| 四川省化石能源领域碳排放区域时空特征及影响因素 |
| 摘要点击 805 全文点击 13 投稿时间：2025-03-14 修订日期：2025-09-15 |
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| 中文关键词 化石能源 碳排放 区域时空特征 STIRPAT模型 影响因素 |
| 英文关键词 fossil energy carbon emissions spatiotemporal regional characteristics STIRPAT model influencing factors |
| DOI 10.13227/j.hjkx.202503163 |
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| 中文摘要 |
| 为刻画四川省化石能源碳排放的时空演化格局及其驱动机制，构建了涵盖能源开采、集输与消费环节的碳排放核算框架，融合局部莫兰指数、LISA时空聚类与重心迁移法揭示碳排放区域集聚与重心迁移特征；并结合STIRPAT模型，引入Pearson相关性分析、岭回归与随机森林方法，识别关键驱动因素. 结果表明：①四川省化石能源碳排放总体呈波动增长趋势，以能源消费环节为主导. ②四川省碳排放具有显著的空间异质性与时序分化特征，呈“西北-东南”空间分布格局，重心呈北向迁移趋势，呈现“资源型”向“产业型”转化趋势；同时，局部集聚性逐步增强，“高-高”型聚集区域由早期的攀西地区扩展至川南城市群，区域集聚格局日益显著. ③2011~2021年四川省整体呈弱脱钩状态，碳排放增长速度低于GDP增长，碳排放效率逐年改善. ④碳排放与城镇化率、货物周转量、地区生产总值及能源消耗量高度相关（|r|为0.63~0.94，强正相关），其中开采、集输和终端消费过程分别受能源规模、物流扩张及经济人口因素主导. |
| 英文摘要 |
| To characterize the spatiotemporal evolution pattern and driving mechanisms of fossil energy carbon emissions in Sichuan Province， a carbon emission accounting framework covering energy extraction， transmission， and consumption stages was established. By integrating local Moran's I， LISA spatiotemporal clustering， and center of gravity migration methods， the study revealed the regional aggregation and dynamic migration features of carbon emissions. Furthermore， the STIRPAT model was combined with Pearson correlation analysis， ridge regression， and random forest methods to identify key driving factors. The results showed that：① Fossil energy carbon emissions in Sichuan Province exhibited a fluctuating upward trend， dominated by the energy consumption stage. ② Significant spatial heterogeneity and temporal differentiation were observed， with the northwest-southeast spatial distribution pattern of carbon emission， and the core of emission migrating northward， reflecting a transition from a “resource-based” to an “industry-based” regional pattern. Meanwhile， local aggregation intensified， with “high-high” clusters expanding from the early Panxi Region to the southern Sichuan Province urban agglomeration， resulting in increasingly prominent regional clustering. ③ From 2011 to 2021， Sichuan generally experienced weak decoupling， with carbon emission growth lagging behind GDP growth and improving emission efficiency. ④ Carbon emissions were strongly positively correlated with urbanization rate， freight turnover， GDP， and energy consumption （|r|0.63-0.94）， with extraction， transmission， and end-use consumption stages primarily driven by energy scale， logistics expansion， and economic and demographic factors， respectively. |
