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Unlocking sludge co-firing synergies between thermal power and wastewater treatment plants

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Abstract

Co-firing municipal wastewater treatment plant (MWWTP) sludge in thermal-fired power plants (TFPPs) offers a low-carbon circular strategy. However, its adoption is constrained by spatio-temporal supply–demand mismatches and economic feasibility. Here we show that an optimized, plant-level monthly allocation framework can overcome these barriers, powered by a high-resolution database of 5,218 MWWTPs and 1,990 TFPPs across China. We find that surplus sludge from 3,735 MWWTPs (~87% capacity) can stably substitute coal on a monthly basis across 421 qualified TFPPs. This symbiotic network reduces total sludge volume by 79% (95% CI 55–88%) and mitigates 15.25 (10.53–17.39) MtCO2eq annually (50% of MWWTP emissions), while yielding median incremental profits of 369.41 (4.18–1,250.46) million CNY yr‒1. Scenario analysis reveals that rising fuel prices can amplify environmental and economic benefits by 11% and 135%. Crucially, despite a shrinking coal-power sector under future energy transitions, long-term co-firing network resilience is sustained through a 35–67% expansion in collaborative TFPPs. These insights provide a scalable framework for implementing cross-sectoral circular strategies tailored to regional sustainability.

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Fig. 1: Framework of the high-resolution monthly sludge allocation model for cross-sectoral scheduling from a life-cycle perspective.
Fig. 2: Cross-sectoral collaboration patterns and associated environmental and economic benefits under different synergy scenarios in China.
Fig. 3: Monthly coordination of sludge co-firing at the plant level under different synergy scenarios in China.
Fig. 4: Temporal dynamics of provincial sludge co-firing under different synergy scenarios in China.
Fig. 5: Provincial environmental benefits of cross-sectoral sludge co-processing under different synergy scenarios in China.

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Data availability

Source data are provided with this paper. The data supporting Figs. 1–5 and detailed data on TFPPs and MWWTPs are also available via figshare at https://doi.org/10.6084/m9.figshare.30994609 (ref. 69) and in the Supplementary Information. All data used for this study are provided or available from the publicly accessible sources cited.

Code availability

The codes that support the findings of this study are available via GitHub at https://github.com/JINNIL16/EnerSludge.

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Acknowledgements

We thank Y.-M. Wei, J.-N. Kang and T. Han for their insightful suggestions and comments in the early stages of this work. We are grateful to Y. Lin for her assistance with language polishing and paper editing. We also thank M. Gong, S. Liu, Y. Zhang, H. Zhang, Z. Liu, R. Qin and J. Qing from our research group for their technical support and assistance with data collection and methodological tools.

Funding

This work is supported by the National Natural Science Foundation of China (award nos. 52425005 to S.Q., 52370189 to S.Q. and 52200228 to Q.Z.), the Jing-Jin-Ji Regional Integrated Environmental Improvement National Science and Technology Major Project (award no. 2025ZD1202203 to S.Q.) and the Beijing Laboratory for System Engineering of Carbon Neutrality, Beijing Municipal Education Commission (award no. BLSECN2025001 to S.Q.). The remaining authors declare no relevant funding.

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Authors and Affiliations

  1. School of Management, Beijing Institute of Technology, Beijing, China

    Jin Lin, Qi Zhou, Linyuan Hu, YuChen Hu, Yunlei She, Yifan Song, Qiang Huang, Jiaqi Jing, Xiuya Wang & Shen Qu

  2. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, China

    Jin Lin, Qi Zhou, YuChen Hu, Yunlei She, Yifan Song, Qiang Huang, Jiaqi Jing, Xiuya Wang & Shen Qu

  3. Beijing Laboratory for System Engineering of Carbon Neutrality, Beijing Institute of Technology, Beijing, China

    Jin Lin, Qi Zhou, YuChen Hu, Yunlei She, Yifan Song, Qiang Huang, Jiaqi Jing, Xiuya Wang & Shen Qu

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Contributions

J.L. collected the data, processed the raw data, designed the study, developed the model, ran and analysed the simulations and wrote the paper. Q.Z. collected the data, conceived of and designed the study and revised the paper. L.H. developed the model and revised the paper. Y.H. and Y. She revised the paper. S.Q. conceived of the study, collected the data and provided useful suggestions for this work. Y. Song analysed the simulations and interpreted the results. Q.H. and J.J. provided environmental assessments. X.W. ran the simulations. All authors critically reviewed the paper and approved the final paper.

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Qi Zhou or Shen Qu.

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Extended data

Extended Data Fig. 1 Conceptual framework and key feasibility drivers of the monthly plant-level sludge allocation model.

The framework integrates three main dimensions into the monthly plant-level sludge allocation optimization: economic feasibility, plant operation, and technical integration. Under the objective of maximizing overall greenhouse gas abetment, the model determines retrofit-eligible thermal power plants, synergized municipal wastewater treatment plant, and dynamic sludge flow allocation across plants.

Extended Data Fig. 2 Provincial distribution and capacities of eligible MWWTPs involved in cross-sectoral sludge co-firing under different scenarios.

The upper panel displays the number of MWWTPs by province, and the lower panel shows their total treatment capacity. Results are presented for four scenario groups (T1C1–T2C2) within each province and categorized by the median monthly stable sludge supply ratio. Detailed results can be found in the Source Data.

Source Data

Extended Data Fig. 3 Provincial distribution and capacities of eligible CFPPs involved in cross-sectoral sludge co-firing under different scenarios.

The upper panel shows the number of CFPPs by province, and the lower panel displays their total generation capacity. Results are presented for four scenario groups (T1C1–T2C2) within each province and categorized by the median monthly stable sludge co-firing ratio and capacity level. Detailed results can be found in the Source Data.

Source Data

Extended Data Fig. 4 Provincial environmental benefits of cross-sectoral sludge co-processing under all synergy scenarios.

The upper panel shows annual provincial sludge co-firing volumes under T1C1–T2C2 scenarios; the lower panel shows annual provincial GHG emission reductions under T1C1–T2C2 scenarios. For all box plots, the center horizontal line indicates the median; the lower and upper limits of the box represent the 25th and 75th percentiles, respectively; and the whiskers extend to the minimum and maximum data points within 1.5 times the interquartile range from the box (n = 1,000 independent Monte Carlo simulation runs for each scenario; outliers are not shown). Detailed results can be found in the Source Data.

Source Data

Extended Data Fig. 5 Analysis of regional drivers for economic synergies in provincial sludge co-firing based on a two-stage ML-SHAP framework.

(a) Interpretation of the first-stage model results, analyzing dominant drivers for the probability of positive incremental profit (provincial incremental profit > 0); (b) Interpretation of the second-stage model results, analyzing dominant drivers for the magnitude of economic synergies. The left column presents the T1C1 scenario (current coal price), and the right column presents the T1C2 scenario (rising coal price). Detailed results can be found in the Source Data.

Source Data

Supplementary information

Supplementary Information (download PDF )

Supplementary Methods 1–6, Figs. 1–10 and Tables 1–21.

Reporting Summary (download PDF )

Peer Review File (download PDF )

Source data

Source Data Fig. 2 (download XLSX )

Statistical source data (model simulation results).

Source Data Fig. 3 (download XLSX )

Statistical source data (plant-level results).

Source Data Fig. 4 (download XLSX )

Statistical source data (provincial dynamics).

Source Data Fig. 5 (download XLSX )

Statistical source data (provincial benefits).

Source Data Extended Data Fig. 2 (download XLSX )

Statistical source data (provincial distribution and capacities of eligible MWWTPs).

Source Data Extended Data Fig. 3 (download XLSX )

Statistical source data (provincial distribution and capacities of eligible CFPPs).

Source Data Extended Data Fig. 4 (download XLSX )

Statistical source data (provincial environmental benefits).

Source Data Extended Data Fig. 5 (download XLSX )

Statistical source data (SHAP analysis).

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Lin, J., Zhou, Q., Hu, L. et al. Unlocking sludge co-firing synergies between thermal power and wastewater treatment plants.
Nat Water 4, 979–991 (2026). https://doi.org/10.1038/s44221-026-00697-8

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