Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A multi-model assessment of food security implications of climate change mitigation

Nature Sustainability volume 2, pages 386–396 (2019)Cite this article

Subjects

Abstract

Holding the global increase in temperature caused by climate change well below 2 °C above pre-industrial levels, the goal affirmed by the Paris Agreement, is a major societal challenge. Meanwhile, food security is a high-priority area in the UN Sustainable Development Goals, which could potentially be adversely affected by stringent climate mitigation. Here we show the potential negative trade-offs between food security and climate mitigation using a multi-model comparison exercise. We find that carelessly designed climate mitigation policies could increase the number of people at risk of hunger by 160 million in 2050. Avoiding these adverse side effects would entail a cost of about 0.18% of global gross domestic product in 2050. It should be noted that direct impacts of climate change on yields were not assessed and that the direct benefits from mitigation in terms of avoided yield losses could be substantial, further reducing the above cost. Although results vary across models and model implementations, the qualitative implications are robust and call for careful design of climate mitigation policies taking into account agriculture and land prices.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Population at risk of hunger under the baseline scenario and food consumption.
Fig. 2: The population at risk of hunger under the baseline and mitigation scenarios.
Fig. 3: Emissions and carbon prices.
Fig. 4: Food consumption, agricultural price and carbon price relationships.
Fig. 5: Complementary food policy cost compared with the mitigation cost.

Data availability

Scenario data are accessible online via the CD-LINKS Scenario Database at https://db1.ene.iiasa.ac.at/CDLINKSDB. Data derived from the original scenario database, which are shown as figures but are not in the above database, are available upon reasonable request from the corresponding author.

References

  1. FAOSTAT 2016 (FAO, accessed 9 February 2016); https://faostat.fao.org

  2. The State of Food Insecurity in the World 2015—Meeting the 2015 International Hunger Targets: Taking Stock of Uneven Progress (FAO: Rome, 2015).

  3. Parry, M. L., Rosenzweig, C., Iglesias, A., Livermore, M. & Fischer, G. Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob. Environ. Change 14, 53–67 (2004).

    Article  Google Scholar 

  4. Nelson, G. C. et al. Food Security, Farming, and Climate Change to 2050, Scenarios, Results, Policy Options (IFPRI, 2010).

  5. Rosenzweig, C. & Parry, M. L. Potential impact of climate change on world food supply. Nature 367, 133–138 (1994).

    Article  Google Scholar 

  6. Hasegawa, T. et al. Climate change impact and adaptation assessment on food consumption utilizing a new scenario framework. Environ. Sci. Technol. 48, 438–445 (2014).

    Article  CAS  Google Scholar 

  7. Hasegawa, T. et al. Consequence of climate mitigation on the risk of hunger. Environ. Sci. Technol. 49, 7245–7253 (2015).

    Article  CAS  Google Scholar 

  8. Obersteiner, M. et al. Assessing the land resource–food price nexus of the sustainable development goals. Sci. Adv. 2, e1501499 (2016).

    Article  Google Scholar 

  9. Lotze-Campen, H. et al. Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison. Agric. Econ. 45, 103–116 (2014).

    Article  Google Scholar 

  10. Stevanović, M. et al. Mitigation strategies for greenhouse gas emissions from agriculture and land-use change: consequences for food prices. Environ. Sci. Technol. 51, 365–374 (2017).

    Article  Google Scholar 

  11. Springmann, M. et al. Mitigation potential and global health impacts from emissions pricing of food commodities. Nat. Clim. Change 7, 69–74 (2017).

    Article  Google Scholar 

  12. Havlík, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).

    Article  Google Scholar 

  13. Stefan, F. et al. Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett. 12, 105004 (2017).

    Article  Google Scholar 

  14. Fujimori, S. et al. Inclusive climate change mitigation and food security policy under 1.5 °C climate goal. Environ. Res. Lett. 13, 074033 (2018).

    Article  Google Scholar 

  15. Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015).

  16. Fujimori, S. et al. Implication of Paris Agreement in the context of long-term climate mitigation goals. SpringerPlus 5, 1–11 (2016).

    Article  Google Scholar 

  17. Popp, A. et al. Land-use protection for climate change mitigation. Nat. Clim. Change 4, 1095–1098 (2014).

    Article  CAS  Google Scholar 

  18. Rose, S. K. et al. Bioenergy in energy transformation and climate management. Climatic Change 123, 477–493 (2014).

    Article  Google Scholar 

  19. Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853 (2014).

    Article  CAS  Google Scholar 

  20. Reilly, J. et al. Using land to mitigate climate change: hitting the target, recognizing the trade-offs. Environ. Sci. Technol. 46, 5672–5679 (2012).

    Article  CAS  Google Scholar 

  21. Kriegler, E. et al. Making or breaking climate targets: the AMPERE study on staged accession scenarios for climate policy. Technol. Forecast. Soc. Change 90, 24–44 (2015).

    Article  Google Scholar 

  22. Fujimori, S. et al. SSP3: AIM implementation of Shared Socioeconomic Pathways. Glob. Environ. Change 42, 268–283 (2017).

    Article  Google Scholar 

  23. van Vuuren, D. P. et al. Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ. Change 42, 237–250 (2017).

    Article  Google Scholar 

  24. 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).

    Article  Google Scholar 

  25. Kriegler, E. et al. Fossil-fueled development (SSP5): an energy and resource intensive scenario for the 21st century. Glob. Environ. Change 42, 297–315 (2017).

    Article  Google Scholar 

  26. Joint Research Centre POLES: Global Energy Model (European Union, 2016).

  27. Emmerling J. et al. The WITCH 2016 Model—Documentation and Implementation of the Shared Socioeconomic Pathways (Elsevier, 2016).

  28. Gernaat, D. E. H. J. et al. Understanding the contribution of non-carbon dioxide gases in deep mitigation scenarios. Glob. Environ. Change 33, 142–153 (2015).

    Article  Google Scholar 

  29. 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).

    Article  Google Scholar 

  30. Hasegawa, T., Fujimori, S., Takahashi, K., Yokohata, T. & Masui, T. Economic implications of climate change impacts on human health through undernourishment. Climatic Change 136, 1–14 (2016).

    Article  Google Scholar 

  31. Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).

    Article  Google Scholar 

  32. Schmidhuber, J. & Tubiello, F. N. Global food security under climate change. Proc. Natl Acad. Sci. USA 104, 19703–19708 (2007).

    Article  CAS  Google Scholar 

  33. Rogelj, J. et al. Special Report: Global Warming of 1.5 ºC (eds Flato, G. et al.) Ch. 2 (IPCC, Cambridge Univ. Press, 2018).

  34. McCollum, D. L., Krey, V. & Riahi, K. An integrated approach to energy sustainability. Nat. Clim. Change 1, 428–429 (2011).

    Article  Google Scholar 

  35. Jewell, J. et al. Comparison and interactions between the long-term pursuit of energy independence and climate policies. Nat. Energy 1, 16073 (2016).

    Article  Google Scholar 

  36. van Vuuren, D. P. et al. Exploring the ancillary benefits of the Kyoto protocol for air pollution in Europe. Energy Policy 34, 444–460 (2006).

    Article  Google Scholar 

  37. McCollum, D. L. et al. Climate policies can help resolve energy security and air pollution challenges. Climatic Change 119, 479–494 (2013).

    Article  Google Scholar 

  38. Cameron, C. et al. Policy trade-offs between climate mitigation and clean cook-stove access in South Asia. Nat. Energy 1, e15010 (2016).

    Article  Google Scholar 

  39. Net ODA (OECD, 2017); https://data.oecd.org/oda/net-oda.htm

  40. Hertel, T. W., Burke, M. B. & Lobell, D. B. The poverty implications of climate-induced crop yield changes by 2030. Glob. Environ. Change 20, 577–585 (2010).

    Article  Google Scholar 

  41. Zekarias, H., Thomas, H. & Alla, G. Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett. 8, 035009 (2013).

    Article  Google Scholar 

  42. van Vuuren, D. P. et al. Alternative pathways to the 1.5 °C target reduce the need for negative emission technologies. Nat. Clim. Change 8, 391–397 (2018).

    Article  Google Scholar 

  43. Roy, J. et al. Special Report: Global Warming of 1.5 ºC (eds Krakovska, S. et al.) Ch. 5 (IPCC, Cambridge Univ. Press, 2018).

  44. Grubler, A. et al. A low energy demand scenario for meeting the 1.5 °C target and sustainable development goals without negative emission technologies. Nat. Energy 3, 515–527 (2018).

    Article  Google Scholar 

  45. Wise, M. et al. Implications of limiting CO2 concentrations for land use and energy. Science 324, 1183–1186 (2009).

    Article  CAS  Google Scholar 

  46. Frank, S. et al. Agricultural non-CO2 emission reduction potential in the context of the 1.5 °C target. Nat. Clim. Change 9, 66–72 (2019).

    Article  CAS  Google Scholar 

  47. von Lampe, M. et al. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP global economic model intercomparison. Agric. Econ. 45, 3–20 (2014).

    Article  Google Scholar 

  48. Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).

    Article  CAS  Google Scholar 

  49. Keith, W. et al. Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios. Environ. Res. Lett. 10, 085010 (2015).

    Article  Google Scholar 

  50. Valin, H. et al. The future of food demand: understanding differences in global economic models. Agric. Econ. 45, 51–67 (2014).

    Article  Google Scholar 

  51. Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat. Clim. Change 8, 626–633 (2018).

    Article  CAS  Google Scholar 

  52. Hall, A. Projecting regional change. Science 346, 1461–1462 (2014).

    Article  CAS  Google Scholar 

  53. Krey, V. et al. Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models. Energy 172, 1254–1267 (2019).

    Article  Google Scholar 

  54. Fujimori S., Masui T. & Matsuoka Y. AIM/CGE [Basic] Manual (National Institute Environmental Studies, 2012).

  55. Fujimori, S., Hasegawa, T., Masui, T. & Takahashi, K. Land use representation in a global CGE model for long-term simulation: CET vs. logit functions. Food Sec. 6, 685–699 (2014).

    Article  Google Scholar 

  56. Stehfest E. et al. (eds) Integrated Assessment of Global Environmental Change with IMAGE 3.0: Model Description and Policy Applications (Netherlands Environmental Assessment Agency, 2014).

  57. Woltjer, G. B. & Kuiper, M. H. The MAGNET Model: Module Description. (LEI Wageningen UR, 2014).

  58. Lucas, P. L., van Vuuren, D. P., Olivier, J. G. J. & den Elzen, M. G. J. Long-term reduction potential of non-CO2 greenhouse gases. Environ. Sci. Policy 10, 85–103 (2007).

    Article  Google Scholar 

  59. Messner, S. & Schrattenholzer, L. MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy 25, 267–282 (2000).

    Article  Google Scholar 

  60. Kindermann, G. E., Obersteiner, M., Rametsteiner, E. & McCallum, I. Predicting the deforestation-trend under different carbon-prices. Carbon Balance Manage. 1, 15 (2006).

    Article  Google Scholar 

  61. Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6–part 1: model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).

    Article  CAS  Google Scholar 

  62. Keramidas K. et al. POLES-JRC Model Documentation (European Union, 2017).

  63. Lotze-Campen, H. et al. Global food demand, productivity growth, and the scarcity of land and water resources: a spatially explicit mathematical programming approach. Agric. Econ. 39, 325–338 (2008).

    Google Scholar 

  64. Bonsch, M. et al. Trade-offs between land and water requirements for large-scale bioenergy production. GCB Bioenergy 8, 11–24 (2016).

    Article  Google Scholar 

  65. Dietrich, J. P., Schmitz, C., Lotze-Campen, H., Popp, A. & Müller, C. Forecasting technological change in agriculture—an endogenous implementation in a global land use model. Technol. Forecast. Soc. Change 81, 236–249 (2014).

    Article  Google Scholar 

  66. Bodirsky, B. L. et al. N2O emissions from the global agricultural nitrogen cycle—current state and future scenarios. Biogeosciences 9, 4169–4197 (2012).

    Article  CAS  Google Scholar 

  67. Bosetti, V. et al. WITCH—a world induced technical change hybrid model. Energy J. 27, 13–37 (2006).

    Google Scholar 

  68. The State of Food Insecurity in the World 2012: Economic Growth is Necessary But Not Sufficient to Accelerate Reduction of Hunger and Malnutrition (FAO, WFP and IFAD, 2012).

  69. Emission Database for Global Atmospheric Research (EDGAR) release version (EC and JRC, accessed 2012); http://edgar.jrc.ec.europa.eu

  70. FAO Methodology for the Measurement of Food Deprivation: Updating the Minimum Dietary Energy Requirements (FAO, 2008).

  71. Food Security Indicators (FAO, 2013).

  72. Energy and Protein Requirements (FAO, WHO and UNU, 1973).

  73. Shared Socioeconomic Pathways (SSP) Database v.0.9.3 (IIASA, 2012).

Download references

Acknowledgements

S.Fujimori, T.H. and K.T. are supported by JSPS KAKENHI Grant Number JP16K18177, JSPS Overseas Research Fellowships and the Environment Research and Technology Development Fund (2-1702) of the Environmental Restoration and Conservation Agency of Japan. J.Després and A.S. are funded by the European Commission. All other authors received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 642147 (CD-LINKS). The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission.

Author information

Authors and Affiliations

  1. Department of Environmental Engineering, Kyoto University, Kyoto, Japan

    Shinichiro Fujimori

  2. Center for Social and Environmental Systems Research, National Institute for Environmental Studies (NIES), Tsukuba, Japan

    Shinichiro Fujimori, Tomoko Hasegawa & Kiyoshi Takahashi

  3. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Shinichiro Fujimori, Tomoko Hasegawa, Volker Krey, Keywan Riahi, Jessica Callen, Stefan Frank, Oliver Fricko & Petr Havlik

  4. Department of Civil and Environmental Engineering, Ritsumeikan University, Kusatsu, Japan

    Tomoko Hasegawa

  5. Graz University of Technology, Graz, Austria

    Keywan Riahi

  6. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

    Christoph Bertram, Benjamin Leon Bodirsky, Florian Humpenöder & Alexander Popp

  7. RFF-CMCC European Institute on Economics and the Environment (EIEE), Centro Euro-Mediterraneo sui Cambiamenti Climatici, Milan, Italy

    Valentina Bosetti, Laurent Drouet & Johannes Emmerling

  8. Department of Economics, Bocconi University, Milan, Italy

    Valentina Bosetti

  9. Joint Research Centre (JRC), European Commission, Seville, Spain

    Jacques Després & Andreas Schmitz

  10. PBL Netherlands Environmental Assessment Agency, The Hague, Netherlands

    Jonathan Doelman & Detlef van Vuuren

  11. Wageningen economic Research, Wageningen University and Research Centre, The Hague, Netherlands

    Jason F. L. Koopman & Hans van Meijl

  12. E-Konzal, Osaka, Japan

    Yuki Ochi

  13. Copernicus Institute for Sustainable Development, Utrecht University, Utrecht, the Netherlands

    Detlef van Vuuren

Authors
  1. Shinichiro Fujimori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomoko Hasegawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Volker Krey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keywan Riahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christoph Bertram
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benjamin Leon Bodirsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Valentina Bosetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jessica Callen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jacques Després
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jonathan Doelman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Laurent Drouet
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Johannes Emmerling
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Stefan Frank
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Oliver Fricko
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Petr Havlik
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Florian Humpenöder
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Jason F. L. Koopman
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hans van Meijl
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Yuki Ochi
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Alexander Popp
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Andreas Schmitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Kiyoshi Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Detlef van Vuuren
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.Fujimori, V.K. and K.R. designed the research; S.Fujimori carried out analysis of the modelling results, created figures and wrote the draft of the paper; T.H. and Y.O. carried out hunger estimation tool simulation; S.Fujimori and T.H. provided AIM data; J.Doelman, J.K., H.v.M. and D.v.V. provided IMAGE data; O.F., S.Frank and P.H. provided MESSAGE–GLOBIOM data; J.Després and A.S. provided POLES data; B.L.B., F.H. and A.P. provided REMIND–MAgPIE data; V.B., L.D. and J.E. provided WITCH data; J.C. edited English language; and all authors contributed to the discussion and interpretation of the results.

Corresponding author

Correspondence to Shinichiro Fujimori.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information

Supplementary Notes, Supplementary Figs. 1–16, Supplementary Tables 1–3, Supplementary Refs. 1–20

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fujimori, S., Hasegawa, T., Krey, V. et al. A multi-model assessment of food security implications of climate change mitigation. Nat Sustain 2, 386–396 (2019). https://doi.org/10.1038/s41893-019-0286-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-019-0286-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing