Regional Differences in Fossil Energy-Related Carbon Emissions in China’s Eight Economic Regions: Based on the Theil Index and PLS-VIP Method

Abstract

Determining differences in regional carbon emissions and the factors that affect these differences is important in the realization of differentiated emissions mitigation policies. This paper adopts the Theil index and the partial least square-variable importance of projection (PLS-VIP) method to analyze the change characteristics, regional differences and causes of carbon emissions, as well as the extent to which various factors influenced carbon emissions in China’s eight economic regions in 2005–2017. The results indicate that (1) during the study period, carbon emissions in the eight economic regions displayed a rigid uptrend with a phased characteristic. The growth rates of carbon emissions were different across the studied regions. (2) The overall difference in regional carbon emissions showed an increasing trend, mainly owing to increasing interregional differences. (3) The extent of the influence and explanatory ability of each factor on regional carbon emissions and discrepancies in carbon emissions were different. Population size, economic development, and energy intensity were found to be the three main factors influencing regional carbon emission changes. Industrial structure and urbanization were also contributors to regional differences in emissions. The influence of energy structure on regional carbon emissions and its explanatory power were weak on the whole, but its elastic coefficients and VIP values changed significantly. Finally, regionally targeted proposals for emissions mitigation are offered.

Analysis of carbon emission performance and regional differences in China’s eight economic regions: Based on the super-efficiency SBM model and the Theil index

“…(3) From the perspective of differences in carbon emission performance, the overall differences in China’s eight economic regions’ carbon emission performance show a fluctuating upward trend. The contribution rate of inter-regional difference shows a slight upward trend, while the contribution rate of intra- regional difference by a downward trend is also consistent with Liu [ 46 ]. Among them, ERMRYR has the highest contribution rate, and the contribution rates of inter-regional and intra-regional differences to the whole country are as high as 43.46% and 67.98%, respectively.…”

Socioeconomic Determinants of Health and Their Unequal Distribution in Poland

“…Compared with the Gini coefficient, when estimating regional differences, the Theil index allows sub-groups to be broken down within the context of larger groups. Thus, it is possible to analyse their contribution to the total differences and to identify the main sources of the overall differences [89]. This is an important property of the Theil index measure, as this additive decomposability implies that the aggregate inequality measure can be broken down into inequality within and between any defined population subgroups [90].…”

Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

“…In this formula, n and m represent the number of regions and the number of provinces within the region, respectively; x i represents the proportion of carbon emissions from agricultural production in region i to total carbon emissions from agricultural production in China; x ij represents the proportion of carbon emissions from agricultural production in province j within region i to total carbon emissions from agricultural production in China; d i represents the ratio of the intensity of carbon emissions from agricultural production in region i to total carbon emissions intensity from agricultural production in China; d ij represents the ratio of the intensity of carbon emissions from agricultural production in province j within region i to total carbon emissions from agricultural production in China. Meanwhile, in order to calculate the contribution of different differences from the overall difference, we used the quotient expression contribution of each difference index and the overall difference index to calculate [ 32 ].…”