Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption

Abstract

Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005–2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China’s “double carbon” target quickly and effectively.

Do Land Use Structure Changes Impact Regional Carbon Emissions? A Spatial Econometric Study in Sichuan Basin, China

“…Because changes in land use structure and carbon emission intensity are influenced by numerous factors, we cannot rule out the possibility that there are other more relevant reasons in these places causing them to have a negative association, or is it simply a statistical error caused by data accuracy? According to some studies, China’s industrial carbon emissions have an inverted U-shaped non-linear relationship with the level of economic development [ 48 ], and it is widely assumed that economic development is consistent with the growth of carbon emissions, however, the backward stage of economic development causes more carbon emissions due to resource underutilization. Therefore, we speculate that there may be certain threshold intervals, and when the information entropy of land use structure is within these intervals, the information entropy shows a negative correlation with carbon emission intensity.…”

Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target

“…Regarding the spatial difference of CEI, the existing literature mainly uses the coefficient of variation [21,22], the Theil index [23,24], spatial autocorrelation [25,26], and the Gini coefficient [27] to reveal the differences of regional CEI. Yang [28] used the Theil index to study CEI's spatial and temporal distribution and regional differences from 2000 to 2019 in China and found that the decrease in CEI from 2000 to 2019 showed an obvious imbalance in spatial and temporal distribution, in which the gap between the North and the South was larger than that between the East and the West.…”

Effects and Spatial Spillover of Manufacturing Agglomeration on Carbon Emissions in the Yellow River Basin, China

“…Many scholars have calculated the carbon emissions of different industries and sectors, such as industry, agriculture, tourism, and transportation. Studies on carbon emissions from land use [14], carbon emissions from energy consumption [15], and embodied carbon emissions from trade [16] have also gradually increased. Regarding the influencing factors of carbon emissions, related research focuses on urbanization [17], technological innovation [18], energy consumption scale [19], and industrial agglomeration [20], these studies use the LMDI model [21], spatial econometric model [22] and threshold effect to conduct in-depth research.…”