Spatio-temporal Evolution，Dynamic Transition，and Convergence Trend of Urban Carbon Emission Intensity in China

被引：0
|
作者：
Yang Q.-K. [1 ]
Wang L. [2 ]
Zhu G.-L. [1 ]
Li Y. [1 ]
Fan Y.-T. [1 ]
Wang Y.-Z. [2 ]
机构：
[1] School of Public Administration, Nanjing University of Finance & Economics, Nanjing
[2] Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing
来源：
Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 04期
关键词：
dynamic transition; spatial convergence; spatio-temporal evolution; urban carbon emission intensity;
D O I：
10.13227/j.hjkx.202305134
中图分类号：
学科分类号：
摘要：
Rational control of urban carbon emission intensity was of great significance for China to achieve the goal of carbon peak and carbon neutrality，to combat tackle climate change. This study used nighttime lighting data to invert urban carbon emissions in China from 2001 to 2020，calculated carbon emission intensity，and used spatial autocorrelation and convergence test models to study the spatiotemporal evolution，dynamic transition，and convergence trend of urban carbon emission intensity in China. The results showed that：① during the research period，urban carbon emission intensity in China continued to decrease，from 0. 279 tons per thousand yuan in 2001 to 0. 088 tons per thousand yuan in 2020，with an average annual decrease of 5. 94%. There was a convergence characteristic in the differences in carbon emission intensity among major regional cities. In terms of spatial distribution，the high value areas of urban carbon emission intensity were concentrated in provinces such as Northeast China，Inner Mongolia，Ningxia，and Shaanxi. The difference between the northern and southern regions was widening，and carbon emission intensity in the central，southern，and eastern regions had decreased significantly，with clear levels of high and low agglomeration. ② The global Moran’ s I of carbon emission intensity in Chinese cities was relatively high，with an average of 0. 436，indicating significant spatial autocorrelation. The probability of spatio-temporal transition between different types was relatively low，with a spatial cohesion index of 82. 57%. The spatial stability of the transition type was relatively high，and there was a spatial locking effect and‘club convergence’phenomenon in the spatiotemporal evolution of carbon emission intensity. ③ The σ convergence in China and the four major regions was not significant，but absolute β convergence and conditions of β convergence existed. The convergence speed of absolute β was different，and the rate of convergence nationwide was 3. 137%. The rate of convergence in the eastern and western regions was slightly lower，at only 3. 043% and 3. 050%，respectively. The frequent flow of factors such as people，funds，and information in the western region led to a higher convergence rate of urban carbon emission intensity. ④ The rate of conditional β convergence had accelerated. The growth rate in the eastern region was the highest，at 3. 772%. The rate of convergence in Northeast China increased slightly，only by 0. 098%. The growth rate of convergence in the central and western regions was in the middle range，at 0. 486% and 0. 661%，respectively. The impact of factors such as economic level，industrial structure，population density，foreign investment，scientific research investment，and road network density on urban carbon emission intensity showed significant heterogeneity. The increase in per capita GDP，population spatial agglomeration，and the transformation of low-carbon technologies brought about by foreign investment，as well as the inclination of fiscal investment in R&D，all had a positive effect on the convergence of urban carbon emission intensity. © 2024 Science Press. All rights reserved.
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页码：1869 / 1878
页数：9
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