Regional disparities and evolution trend of city-level carbon emission intensity in China

被引：59
作者：
Ke, Nan [1 ]
Lu, Xinhai [1 ]
Kuang, Bing [2 ]
Zhang, Xupeng [3 ]
机构：
[1] Huazhong Univ Sci & Technol, Coll Publ Adm, Wuhan 430074, Peoples R China
[2] Cent China Normal Univ, Coll Publ Adm, Wuhan 430079, Peoples R China
[3] China Univ Geosci, Sch Publ Adm, Wuhan 430074, Peoples R China
来源：
SUSTAINABLE CITIES AND SOCIETY | 2023年 / 88卷
基金：
中国国家自然科学基金;
关键词：
Carbon emission intensity; Urban green economy transition; Nighttime light data; Regional disparities; Evolution trend; CO2; EMISSIONS; DIOXIDE EMISSIONS; OECD COUNTRIES; ENERGY; DECOMPOSITION; PRODUCTIVITY; EFFICIENCY; INDUSTRY; TARGET; CITIES;
D O I：
10.1016/j.scs.2022.104288
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Carbon emission intensity (CEI) plays a crucial role in promoting urban green economy transition, which has attracted strong attention from scholars and society. However, the regional disparities and evolution trend of city-level CEI have not been yet fully investigated. This paper addresses this gap by exploring the regional disparities and evolution trend of China's city-level CEI from 2000 to 2017, based on nighttime light data and the methods of Dagum Gini coefficient, exploratory spatial data analysis (ESDA), kernel density estimation (KDE), and spatial Markov chains. The research results revealed that China's city-level CEI showed an obvious decline trend from 3.677 kgCO2/USD in 2000 to 1.696 kgCO2/USD in 2017. There are evident regional disparities of China's city-level CEI, and inter-regional disparities are the main source. China's city-level CEI shows a positive spatial autocorrelation, and there are obvious differences in the distribution rules of cities with different spatial agglomeration forms. Moreover, China's city-level CEI presents obvious polarization and siphon effect. This paper therefore formulates the differentiated urban green economy transition strategies based on the regional disparities and evolution trend of China's city-level CEI.
引用
收藏
页数：11
相关论文
共 70 条
[1]   Modelling carbon emission intensity: Application of artificial neural network [J].
Acheampong, Alex O. ;
Boateng, Emmanuel B. .
JOURNAL OF CLEANER PRODUCTION, 2019, 225 :833-856
[2]   Proximity effects in obesity rates in the US: A Spatial Markov Chains approach [J].
Agovino, Massimiliano ;
Crociata, Alessandro ;
Sacco, Pier Luigi .
SOCIAL SCIENCE & MEDICINE, 2019, 220 :301-311
[3]   Process-to-Policy (P2Pol): using carbon emission pinch analysis (CEPA) tools for policy-making in the energy sector [J].
Andiappan, Viknesh ;
Foo, Dominic C. Y. ;
Tan, Raymond R. .
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY, 2019, 21 (07) :1383-1388
[4]   Carbon emission intensity in electricity production: A global analysis [J].
Ang, B. W. ;
Su, Bin .
ENERGY POLICY, 2016, 94 :56-63
[5]  
[Anonymous], 2021, Outline of the 14th Five -Year Plan (2021-2025) for National Economic and Social Development and Vision 2035 of the Peoples Republic of China
[6]   GeoDa:: An introduction to spatial data analysis [J].
Anselin, L ;
Syabri, I ;
Kho, Y .
GEOGRAPHICAL ANALYSIS, 2006, 38 (01) :5-22
[7]   LOCAL INDICATORS OF SPATIAL ASSOCIATION - LISA [J].
ANSELIN, L .
GEOGRAPHICAL ANALYSIS, 1995, 27 (02) :93-115
[8]   Spatiotemporal carbon emissions across the spectrum of Chinese cities: Insights from socioeconomic characteristics and ecological capacity [J].
Chen, Boyang ;
Xu, Chong ;
Wu, Yinyin ;
Li, Zhiwen ;
Song, Malin ;
Shen, Zhiyang .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2022, 306
[9]   Decomposition and prediction of China's carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level [J].
Chen, Hao ;
Qi, Shaozhou ;
Tan, Xiujie .
SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 825
[10]   County-level CO2 emissions and sequestration in China during 1997-2017 [J].
Chen, Jiandong ;
Gao, Ming ;
Cheng, Shulei ;
Hou, Wenxuan ;
Song, Malin ;
Liu, Xin ;
Liu, Yu ;
Shan, Yuli .
SCIENTIFIC DATA, 2020, 7 (01)
1234567