| 中国碳中和潜力的区域差异及影响因素的时空异质性分析 |
| 摘要点击 261 全文点击 7 投稿时间：2024-06-30 修订日期：2024-09-08 |
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| 中文关键词 碳中和 碳源碳汇 Dagum基尼系数 核密度估计 STRIPAT-GTWR模型 |
| 英文关键词 carbon neutrality carbon sources and sinks Dagum Gini coefficient kernel density estimation STRIPAT-GTWR model |
| DOI 10.13227/j.hjkx.20250707 |
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| 中文摘要 |
| “碳中和”是中国当前为应对气候变化作出的重大战略，也是未来经济社会建设发展中的主要任务和目标. 采用碳源碳汇估计模型计算了2003~2021年中国30个省份的碳中和潜力，利用Dagum基尼系数，从国家及区域的角度考察了我国碳中和潜力的区域差异，并采用扩展的STIRPAT-GTWR模型揭示了碳中和潜力的时空异质性影响因素. 结果表明：①研究期间，中国平均碳源量为227.73 Mt，而平均碳汇量在78.84 Mt左右，是碳汇量的3倍左右，且碳源的增长速度远大于碳汇增长速度；②中国碳中和潜力在研究期内普遍下降，各省市碳中和能力发展不容乐观，其空间分布与碳汇分布相一致，均呈现出西北高-东南低的空间格局；③中国碳中和潜力存在区域差异性，东部地区差异最大，东北和西部次之，中部最小，且组内差异是造成碳中和潜力整体差异扩大的主要原因；④中国碳中和潜力的影响因素存在明显的时空异质性，能源强度、能源结构、经济发展水平和城市化水平是我国碳中和潜力的主要影响因素. 结果可以明确目前我国各省市碳中和潜力现状及其异质性驱动因素，便于各省市制定差异化和针对性的“增汇减碳”策略，以提升地区碳中和潜力，助力中国碳中和目标的早日实现. |
| 英文摘要 |
| Carbon neutrality is an important strategy for China to cope with the present climate change， and it is also the main task and goal in future economic and social development. This study uses the carbon source and carbon sink estimation model to calculate the carbon neutral capacity （CNC） of 30 provinces in China from 2003 to 2021. Furthermore， this study investigates the regional differences of CNC in China by using the Dagum Gini coefficient， and applies the STIRPAT-GTWR model to reveal the influencing factors of spatial-temporal heterogeneity. The results were as follows： ① During the study period， the average carbon sink in China was approximately 78.84 Mt， and that in Inner Mongolia， Heilongjiang， Sichuan， and Yunnan was as high as 166 Mt， mainly in the western region. However， the average carbon source was 227.73 Mt， which was about three times that of the carbon sink. The carbon sources in Shandong， Hebei， and Jiangsu were as high as 400 Mt， mainly concentrated in the eastern region. In addition， the growth rate of carbon sources was much faster than that of carbon sinks； the carbon emissions generated by energy consumption were especially growing rapidly. ② The CNC was generally decreasing in the study period， which suggest that CNC development in provinces and cities was not optimistic， and its spatial distribution was consistent with that of carbon sinks， showing a spatial pattern of high in the northwest and low in the southeast. ③ There were regional differences in China's CNC， with the largest difference in the eastern region， followed by the northeast and west， and the smallest in the central region， and the intra-group differences were the main reasons for the overall differences in CNC. ④ There was obvious spatio-temporal heterogeneity in the influencing factors of CNC in China. Energy intensity， energy structure， economic development level， and urbanization level were the main factors that affected the CNC of China. These results can clarify the status quo of CNC and its heterogeneous driving factors in China's provinces and cities and help provinces and cities to formulate differentiated and targeted strategies of "increasing carbon sinks and reducing carbon emissions" to enhance the regional CNC and help China achieve carbon neutrality at an early date. |
