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Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance

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  • Juanjuan Tian

    (College of Energy Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Research Center for Energy Economy and Management, Xi’an University of Science and Technology, Xi’an 710054, China
    These authors contributed equally to this work.)

  • Xiaoqian Song

    (China Institute of Urban Governance, Shanghai Jiao Tong University, Shanghai 200030, China
    China SJTU-UNIDO Joint Institute of Inclusive and Sustainable Industrial Development, Shanghai Jiao Tong University, Shanghai 200030, China
    School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200030, China
    These authors contributed equally to this work.)

  • Jinsuo Zhang

    (Research Center for Energy Economy and Management, Xi’an University of Science and Technology, Xi’an 710054, China
    School of Management, Yan’an University, Yan’an 716000, China)

Abstract

The improvement in city-level carbon efficiency ( CE ) is crucial for China to achieve its CO 2 emission targets. Based on the panel data from 2003 to 2017, total factor CE values of 283 prefecture-level cities were measured using the super-efficiency SBM model. Through the exploratory spatial data analysis (ESDA), we found that the average city-level CE from 2003 to 2017 showed a “W”-type growth trend. Additionally, there are significant spatial heterogeneity and spatial dependency characteristics of city-level CE . The results of local spatial correlation analysis showed that the Low–Low clusters are distributed in all cities of Shanxi and Northern Shaanxi, and gradually expand to Inner Mongolia, Gansu, Ningxia, and Hebei over time, and the High–High clusters are mainly located in the southeast coastal cities and central and eastern Sichuan. High–Low clusters are generally scattered in cities with relatively superior political–economic status in Northeast China, North China, and Northwest China, and gradually concentrated in North China during 2003–2017. Additionally, the dynamic spatial econometric model was employed to investigate the influencing factors of CE , and we found that the city-level CE has the characteristic of path dependence on time. Factors such as industrial structure upgrading and environmental regulation have significant improvement effects on city-level CE , while technological progress, financial development, energy intensity, and government intervention can significantly inhibit city-level CE . Compared with short-term effects, the long-term effects are insignificant with higher absolute values, indicating the long-term persistence and gradual strengthening characteristics of driving factors on city-level CE ; however, the acting long-term mechanism has not been formed. Additionally, the regional spillover effect of driving factors on CE is more significant in the short term. Based on the empirical results, some policy implications for cities to improve CE are proposed.

Suggested Citation

  • Juanjuan Tian & Xiaoqian Song & Jinsuo Zhang, 2022. "Spatial-Temporal Pattern and Driving Factors of Carbon Efficiency in China: Evidence from Panel Data of Urban Governance," Energies, MDPI, vol. 15(7), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2536-:d:783321
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