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Evaluation and prediction of urban low-carbon transition effectiveness based on a game-theoretic combination empowerment and overlay integration model: A study of 292 Chinese cities

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  • Niu, Dongxiao
  • Du, Ruoyun
  • Xu, Xiaomin
  • Zhao, Yanpei

Abstract

The low-carbon transition is imperative for addressing global climate change and advancing sustainable development. In response to China's “3060” strategy, Chinese provinces and cities have implemented measures to ensure sustainable development. This study constructs a low-carbon transition efficacy (LCTE) assessment system covering energy consumption and carbon emissions, energy structure and efficiency, green economy and innovation, and the urban environment and ecology. By adopting the game empowerment method, combining the BBWM and IDOCRIW methods, the approach proposed in this paper mitigates the limitations of single-method weighting. Furthermore, a Stacking model is used to predict Chinese cities' LCTE trends from 2024 to 2030. The results demonstrate an overall upward trend in LCTE across 292 Chinese cities from 2006 to 2022, with significant regional disparities. The eastern region maintains multidimensional leadership, bolstered by robust economic foundations and policy incentives; the central region exhibits steady yet gradual progress; and the western and northeastern regions encounter heightened transition pressures. The LCTE is forecast to increase modestly nationwide from 2024 to 2030. By systematically evaluating and forecasting the trend of Chinese LCTE, this study comprehensively explores the diversity and specificity of Chinese cities in the low-carbon economic transition and proposes targeted recommendations, providing an important theoretical basis for urban planning and coordinated regional development.

Suggested Citation

  • Niu, Dongxiao & Du, Ruoyun & Xu, Xiaomin & Zhao, Yanpei, 2025. "Evaluation and prediction of urban low-carbon transition effectiveness based on a game-theoretic combination empowerment and overlay integration model: A study of 292 Chinese cities," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s0360544225042549
    DOI: 10.1016/j.energy.2025.138612
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