Study and Analysis on the Driving Factors of Carbon Emissions at the County Level Based on Spatio-Temporal Evolution
Year: 2025
Type: article
Abstract: 县域碳排放驱动因素研究有助于精准剖析县域碳排放的内在机制，为制定因地制宜的低碳发展策略、优化资源配置、推动绿色转型及实现“双碳”目标提供关键支撑。文章以福建省83个县区作为研究对象，采用探索性空间数据分析方法探究县域碳排放的时空格局，研究表明，福建省碳排放量呈逐年递增趋势，碳排放在空间上表现出显著的集聚分布特征。进一步采用地理探测器和时空地理加权回归(GTWR)模型探究碳排放的主导因子。结果表明农业发展水平、工业化水平和能源消耗量与碳排放之间成正相关关系，能源消耗量呈现稳定且显著的促进作用，人口回归系数人口规模对碳排放的影响呈倒U型，而经济指标表现出复杂的波动特征，其回归系数呈现正负交替现象。The study of the driving factors of carbon emissions at the county level helps to accurately analyze the internal mechanism of carbon emissions at the county level, and provides key support for formulating low-carbon development strategies tailored to local conditions, optimizing the allocation of resources, promoting green transformation, and achieving the “dual carbon” goals. This article takes 83 counties and districts in Fujian Province as the research objects, and uses exploratory spatial data analysis methods to explore the spatio-temporal pattern of carbon emissions at the county level. The research shows that the carbon emissions in Fujian Province show a trend of increasing year by year, and the carbon emissions exhibit significant agglomeration distribution characteristics in space. Furthermore, the Geodetector and the Geographically and Temporally Weighted Regression (GTWR) model are adopted to explore the dominant factors of carbon emissions. The results show that there is a positive correlation between the level of agricultural development, the level of industrialization, energy consumption and carbon emissions. Energy consumption shows a stable and significant promoting effect. The regression coefficient of the population indicates that the impact of the population size on carbon emissions presents an inverted U-shaped pattern, while the economic indicators show complex fluctuation characteristics, and their regression coefficients alternate between positive and negative.
Source: Modeling and Simulation
Author: 明晟 邱
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4411467512