| 数据驱动下广东省碳排放时空分异特性及影响因素 |
| 摘要点击 904 全文点击 161 投稿时间：2024-03-06 修订日期：2024-05-19 |
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| 中文关键词 碳排放 时空特性 城市聚类 因素分解 绿色发展 |
| 英文关键词 carbon emissions spatio-temporal characteristics urban clustering factor decomposition green development |
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| 中文摘要 |
| 科学评估碳排放特性是精准施策、有序实现碳达峰碳中和的关键环节. 以广东省21个地级市为研究对象,结合2013~2022年经济活动、能源消耗和人口分布等年鉴数据,借助碳排放核算方法分析广东省各市近10 a碳排放变化及空间分布特性,利用主成分分析和K均值聚类算法对城市特征降维及聚类划分,并应用广义迪氏指数分解模型进行影响因素解析,进而提出广东省4类典型城市的减碳策略. 结果表明：①2013~2022年间广东省碳排放总量持续增长,空间分布上呈现较为明显的区域不平衡性. 其中,珠三角地区的碳排放量贡献高达42.50%,粤北地区碳排贡献最低. ②广东省各市在绿色发展、经济发展和工业发展方面表现各异,大体可分为减排示范型、资源依赖型、传统耗能型和节能降碳型,占比约为14.29%、28.57%、19.05%和38.09%. ③“经济活动”是广东省所有城市碳排放的共有动因,“能源消耗”和“生活方式”是资源依赖型城市和传统耗能型城市碳排放的核心驱动因素；双碳目标提出后,“能源构成”成为减排示范型城市和节能降碳型城市的关键动因. |
| 英文摘要 |
| Scientific analysis of carbon emission characteristics is a key link for precise policy implementation and orderly achievement of Carbon Peak and Carbon Neutrality. Taking 21 prefecture level cities in Guangdong Province as research objects, data including their economic activity, energy consumption, and population distribution from 2013 to 2022 was collected. First, the carbon emission accounting method was used to analyze the time changes and spatial distribution characteristics of carbon emissions in the past decade. Second, the principal component analysis and K-means method was used to reduce the dimensionality and cluster division for Guangdong urban characteristics. Third, the Generalized Schur Decomposition model was applied to analyze the influencing factors. Furthermore, carbon reduction strategies for four typical cities in Guangdong Province were proposed. The results showed that： ① From 2013 to 2022, the total carbon emissions in Guangdong Province continued to increase and showed a significant regional imbalance in its spatial distribution. The carbon emissions contribution in the Pearl River Delta region was as high as 42.50%, whereas that in northern Guangdong was the lowest. ② Each city in Guangdong Province performed differently in green development, economic development, and industrial development. All 21 cities could be broadly divided into four categories, namely, Demonstration City, Resource-dependent City, Traditional Industrial City, and Emission-reducing City, accounting for approximately 14.29%, 28.57%, 19.05%, and 38.09%, respectively. ③ Economic activities were the common driving factors for carbon emissions in all cities, whereas energy consumption and lifestyle were the core driving factors for carbon emissions in resource-dependent cities and traditional industrial cities. After the proposal of Carbon Peak and Carbon Neutrality, energy composition became a key driving force for demonstration cities and emission-reducing cities. |
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