Li Lei, Li Junfeng, Wang Xilong, Sun Shujie
School of Geography and Tourism, Anhui Normal University, Wuhu, Anhui, China.
School of Applied Economics, University of Chinese Academy of Social Sciences, Beijing, China.
Heliyon. 2023 May 24;9(6):e16596. doi: 10.1016/j.heliyon.2023.e16596. eCollection 2023 Jun.
The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most densely built and economically vibrant regions in China, it is of vital importance to study the spatio-temporal heterogeneity and influence mechanisms of its carbon emissions against the backdrop of peaking carbon dioxide emissions and achieving carbon neutrality. However, systematic research on this area is still lacking. Therefore, this study uses spatial autocorrelation, kernel density estimation, and standard deviation ellipses to construct an exploratory spatial data analysis (ESDA) framework to analyze the spatio-temporal evolutionary characteristics of carbon emissions from GBA and combine it with the geographically and temporally weighted regression (GTWR) model to identify the various influencing factors of carbon emissions in GBA and reveal its implications. The results showed that: (1) Between 2009 and 2019, the total carbon emissions in GBA remained stable and gradually decreased. The gap between the carbon emission intensity of the cities narrowed. (2) The GBA urban agglomeration exhibited spatial autocorrelation, but characteristics of the global spatial pattern had not yet formed a steady state. The kernel density of carbon emissions in GBA showed an obvious "monopolar" phenomenon. (3) The gravity centre of carbon emissions in GBA was located to the southeast of the geometric centre of the whole region, shifting toward the northwest. (4) Population size, level of economic development and energy intensity have a strong positive contribution to carbon emissions, compared to the level of opening up and industrialization level, which has a weaker impact. There is significant spatial heterogeneity in the distribution of regression coefficients for each factor, and GBA should take full account of the characteristics of different types of cities in terms of carbon emissions and implement targeted emission reduction strategies. Our research provides a comprehensive analytical framework for regional carbon emissions, offering theoretical support for low-carbon development in the GBA.
粤港澳大湾区是中国建设最密集、经济最具活力的地区之一,在二氧化碳排放达峰和实现碳中和的背景下,研究其碳排放的时空异质性及影响机制至关重要。然而,目前针对该地区的系统研究仍较为缺乏。因此,本研究运用空间自相关、核密度估计和标准差椭圆构建探索性空间数据分析(ESDA)框架,以分析大湾区碳排放的时空演变特征,并结合地理加权回归(GTWR)模型识别大湾区碳排放的各类影响因素,揭示其内在含义。研究结果表明:(1)2009年至2019年间,大湾区碳排放总量保持稳定并呈逐渐下降趋势,各城市碳排放强度差距缩小。(2)大湾区城市群呈现空间自相关,但全局空间格局特征尚未形成稳态,大湾区碳排放的核密度呈现明显的“单极”现象。(3)大湾区碳排放重心位于全区几何中心的东南部,且向西北方向移动。(4)人口规模、经济发展水平和能源强度对碳排放有较强的正向贡献,相比之下,对外开放水平和工业化水平的影响较弱。各因素回归系数分布存在显著的空间异质性,大湾区在碳排放方面应充分考虑不同类型城市的特点,实施针对性的减排策略。本研究为区域碳排放提供了一个全面的分析框架,为大湾区低碳发展提供了理论支持。