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

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