[Data-driven Analysis on the Spatio-temporal Characteristics and Influencing Factors of Carbon Emissions in Guangdong Province]

Abstract

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.

Keywords: carbon emissions; factor decomposition; green development; spatio-temporal characteristics; urban clustering.