基于现代化的中国省级碳排放时空演变及影响因素研究

doi:10.12006/j.issn.1673-1719.2022.230

摘要/Abstract

摘要：

使用2000—2020年中国省级碳排放及现代化指标数据，采用传统统计和空间自相关分析方法分析省级碳排放及现代化指标的时空演变特征，并利用地理探测器方法探测现代化水平对碳排放的影响。结果表明：中国30个省的碳排放总量在近20年整体上呈现持续增长，碳排放总量增长率却在波动下降；碳排放总量呈现由东部向西部梯次递减的走势，并且东、中部地区之间的碳排放量差距有所缩小；中国碳排放量高值区域主要集中在环渤海地区，以2015年为转折点，碳排放量高值区域的变化大致划分为扩张阶段和收缩阶段；中国省域尺度碳排放具有显著的空间正向相关性，省级碳排放的空间集聚水平表现出先不断提升再逐渐降低的特征；在局部空间自相关分析中，呈正向空间关联的区域数量均多于负向空间关联的区域数量，中国大多数省份的碳排放量受其周围省份的影响较大；现代化指标时间变化趋势显著，空间分布演变特征鲜明；交通运输、平均受教育年限、信息化水平、人口绝对规模、工业化水平对碳排放有较强的解释力度，影响因子两两共同作用对碳排放的影响均强于单个因子的作用。

关键词: 现代化, 碳排放, 时空特征, 影响因素

Abstract:

Based on the data of China’s provincial carbon emission and modernization index from 2000 to 2020, the spatial and temporal evolution characteristics of provincial carbon emission and modernization index were analyzed by traditional statistics and spatial autocorrelation analysis methods, and the influence of modernization level on carbon emission was detected by geographic detector method. The results show that the total carbon emission of 30 provinces in China has shown continuous growth in the past 20 years, while the total carbon emission growth rate has fluctuated down. It is worth noting that total carbon emissions showed negative growth in 2015. From the perspective of different regions, the total carbon emission showed a descending trend from east to west, and the carbon emission gap between east and central regions narrowed. From the perspective of spatial distribution characteristics of carbon emissions, high-value regions of China’s carbon emissions are mainly concentrated in the Bohai Rim region. With 2015 as the turning point, the changes of high-value regions of carbon emissions can be roughly divided into expansion stage and contraction stage. In the spatial correlation analysis, carbon emissions at the provincial scale in China have a significant spatial positive correlation, and the spatial concentration level of carbon emissions at the provincial level shows the characteristics of increasing first and then decreasing gradually. The number of regions with positive spatial correlation is more than that of regions with negative spatial correlation. Carbon emissions of most provinces in China are greatly affected by their neighboring provinces. Hebei is always in the high-high agglomeration area, and Shandong, Henan and Shanxi also show the characteristics of high-high agglomeration for many times. In the aspect of the modernization index research, the time change trend of modernization index is significant, and the spatial distribution evolution characteristics are distinct, which accords with China’s national conditions. From the perspective of influencing factors, transportation, average years of education, informatization level, absolute population size and industrialization level have strong explanatory power for carbon emissions. The combined effect of the two factors on carbon emissions is stronger than that of a single factor. The key interaction factors, including environmental regulation, absolute population size and industrialization level, have strong interaction effects on the spatial differentiation of provincial carbon emissions.

Key words: Modernization, Carbon emissions, Spatial and temporal characteristics, Influencing factors

杨红雄, 杨光. 基于现代化的中国省级碳排放时空演变及影响因素研究[J]. 气候变化研究进展, 2023, 19(4): 457-471.

YANG Hong-Xiong, YANG Guang. Spatial-temporal evolution and influencing factors of provincial carbon emissions in China based on modernization[J]. Climate Change Research, 2023, 19(4): 457-471.

图/表 10

表1 中国省级现代化指标

Table 1 Indicators of provincial modernization in China

图1 2000—2020年中国30省碳排放量及增长率变化

Fig. 1 Change of carbon emission and growth rate in 30 provinces in China from 2000 to 2020

图2 2000—2020年中国分区域碳排放量

Fig. 2 Carbon emissions of China by region from 2000 to 2020

图3 2000—2020年中国省级碳排放空间分布图

Fig. 3 Spatial distribution of provincial carbon emissions in China from 2000 to 2020

表2 碳排放全局空间自相关分析

Table 2 Spatial autocorrelation analysis of carbon emissions

图4 2000—2020年中国省级碳排放局部空间自相关图（LISA）

Fig. 4 LISA map of China’s provincial carbon emissions from 2000 to 2020

图5 2020年中国现代化指标时间趋势

Fig. 5 Time trend of China’s modernization index in 2020

图6 2020年中国省级现代化指标空间分布

Fig. 6 Spatial distribution of China’s provincial-level modernization indicators in 2020

表3 因子探测器结果

Table 3 Factor detector results

表4 交互作用探测器结果

Table 4 Interaction detector results

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