scenarios and
uniform scenario changes are indicated. Results are
across both rainfed and irrigated farming systems.
Continental impacts with
agronomic models
1
The six GCM models used by Cline, 2007 include:
ECHAM4/OPYC3, HadCM3, CSIRO-MK2, CGCM2,
GFDL-R30, CCSR/NIES.
4
Similar to the Ricardian approach, selected crop
and combined CGE model studies yield multiple
lines of evidence for analysis. Production impacts
on major crops are the primary model outputs,
which in turn determine the outcome of other
indicators including GDP, welfare and child
malnutrition. Selected studies for the AdaptCost
review included Cline, 2007, Nelson et al., 2009 xi,
Müller et al., 20105, and Calzadilla et al., 2009xii.
These studies explore agro-economic approaches
using standalone crop models or crop models
combined with partial (e.g. IFPRI’s IMPACT
model) or a computable general equilibrium model
(e.g. GTAP-W).
GDP
Child
malnutrition
Changes to
impact cost
category
% production
changes
GDP losses
Welfare
Child
malnutrition
Although crop production impacts across studies
vary by magnitude and, in the case of Müller et al.
2010, direction, most agree that production
impacts will be large and negative for a variety of
important crops in Africa. A summary of climate
change impacts across the reviewed studies
including percentage production changes, GDP
and child malnutrition with and without CO2
fertilization effects are summarized below.
With CO2 fertilization effects
2050
2080
Total average:
-20.3% (low-1.6%4
income calorie
+7.5%5
importers)2
Wheat: 24%4
-5.8% (middleSugar cane:
income calorie
11%4
exporters)2
3,330 (0.2%
GDP)4
+2,0004
+1.8 million4
Note: Impact values derived from Kurukulasuriya and
Mendelsohn, 20061, Cline et al., 20072, Nelson et al.,
20093, Calzadilla et al., 20094 and Müller et al., 2010
Calzadilla et al., 2009 presents the only GDP and
welfare impacts from climate change among the
studies. With no CO2 fertilization effects, losses
by 2050 are an estimated US$ 3.33 billion (0.2%
GDP), increasing to US$ 4.46 billion when CO2
fertilization effects are not considered. Despite
this, overall welfare in Sub-Saharan Africa is
estimated to increase by US$ 2 billion for a variety
of reasons such as impacts on African crops
being relatively less than other world regions.
Although this improves welfare (+2 billion),
malnutrition in SSA increases from 30.2 million
children under the age of 5, projected for 2050
with no climate change, to 32 million with climate
change (20 million lower than the Nelson et al.,
2009 results either way).
For the 2050s, Nelson et al., 2009 projects large
losses in rice (15%), wheat (34%), and maize
(10%), with no CO2 effect against a 2050 baseline
production (no climate change). With climate
change and no adaptation, Calzadilla et al., 2009
projects rainfed crop production increases by
0.7% and irrigated production decreases
significantly by 15.3%, resulting in overall
production losses for Sub-Saharan Africa of 1.6%,
with most of the decline attributed to wheat (24%)
and sugar cane (11%). Müller et al., 2010
projects relatively smaller production changes of 7.6% without CO2 fertilization effects, but +7.5%
increases with full CO2 fertilization effects relative
to a 1996-2005 baseline on current cropland.
In contrast, Nelson et al., 2009 reports much
larger increases in child malnutrition due to
decreased cereals consumption by 2050. Child
malnutrition numbers increase to 52 million, 10
million more than the 42 million expected without
climate change effects. CO2 fertilization effects
have marginal benefits to meat and cereals
consumption and malnutrition against no CO2
fertilization effects for both AOGCM models
applied.
By the 2080s, Cline et al., 2007 projects losses of
20.3% for low-income calorie importers and 5.8%
losses for middle income calorie exporters, with
17.5% CO2 effect.
Changes to
impact
category
% production
% production
Maize: -10%3
4,4607
+10 or 20 million3
Without CO2 fertilization
2050
2080
Total average:
-28%2
-7.6%5
Rice: -15%3
Wheat: -34%3
Moreover, across both studies in which IFPRI’s
partial equilibrium model (IMPACT) was used,
5
child malnutrition rates are projected to increase
by at least 1.8 million and up to 10 or 20 million,
depending on the projected 2030 base (32 or 42
million) of the studies. Results demonstrate that
Sub-Saharan Africa’s food security, and children’s
health and well-being could be severely affected
by climate change, a threat only marginally
reduced by CO2 fertilization effects.
Across country studies, net climate impacts to
crop production and the economy were
consistently negative, particularly incomes of poor
rural households and unskilled labor.
GDP
impacts between Zambia and Namibian studies
range between 0.9% and 3.5% per year in the
near term.
Similar to continental level findings, dryland
farming systems in semi-arid areas suffer greater
losses relative irrigated systems in sub-humid
regions. It is also interesting to note that the
effects of CO2 fertilization, which featured so
centrally in continental analyses, were not
considered in the country studies. This is in line
with the suggestion that national-level impacts will
be dominated by temperature and precipitation
changes, rather than potential productivity gains
under higher CO2 concentrations, which in turn
require sufficient amounts of soil nutrients that are
limited in many African country contexts (Müller et
al., 2010).
Explicit adaptation considerations were also
explored by Calzadilla et al., 2009 under two
adaptation scenarios to reduce impacts including
doubling irrigation and a 25% productivity to
achieve higher yields and revenues from crop
production. By doubling irrigation, estimated GDP
increases of US$ 113 do not offset GDP losses
due to climate change of US$ 3.3 billion, while the
number of malnourished children declines by only
0.3 million. However, by increasing agricultural
productivity 25%, GDP gains of US$ 25.7 billion
widely overcome estimated losses from climate
change, with 1.6 million fewer malnourished
children.
Results of Ricardian analysis under the CEEPAWorld Bank study indicate that climate change
has net negative impacts on all 11 study countries
by 2050, with a clear distribution of losses
emerging based on initial biophysical conditions.
Countries already characterized by hot and dry
climates, such as Burkina Faso and Niger, suffer
the greatest productivity losses (-19.9% and 30.5%). Countries within cooler climates such as
Ethiopia and South Africa suffer relatively little (1.3% and -3.0%).
These results suggest that there is potentially
insufficient
attention
given
to
improved
management and productivity as an adaptation
strategy. Studies to date focus primarily on
irrigation as an adaptation strategy, such as
Ricardian methods or Fischer et al., 2009. As a
result, improved management, policy and
technology changes are not taken into
consideration. This may over or under estimate
net economic costs of climate change. Damage
costs may also be underestimated if measures
such as irrigation system investments result in
increased risk exposure and maladaptation.
However, these results are constrained by
important caveats, particularly the need to
incorporate impacts of changing temperature and
precipitation on runoff and water supply.
Hydrology studies for Cameroon underscore this
message. Increased temperatures from climate
change are likely to raise the rate of potential
evapotranspiration,
but
reduce
actual
evapotranspiration. Declines in rainfall and runoff
may result, and major rivers such as the Mayo
Tsanga and Mayo Sara, which feed the already
rapidly shrinking Lake Chad, cease to flow
altogether (Molua and Lambi, 2006xiii).
Selected National Level Impacts
Studies
To capture country-specific economic impacts of
climate change, selected studies using both
Ricardian and crop-CGE modelling were
reviewed. Overall results illustrate the sensitivity
of national and agricultural economies to climate
risks. These risks are dominated by current
climate variability in the near future, which may
compound climate change impacts.
Results from Reid et al., 2009xiv indicate that
estimated losses to Namibia’s agricultural sector
are likely to be greater than gains, based on
6
consensus from an expert workshop and CGE
modelling. In line with the CEEPA/WB study
results, there was agreement that dryland
subsistence cropping will likely shrink or even
disappear in some central areas under climate
scenarios of reduced rainfall, higher temperatures
and increased evaporation. However, in contrast
to the CEEPA/WB Ricardian analysis, the
roundtable consensus did not agree that livestock
would likely substitute for crop production when
lands become drier under climate change.
Rather, livestock and small stock production in all
regions of Namibia are expected to fall as carrying
capacity of lands decrease and ranges shift.
under the worst-case scenario (considering only
climate variability) amount to 0.9% annually, or
accumulated total losses of US$ 7.1 billion.
Sensitivity of agricultural GDP is even more
pronounced compared to total economy-wide
GDP losses over this period.
By the year 2016, modelling indicates climate
variability causes a loss in agricultural GDP of
US$ 2.2 billion on average, equivalent to an
annual 1% reduction in agriculture’s growth rate,
or US$ 0.3 billion below what it would have been
under the ‘normal’ rainfall scenario with no climate
variability. Under the worst-case scenario, annual
agricultural GDP losses increase to 2%. Negative
effects of climate variability are especially severe
for maize, Zambia’s staple food crop, and occur
mostly in the southern and central regions.
Estimates suggest that climate variability will keep
300,000 people below the national poverty line by
2016.
The Namibia work also estimated direct GDP
impacts of climate change, based on use values
for the agriculture sector (including pastoralism),
might range from a loss of 1.5% of GDP in the
best case to 3.5% in the worst case. CGE model
results fell roughly within this range with losses
ranging between 1.1 and 2.6% of GDP.
Distributional impacts from reduced agricultural
productivity resulted in employment losses
(largely to unskilled labour), causing expansion in
other sectors while also pushing down wages.
The poorest household group in the agriculture
sector were the most negatively affected from
climate change (12.2% income reductions)
relative to wealthier urban and rural agriculture
groups (6.8% income reductions) in the worstcase scenario.
Under a uniform scenario of 15% reductions in
rainfall, negative economic impacts of climate
variability are increased by a factor of 1.5,
pushing an additional 30,000 people below the
poverty line by 2016, thus, climate change and
variability in Zambia compound each other,
raising the number of poor people to 74,000 under
the most negative of both scenarios. Negative
impacts of climate variability are dampened under
a uniform scenario of 15% increased rainfall.
Moreover, climate change impacts on Namibia’s
agricultural sector were estimated to have
macroeconomic impacts of 1.1% up to 3.5% of
GDP, with highly uneven distributional impacts on
unskilled rural labour and households. The time
period covered in this analysis is unclear.
Moreover, across country studies, impacts of
climate change are anticipated to be significant in
economic
and
productivity
terms,
likely
compounding risks of current climate variability.
These risks increase with rising temperatures and
accelerated
rainfall
regimes,
and
are
disproportionately large for rural households and
unskilled labour.
In contrast to other regional or country-level
studies, Thurlow et al., 2009xv is important in
suggesting that in the case of Zambia, effects of
current patterns of climate variability are likely to
both dominate and compound effects of climate
change in the near future.
Analysis of the
historical period (1975-2007) indicates that on
average Zambia lost 0.4% of growth annually, or
US$13.8 billion total, due to climate variability.
Aggregate Costs of Adaptation
for Africa
Based on work to date, the risks of climate
change posed to Africa’s agricultural sector and
economic development signifies the need for
Between 2007 and 2016, DCGE model results
suggest that accumulated economy-wide losses
7
Africa to adapt.
The AdaptCost study has
reviewed recent assessments identifying a wide
range of adaptation options for the sector and
their economic costs.
investments above future BAU investment
projections driven by population and economic
growth. In order to calculate additional adaptation
financing needs in 2030, a business as usual
(BAU) scenario was calculated based on historical
investment trends in the agricultural sector from
ODA.
Some distinguishing factors between the studies
include how ‘business as usual’ investment
baselines are derived against adaptation
investments, and how additional adaptation
investments are calculated to estimate future
financing needs.
Categories of investment included research,
extension services, and fixed capital formation
across public (domestic and foreign) and private
investors. Specific components of these cost
categories were not indicated.
In many cases, studies attempt to identify
additional investment needs for climate change
adaptation from current or planned development
investment. In other cases, climate adaptation
and development investments are taken together.
Special consideration is given to irrigation, as it
constitutes both a widely recommended
adaptation strategy in adaptation economics and
a key pillar of Africa’s existing development
investment strategies.
For the purposes of this AdaptCost review, Africaspecific BAU and adaptation costs were
calculated based on the percentage markup
values for the three investment types
(percentages cited in Table 16, section 7.4 of
McCarl, 2007). Given the marginal difference
between climate impacts under the A1-B and B2
(mitigation) scenario used for the study, only
results of the A1-B (“with climate change”) were
assessed for AdaptCost. Results for additional
annual investment needs for adaptation by 2030
are summarized in the table below.
1) UNFCCC (2007)
The UNFCCC study on Investment and Financial
Flows to address Climate Change (2007xvi)
estimated the total global adaptation costs to
climate change impacts (all sectors) at $50 to
$170 billion/year by 2030, of which $28 to $67
billion/year was anticipated in developing
countries (Non-Annex1 parties).
Additional annual
adaptation by 2030
Investment
type
investment
A key background study for the UNFCCC report is
McCarl, 2007 covering ‘Adaptation options for
agriculture, forestry and fisheries’.
This
background study contains more Africa-specific
information and uses an investment and financial
flows (IFF) approach to estimate additional
adaptation financing needs.
for
Added investment above BAU with
climate change (USD million)
$
The study estimated global agriculture adaptation
costs (the increase in global financial flows) at
between $11.3 and $12.6 billion per year by 2030,
depending on the emission scenario, but did not
produce values by region.
needs
% over current
Research
131
8.8
Extension
staff and
expenditure
Fixed capital
formation
Africa Total
11
2.0
639
3.1
781
3.4
Source: Adapted from Table 16, section 7.4 of
McCarl, 2007.
Based on this analysis, additional agricultural
adaptation investments above BAU total $781
million by 2030 for agricultural research,
extension and capital formation.
These
adaptation finance needs represent 2.4% of
added development investment needs estimated
at around $33 billion by 2030.
Adaptation
investment needs for Africa represent roughly
17% of overall annual adaptation investment
McCarl, 2007 estimated additional financing
needs based on expert elicitation and three
climate futures (no climate change, climate
change, mitigation).
Cost estimates involved
percentage increases for climate adaptation
8
needs for the agricultural sector estimated for
developing countries ($4.7 billion).
However, the study recommends more bottom-up
assessments are recommended for future work.
2) Parry et al (2009) (IIED/Grantham):
Assessing the Costs of Adaptation to
Climate Change
3) Nelson et al., 2009; World Bank, 2010:
A recent study by IFPRI (Nelson et al., 2009) for
the World Bankxviii, explores the costs of
productivity-enhancing investments that reduce
child malnutrition, projected to increase under
climate change scenarios for Sub-Saharan Africa.
This work is in part a response to methodological
criticisms of the percentage markup IFF approach
of the UNFCCC report, as it makes explicit
analytical links between climate change and
investments needed to adapt to or avoid impacts.
In a recent report, Parry et al., 2009 and the
IIED/Grantham Institute assessed the costs of
adaptation (Parry et al, 2009xvii). This is primarily a
review of the numbers cited in the UNFCCC
(2007) study above.
It highlights that the
UNFCCC study, and other similar ones, have a
number of deficiencies. It also undertakes a reassessment of the UNFCCC review, reporting that
the overall costs of adaptation (all sectors) from
this report are a substantial under-estimate,
though these are biased by underestimation in
certain sectors (rather than for agriculture). The
reasons given for the underestimates are due to
the inclusion of estimated initial capital
investments only, and omission of estimated
preparation,
operations
and
maintenance,
decommissioning,
and
institutional
and
administrative costs, including those for building
planning capacity. In the case of agriculture,
there is also no analytical work examining the
higher-order costs and inter-linkages between
adaptation in water and agriculture sectors.
Both IFPRI and World Bank studies reported
results of the linked IMPACT and DSSAT
agronomic modelling discussed in the impacts
section above. Results between the studies differ
only slightly, likely due to different base year
prices (US$ 2000 in Nelson et al. and US$ 2005
in the World Bank), although each study used
2000 as the base year for investment and asset
stock accounting. The studies assess adaptation
costs as the costs of initiatives needed to restore
welfare to levels prevailing before climate change
along the projected development baseline (note
that this assumption has been the subject of
considerable comment2).
A number of further methodological issues were
highlighted in the study:

Scope: Limited range of impacts considered

Depth: Focus on hard adaptation, no costeffectiveness test

Depth: Focus on planned, public adaptation,
not including private (autonomous) adaptation

Costing: Ignores preparation, lifetime, and
residual costs

Costing: Ignores higher-order effects – i.e. if
mitigation and development costs are not
made

Uncertainty: Ignores the need to plan for a
range of outcomes.
In addition, investment needs estimates do not
account for overlaps in spending with baseline
(BAU) growth or of adaptation costs for other
sectors. Therefore, development, adaptation and
cross-sectoral investment needs are not
differentiated. This may result in overestimates of
adaptation financing needs.
The studies also focus on planned (public sector)
adaptation costs. These primarily consider “hard”
options involving engineering solutions. Different
aggregation rules accounting for positive as well
2
There are two issues here. First, it is questionable whether
this degree of adaptation is actually physically possible. Other
literature (e.g. Parry et al. 2009) above indicates that
adaptation might only be able to reduce economic costs by
around 50% in developing countries, thus leaving considerable
residual impacts. Second, it assumes that such a level of
adaptation is (economically) rational - which may be
questioned, and leads to a potential overestimation of costs.
Overall, the top-down approach used in the
UNFCCC 2007 study is accepted as a reasonable
first approximation of adaptation costs in the
agriculture, forestry and fisheries sector.
9
as negative effects of climate change and
adaptation costs are also considered.3
5) Grantham Institute (2009)
The Grantham Institute for Climate Change
produced a fact-base on climate change in Africa,
including impacts, required actions and adaptation
costs, presented at the CAHOSCCxix.
Estimated adaptation investment needs were
presented based on two scenario climate futures
simulated by the (wetter) NCAR and (drier)
CSIRO models. Annualized additional investment
costs needed to counteract the effects of climate
change under the two scenarios are presented in
the table below. World Bank results only are
shown as they are based on the most recent base
year (2005).
The report has some recommendations by sector
for the near (2015) and longer (2030) term
periods. In the short term, the challenge of
distinguishing adaptation from development is
highlighted.
ODA commitments to meet the
MDGs (estimated at $72 billion per year for Africa
compared to the $29 billion delivered in 2004)
should also be met because without them
adaptation will be much more costly.
Net annual cost of adaptation for agriculture in
SSA—counteracting the effects of climate change
on children’s nutrition levels by cost type, 20102050 ($ billion at 2005 prices, no discounting).
Cost type
and
investment
category
Agricultural
research
Irrigation
efficiency
Irrigation
expansion
Roads
Total
NCAR
(wetter
scenario)
CSIRO
(drier
scenario)
Scenario
average
0.3
0.3
0.3
0.2
0.2
0.3
0.6
0.6
0.6
2.2
3.3
2.1
3.2
2.15
3.25
For agricultural sector, the report recommends the
need to develop and climate-proof agricultural
productivity by improving agricultural techniques
and adopting higher-yielding, and resilient crops
in the near term. Costs of measures are based on
UNFCCC estimates of $1 billion per year for
Africa by 2030, on top of development spending.
Implementation will also require capacity building,
access to new agricultural techniques and inputs
and large-scale knowledge dissemination.
Country Studies of the Costs of
Adaptation for Agriculture
Source: Adapted from World Bank, 2010.
Overall results indicate that public investments
(planned adaptation versus autonomous) are an
estimated $8 billion annually between 2010 and
2050 to restore childhood nutritional levels
worldwide. Investment needs in Sub-Saharan
Africa accounts for about a third of global needs,
with $3.2 to $3.3 billion, mostly going to rural
roads investments.
In contrast to the high-level, top-down global and
regional studies reported above that primarily
serve to scope out the scale of potential
aggregate costs, a number of recent and on-going
studies adopt the national level of analysis. These
studies benefit from a greater degree of
contextualisation and are designed to inform the
design of adaptation strategies for national and
sub-national authorities. A sample of these
studies is outlined below.
While the approach used by the IFPRI and World
Bank studies have some similarities to previous
global studies, they also mark a significant
advance forward by working with a more explicit
economic framework, bottom up costs, and
linkages to a range of climate impacts.
1) AIACC (2006) - Gambia
As part of the 2006xx Assessments of Impacts and
Adaptations to Climate Change (AIACC) studies,
agricultural adaptation actions were explored for
millet production in The Gambia. The project
carried out a detailed cost benefit analysis for
irrigation and its potential to mitigate expected
3
For some sectors, in some regions, climate change leads to
a potential economic benefits – the study applied different
rules when netting these against adaptation costs, by country
or region. The main results are presented as X-sums, where
positive and negative items are netted within countries but not
across countries.
10
yield variations and losses from potentially
extreme reductions in future rainfall under climate
change for the near (2010-2039) and the distant
future (2070-2099). Consideration of the irrigation
option for economic appraisal was based on
positive production results across ‘no adaptation’,
introducing a ‘new crop variety’, ‘fertilizer
application’,
‘irrigation’,
and
‘supplemental
irrigation’
options
using
the
Soil-WaterAtmosphere-Plant (SWAP) model.
2) Economics of Climate Adaptation
(2009) – Mopti, Mali
The Economics of Climate Adaptation Working
Group (ECA, 2009xxi) undertook case studies on
the costs of climate change risks, and costs (and
benefits) of adaptation. The level of application is
small scale and single sector at the sub-national
level, and has a useful focus on short-term
adaptation costs and benefits.
The study
identified a number of interventions with net
economic benefits under three climate change
scenarios.
Results of averted losses from
adaptation measures were presented for the high
(negative) scenario. Categories of cost effective
interventions included:
Direct benefits measured for CBA work included
increased farm production and income from
irrigation investments. Cost considerations were
monetized annually using a discount rate of 9%
and project horizon of 60 years based on capital
investment,
operation,
maintenance
and
replacement costs. Operations and maintenance,
and distribution costs represented the two largest
cost components.
Net Benefit and Benefit-Cost ratios associated
with irrigation water delivery for specified
price of millet.
Cost category
Irrigation costs
Benefits
Net benefits
(US/ha)
B/C ratio
3% discount
rate
2171
300
-1871
14% discount
rate
3233
300
-2933
0.14
0.09

Infrastructure and asset-based investments
(agriculture and livestock)

Migration of people and assets to increase
agricultural productivity in high potential areas

Institutional measures and reforms (policies,
institutions, access to finance)

Agricultural value chain improvements
(technological developments, farmer training,
access to finance)
It was noted that revenue benefits (an estimated
$2 billion annually) of developing cash crops on
new lands including delta and non-delta areas
and small rice perimeters in the Mopti region
could cover a large portion, if not all, expected
losses from climate change for the entire country.
Based on the assumptions of the analysis, the net
benefits of irrigation development are negative,
translating to B/C values closer to zero than 1.
Despite this, further analysis shows that increased
production yields from irrigation would eliminate
the need for commercial cereal import/food aid in
The Gambia. This would also result in substantial
foreign exchange savings that could be reinvested
in agriculture development. For example, roughly
$22 million in savings could fund the development
for 7500 ha of millet using diesel-pumped
groundwater.
This supports the overall finding that many cost
effective adaptation options are essentially
economic development actions that are robust in
the face of uncertainties climate change. These
actions also address Mali’s currently shifting agroclimatic and agro-ecological zones caused by the
southward encroachment of the Sahara desert,
coupled with land use changes.
3) World Bank EACC – Mozambique,
Ethiopia, Ghana
Moreover, while “preliminary results of BenefitCost analysis indicated substantial benefits at a
macro economic level, increased income from
irrigation is not matched by costs incurred by
farming households, suggesting the need for
further policy measures to support irrigation.”
As part of the World Bank Economics of
Adaptation to Climate Change (EACC) 2010
project, three national level country studies were
carried out for Mozambique, Ethiopia and Ghana.
Each of the studies assessed economic impacts
11
and costs of adaptation for the agriculture sector
using CGE modeling across two uniform and two
GCM climate change scenarios.
Qualitative
interviews and participatory workshops were also
carried out to compare economic and social
prioritization of adaptation options.
or modifications
programs.
of
–
current
government
For the agriculture sector, adaptation actions
include increasing irrigated cropland and investing
in agricultural research and development. Both
transport and hydropower sectoral actions also
influence agriculture sector resilience and
adaptation options.
Transport sector options
include increasing the share of paved and
hardened
roads.
Hydropower
sector
recommendations are to alter the scale and timing
of planned projects, as well as constrain
downstream flow and irrigation flow. CGE analysis
finds the cost of adaptation options to be
significant, ranging from 2-5% of annual gross
fixed capital formation, or about 10% of the
annual current account deficit of Ethiopia.
Across country studies, the possible adaptation
benefits of improved agricultural productivity and
infrastructure investments, primarily irrigation,
were highlighted. However, the study stresses
that use of aggregate CGE modeling to assess
the costs and benefits of adaptation may mask
residual damages that occur at micro and sectoral
levels.
Furthermore, the need to validate
economic prioritization of adaptation options
based on economic efficiency with social priorities
was critical.
Mozambique - In the case of Mozambique, CGE
analysis determined sealing unpaved roads
reduces the worst-case climate changes across
alternate wet and dry scenarios for the 2050
period. With little additional cost implications, this
is a no-regret action advisable even under the
baseline. Improved agricultural productivity or
human capital accumulation could compensate for
remaining welfare losses. Raising agricultural
productivity by 1% each year over baseline
productivity offsets remaining damages, as does
providing primary education to 10% of the 2050
workforce. Investment costs to restore welfare
are estimated to be less than US$390 million per
year over 40 years.
Welfare implications of adaptation against a noclimate change baseline indicates that adaptation
lowers income variability of agriculture GDP
growth by 40-50%, and reduces welfare losses
under climate change by half.
Potential
investment programs to offset residual welfare
losses might include an additional labor-upgrading
program in a national adaptation strategy. Under
such a scenario, 0.1% of rural unskilled labor
might be transferred to urban regions, and there is
upgrading so that urban skilled and unskilled labor
categories grow uniformly faster than in the
baseline scenario.
Tested over the Wet2
scenario, the strategy more than offsets remaining
negative welfare impacts, and accelerates the
diversification of the economy away from highly
climate sensitive sectors including agriculture.
Other hard and soft planned and autonomous
adaptation options were explored as well. Some
suggested softer options, which are challenging to
cost, include access to more credit and financial
services for small businesses, diversification of
livelihoods away from agriculture, and better
education and information for rural areas.
Ghana - In the case of Ghana, adaptation
considerations were made to restore aggregate
absorption to the baseline, rather than individual
sectors. This is to achieve efficient allocation of
an adaptation envelope constrained by the cost of
returning aggregate welfare to the baseline
caused under each climate scenario.
Ethiopia - In the case of Ethiopia, drought and
flood related damages to agriculture, roads and
dams providing hydropower and irrigation were
highlighted as significant across wet and dry
climate scenarios.
Ambitious government
infrastructure investment programs already exist
in Ethiopia, which are likely to enhance Ethiopia’s
resilience to climate change. Therefore, the study
identified adaptation strategies as additions to –
Adaptation actions for the agriculture sector
involve expansion of irrigated land from 2012
onwards. Adaptation of road infrastructure for the
transport sector also benefits the sector through
reductions in overall welfare losses, with the
exception of the Ghana Dry GCM scenario.
12
Therefore, road design change is not necessarily
an unequivocal no-regret adaptation measure.
least cost option ($7.9 million by 2030), followed
by promotion of intensive cattle farming ($36.04
million), and improvement of existing cattle
farming ($246.54). Promotion of irrigated farming
was by far the highest cost option at $4.42 billion
by 2030.
Under the uniform global wet scenario, the total
cost of returning aggregate welfare to the baseline
using agriculture and road design adaptations is
slightly lower than the estimated envelop of
adaptation investment expenditure ($16 billion).
In the other wet and dry climate scenarios, some
negative residual impacts remain although the
agriculture-focused strategy based on irrigation
investments nearly restores aggregate welfare. It
is recommended in these cases to prioritize
investments to high return crops and regions, and
use
remaining
resources
for
lump-sum
compensation payments.
Furthers study results for IFF pilot countries are
anticipated in 2010.
Research Priorities
The available evidence base – at both continental
and country level remains low – and there is need
for further analysis on the costs, benefits and
effectiveness of adaptation at national and subnational levels to validate and crosscheck
eeconomic prioritization of adaptation options
based on economic efficiency with social priorities
at national and sub-national levels.
In addition, investment in education represents an
adaptation strategy that spurs growth and sectoral
performance,
which
indirectly
reduces
vulnerability to negative climate change shocks
and maintain welfare.
However, the study
emphasizes that aggregate estimations of
adaptation costs and benefits at the macro level
mask residual damages that can occur at micro or
sectoral levels.
Areas for adaptation investment priorities are
illustrated in figure below.
Final recommendations call for investments in
R&D related to the impacts of climate change on
crops and livestock products and pest control, as
well as early-maturing varieties, improved water
storage capacity to utilize excess water in wet
years, and improved agricultural extension and
marketing networks. Investment in mid-sized
irrigation facilities, improvement of the land tenure
system and entrepreneurial skills to generate off
farm income were also recommended.
Key study recommendations from the review
include the need for agriculture impacts and
adaptation:
UNDP Investment and Financial Flows
– African study countries


Supported by UNDP, work is currently being
undertaken by African regional partners assessing
investment and financial flows needed for
adaptation. Study countries include Algeria, The
Gambia, Namibia, Niger, Liberia, and Togo.


Preliminary results for Niger indicate costs of all
options totalled $4.71 billion (2005 dollars) or
$188 million per year up to 2030. Investments in
rainfed crops production improvements were the
13
More bottom-up national and sub-national
impacts and adaptation economics
Improved analysis of ‘soft’ costs of climate
impacts and adaptations
Emphasis
on
adaptation-development
synergies as a primary adaptation strategy to
increase resilience to uncertain future risks
Use of analytical methods balancing
economic and social investment prioritisation
The AdaptCost Project
v
Dinar et al., 2009. Climate change and agriculture in Africa.
Earthscan, UK.
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.
vi
Fischer, G., Tubiello, F., Velthuizen, H., and Wiberg, D.,
2006. Climate Change Impacts on Irrigation Water
Requirements: Effects of Mitigation, 1920-2080, Technological
Forecasting and Social Change,
doi:10.1016/j.techfore.2006.05.021.
vii
Kurukulasuriya, P. and Mendelsohn, R., 2006. A Ricardian
analysis of the impact of climate change on African cropland.
CEEPA Discussion Paper No. 8. Special series on climate
change and agriculture in Africa. Discussion Paper ISBN 1920160-08-6.
viii
Thornton et al., 2006. Mapping climate vulnerability and
poverty in Africa. Report to the Department for International
Development, International Livestock Research Institute,
Nairobi, Kenya.
ix
Sungono Seo and Robert Mendelsohn, 2006. The impact of
climate change on livestock management in Africa: A
structural Ricardian analysis. CEEPA Discussion Paper No.
23. Special series on climate change and agriculture in Africa
x
William R. Cline, 2007. Global Warming and Agriculture:
Impact Estimates by Country. Center for Global Development
and Peterson Institute for International Economics.
Washington, DC.
xi
Nelson, G.C. et al. 2009. Climate Change. Impact on
Agriculture and Costs of Adaptation. IFPRI, Washington D.C.
xii
Calzadilla et al., 2009. Economy-wide impacts of climate
change on agriculuture in sub-Saharan Africa. Kiel Institute for
the World Economy, Working Paper FNU-170.
xiii
Ernest Molua and Cornelius Lambi, 2006. The economic
impact of climate change on agriculture in Cameroon. CEEPA
Discussion Paper No. 17. Special series on climate change
and agriculture in Africa.
xiv
Reid et al., 2007. The economic impact of climate change in
Namibia. International Institute for Environment and
Development.
xv
Thurlow et al., 2009. The impact of climate variability and
change on economic growth and poverty in Zambia.
International Food Policy Research Institute.
xvi
UNFCCC (2007). Investment and financial flows relevant to
the development of an effective and appropriate international
response to Climate Change (2007). United Nations
Framework Convention on Climate Change
xvii
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.
xviii
World Bank, 2010. The Economics of Adaptation to
Climate Change (EACC) synthesis report.
xix
African Partnership Forum and Conference of African Heads
of State and Government on Climate Change (CAHOSCC), at
the special session on climate change. September 3rd 2009,
Addis Ababa.
http://www.uneca.org/apf/index.asp
Grantham Institute (2009). Possibilities for Africa in global
action on climate change.
Executive Summary. July 2009
Documents available at:
 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.
This briefing note was prepared by Jillian
Dyszynski (SEI-Oxford Office).
Footnotes and References
i
FAO, 2002, Comprehensive Agriculture Development
Programme (CAADP).
ii
Boko, M., I. Niang, A. Nyong, C. Vogel, A. Githeko, M.
Medany, B. Osman-Elasha, R. Tabo and P. Yanda, 2007:
Africa. 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, 433-467.
iii
J. C. Nkomo, Ph.D. University of Cape Town, South Africa,
A. O. Nyong, Ph.D. University of Jos, Nigeria, K. Kulindwa,
Ph.D. University of Dar es Salaam, Final Draft Submitted to
The Stern Review on the Economics of Climate Change July,
2006.
iv
Strzepek, Kenneth and McCluskey, Alyssa, 2006. District
level hydroclimatic time series and scenario analyses to
assess the impacts of climate change on regional water
resources and agriculture in Africa. CEEPA Discussion Paper
No. 13. Special series on climate change and agriculture in
Africa. Discussion Paper ISBN 1-920160-13-2.
14
http://www.uneca.org/apf/documents.asp
xx
Nkomo, J.C. and Gomes, Bernard, 2006. Estimating and
comparing costs and benefits of adaptation projects: Case
studies in South Africa and The Gambia. A final report
subimitted to Assessments of Impacts and Adaptations to
Climate Change (AIACC), Project No. AF 47.
xxi
ECA (2009). Shaping Climate-resilient Development a
framework for decision-making. A report of the economics of
climate Adaptation working group. Economics of Climate
Adaptation. Available at:
http://www.swissre.com/resources/387fd3804f928069929e92b
3151d9332ECA_Shaping_Climate_Resilent_Development.pdf (Accessed
January 2010).
15