Spatial Driven Effects of Multi-Dimensional Urbanization on Carbon Emissions: A Case Study in Chengdu-Chongqing Urban Agglomeration

• Published in: Land (Basel) Vol. 11; no. 10; p. 1858
• Main Authors: Chang, Jie, Sun, Pingjun, Wei, Guoen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Agglomeration; Agricultural production; Anthropogenic factors; Bivariate analysis; Carbon dioxide; carbon emission; Chengdu-Chongqing urban agglomeration (CUA); Climate change; Decay; Economic aspects; Emissions; Emissions (Pollution); Emissions control; Environmental aspects; Environmental policy; GDP; Gross Domestic Product; Heterogeneity; Human influences; Land management; Metropolitan areas; Mountainous areas; multi-dimensional urbanization; Partial differential equations; Population; spatial durbin model (SDM); Spatial heterogeneity; spatial spillover effect; Temporal variations; Trends; Urban areas; Urbanization; Variables; China; Beijing China; Yangtze River Delta; Yangtze River
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11101858

Previous studies lacked attention to the spatial heterogeneity of the impact of urbanization on carbon emissions. To fill this knowledge gap, this study analyzed the spatio-temporal variations of carbon emissions (TCE), the per capita carbon intensity (PCI), and the economic carbon intensity (ECI) in the Chengdu-Chongqing urban agglomeration (CUA) based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) from 2000–2018. Bivariate spatial autocorrelation, and spatial Durbin models were combined to quantify the spatial correlation and driving mechanisms between carbon emission intensity and multi-dimensional urbanization (population, economic, and land urbanization). The following are the main results: (1) The TCE in CUA increased by 3.918 million tons at an average annual growth of 6.86%; CUA ranked last among China’s national strategic urban agglomerations in terms of TCE, PCI, and ECI. (2) High carbon emission values were concentrated in the Chengdu and Chongqing metropolitan areas, presenting a spatial feature of “Core-Periphery” gradient decay. (3) Nearly 30% of the agglomeration had carbon emission growth at low rates, with the growth cores concentrated in the main urban areas of Chengdu and Chongqing. (4) The “Low-Low” positive correlation was the main correlation type between multi-dimensional urbanization and carbon emissions and was distributed mainly in mountainous areas (e.g., Leshan and Ya’an). (5) Among the urbanization dimensions, the impacts on carbon emissions in local and adjacent areas exhibited varying levels of spatial heterogeneity. Economic urbanization was found to have the strongest positive direct and spillover effects; land urbanization inhibited the growth of carbon emissions in local and adjacent areas; population urbanization promoted carbon emission reduction in adjacent areas. Our findings provide support for CUA to carry out cross-city joint governance strategies of carbon emissions, also proving that regional carbon emission reduction should be an integration of various efforts including low-carbon living of residents, green transformation of economy and optimal land management.