| 黑龙江省县域碳排放时空变化及其影响因素分析 |
| 摘要点击 330 全文点击 19 投稿时间：2025-08-12 修订日期：2025-10-28 |
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| 中文关键词 县域 碳排放 空间分析 STIRPAT模型 影响因素 |
| 英文关键词 county level carbon emissions spatial analysis STIRPAT model influencing factors |
| DOI 10.13227/j.hjkx.202508118 |
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| 中文摘要 |
| “双碳”战略规划下，研究黑龙江省县域碳排放的时空演变规律及其影响因素，可对黑龙江省的可持续发展提供有益借鉴. 以黑龙江省121个县（区）为研究对象， 采用空间自相关分析法、标准差椭圆分析法对2000~2020年黑龙江省县域尺度碳排放时空演变特征进行研究；并基于扩展的STIRPAT模型、岭回归模型分析其影响因素并进行定量研究. 结果表明，2000~2020年黑龙江省碳排放量总体呈现上升趋势，年均增长率为5%，其中2010~2020年呈波动增长趋势. 碳排放空间分布差异明显，较高值区主要分布在黑龙江省东部的佳木斯市、鹤岗市、鸡西市及西南部哈尔滨市、大庆市、齐齐哈尔市的区县，呈扩大趋势. 黑龙江省县域碳排放量存在显著的空间自相关性，空间集聚特征明显；标准差椭圆分析表明，黑龙江省县域碳排放空间分布形态呈现“圆化”趋势，碳排放方向趋势减弱，重心点主要集中于哈尔滨市的通河县和木兰县， 表明碳排放空间格局总体上相对稳定. 对黑龙江省碳排放有显著正向影响的核心要素有：能源结构、人均GDP、财政支出和人口数量，次要要素为产业结构和居民消费水平；碳排放强度对碳排放有显著负向影响. 因此，各区县政府应根据发展实际情况，制定合理化和差异化的碳减排政策，同时加强区域间的合作交流，保证环境低碳和绿色的可持续发展. |
| 英文摘要 |
| Under the “Dual Carbon” strategic goals （peak carbon emissions and carbon neutralit）， it is of great significance to study the spatial-temporal evolution law and influencing factors of carbon emission for the sustainable development of Heilongjiang Province. Based on the 121 counties （districts） in Heilongjiang Province， this study employed spatial autocorrelation and standard deviation ellipse analysis to explore the spatiotemporal changes of carbon emissions from 2000 to 2020. Additionally， an extended STIRPAT model and ridge regression analysis were applied to quantitatively assess the influencing factors. The results showed that from 2000 to 2020， carbon emissions in Heilongjiang Province exhibited an overall upward trend， with an average annual growth rate of 5%， though fluctuations were observed from 2010 to 2020. Spatially， carbon emissions displayed significant disparities， with higher values concentrated in counties/districts of Jiamusi， Hegang， and Jixi in eastern Heilongjiang， as well as Harbin， Daqing， and Qiqihar in the southwest， showing an expanding trend. County-level carbon emissions demonstrated strong spatial autocorrelation， with evident clustering characteristics. Standard deviation ellipse analysis revealed a “circularization” trend in the spatial distribution of emissions， accompanied by a weakening directional trend. The centroid of emissions primarily shifted between Tonghe County and Mulan County （Harbin）， suggesting a relatively stable overall spatial pattern. The key factors exerting a significant positive impact on carbon emissions included energy structure， per capita GDP， fiscal expenditure， and population size， while industrial structure and household consumption levels played secondary roles. In contrast， carbon emission intensity had a notable negative effect. Accordingly， county governments should formulate tailored and differentiated emission reduction policies based on local conditions， while strengthening interregional collaboration to ensure low-carbon， green， and sustainable development. |
