The static and dynamic carbon emission efficiency of transport industry in China. (1st July 2023)

• Record Type: Journal Article
• Authors: Meng, Conghui; Du, Xiaoyun; Zhu, Mengcheng; Ren, Yitian; Fang, Kai
• Is Part Of: Energy. Volume 274(2023)
• Publication Date: 2023-07-01
• Subjects: Carbon emission efficiency -- Transport industry -- Super slack-based measure model -- Global malmquist-luenberger index; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2023.127297
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Transport industry is one of the major contributors to energy-related carbon emissions in China, thus becoming a topical area of concern where efforts to improve carbon emission efficiency need to be prioritized. By establishing a Static-Dynamic Efficiency (SDE) framework and using panel data from 30 provinces in China from 2010 to 2019, we develop a super slack-based measure model to assess the static efficiencies and potential for improvement of the TICEE at the provincial level, adopt the global Malmquist-Luenberger index to examine the dynamic changes in TICEE and the underlying driving factors, and propose improvement strategies by integrating the static and dynamic perspectives. We observe: (1) severe carbon emission inefficiency in transport industry in China, particularly in western, central and north-eastern regions; (2) a slow downward trend in static efficiency due to the slow progressing technological innovation; and (3) high potential for saving energy and reducing emissions in the provinces that fall into the "inefficient-deterioration" category. Our research findings extend the literature by bringing together the static and dynamic aspects into the SDE framework, as well as assist policy makers in better understanding the way to improve TICEE. Graphical abstract: Image 1 Highlights: We assess the carbon emission efficiency of transport industry in Chinese provinces. Static efficiency and improvement potential of the TICEE in China are quantified. Underlying drivers of the varying TICEE are identified by decomposing GML index. Improvement strategies are proposed by linking the static and dynamic perspectives.