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

被引:0
|
作者
Tian, Juanjuan [1 ,2 ]
Song, Xiaoqian [3 ,4 ,5 ]
Zhang, Jinsuo [2 ,6 ]
机构
[1] Xian Univ Sci & Technol, Coll Energy Sci & Engn, Xian 710054, Peoples R China
[2] Xian Univ Sci & Technol, Res Ctr Energy Econ & Management, Xian 710054, Peoples R China
[3] Shanghai Jiao Tong Univ, China Inst Urban Governance, Shanghai 200030, Peoples R China
[4] Shanghai Jiao Tong Univ, China SJTU UNIDO Joint Inst Inclus & Sustainable, Shanghai 200030, Peoples R China
[5] Shanghai Jiao Tong Univ, Sch Int & Publ Affairs, Shanghai 200030, Peoples R China
[6] Yanan Univ, Sch Management, Yanan 716000, Peoples R China
关键词
carbon efficiency; super-efficiency SBM model; spatial-temporal pattern; dynamic spatial econometric model; driving factors; CO2 EMISSION PERFORMANCE; PROVINCIAL INDUSTRIAL SECTOR; SBM-DEA MODEL; ENERGY-CONSUMPTION; TECHNOLOGICAL-PROGRESS; FINANCIAL DEVELOPMENT; ENVIRONMENTAL-POLICY; INTENSITY; TRADE; INDEX;
D O I
10.3390/en15072536
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The improvement in city-level carbon efficiency (CE) is crucial for China to achieve its CO2 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.
引用
收藏
页数:24
相关论文
共 50 条
  • [31] Urban green land use efficiency of resource-based cities in China: Multidimensional measurements, spatial-temporal changes, and driving factors
    Li, Weiming
    Cai, Zhaoyang
    Jin, Leshan
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2024, 104
  • [32] The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis
    Wu, Rong
    Li, Zhigang
    Wang, Shaojian
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 766
  • [33] Analysis of the spatial-temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt
    Jin, Yanzhi
    Zhang, Kerong
    Li, Dongyang
    Wang, Siyuan
    Liu, Wuyi
    [J]. ECOLOGICAL INDICATORS, 2024, 165
  • [34] Spatial pattern and driving factors of synergistic governance efficiency in pollution reduction and carbon reduction in Chinese cities
    Zha, Qifen
    Liu, Zhen
    Wang, Jian
    [J]. ECOLOGICAL INDICATORS, 2023, 156
  • [35] Exploring Spatial-Temporal Coupling and Its Driving Factors of Green and Low-Carbon Urban Land Use Efficiency and High-Quality Economic Development in China
    Peng, Lina
    Liang, Juan
    Wang, Kexin
    Xiao, Wenqian
    Zou, Jian
    Hong, Yuxuan
    Ding, Rui
    [J]. SUSTAINABILITY, 2024, 16 (08)
  • [36] Spatial-Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009-2023)
    Tian, Mi
    Wu, Chao
    Zhu, Xin
    Hu, Qinghai
    Wang, Xueqiu
    Sun, Binbin
    Zhou, Jian
    Wang, Wei
    Chi, Qinghua
    Liu, Hanliang
    Liu, Yuheng
    Yang, Jiwu
    Li, Xurong
    [J]. LAND, 2024, 13 (07)
  • [37] Spatial-Temporal Pattern and Influencing Factors of Drought Impacts on Agriculture in China
    Deng, Xiyuan
    Wang, Guoqing
    Yan, Haofang
    Zheng, Jintao
    Li, Xuegang
    [J]. FRONTIERS IN ENVIRONMENTAL SCIENCE, 2022, 10
  • [38] The spatial-temporal pattern evolution and influencing factors of county-scale tourism efficiency in Xinjiang, China
    Yang, Yiwan
    Zhang, Chunxiang
    Qin, Ziwei
    Cui, Yingyin
    [J]. OPEN GEOSCIENCES, 2022, 14 (01) : 1547 - 1561
  • [39] The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization
    Fu, Jun
    Ding, Rui
    Zhang, Yilin
    Zhou, Tao
    Du, Yiming
    Zhu, Yuqi
    Du, Linyu
    Peng, Lina
    Zou, Jian
    Xiao, Wenqian
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2022, 19 (23)
  • [40] Spatial-temporal distribution and key factors of urban land use ecological efficiency in the Loess Plateau of China
    Zhang, Lanyue
    Xiao, Yi
    Guo, Yimeng
    Qian, Xinmeng
    [J]. SCIENTIFIC REPORTS, 2023, 13 (01)