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Regionalized life-cycle monetization can support the transition to sustainable rural food waste management in China

Nature Food volume 4, pages 797–809 (2023)Cite this article

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Abstract

Innovative recycling technologies can help curb food waste, yet their implementation often involves trade-offs among different environmental issues and among environmental, economic and social issues. Monetization can provide a solution to integrate all environmental impacts across the life cycle of food waste and to enable a normalized evaluation with economic accounting. Herein, a Chinese regionalized monetization model was applied to various indicators related to the environment, resource depletion and human health to assess ten typical rural food waste recycling technologies in Zhejiang province. The results reveal that biodrying and maturity and two bioconversion options are promising solutions, considering both environmental and economic impacts as well as the shifting of environmental impacts among different compartments as hidden risks. The monetization method proposed here could be applied to other sectors to support decision-making towards more sustainable development.

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Fig. 1: Flow chart for establishing the monetization framework in this work.
Fig. 2: Comparison of monetization factors.
Fig. 3: LCA results of ten rural food waste treatment technologies.
Fig. 4: Monetization results and environmental risk indexes.
Fig. 5: Economic analysis and potential trade-offs between LCA and LCC analysis results for rural food waste recycling technologies.
Fig. 6: Scenario analysis of monetization results using different substitution methods and under various policy scenarios. (Note: EPtotal are the total environmental impacts of each indicator, EPavoid are the environmental credits of products that replace the conventional products, EPnet are the values obtained by subtracting EPavoid from EPtotal)

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Data availability

The data supporting the findings of this study can be found in Supplementary Data 1. Source data are provided with this paper.

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Acknowledgements

We acknowledge support from the Westlake Education Foundation and the Research Center for Industries of the Future at Westlake University (L.W.), the Zhejiang Provincial Department of Science and Technology (grant no. 2023SDXHDX0006, L.W.), and the Key Research and Development Project of Zhejiang Province, China (grant nos 2021C03024 and 2022C03001, W.W.). We thank X. Yang (at Westlake University) for his assistance in manuscript editing, the Westlake Language Center for providing their services and FanRuan Software Co, Ltd 2019 for providing free service in figure drafting.

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Author notes

  1. These authors contributed equally: Fang Liu, Liqing Xin.

Authors and Affiliations

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

    Fang Liu, Liqing Xin, Hang Tang, Yong Qin & Weixiang Wu

  2. Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, Hangzhou, China

    Fang Liu, Lei Zhang, Xinyue Dong, Yi Zhang & Lei Wang

  3. Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, China

    Fang Liu, Lei Zhang, Xinyue Dong, Yi Zhang & Lei Wang

  4. Research Center for Industries of the Future, Westlake University, Hangzhou, China

    Lei Wang

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Contributions

F.L. and L.W. conceptualized the project and devised the methodology. F.L. managed the software, wrote the original draft of the paper and visualized the data. F.L., L.X., H.T., L.Z., X.D., Y.Z., W.W. and L.W. validated the results. F.L. and L.X. conducted the formal analysis. F.L., L.X., H.T., Y.Q., L.Z., X.D. and L.W. conducted the investigation. F.L., L.X., H.T., Y.Q. and L.Z. curated the data. L.X., W.W. and L.W. revised the manuscript. W.W. and L.W. provided the resources and acquired the funding for the project. L.W. supervised the project and reviewed and edited the manuscript.

Corresponding authors

Correspondence to Weixiang Wu or Lei Wang.

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Nature Food thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Main, Results, Methods, Figs. 1–8, Tables 1–8 and references.

Reporting Summary

Supplementary Data 1

The assumptions, relevant pollution equivalent values, applicable taxes in 31 provinces, estimation details of the monetization method Chinatax, LCA and LCC results, monetization results in 31 provinces, and sensitivity analysis datasets (sheets 0 to 25).

Source data

Source Data Fig. 2

Source data for the monetization factors in ten monetization methods and for the monetization factors of ChinataxRCP in China’s 31 provinces.

Source Data Fig. 3

LCA results of ten rural food waste treatment technologies for nine environmental impact categories.

Source Data Fig. 4

Monetization results for nine rural food waste management technologies.

Source Data Fig. 5

Economic analysis source data and potential trade-offs between environmental and economic analysis results for rural food waste management technologies.

Source Data Fig. 6

Scenario analysis source data for monetization results using different substitution methods and under various policy scenarios.

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Liu, F., Xin, L., Tang, H. et al. Regionalized life-cycle monetization can support the transition to sustainable rural food waste management in China. Nat Food 4, 797–809 (2023). https://doi.org/10.1038/s43016-023-00842-6

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