中国四大城市群碳排放驱动因素时空分解研究

doi:10.12006/j.issn.1673-1719.2023.148

摘要/Abstract

摘要：

城市群是中国经济发展和能源消耗的集聚区域，也是碳排放的主要来源。研究中国典型城市群碳排放的时空演变特征及其影响因素对实现“双碳”目标具有重要意义。文中应用ST-IDA模型（时空指数分解分析法）和LMDI（对数平均迪氏指数法）分解法，分析2000—2019年京津冀、长三角、珠三角和成渝城市群的碳排放驱动因素（人口规模、经济水平、产业结构、能源强度和能源结构)。研究发现：2000—2019年间，四大城市群能源活动碳排放总体趋势均由高速增长阶段步入平稳增长阶段，其中成渝城市群已基本实现碳达峰；能源强度效应是影响碳排放空间差异的主要因素；人口规模扩张、经济发展水平提高和能源强度上升是促进碳排放增长的主要因素，产业结构和能源消费结构优化起到抑制作用；四大城市群碳排放的时空演变主要取决于工业部门。鉴于四大城市群呈现出不同的碳排放特征，未来应探索差异化、多元化的城市群减排路径，促进城市群碳减排。

关键词: 碳排放, 驱动因素, ST-IDA模型, 时空分解, 城市群

Abstract:

Urban agglomerations are the agglomerations of China’s economic development and energy consumption, as well as the main source of carbon emissions. The study of the spatial-temporal evolution characteristics and driving factors of carbon emissions in China’s typical urban agglomerations is of great significance for the achievement of carbon peaking and carbon neutrality goals. The carbon emission driving factors (population size, economic level, industrial structure, energy intensity and energy structure) of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing urban agglomerations were analyzed by using ST-IDA model and LMDI decomposition method. The results are as follows. During 2000-2019, the overall trend of CO₂ emissions from energy activities in the four major urban agglomerations stepped from the “high growth stage” to the “stable growth stage”, among which the Chengdu-Chongqing urban agglomeration have basically achieved the carbon peak; The energy intensity effect is the main factor affecting the spatial differences in carbon emissions; The expansion of population size, the increase of economic development level and the rise of energy intensity are the main factors contributing to the growth of carbon emissions in the four major urban agglomerations, while the optimization of industrial structure and energy consumption structure plays a suppressive role; The spatial and temporal evolution of carbon emissions in the four major urban agglomerations depends mainly on the industrial sector. Since the four major urban agglomerations present different carbon emission characteristics, differentiated and diversified emission reduction paths should be explored in the future to promote carbon emission reduction in urban agglomerations.

Key words: Carbon emissions, Driving factors, ST-IDA model, Spatial-temporal decomposition, Urban agglomerations

刘元欣, 贺铄, 江雅婧, 罗旭, 袁家海. 中国四大城市群碳排放驱动因素时空分解研究[J]. 气候变化研究进展, 2024, 20(2): 231-241.

LIU Yuan-Xin, HE Shuo, JIANG Ya-Jing, LUO Xu, YUAN Jia-Hai. Spatial-temporal decomposition of carbon emissions in China’s four major urban agglomerations[J]. Climate Change Research, 2024, 20(2): 231-241.

图/表 5

图1 2000—2019年中国碳排放总量及增长率

Fig. 1 Total carbon emissions and growth rate in China from 2000 to 2019

图2 2000—2019年四大城市群碳排放变化(a)及各阶段增长率(b)

Fig. 2 Changes (a) and growth rates (b) of carbon emissions in four major urban agglomerations from 2000 to 2019

图3 2000—2019年四大城市群碳排放空间分解结果

Fig. 3 Spatial decomposition of carbon emissions in four urban agglomerations from 2000 to 2019

图4 2000—2019年四大城市群能源结构注：数据来自于各市《统计年鉴》。

Fig. 4 Energy structure of four major urban agglomerations from 2000 to 2019

图5 2000—2019年四大城市群碳排放时间分解结果

Fig. 5 Temporal decomposition of carbon emissions in four major urban agglomerations from 2000 to 2019

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