Assessing urban carbon health in China's three largest urban agglomerations: Carbon emissions, energy-carbon emission efficiency and carbon sinks

Angzu Cai, Ru Guo, Yuhao Zhang, Leyi Wang, Ruimin Lin, Haoran Wu, Runyao Huang, Jing Zhang and Jiang Wu

Applied Energy, 2025, vol. 383, issue C, No S030626192500056X

Abstract: Energy consumption is the most important source of carbon emissions in China. Research on carbon emissions is of great significance for energy transition and regional sustainable development. Urban agglomerations (UAs) in China face significant challenges, including uneven development, rising carbon emissions, and encroachment on ecological green spaces. This study introduces a novel index, Carbon Health Index (CHI), to comprehensively evaluate the interrelated dynamics within the carbon emission intensity-energy carbon emission efficiency‑carbon sink (IES) system. The term “health” in this context reflects the balance and sustainability of the carbon system, where lower carbon emission intensity (negative impact), higher energy carbon emission efficiency (positive impact), and enhanced carbon sink capacity (positive impact) indicate a more sustainable and environmentally balanced system. Taking the three largest UAs in China as a case, this study explores the interrelationships within the carbon system. The study employed a coupling index and geostatistical methods to calculate the CHI and its spatial autocorrelation over the two-decade period. Machine learning methods were then used to assess the impact of driving factors on CHI. Key findings indicate that during the study period, the carbon emission intensity of the three UAs decreased by an average of 66.11 % compared to 2000. Energy carbon emission efficiency improved, with an average growth rate of 1.42 %, while carbon sink intensity remained relatively stable. The CHI exhibited a positive growth trend, with an average annual increase of 8.22 %. Spatial analysis revealed a pattern of higher CHI values on the peripheries and lower values at the core of these UAs. The primary positive contributors to CHI were GDP and ecological carbon sink land, whereas energy consumption was a significant negative contributor. This study concludes that optimizing energy structure, enhancing ecological carbon sequestration capacity, and strengthening regional collaborative governance are essential strategies for decoupling regional carbon emissions from economic growth and improving the CHI.

Keywords: Energy consumption; Carbon emission; Urban agglomeration; Machine learning; Driving factor (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125326

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