时空视角下中国省域碳排放强度影响因素与演变趋势研究

Research on the influencing factors and evolution trends of carbon intensity in Chinese provincial regions from a spatial perspective

  • 摘要: “十五五”时期是我国实现2030年前碳达峰目标的关键阶段,在分析我国各省(区、市)2011—2022年碳排放强度数据的基础上,分别运用莫兰指数、空间杜宾模型及马尔可夫链转移概率矩阵,揭示各省(区、市)碳排放强度的空间相关性、影响因素及时空动态演变特征。结果显示,我国各省(区、市)碳排放强度在空间上并非完全随机分布,不同年份均具有正向的空间效应,呈现“高高”集聚或“低低”集聚。产业结构、研发强度、城市化水平、可再生能源发展水平、外商投资水平及对外开放程度等,均对我国省域碳排放强度有显著的负向影响,而工业化水平、金融发展水平的提高则会促进碳排放强度的升高。单变量马尔可夫链转移概率矩阵结果显示,多数省(区、市)的碳排放强度能够维持初始状态,河北、辽宁、山东、宁夏的碳排放强度可能进一步攀升,其中,辽宁碳排放强度升高的可能性最大;天津、吉林、黑龙江向低碳排放强度转移的概率大于向高强度转移的概率;内蒙古呈现转移不明的情况。空间马尔可夫链转移概率矩阵结果显示,随着跨越期的延长,各省(区、市)从碳排放中低强度、中高强度向低强度、中低强度“跃升”的概率大幅提升。建议构建跨区域低碳协同发展机制,推行差异化节能减排路径,稳步推进低碳政策技术落地。

     

    Abstract: The 15th Five-Year Plan period is a crucial stage for ensuring carbon peak by 2030. Based on the analysis of carbon emission intensity data of all provinces in China from 2011 to 2022, this paper uses the Moran index, Spatial Durbin model, and Markov chain transition probability matrix to reveal the spatial correlation, influencing factors, and temporal dynamic evolution characteristics of carbon emission intensity in each province. The results show that the carbon emission intensity of all provinces in China is not randomly distributed spatially. In different years, there is a positive spatial effect, presenting a high-high clustering or low-low clustering. Industrial structure, R&D intensity, urbanization level, renewable energy development level, foreign investment level, and the degree of opening up all have significant negative impacts on the carbon emission intensity of provincial regions in China. The improvement of industrialization level and financial development level will promote the increase in carbon emission intensity. The results of single-variable Markov chain show that the carbon emission intensity of most provinces can maintain the initial state, while the carbon emission intensity of Hebei, Liaoning, Shandong, and Ningxia may further increase. Among them, Liaoning has the greatest possibility of an increase in carbon emission intensity. The probability of transferring from high to low or from high-high to low-low carbon emission intensity is greater for Tianjin, Jilin, Heilongjiang, and is greater than the probability of transferring to high or high-high. Inner Mongolia shows an unclear transfer situation. The results of the spatial Markov transition probability matrix show that with the extension of the transition period, the probability of the low-intensity and medium-high-intensity carbon emissions in each province "jumping" to low-intensity and medium-low-intensity significantly increases. This paper proposed to construct a cross-regional mechanism for coordinated low-carbon development and implementing differentiated energy conservation and emission reduction paths, and steadily promoting the implementation of low-carbon policies and technologies.

     

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