TY - JOUR
T1 - The convergence characteristics of China's carbon intensity
T2 - Evidence from a dynamic spatial panel approach
AU - Huang, Junbing
AU - Liu, Chuanhui
AU - Chen, Shuxing
AU - Huang, Xin
AU - Hao, Yu
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/6/10
Y1 - 2019/6/10
N2 - In recent years, China's CO 2 emissions have surged as the country's economy has expanded rapidly. Faced with mounting international and domestic pressures, China has made great efforts to curb CO 2 emissions. To formulate targeted regional reduction plans, the features and characters of provincial carbon intensity should be carefully evaluated. In this study, the existence of convergence in China's carbon intensity and possible technological factors that may influence the convergence, are carefully investigated by building a united framework and employing dynamic spatial panel approach. The estimation results verify that there is significant spatial correlation in China's provincial carbon intensity. There is also evidence that the three different types of convergence (i.e., stochastic convergence, σ-convergence and β-convergence) exist during the sample period of 2000–2016. Moreover, among the technological factors of conditional β-convergence, indigenous innovation activity is most critical. Not all foreign innovations stem from the inflow of foreign direct investment and trade exert a positive effect on carbon intensity reduction. Besides, it is estimated that the spatial spillovers effects from neighboring provinces are important to carbon intensity.
AB - In recent years, China's CO 2 emissions have surged as the country's economy has expanded rapidly. Faced with mounting international and domestic pressures, China has made great efforts to curb CO 2 emissions. To formulate targeted regional reduction plans, the features and characters of provincial carbon intensity should be carefully evaluated. In this study, the existence of convergence in China's carbon intensity and possible technological factors that may influence the convergence, are carefully investigated by building a united framework and employing dynamic spatial panel approach. The estimation results verify that there is significant spatial correlation in China's provincial carbon intensity. There is also evidence that the three different types of convergence (i.e., stochastic convergence, σ-convergence and β-convergence) exist during the sample period of 2000–2016. Moreover, among the technological factors of conditional β-convergence, indigenous innovation activity is most critical. Not all foreign innovations stem from the inflow of foreign direct investment and trade exert a positive effect on carbon intensity reduction. Besides, it is estimated that the spatial spillovers effects from neighboring provinces are important to carbon intensity.
KW - Carbon intensity
KW - Convergence
KW - Dynamic spatial panel approach
KW - Technological factors
UR - http://www.scopus.com/inward/record.url?scp=85062505863&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2019.02.413
DO - 10.1016/j.scitotenv.2019.02.413
M3 - Article
C2 - 30856577
AN - SCOPUS:85062505863
SN - 0048-9697
VL - 668
SP - 685
EP - 695
JO - Science of the Total Environment
JF - Science of the Total Environment
ER -