Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41558-020-0847-4
1Department of Earth and Environmental Sciences, University of Leuven (KU Leuven), Heverlee, Belgium. 2Ecosystems Services and Management Program,
International Institute for Applied System Analysis (IIASA), Laxenburg, Austria. 3RTI International, Durham, NC, USA. 4College of Science and Engineering,
Ritsumeikan University, Kusatsu, Japan. 5United States Environmental Protection Agency, Washington, DC, USA. 6Department of Economics and Social
Sciences, University of Natural Resources and Life Sciences, Vienna, Austria. ✉e-mail: charlotte.janssens@kuleuven.be
A
pproximately 11% of the world population in 2017, or 821 mil-
lion people, suffered from hunger1. Undernourishment has
been increasing since 2014 due to conflict, climate vari-
ability and extremes, and is most prevalent in sub-Saharan Africa
(23.2% of population), the Caribbean (16.5%) and Southern Asia
(14.8%)1. Climate change is projected to raise agricultural prices2
and to expose an additional 77 million people to hunger risks by
2050 (ref. 3), thereby jeopardizing the UN Sustainable Development
Goal to end global hunger4. Adaptation policies to safeguard food
security range from new crop varieties and climate-smart farming
to reallocation of agricultural production2,5.
International trade can be an important adaptation mechanism6,7.
Trade links countries with a food deficit with countries with a food
surplus and raises consumption possibilities through specialization
according to comparative advantage. Climate change affects regions
and crops differently8, possibly shifting regional comparative
advantages and altering trade patterns. Studies report that restrict-
ing trade exacerbates the impact of climate change on agricultural
production, whereas liberalizing trade alleviates it9–14. However, the
current literature is incomplete in its scenario design and does not
comprehensively assess whether and—if so, why—the role of trade
becomes larger under climate change (see Methods; Supplementary
Text). The ‘adaptation illusion hypothesis’ argues that many farm
practices are wrongly identified as adaptation because they have
equal beneficial impacts with or without climate change15,16. Here
we investigated the case of adaptation through trade, and reveal
whether climate change alters the pattern of comparative advantage
and increases the impact of trade integration on hunger. With the
emerging integration between climate and trade policy agendas17,
a better understanding is needed to guide international policies to
reduce hunger.
Prevailing trade barriers may affect the adaptation potential of
trade. Border protection is widespread and has an important influ-
ence on agrifood trade18,19. Despite substantial liberalization efforts
under the ongoing Doha Round, tariffs remain high for agricultural
products20. We investigated the impact of pre-Doha tariff levels as
well as further liberalization of agricultural tariffs. Other trade costs
associated with infrastructure, logistics and custom procedures are
high, particularly in agricultural trade and in developing countries21.
Reducing such barriers could create larger trade gains than reduc-
tions in border protection18. We compared the adaptation potential
of trade liberalization, through the reduction in tariff barriers, and
trade facilitation, through the reduction in other trade costs.
We focused on global hunger projections to 2050 and analysed
how climate change and trade interact in their impact on hunger.
Our economic (Global Biosphere Management Model (GLOBIOM))
and crop (Environment Policy Integrated Model (EPIC)) modelling
approach (see Methods) is well established for investigating agricul-
tural climate change impacts22–25. We advance on the current litera-
ture by analysing 60 integrated scenarios that capture variability in
trade barriers and in climate projections originating from general
circulation models (GCMs), emissions scenarios (Representative
Concentration Pathways (RCPs)) and assumptions about CO2 fer-
tilization. By statistically analysing the scenario sample, we assessed
whether, where and how climate change influences the effect of
trade on the risk of hunger.
The adaptive effect of international trade on global hunger
Building on a previous study24, we used ten climate change and six
trade scenarios, and analysed hunger effects at the global and regional
levels. Four RCPs (2.6 W m−2, 4.5 W m−2, 6.0 W m−2 and 8.5 W m−2)
are projected by HadGEM2–ES. RCP 8.5 is also implemented with
Global hunger and climate change adaptation
through international trade
Charlotte Janssens   1,2 ✉, Petr Havlík2, Tamás Krisztin2, Justin Baker   3, Stefan Frank2,
Tomoko Hasegawa   2,4, David Leclère   2, Sara Ohrel   5, Shaun Ragnauth   5, Erwin Schmid   6,
Hugo Valin   2, Nicole Van Lipzig1 and Miet Maertens   1
International trade enables us to exploit regional differences in climate change impacts and is increasingly regarded as a
potential adaptation mechanism. Here, we focus on hunger reduction through international trade under alternative trade sce-
narios for a wide range of climate futures. Under the current level of trade integration, climate change would lead to up to
55 million people who are undernourished in 2050. Without adaptation through trade, the impacts of global climate change
would increase to 73 million people who are undernourished (+33%). Reduction in tariffs as well as institutional and infra-
structural barriers would decrease the negative impact to 20 million (−64%) people. We assess the adaptation effect of trade
and climate-induced specialization patterns. The adaptation effect is strongest for hunger-affected import-dependent regions.
However, in hunger-affected export-oriented regions, partial trade integration can lead to increased exports at the expense of
domestic food availability. Although trade integration is a key component of adaptation, it needs sensitive implementation to
benefit all regions.
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
829

Articles
Nature Climate Change
four alternative climate models (GFDL–ESM2M, NorESM1–M,
IPSL–CM5A–LR and MIROC–ESM–CHEM). RCP 2.6 represents
climate stabilization at 2 °C, whereas RCP 8.5 represents a probable
temperature range of 2.6–4.8 °C (ref. 26). We compared the stron-
gest climate change impacts (RCP 8.5) with the intermediate climate
scenarios (RCP 2.6 to RCP 6.0). EPIC projects yields for climatic
conditions of each RCP × GCM combination including CO2 fertil-
ization that are compared to yields without climate change impacts
(no climate change scenario). RCP 8.5 × HadGEM2–ES was also
run without CO2 fertilization effects, representing the worst pos-
sible outcome. Our approach follows the ISI-MIP (www.isimip.org)
Fast Track Protocol, which considers scenarios with CO2 fertiliza-
tion as the default, and prioritizes RCP 8.5 × HadGEM2–ES for CO2
sensitivity analyses. We provide a complete CO2 sensitivity analy-
sis across RCPs in the Supplementary Text. In the baseline trade
scenario, trade barriers were kept constant at 2010 level, but trade
patterns vary endogenously across different climate impact scenar-
ios. The fixed imports scenario prevents agricultural imports from
exceeding levels from the no climate change scenario. The pre-Doha
tariffs scenario represents the trade environment before global trade
liberalization launched by the Doha Round. In the facilitation sce-
nario, additional costs from expanding trade volume beyond the
current level (for example infrastructure costs) were set close to
zero. Under the tariff elimination scenario agricultural tariffs were
progressively phased out from −25% in 2020 to −100% in 2050. The
facilitation + tariff elimination scenario combines the previous two
scenarios. Socioeconomic developments were modelled with the
second Shared Socioeconomic Pathway (SSP2)27. The scenarios are
discussed further in the Methods.
Through adjustments in trade, supply and demand, the 2050
global population at risk of hunger under climate change and trade
scenarios deviates substantially from the SSP2 baseline (the base-
line trade + no climate change scenario; Fig. 1). Lower trade costs
reduce importer prices, increase traded quantities and/or increase
exporter prices, whereas lower climate-induced crop yields increase
prices. On the supply side, this influences the optimal land alloca-
tion within each pixel in terms of land cover, crop and management
system. On the demand side, regions determine the optimal level of
consumption and trade of each product in response to new price lev-
els. Within-country distributional impacts of price changes through
agricultural income effects were not considered (see Methods).
In the baseline trade scenario, price changes across RCP 8.5 sce-
narios lead to a reduction in global food availability of −0.2% to
−3% compared with the baseline. The corresponding hunger
effects are large—an additional 7–55 million people are projected
to become undernourished (+6% to +45%). Across the RCP 8.5
scenarios, global cropland area changes by −2% to +3% and the
share of irrigated area increases from +1% to +7%. Total agricul-
tural trade volume increases by +1% to +7% across RCP 8.5 sce-
narios through an expansion at the intensive and extensive margin
(new flows representing 1–3% of total trade volume; Supplementary
Table 1). Hunger impacts under intermediate climate change range
from a decrease of 1 million to an increase of 14 million undernour-
ished people. In RCP 2.6, undernourishment is lower than in the
no climate change scenario because crop yields in several regions
increase or remain unaffected partly due to the CO2 fertilization
effect (Extended Data Fig. 1, Supplementary Fig. 12). When adap-
tation through trade is constrained in the fixed imports scenario,
hunger exacerbates across all of the RCP 8.5 scenarios, up to an
additional 73 million undernourished people compared with the
baseline (+60%). By preventing endogenous market responses to
climate change, the fixed imports scenario results in lower global
crop production efficiency (−1% to −2.5%), lower global food avail-
ability (−10 to −37 kcal per capita per day) and higher agricultural
prices (+2% to +17%) across the RCP 8.5 scenarios compared with
the baseline trade scenario (Supplementary Table 2). The pre-Doha
tariffs scenario leads to up to 81 million additional undernourished
people compared with the baseline scenario (+67%), highlighting
the importance of trade integration that has already been achieved
through the Doha Round in alleviating the potential long-term
impacts of climate change on hunger.
The facilitation and tariff elimination scenarios reduce the global
risk of hunger from climate change to a comparable extent, and the
facilitation + tariff elimination scenario can even compensate for
0
50
100
150
200
Baseline trade
Fixed imports
Pre-Doha tariffs
Facilitation
Tariff elimination
Facilitation +
tariff elimination
Population at risk of hunger (×106)
GCM:
None
GFDL–ESM2M
IPSL–CM5A–LR
MIROC–ESM–CHEM
NorESM1–M
HadGEM2–ES
Climate scenario:
No CC
RCP 2.6
RCP 4.5
RCP 6.0
RCP 8.5
RCP 8.5 without the CO2 effect
Fig. 1 | Global population at risk of hunger in 2050 across climate change and trade scenarios. Climate change scenarios include the effect of CO2
fertilization on crop yields. RCP 8.5 is implemented with and without the CO2 effect. The black dotted horizontal line indicates the population at risk of
hunger in the SSP2 baseline (122 million).
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
830

Articles
Nature Climate Change
the impact of all but the most extreme climate change scenario.
Trade liberalization and facilitation reduce hunger by enhancing
climate-induced trade adjustments—across RCP 8.5 scenarios, total
agricultural trade increases by 166% to 262% under the facilita-
tion + tariff elimination scenario—by reducing agricultural prices,
and by increasing food availability and crop production efficiency
(Supplementary Tables 1 and 2). The hunger effect under extreme
climate change (RCP 8.5 without the CO2 effect) is reduced by 31%
under the facilitation scenario, 11% under the tariff elimination sce-
nario and 64% under the facilitation + tariff elimination scenario.
These effects are consistent with other studies that reported 44%
lower hunger effects under market integration13 and 46% lower
price effects under trade liberalization10 (Supplementary Fig. 5).
Regional perspective on climate change, hunger and trade
The hunger outcomes of the climate and trade scenarios differ
substantially among the hunger-affected regions (Fig. 2). Climate
change has little impact on regions facing positive or small nega-
tive crop yield impacts (Russia and West Asia (CSI), and the
Middle-East and North-Africa (MNA)) or maintaining a high crop
yield (Latin American countries (LAC); Extended Data Fig. 1 (for
average crop yield), Supplementary Figs. 1–4 (for the four main
crops)). Regions with negative impacts on medium crop yields face
larger hunger impacts (East Asia (EAS) and Southeast Asia (SEA)).
South Asia (SAS) and sub-Saharan Africa (SSA) face the most
severe hunger impacts from climate change. They experience nega-
tive impacts on already low yields, also when including the impact
of supply-side adaptation on yields (Extended Data Fig. 2). Across
the RCP 8.5 scenarios, projections for the baseline trade scenario
range from an increase of 13–181% and 2–51% in the population at
risk of hunger for SAS and SSA, respectively. The effect of the trade
scenarios on regional undernourishment is largest among baseline
net-importing regions (SSA, MNA, EAS and SAS) and regions in
which climate change reduces net exports (SEA; Extended Data
Figs. 3 and 4). The fixed imports scenario enlarges hunger impacts
in the extreme climate change scenario in SSA, SAS and SEA by rais-
ing agricultural prices (Extended Data Figs. 5 and 6), increasing net
exports in SEA, and reducing net imports in SSA and SAS. Adverse
effects from trade restriction, such as the export bans observed
during the 2007–2008 world food crisis28,29 and those feared as a
result of the global COVID-19 pandemic30,31, may pose severe
hunger risks under climate change. Under the pre-Doha tariffs
scenario undernourishment in SSA, SAS and EAS is substantially
higher compared with the baseline trade scenario. Tariff liberaliza-
tion between 2001 and 2010 reduced average import tariffs in SSA,
SAS and EAS by around 30% (Supplementary Table 6). The lower
0
10
Baseline
Population at
risk of hunger (×10
6)
Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
CAN
0
10
CSI
0
10
20
EAS
0
10
EUR
0
10
LAC
0
10
20
MNA
0
10
OCE
0
10
20
30
40
50
SAS
0
10
20
SEA
0
10
20
30
40
50
60
SSA
0
10
USA
Region
CAN
CSI
EAS
EUR
LAC
MNA
OCE
SAS
SEA
SSA
USA
Climate scenario
No CC
RCP 2.6
RCP 4.5
RCP 6.0
RCP 8.5
RCP 8.5 without CO2 effect
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline
Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Baseline Fixed
imports
Pre-Doha
tariffs
Fac. +
tariff el.
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Population at
risk of hunger (×10
6)
Fig. 2 | Population at risk of hunger in 2050 across climate change and trade scenarios in each region. The results from the GCM HadGEM2–ES are
shown; the full scenario set is provided in Extended Data Fig. 7. The following regions are included: USA, Russia and West Asia (CSI), East Asia (EAS),
Southeast Asia (SEA), South Asia (SAS), Middle East and North Africa (MNA), sub-Saharan Africa (SSA), Latin American Countries (LAC), Oceania
(OCE), Canada (CAN) and Europe (EUR). The black dotted horizontal lines indicate the population at risk of hunger in the SSP2 baseline. Fac. + tariff el.,
facilitation + tariff elimination.
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
831

Articles
Nature Climate Change
tariffs reduce the overall level of trade costs by 2050 (Supplementary
Table 7) and enable larger agricultural net imports in SSA, SAS
and EAS across all climate scenarios (Extended Data Fig. 3). In
the MNA region, the average import tariff reduced marginally and
in SEA it was already low (Supplementary Table 6). The facilica-
tion + tariff elimination scenario reduces hunger in the SSA, MNA
and EAS regions across all climate scenarios by decreasing average
trade costs (Supplementary Table 7), thereby reducing agricultural
prices and raising agricultural imports (Extended Data Figs. 3
and 5). In some cases, trade integration increases rather than
decreases the level of undernourishment in a region under cli-
mate change. The largest adverse effects occur under the tariff
elimination scenario in the SEA and SAS regions (Extended Data
Fig. 7). Whereas the facilitation scenario reduces hunger in the
extreme climate change scenario by 16% and 8%, the tariff elimi-
nation scenario increases hunger impacts by 4% and 16% in SEA
and SAS, respectively. Both trade scenarios reduce average trade
costs (Supplementary Table 7), but the tariff elimination scenario
increases rice exports from SAS and SEA, thereby reducing domes-
tic calorie availability. The facilication + tariff elimination scenario
compensates for calorie loss from rice exports through increased
imports of other agricultural goods and decreases the hunger effect
of extreme climate change by 26% and 11% in SEA and SAS, respec-
tively. Our sensitivity analysis shows that the effects of trade on
climate-induced hunger are robust to CO2 fertilization assumptions
(Supplementary Figs. 13 and 14).
A larger role for trade under climate change
To reveal whether the effect of trade increases under climate change
and, therefore, has a real adaptation role, we analysed hunger out-
comes from GLOBIOM on crop yield shifts projected by EPIC and
average trade costs in regional-level regression models (Table 1).
We interpret these results for a 5.4% reduction in crop yields and
a 23% reduction in average trade costs, which correspond to the
average impacts of climate change and the trade integration scenar-
ios, respectively. Regression results revealed that a 5.4% reduction
in crop yields within a region leads to an average food availability
reduction of 11 kcal per capita per day (95% confidence interval
(CI), 15–8 kcal per capita per day) and an additional 0.52 million
people at risk of hunger (CI = 0.25–0.79 million). For a 23% decrease
in trade costs, we project an increase in average food availability
within a region of 13 kcal per capita per day (CI = 9–16 kcal per cap-
ita per day) and a decrease in undernourished people of 1.22 million
(CI = 1.52–0.93 million). When excluding regions that experience
negative impacts in some trade scenarios (SAS and SEA), we found
a significant negative interaction effect between trade costs and crop
yields (P = 0.014). For example, under extreme climate change (that
is, a 20% crop yield reduction), the positive effect of a 23% reduc-
tion in trade costs is 1.97 million fewer people undernourished,
consisting of a direct (−1.50 million) and a climate-induced trade
effect (−0.47 million). These results confirm the existence of posi-
tive trade effects on food availability and hunger alleviation13,32 and
reveal an additional climate-induced effect of lowering trade costs.
We ran the regressions presented in Table 1 with regional inter-
action effects (Supplementary Table 3). In most of the regions,
climate-induced decreases in crop yields reduce food availability
and increase hunger while reduced trade costs have opposite effects.
The food availability impacts of crop-yield changes are largest
for SAS, SSA and SEA, whereas the effect of trade costs is largest
for regions maintaining net imports under climate change (SSA,
MNA and EAS). The corresponding impact on hunger is largest
in low-income regions (SSA and SAS), followed by middle-income
regions (EAS, MNA, and SEA). The interaction effect, which reveals
whether climate change alters the relationship between trade costs
and hunger, is most pronounced in SSA, followed by EAS. Figure 3
plots the predicted hunger–yield relationship in EAS and SSA for
different levels of trade cost, showing that hunger is less sensitive to
climate-induced yield changes under reduced trade costs.
Inter-regional specialization
We assessed the extent that climate change shifts the pattern of com-
parative advantage of four important crops (corn, wheat, soya and
rice; Fig. 4). Consistent with Ricardo’s theory of comparative advan-
tage, a region is regarded as having a comparative advantage when it
specializes in a certain crop, such that its share of world production
Table 1 | Results from OLS estimation of the impact of crop
yields, trade costs and their interaction on regional hunger and
food availability
Population at risk of
hunger (million)
Food availability (kcal per
capita per day)
Sample
(1) All
regions
(2)
Without
SAS and
SEA
(1) All
regions
(2) Without
SAS and
SEA
Crop yield
(percentage change)
−9.70***
−1.80
213.00***
173.00***
(2.60)
(1.40)
(29.00)
(31.00)
Trade cost
(log[US$/106 kcal])
4.70***
5.80***
−49.00***
−80.00***
(0.58)
(0.73)
(7.40)
(9.40)
Crop yield × trade
cost
3.30
−8.90**
14.00
191.00***
(6.20)
(3.60)
(60.00)
(74.00)
*P < 0.1; **P < 0.05; ***P < 0.01. Regional fixed effects are included. The values in brackets show
the heteroskedastic robust s.e.; n = 550 (1) and n = 450 (2). Adjusted R2 = 0.890 (1) and adjusted
R2 = 0.930 (2) for the hunger regressions; and adjusted R2 = 0.950 (1) and adjusted R2 = 0.920 (2)
for the food availability regressions. Observations are GLOBIOM output for the 11 world regions
under 5 different trade scenarios (baseline, pre-Doha tariffs, facilitation, tariff elimination and
facilitation + tariff elimination) and ten climate change scenarios in 2050. The regression models
are described in the Methods.
EAS
SSA
−0.4
−0.2
0
0.2
0.4 −0.4
−0.2
0
0.2
0.4
−40
0
40
80
120
Crop yield change due to climate change (decimal fraction)
Population at risk of hunger (×106)
Trade cost
First decile
Median
Ninth decile
Fig. 3 | Fitted linear response of population at risk of hunger to
climate-induced crop yield change in EAS and SSA for different values
of trade costs. The shaded areas indicate the 95% prediction intervals.
Prediction on the basis of an ordinary least squares (OLS) estimation of
the regional level linear regression of the impact of crop yield change, trade
costs and their interaction on population at risk of hunger. The regression
results are shown in Supplementary Table 3 and the regression model
is described in the Methods. The fitted response for all of the regions is
shown in Extended Data Fig. 8.
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
832

Articles
Nature Climate Change
increases when trade costs decrease (see Methods; Supplementary
Text). Under no climate change, trade integration increases the
global production share of the United States (USA) in corn; LAC in
soya; CSI, Europe (EUR) and LAC in wheat; and SAS and EAS in
rice (Fig. 4a). Trade integration has similar impacts on specializa-
tion under climate change (Fig. 4b). Figure 4c compares the spe-
cialization of regions in response to trade-cost reduction; negative
values indicate decreases and positive values indicate increases in
comparative advantage under climate change compared with no cli-
mate change. For example, MNA still decreases its share of global
wheat production in response to trade integration under climate
change, but to a lesser extent than under no climate change. The
small and mainly insignificant values indicate that the pattern of
comparative advantage of the four crops remains similar under cli-
mate change. Although climate change affects crop yields and cost
competitiveness of regions, it does not substantially alter the relative
position between regions (Supplementary Figs. 8–10). This finding
is corroborated by alternative indicators of comparative advantage,
including crop shares in a region’s total production, export shares in
a region’s crop production and the revealed comparative advantage
(RCA) index (Supplementary Figs. 6, 7 and 11).
Adaptation to climate change occurs through changes in exist-
ing and new inter-regional trade flows (Supplementary Tables 8–11).
Across the RCP 8.5 scenarios, the largest export growth originates
from major baseline producing regions (corn from USA and LAC,
soya from LAC and USA, rice from SAS and SEA, and wheat from
EUR and Canada (CAN); Supplementary Fig. 9). The largest new
trade flows are new corn exports from USA to EAS, CAN, LAC and
SEA, from EUR to MNA and from LAC to EAS; new soya exports
from LAC to SAS and from USA to CAN and MNA; and new wheat
exports from CSI to EUR, and from MNA to SSA. Climate change
does not induce substantial new rice trade flows. There is uncertainty
across RCP 8.5 scenarios in bilateral trade patterns, but several exports
to hunger-affected regions increase consistently (such as wheat from
EUR to SSA, soya from LAC to SAS, or corn from LAC to MNA).
However, hunger-affected regions are not only engaging in trade at
the importer side, but also increase certain exports (wheat in MNA,
corn in SSA, and rice in EAS and SAS; Extended Data Fig. 10).
No climate change
a
b
c
Corn
Rice
Soya
Wheat
0
20
40
60
80
Share of global production (%)
Trade scenario
Baseline
Facilitation + tariff elimination
Climate change
CC – no CC
−0.2
0.0
0.2
−0.2
0.0
0.2
0
20
40
60
80
−0.2
0.0
0.2
−0.2
0.0
0.2
0
20
40
60
80
−0.2
0.0
0.2
−0.2
0.0
0.2
0
20
40
60
80
−0.2
0.0
0.2
−0.2
0.0
0.2
Impact of 1% trade-cost reduction
on share of global production (%)
Impact of 1% trade-cost reduction
on share of global production (%)
Region
USA
CAN
EUR
CSI
EAS
SEA
SAS
MNA
SSA
LAC
OCE
Fig. 4 | Inter-regional specialization in corn, rice, soya and wheat in response to trade-cost reduction in 2050. a, The share of global production under
no climate change in the baseline trade and facilitation + tariff elimination scenarios. b,c, The impact of 1% trade-cost reduction on the share of global
production where the dependent variable is either the outcome under climate change (b) or the difference in outcome between climate change (CC) and
no climate change (c). Each point shows the estimated impact of a 1% trade-cost reduction for each region on share of world production (%), with lines
denoting the corresponding 95% confidence interval (heteroskedastic robust s.e.). The regression models are described in the Methods.
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
833

Articles
Nature Climate Change
Discussion
International trade contributes globally to climate change adapta-
tion. The impact of the worst climate change scenarios on global
risk of hunger increases by 33–47% under restricted trade scenar-
ios, and decreases by 11–64% under open trade scenarios. The gain
from reducing trade costs is largest for regions that remain import
dependent under climate change. Climate change increases the role
of trade in reducing the risk of hunger for some regions, although
it does not substantially alter the pattern of comparative advantage
of main staple crops. It is the ability to link food surplus with defi-
cit regions that underpins trade’s adaptation effect. These conclu-
sions are robust across RCPs, and independent from the assumption
on CO2 fertilization effects. Finally, we found that the number of
undernourished people increases with climate change, irrespective
of trade scenarios. Thus, climate change mitigation remains crucial
for eradicating hunger.
Our study is comprehensive in its scenario design and rigorous
in its analysis of the processes driving adaptation through trade.
Nevertheless, it is important to emphasize that this study focuses
on the global scale in the long term. Trade policies and climate
change have important within-country distributional consequences
through income and food-access effects33–35, which are theoretically
ambiguous and which our modelling approach does not consider.
Across households with different food access channels, from urban
net-consumers to rural subsistence farmers, impacts can differ even
in their direction34. Also, current global studies, including ours,
focus on crop- and grass-yield impacts, and other direct and indi-
rect climate change effects are not represented to date—for example,
heat stress on animals, pest and disease incidence, sea level rise or
reduced pollination. Finally, we take a long-term equilibrium per-
spective ignoring the negative effects of extreme weather events. All
of these aspects require substantial new research.
Despite the limitations described above, our study brings novel
policy implications. We found that liberalization that has already
been achieved under the Doha Round substantially reduces
climate-induced hunger impacts. A careful approach to trade
integration covering different types of trade barriers can further
limit hunger risks. The full removal of agricultural tariffs leads
to increases in food availability in SSA, MNA and EAS, but may
increase exports and lower regional food availability in SEA and
SAS. Further trade facilitation can reduce undernourishment in
all hunger-affected regions. However, the effective realization of
trade facilitation requires considerable investments in transport
infrastructure and technology. Especially in low-income regions,
such as SSA, infrastructure is weak36. An estimated US$130–170
billion a year is needed to bridge the infrastructure gap in SSA
by 2025 (ref.37). Infrastructure finance averaged US$75 billion in
recent years, with the largest contribution from budget-constrained
national governments37. Alternative financing through institutional
and private investments, called for by the African Development
Bank Group and the World Bank Group36,37, could be not only cru-
cial for economic growth, but also for climate change adaptation. In
essence, our results demonstrate that trade instruments can mitigate
an important part of the adverse hunger effects of long-term cli-
mate change. Our results thereby confirm the importance of holistic
approaches to international trade negotiations, and could prove also
relevant in the face of trade-policy reactions in more acute crisis
situations, such as the global COVID-19 pandemic.
Online content
Any methods, additional references, Nature Research report-
ing summaries, source data, extended data, supplementary infor-
mation, acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41558-020-0847-4.
Received: 19 July 2019; Accepted: 16 June 2020;
Published online: 20 July 2020
References
	1.	 FAO, IFAD, UNICEF, WFP & WHO The State of Food Security and Nutrition
in the World 2018. Building Climate Resilience for Food Security and Nutrition
(FAO, 2018).
	2.	 Nelson, G. C. et al. Climate change effects on agriculture: economic responses
to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).
	3.	 2019 Global Food Policy Report (IFPRI, 2019).
	4.	 Hoegh-Guldberg, O. et al. in Special Report on Global Warming of 1.5 °C
(eds Masson-Delmotte, V. et al.) Ch. 3 (WMO, 2018).
	5.	 Hertel, T. W. Climate Change, Agricultural Trade and Global Food Security.
The State of Agricultural Commodity Markets (SOCO) 2018: Background Paper
9 (FAO, 2018).
	6.	 Huang, H., von Lampe, M. & van Tongeren, F. Climate change and trade in
agriculture. Food Policy 36, S9–S13 (2011).
	7.	 Brown, M. E. et al. Do markets and trade help or hurt the global food system
adapt to climate change? Food Policy 68, 154–159 (2017).
	8.	 Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st
century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci.
USA 111, 3268–3273 (2014).
	9.	 Stevanović, M. et al. The impact of high-end climate change on agricultural
welfare. Sci. Adv. 24, e1501452 (2016).
	10.	Wiebe, K. et al. Climate Change impacts on agriculture in 2050 under a range
of plausible socioeconomic and emissions scenarios. Environ. Res. Lett. 10,
085010 (2015).
	11.	Gouel, C. & Laborde, D. The Crucial Role of International Trade in Adaptation
to Climate Change Working Paper No. 25221 (National Bureau of Economic
Research, 2018).
	12.	Costinot, A., Donaldson, D. & Smith, C. Evolving comparative advantage and
the impact of climate change in agricultural markets: evidence from 1.7
million fields around the world. J. Polit. Econ. 124, 205–248 (2016).
	13.	Baldos, U. L. C. & Hertel, T. W. The role of international trade in
managing food security risks from climate change. Food Secur. 7,
275–290 (2015).
	14.	Cui, H. D., Kuiper, M., van Meijl, H. & Tabeau, A. Climate Change and
Global Market integration: Implications for Global Economic Activities,
Agricultural Commodities, and Food Security. The State of Agricultural
Commodity Markets (SOCO) 2018: Background Paper (FAO, 2018).
	15.	Lobell, D. B. Climate change adaptation in crop production: beware of
illusions. Glob. Food Sec. 3, 72–76 (2014).
	16.	Moore, F. C., Baldos, U. L. C. & Hertel, T. Economic impacts of climate
change on agriculture: a comparison of process-based and statistical yield
models. Environ. Res. Lett. 12, 065008 (2017).
	17.	Zimmermann, A., Benda, J., Webber, H. & Jafari, Y. Trade, Food Security and
Climate Change: Conceptual Linkages and Policy Implications Background
Paper for The State of Agricultural Commodity Markets (SOCO) 2018
(FAO, 2018).
	18.	Hoekman, B. & Nicita, A. Trade policy, trade costs, and developing country
trade. World Dev. 39, 2069–2079 (2011).
	19.	Disdier, A. C. & van Tongeren, F. Non-tariff measures in agri-food trade:
what do the data tell us? Evidence from a cluster analysis on OECD imports.
Appl. Econ. Perspect. Policy 32, 436–455 (2010).
	20.	Bureau, J., Guimbard, H. & Jean, S. Agricultural trade liberalisation in the
21st century: has it done the business? J. Agric. Econ. 70, 3–25 (2018).
	21.	Arvis, J. F., Duval, Y., Shepherd, B., Utoktham, C. & Raj, A. Trade costs in the
developing world: 1996-2010. World Trade Rev. 15, 451–474 (2016).
	22.	Mosnier, A. et al. Global food markets, trade and the cost of climate change
adaptation. Food Secur. 6, 29–44 (2014).
	23.	Leclère, D. et al. Climate Change induced transformations of agricultural
systems: insights from a global model. Environ. Res. Lett. 9, 124018 (2014).
	24.	Baker, J. et al. Evaluating the effects of climate change on US agricultural
systems: sensitivity to regional impact and trade expansion scenarios.
Environ. Res. Lett. 13, 064019 (2018).
	25.	Havlík, P. et al. Climate Change Impacts and Mitigation in the Developing
World, An Integrated Assessment of the Agriculture and Forestry Sectors.
Background Paper for the World Bank Report: “Shock Waves: Managing the
Impacts of Climate Change on Poverty.” Policy Research Working Paper
WPS7477 (The World Bank, 2015).
	26.	IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team,
Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).
	27.	Fricko, O. et al. The marker quantification of the Shared Socioeconomic
Pathway 2: A middle-of-the-road scenario for the 21st century. Glob. Environ.
Change 42, 251–267 (2017).
	28.	Bouët, A. & Laborde Debucquet, D. in Food Price Volatility and Its
Implications for Food Security and Policy (eds Kalkuhl, M. et al.) 167–179
(Springer, 2016); https://doi.org/10.1007/978-3-319-28201-5_8
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
834

Articles
Nature Climate Change
	29.	Kornher, L. Maize Markets in Eastern and Southern Africa (ESA) in the
Context of Climate Change. The State of Agricultural Commodity Markets
(SOCO) 2018: Background Paper (FAO, 2018).
	30.	Glauber, J., Laborde, D., Martin, W. & Vos, R. COVID-19: Trade Restrictions
are Worst Possible Response to Safeguard Food Security (IFPRI, 2020); https://
www.ifpri.org/blog/covid-19-trade-restrictions-are-worst-possible-response-
safeguard-food-security
	31.	Sulser, T. & Dunston, S. COVID-19-Related Trade Restrictions on Rice and
Wheat Could Drive Up Prices and Increase Hunger (IFPRI, 2020); https://
www.ifpri.org/blog/covid-19-related-trade-restrictions-rice-and-wheat-could-
drive-prices-and-increase-hunger
	32.	Dithmer, J. & Abdulai, A. Does trade openness contribute to food security? A
dynamic panel analysis. Food Policy 69, 218–230 (2017).
	33.	Bureau, J. C. & Swinnen, J. EU policies and global food security. Glob. Food
Sec. 16, 106–115 (2018).
	34.	Porter, J. R. et al. Food security and food production systems. in Climate
Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.)
485–533 (Cambridge Univ. Press, 2014).
	35.	Swinnen, J. & Squicciarini, P. Mixed messages on prices and food security.
Science 335, 405–406 (2012).
	36.	Calderon, C., Cantu, C. & Chuhan-Pole, P. Infrastructure Development in
Sub-Saharan Africa: A Scorecard Policy Research Working Paper WPS8425
(The World Bank, 2018).
	37.	African Economic Outlook 2018 (African Development Bank, 2018).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Nature Climate Change | VOL 10 | September 2020 | 829–835 | www.nature.com/natureclimatechange
835

Articles
Nature Climate Change
Methods
Modelling framework. We used the GLOBIOM, a recursive dynamic, spatially
explicit, economic partial equilibrium model of the agriculture, forestry and
bioenergy sector with bilateral trade flows and costs that can model new trade
patterns38. The model computes a market equilibrium in 10-year time steps
from 2000 to 2050 by maximizing welfare (the sum of consumer and producer
surplus) subject to technological, resource and political constraints. In each time
step, market prices adjust endogenously to equalize supply and demand for each
product and region. On the demand side, a representative consumer for each of 30
economic regions optimizes consumption and trade in response to product prices
and income. Food demand depends endogenously on product prices through an
isoelastic demand function and exogenously on GDP and population projections39.
We mainly present model results aggregated to 11 regions (Supplementary Table 4):
USA, CAN, EUR, OCE, SEA, SAS, SSA, MNA, EAS, CSI and LAC. GLOBIOM
is a bottom-up model that builds on a high spatial grid-level resolution on the
supply side. Land is disaggregated into simulation units—clusters of 5 arcmin
pixels that are created based on altitude, slope and soil class, 30 arcmin pixels, and
country boundaries. GLOBIOM’s crop production sector includes 18 major crops
(barley, beans, cassava, chickpeas, corn, cotton, groundnut, millet, palm oil, potato,
rapeseed, rice, soybean, sorghum, sugarcane, sunflower, sweet potato and wheat)
under 4 management systems (irrigated, high input; rainfed, high input; rainfed,
low input; and subsistence). The allocation of acreage by the crop and management
system is determined by potential yields, production costs and expansion
constraints23. Crop production parameters are based on the detailed biophysical
crop model EPIC. Additional biophysical models were used to represent the
livestock (RUMINANT40) and forestry (G4M41) sectors. Further information on
model structure and parameters was described previously42,43.
As a partial equilibrium model, GLOBIOM focuses only on specific sectors of
the economy and does not represent feedbacks on consumer income and GDP from
trade and climate change. However, the partial equilibrium model allows for more
detail in the represented sectors, and a more accurate assessment of biophysical
impacts. This is due to the high spatial and commodity resolution as well as the
physical rather than monetary representation of variables compared with the general
equilibrium models that explicitly cover income feedbacks. Crop yields adjust
endogenously through the management system or location of production, and
exogenously according to long-term technological development and climate change
impacts23. The output from EPIC was used to compute, for each time step, yield
shifters for each climate change scenario and each crop and management system at a
disaggregated spatial scale (simulation unit). EPIC simulates scenario-specific yields
on the basis of inputs from climate models (daily climatic conditions including solar
radiation, minimum and maximum temperature, precipitation, wind speed, relative
humidity and CO2 concentration). Climate change impacts on livestock production
are modelled through crop and grassland yield impacts on feed availability. EPIC
crop and grassland yield impacts, as well as their implementation in GLOBIOM, are
further explained in Leclère et al.23 and Baker et al.24.
International trade. International trade is represented in GLOBIOM through the
Enke–Samuelson–Takayama–Judge spatial equilibrium assuming homogenous
goods38,44. Bilateral trade flows between the 30 economic regions were determined
by the initial trade pattern, relative production costs of regions and the
minimization of trading costs38. The initial trade pattern was informed by the
BACI database from CEPII averaging across 1998–2002 (ref. 45). Trade costs are
composed of tariffs from the MAcMap-HS6 database46, transport costs47 and a
nonlinear trade expansion cost. The MAcMap-HS6 2001 release from CEPII-ITC
provides ad valorem and specific tariffs, and shadow tariff rates of tariff rate quotas
for the model calibration in the base year 2000 (ref. 48). To incorporate trade
liberalization developments under the Doha Round, the tariff data is updated in
the 2010 time step with the 2010 release of MAcMap-HS6 (ref. 49; Supplementary
Table 6). We used the estimation by Hummels47 to compile input data on bilateral
transport costs on the basis of the distance between trade pairs and the weight–
value ratio of agricultural products. Transport costs were set to US$30 per ton
minimum, on the basis of the fifth percentile of the OECD Maritime Transport
Cost database (2003–2007), and were kept constant at base year level over the
simulation period as the drivers of transport costs (for example, fuel prices and
containerization50) are not represented in the partial equilibrium model. In the
scenario simulations, the nonlinear expansion cost raises per-unit trade costs when
the traded quantity increases over time to model persistency in trade flows. A
constant elasticity function was used for trade flows observed in the base year, and
a quadratic function was used for new trade flows. The nonlinear element reflects
the cost of trade expansion in terms of infrastructure and capacity constraints in
the transport sector and was reset after each 10-year time step. Compared to other
global economic models, GLOBIOM’s trade representation is positioned between
the rigid Armington approach of general equilibrium models and the flexible world
pool market approach of many partial equilibrium models.
Risk of hunger. We measured the population at risk of hunger, or the number
of people whose food availability falls below the mean minimum dietary energy
requirement, on the basis of previous studies51–53. The following four parameters
were used: the mean minimum dietary energy requirement, the coefficient of
variation of the distribution of food within a country, the mean food availability
in the country (kcal per capita per day) and total population. Minimum dietary
energy requirements are exogenously calculated on the basis of demographic
composition (age, sex) of future population projections. Future changes in
the inequality of food distribution within a country are exogenous and follow
projected national income growth. This is based on an estimated relationship
between income and the coefficient of variation of food distribution with
observed historical national-level data. Poor infrastructure, remoteness and a
high prevalence of subsistence farming limit local markets in distributing food
equally across households7. Income is lowest in SAS and SSA, regions in which
the share of land under subsistence farming is the largest (27% in SAS and 43%
in SSA)54. Food availability in kcal per capita per day is endogenously determined
by GLOBIOM at the regional level. One limitation of the approach is that it does
not include within-country distributional consequences of trade integration and/
or climate change through income effects. Trade policies and climate change alter
food prices, which affects individual incomes, purchasing power and food access
depending on households being net consumers or net producers of food33. At the
aggregate regional level, the bias from not considering these distributional effects
may be upward or downward, depending on the share of net-consuming versus
net-producing households; degree of subsistence farming versus agricultural wage
work; and share of rural versus urban population in each country.
Climate change adaptation. Climate change adaptation is defined by the IPCC
as “the process of adjustment to actual or expected climate and its effects”26.
Adaptation of the agricultural sector to climate-induced changes in crop yields
may include adjustments in consumption, production and international trade2.
Demand-side adaptation is captured in GLOBIOM by changes in regional
consumption levels in response to market prices. Supply-side adaptation includes
the reallocation of land for each crop by a grid-cell and management system, and
the expansion of cropland to other land covers23. Whereas Leclère et al.23 assess
supply-side adaptation, here we focused on international market responses, in
which our analysis approach is inspired by the ‘adaptation illusion hypothesis’
postulated by Lobell15 and confirmed by Moore et al.55. They argue that farm-level
practices identified as adaptation measures by many crop modelling studies
cannot be referred to as climate adaptation as they have the same yield impact
in current climate as under climate change. In a similar manner, we intended to
investigate whether, where and, if so, why trade integration has a larger positive
impact on the risk of hunger under climate change. We defined the adaptation
effect of trade as the sum of the effect of reducing trade costs on hunger under
current climate (direct trade effect), and any additional positive or negative
impact of trade integration under climate change (climate-induced trade effect).
The adaptation effect of trade can be understood through Ricardo’s theory of
comparative advantage (Supplementary Text)11,12. Reducing trade costs promotes
trade according to comparative advantage56 and facilitates the role of trade as
a transmission belt in linking food-deficit and food-surplus regions57. Climate
change impacts differ across crops and regions8. Depending on the spatial
distribution of these impacts, the current pattern of comparative advantage may
be intensified, maintained or substantially altered. This may lead to increased
food deficits in certain regions. Trade is argued to have a larger role under climate
change as it facilitates adjustment to changes in comparative advantage11,12 and
enables food surplus to be linked with food deficit regions6,7,57.
Scenario design. Our choice of climate change scenarios was determined by the
ISI-MIP Fast Track Protocol used by crop modellers to calculate crop and grass yield
impacts8,58. We used all four RCPs that reflect increasing levels of radiative forcing by
2100 (the 2.6 W m−2, 4.5 W m−2, 6 W m−2 and 8.5 W m−2 scenarios)59 as projected by
the HadGEM2–ES GCM60,61. RCP 8.5 was implemented with four additional GCMs
to reflect uncertainty in climate models: GFDL–ESM2M62, IPSL–CM5A–LR63,
MIROC–ESM–CHEM64 and NorESM1–M65. RCP 2.6 represents climate stabilization
at 2 °C and RCP 8.5 a temperature range of 2.6–4.8 °C (ref. 26). Yield impacts are
based on simulations from the crop model EPIC23,24. Each RCP × GCM combination
was modelled including CO2 fertilization effects. RCP 8.5 × HadGEM2–ES was
additionally simulated without the CO2 effect, which reflects the most severe climate
change scenario. These scenarios represent the tier 1 set of ISI-MIP scenarios and
climate change impacts are simulated individually for all 18 GLOBIOM crops, except
for oil palm, and for grasslands. Scenarios without CO2 fertilization for RCPs other
than RCP 8.5 were considered to be of secondary importance in the ISI-MIP Fast
Track—and in the latest simulation protocol for ISI-MIP 3b—and were therefore
available only for the four main crops (corn, rice, soya and wheat). We carry out a
comprehensive sensitivity analysis with respect to the CO2 fertilization effect for
all RCPs, however, as this requires extrapolating climate change impacts from the
four crops to the other crops, and thus would introduce inconsistency with the Tier
1 scenarios, the analysis is presented separately in Supplementary Text. In the no
climate change scenario, exogenous yield change originates only from long-term
technological development assumptions.
We implemented six trade scenarios to analyse the role of trade in climate
change adaptation. The first scenario—fixed imports—limits imports to the level
observed in the no climate change scenario or less. This represents restricting trade
flow adjustments in response to climate change, or limiting trade as an adaptation
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
mechanism. The second scenario, pre-Doha tariffs, excludes the tariff update
in 2010, representing the trade environment before global trade liberalization
launched by the Doha Round (a comparison of average tariff rates is provided in
Supplementary Table 6). We also implemented three trade integration scenarios
to assess promotion of the trade adaptation mechanism. In the first scenario, the
facilitation scenario, the nonlinear part of trade costs is set close to zero from
2020 onwards on the basis of Baker et al.24. This reflects the impact of reducing
transaction costs, infrastructure costs and other institutional barriers limiting the
expansion of trade. Trade facilitation is defined by the WTO as the “simplification
of trade procedures”66. In economic literature it refers to the reduction in trade
transaction costs that are determined by the efficiency of customs procedures,
infrastructure services and domestic regulations 18,66. Other trade costs that are
relevant in agricultural trade, which were not included in this study, are non-tariff
measures (NTMs). UNCTAD defines NTMs as “all policy-related trade costs
incurred from production to final consumer, with the exclusion of tariffs”67. Typical
examples of NTMs are technical measures, such as sanitary and phytosanitary
measures (SPS), and price and quantity control measures, such as quotas and
subsidies. Some studies include also the above-mentioned transaction costs in the
category of NTMs68,69, whereas others make the explicit distinction18,70. The per-unit
transport costs were kept constant at the base year level. In the second scenario,
tariff elimination, all agricultural tariffs were progressively phased out between
2020 and 2050, that is −25% in 2020, −50% in 2030, −75% in 2040 and −100% in
2050. This scenario leads to a 70% growth in total agricultural trade (Supplementary
Table 1), comparable in magnitude to the agricultural import (+36%) and export
(+60%) growth under tariff liberalization reported by Anderson and Martin71. The
final scenario, facilitation + tariff elimination, is a combination of the previous two
scenarios and presents the most extensive open trade scenario. In the baseline trade
scenario, trade barriers are kept constant at 2010 levels, but trade patterns vary
endogenously across the different climate impact scenarios. Supplementary Table 7
provides a comparison of average trade costs across the different scenarios.
Socioeconomic developments were modelled according to the SSP2, which
reflects a middle-of-the-road scenario in which the population reaches 9.2 billion
by 2050 and income grows according to historical trends in each region27. The
technological development assumed by SSP2 leads to an increase in global average
crop yields of 66% between 2000 and 2050 (Supplementary Table 12). The SSP
scenarios are widely discussed and are often used as a basis for harmonizing key
macroeconomic assumptions for integrated assessment modelling of different
climate futures72. SSP2 projects a decrease in the global population at risk of hunger
from 867 million in 2000 to 122 million by 2050. This because of an increase in
food consumption—global food availability increases from 2,700 to 3,007 kcal per
capita per day—and an improved food distribution within regions, which are both
related to the assumed income growth under SSP2 (ref. 53). Income projections lead
to changes in food preferences. Under SSP2, the share of livestock products in diets
increases globally from 16% in 2000 to 17.3% in 2050, with the largest increases in
Asian regions73. Such changes affect the baseline trade pattern—for example,
increased production and consumption of livestock products in SAS, EAS and SEA
imply an increase in imports of feed crops such as corn and soya by 2050.
Hunger statistical analysis. We analysed the results of the scenario runs using a
regional-level linear regression model to infer the underlying relationship between
trade costs, crop-yield changes and hunger as predicted by GLOBIOM. The
following models were estimated using OLS (Table 1):
Population at risk of hungeritr ¼
β 1
ð Þ
1 Crop yieldir þ β 1
ð Þ
2 Trade costsitr
þβ 1
ð Þ
3 Crop yieldir ´ Trade costsitr þ P
i
β 1
ð Þ
4i Regioni þ ε 1
ð Þ
itr
Food availabilityitr ¼
β 2
ð Þ
1 Crop yieldir þ β 2
ð Þ
2 Trade costsitr
þβ 2
ð Þ
3 Crop yieldir ´ Trade costsitr þ P
i
β 2
ð Þ
4i Regioni þ ε 2
ð Þ
itr
We estimated the models also with regional interaction terms (Fig. 3,
Supplementary Table 3):
Population at risk of hungeritr ¼
P
i
β 3
ð Þ
1i Crop yieldir ´ Regioni þ β 3
ð Þ
2i Trade costsitr ´ Regioni

þβ 3
ð Þ
3i Crop yieldir ´ Trade costsitr ´ Regioni þ β 3
ð Þ
4i Regioni

þ ε 3
ð Þ
itr
Food availabilityitr ¼
P
i
β 4
ð Þ
1i Crop yieldir ´ Regioni þ β 4
ð Þ
2i Trade costsitr ´ Regioni

þβ 4
ð Þ
3i Crop yieldir ´ Trade costsitr ´ Regioni þ β 4
ð Þ
4i Regioni

þ ε 4
ð Þ
itr
where Population at risk of hungeritr gives the number of people at risk of
hunger (million) and Food availabilityitr gives the food availability (kcal per capita
per day) in 2050 in each region i, trade scenario t and climate change scenario r.
Crop yieldir gives the change in average crop yield (kcal ha−1) compared with
the average crop yield in the no climate change scenario in 2050. Trade costitr
gives the log-transformed weighted average trade costs (US$ per 106 kcal)
on all trade flows in 2050. To obtain a measure that reflects the implication
of trade scenarios on overall trading costs, we calculated the trade-weighted
average of trade costs over all agricultural imports, exports and intraregional
trade flows for each region i, trade scenario t and climate change scenario r:
Average trade costitr ¼ P
k
xiktr
Totalxitr ´ Trade costiktr
I
 where xiktr are the trade flows of
crop k in, out and within region i in each scenario (t,r) and Totalxitr
I
 is the sum of
all trade flows in, out and within region i in each scenario (t,r). The variables Crop
yieldir and Trade costitr are centred (demeaned) to solve structural multicollinearity.
For the regional fixed effects (Regioni) dummy variables were used.
β m
ð Þ
ki
I
 are the slope coefficients to be estimated for variable k in regression
model m (with k = 1, …, 4 and m = 1, …, 4). ε m
ð Þ
itr
I
 is an independently and identically
normally distributed error term with zero mean and σ2
m
ð Þ
I
 variance. Standard
errors were estimated robust to heteroscedasticity using the HC3 method as
recommended by Long and Ervin74. HC3 is a refined version of White’s method
for estimating heteroskedastic s.e. (HC0). Long and Ervin74 demonstrated using
Monte Carlo simulations that the HC3 method outperforms HC0 for small sample
sizes (n < 250). The calculation of s.e. of the regional interaction effects was
performed using the delta method. The F statistic of overall significance rejects
the null hypothesis at the 1% significance level for all of the models. The sample
was composed of GLOBIOM regional output under five different trade scenarios
(baseline, pre-Doha tariffs, facilitation, tariff elimination and facilitation + tariff
elimination) and ten climate change scenarios in 2050. The sample size was 550
for models with regional fixed effects (11 regions × 5 trade × 10 climate change
scenarios) and 450 for models with regional interaction terms (9 regions (EUR and
CAN excluded) × 5 trade × 10 climate change scenarios). Summary statistics of all
of the variables are shown in Supplementary Table 5.
Comparative advantage statistical analysis. To assess comparative advantage, we
estimated linear regression models of the effect of trade-cost reduction on the share
of production of a specific crop that region i represents in total world production
of that crop in each trade scenario t and climate change scenario r (Share of world
productionitr); the share of a specific crop in a region’s total crop production
(Share of regional crop productionitr); and the share of a region’s production of
a specific crop that is exported (Share of production exporteditr). The following
models were estimated separately for wheat, corn, rice and soya using OLS (Fig. 4,
Supplementary Figs. 16 and 17):
Share of world productionitr ¼
P
i
β 5
ð Þ
1i Trade costsitr ´ Regioni
þβ 5
ð Þ
2i Regioni þ ε 5
ð Þ
itr
Share of regional crop productionitr ¼
P
i
β 6
ð Þ
1i Trade costsitr ´ Regioni
þβ 6
ð Þ
2i Regioni þ ε 6
ð Þ
itr
Share of production exporteditr ¼
P
i
β 7
ð Þ
1i Trade costsitr ´ Regioni
þβ 7
ð Þ
2i Regioni þ ε 7
ð Þ
itr
For Fig. 4b, the dependent variable is the outcome under climate change, whereas,
for Fig. 4c, the dependent variable is the difference in outcome between climate
change and no climate change. Trade costsitr is the log-transformed trade-weighted
average of trade costs (US$ per ton) per region i, trade scenario t and climate
change scenario r (Supplementary Table 7). The variable Trade costitr was centred
(demeaned) to solve structural multicollinearity. Dummy variables were used
for regional fixed effects (Regioni). Observations were taken from the nine
RCP × GCM scenarios and four trade integration scenarios (baseline trade,
facilitation, tariff and facilitation + tariff); regions were excluded that have a deficit
production in at least 90% of the trade and climate change scenarios; n = 189
(corn), n = 180 (rice), n = 98 (soya) and n = 246 (wheat); s.e. was estimated robust
to heteroscedasticity using the HC3 method and s.e. of regional interaction effects
was calculated using the delta method.
Data availability
The authors declare that the main data supporting the findings of this study are
available within the Article and the Supplementary Information. Additional data
are available from the corresponding author on request. Source data are provided
with this paper.
Code availability
Code used for the statistical analysis of the scenario data is available from the
corresponding author on request.
References
	38.	Mosnier, A. Tracking Indirect Effects of Climate Change Mitigation and
Adaptation Strategies in Agriculture and Land Use Change With a Bottom-Up
Global Partial Equilibrium Model (Univ. Natural Resources and Life Sciences
(BOKU), 2014).
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
	39.	Valin, H. et al. The future of food demand: understanding differences in
global economic models. Agric. Econ. 45, 51–67 (2014).
	40.	Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse
gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110,
20888–20893 (2013).
	41.	Forsell, N. et al. Assessing the INDCs’ land use, land use change, and forest
emission projections. Carbon Balance Manag. 11, 26 (2016).
	42.	Havlík, P. et al. Global land-use implications of first and second generation
biofuel targets. Energy Policy 39, 5690–5702 (2011).
	43.	Havlik, P. et al. Climate change mitigation through livestock system
transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).
	44.	Takayama, T. & Judge, G. G. Spatial and Temporal Price and Allocation
Models (North-Holland Publishing Company, 1971).
	45.	Gaulier, G. & Zignago, S. BACI: International Trade Database at the
Product-Level. The 1994-2007 Version Working Paper 1–35 (CEPII, 2010);
https://doi.org/10.2139/ssrn.1994500
	46.	Bouët, A., Decreux, Y., Fontagné, L., Jean, S. & Laborde, D. Assessing applied
protection across the world. Rev. Int. Econ. 16, 850–863 (2008).
	47.	Hummels, D. Toward a Geography of Trade Costs (Purdue University, 2001).
	48.	Bouët, A., Decreux, Y., Fontagné, L., Jean, S. & Laborde, D. A Consistent,
Ad-Valorem Equivalent Measure of Applied Protection Across the World: The
MAcMap-HS6 Database Working Papers 2004-22 (CEPII Research Center,
2004); http://cepii.fr/PDF_PUB/wp/2004/wp2004-22.pdf
	49.	Guimbard, H., Jean, S., Mimouni, M. & Pichot, X. MAcMap-HS6 2007, an
Exhaustive and Consistent Measure of Applied Protection in 2007. J. Int.
Econ. 130, 99–122 (2012).
	50.	Hummels, D. Transportation costs and international trade in the second era
of globalization. J. Econ. Perspect. 21, 131–154 (2007).
	51.	Hasegawa, T. et al. Consequence of climate mitigation on the risk of hunger.
Environ. Sci. Technol. 49, 7245–7253 (2015).
	52.	Hasegawa, T. et al. Risk of increased food insecurity under stringent global
climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).
	53.	Hasegawa, T., Fujimori, S., Takahashi, K. & Masui, T. Scenarios for the risk of
hunger in the twenty-first century using Shared Socioeconomic Pathways.
Environ. Res. Lett. 10, 014010 (2015).
	54.	Global Spatially-Disaggregated Crop Production Statistics Data for 2000
Version 3.0.7 (IFPRI, 2019); https://doi.org/10.7910/DVN/A50I2T
	55.	Moore, F. C., Baldos, U., Hertel, T. & Diaz, D. New science of climate change
impacts on agriculture implies higher social cost of carbon. Nat. Commun. 8,
1607 (2017).
	56.	Costinot, A., Donaldson, D. & Komunjer, I. What goods do countries trade? A
quantitative exploration of Ricardo’s ideas. Rev. Econ. Stud. 79, 581–608 (2012).
	57.	Clapp, J. Food Security and International Trade: Unpacking disputed narratives.
The State of Agricultural Commodity Markets 2015-2016 (FAO, 2015).
	58.	Warszawski, L. et al. The inter-sectoral impact model intercomparison project
(ISI-MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).
	59.	van Vuuren, D. P. et al. The representative concentration pathways: an
overview. Climatic Change 109, 5–31 (2011).
	60.	Martin, G. M. et al. The HadGEM2 family of Met Office unified model
climate configurations. Geosci. Model Dev. 4, 723–757 (2011).
	61.	Collins, W. J. et al. Development and evaluation of an Earth-system
model—HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).
	62.	Dunne, J. P. et al. GFDL’s ESM2 global coupled climate-carbon Earth system
models. part I: physical formulation and baseline simulation characteristics.
J. Clim. 25, 6646–6665 (2012).
	63.	Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth
system model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).
	64.	Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of
CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).
	65.	Bentsen, M. et al. The Norwegian Earth System Model, NorESM1-M—part 1:
description and basic evaluation of the physical climate. Geosci. Model Dev. 6,
687–720 (2013).
	66.	Moïsé, E. & Sorescu, S. Trade Facilitation Indicators—The Potential Impact of
Trade Facilitation on Developing Countries’ Trade OECD Trade Policy Papers
No. 144 (OECD Publishing, 2013).
	67.	Non-Tariff Measures to Trade: Economic and Policy Issues for Developing
Countries (UNCTAD, 2013).
	68.	Minor, P. J. Time as a Barrier to Trade: A GTAP Database of Ad Valorem
Trade Time Costs 2nd edn (ImpactEcon, 2013).
	69.	Petri, P. A. & Plummer, M. G. The Economic Effects of the Trans-Pacific
Partnership: New Estimates Working Paper 16-2 (Peterson Institute for
International Economics, 2016).
	70.	Balistreri, E. J., Maliszewska, M., Osorio-Rodarte, I., Tarr, D. G. &
Yonezawa, H. Poverty, welfare and income distribution implications of
reducing trade costs through deep integration in eastern and Southern
Africa. J. Afr. Econ. 27, 172–200 (2018).
	71.	Anderson, K. & Martin, W. Agricultural Trade Reform and the Doha
Development Agenda (The World Bank and Palgrave Macmillan, 2006).
	72.	Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: an overview. Glob. Environ.
Change 42, 153–168 (2017).
	73.	Valin, H. et al. Agricultural productivity and greenhouse gas emissions:
trade-offs or synergies between mitigation and food security? Environ. Res.
Lett. 8, 035019 (2013).
	74.	Long, J. S. & Ervin, L. H. Using heteroscedasticity consistent standard errors
in the linear regression model. Am. Stat. 54, 217–224 (2000).
Acknowledgements
We thank H. Guimbard and staff at CEPII for their contribution in terms of trade policy
data and A. Mosnier for her support in the trade modelling work before this study. We
acknowledge research funding from Research Foundation Flanders (FWO contract,
180956/SW) and support from the US Environmental Protection Agency (EPA, contract
BPA-12-H-0023; call order, EP-B15H-0143). The views and opinions expressed in this
paper are those of the authors alone and do not necessarily state or reflect those of
the EPA, and no official endorsement should be inferred. This paper has also received
funding from the EU Horizon 2020 research and innovation programme under grant
agreement no. 776479 for the project CO-designing the Assessment of Climate CHange
costs (https://www.coacch.eu/), and from the European Structural and Investments
Funds for the project SustES, Adaptive Strategies for Sustainability of Ecosystems
Services and Food Security in Harsh Natural Conditions (reg. no. CZ.02.1.01/0.0/0.0/16
_019/0000797).
Author contributions
All of the authors have contributed substantially to the manuscript. P.H., J.B., T.K. and
C.J. developed the concept and designed scenarios. P.H., E.S., T.H., C.J. and D.L. provided
code and model simulations. C.J., T.K. and P.H. analysed the data. C.J., P.H., T.K., J.B. and
M.M. interpreted the data and wrote the manuscript on which S.F., H.V., N.V.L., E.S.,
T.H., S.O. and S.R. commented.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41558-020-0847-4.
Supplementary information is available for this paper at https://doi.org/10.1038/
s41558-020-0847-4.
Correspondence and requests for materials should be addressed to C.J.
Peer review information: Nature Climate Change thanks Maksym Chepeliev and the
other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 1 | Biophysical impact of climate change on average crop yield in each region by 2050 as projected by the EPIC crop model. Yields in
ton dry matter per ha. The x-axis indicates the crop yield under no climate change and y-axis the crop yield under climate change for different RCP x GCM
combinations without market feedback and adaptation measures. Under no climate change yields are determined by base year yield and assumptions on
technological development over time, under climate change an additional climate impact shifter is applied. Points above the black line indicate an increase
in crop yield, points below a decrease in crop yield.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 2 | Impact of climate change on average crop yield after supply-side adaptation in each region by 2050 as projected by GLOBIOM.
Yields in ton dry matter per ha. The x-axis indicates the crop yield under no climate change and y-axis the crop yield under climate change for different
RCP x GCM combinations with GLOBIOM market feedback and supply-side adaptation (changes in management system and reallocation of production
across spatial units in response to price changes). Points above the black line indicate an increase in crop yield, points below a decrease in crop yield.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 3 | Net agricultural trade of baseline net importing regions in 2050 under trade and climate change scenarios. Net agricultural trade
in ton dry matter. Fac. = Facilitation, Tariff elim. = Tariff elimination.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 4 | Net agricultural trade of baseline net exporting regions in 2050 under trade and climate change scenarios. Net agricultural trade
in ton dry matter. Fac. = Facilitation, Tariff elim. = Tariff elimination.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 5 | Change in agricultural prices of baseline net importing regions in 2050 under trade and climate change scenarios compared to
SSP2 baseline. Fac. = Facilitation, Tariff elim. = Tariff elimination.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 6 | Change in agricultural prices of baseline net exporting regions in 2050 under trade and climate change scenarios compared to
SSP2 baseline. Fac. = Facilitation, Tariff elim. = Tariff elimination.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 7 | Change in population at risk of hunger in 2050 in hunger-affected regions under climate change and trade scenarios compared
to SSP2 baseline. Fac. = Facilitation, Tariff elim. = Tariff elimination. The estimated risk of hunger in the other world regions is zero (CAN, EUR) or very low
(OCE, USA).
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 8 | Plot of the fitted linear response of population at risk of hunger (million) to climate-induced crop yield change for different
values of trade costs (1st decile, median, 9th decile). Shaded areas indicate prediction intervals. Prediction based on an OLS estimation of a regional level
linear regression of the impact of crop yield change, trade costs and their interaction on population at risk of hunger. Regression results are shown in
Supplementary Table 3 and the regression model is described in Method.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 9 | Share of production volume that each region represents of total world production for corn, rice, soya and wheat in the SSP2
baseline in 2050. The projected total world production by 2050 in the SSP2 baseline is 1213 Mt for corn, 884 Mt for rice, 309 Mt for soya and 794 Mt
for wheat.
Nature Climate Change | www.nature.com/natureclimatechange

Articles
Nature Climate Change
Extended Data Fig. 10 | Net trade (1000 ton) in East Asia (EAS), Middle East and North Africa (MNA), South Asia (SAS) and Sub-Saharan Africa (SSA)
for corn, rice, soya and wheat under climate change and trade scenarios in 2050. Net agricultural trade in ton dry matter. Values above zero indicate net
exports, negative values indicate net imports.
Nature Climate Change | www.nature.com/natureclimatechange

    Policy Brief:

Answer:
**International trade is a key component of climate change adaptation**

**Policy Problem**Climate change is expected to reduce crop yields and increase food prices globally, which will threaten the achievement of UN Sustainable Development Goal 2 to end global hunger. Yet there are regional differences in climate change impacts, and international trade can be an important adaptation mechanism by connecting regions of food deficit with those of food surplus. Even though climate and trade policy agendas have started to converge, the extent to which future climate change impacts should be considered in the development of international trade policies remains unclear. Trade barriers such as import tariffs, export restrictions, limited infrastructure or inefficient customs procedures substantially limit agricultural trade flows and may affect the capacity of trade to mitigate climate change impacts. Climate change adaptation may therefore need to be addressed by the full spectrum of the global trade policy agenda, from agreements on free trade to rules on trade restrictions.

**Scientific Research Findings**Under current trade barriers, a pessimistic scenario of high global warming (+4 °C by 2100) with no benefits from enhanced atmospheric CO2 on crops could cause up to an additional 55 million people to be undernourished by 2050, mostly in Sub-Saharan Africa and South Asia. If trade restrictions that prevent increased trading under climate change were imposed, the impact could increase to an additional 73 million people. Reduction in tariffs and improvements in trade infrastructure would limit the impact to an additional 20 million people. For export-oriented regions, however, partial trade integration could lead to lower domestic food availability. The findings show that trade policies clearly influence the sensitivity of hunger to climate change. The study focuses on the impacts of trade and climate change on food availability and does not account for income effects, which determine people’s access to food. Furthermore, it does not investigate extreme weather events such as droughts and floods, which are likely to accentuate the importance of trade for adaptation.

**Scientific Research Study Methods**The analysis builds on an integrated framework of climate, crop and economic simulation models that assesses the long-term impact of climatic and socio-economic changes on global hunger. We develop a baseline at which global hunger is reduced by 2050 following population and income growth and reduced inequalities, and compare this with scenarios that vary in projected climate outcomes (from 2 °C to 4 °C global warming) and trade barriers. The climate models estimate temperature and precipitation under alternative concentrations of greenhouse gas emissions, which the crop model translates into yield impacts. The yield changes are incorporated into the economic model at detailed spatial scale to accurately represent biophysical effects, and we model the interaction of these with changes in import tariffs and other international trade costs.

**Policy Implications**• International trade can substantially contribute to climate change adaptation by reducing global hunger driven by the heightened pressure of climate change on agricultural markets.
• Tariffs and quantitative trade restrictions, which countries sometimes implement in response to crisis situations, tend to aggravate the increase in hunger caused by climate change.
• Lowering tariffs and infrastructure-related barriers mitigates impacts on hunger in import-oriented regions, although caution is needed to avoid food price increases in export-oriented regions.
• Opening to trade may further expose farmers in severely hit import-oriented regions, which calls for holistic development programs to redistribute benefits and compensate income losses.
• Trade agreements should better account for adaptation by preventing border restrictions, facilitating trade and identifying distribution consequences and safeguards in ex-ante impact assessments.