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Enhanced food system efficiency is the key to China’s 2060 carbon neutrality target

Nature Food volume 4, pages 552–564 (2023)Cite this article

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Abstract

Bioenergy with carbon capture and storage, among other negative-emission technologies, is required for China to achieve carbon neutrality—yet it may hinder land-based Sustainable Development Goals. Using modelling and scenario analysis, we investigate how to mitigate the potential adverse impacts on the food system of ambitious bioenergy deployment in China and its trading partners. We find that producing bioenergy domestically while sticking to the food self-sufficiency ratio redlines would lower China’s daily per capita calorie intake by 8% and increase domestic food prices by 23% by 2060. Removing China’s food self-sufficiency ratio restrictions could halve the domestic food dilemma but risks transferring environmental burdens to other countries, whereas halving food loss and waste, shifting to healthier diets and narrowing crop yield gaps could effectively mitigate these external effects. Our results show that simultaneously achieving carbon neutrality, food security and global sustainability requires a careful combination of these measures.

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Fig. 1: Effects of bioenergy deployment in China on domestic land use, production, consumption and trade of agricultural products.
Fig. 2: Impacts of bioenergy deployment in China on domestic sustainability in 2060.
Fig. 3: Virtually imported environmental impacts from China’s trading partners due to agricultural imports.
Fig. 4: Agricultural product import and embedded environmental impacts.
Fig. 5: Sustainability impacts of bioenergy deployment in China under different alternative futures in 2060.

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Data availability

The data supporting the findings of this study are available within the Article and Supplementary Data. Source data are provided with this paper.

Code availability

The code used to present the results in this study is available from the corresponding author upon request. The documentation for the GLOBIOM model is available online at https://iiasa.github.io/GLOBIOM/.

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Acknowledgements

This study was supported by the National Science Fund for Outstanding Young Scholars (grant no. 72222001) (H.D.), the National Key Research and Development Program of the Ministry of Science and Technology of China (grant no. 2022YFE0138300) (H.D.), the National Natural Science Foundation of China (grant nos 72073003, 71810107001 and 72234002) (H.D.), the National Social Science Foundation of China (grant no. 21AZD060) (H.D.), the European Union’s Horizon 2020 research and innovation programme under the ENGAGE (grant no. 821471) (P.H.) and NAVIGATE (grant no. 821124) (P.H.) projects, the China Postdoctoral Science Foundation (grant no. 2022M720212) (C.H.) and the Youth Academic Program in Area Studies of Peking University (grant no. 7101602310) (C.H.). We acknowledge the Peking University–IIASA Postdoctoral Program for providing funding and collaboration opportunities (no grant number).

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Authors and Affiliations

  1. College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Ming Ren, Chen Huang, Yazhen Wu, Yuyao Zhu, Chen Fang, Xiaotian Ma, Yong Liu, Shasha Xu & Hancheng Dai

  2. Institute of Carbon Neutrality, Peking University, Beijing, China

    Ming Ren & Hancheng Dai

  3. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Ming Ren, Chen Huang, Andre Deppermann, Stefan Frank, Petr Havlík, Yuyao Zhu & Shasha Xu

  4. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Hao Zhao & Jinfeng Chang

  5. Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang, China

    Hao Zhao, Lin Ma & Zhaohai Bai

  6. Institute for Global Health and Development, Peking University, Beijing, China

    Hancheng Dai

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Contributions

M.R., H.D. and P.H. designed the study. M.R. ran the model and performed the analysis with the help of A.D. and S.F. C.H. drew the figures. M.R., H.D. and P.H. wrote the manuscript with major contributions from all co-authors.

Corresponding author

Correspondence to Hancheng Dai.

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Extended data

Extended Data Fig. 1

The conceptual research framework of interconnected impacts of bioenergy deployment on selected sustainability indicators.

Extended Data Fig. 2

Overview of detailed modeling framework.

Extended Data Fig. 3 Projections of China’s agricultural imports in 2060.

a–f, The lengths of the red suspended bars indicate the absolute marginal change in each scenario compared with the scenario to its above; the number beside each red bar is obtained by dividing the abovementioned absolute change by the corresponding value in the Reference scenario. The length of the final bar is the value for the FoodSystem scenario.

Source data

Extended Data Fig. 4 Sensitivity of sustainability impacts to changes in uncertain input variables.

Absolute changes (top) in sustainability indicators and elasticity (bottom) in 2060. The mapping of sensitivity scenarios, baseline scenarios, and inputs and the proxy variables of inputs for calculating elasticities is shown in Supplementary Table 11. Methods and Supplementary Table 8 present sensitivity scenarios in further detail. The right side shows the uncertain input variables and the left side shows the sensitivity scenarios. The first six indicators (top) are China’s domestic sustainability, and the last four indicators are virtually imported environmental impacts from China’s trading partners.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–34, Tables 1–14, Methods and Discussion.

Reporting Summary

Supplementary Data 1

Data supporting the findings of this study including China’s domestic sustainability, trade of agricultural products and virtually imported environmental impacts from China’s trading partners due to agricultural imports.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

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Ren, M., Huang, C., Wu, Y. et al. Enhanced food system efficiency is the key to China’s 2060 carbon neutrality target. Nat Food 4, 552–564 (2023). https://doi.org/10.1038/s43016-023-00790-1

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