Abstract

Industrial carbon emissions efficiency requires a balance between economic development and carbon reduction to be achieved through structural adjustment and technological change, which is the key to low-carbon economic development and the essence of China’s carbon reduction commitment. Existing research has focused more on static carbon emission efficiency and provides insufficient analysis of the internal causes of inter-regional differences in carbon emission efficiency. This paper aims to fill this gap in the existing research. The data of 30 provinces in China, obtained from the Chinese Official Statistical Yearbook, were taken as the sample. We accurately measured the dynamic carbon emission efficiency of industrial energy (DCEEIE) in China by combining the three-stage slacks-based measure data envelopment analysis (SBM-DEA) model and the Malmquist-Luenberger model. This study further uses Dagum’s Gini coefficient decomposition and the panel vector auto regression (PVAR) model to empirically estimate regional differences in DCEEIE and the effects of its internal structure. The results show, first, that DCEEIE in China is increasing, and that overall energy efficiency is at a high level. Although both efficiency change and technological change are fundamental to efficiency, an attenuation of efficiency and a diffusion of technology are evident. Second, the total Gini coefficients of dynamic efficiency, efficiency change (EC), and technological change (TC) are all decreasing; regional differences and transvariation intensity are the main reasons for long-term gaps. Third, TC improves dynamic efficiency in the initial stages, but the main driving force in the long run is EC. The limitation of this paper is that we do not fully discuss the external factors which might impact TC and EC, nor do we continue the discussion on how to transform the energy mix by improving carbon efficiency, which is the future direction for green development. These issues represent avenues for future research.

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