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The multiple benefits of Chinese dietary transformation

Nature Sustainability volume 8, pages 606–618 (2025)Cite this article

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Abstract

The transition to more sustainable diets is critical to achieve the Sustainable Development Goals and meet the Paris Agreement commitments. In China, this transition is particularly urgent due to the double burden of malnutrition and environmental degradation. In this study, we explored the potential of alternative diets in China to enhance public health, ensure food affordability and reduce adverse environmental impacts. We assessed these patterns through a multi-objective diet optimization model combined with an agro-economic modelling framework that captures key socio-economic and biophysical dynamics in China. The proposed healthy, affordable and low-environmental-impact diets substantially improve dietary quality and are projected to reduce food expenditures by 20–28% (US$128–186 capita−1 in power purchasing parities of 2005) by 2050. These diets also bring environmental benefits, including a 3–11% (4–13 Mha) expansion of non-forest natural vegetation area and modest biodiversity gains by 2050, a 9–40% (3–13 Gt CO2-equivalent) reduction in greenhouse gas emissions and a 5–12% (347–772 km3) decrease in freshwater withdrawals between 2020 and 2050. Our findings underscore the potential to achieve multiple co-benefits through long-term and target-oriented dietary transformations, while also balancing the transformation feasibility with achievable gains.

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Fig. 1: Food intakes in the reference, HEALCDG, HEALEAT and HEALbase scenarios.
Fig. 2: Impact of dietary transformations on dietary quality indices.
Fig. 3: Changes in food expenditures and agricultural producer prices in the three HEAL scenarios compared to the reference scenario in 2050.
Fig. 4: LUC and GHG emissions in the reference and three HEAL scenarios.
Fig. 5: Agricultural water use and BII dynamics in China in the reference and three HEAL scenarios.

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Data availability

The data supporting the findings of this study can be found in Methods and Supplementary Information. Source data are provided with this paper.

Code availability

The MAgPIE code, including the food demand model, is available under GNU Affero General Public License, version 3 (AGPLv3) via GitHub at https://github.com/magpiemodel/magpie. The release (version 4.6.5) used in this paper is available via Zenodo at https://doi.org/10.5281/zenodo.7782037 (ref. 74). The technical model documentation is available at https://rse.pik-potsdam.de/doc/magpie/4.6.5/. Additional codes and details for the MODOpt model and MAgPIE-China are available from the corresponding author upon reasonable request.

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Acknowledgements

X.W., H.C. and J.X. were supported by the National Science Foundation of China (grant nos 72273126 and 72134006) and the National Key Research and Development Program of China (grant no. 2020YFA0608604). X.W., C.Y., H.C. and J.X. acknowledge support from the Fundamental Research Funds for the Central Universities. We thank W. Lu from the Department of Agricultural Economics and Management, Zhejiang University for providing valuable suggestions during the conceptualization of this work. We also thank M. Obersteiner and H. C. J. Godfray at the University of Oxford and C. Yue at Northwest Agriculture and Forestry University for insights during the revision.

Author information

Author notes

  1. These authors contributed equally: Hao Cai, Jiaqi Xuan, Xiaoxi Wang, Changzheng Yuan.

Authors and Affiliations

  1. China Academy for Rural Development, Department of Agricultural Economics and Management, Zhejiang University, Hangzhou, China

    Hao Cai, Jiaqi Xuan, Xiaoxi Wang & Hermann Lotze-Campen

  2. MAgPIE-China Research Group, Hangzhou, China

    Hao Cai, Jiaqi Xuan & Xiaoxi Wang

  3. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

    Xiaoxi Wang, Benjamin Leon Bodirsky, Miodrag Stevanović, Jan Philipp Dietrich, Alexander Popp & Hermann Lotze-Campen

  4. Environmental Change Institute, University of Oxford, Oxford, UK

    Xiaoxi Wang

  5. School of Public Health, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Changzheng Yuan

  6. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Changzheng Yuan

  7. Faculty of Organic Agricultural Sciences, University of Kassel, Kassel, Germany

    Alexander Popp

  8. Department of Agricultural Economics, Humboldt-Universität zu Berlin, Berlin, Germany

    Hermann Lotze-Campen

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  1. Hao Cai
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Contributions

X.W., C.Y., H.L.-C. and H.C. conceptualized the study. X.W., H.C., J.X., C.Y., B.L.B., M.S., J.P.D., A.P. and H.L.-C. contributed data and devised the methodology. H.C., J.X., X.W. and C.Y. performed the analysis and interpreted the results. H.C., J.X. and X.W. produced the visualizations. X.W. acquired the funding and managed the project administration. H.C., J.X. and X.W. wrote the original draft of the paper. All authors participated in discussing the results and contributed to reviewing and editing the paper.

Corresponding author

Correspondence to Xiaoxi Wang.

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Nature Sustainability thanks Kostas Dellis, Liangjie Xin 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 Food intakes in terms of aggregated food groups in three alternative diets for specific gender and age group.

Each panel shows food intakes of a specific demographic group in the three alternative dietary scenarios.

Source data

Extended Data Fig. 2 Overall demand in 2050 in terms of different demand types for aggregated food groups in the reference and three alternative scenarios.

There are eight demand types including food, feed, processed, other utilization, bioenergy, seed, waste, and domestic balance flow considered in MAgPIE.

Source data

Extended Data Fig. 3 Expenditures on different food groups in MAgPIE-China in the reference and three alternative scenarios between 2020 and 2050.

a. Food expenditures in the reference scenario. b. Food expenditures in the HEALCDG scenario. c. Food expenditures in the HEALEAT scenario. d. Food expenditures in the HEALbase scenario.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–32, Tables 1–8 and references.

Reporting Summary

Source data

Source Data Fig. 1

Source data from the model results to generate Fig. 1.

Source Data Fig. 2

Source data from the model results to generate Fig. 2.

Source Data Fig. 3

Source data from the model results to generate Fig. 3.

Source Data Fig. 4

Source data from the model results to generate Fig. 4.

Source Data Fig. 5

Source data from the model results to generate Fig. 5.

Source Data Extended Data Fig. 1

Source data from the model results to generate Extended Data Fig. 1.

Source Data Extended Data Fig. 2

Source data from the model results to generate Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Source data from the model results to generate Extended Data Fig. 3.

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Cai, H., Xuan, J., Wang, X. et al. The multiple benefits of Chinese dietary transformation. Nat Sustain 8, 606–618 (2025). https://doi.org/10.1038/s41893-025-01560-6

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