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.

  • Article
  • Published:

Bundled measures for China’s food system transformation reveal social and environmental co-benefits

Nature Food volume 6, pages 72–84 (2025)Cite this article

Subjects

Abstract

Food systems are essential for the achievement of the United Nations Sustainable Development Goals in China. Here, using an integrated assessment modelling framework that considers country-specific pathways and covers 18 indicators, we find that most social and environmental targets for the Chinese food system under current trends are not aligned with the United Nations Agenda 2030. We further quantify the impacts of multiple measures, revealing potential trade-offs in pursuing strategies aimed at public health, environmental sustainability and livelihood improvement in isolation. Among the individual packages of measures, a shift towards healthy diets exhibits the lowest level of trade-offs, leading to improvements in nutrition, health, environment and livelihoods. In contrast, focusing efforts on climate change mitigation and ecological conservation, or promoting faster socioeconomic development alone, have trade-offs between social and environmental outcomes. These trade-offs could be minimized by bundling all three aspects of measures.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Health, environmental and livelihood indicators associated with the Chinese food system under the BASESSP2, FSTSDP_China and three package scenarios.
Fig. 2: Spatially explicit environmental indicators in the BASESSP2 scenario for the years 2020 and 2050, and in the FSTSDP_China scenario for the year 2050.
Fig. 3: Dietary patterns and agricultural employment in the BASESSP2 and FSTSDP_China scenarios in 2020, 2030 and 2050.
Fig. 4: Co-development of indicators related to public health, the environment and livelihoods in the Chinese food system.

Similar content being viewed by others

Data availability

More details 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 the GNU Affero General Public License, version 3 (AGPLv3) via GitHub at https://github.com/magpiemodel/magpie. The release used in this paper (version 4.6.5) is available via Zenodo at https://doi.org/10.5281/zenodo.7782037 (ref. 72). The technical model documentation is available at https://rse.pik-potsdam.de/doc/magpie/4.6.5/. Additional codes and details for all the participating models tailored to China can be provided upon reasonable request. Data analysis was conducted using R (version 4.1.3), GAMS (version 38.3.0) and the libraries of Potsdam Integrated Assessment Modelling via GitHub at https://github.com/pik-piam.

References

  1. Wang, X., Bodirsky, B. L., Müller, C., Chen, K. Z. & Yuan, C. The triple benefits of slimming and greening the Chinese food system. Nat. Food 3, 686–693 (2022).

    Article  PubMed  MATH  Google Scholar 

  2. Wang, X. et al. Reforming China’s fertilizer policies: implications for nitrogen pollution reduction and food security. Sustain. Sci. 18, 407–420 (2023).

    Article  MATH  Google Scholar 

  3. Yu, C. et al. Managing nitrogen to restore water quality in China. Nature 567, 516–520 (2019).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  4. He, Y. et al. The dietary transition and its association with cardiometabolic mortality among Chinese adults, 1982–2012: a cross-sectional population-based study. Lancet Diabetes Endocrinol. 7, 540–548 (2019).

    Article  PubMed  MATH  Google Scholar 

  5. Chen, H. et al. Plant-based dietary patterns in relation to mortality among older adults in China. Nat. Aging 2, 224–230 (2022).

    Article  CAS  PubMed  MATH  Google Scholar 

  6. Repositioning Nutrition as Central to Development: a Strategy for Large Scale Action (World Bank, 2006).

  7. Liu, J., Lundqvist, J., Weinberg, J. & Gustafsson, J. Food losses and waste in China and their implication for water and land. Environ. Sci. Technol. 47, 10137–10144 (2013).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  8. Gong, P. et al. Urbanisation and health in China. Lancet 379, 843–852 (2012).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  9. Zhai, F. et al. Dynamics of the Chinese diet and the role of urbanicity, 1991–2011. Obes. Rev. 15, 16–26 (2014).

    Article  PubMed  MATH  Google Scholar 

  10. UNFSS Action Tracks. United Nations https://www.un.org/en/food-systems-summit/action-tracks (2022).

  11. Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  12. Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).

    Article  PubMed  MATH  Google Scholar 

  13. Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  14. Breure, T. S. et al. A systematic review of the methodology of trade-off analysis in agriculture. Nat. Food 5, 211–220 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Soergel, B. et al. A sustainable development pathway for climate action within the UN 2030 Agenda. Nat. Clim. Change 11, 656–664 (2021).

    Article  ADS  MATH  Google Scholar 

  16. Bodirsky, B. et al. A food system transformation can enhance global health, environmental conditions and social inclusion. Preprint at https://doi.org/10.21203/rs.3.rs-2928708/v1 (2023).

  17. O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environ. Change 42, 169–180 (2017).

    Article  MATH  Google Scholar 

  18. Popp, A. et al. Land-use futures in the shared socio-economic pathways. Global Environ. Change 42, 331–345 (2017).

    Article  MATH  Google Scholar 

  19. Chinese Nutrition Society. Chinese Dietary Guidelines (eds Wang, F. et al.) (People’s Medical Publishing House, 2022).

  20. Chen, P., Li, F. & Harmer, P. Healthy China 2030: moving from blueprint to action with a new focus on public health. Lancet Public Health 4, e447 (2019).

    Article  PubMed  MATH  Google Scholar 

  21. Notice of the State Council on Printing and Distributing the Action Plan for the Prevention and Control of Soil Pollution (The State Council, 2015); http://www.gov.cn/zhengce/content/2016-05/31/content_5078377.htm

  22. Notice of the State Council on Printing and Distributing the Action Plan for the Prevention and Control of Air Pollution (The State Council, 2013); http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm

  23. Notice of the State Council on Printing and Distributing the Action Plan for the Prevention and Control of Water Pollution (The State Council, 2015); http://www.gov.cn/zhengce/content/2015-04/16/content_9613.htm

  24. Kunming–Montreal Global Biodiversity Framework. CBD/COP/15/L25 (Convention on Biological Diversity, 2022); https://www.cbd.int/conferences/2021-2022/cop-15/documents

  25. Soergel, B. et al. Combining ambitious climate policies with efforts to eradicate poverty. Nat. Commun. 12, 2342 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  26. 2021 Global Food Policy Report: Transforming Food Systems after COVID-19 (International Food Policy Research Institute, 2021); https://ebrary.ifpri.org/digital/collection/p15738coll2/id/134343

  27. Wang, X. et al. Taking account of governance: Implications for land-use dynamics, food prices, and trade patterns. Ecol. Econ. 122, 12–24 (2016).

    Article  MATH  Google Scholar 

  28. Wang, X. et al. Beyond land-use intensity: assessing future global crop productivity growth under different socioeconomic pathways. Technol. Forecast. Soc. Change 160, 120208 (2020).

    Article  Google Scholar 

  29. Evenson, R. E. & Gollin, D. Assessing the impact of the green revolution, 1960 to 2000. Science 300, 758–762 (2003).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  30. Jones, S. K., Monjeau, A., Perez-Guzman, K. & Harrison, P. A. Integrated modeling to achieve global goals: lessons from the Food, Agriculture, Biodiversity, Land-use, and Energy (FABLE) initiative. Sustain. Sci. 18, 323–333 (2023).

    Article  Google Scholar 

  31. Mosnier, A. et al. How can diverse national food and land-use priorities be reconciled with global sustainability targets? Lessons from the FABLE initiative. Sustain. Sci. 18, 335–345 (2023).

    Article  Google Scholar 

  32. Dietrich, J. P. et al. MAgPIE 4—a modular open-source framework for modeling global land systems. Geosci. Model Dev. 12, 1299–1317 (2019).

    Article  ADS  CAS  MATH  Google Scholar 

  33. 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. https://doi.org/10.1111/j.1574-0862.2008.00336.x (2008).

    Article  Google Scholar 

  34. Guidelines of the Eleventh Five-Year Plan for National Economic and Social Development (National Development and Reform Commission, 2006); https://www.gov.cn/gongbao/content/2006/content_268766.htm

  35. Opinions of Applying the Strictest Water Resources Control System (The State Council, 2012); https://www.gov.cn/zhuanti/2015-06/13/content_2878992.htm

  36. Schneider, K. R. et al. The state of food systems worldwide in the countdown to 2030. Nat. Food 4, 1090–1110 (2023).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  37. Popkin, B. M., Corvalan, C. & Grummer-Strawn, L. M. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet 395, 65–74 (2020).

    Article  PubMed  MATH  Google Scholar 

  38. Chang, J. et al. Reconciling regional nitrogen boundaries with global food security. Nat. Food 2, 700–711 (2021).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  39. Stevanović, M. et al. The impact of high-end climate change on agricultural welfare. Sci. Adv. 2, e1501452 (2016).

    Article  ADS  PubMed  PubMed Central  MATH  Google Scholar 

  40. Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science https://doi.org/10.1126/science.aal4369 (2017).

    Article  PubMed  MATH  Google Scholar 

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

    Article  ADS  MATH  Google Scholar 

  42. Fanzo, J. et al. Viewpoint: rigorous monitoring is necessary to guide food system transformation in the countdown to the 2030 global goals. Food Policy 104, 102163 (2021).

    Article  MATH  Google Scholar 

  43. Jaureguiberry, P. et al. The direct drivers of recent global anthropogenic biodiversity loss. Sci. Adv. 8, eabm9982 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Employment by Sex, Age and Economic Activity (Thousands) (ILOSTAT, 2024); https://ilostat.ilo.org/data/

  45. Agricultural Policy Monitoring and Evaluation 2022: Reforming Agricultural Policies for Climate Change Mitigation (OECD, 2022); https://doi.org/10.1787/7f4542bf-en

  46. Bodirsky, B. L. et al. The ongoing nutrition transition thwarts long-term targets for food security, public health and environmental protection. Sci. Rep. 10, 19778 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  47. Gautam, M. et al. Repurposing Agricultural Policies and Support: Options to Transform Agriculture and Food Systems to Better Serve the Health of People, Economies, and the Planet (World Bank, 2022); https://doi.org/10.1596/36875

  48. Humpenöder, F. et al. Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett. 9, 064029 (2014).

    Article  ADS  Google Scholar 

  49. Eker, S., Reese, G. & Obersteiner, M. Modelling the drivers of a widespread shift to sustainable diets. Nat. Sustain. 2, 725–735 (2019).

    Article  MATH  Google Scholar 

  50. Bauer, J. M., Bietz, S., Rauber, J. & Reisch, L. A. Nudging healthier food choices in a cafeteria setting: a sequential multi-intervention field study. Appetite 160, 105106 (2021).

    Article  PubMed  Google Scholar 

  51. Balcombe, K., Fraser, I., Lowe, B. & Souza Monteiro, D. Information customization and food choice. Am. J. Agric. Econ. 98, 54–73 (2016).

    Article  MATH  Google Scholar 

  52. Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357–23362 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Faccioli, M. et al. Combined carbon and health taxes outperform single-purpose information or fiscal measures in designing sustainable food policies. Nat. Food 3, 331–340 (2022).

    Article  CAS  PubMed  MATH  Google Scholar 

  54. 2022 Global Food Policy Report: Climate Change and Food Systems (International Food Policy Research Institute, 2022); https://ebrary.ifpri.org/digital/collection/p15738coll2/id/135889

  55. Prag, A., Lyon, T. & Russillo, A. Multiplication of Environmental Labelling and Information Schemes (ELIS): Implications for Environment and Trade (OECD, 2016); https://doi.org/10.1787/5jm0p33z27wf-en

  56. von Bloh, W. et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geosci. Model Dev. 11, 2789–2812 (2018).

    Article  ADS  MATH  Google Scholar 

  57. Scholes, R. J. & Biggs, R. A biodiversity intactness index. Nature 434, 45–49 (2005).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  58. Purvis, A. et al. in Advances in Ecological Research (eds Bohan, D. A., Dumbrell, A. J., Woodward, G. & Jackson, M.) Vol. 58, 201–241 (Academic, 2018).

  59. Baumstark, L. et al. REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits. Geosci. Model Dev. 14, 6571–6603 (2021).

    Article  ADS  CAS  MATH  Google Scholar 

  60. Springmann, M. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  61. GBD 2019 Demographics Collaborators.Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1160–1203 (2020).

    Article  Google Scholar 

  62. Aune, D. et al. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality—a systematic review and dose-response meta-analysis of prospective studies. Int. J. Epidemiol. 46, 1029–1056 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. The Global BMI Mortality Collaboration.Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet 388, 776–786 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Schwingshackl, L. et al. Food groups and risk of colorectal cancer: food groups and colorectal cancer. Int. J. Cancer 142, 1748–1758 (2018).

    Article  CAS  PubMed  MATH  Google Scholar 

  65. Bechthold, A. et al. Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit. Rev. Food Sci. Nutr 59, 1071–1090 (2019).

    Article  CAS  PubMed  MATH  Google Scholar 

  66. Yuan, Y.-Q. et al. The development of a Chinese healthy eating index and its application in the general population. Nutrients 9, 977 (2017).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  67. Ren, Y., Zhao, J., Glauben, T. & Castro Campos, B. Supermarket environment and nutrition outcomes: evidence from rural China. J. Rural Stud. 92, 79–92 (2022).

    Article  Google Scholar 

  68. Stubbendorff, A. et al. Development of an EAT–Lancet index and its relation to mortality in a Swedish population. Am J. Clin. Nutr. 115, 705–716 (2022).

    Article  PubMed  Google Scholar 

  69. Rao, N. D., Sauer, P., Gidden, M. & Riahi, K. Income inequality projections for the Shared Socioeconomic Pathways (SSPs). Futures 105, 27–39 (2019).

    Article  Google Scholar 

  70. Database collection of the Food and Agriculture Organization of the United Nations (FAOSTAT, 2015).

  71. Kreidenweis, U. et al. Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett. 11, 085001 (2016).

    Article  ADS  MATH  Google Scholar 

  72. Dietrich, J. P. et al. MAgPIE—an open source land-use modeling framework. Zenodo https://doi.org/10.5281/zenodo.7782037 (2023).

Download references

Acknowledgements

X.W., H. Cai, J.X., R.D., B.L. and M.X. were supported by the National Science Foundation of China (grant nos. 72273126, 72134006 and 72074193). L.Y. acknowledges funding from the National Science Foundation of China (grant no. 72104213), and the Fundamental Research Funds for the Central Universities. X.W., L.Y., H. Cai, J.X., R.D., B.L. and M.X. acknowledge funding from the National Key Research and Development Program of China (grant no. 2020YFA0608604). X.W., H. Cai, J.X., R.D., B.L., B.L.B., M. Stevanović, Q.C., M.C., F.B., M.X., M. Springmann, D.L., D.M.-C.C., F.H., P.v.J., B.S., J.P.D., A.P. and H.L.-C. acknowledge support from Food System Economics Commission (G-2009-01682). M. Springmann acknowledges funding from Wellcome Trust (award no. 225318/Z/22/Z). D.L. acknowledges funding from Hans-Böckler-Stiftung’s Doctoral Scholarship. X.W. acknowledges support from the Key Research Base for Humanities and Social Sciences under the Ministry of Education (22JJD790075). X.W., C.Y., H. Chen, H. Cai, J.X., R.D., B.L. and M.X. acknowledge support from the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

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

    Xiaoxi Wang, Hao Cai, Jiaqi Xuan, Ruiying Du, Bin Lin, Meng Xu & Hermann Lotze-Campen

  2. MAgPIE-China Research Group, Hangzhou, China

    Xiaoxi Wang, Hao Cai, Jiaqi Xuan, Ruiying Du, Bin Lin & Meng Xu

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

    Xiaoxi Wang, Benjamin Leon Bodirsky, Miodrag Stevanović, Quitterie Collignon, Michael Crawford, Felicitas Beier, Debbora Leip, David Meng-Chuen Chen, Florian Humpenöder, Patrick von Jeetze, Bjoern Soergel, Jan Philipp Dietrich, Christoph Müller, Alexander Popp & Hermann Lotze-Campen

  4. Department of Agricultural Economics and Rural Development, Georg-August-University Göttingen, Göttingen, Germany

    Quitterie Collignon

  5. School of Public Health, the Children’s Hospital, and National Clinical Research Center for Child Health, Zhejiang University School of Medicine, Hangzhou, China

    Changzheng Yuan & Hui Chen

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

    Changzheng Yuan

  7. Department of Land Management, School of Public Affairs, Zhejiang University, Hangzhou, China

    Lu Yu

  8. German Institute of Development and Sustainability, Bonn, Germany

    Lu Yu

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

    Felicitas Beier, Debbora Leip, Patrick von Jeetze & Hermann Lotze-Campen

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

    Marco Springmann

  11. Institute for Global Health, University College London, London, UK

    Marco Springmann

  12. IRI THESys, Humboldt-Universität zu Berlin, Berlin, Germany

    David Meng-Chuen Chen

  13. Academy of Global Food Economics and Policy, China Agricultural University, Beijing, China

    Shenggen Fan

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

    Alexander Popp

Authors
  1. Xiaoxi Wang
    View author publications

    Search author on:PubMed Google Scholar

  2. Hao Cai
    View author publications

    Search author on:PubMed Google Scholar

  3. Jiaqi Xuan
    View author publications

    Search author on:PubMed Google Scholar

  4. Ruiying Du
    View author publications

    Search author on:PubMed Google Scholar

  5. Bin Lin
    View author publications

    Search author on:PubMed Google Scholar

  6. Benjamin Leon Bodirsky
    View author publications

    Search author on:PubMed Google Scholar

  7. Miodrag Stevanović
    View author publications

    Search author on:PubMed Google Scholar

  8. Quitterie Collignon
    View author publications

    Search author on:PubMed Google Scholar

  9. Changzheng Yuan
    View author publications

    Search author on:PubMed Google Scholar

  10. Lu Yu
    View author publications

    Search author on:PubMed Google Scholar

  11. Michael Crawford
    View author publications

    Search author on:PubMed Google Scholar

  12. Felicitas Beier
    View author publications

    Search author on:PubMed Google Scholar

  13. Meng Xu
    View author publications

    Search author on:PubMed Google Scholar

  14. Hui Chen
    View author publications

    Search author on:PubMed Google Scholar

  15. Marco Springmann
    View author publications

    Search author on:PubMed Google Scholar

  16. Debbora Leip
    View author publications

    Search author on:PubMed Google Scholar

  17. David Meng-Chuen Chen
    View author publications

    Search author on:PubMed Google Scholar

  18. Florian Humpenöder
    View author publications

    Search author on:PubMed Google Scholar

  19. Patrick von Jeetze
    View author publications

    Search author on:PubMed Google Scholar

  20. Shenggen Fan
    View author publications

    Search author on:PubMed Google Scholar

  21. Bjoern Soergel
    View author publications

    Search author on:PubMed Google Scholar

  22. Jan Philipp Dietrich
    View author publications

    Search author on:PubMed Google Scholar

  23. Christoph Müller
    View author publications

    Search author on:PubMed Google Scholar

  24. Alexander Popp
    View author publications

    Search author on:PubMed Google Scholar

  25. Hermann Lotze-Campen
    View author publications

    Search author on:PubMed Google Scholar

Contributions

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

Corresponding authors

Correspondence to Xiaoxi Wang or Hermann Lotze-Campen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Ilona Otto, Kate Schneider and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Scenario design and description of the food system transformation measures.

The three package scenarios, Diets, SustEnvironment, and Livelihoods are designed based on the BASESSP2 scenario with specific measures targeting progress on public health, the environment, and livelihoods, respectively. The FSTSDP_China is a holistic food system transformation pathway including all measures in the three package scenarios. Credit: icons, Freepik.com.

Extended Data Fig. 2 Health, environmental, and livelihood indicators associated with the Chinese food system under the scenarios of BASESSP2, FSTSDP_China, three package measures, and individual measures.

The colour schemes indicate positive (green) and negative (red) impacts on indicators relative to the values in the 2050 BASESSP2 scenario. Grey cells with values indicate that these output indicators are not affected by the policy measures of the respective scenarios.

Extended Data Fig. 3 Details about co-developments of indicators related to public health, the environment, and livelihoods in the Chinese food system under the FSTSDP_China, and three package scenarios.

a, Co-developments of indicators under the FSTSDP_China scenario. b, Co-developments of indicators under the Diets scenario. c, Co-developments of indicators under the SustEnvironment scenario. d, Co-developments of indicators under the Livelihoods scenario. The red color indicates a trade-off between two indicators, while the green color indicates a co-benefit. The grey color suggests no interaction between the two indicators in this scenario. The purple color indicates both indicators are deteriorating in the respective scenario. A plus sign following the indicator’s name suggests that a higher value is favorable, while a minus sign indicates that a lower value is preferable. Notably, drivers of these co-developments are the measures within the respective policy set and we do not imply any direct correlations between each pair of indicators.

Source data

Extended Data Table 1 Key interventions and assumptions in scenarios
Full size table

Supplementary information

Supplementary Information

Supplementary Methods, sensitivity analysis, Figs. 1–27 and Tables 1–3.

Reporting Summary

Source data

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 Extended Data Fig. 3

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Cai, H., Xuan, J. et al. Bundled measures for China’s food system transformation reveal social and environmental co-benefits. Nat Food 6, 72–84 (2025). https://doi.org/10.1038/s43016-024-01100-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43016-024-01100-z

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