The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation

Ruijing Zheng, Yu Cheng, Haimeng Liu, Wei Chen, Xiaodong Chen and Yaping Wang
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Ruijing Zheng: College of Geography and Environment, Shandong Normal University, Jinan 250358, China
Yu Cheng: College of Geography and Environment, Shandong Normal University, Jinan 250358, China
Haimeng Liu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Wei Chen: College of Geography and Environment, Shandong Normal University, Jinan 250358, China
Xiaodong Chen: College of Management, Sichuan Anticultural University, Chengdu 611130, China
Yaping Wang: College of Geography and Environment, Shandong Normal University, Jinan 250358, China

IJERPH, 2022, vol. 19, issue 15, 1-22

Abstract: Urban agglomerations have become the core areas for carbon reduction in China since they account for around 75% of its total emissions. Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and the Pearl River Delta (PRD), which are its most important poles of regional development and technological innovation, are key to achieving China’s carbon peak emissions target. Based on the panel data of these three major urban agglomerations from 2003 to 2017, this study estimated the carbon emission efficiency (CEE) by the super-efficiency slacks-based measure (super-SBM) model and analyzed its spatiotemporal distribution pattern. The Dagum Gini coefficient was used to evaluate the difference in CEE between the three major agglomerations, while panel data models were established to analyze the impact of technological innovation on the three agglomerations. The overall CEE showed an upward trend during the study period, with significant spatial and temporal variations. Additionally, the main source of urban agglomeration difference in CEE evolved from inter-regional net differences to intensity of transvariation. While technological innovations are expected to significantly improve CEE, their effect varies among urban agglomerations. These results provide policymakers with insights on the collaborative planning of urban agglomerations and the low-carbon economy.

Keywords: carbon emission efficiency; super-SBM model; spatiotemporal pattern; technological innovation; panel data models; urban agglomerations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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