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Improving carbon emission efficiency in Chinese manufacturing: A study considering technological heterogeneity and noise

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  • Hu, Shuo
  • Yuan, Zheng
  • Wang, Ailun

Abstract

Improving carbon emission efficiency in Chinese manufacturing is crucial for achieving dual benefits of economic growth and energy conservation. However, traditional data envelopment analysis methods fail to simultaneously consider technological heterogeneity and data noise. In this study, we propose a novel meta-frontier carbon emission efficiency indicator that accounts for both technological heterogeneity and noise, utilizing an improved Shephard carbon distance function. To uncover the sources of efficiency loss, we analyze efficiency from the perspectives of production technology and management, decomposing meta-frontier carbon emission inefficiency into technological gap inefficiency and management inefficiency. Based on this framework, we calculate the meta-frontier carbon emission efficiency for 27 manufacturing industries in China from 2011 to 2020 and analyze the underlying causes of their low carbon emission efficiency. The results indicate that Chinese manufacturing exhibits relatively low meta-frontier carbon emission efficiency with significant industrial heterogeneity. Industries with high technological levels outperform those with medium and low technological levels. The main cause of carbon emission inefficiency in Chinese manufacturing is attributed to technological gaps. Building upon these findings, we propose measures to enhance carbon emission efficiency for different manufacturing industries at varying technological levels. Our research sheds light on important patterns among different sectors.

Suggested Citation

  • Hu, Shuo & Yuan, Zheng & Wang, Ailun, 2024. "Improving carbon emission efficiency in Chinese manufacturing: A study considering technological heterogeneity and noise," Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:energy:v:291:y:2024:i:c:s0360544224001634
    DOI: 10.1016/j.energy.2024.130392
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