%0 Journal Article %T 山西省碳排放空间格局及低碳化空间规划策略
The Spatial Pattern of Carbon Emissions and the Low-Carbon Spatial Planning Strategy in Shanxi Province %A 梁变变 %J Open Journal of Nature Science %P 386-396 %@ 2330-1732 %D 2025 %I Hans Publishing %R 10.12677/ojns.2025.132040 %X “双碳”目标背景下，山西作为典型的煤炭资源消耗区，在全国碳减排任务中占据重要地位。研究借助融合校正后的NPP-VIIRS夜间灯光数据，结合省级碳排放，构建碳排放拟合模型，借助探索性空间数据分析山西省县域碳排放的时空格局演化特征，采用GTWR模型研究碳排放的影响因素，最后提出低碳化空间发展策略。结果表明：(1) 2012~2022年山西省碳排放总量逐渐增多，但增速放慢。其中，中部城市群碳排放占主导地位，省会太原市贡献最高，省域边缘县域碳排放量较低，不均衡性突出。(2) 各县级市之间碳排放呈现显著的正相关，空间关联程度逐渐加强。碳排放高值区主要分布于省内高速公路、汾河两岸，低值区广泛分布，整体以缓慢增长型区域为主。(3) 碳排放的5种因素按照相关性排序为年末总人口 > 城镇化率 > 人均GDP > 人均可支配收入 > 第二产业产值占比。建议规划合理安排三生空间、能矿空间、完善支撑体系、开展区域协同减排并落实到国土空间规划的全过程。
Under the background of the “dual carbon” goals, Shanxi, as a typical coal resource consumption area, plays a crucial role in national carbon reduction efforts. This study utilizes the fusion-corrected NPP-VIIRS nighttime light data and integrates it with provincial carbon emission data, constructs a carbon emission fitting model, analyzes the spatiotemporal evolution characteristics of carbon emissions in counties of Shanxi Province using exploratory spatial data analysis, employs the GTWR model to investigate the determinants of carbon emissions, and proposes low-carbon spatial development strategies. The results demonstrate that: (1) The total carbon emissions in Shanxi Province showed a gradual increase from 2012 to 2022, with a decelerating growth rate. Specifically, carbon emissions from the central urban agglomeration dominate, with the provincial capital Taiyuan contributing the most. Carbon emissions from peripheral counties remain relatively low, indicating a prominent imbalance. (2) A significant positive spatial autocorrelation exists among county-level cities’ carbon emissions, with gradually intensifying spatial clustering. The high-emission clusters are predominantly distributed along major transportation corridors and the Fen River basin, whereas low-emission areas exhibit a dispersed pattern, primarily consisting of slow-growth regions. (3) The five influencing factors are ranked by correlation coefficient as follows: year-end total population > urbanization rate > per capita GDP > per capita disposable income > proportion of secondary industry output value. It is recommended to strategically plan and allocate ecological-living-production spaces, optimize energy and mining spatial arrangements, enhance supporting systems, implement regional collaborative emission reduction initiatives, and integrate these measures throughout the national spatial planning process. %K 煤炭资源型地区， %K 碳排放， %K 空间格局， %K GTWR， %K 规划策略
Coal Resource-Based Regions %K Carbon Emissions %K Spatial Pattern %K GTWR %K Planning Strategy %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=109787