Nexus Between Carbon Emissions, Energy Consumption, Urbanization and Economic Growth in Asia: Evidence From Common Correlated Effects Mean Group Estimator (CCEMG)

Abstract

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.

Probing the Effect of Governance of Tourism Development, Economic Growth, and Foreign Direct Investment on Carbon Dioxide Emissions in Africa: The African Experience

“…On the basis of these fascinating findings, the authors conclude that the selected African countries are linked through economic growth, FDI inflows, technological advancement, tourism development, and governance. Furthermore, CADF and CIPS unit root testing were adopted because of their effectiveness in stationary checking, as posited in a series of investigations [111,112,130]. Table 4 demonstrates the CADF and CIPS, revealing that it is not feasible to disprove the null hypothesis; however, none of the series (lnCO 2 , lnECG, lnFDI, lnTOUR, and lnGOV) were found to be stationed at the first difference level.…”

The Effect of Paid Maternity Leave on Child Health in the Middle East and North Africa

“…For this, due to cross-sectional dependence, the Pesaran unit root test (CIPS test) was used ( 22 ). If the panel data are cross-sectionally dependent, using panel data unit root tests such as Im, Pesaran, and Shin (IPS), Levin, Lin, and Chu (LLC), and Augmented Dickey Fuller - Fisher (ADF-Fisher) will produce misleading results and raise the risk of erroneous unit root results ( 23 ). By converting the IPS test and considering the cross-sectional dependence, Pesaran has proposed a test to check the presence or absence of a unit root, which is known as the CIPS test ( 22 ).…”

“…It should be noted that the Westerlund cointegration test can be used for both cross-sectional dependence and cross-sectional independence ( 20 ). If the cross-sectional dependence is proved, the Westerlund bootstrap method should be used ( 23 ).…”

The Impact of Urbanization on the Relationship between Carbon Storage Supply and Demand in Mega-Urban Agglomerations and Response Measures: A Case of Yangtze River Delta Region, China

“…It has been reported that urbanization has affected carbon emissions in multifaceted and complex ways [ 10 ]; such effects could be positive [ 11 ], negative [ 12 ], offering no significant effect [ 13 ], or described as a “hump-shaped” relationship [ 14 ]. Urbanization refers to a series of changes, such as population shift, land use change, lifestyle change and industrial restructuring, all of which might affect carbon emissions in different ways [ 14 , 15 , 16 , 17 ]. Furthermore, some research has focused on the impact threshold of urbanization on carbon emissions to find the optimal level of urbanization for sustainable development [ 18 ].…”