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Global hunger and climate change adaptation through international trade

Nature Climate Change volume 10, pages 829–835 (2020)Cite this article

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Abstract

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 scenarios 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 infrastructural 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.

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Fig. 1: Global population at risk of hunger in 2050 across climate change and trade scenarios.
Fig. 2: Population at risk of hunger in 2050 across climate change and trade scenarios in each region.
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.
Fig. 4: Inter-regional specialization in corn, rice, soya and wheat in response to trade-cost reduction in 2050.

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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.

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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 information

Authors and Affiliations

  1. Department of Earth and Environmental Sciences, University of Leuven (KU Leuven), Heverlee, Belgium

    Charlotte Janssens, Nicole Van Lipzig & Miet Maertens

  2. Ecosystems Services and Management Program, International Institute for Applied System Analysis (IIASA), Laxenburg, Austria

    Charlotte Janssens, Petr Havlík, Tamás Krisztin, Stefan Frank, Tomoko Hasegawa, David Leclère & Hugo Valin

  3. RTI International, Durham, NC, USA

    Justin Baker

  4. College of Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Tomoko Hasegawa

  5. United States Environmental Protection Agency, Washington, DC, USA

    Sara Ohrel & Shaun Ragnauth

  6. Department of Economics and Social Sciences, University of Natural Resources and Life Sciences, Vienna, Austria

    Erwin Schmid

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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.

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Correspondence to Charlotte Janssens.

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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.

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Extended data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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).

Source data

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.

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.

Source data

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.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Tables 1–12, text and references.

Source data

Source Data Fig. 1

Numerical data to generate the graph.

Source Data Fig. 2

Numerical data to generate the graph.

Source Data Extended Data Fig. 1

Numerical data to generate the graph.

Source Data Extended Data Fig. 2

Numerical data to generate the graph.

Source Data Extended Data Fig. 3

Numerical data to generate the graph.

Source Data Extended Data Fig. 4

Numerical data to generate the graph.

Source Data Extended Data Fig. 5

Numerical data to generate the graph.

Source Data Extended Data Fig. 6

Numerical data to generate the graph.

Source Data Extended Data Fig. 7

Numerical data to generate the graph.

Source Data Extended Data Fig. 9

Numerical data to generate the graph.

Source Data Extended Data Fig. 10

Numerical data to generate the graph.

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Janssens, C., Havlík, P., Krisztin, T. et al. Global hunger and climate change adaptation through international trade. Nat. Clim. Chang. 10, 829–835 (2020). https://doi.org/10.1038/s41558-020-0847-4

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