CO2 emissions are first aggravated and then alleviated with economic growth in China: a new multidimensional EKC analysis

Abstract

CO2 emissions have become a topical issue worldwide, but few studies have explored the relationship between CO2 emissions and income by establishing direct, indirect and total environmental Kuznets curves (EKCs). Using an annual panel dataset collected over the 1997-2017 period in China, this study first analyzed the spatiotemporal evolutionary process of CO2 emissions and subsequently developed direct, indirect and total EKCs based spatial Durbin model (SDM) and partial derivative approach. These results indicate that, first, CO2 emissions have characteristic positive spatial autocorrelation, with gravity centers that have shifted westward. Second, the direct EKC forms a line, while the total EKC resembles a lying-S shape as well as the total EKC, which indicates that compared to local economic growth, neighboring growth plays a very different role in impacting local CO2 emissions. Furthermore, neighboring economic growth seems to have stronger impacts on local emissions, and the turning point of the total EKC comes much earlier than that of the conventional EKC due to the spillover effects of economic growth. Finally, the growth of the population, as well as the rise of energy intensity, can stimulate CO2 emissions in both local and neighboring regions. Industrialization seems to have a nonsignificant impact on emissions changes due to the offsetting effects of the positive direct and negative indirect impacts of the share of secondary industry. Improvements in local urbanization may lead to an increase in emissions, while neighboring improvements may have stronger restricting effects; thus, urbanization improvement is beneficial to emissions reduction. This study provides more scientific information from both local and neighboring perspectives, which may differ from conventional results but still be beneficial for emissions reduction policy makers to introduce corresponding measures.

Can digital technology reduce carbon emissions? Evidence from Chinese cities

“…Alam et al (2012) based on the IPCC method, calculated the relationship between energy consumption, electricity consumption, carbon emissions and economic growth; Chang et al (2022) studied the changes in carbon emissions from 2003 to 2017 through the consumption side in China's national and regional power sectors using the logaverage index (LMDI) model and estimated the carbon emissions from the power sector in each region through the production and consumption accounting principles, using two-factor ANOVA and one-factor ANOVA. The differences in regional power sector carbon emissions were compared by two principles; Feng et al (2022) used the annual panel data of China from 1997 to 2017 to first analyze the spatial and temporal evolution process of CE, and then developed a spatial Durbin model and partial derivative method based on direct, indirect and total EKC, which yielded a positive spatial autocorrelation of CE with the center of gravity shifting westward. However, this method is difficult to calculate CE in the absence of carbon emission factor data, and the carbon emission factors may be affected by the level of technology, production status, energy use and process with large uncertainties.…”