Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China

Abstract

Estimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and analyses the driving force of carbon emission growth using Henan Province as a case study. Based on the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82 percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and northern regions and low emissions in the southern. However, their carbon emissions have constantly decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of carbon emission reduction programs.

Keywords

• carbon emission, driving force analysis, down-scaling temporal and spatial, distribution