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<p>Municipal solid waste (MSW) management is a significant source of greenhouse gas (GHG) emissions, posing a major challenge for urban sustainability. This study presents the first comprehensive assessment of GHG emissions from MSW treatment in Taizhou, China, from 2010 to 2023, based on the IPCC 2006 guidelines and local inventories. To enhance the analytical depth, this research integrates the Logarithmic Mean Divisia Index (LMDI) decomposition method with a Grey prediction GM(1,1) model to identify driving factors and forecast future emissions. The results indicate that emissions increased from 62.55 × 10<sup>4</sup> tons to 72.37 × 10<sup>4</sup> tons CO<sub>2</sub>-eq. Economic output and population size were the primary drivers, while the treatment pattern was the strongest inhibiting factor. Spatial analysis revealed significant disparities across districts, correlating with economic activity. The GM(1,1) model projects a rise to 104.89 × 10<sup>4</sup> tons by 2035 under a business-as-usual scenario. Furthermore, a mitigation scenario demonstrates the significant potential of policy interventions. The study concludes that enhancing incineration efficiency, strictly enforcing waste classification, and exploring carbon capture technologies are crucial strategies for Taizhou to mitigate future emissions and align its waste sector with carbon neutrality goals.</p>

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Study on greenhouse gas emission characteristics and emission prediction of municipal solid waste treatment in Taizhou

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Kejia Lu,
  • Zhiyi Shu,
  • Yuanyuan Wang,
  • Taoxiang Wang,
  • Thebe Sosome,
  • Guanghua Xia,
  • Ying Lu

摘要

Municipal solid waste (MSW) management is a significant source of greenhouse gas (GHG) emissions, posing a major challenge for urban sustainability. This study presents the first comprehensive assessment of GHG emissions from MSW treatment in Taizhou, China, from 2010 to 2023, based on the IPCC 2006 guidelines and local inventories. To enhance the analytical depth, this research integrates the Logarithmic Mean Divisia Index (LMDI) decomposition method with a Grey prediction GM(1,1) model to identify driving factors and forecast future emissions. The results indicate that emissions increased from 62.55 × 104 tons to 72.37 × 104 tons CO2-eq. Economic output and population size were the primary drivers, while the treatment pattern was the strongest inhibiting factor. Spatial analysis revealed significant disparities across districts, correlating with economic activity. The GM(1,1) model projects a rise to 104.89 × 104 tons by 2035 under a business-as-usual scenario. Furthermore, a mitigation scenario demonstrates the significant potential of policy interventions. The study concludes that enhancing incineration efficiency, strictly enforcing waste classification, and exploring carbon capture technologies are crucial strategies for Taizhou to mitigate future emissions and align its waste sector with carbon neutrality goals.