Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province

Abstract

China has committed to peaking carbon dioxide emissions by 2030 and has set a goal of working towards carbon neutrality by 2060. Hunan province is a vital undertaking place for national industrial transfer. It is of great significance for promoting energy conservation and emission reduction to investigate the influencing factors and spatial effects of carbon emissions in Hunan province. Firstly, based on the energy consumption data of Hunan province from 2005 to 2017, this paper uses the method recommended by the Intergovernmental Panel on Climate Change (IPCC) to measure the carbon emissions of Hunan province and its economic zones. Secondly, the five-factor Logarithmic Mean Divisia Index (LMDI) model is constructed to analyze the influence degree of population size, economic development, industrial structure, energy intensity, and energy structure on carbon emissions. Finally, the spatial differences of the influencing factors in the four economic zones of Hunan province are analyzed. The research shows that: (1) An overall carbon emission reduction has been achieved in Hunan province since 2011. (2) Changsha–Zhuzhou–Xiangtan Economic Zone is the key area to achieve carbon emission reduction, while there is still the phenomenon of emission increase in the other three economic zones. (3) For all economic zones, economic development contributes the most to the increase in carbon emissions, while energy intensity shows the strongest inhibitory effect. Other factors have various effects on the four economic zones.

Decomposing and Decoupling the Energy-Related Carbon Emissions in the Beijing–Tianjin–Hebei Region Using the Extended LMDI and Tapio Index Model

“…In line with the findings of Peng and Liu [13], Qin et al [20] showed that the main factors promoting and hindering Xinjiang's carbon emissions were also economic development and energy intensity effects, respectively. Similar findings were obtained by Yang et al [18] and Wang et al [19], who studied the drivers of carbon emissions in Hunan and Guangdong provinces, respectively.…”

“…Dong et al [15] pointed out that the intense economic activity in Northwest China is the main reason for the growth of carbon emissions, and the current extensive economic development mode urgently needs to be amended. In addition, scholars [16][17][18][19] analysed the components of carbon emissions from a provincial perspective. Xia et al [17] found that the inhibitory effect of reducing energy intensity on carbon emissions in Zhejiang transitioned from being unable to offset the emissions-promoting effect of economic development to offsetting it, which indicated that Zhejiang had some success in achieving a low-carbon development.…”

Assessing Carbon Emissions from Animal Husbandry in China: Trends, Regional Variations and Mitigation Strategies

“…Their study's findings indicate that although carbon intensity is the primary factor regulating carbon emissions, population growth and the economy are the primary factors causing emissions to rise. Yang et al conducted an analysis of the impact of population size, economic development, industrial structure, energy intensity, and energy structure on carbon emissions in Hunan Province using an LMDI model [25]. According to the findings, Hunan Province's economic expansion has the biggest influence on rising carbon emissions, while energy consumption intensity is the biggest deterrent.…”

Can structure optimization of mining industry reduce carbon emission in China? Evidence from kernel density estimation and PVAR approach

“…In the context of carbon neutrality, Chen et al investigated the decoupling relationship between carbon emissions and economic growth in China's mining industry and the results revealed that the intensity of carbon emissions from the industry has decreased and that the relationship between its carbon emissions and economic growth is inverted U-shaped (Chen and Yan, 2022). (Yang et al,2022) (Xu et al, 2017). Some scholars have used vector autoregressive models (VAR), spatial econometric models, and geographically weighted regression models to explore the impact of various factors on carbon emissions.…”