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Expediting co-benefits of tailored municipal solid waste management strategies globally

Nature Sustainability (2025)Cite this article

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Abstract

Managing municipal solid waste (MSW) amid global environmental and public health concerns is increasingly challenging with population growth and urbanization. Developing tailored MSW management strategies that target environmental co-benefits within diverse national development and waste composition contexts is complex and urgently required. We combine a machine-learning-derived MSW generation database with life cycle inventories of full technical modules to model a process-, component- and technology-specific MSW management system. The human health, ecosystem quality and resource scarcity co-benefits in 171 countries by 2050 are assessed in 11 scenarios integrating hierarchical management intensities with Shared Socioeconomic Pathways. Results highlight that 63.9% of health damage can be mitigated, ecosystem damage can be completely offset and reversed to net benefits, and resource benefits can increase by 137.5% during 2020–2050 in the ideal sustainability-focused scenario. Prioritizing lower- and middle-income countries (such as India), which could cumulatively contribute 40.5%, 37.8% and 27.3% of health, ecosystem and resource benefits, respectively, over 30 years, is crucial to averting prolonged damage peak and neutralization by accelerating transformation of their MSW management systems.

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Fig. 1: Life cycle flow of the MSW management system.
Fig. 2: Projected global MSW profiles under SSPs.
Fig. 3: Global distribution of waste components in 2050 under the SSP2 scenario.
Fig. 4: Comparison of MSW management modes.
Fig. 5: Damage associated with MSW management under main scenarios.
Fig. 6: Cumulative damage associated with MSW management, and mitigation potential under SSP1.

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Data availability

All data supporting this study are freely available in the published Extended Data and Supplementary Information files. Specifically, the datasets underlying Figs. 2 and 3 and Supplementary Figs. 1 and 2 can be found in Supplementary Table 19; those for Fig. 4 and Supplementary Fig. 3 are provided in Supplementary Tables 20–25; and the data for Figs. 5 and 6, Extended Data Figs. 1–3 and Supplementary Fig. 4 are contained in Supplementary Table 26. Feature‐importance rankings displayed in Extended Data Fig. 4 were generated internally from our random forest model outputs. The Supplementary Information also includes complete LCI parameters for each technical module and step-by-step protocols required to reproduce all analyses. Source data are provided with this paper.

Code availability

All custom scripts and models used in this study are publicly available in the GitHub repository (https://github.com/Kirin-Ciao/Tailored-Municipal-Solid-Waste-Management-Globally) and have been deposited in a DOI-minting repository for persistent access and citation. The random forest code (Python v.3.8+, scikit-learn, numpy and pandas) and the LCIA models for openLCA v.2.0.4 (CSV/JSON, ILCD-compatible) are available via Zenodo at https://doi.org/10.5281/zenodo.15708736 (ref. 66) and https://doi.org/10.5281/zenodo.15708746 (ref. 67). Subsequent code versions will be managed via GitHub releases and Zenodo versioning. All code is released under the Apache-2.0 license (OSI-approved) with no additional restrictions. An ILCD-compatible version of the same LCIA models is also hosted on the TianGong database and can be downloaded at https://www.tiangong.earth/data (ref. 65).

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (grant no. 2023YFC3708602) (J.S.), Outstanding Youth Fund of Natural Science Foundation of Jilin Province (grant no. 20240101030JJ) (J.S.), Key Research and Development Program of Jilin Provincial Science and Technology Department (grant no. 20230203005SF) (J.S.) and Outstanding Young Scientific and Technological Talents Program of Changchun Science and Technology Bureau (grant no. 23YQ04) (J.S.).

Author information

Authors and Affiliations

  1. Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, China

    Qilin Cao, Junnian Song, Chaoshuo Liu & Wei Yang

  2. Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun, China

    Qilin Cao, Junnian Song, Chaoshuo Liu & Wei Yang

  3. College of New Energy and Environment, Jilin University, Changchun, China

    Qilin Cao, Junnian Song, Chaoshuo Liu & Wei Yang

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

    Junnian Song, Heran Zheng & Zhifu Mi

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

    Wei Yang

  6. Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan

    Helmut Yabar

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  1. Qilin Cao
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  2. Junnian Song
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Contributions

J.S. and W.Y. conceived the study and designed the overall research framework. Q.C. performed model construction and calibration, data curation and all primary analyses. C.L. assisted in conducting the LCIA modelling. J.S., W.Y., Z.M., H.Z. and H.Y. collaboratively defined the scenario design, data visualization and uncertainty analysis. J.S. supervised the project and secured funding. Q.C. drafted the manuscript, and all authors reviewed and edited subsequent versions.

Corresponding authors

Correspondence to Junnian Song, Wei Yang or Zhifu Mi.

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Extended data

Extended Data Fig. 1 Trends in human health damage across different income brackets.

BAU, Business as Usual; EMS: Enhanced Management for Sustainability; FPS: Full Potential Scenario; RPS: Realistic Potential scenarios; SPS: Slacking Potential Scenario. HIC-I, HIC-II, and HIC-III represent groups of HICs following the JPN, the EU, and the US modes, respectively.

Source data

Extended Data Fig. 2 Trends in ecosystem quality damage across different income brackets.

BAU, Business as Usual; EMS: Enhanced Management for Sustainability; FPS: Full Potential Scenario; RPS: Realistic Potential scenarios; SPS: Slacking Potential Scenario. HIC-I, HIC-II, and HIC-III represent groups of HICs following the JPN, the EU, and the US modes, respectively.

Source data

Extended Data Fig. 3 Trends in resource scarcity damage across different income brackets.

BAU, Business as Usual; EMS: Enhanced Management for Sustainability; FPS: Full Potential Scenario; RPS: Realistic Potential scenarios; SPS: Slacking Potential Scenario. HIC-I, HIC-II, and HIC-III represent groups of HICs following the JPN, the EU, and the US modes, respectively.

Source data

Extended Data Fig. 4 Top 10 factors driving the generation of MSW and its different components.

Key demographic (genders, age and education), economic (GDP) and social (GINI and poverty) factors driving the generation of 8 MSW components. F=Female; M=Male; Age is stratified into 5-year intervals from 0 to 100 years; N/P/S/T=No/Primary/Secondary/Tertiary education level. For example, Pop_F_85–89_P denotes the female cohort aged 85–89 who have completed primary education. The correlation may not necessarily be positive, and the ranking does not imply a linear relationship.

Source data

Extended Data Fig. 5 Temporal progression of MSW management reinforcement levels in different income brackets across future patterns.

This figure displays the temporal progression of MSW management reinforcement levels among different income brackets (LIC, LMC, UMC, HIC-US/EU/JPN) under three distinct future development scenarios: Full Potential Scenario (FPS), Realistic Potential Scenario (RPS), and Slacking Potential Scenario (SPS). The reinforcement levels range from 0 (no reinforcement) to V (highest reinforcement). The arrows in the figure represent the progression of reinforced management. By advancing through levels I to V, a country in the PMM can transition to an FPS state. The reinforcement process follows a stepwise, realistic pathway, with level III to level V being the most challenging. For more detailed information on the classification of country groups and the specific reinforcement pathways, refer to the SI.

Supplementary information

Supplementary Information

Abbreviations and Supplementary results (Sections 1.1–1.3), model and system details (Sections 2.1–2.5), Figs. 1–6, Tables 1–18 and references.

Reporting Summary

Supplementary Table 19

Historical MSW generation/composition samples and SSP1–5 forecasts (171 countries, 2030–2050).

Supplementary Table 20

Detailed inventories for processing 1 ton of MSW under the Japan mode.

Supplementary Table 21

Detailed inventories for processing 1 ton of MSW under the EU mode.

Supplementary Table 22

Detailed inventories for processing 1 ton of MSW under the USA mode.

Supplementary Table 23

Detailed inventories for processing 1 ton of MSW under the China mode.

Supplementary Table 24

Detailed inventories for processing 1 ton of MSW under the India mode.

Supplementary Table 25

Detailed inventories for processing 1 ton of MSW under the permissive management mode.

Supplementary Table 26

Environmental impacts and co-benefits from effective MSW management under 11 future scenarios.

Supplementary Table 27

Grid coefficient assumptions.

Source data

Source Data Figs. 2 and 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Figs. 5 and 6 and Extended Data Figs. 1–3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

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Cao, Q., Song, J., Liu, C. et al. Expediting co-benefits of tailored municipal solid waste management strategies globally. Nat Sustain (2025). https://doi.org/10.1038/s41893-025-01613-w

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