华北水利水电大学学报（社会科学版）

科学有序推进煤炭资源型地区低碳转型，是中国经济社会平稳运行和高质量发展的重要支撑。本研究基于2000—2017年山西省118个县域的碳排放数据及相关的社会经济和自然环境数据，采用空间自相关及地理探测器方法，揭示了山西省县域碳排放的时空格局演变与影响因素。研究发现，2000—2017年山西省碳排放整体呈不断增长趋势，年均增长率为5.20%。大部分县域碳排放表现出显著增长趋势，山西省碳排放整体空间分布格局为两侧低、中间高。研究期间，山西省县域碳排放在空间上具有显著的空间集聚特征，高-高聚集和低-低聚集为县域碳排放局部空间分布的主要特征。社会经济因子对山西省县域碳排放空间分异的影响明显，自然环境因子的影响程度相对较小，但自然环境因子与社会经济因子交互作用后对山西省县域碳排放空间分异的解释力明显增强。对晋北、晋中、晋南碳排放空间分异解释力最高的分别为工业能源强度∩年平均降水量、年平均气温∩植被覆盖度、一般公共预算支出∩产业结构。研究结果可为山西省低碳转型和经济高质量发展提供差异化和精细化的决策支持，对其他煤炭资源型地区可持续发展提供参考和借鉴。

Scientific and orderly promotion of low-carbon transformation in coal resource-based areas is an important support for the smooth operation and high-quality development of China's economy and society. With Shanxi Province as an example, we used spatial autocorrelation and geodetector methods to reveal the temporal and spatial pattern evolution and influencing factors of carbon emissions at the county level in Shanxi Province, based on the carbon emission data of 118 districts and counties in Shanxi Province from 2000 to 2017, as well as related socio-economic and natural environmental data. The study found that the overall carbon emissions in Shanxi Province showed a continuous growth trend from 2000 to 2017, with an average annual growth rate of 5.20%. The carbon emissions of most districts and counties show a significant growth trend, and the overall distribution pattern of carbon emissions is high in the center and low on both sides. During the study period, the county carbon emissions in Shanxi Province have significant spatial agglomeration characteristics, and high-high aggregation and low-low aggregation are the main characteristics of the local spatial distribution of county carbon emissions. The impact of socioeconomic factors on the spatial differentiation of county carbon emissions in Shanxi Province is obvious, while the impact of natural environmental factors is relatively small, but the explanatory power of natural environmental factors and socioeconomic factors on the spatial differentiation of county carbon emissions in Shanxi Province is significantly enhanced after interaction. Industrial energy intensity ∩ annual average precipitation, annual average temperature ∩ vegetation coverage, and general public budget expenditure ∩ industrial structure have the highest explanatory power for the spatial differentiation of carbon emissions in northern, central and southern Shanxi, respectively. The research results can provide differentiated and refined decision support for the low-carbon transformation and high-quality economic development of Shanxi Province, and provide reference for the sustainable development of other coal resource-based regions.