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.

The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies

“…Total carbon emissions ascended from 449,470.14 million tons in 2004 to 1,057,492.36 million tons in 2020, presenting an annual growth rate of 5.1%. 2 With respect to the spatio-temporal evolutionary characteristics, and using the 'green economy' as a pivot, energy carbon emissions manifested a swift upward trajectory from 2004 to 2011, boasting a growth rate of 96.87%. However, the growth rate decelerated to 6.23% during 2011-2016, before slightly recovering to 12.46% during 2016-2020.…”

“…Furthermore, all variables exh With positive values at the 1% significance level, it is implied that carbon emissions exhibit a generally positive spatial autocorrelation. 2 Analyzing provinces at a regional level, such as Henan (HN), Shandong (SD), Jiangsu (JS), Hebei (HB), and Shanxi (SX), which are situated in Northnorth and Centralcentral China, demonstrates high-high value agglomerations, thus exhibiting pronounced diffusion impacts. These provinces not only have elevated carbon emissions but also impact neighboring provinces by amplifying their carbon emissions.…”

“…Global warming, with its negative impact on human existence, is one of the paramount environmental quandaries confronting our world today [1]. China, owing to its vast populace and substantial energy consumption, has ascended to the undesirable position of the world's largest carbon emitter [2]. This predicament stems largely from the country's extensive and expansive economic growth paradigm.…”

Decomposition of carbon emission factors in china's power industry: considering transmission and distribution

“…In terms of models, most scholars take economy or income, population, energy consumption intensity, fuel structure, carbon emission intensity, energy structure and industrial structure as factors influencing the change of carbon emissions in the power industry [14], [15], [16], [17], [18]. Most scholars have generally proved that socioeconomic factors such as economic development and population growth are the main driving factors of carbon emissions in the power industry, while energy factors such as energy structure can inhibit carbon emissions [19,20]. For example, Li et al [19] show that the main factors affecting carbon emissions in public buildings in Henan Province were identified as the urbanization rate, public floor area per capita, and energy intensity per unit of public floor area.…”

“…Most scholars have generally proved that socioeconomic factors such as economic development and population growth are the main driving factors of carbon emissions in the power industry, while energy factors such as energy structure can inhibit carbon emissions [19,20]. For example, Li et al [19] show that the main factors affecting carbon emissions in public buildings in Henan Province were identified as the urbanization rate, public floor area per capita, and energy intensity per unit of public floor area. Jiang et al [20] applied the electricity elasticity of carbon emissions to a decoupling index.…”