Analysis of the spatial-temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt

被引:0
|
作者
Jin, Yanzhi [1 ]
Zhang, Kerong [1 ,3 ,4 ]
Li, Dongyang [1 ]
Wang, Siyuan [1 ]
Liu, Wuyi [2 ,5 ,6 ]
机构
[1] Fuyang Normal Univ, Sch Business, Fuyang 236037, Peoples R China
[2] Fuyang Normal Univ, Sch Biol Sci & Food Engn, Fuyang 236037, Peoples R China
[3] Fuyang Normal Univ, Sch Business, Fuyang 236037, Peoples R China
[4] Fuyang Normal Univ, Sch Business, Qing He West Rd 100, Fuyang 236037, Peoples R China
[5] Fuyang Normal Univ, Sch Biol Sci & Food Engn, Fuyang 236037, Peoples R China
[6] Fuyang Normal Univ, Sch Biol Sci & Food Engn, Qing He West Rd 100, Fuyang 236037, Peoples R China
关键词
Yangtze River Economic Belt; Carbon Emission Efficiency; Spatial and Temporal Evolution; Driving Factors; ENERGY-CONSUMPTION; CO2; EMISSIONS; CHINA; URBANIZATION;
D O I
10.1016/j.ecolind.2024.112092
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial-temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial-temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor's effecting intensity and impacting direction, as well as the dual-factors' interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006-2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of "downstream areas > midstream areas > upstream areas", "high in the east and low in the west", "hot in the east and cold in the west", while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.
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页数:13
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