Examining the spatiotemporal variations and inequality of China’s provincial CO2 emissions

Tremendous energy consumption appears as rapid economic development, leading to large amount of CO₂ emissions. Although plentiful studies have been made into the driving factors of CO₂ emissions, the existing literatures that take the spatial differences and temporal changes into consideration are few. Therefore, this study first analyzes the variations of total CO₂ emissions’ spatial distribution from 2008 to 2017 and present the changes of driving factors, finding significant spatial autocorrelation in CO₂ emissions at the province level, and that urbanization rate, per capita GDP and per capita CO₂ emissions increased significantly, but energy consumption structure and trade openness decreased. We then compared the effects of different factors affecting CO₂ emissions, using classic linear regression model, panel data model, and the geographically weighted regression (GWR) model, and the three models roughly agree on the effects of factors. The GWR model considering spatial heterogeneity provides more detailed results. Population, urbanization rate, per capita carbon emissions, energy consumption structure, and trade openness all have positive effects, while per capita GDP has a two-way impact on CO₂ emissions. The influence of urbanization rate and energy consumption structure in the central and western regions increased even faster than in eastern regions, and the impacts of trade openness in lower and higher opening areas are more significant. The population and per capita CO₂ emission have declining influences, among which the influence of population in coastal areas declined more slowly, while the rate of decline of per capita CO₂ emission was positively correlated with the local total CO₂ emissions. The Lorenz curve and the Gini coefficient reveal the inequality distribution of CO₂ emissions in various regions, with the highest CO₂ emissions growth in the medium-economic-level areas, where the key area of carbon mitigation is. Finally, per capita GDP reveals that China as a whole has the trend of inverted N-shape Kuznets curve, and the underdeveloped regions are in the rising stage between the two inflection points, while developed regions are at the end of the rising stage and about to reach the second inflection point.