TY - JOUR
T1 - Spatial-temporal characteristics and drivers of the regional residential CO2 emissions in China during 2000–2017
AU - Li, Hao
AU - Zhao, Yuhuan
AU - Wang, Song
AU - Liu, Ya
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/12/10
Y1 - 2020/12/10
N2 - Residential sector is an important CO2 emitter in China. This study first analyzes spatial-temporal characteristics of residential CO2 emissions (RCE) of China's 30 provinces from 2000 to 2017, and then identify the key driving forces of RCE from both temporal and spatial perspectives. Main findings includes: (1) Rural residents experienced a relatively higher growth in RCE than the urban; (2) During 2000–2017 population scale effect contributed most of 35.5% to the increase of RCE while energy structure effect was the key element to reduce RCE by 34.1%. Urbanization effect performed an inverted “U-shaped” evolution trajectory; (3) From the spatial perspective, population scale effect, energy structure effect, energy intensity effect and income improvement effect all worked for enlarging the inequality of RCE among provinces, and their average contribution degree were 31.4%, 23.1%, 22.9% and 16.4%, respectively; (4) In order to attain larger mitigation in RCE, Hebei, Inner Mongolia and Shanxi could focus more on the improvement of energy efficiency and structure while Guangdong, Shandong and Jiangsu on low-carbon consumption and travelling. The improved M-R spatial decomposition model could be applied to investigate city-level RCE, and the results help China to design targeted mitigation measures for provincial residential sector.
AB - Residential sector is an important CO2 emitter in China. This study first analyzes spatial-temporal characteristics of residential CO2 emissions (RCE) of China's 30 provinces from 2000 to 2017, and then identify the key driving forces of RCE from both temporal and spatial perspectives. Main findings includes: (1) Rural residents experienced a relatively higher growth in RCE than the urban; (2) During 2000–2017 population scale effect contributed most of 35.5% to the increase of RCE while energy structure effect was the key element to reduce RCE by 34.1%. Urbanization effect performed an inverted “U-shaped” evolution trajectory; (3) From the spatial perspective, population scale effect, energy structure effect, energy intensity effect and income improvement effect all worked for enlarging the inequality of RCE among provinces, and their average contribution degree were 31.4%, 23.1%, 22.9% and 16.4%, respectively; (4) In order to attain larger mitigation in RCE, Hebei, Inner Mongolia and Shanxi could focus more on the improvement of energy efficiency and structure while Guangdong, Shandong and Jiangsu on low-carbon consumption and travelling. The improved M-R spatial decomposition model could be applied to investigate city-level RCE, and the results help China to design targeted mitigation measures for provincial residential sector.
KW - China
KW - Driving forces
KW - LMDI
KW - M-R spatial decomposition
KW - Residential CO emissions
UR - http://www.scopus.com/inward/record.url?scp=85091626687&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2020.124116
DO - 10.1016/j.jclepro.2020.124116
M3 - Article
AN - SCOPUS:85091626687
SN - 0959-6526
VL - 276
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 124116
ER -