Journal of African Economies, Vol. 23, AERC Supplement 2, pp.

ii17 –ii49
doi:10.1093/jae/eju011

The Economic Impacts of Climate

Change on Agriculture in Africa
John Asafu-Adjaye*
School of Economics, The University of Queensland, Brisbane, QLD 4072, Australia

* Corresponding author: John Asafu-Adjaye. E-mail: j.asafuadjaye@uq.edu.au

Abstract
The African continent is projected to suffer adverse impacts from climate
change, which are disproportionate to its contribution to global carbon
dioxide emissions. Africa is particularly vulnerable because it is among the
hottest places on the Earth and therefore any further warming will likely
have adverse socioeconomic consequences; and most of the economies in
this region rely mainly on natural resources and rain-fed agriculture, which
are very sensitive to climate change and variability. This article investigates
the impacts of climate change on African agriculture and discusses the
policy implications for managing these impacts. The modelling results show
that Africa will experience the largest impacts from climate change in terms
of decline in economic growth and welfare losses. The disaggregated
results show that Southern Africa will be the hardest hit region, followed
by the Rest of sub-Saharan Africa, North Africa and East Africa, in that
order. There is therefore need for concerted efforts at adaptation, which
should include introduction of temperature-sensitive varieties, diversifica-
tion of production systems and livelihoods, shift to sustainable agricultural
intensification, shift to irrigation agriculture, addressing institutional chal-
lenges such as poor physical and social infrastructure, market imperfections,
lack of access to credit, and lack of crop insurance, etc. In the long run,
emphasis should be placed on diversifying away from agriculture to industry
and services.
Keywords: Africa, climate change, agricultural productivity
JEL classification: Q54, C68

# The author 2014. Published by Oxford University Press on behalf of the Centre for the
Study of African Economies. All rights reserved. For permissions, please email: journals.
permissions@oup.com
ii18 | John Asafu-Adjaye

1. Introduction
The overwhelming weight of current and historical data indicates that global
warming is unequivocal. Global surface temperatures (including over land
and sea) have increased by 0.88C over the period 1901 –2010 and about
0.58C over the period 1979– 2010. Furthermore, the evidence suggests that
all of the 10 warmest years in the global temperature records up to 2011
have occurred since 1997, with 2005 and 2010 being the warmest two years
in more than a century of global records (AMS, 2012). Globally, the sea
level has risen by an average of about 17 cm in the twentieth century, with
the rate of increase accelerating since the early 1990s. The majority of scien-
tists now believe that human activities, particularly burning of fossil fuels and
deforestation, are among the prime causes of the changes observed in the
twentieth century and are likely to contribute to further changes in the
twenty-first century (IPCC, 2007).
Although the African continent accounts for less than 7% of total global
carbon dioxide (CO2) emissions and its emissions per capita are less than
half the global average, she will bear the brunt of further global warming
(AfDB, 2011).1 Africa is particularly vulnerable because it is among the
hottest places on the Earth and therefore any further warming is likely to
have adverse socioeconomic consequences. Africa’s vulnerability is heigh-
tened by the fact that most of the economies in this region rely mainly on
natural resources and rain-fed agriculture, which are very sensitive to
climate change and variability. For example, biomass provides about 80%
of the primary domestic energy supply in Africa, while rain-fed agriculture
contributes some 30% of GDP and employs about 70% of the population,
and is the main safety net of the rural poor (World Bank, 2012). In addition
and perhaps more importantly, Africa’s vulnerability is exacerbated by the
fact that Africa is home to the largest numbers of the world’s poor, with
extreme poverty as high as 48% (AfDB, 2011)—which weakens Africa’s adap-
tive capacity.
Many African countries are already under various forms of climate related
stress (e.g., drought, floods, rainfall variability, etc.) which, coupled with
low adaptive capacity, make them highly vulnerable to climate change.2
Africa is already suffering from many stress factors that impinge on economic
development in the continent and climate change will elevate these stress
factors. For instance, factors such as population growth and desertification
1
Specific details of the impacts of climate change on Africa are discussed below.
2
For instance, women in poor communities will be particularly affected because of their trad-
itional role as providers of food, water and firewood for the household.
 The Economic Impacts of Climate Change | ii19

will continue to put pressure on Africa’s natural resources in the next 50

years. Expansion in both subsistence and small-scale commercial farming,
as well as livestock grazing, will continue to put pressure on Africa’s forest
resources. According to AfDB (2011), conversion to small-scale agriculture
accounted for about 59% of forestland lost to farming within the period
1990–2000.
Another existing stress factor in Africa that could be compounded by
climate change is soil erosion. Lal (1995) estimates that yield reductions
due to past erosion may range from 2 to 40%, with a mean of 8.2% for the
continent of Africa and 6.2% for sub-Saharan Africa (SSA). There will also
be pressure on water resources. Given the current population growth
trends and water use patterns in Africa, the research findings indicate that
a number of countries will exceed the limits of their land-based water re-
source capabilities by 2025 (Ashton, 2002). Climate change will increase
the number of people at risk of increased water stress. Morbidity could
also be compounded by climate change. Africa’s disease burden is high,
and is estimated to be at least twice as high as any other region of the
world (PACJA, 2009). Climate change will likely affect people’s exposures
to physical and biological risks such as extreme weather and new diseases,
as well as the geographic range of vector and water-borne diseases (PACJA,
2009; IPCC, 2014). An increase in morbidity will compound the impacts
of the HIV/AIDS pandemic—further undermining prospects for develop-
ment in Africa.3
Because of its compounding effect on the existing sources of stress as dis-
cussed above, climate change presents a formidable developmental challenge
to policymakers in Africa. The main objectives of this article are twofold.
First, it investigates the impacts of climate change in Africa, with specific ref-
erence to the agricultural sector. Secondly, it discusses the implications of the
findings for managing the impacts.
Although the focus of this article is to investigate the impacts of
climate change on agriculture, it is instructive to note in passing that
there is a bidirectional relationship between the two. That is, production
of agricultural commodities also has an impact on climate change.
Using estimates from 2005, 2007 and 2008, Vermeulen et al. (2012)
found that agriculture contributes the lion’s share of greenhouse gases
3
More than 69% of all people living with HIV (including 91% of the world’s HIV-positive
children) are found in Sub-Sahara Africa. In 2011, an estimated 1.8 million people in the
region became newly infected. An estimated 1.2 million adults and children died of
AIDS, accounting for 71% of the world’s AIDS deaths in 2011 (The Foundation for AIDS
Research, 2012).
ii20 | John Asafu-Adjaye

(GHGs) emanating from the food system,4 releasing up to 12,000 mega-

tonnes of carbon dioxide equivalent (CO2-e) a year—up to 86% of all
food-related anthropogenic greenhouse-gas emissions. Overall, they
found that the entire food system released 9,800 – 16,900 megatonnes of
CO2-e into the atmosphere in 2008, which is equivalent to 19 – 29% of
global GHG emissions.
The remainder of the article is organised as follows. The next section pro-
vides the context for the study by briefly discussing the role agriculture plays
in Africa’s economic growth and development. Section 3 presents the latest
available climate change projections for Africa. The discussion includes
impacts on temperature and precipitation. This is followed by a review of
the latest research on the impacts of climate change on African agriculture
in Section 4. In Section 5, some of the predicted estimates of the decline in
African agricultural output are fed into a global economic model to yield esti-
mates for economic growth, welfare and prices for selected African countries.
Section 6 discusses the policy implications of the findings, while Section 7
concludes.

2. Sub-Saharan Africa’s recent economic performance

and the role of Agriculture
After years of low and sometimes negative growth in the eighties and nineties,
the growth performance of several SSA economies began to pick up at the be-
ginning of 2000. Figure 1 shows growth rates for SSA in comparison with
South Asia, the OECD and the world average. It can be seen that SSA coun-
tries grew at an annual average rate of about 5% in the period 2005– 2010,
which was twice the world average in the same period. Even though SSA eco-
nomic growth declined in 2008 and 2009 when the effects started to be felt, it
was well above that of the other regions. Economic growth in SSA rebounded
in 2010 with an average of over 5%.
The World Bank projects African economies to grow by more than 5% in
2013. This growth will be fuelled by, among other things, increasing invest-
ment and a general pick-up in the world economy. Overall, the strong
growth in Africa over the past decade has led to a significant reduction in
the levels of poverty. According to the World Bank’s provisional figures,
the proportion of Africans living on less than $1.25 a day fell from 58 to
48.5% between 1996 and 2010. In a number of SSA countries, the strong
4
The ‘food system’ comprises agricultural production (livestock, crops and fisheries),
processing, storage, transport and retail.
 The Economic Impacts of Climate Change | ii21

Figure 1: Real GDP Growth for Selected Regions, 2003–2010.

Source: Data from World Bank (2012).

growth performance has been driven by mineral and mining exports.

However, in most of these countries (e.g., Ghana), this has not had a signifi-
cant effect on employment. This is because mining tends not only to be
capital-intensive but also to have weak backward and forward linkages
with the rest of the economy.5 This situation is worsened by many of the
multinational companies repatriating their profits and spending very little
in the economies in which they operate.
Despite the strong growth of natural resource exports in Africa (particu-
larly minerals and oil and gas), most of the countries in this region (especially
in SSA) primarily rely on agriculture for their national income and to support
livelihoods.6 Agriculture contributes roughly a quarter of GDP on average,
and for some countries close to 50% of GDP (Table 1). Although the share
of agriculture in national output has declined since 1990 in most countries,
agriculture remains the mainstay of many African economies, and the
primary vehicle for poverty alleviation and food security, especially given
its subsistence nature. With the exception of a few mineral resource-rich
countries, the trade sector (exports and imports) in most SSA countries is
dominated by the export of agricultural commodities—pointing to the

5
The majority of mining inputs, including capital are generally imported, while there is very
little processing in source countries.
6
For example, in Ethiopia agriculture employs 85% of the population and accounts for 82%
of foreign earnings.
ii22 | John Asafu-Adjaye

Table 1: Selected Economic Indicators for Selected Sub-Saharan African Countries, 1990,
2005 and 2010

Country Agriculture valued Manufacturing Trade (% GDP)

added (% GDP) value added
(% GDP)

1990 2005 2010 1990 2005 2010 1990 2005 2010

Benin 36 32 32 8 8 8 41 40 40
Botswana 5 2 2 5 4 4 105 85 73
Burkina Faso 28 – 35 15 – 8 36 – 42
Burundi 56 35 35 13 9 11 36 45 43
Cameroon 25 21 – 15 19 – 37 49 55
Central African Rep. 48 54 – 11 – – 42 – 35
Chad 29 21 – 14 5 – 41 94 69
Congo, Dem. Rep. 31 46 46 11 7 5 59 71 146
Congo, Rep. 13 5 4 8 5 4 99 137 140
Cote d’Ivoire 32 22 23 21 17 19 59 91 77
Equatorial Guinea 62 3 – – 8 – 102 144 123
Eritrea – 23 – – 8 – – 65 –
Ethiopia 56 47 47 – 5 4 14 55 47
Gabon 7 5 4 6 4 4 77 90 97
Gambia 29 33 29 7 5 5 131 110 66
Ghana 45 37 30 10 8 7 43 98 71
Guinea 24 20 22 5 4 7 65 58 65
Guinea-Bissau 61 60 – 8 9 – 47 88 –
Kenya 30 27 25 12 11 11 57 61 68
Malawi 45 35 30 19 13 12 57 79 74
Mali 46 37 – 9 3 – 51 63 66
Mozambique 37 22 30 10 14 14 44 73 71
Namibia 12 12 8 14 13 14 119 93 102
Nigeria 33 23 – 6 – – 72 90 65
Rwanda 33 42 32 18 8 7 20 42 41
Senegal 20 18 17 13 11 13 56 69 68
South Africa 5 3 2 24 19 15 43 55 55
Tanzania 46 46 28 9 7 10 50 54 66
Togo 34 44 31 10 10 8 79 84 97
Uganda 57 33 24 6 9 8 27 40 58
Zambia 21 19 20 36 12 9 72 42 82
Zimbabwe 16 19 18 23 14 18 46 130 126
Average (sample) 33 27 24 13 9 9 59 77 74
Sub-Sahara Africa 20 16 13 17 14 12 53 68 66

Source: Data from World Bank (2012).

 The Economic Impacts of Climate Change | ii23

important role that agriculture will continue to play in Africa’s development,

at least in the near term.7
Sub-Saharan African agriculture can be described as small scale, low input,
mainly subsistence based with limited use of organic and inorganic fertilisers.
There is heavy dependence on rainfall which means that economic output is
easily influenced by agro-climatic conditions. In general, the rate and inten-
sity of technology adoption in SSA agriculture is very low. For example, a
study conducted in Ethiopia by Dercon and Christiansen (2011) estimated
that only 22% of surveyed households used fertiliser consistently. Factors
constraining the use of fertiliser include access to and high cost of inputs
(e.g., improved seeds) and credit, and high fertiliser prices. SSA’s agricultural
productivity is further hampered by market imperfections. In general, the
level of farmers’ participation in markets (as either buyers and/or sellers) is
quite low. This low level of market participation has been attributed to
high transaction costs (Alene et al., 2008). Transaction costs include the op-
portunity cost that farmers incur in the process of searching potential trade
partners, bargaining and monitoring transactions. The problem is exacer-
bated by the fact that rural markets are highly segregated from major
markets. Many farmers are forced to sell their produce immediately after
harvest owing to, inter alia, imperfect or missing credit markets and lack of
storage facilities. There is also market imperfection in the input markets
and services including land, labour, credit and extension (Holden et al.,
2005). According to Spielman et al. (2010), lack of an appropriate role by
the public and private sectors is the major reason for the poor performance
observed in input markets (e.g., for improved seed and inorganic fertiliser)
and extension services.
The market failure in SSA agriculture exacerbates the vulnerability of
resource-poor farmers and aggravates the effects of climate-induced
shocks, making it more difficult for them to cope with shocks and to
protect their resource base and livelihoods. Climate change has two types
of impacts on the farmers—ex-ante and ex-post impacts. The ex-ante
impact is related to the opportunity cost they incur in fear of adverse effect
of climate variability (e.g., adopting less risky and therefore less profitable
7
It is worth noting that the decline in agriculture’s share of GDP has not been matched by an
increase in manufacturing, as the share of manufacturing actually declined from 17% of GDP
in 1990 to less than 10% of GDP in 2010 for SSA as a whole. The services sector is the fastest
growing, fuelled by growth in telecommunication companies. But again this sector tends to
be capital-intensive and does not directly contribute significantly to employment creation
and also does not immediately translate to poverty reduction. Where present, mobile
phone penetration likely reduces transactions costs and increases market integration.
ii24 | John Asafu-Adjaye

technologies, limited use of fertiliser, etc.). This cost is incurred even in good
seasons. The ex-post impact is incurred in terms of the adverse effects of
climate change (e.g., crop failure due to drought). We argue later in this
article that SSA farmers can be aided to cope with climate change by improv-
ing both physical and social infrastructure and by introducing institutional
innovations such as contracts, micro finance and crop insurance.

3. Climate change projections for Africa in the next 50 years

3.1 Temperature
In the IPCC’s Third Assessment Report (TAR), the major conclusion regard-
ing temperature change was that it is very likely that all land areas will warm
by more than the global average (IPCC, 2001).8 While the temperature pro-
jections in the TAR were based mainly on general circulation models
(GCMs), the regional projections in the Fourth Assessment Report (AR4)
are based on more reliable atmosphere-ocean general circulation models
(AOGCMs). The new results presented in the AR4 generally corroborate
with those reported in the TAR. That is, continued GHG emissions at or
above the current rates will lead to further global warming and will induce
many changes in the global climatic system in the current century that
would very likely be greater than what has been observed in the previous
century. Table 2 provides data on temperature and rainfall for the Africa
region generated from the IPCC’s multi-model dataset (MMD) involving
21 different models.
The models track seasonal and annual changes in climate for the period
1980– 1999 and then provide climate projections for the period 2080 –
2099. The table shows the minimum, maximum, median (50%), 25 and
75% quartile values among the 21 models, for temperature (8C) and precipi-
tation (%) change.
The temperature data show a fair amount of seasonal and regional variabil-
ity. The highest projected mean temperature increase is 4.18C for the Sahara
region in June, July and August (JJA), followed by 3.78C in September,
October and November (SON) for the Southern Africa region. On average,
temperatures will rise by 3.68C in the hottest part of the continent—the
Sahara region, while they will rise by an average of 3.28C in the coolest
part—the East Africa region. Temperatures will rise by an average of 3.3
and 3.48C in the West and Southern parts of Africa, respectively. For the
8
The only exceptions are Southeast Asia and South America in June, July and August.
 The Economic Impacts of Climate Change | ii25

Table 2: Regional Averages of Temperature and Precipitation Projections for Africa

Region/season Temperature response (88 C) Precipitation response (%)

Min 25 50 75 Max Min 25 50 75 Max

West Africa
DJF 2.3 2.7 3.0 3.5 4.6 216 22 6 13 23
MAM 1.7 2.8 3.5 3.6 4.8 211 27 23 5 11
JJA 1.5 2.7 3.2 3.7 4.7 218 22 2 7 16
SON 1.9 2.5 3.3 3.7 4.7 212 0 1 10 15
Annual 1.8 2.7 3.3 3.6 4.7 29 22 2 7 13
East Africa
DJF 2.0 2.6 3.1 3.4 4.2 23 6 13 16 33
MAM 1.7 2.7 3.2 3.5 4.5 29 2 6 9 20
JJA 1.6 2.7 3.4 3.6 4.7 218 22 4 7 16
SON 1.9 2.6 3.1 3.6 4.3 210 3 7 13 38
Annual 1.8 2.5 3.2 3.4 4.3 23 2 7 11 25
Southern Africa
DJF 1.8 2.7 3.1 3.4 4.7 26 23 0 5 10
MAM 1.7 2.9 3.1 3.8 4.7 225 28 0 4 12
JJA 1.9 3.0 3.4 3.6 4.8 243 227 223 27 23
SON 2.1 3.0 3.7 4.0 5.0 243 220 213 28 3
Annual 1.9 2.9 3.4 3.7 4.8 212 29 24 2 6
Sahara Region
DJF 2.4 2.9 3.2 3.5 5.0 247 231 218 212 31
MAM 2.3 3.3 3.6 3.8 5.2 242 237 218 210 13
JJA 2.6 3.6 4.1 4.4 5.8 253 228 24 16 74
SON 2.8 3.4 3.7 4.3 5.4 252 215 6 23 64
Annual 2.6 3.2 3.6 4.0 5.4 244 224 26 3 57

Source: Based on Table 11.1, IPCC (2007).

Notes: JJA—June, July August; MAM—March, April, May; JJA—June, July August; SON—
September, October, November.

continent as a whole, the MMD estimates project median temperature

increases of between 3 and 48C, which are one-and-a-half times greater
than the global mean response. Fifty per cent of the models predict
warming within about 0.58C of these median values. The basic pattern of
the projected warming in Africa (as in other parts of the world) in AR4 is
very similar to the TAR.

3.2 Precipitation
The prediction of changes for precipitation is complicated by the consider-
able spatial and temporal variability in rainfall across Africa. However, in
ii26 | John Asafu-Adjaye

recent years, some advances have been made to improve our understanding
of the complex mechanisms responsible for rainfall variability on the contin-
ent (e.g., see Warren et al., 2006; Washington and Preston, 2006; Christensen
et al., 2007). The research has steadily moved away from explanations for
rainfall variations in this region as primarily due to land use changes and
towards explanations based on changes in sea surface temperatures (SSTs).
For example, the pattern of drying of the Sahara region observed since the
1970s has now been linked to a positive trend in equatorial Indian Ocean
SSTs. In the Southern Africa region, changing SSTs are now believed to be
more significant than changing land use patterns in controlling warm
season rainfall variability and trends. For example, Hoerling et al. (2006)
have shown evidence of strong links between rainfall patterns and Indian
Ocean SSTs. Rainfall patterns in South-West Africa have also been found
to be influenced by El Niño Southern Oscillation (ENSO) variations, and
also partly influenced by the North Atlantic Oscillation (Nicholson and
Selato, 2000). The ENSO has also been known to influence regional
weather patterns in Southern Africa (Fauchereau et al., 2003), while a rela-
tionship has also been identified between the warm Mediterranean Sea and
abundant rainfall (Rowell, 2003). Despite the influence of SSTs, vegetation
patterns are still important because they help shape the climatic zones
throughout much of Africa by providing a positive feedback to climate
change (e.g., see Tadross et al., 2005a, b).
The data in Table 2 indicate that rainfall increases are projected in East
Africa and to some extent in West Africa. On the other hand, Southern
Africa and the Sahara region are likely to experience drying. In the Sahara
region, drying will occur throughout the year with an average decrease of
18% in rainfall in December, January and February (DJF) and March,
April and May (MAM). Southern Africa is projected to experience a mean
decrease of 23% in JJA and 13% in SON, while the West Africa region is pro-
jected to experience a 2% mean increase in rainfall. However, this will vary
over the year with drying occurring during MAM and the highest mean in-
crease of 6% occurring in DJF. The projection for increased rainfall in East
Africa is fairly robust, with 18 of the 21 models projecting an increase in
most of this region, especially east of the Great Lakes. The table shows that
there will be increased rainfall throughout the year with nearly half of it oc-
curring in DJF. The finding of rainfall increase in East Africa is also supported
by independent research conducted by Hulme et al. (2001) and Ruosteenoja
et al. (2003). Recent findings for the Sahel region of Africa indicate that the
timing of critical rains will shift, shortening the growing seasons (Biasutti
 The Economic Impacts of Climate Change | ii27

and Sobel, 2009), and more extensive periods of drought may result as tem-
peratures rise (Lu, 2009).

4. Impacts of climate change on agriculture in Africa

This section of the article discusses in a little more detail the impacts of climate
change and variabilityonvarious sectors in Africa, with particular focus on agri-
cultural production, water resources and desertification. Several independent
studies using a variety of climate models and emissions scenarios indicate
that climate change will have an overall negative impact on African agriculture.
Some of the major assessments on African agriculture are discussed below.

4.1 Crops
Using the FAO/IIASA Agro-Ecological Zones model and climate variables
from five different GCMs under four emissions scenarios, Fischer et al.
(2005) estimate that by the 2080s, there will be a significant decrease in suit-
able rain-fed land extent and production potential for cereals due to climate
change. For the same time horizon, they also project that the area of arid and
semi-arid land in Africa could increase by 5– 8%, which is equivalent to
60–90 million hectares. The study shows that wheat production is likely to
disappear from Africa by the 2080s. Local level assessments also indicate sub-
stantial crop losses for various countries.
Stige et al. (2006) have projected significant reductions in maize produc-
tion in southern Africa under possible increased ENSO conditions, assuming
no adaption. In Egypt, climate change could decrease national production of
many crops (ranging from –11% for rice to –28% for soybeans) by 2050,
compared with their production under current climate conditions
(Abou-Hadid, 2006). Thornton (2012) estimates that by 2050, climate
change could cause irrigated wheat yields in developing countries to drop
by 13%, and irrigated rice could fall by 15%. In Africa, maize yields could
drop by 10– 20% over the same time frame (Thornton, 2012). A number
of the studies (e.g., Benhin, 2006) show that it is possible to offset some of
the negative impacts through adaptation.
The climate change research indicates that agricultural production in
some parts of Africa (e.g., East Africa) may actually be enhanced due to
increased rainfall and temperature, as well as an increased growing season
(e.g., see Thornton et al., 2006). However, these gains are likely to be offset
by the projected losses in central, western and southern Africa.
ii28 | John Asafu-Adjaye

4.2 Livestock
Livestock is an important component of African agriculture, and approxi-
mately 80% of the potential cropland is also used for grazing. The impact of
climate change on livestock farming in Africa has been examined by Seo and
Mendelsohn (2006, 2007). They considered various scenarios including a
uniform increase in temperature of 2.5 and 5.08C and a uniform change
in rainfall of 215% and +15% across all of Africa. Their model predicts
a 32% loss in expected net revenue with a 2.58C warming, and a 70% loss
with a 58C warming. Rainfall effects were found to be relatively smaller.
For example, a 15% increase in rainfall leads to a loss of 1% in expected
net revenue per household from livestock and a 15% decrease in rainfall
leads to a gain of 2%.
The effect of global warming was found to be considerably more severe on
large farms than on small farms. For example, the expected income of small
farmers falls by an average of 13% with 2.58C warming, but by a negligible
amount with 58C warming (Seo and Mendelsohn 2006, 2007). A 15% de-
crease in precipitation increases small livestock farm incomes by 6%. On
the other hand, large farmers’ expected income falls of an average of 26%
with 2.58C warming and 67% with a 58C warming, while a 15% decrease
in precipitation increases large livestock farmers’ incomes by 2%. Small
farms do relatively better because they are more diversified in dairy cattle,
sheep and goats, whereas large farms are specialised in mainly beef cattle.
Small farmers are less affected by global warming because they have more
heat tolerant animals (e.g., sheep and goats) in their portfolio. The reduction
in income for large livestock farms would probably result both from a decline
in the number of stock and a reduction in the net revenue per animal.

4.3 Impacts on agricultural productivity

Cline (2007) conducted one of the most comprehensive analyses of the
impacts of climate change on global agriculture through the 2080s. First,
he examined a series of agricultural impact models, namely crop models
and Ricardian models. Next, he applied detailed climate projections to the
various agricultural impact models to develop a set of alternative impact esti-
mates. He then arrived at a set of preferred estimates, applying judgmental
weighting of estimates by likely reliability, of the likely impacts of climate
change with and without carbon fertilisation.9
9
Carbon fertilization is the process by which rising levels of atmospheric CO2 increase crop
and forest growth rates by stimulating photosynthesis (e.g., see Reilly, 992, 1994).
 The Economic Impacts of Climate Change | ii29

Cline’s Ricardian forecasts show declines in agricultural output for all the
African countries in the sample. Consistent with the rainfall forecasts, the
most severe losses are in the northern Sahara region, followed by Southern
Africa, while the smallest losses are in tropical and Eastern Africa. These
losses are reduced to some extent in countries with a significant share of crop-
land under irrigation. The weighted average crop losses for a Business As Usual
(BAU) scenario without carbon fertilisation range from 84% (Senegal) to
2.5% (Uganda), with a mean decline of 27.8% for the sample. An issue of
major concern is the high number of severe effects for dryland agriculture.
Countries such as Sudan, Senegal, Niger and Mali which have low proportions
of irrigated land are projected to suffer declines of 100%, while of 50% or more
are reported for countries such as South Africa and Zambia.
The crop model forecasts are similar to the Ricardian estimates and show
less variability because the parameters are not as country-specific as those of
the Ricardian estimates. He estimates that BAU climate change by the 2080s
will reduce agricultural production by about 28% on average without carbon
fertilisation. With carbon fertilisation, the crop losses are lower, with prod-
uctivity declining by 18% on average. Globally, a decline of 3% is predicted
with carbon fertilisation.

5. Simulation of the economic impacts of climate change

on African economies
The Stern Review and other global impact studies (including the IPCC’s)
tend to underestimate or mask the impacts on individual developing econ-
omies. For example, in the Stern Review (Stern, 2006), consumption
effects are calculated by normalising for per-capita global GDP, rather than
regional GDP. Such an approach does not capture the full range of costs
because the models are highly aggregated and do not sufficiently capture re-
gional and location specific impacts. In this section, we present quantitative
estimates of how climate change will affect economic growth in African coun-
tries. The remainder of the section is set out as follows. First, the rationale for
the modelling approach is explained. This is followed by a brief description of
the model structure and the simulation scenarios. Next, the simulation
results are presented and discussed.

5.1 The modelling approach

Climate change is a complex process which has multiple direct and indirect
impacts at different levels in an economy. Traditional analytical approaches
ii30 | John Asafu-Adjaye

using partial equilibrium (e.g., econometrics) models are inadequate

because they do not capture the economic feedbacks involved. Recent
studies have used computable general equilibrium (CGE) models to
assess the sectoral impacts of climate change policies (e.g., see McKibbin
et al., 2011; Asafu-Adjaye and Mahadevan, 2013). General equilibrium
models are noted for their ability to estimate the impacts of external
shocks or policy changes on several sectors simultaneously. However,
most of these studies employ a comparative static approach, which over-
looks the dynamic effects. Here, we improve on the previous studies by
employing a disaggregated dynamic CGE model of the global economy.
This approach takes into account the fact that the magnitude of physical
and economic impacts will rise over time. It also considers the fact that
endogenous growth dynamics will be affected by interactions between
changes in income levels, savings, and actual and expected rates of returns
on capital.
The CGE model used here is the dynamic Global Trade Analysis Project
(GTAP-Dyn) model (Ianchovichina and McDougall, 2000). The model
uses the GTAP 6 database which captures world economic activity in 57 dif-
ferent industries of 87 regions of the world. The industrial sectors include 14
agricultural industries, 14 manufacturing industries and 15 service indus-
tries, as well as a number of extractive industries (e.g., forestry mining, oil
and gas). In view of the large number of industries and countries, it was
decided to aggregate the countries and industries involved. In this simula-
tion, we have aggregated the database into eight regions—North America,
EU, Asia, four African regions and the Rest of the World (ROW). The
African regions are North Africa, East Africa, Southern Africa and the rest
of SSA. The countries in these regional aggregates are shown in Table 3.
We have also aggregated to five productive sectors: agriculture, food pro-
cessing, extractive industries, manufacturing and services. The database dif-
ferentiates food processing from manufacturing. The latter includes light
manufacturing (e.g., clothing, textiles, wood/paper processing) and heavy
manufacturing (e.g., metals, chemicals and plastics).

5.2 Assumptions
For the purposes of the simulations, agricultural productivity is defined as
the annual percentage change in agricultural output from 2010 to 2050.
The baseline scenario is assumed to be a world where there are no climate
change impacts on future economic growth. This situation is then compared
to a BAU climate change scenario without carbon fertilisation. The BAU
 The Economic Impacts of Climate Change | ii31

Table 3: Countries in the Regional Aggregates

North East Africa European Asia Rest of the

America Union World

Canada Tanzania Austria China All other

countries
United States Uganda Belgium Hong Kong
Mexico Malawi Denmark Japan
Mozambique Finland Korea
North Africa Zambia France Taiwan
Algeria Madagascar Germany Indonesia
Morocco UK Malaysia
Tunisia Greece Philippines
Rest of North
Africa Rest of SSA Italy Singapore
All other SSA Luxemburg Thailand
countries
Southern Netherlands Vietnam
Africa
Botswana Portugal Rest of SE Asia
South Africa Spain Bangladesh
Zimbabwe
Rest of SACU Sweden India
Sri Lanka
Rest of South
Asia

projections for GDP growth, gross domestic investment, capital stocks and
population by age groups are taken from Walmsley (2006), with an allowance
made for the slowdown in economic growth in 2008 –2010 due to the global
financial crisis. African countries are projected to grow at a rate of 6% per
annum by the 2050s, with the advanced countries growing at 2.5% per
annum. For simplicity, our climate change scenario is confined solely to
the impacts on agricultural productivity and we have not factored in the
fact that adaptation could mitigate the decline in agricultural productivity.
Forecasting the effect of adaptation of African countries over 70 years is a
complex task and we have therefore omitted this factor. The impact estimates
used in the modelling exercise are based on Cline’s preferred estimates of the
impacts of baseline global warming on agriculture by the 2080s (Cline, 2007)
and are shown in Table 4.
Cline’s estimates of agricultural productivity losses are comparative static
estimates for the 2080s. To implement this scenario in our dynamic model,
ii32 | John Asafu-Adjaye

Table 4: Impact of Global Warming on Agricultural Productivity by the 2080s Without

Carbon Fertilisation

Country/region % Change

North Americaa 214.5

EUb 29.4
Asiab 219.3
North Africac 221.2
East Africac 23.8
Southern Africac 233.4
Rest of Sub-Saharan Africac 218.5
Rest of the Worldb 219.7
a
Average for Canada, USA and Mexico taken from Cline (2007), Table 5.10.
b
Taken from Cline (2007), Table 5.10.
c
Average for the countries in respective regions in Table 5.8 of Cline (2007).

we fitted the data to a polynomial function10 to obtain estimated pro-

ductivity losses for 5-year periods from 2010 to 2080 (Appendix A1).
These agricultural productivity losses were then fed into the model as exter-
nal shocks.

5.3 Simulation results

The simulation results shown below represent the cumulative differences
between the baseline scenario and the BAU (climate change) scenario for
the period 2010 –2050. We first consider the impacts on GDP (Figure 2). It
is important to note that these impacts pertain solely to agriculture and ab-
stract from impacts in other sectors of the economy. As expected, climate
change will have the least economic impacts on the EU and North
America, and the largest impacts on African economies. Southern Africa
and the rest of SSA will be the hardest hit with a decline in GDP growth of
nearly 2 percentage points per annum each by the 2050s, followed by
North Africa (21.4 percentage points per annum). In line with the climatic
evidence, the East African region will experience the least loss with output
decline of about 0.6 percentage points per annum. Cumulatively, the
impacts of reduced agricultural output on African economic growth from
2010 to 2080 are 26 percentage points (Southern Africa and rest of SSA),
24 percentage points (North Africa) and 22 percentage points (East
10
A polynomial function is appropriate when the underlying response function is unknown,
in which case it provides a good approximation to the true function.
 The Economic Impacts of Climate Change | ii33

Figure 2: Impacts of the Decline in Agricultural Productivity on GDP Growth.

Source: Author’s estimates from model simulation.

Figure 3: Impacts of the Decline in Agricultural Productivity on Welfare (Equivalent

Variation) in Africa by the 2050s.
Source: Author’s estimates from model simulation.

Africa). By comparison, the impacts on other regions are as follows: North

America (0.2 percentage points), EU (0.03 percentage points), Asia (21.2
percentage points) and ROW (22.5 percentage points). The global
economy is projected to contract by 3 percentage points per annum by
2080 due to reduced agricultural productivity.
Figure 3 presents the welfare impacts (defined by change in household
income) on African countries in millions of US dollars. In line with the pro-
jected size of the climatic impacts, it can be seen that Southern Africa is the
most severely affected with annual welfare losses of over $6 billion by the
ii34 | John Asafu-Adjaye

2050s. It is followed by the rest of SSA (2$470 million), North Africa (2$360
million) and East Africa (2$37 million). By comparison, welfare increases in
the EU and North America, while it declines for Asia and ROW (Figure 4).
The welfare losses are caused by a combination of factors including decline
in household incomes due to falling agricultural production, adverse
terms of trade and rising domestic prices.
Figure 5 indicates that the ratio of domestic to imported prices for agri-
cultural products will fall in North America and the EU but will rise substan-
tially in developing countries by 2050. The steepest price rises will occur in
Southern Africa, followed by North Africa and the rest of SSA, in that order.
These price rises will put upward pressure on inflation, as confirmed by the
increases in the GDP deflator (not shown here), with the highest inflation
pressures being felt in African countries. This will have serious effects on

Figure 4: Impacts of the Decline in Agricultural Productivity on Welfare (Equivalent

Variation) Globally by the 2050s.
Source: Author’s estimates from model simulation.

Figure 5: Ratio of Domestic to Imported Prices for Agricultural Products (Per cent).
Source: Author’s estimates from model simulation.
 The Economic Impacts of Climate Change | ii35

poverty in most countries. The simulation results also indicate that, as can
be expected, the decline in agricultural output has a negative impact on the
food processing sector, with African countries being the most severely
affected (Appendix A2). Even though there is some growth in the extractive
resources sector in Southern Africa, the manufacturing and services con-
tract in output. It is important to note that these estimates take into
account only projected agricultural productivity losses. Therefore, includ-
ing specific climate change impacts in the other sectors will result in even
larger declines in growth by 2050. This is largely because possible adaption
is not accounted for.

5.4 Comparison with other studies

The results of this study are broadly consistent with other studies that have
used more aggregated models. Parrya et al. (2004) assessed the effects of
climate change on global food production under the IPCC’s SRES scenarios.
They found that the world would continue to feed itself through this century
because production in the developed countries (which mostly benefit from
climate change) would compensate for the projected decline in agricultural
output in the developing countries. However, they also found that there
would be substantial increases in food prices and risk of hunger among the
poorer nations.
Bosello et al. (2010) undertook an inter-model comparison using five dif-
ferent models that provide a regional break-down of overall climate change
impacts as a percentage of GDP, for a temperature increase of 2.58C. In
two of the models, SSA is the region which suffers the highest climate
damages. In a further two models, it is the second most adversely affected
region and is the third most adversely affected region in the fifth model.
Another study by Barr et al. (2010) also showed African countries to be the
most vulnerable in the world, with the highest expected impacts of climate
change and the lowest capacity to adapt.
Using the Regional Integrated model of Climate and the Economy (RICE)
model, Vivid Economics (2011) found that climate damages in Africa (as a
percentage of GDP) are expected to be higher than in any other region in
the world, more than 10 percentage points higher than the next most
exposed region (India) and more than twice as high as in the USA, Russia,
Eurasia, and Latin America. Furthermore, it was found that not only is
Africa expected to be more vulnerable to climate change than any other
region in the world, but also this vulnerability will increase with temperature
increases.
ii36 | John Asafu-Adjaye

6. Policy choices and strategies

The policy responses to climate change and variability need to be holistic and
multifaceted. In view of the fact that the climate is already changing the first
line of action should be to assist farmers to better adapt to the new and evolv-
ing conditions. However, although adaptation to warmer and drier condi-
tions in Africa is necessary, it would not be a sufficient response if BAU
climate change is allowed to occur unchecked. Therefore, in the short to
medium term, both developed and developing countries would need to con-
sider mitigation strategies as part of the policy response. In addition to adap-
tation and mitigation, we need to consider complementary policies that are
necessary to promote the sustainability of agricultural production. Finally,
there is the need to address institutional challenges as part of the implemen-
tation of these policies. Each of these issues is briefly discussed below.

6.1 Investing in climate change adaptation

The IPCC defines adaptation as ‘any adjustment in natural or human systems
in response to actual or expected climatic stimuli or their effects, which mod-
erates harm or exploits beneficial opportunities’. Adaptation includes pol-
icies and measures to reduce exposure to climate variability and extremes,
as well as the strengthening of adaptive capacity. Adaptation can be autono-
mous or anticipatory (or proactive).11 There is evidence that autonomous
adaptation is already occurring in Africa in response to climate variability.12
However, future climate change presents a new set of risks and challenges that
require a proactive adaptation response. Effective adaptation to climate
change will require actions and co-operation of different stakeholders at
the farm/household, local/state, and national levels.
Current adaptation practices in Africa include changing crop selection,
changing planting dates, and enterprise diversification such as mixing
crops and livestock (Kurukulasuriya and Mendelsohn, 2006). However,
such measures may only be effective at moderate levels of warming (e.g.,
1 – 28C). For higher temperatures, adaptation measures such as introduction
of temperature-tolerant varieties, diversification of production systems and
livelihoods, and a shift to intensive agriculture may be required. These
measures are necessary to reduce risks and build resilience, and they fall
11
Autonomous adaptation is undertaken ‘naturally’ by individuals or groups in response to
actual or anticipated climate change. On the other hand, planned adaptation is often
undertaken following a policy decision by public agencies.
12
See examples for Kenya (Eriksen, 2005) and Uganda (Ellis and Bahiigwa, 2003).
 The Economic Impacts of Climate Change | ii37

within the responsibility of government agencies and NGOs. Another adap-

tation strategy is to shift to off-farm means of livelihoods. There is evidence
that off-farm incomes are increasing in some parts of Africa—up to 60 –80%
of total incomes in some cases (Bryceson, 2002). It is expected that this trend
will continue as a form of income diversity in the face of climate change and
variability.
In the long run, efforts to enhance adaptive capacity of farmers need to
focus on means to improve literacy levels and skills, improve health status,
reduce extreme poverty, and provide access to credit and technology (e.g.,
modern farm inputs, electricity, fertiliser, etc.). This implies that the capacity
of agricultural extension officers in the various countries would need to be
strengthened in order for them to play a more effective role in introducing
the new technology to the farmers. Furthermore, there is a need to invest
more in agricultural research to breed and test new cultivars for drought
and high temperature resistance. The researchers would need to work
closely with the farmers and extension officers for a more effective transfer
of the research results into the field. Alongside the scientific work, there
would be a need for socioeconomic research to investigate the factors that
constrain farmers to adapt new technology, and from this to draw up
policy recommendations to improve adoption rates in the various countries.
African farmers can be better assisted to cope with climate change with the
establishment of early warning systems giving information on the timing,
length and adequacy of rainfall. Finally, there is a need to promote small-
and medium-scale irrigation schemes. The former could include techniques
such as shallow ground irrigation as well as other simple water harvesting
techniques. Extreme climatic events may have a major impact on the avail-
ability of, and access to, seed. There is therefore a need to strengthen formal
and informal seed systems as part of the adaptive strategy to address climate
change. A number of National Adaptation Programmes of Action (NAPAs)
have been lodged by African countries. Out of a total of 49 NAPAs submitted
to the UNFCCC to date, 29 are African countries (UNFCCC, 2013). Most of
these NAPAs attempt to identify the impacts of climate change on their coun-
tries and go on to identify priority adaptation projects.

6.2 Complementary policies

Given the projected growth of the population on the African continent, in-
creasing agricultural production by area expansion is not a viable option
for the future in view of the fixity of arable land. To address the challenges
of ensuring food security in the face of climate change and other factors
ii38 | John Asafu-Adjaye

(e.g., competing land uses), policy makers would need to consider comple-
mentary policies which may include sustainable agricultural intensification
(SAI) and genetic modification technologies. Each of these is briefly dis-
cussed below.

6.2.1 Sustainable agricultural intensification

The basic idea of SAI13 technology is to produce more food on existing agri-
cultural land while at the same time minimising the adverse environmental
impacts (The Royal Society, 2009). In technical terms, it can be defined as an
increase in agricultural production per unit of inputs (which may be labour,
land, time, fertiliser seed, or cash) or maintaining the level of production
while certain inputs are decreased (such as by more effective delivery of
smaller amounts of fertiliser, better targeting of plant or animal protection,
and mixed or relay cropping on smaller fields). The potential benefits of SAI
include the following: (i) it enhances the resource base of the farmer and thus
improves land productivity; (ii) it meets the production goals of the farmer
(for food and/or cash), and (iii) it is profitable. In general, African farmers are
constrained in practicing SAI due to high prices and other financial con-
straints that prevent them from using adequate organic and inorganic fertil-
iser. There is therefore an urgent need for governments to promote SAI
practices which include low-tillage techniques, using crop residue as
mulch, crop diversification and low-cost water-saving techniques.

6.2.2 GM technology
Genetically modified foods (GM foods) have been the subject of much con-
troversy in recent years. The term GM foods or GMOs (genetically modified
organisms) is most commonly used to refer to crops created for human or
animal consumption using the latest molecular biology techniques. These
crops are modified in the laboratory to enhance desired traits (e.g., drought
tolerance, improved nutritional content, etc.). Given the projected growth of
the African population and the challenges posed by climate variability and
change, GM foods could promote food security by achieving desirable objec-
tives such as drought/salinity tolerance, improved nutritional quality and
disease/pest resistance. Objections to GM technology have been raised mainly
on environmental and human health grounds. The environmental concerns
relate to unintended harm to other organisms or gene transfer to non-target
13
SAI is also referred to elsewhere as agricultural intensification or conservation agriculture.
 The Economic Impacts of Climate Change | ii39

species, while the health concerns mostly relate to the possibility of unexpect-
ed and negative impacts of GMOs on human health. In general, many scien-
tists do not believe that GM foods present a risk to human health and most of
the objections have been raised by religious groups, public interest groups,
environmental activists and some professional associations. GM technology
has the potential to significantly contribute to enhancing food security.
However, there is a need for further study particularly in the area of safety
testing and regulation of GM foods. GMOs raise the potential issue of afford-
ability by small-scale farmers as patents are currently held by private corpora-
tions. If this option is to be pursued, there would be the need to explore
avenues by which they could be subsided as has been the case with drug
manufacturers.

6.3 Addressing the institutional challenges

Institutional and governance factors have been identified as playing a critical
role in local communities’ abilities to adapt to and cope with climate variabil-
ity and change. Yet, in many SSA countries, the institutional and legal frame-
works have been found to be insufficient to deal with environmental
degradation and disaster risks (Sokona and Denton, 2001; Beg et al. 2002).
In a case study of response to multiple stressors in a rural community in
KwaZulu-Natal, South Africa, Reid and Vogel (2006) identified institutional
organisation and governance as being among the factors which reduce the
ability of farmers to secure sustainable livelihoods and cope with multiple
stresses, including climate. They highlight the need for a better understand-
ing of both formal and informal institutions at the local level in efforts to
enhance adaptation to climate change and variability. Brooks et al. (2005)
have also shown that adaptation can be successful and sustainable when
linked to effective governance systems, civil and political rights and literacy.
Other institutional measures that could enhance adaptation include devel-
oping appropriate land use and water management plans to regulate land
and water for future use. In some cases, new policies and institutions would
need to be developed to support the new land use and water management
arrangements. Improvements in the physical infrastructure may improve adap-
tive capacity (Sokona and Denton, 2001). For example, a general deterioration
in infrastructure could threaten water supply in periods of droughts and floods.
The construction of defensive structures such as mangrove belts, tree shelter-
belts and levees are also useful proactive strategies to enhance adaptation.
Climate change will expose buyers and sellers of food in local markets to a
higher degree of price volatility by increasing the variability (or imbalance) in
ii40 | John Asafu-Adjaye

local food supply and demand, thereby increasing income and consumption
risks. This effect is aggravated by high physical and institutional transaction
costs in African markets that cause rural food markets to be segregated from
major markets, so that excess demand in rural markets cannot be satisfied and
farmers cannot take advantage of spatial arbitrage. Better integration of rural
markets into major markets may therefore downsize climate change-induced
price volatility. Market imperfections are also present in input markets and
services, affecting production costs and access to technology. Reducing trans-
actions costs through improvements in infrastructure (e.g., transportation
and communication) will increase the integration of rural markets into major
markets, and thereby help to reduce climate change-induced market risks.
Finally, institutional innovations, such as contracts, micro finance and crop in-
surance, can increase farmers’ ability to cope with climate change-induced
production risks, while better infrastructure can help reduce market risks by
reducing physical and institutional transaction costs.

7. Conclusions
Climate change and variability is a serious environmental threat to develop-
ing countries in general and Africa in particular. The African continent is es-
pecially vulnerable because it is already warmer than other non-tropical
regions and it is already confronting both climate and non-climate related
stresses that further increase vulnerability and reduce adaptation capacity.
Furthermore, most African countries are dependent on agriculture, which
is the most climate-sensitive sector. Contrary to classic development models
that project that the industrialisation will increase as development proceeds,
in Africa the rate of industrialisation has historically declined in contrast to
other regions. Urgent policy measures are therefore needed to address the
challenges posed by climate change and to ensure sustainable economic
development in the long term.
The current climate modelling results indicate that the African continent
will warm by more than 38C on average by the 2080s, with average tempera-
tures in the Sahara region rising by 3.68C. Most regions, except East Africa
and parts of West Africa, will experience a reduction in rainfall, and there
is an increased probability of extremely warm, extremely wet and extremely
dry seasons. Populations living along the western and eastern coasts of Africa,
as well as along the Nile delta could be affected by projected rise in sea levels.
Crop and livestock production will be affected by even moderate warming.
Forecasts indicate that agricultural productivity for Africa as a whole will
decline by about 28% on average without carbon fertilisation by the 2080s,
 The Economic Impacts of Climate Change | ii41

and about 18% with carbon fertilisation. However, these statistics mask the
extreme nature of the losses for some countries. In particular, for countries
that are dependent on dryland agriculture, productivity losses could be as
much as 100%. By contrast, agricultural productivity loss for the rest of
the world would be about 14% on average. Other projected impacts of
climate change include an increase in the number of people affected by
water stress, increased desertification, a decline in biodiversity and increase
in malaria and infectious diseases.
The modelling exercise undertaken in this study indicates that Africa will
experience the largest impacts from climate change in terms of decline in eco-
nomic growth and welfare losses, whereas European and North American
economies will experience the least impacts. The disaggregated results indi-
cate that Southern Africa will be the hardest hit, followed by the rest of SSA,
North Africa and East Africa, in that order. The welfare losses are caused by a
combination of factors including decline in household incomes due to falling
agricultural production, adverse terms of trade and rising domestic prices.
The decline in agricultural productivity will have a negative impact on the
food processing sector, as well as the other sectors. And the rise in domestic
prices will put upward pressure on inflation and present challenges for
poverty reduction efforts.
African countries have no choice but to undertake adaption, the only near
term means to offset the negative impacts of climate change. There is evi-
dence of autonomous adaptation already taking place in Africa. However,
this will be insufficient at warming of more than 28C. Proactive measures
that will be required include introduction of temperature-sensitive varieties,
diversification of production systems and livelihoods and a shift to SAI.
Other adaptation measures should include shifting from dryland to irriga-
tion agriculture, and investing in early warning systems, irrigation systems,
research, extension and improvement of access to seeds. In the long-run, em-
phasis should be placed on diversifying away from agriculture to industry and
services.
These measures should be accompanied by complementary policies to
achieve more effective results. We propose that governments investigate the
use of GM technology to address the challenges of climate change, popula-
tion growth and the likely scarcity of fertile agricultural land. Finally, the
process of assisting farmers to cope with climate change can be facilitated
by addressing institutional challenges such as poor physical and social infra-
structure, market imperfections, lack of appropriate land use and water man-
agement planning, lack of access to credit and lack of crop insurance. Future
research in this area should consider developing country-based models to
ii42 | John Asafu-Adjaye

better identify the sectoral and socioeconomic impacts of climate change, as

well as the costs of alternative adaptation strategies. This information would
enable more effective adaptation policies to be developed given the limited
resources at hand.

Funding
The author is grateful for financial support provided by the African
Economic Research Consortium for preparing this article.

Acknowledgements
This article was originally prepared for the AERC Biannual Research
Workshop, Plenary Session held in Arusha, Tanzania in June 2013. I would
like to acknowledge constructive comments made by the discussant and
seminar participants, and the Editors, which have richly improved the
paper. All remaining errors are mine.

References
Abou-Hadid, A. F. (2006) ‘Assessment of Impacts, Adaptation and Vulnerability to
Climate Change in North Africa: Food Production and Water Resources’,
Assessments of Impacts and Adaptations to Climate Change (AIACC) Final
Report (AF 90), Washington, DC.
African Development Bank, AfDB (2011) The Cost of Climate Change in Africa.
Tunis: African Development Bank.
Alene, A. D., V. M. Manyong, G. Omanya, H. D. Mignouna, M. Bokanga and G.
Odhiambo (2008) ‘Smallholder Market Participation Under Transactions
Costs: Maize Supply and Fertilizer Demand in Kenya’, Food Policy, 33(4): 318– 28.
American Meteorological Society, AMS (2012) http://www.ametsoc.org/policy/
2012climatechange.html.
Asafu-Adjaye, J. and R. Mahadevan (2013) ‘Implications of CO2 Reduction Policies
For a High Carbon Emitting Economy’, Energy Economics, 38: 32– 41.
Ashton, P. J. (2002) ‘Avoiding Conflicts over Africa’s Water Resources’, Ambio, 31:
236 – 42.
Barr, R., S. Fankhauser and K. Hamilton (2010) ‘The Allocation of Adaptation
Funding’, Grantham Research Institute on Climate Change and the Environment.
Beg, N., J. Corfee Morlot, O. Davidson, Y. Afrane-Okesse, L. Tyani, F. Denton, Y.
SokonaJ. P. Thomas and Co-authors (2002) ‘Linkages Between Climate Change
and Sustainable Development’, Climate Policy, 2: 129 – 44.
Benhin, J. K. A. (2006) ‘Climate Change and South African Agriculture: Impacts and
Adaptation Options’, CEEPA Discussion Paper No. 21, Special Series on Climate
 The Economic Impacts of Climate Change | ii43

Change and Agriculture in Africa. Centre for Environmental Economics and

Policy in Africa, University of Pretoria, Pretoria.
Biasutti, M. and A. H. Sobel (2009) ‘Delayed Sahel Rainfall and Global Seasonal
Cycle in a Warmer Climate’, Geophysical Research Letters, 36(23). doi:10.1029/
2009GL041303.
Bosello, F., C. Carraro and E. de Cian (2010) Market- and Policy-Driven Adaptation,
Chapter 6 of Lomborg, B., 2010, Smart Solutions to Climate Change. Cambridge:
Cambridge University Press.
Brooks, N., W. N. Adger and P. M. Kelly (2005) ‘The Determinants of Vulnerability
and Adaptive Capacity at the National Level and the Implications for Adaptation’,
Global Environmental Change, 15: 151 –63.
Bryceson, D.F. (2002) ‘The Scramble in Africa: Reorienting Rural Livelihoods’, World
Development, 30: 725 –39.
Christensen, J. H., T. R. Carter, M. Rummukainen and G. Amanatidis (2007)
‘Evaluating the Performance and Utility of Regional Climate Models: The
PRUDENCE Project’, Climatic Change, 81: 7– 30.
Cline, W. (2007) Global Warming and Agriculture: Impact Estimates by Country.
Washington, DC: Center for Global Development and Peterson Institute for
International Economics. www.cgdev.org/content/publications/detail/14090#Chpt
(accessed 13 December 2007).
Dercon, S. and L. Christiansen (2011) ‘Consumption Risk, Technology Adoption
and Poverty Traps: Evidence from Ethiopia’, Journal of Development Economics,
96(2): 159– 73.
Ellis, F. and G. Bahiigwa (2003) ‘Livelihoods and Rural Poverty Reduction in Uganda’,
World Development, 31: 997 – 1013.
Eriksen, S. (2005) ‘The Role of Indigenous Plants in Household Adaptation to
Climate Change: The Kenyan Experience’, in P. S. Low (ed.), Climate Change
and Africa. Cambridge: Cambridge University Press, pp. 248 –59.
Fauchereau, N., S. Trzaska, Y. Richard, P. Roucou and P. Camberlin (2003) ‘Sea
Surface Temperature Co-variability in the Southern Atlantic and Indian Oceans
and its Connections with the Atmospheric Circulation in the Southern
Hemisphere’, International Journal of Climatology, 23: 663 – 77.
Fischer, G., M. Shah, F. N. Tubiello and H. van Velthuizen (2005) ‘Socio-Economic
and Climate Change Impacts on Agriculture: An Integrated Assessment, 1990 –
2080’, Philosophical Transactions of the Royal Society of London B, 360: 2067 – 83.
Hoerling, M. P., J. W. Hurrell and J. Eischeid (2006) ‘Detection and Attribution of
20th Century Northern and Southern African Monsoon Change’, Journal of
Climatology, 19(16): 3989 – 4008.
Holden, S., H. Lofgren and B. Shiferaw (2005) ‘Economic Reforms and Soil
Degradation in the Ethiopian Highlands: A Micro CGE Model with Transaction
Costs’. Paper presented at the International Conference on Policy Modeling
(EcoMod2005), Istanbul, 29 June – 1 July, 2005.
ii44 | John Asafu-Adjaye

Hulme, M., R. Doherty and T. Ngara (2001) ‘African Climate Change: 1900 – 2100’,
Climate Research, 17: 145 – 68.
Ianchovichina, E. and R. McDougall (2000) ‘Theoretical Structure of Dynamic
GTAP’, GTAP Technical Paper No. 17, Center for Global Trade Analysis, Purdue
University, West Lafayette, IN.
Intergovernmental Panel on Climate Change, IPCC (2001) Climate Change 2001:
The Scientific Basis. Contribution of Working Group I to the Third Assessment
Report of the Intergovernmental Panel on Climate Change, J. T. Houghtonet al.
(eds). Cambridge: Cambridge University Press.
Intergovernmental Panel on Climate Change, IPCC (2007) ‘Regional Climate
Projections’, in S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B.
Averyt and M. Tignor and H. L. Miller (eds.), Climate Change 2007: The
Physical Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Cambridge:
Cambridge University Press.
Intergovernmental Panel on Climate Change, IPCC (2014) ‘Fifth Assessment
Report - Climate Change 2014: Impacts, Adaptation, and Vulnerability’, IPCC,
accessed at www.ipcc.ch/report/ar5/wg2/.
Kurukulasuriya, P. and R. Mendelsohn (2006) ‘Crop Selection: Adapting to Climate
Change in Africa’, Centre for Environmental Economics and Policy in Africa
(CEEPA) Discussion Paper No. 26. University of Pretoria, Pretoria.
Lal, R. (1995) ‘Erosion-Crop Productivity Relationships for Soils of Africa’, Soil
Science Society of America Journal, 59(3): 661 – 7.
Lu, J. (2009) ‘The Dynamics of the Indian Ocean Sea Surface Temperature Forcing of
Sahel Drought’, Climate Dynamics, 33(4): 445–60. doi:10.1007/s00382-009-0596-6.
McKibbin, W., A. Morris and P. Wilcoxen (2011) ‘Comparing Climate
Commitments: A Model Based Analysis of the Copenhagen Accord’, Climate
Change Economics, 2(2): 79 – 103.
Nicholson, S. E. and J. C. Selato (2000) ‘The Influence of La Nina on African Rainfall’,
International Journal of Climatology, 20: 1761 – 76.
Pan African Climate Justice Alliance, PACJA (2009) ‘The Economic Cost of
Climate Change in Africa’, PACJA, 41 pp.
Parrya, M. L., C. Rosenzweigb, A. Iglesiasc, M. Livermored and G. Fischere
(2004) ‘Effects of Climate Change on Global Food Production Under SRES
Emissions and Socio-Economic Scenarios’, Global Environmental Change, 14:
53 – 67.
Reid, P. and C. Vogel (2006) ‘Living and Responding to Multiple Stressors in South
Africa—Glimpses from KwaZulu-Natal’, Global Environmental Change, 16(2):
195 – 206.
Reilly, J. (1992) ‘Carbon Dioxide Emissions Control and Global Environmental
Change’, in G. I. Pearman (ed.), Limiting Greenhouse Effects: Controlling Carbon
Dioxide Emissions. Chichester, England: John Wiley & Sons.
Reilly, J. (1994) ‘Crops and Climate Change’, Nature, 367: 118– 9.
 The Economic Impacts of Climate Change | ii45

Rowell, D. P. (2003) ‘The Impact of Mediterranean SSTs on the Sahelian Rainfall

Season’, Journal of Climate, 16(5): 849– 62.
Ruosteenoja, K., T. R. Carter, K. Jylhä and H. Tuomenvirta (2003) ‘Future Climate in
World Regions: An Intercomparison of Model-Based Projections for the New
IPCC Emissions Scenarios’, Finnish Environment Institute, Helsinki, 83 pp.
Seo, S. N. and R. Mendelsohn (2006) ‘Climate Change Impacts on Animal
Husbandry in Africa: A Ricardian Analysis’, Centre for Environmental
Economics and Policy in Africa (CEEPA) Discussion Paper No. 9, University of
Pretoria, Pretoria, 42 pp.
Seo, S. N. and R. Mendelsohn (2007) ‘The Impact of Climate Change on Livestock
Management in Africa: A Structural Ricardian Analysis’, World Bank Policy
Research Working Paper 4279, Washington, DC. http://www-wds.worldbank.
org/external/default/WDSContentServer/IW3P/IB/2007/07/06/000016406_2007
0706160359/Rendered/PDF/wps4279.pdf (accessed 17 December 2007).
Sokona, Y. and F. Denton (2001) ‘Climate Change Impacts: Can Africa Cope with the
Challenges?’, Climate Policy, 1: 117 –23.
Spielman, D. J. and D. Byerleeet al. (2010) ‘Policies to Promote Cereal Intensification
in Ethiopia: The Search for Appropriate Public and Private Roles’, Food Policy,
35(3): 185– 94.
Stern, N. (2006) The Economics of Climate Change: The Stern Review. Cambridge:
Cambridge University Press.
Stige, L. C., J. Stave, K. S. Chan, L. Ciannelli, N. Pretorelli, M. Glantz, H. R. Herren
and N. C. Stenseth (2006) ‘The Effect of Climate Variation on Agro-Pastoral
Production in Africa’, Proceedings of the National Academy of Science USA, 103:
3049 – 53.
Tadross, M. A., B. C. Hewitson and M. T. Usman (2005a) ‘The Interannual Variability
of the Onset of the Maize Growing Season Over South Africa and Zimbabwe’,
Journal of Climate, 18(16): 3356 – 72.
Tadross, M. A., C. Jack and B. Hewitson (2005b) ‘On RCM-Based Projections of
Change in Southern African Summer Climate’, Geophysical Research Letters, 32:
L23713. doi:10.1029/2005GL024460.
The Foundation for AIDS Research (2012) Statistics Worldwide. accessed at http://
www.amfar.org/about_hiv_and_aids/facts_and_stats/statistics__worldwide/
The Royal Society (2009) ‘Reaping the Benefits: Science and the Sustainable
Intensification of Global Agriculture’, London.
Thornton, P. K. (2012) Recalibrating Food Production in the Developing World: Global
Warming Will Change More Than Just the Climate. Climate Change, Agriculture
and Food Security Policy Brief no. 6, CGIAR Research Program on Climate
Change, Agriculture and Food Security Policy, Nairobi.
Thornton, P. K., P. G. Jones, T. M. Owiyo, R. L. Kruska, M. Herero, P. Kristjanson, A.
NotenbaertN. Bekeleet al. (2006) ‘Mapping Climate Vulnerability and Poverty in
Africa’, Report to the Department for International Development, ILRI, Nairobi,
200 pp.
ii46 | John Asafu-Adjaye

United Nations Framework Convention on Climate Change, UNFCCC (2013)

‘NAPAs Received by the Secretariat’, available at http://unfccc.int/adaptation/
workstreams/national_adaptation_programmes_of_action/items/4585.php.
Vermeulen, S. J., B. M. Campbell and J. S. I. Ingram (2012) ‘Climate Change and Food
Systems’, Environment and Resources, 37: 195– 222.
Vivid Economics (2011) ‘Aggregating, Presenting and Valuing Climate Change
Impacts’, report for the UK Department of Economics and Climate Change,
June, London, UK.
Walmsley, T. (2006) ‘A Baseline Scenario for the Dynamic GTAP Model’, Mimeo,
Center for Global Trade Policy Analysis, Purdue University, West Lafayette, IN.
Warren, R., N. Arnell, R. Nicholls, P. Levy and J. Price (2006) ‘Understanding the
Regional Impacts of Climate Change: Research Report Prepared for the Stern
Review on the Economics of Climate Change’, Tyndall Centre for Climate
Change Research, Working Paper 90, University of East Anglia, Norwich, 223 pp.
Washington, R. and A. Preston (2006) ‘Extreme Wet Years Over Southern Africa:
Role of Indian Ocean Sea Surface Temperatures’, Journal of Geophysical
Research—Atmosphere, 111: D15104. doi:10.1029/2005JD006724.
World Bank (2012) World Development Indicators, online version. Washington, DC:
World Bank.
 The Economic Impacts of Climate Change | ii47

Appendix

Table A1: Agricultural Productivity Losses from Climate Change (Percentage Points)

Year North EU Asia North East Southern Rest of Rest of

America Africa Africa Africa SSA the
World

2010 20.097 20.063 20.129 20.150 20.129 20.241 20.155 20.131

2015 20.097 20.063 20.129 20.196 20.168 20.313 20.202 20.131
2020 20.193 20.125 20.257 20.301 20.259 20.481 20.311 20.263
2025 20.242 20.157 20.322 20.436 20.375 20.698 20.451 20.328
2030 20.387 20.251 20.515 20.632 20.544 21.011 20.653 20.552
2035 20.483 20.313 20.643 20.827 20.712 21.324 20.855 20.657
2040 20.580 20.376 20.772 21.053 20.906 21.685 21.088 20.814
2045 20.773 20.501 21.029 21.293 21.113 22.070 21.337 21.051
2050 21.063 20.689 21.415 21.654 21.424 22.647 21.710 21.445
2055 21.160 20.752 21.544 21.805 21.554 22.888 21.866 21.576
2060 21.257 20.815 21.673 21.955 21.683 23.129 22.021 21.707
2065 21.933 21.253 22.573 23.008 22.589 24.813 23.109 22.627
2070 21.982 21.316 22.702 23.158 22.719 25.054 23.265 22.758
2075 22.012 21.391 22.959 23.309 22.978 25.295 23.420 22.889
2080 22.223 21.397 22.869 23.459 22.900 25.367 23.467 22.929
Total 214.482 29.463 219.532 223.237 220.054 237.015 223.911 219.858
ii48 | John Asafu-Adjaye

Table A2: Impact of Climate Change on Sectoral Output (Per cent)

2010 2015 2020 2025 2030 2035 2040 2045 2050 Total

Agriculture
North 0.1 0.1 0.1 0.2 0.5 0.8 1.3 1.9 2.8 7.8
America
European 0.1 0.2 0.2 0.4 0.8 1.3 2.0 2.9 4.1 11.9
Union
Asia 0.0 20.1 20.1 20.1 20.2 20.3 20.3 20.4 20.4 21.9
North 0.0 20.1 20.1 20.2 20.4 20.5 20.8 21.0 21.3 24.5
Africa
East Africa 20.1 20.2 20.2 20.3 20.6 20.9 21.2 21.6 22.0 27.0
Southern 20.1 20.3 20.3 20.7 21.2 21.9 22.7 23.7 24.8 215.8
Africa
Rest of SSA 20.1 20.2 20.2 20.3 20.6 20.9 21.3 21.7 22.2 27.4
Rest of the 0.0 0.0 0.0 20.1 20.1 20.2 20.3 20.4 20.5 21.6
World
Food
North 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.2 0.4 0.9
America
European 0.0 0.0 0.0 0.1 0.1 0.2 0.4 0.5 0.8 2.2
Union
Asia 0.0 20.1 20.1 20.1 20.2 20.3 20.4 20.5 20.6 22.2
North 20.1 20.1 20.1 20.3 20.6 20.9 21.2 21.7 22.2 27.3
Africa
Southern 20.1 20.3 20.3 20.6 21.0 21.5 22.0 22.7 23.5 211.8
Africa
East Africa 0.0 20.1 20.1 20.2 20.4 20.7 21.0 21.4 21.8 25.8
Rest of SSA 20.1 20.1 20.1 20.3 20.5 20.8 21.1 21.6 22.1 26.6
Rest of the 0.0 20.1 20.1 20.1 20.3 20.4 20.6 20.8 21.1 23.4
World
Extractive sector
North 0.0 20.1 20.1 20.2 20.3 20.5 20.7 21.0 21.4 24.3
America
European 0.0 20.1 20.1 20.2 20.3 20.4 20.6 20.8 21.0 23.4
Union
Asia 0.0 0.0 0.0 20.1 20.1 20.1 20.2 20.3 20.4 21.2
North 0.0 0.0 0.0 0.0 0.1 0.1 0.2 0.2 0.2 0.9
Africa
East Africa 20.1 20.2 20.2 20.4 20.6 21.0 21.3 21.7 22.1 27.5
Southern 0.0 0.1 0.1 0.2 0.3 0.4 0.5 0.7 0.9 3.2
Africa
Rest of SSA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20.1 0.0
Rest of the 0.0 0.0 0.0 0.0 0.0 20.1 20.1 20.2 20.3 20.8
World

(continued on next page)

 The Economic Impacts of Climate Change | ii49

Table A2: Continued

2010 2015 2020 2025 2030 2035 2040 2045 2050 Total

Manufacturing
North 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1
America
European 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.3
Union
Asia 0.0 0.0 0.0 0.0 0.0 0.0 20.1 20.1 20.1 20.3
North 0.0 20.1 20.1 20.2 20.3 20.5 20.7 20.9 21.2 23.9
Africa
East Africa 0.0 20.1 20.2 20.3 20.6 21.0 21.5 22.2 23.1 29.1
Southern 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Africa
Rest of SSA 20.1 20.1 20.1 20.2 20.4 20.7 21.0 21.4 21.9 26.0
Rest of the 0.0 0.0 0.0 20.1 20.1 20.2 20.3 20.4 20.5 21.6
World
Services
North 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.2
America
European 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Union
Asia 0.0 0.0 0.0 0.0 20.1 20.1 20.1 20.2 20.2 20.8
North 0.0 0.0 20.1 20.1 20.2 20.3 20.5 20.7 20.9 22.8
Africa
East Africa 20.1 20.2 20.2 20.4 20.7 21.1 21.6 22.2 22.9 29.4
Southern 0.0 0.0 0.0 0.0 20.1 20.1 20.2 20.2 20.3 21.0
Africa
Rest of SSA 0.0 20.1 20.1 20.1 20.3 20.4 20.6 20.8 21.1 23.5
Rest of the 0.0 0.0 0.0 20.1 20.1 20.2 20.3 20.4 20.5 21.6
World