Characteristics and Impact Mechanisms of Spatiotemporal Patterns of Carbon Emissions in China’s Urban Agglomerations
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Graphical Abstract
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Abstract
Taking 18 large-scale urban agglomerations (UAs) in China as the research objects, this study analyzes the characteristics of the spatiotemporal patterns of carbon emissions in UA areas in China and their impact mechanisms by citing Moran’s I and geographically weighted regression (GWR). The research findings are as follows: 1) obvious differences are found in carbon emissions among different UAs. The cities with higher absolute carbon emissions are mainly distributed in the major cities of the Hohhot-Baotou-Ordos-Yulin UA. 2) From 2011 to 2021, the carbon emission levels of China’s UAs grew obviously, but the spatial differences are pronounced, among which the Hohhot-Baotou-Ordos-Yulin UA and others had the highest growth rates. The carbon emission patterns of UAs also present obvious spatial clustering characteristics. The regions with the most obvious growth rates of carbon emissions at the urban scale are mainly distributed in the Hohhot-Baotou-Ordos-Yulin UA and Mid-Yangtze River UA. 3) The value of secondary industry (Xvsi), number of urban enterprises (Xnue), public library holdings (Xplh), and urban passenger volume (Xupv) have an obvious effect on carbon emissions. However, the regression cofficients exhibit obvious spatial variation. Among them, Xvsi has an obvious positive effect on carbon emissions, indicating that spatial agglomeration of the real economy substantially increases reginal carbon emission levels. The high Xnue regression coefficients are mainly distributed in Harbin-Changchun UA, indicating that the growth of enterprises in this region is still dominated by traditional high-carbon-emission enterprises, the urgent task of low-carbon transformation and upgrading for traditional industries in old industrial regions. The regression coefficients of Xplh and Xupv are generally negative, suggesting that improving public service facilities and strengthening regional transportation links can help to reduce the level of carbon emissions.
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