期刊
FRONTIERS IN ENERGY RESEARCH
卷 8, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fenrg.2020.610577
关键词
cross-sectional dependence; Westerlund panel cointegration; common correlated effects mean group; economic growth; carbon emissions; urbanization; energy consumption
The study examines the dynamic relationships between carbon emission, urbanization, energy consumption, and economic growth in 42 Asian countries from 2000 to 2014 using dynamic common correlated effects panel data modeling. Results show high average coefficient of 0.602 between carbon emission and energy consumption, indicating cointegration among carbon emission, urbanization, energy consumption, and economic growth. Robust to PMG-ARDL estimator, findings demonstrate long run relationships attributed to carbon taxation and energy prices, with implications for renewable energy policy and urban planning insights for policymakers.
This study investigates the dynamic relationships between carbon emission, urbanization, energy consumption, and economic growth in a panel of 42 Asian countries for the period 2000-2014 using dynamic common correlated effects panel data modeling. This study employs second generation cross-sectional Pesaran (J. Appl. Econom., 2007, 22(2), 265-312) panel unit root, Westerlund panel cointegration tests (Econom. Stat., 2007, 69(6), 709-748), and Pesaran's (Econometrica, 2006, 74(4), 967-1012) common correlated effects mean group estimation technique. These approaches allow for cross-sectional dependence, and are robust to the presence of common factors, serial correlation, and slope heterogeneity. The Common Correlated Effect Mean Group test reveals a high average coefficient of 0.602 between carbon emission and energy consumption while low coefficients of 0.114 and 0.184 for the pairs of carbon emission-urbanization and carbon emission-GDP, respectively for the panel as a whole, suggesting a cointegration between carbon emission, urbanization, energy consumption, and economic growth. The results indicate that there is relatively high carbon emission especially for highly populated and geopolitical risk Asian countries in the short run. Findings reveal long run relationships between the variables, which is attributed to the on-going carbon taxation and energy prices. Our results are robust to PMG-ARDL estimator. Overall, these findings cast important implications on renewable energy policy and urban planning insights for the policymakers.
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