聚类分析视角下的中国省域碳排放时空格局及驱动因素分析

中国碳排放的时空格局对全球气候治理极为重要,其驱动机制是"减排"的关键所在。对中国省域碳排放时空特征和驱动模式进行聚类研究,揭示不同省域的模式相似性与差异性,对于各省域的 "双碳"目标达成和路径安排具有重要影响和现实支撑。研究采用1997-2019年碳排放量及社会经济数据,从聚类分析角度探讨了中国省域碳排放的趋势及驱动模式,基于偏最小二乘法(PLS)模型的归因结果,识别不同省域碳排放的驱动因素的贡献度和敏感性,进一步探寻省域减排方案。研究表明:①研究期内中国碳排放以499.25mt/a的速率上升,呈低位平缓波动-大幅上升-高位缓慢波动的趋势变化,省域碳排放呈北高南低、东高西低格局。②省域碳排放模式具有差异性,北京、天津等为低起点低速发展类,趋势呈扁平"S"型;吉林、新疆等为低起点高速发展类,呈上升"S"型;河南、广东等为中起点高速发展类,呈扩张"S"型。山东、山西为高起点超高速发展类,呈指数"S"型。③中国省域碳排放驱动因子的贡献度和敏感性存在空间差异。经济发展、产业结构、城市化水平与能源消耗等对碳排放的贡献度较高,其中地区GDP、人均GDP、第二产业占比GDP、年底非农人口比例、地区能源消耗总量等是碳排放的主要贡献因子。碳排放对产业结构、科技发展、环境规制的敏感性较强。④中国省域碳排放驱动模式的分异性较为明显,同类驱动模式省域多在地理空间范围内形成"集聚"现象。因此,不同省域政府减排策略的落实应考虑其碳排放的发展规模及驱动机制的差异,实现地区发展和减排 "两手抓"的同时利用地区优势,资源互通、交流合作,加强省域间碳排放的"共治",进一步促进高质量发展。

Spatiotemporal pattern and driving factors of carbon emissions in Chinese provinces from the perspective of cluster analysis

The spatiotemporal pattern of China''s carbon emissions is extremely important for global climate governance, and its driving mechanism is the key to emission reduction. The clustering analysis of the temporal and spatial characteristics and driving patterns of China''s provincial carbon emissions has important influence and practical support for promoting the realization and path arrangement of "dual carbon" in provinces. This article used carbon emissions and socio-economic data from 1997 to 2019 to explore the trend and driving models of carbon emissions in China''s provinces from the perspective of cluster analysis. Based on the attribution results of the PLS model, the contribution and sensitivity of driving factors of carbon emissions in different provinces were identified, and further exploration of provincial emission reduction plans was carried out. The results were found in this research. Firstly, during the research period, China''s carbon emissions increased at a rate of 499.25 mt/a, showing a trend of low level gentle fluctuation, significant increase, and high level slow fluctuation. The provincial carbon emissions showed a pattern of high in the north, low in the south, and high in the east and low in the west. Then, there were differences in the models of carbon emissions across provinces. Beijing, Tianjin, etc are the low starting point of low-speed development category for carbon emissions, whose trend was flat "S" type. Jilin, Xinjiang, etc., for carbon emissions the low starting point of high-speed development category, whose trend was rising "S" type. Henan, Guangdong, etc., for carbon emissions the medium starting point of high-speed development category, whose trend was expanding "S" type. Shandong, Shanxi, etc., for carbon emissions the high starting point of ultra-high-speed development category, whose trend was exponential "S" type. Meanwhile, there were spatial differences in the contribution degree and sensitivity of provincial carbon emission drivers in China. Economic development, industrial structure, urbanization level and energy consumption had high contribution to carbon emissions, among which regional GDP, per capita GDP, the proportion of secondary industry in GDP, the proportion of non-agricultural population at the end of the year, and regional total energy consumption were the main contributing factors. In addition, the sensitivity of carbon emissions to industrial structure, scientific and technological development and environmental regulation was strong, and the sensitivity was most obvious in the proportion of industries at all levels to GDP, the proportion of college students and above, and the proportion of national fiscal education funds to GDP. At last, the differentiation of carbon emission driving modes was relatively obvious, and similar driving modes were prone to forming agglomeration phenomenon within a certain geographical and spatial range. Therefore, the implementation of emission reduction strategies by different provincial governments should take into account the variations in their carbon emissions'' development scale and driving mechanisms. This necessitates achieving a "dual grasp" on regional development and emission reduction, leveraging regional advantages, promoting resource exchanges and cooperation, strengthening inter-provincial co-governance of carbon emissions, and further advancing high-quality development.