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Health burden from food systems is highly unequal across income groups

Nature Food volume 5, pages 251–261 (2024)Cite this article

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Abstract

Food consumption contributes to the degradation of air quality in regions where food is produced, creating a contrast between the health burden caused by a specific population through its food consumption and that faced by this same population as a consequence of food production activities. Here we explore this inequality within China’s food system by linking air-pollution-related health burden from production to consumption, at high levels of spatial and sectorial granularity. We find that low-income groups bear a 70% higher air-pollution-related health burden from food production than from food consumption, while high-income groups benefit from a 29% lower health burden relative to their food consumption. This discrepancy largely stems from a concentration of low-income residents in food production areas, exposed to higher emissions from agriculture. Comprehensive interventions targeting both production and consumption sides can effectively reduce health damages and concurrently mitigate associated inequalities, while singular interventions exhibit limited efficacy.

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Fig. 1: Provincial-level distributions and inequalities of premature mortalities due to food production and consumption in China.
Fig. 2: Inequality within the food system across food types.
Fig. 3: Mortality disparity across income groups.
Fig. 4: The net gap of premature mortalities between production and consumption (net ΔM) is shown for each pair of income groups.
Fig. 5: The changes in food system inequality and premature mortalities in response to different intervention strategies.

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Data availability

The data supporting the findings of this study are available within the paper and Supplementary Information. Population, residents’ disposable income and food intake data at the site scale from the National Bureau of Statistics of China are available at http://data.stats.gov.cn/. The China MRIO tables are available at http://www.ceads.net/data/input_output_tables/. Livestock feeding data are derived from https://www.fao.org/faostat/. Source data for premature mortalities are accessible via Zenodo: https://doi.org/10.5281/zenodo.10645774.https://doi.org/10.5281/zenodo.10645774.

Code availability

Python 3.8 was used for developing the EEIOA and for data analysis. The source codes used in this study are accessible via Zenodo: https://doi.org/10.5281/zenodo.10645774.https://doi.org/10.5281/zenodo.10645774.

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Acknowledgements

G.S. and H.S. acknowledge funding from the Ministry of Science and Technology of the People’s Republic of China (2023YFE0112900). H.S. acknowledges funding from the National Natural Science Foundation of China (42192510). F.Z. acknowledges funding from the National Natural Science Foundation of China (42225102). S.T. acknowledges funding from the National Natural Science Foundation of China (41991312, 41821005 and 41830641). T.-M.F. and H.S. acknowledge funding from the Shenzhen Science and Technology Program (JCYJ20220818100611024). G.S. acknowledges funding from the National Natural Science Foundation of China (42077328). X.Y., T.-M.F. and H.S. acknowledge funding from the Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks (ZDSYS20220606100604008), Department of Science and Technology of Guangdong Province (2021B1212050024), and Department of Education of Guangdong Province (2021KCXTD004). H.S. acknowledges support from the Center for Computational Science and Engineering at Southern University of Science and Technology.

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Authors and Affiliations

  1. Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China

    Lianming Zheng, Yilin Chen, Peng Guo, Jinling He, Chen Wang, Jianhuai Ye, Lei Zhu, Tzung-May Fu, Xin Yang, Shu Tao & Huizhong Shen

  2. Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Southern University of Science and Technology, Shenzhen, China

    Lianming Zheng, Yilin Chen, Peng Guo, Jinling He, Chen Wang, Jianhuai Ye, Lei Zhu, Tzung-May Fu, Xin Yang, Shu Tao & Huizhong Shen

  3. Institute of Carbon Neutrality, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Wulahati Adalibieke, Feng Zhou, Yuanzheng Zhang, Guofeng Shen & Shu Tao

  4. College of Geography and Remote Sensing, Hohai University, Nanjing, China

    Feng Zhou

  5. School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK

    Pan He

  6. School of Urban Planning and Design, Peking University, Shenzhen Graduate School, Shenzhen, China

    Yilin Chen

  7. Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China

    Peng Xu

  8. Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada

    Shunliu Zhao & Amir Hakami

  9. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Armistead G. Russell

  10. The Bartlett School of Sustainable Construction, University College London, London, UK

    Jing Meng

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  1. Lianming Zheng
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Contributions

H.S., J.M., P.H. and F.Z. conceived and initiated the study. L. Zheng and W.A. processed and analysed the data. Y.C., P.G., J.H. and Y.Z. provided support with data collection and processing. P.X., C.W., J.Y. and L. Zhu assisted in the development of the model framework. L. Zheng drafted the paper, and G.S., T.-M.F. and X.Y. participated in the result discussions. H.S., F.Z., J.M., P.H., S.Z., A.H., S.T. and A.G.R. provided critical revisions.

Corresponding authors

Correspondence to Feng Zhou, Pan He, Jing Meng or Huizhong Shen.

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Nature Food thanks Monica Crippa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–28, Text 1–4 and Tables 1–10.

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Zheng, L., Adalibieke, W., Zhou, F. et al. Health burden from food systems is highly unequal across income groups. Nat Food 5, 251–261 (2024). https://doi.org/10.1038/s43016-024-00946-7

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