School of Public Administration, Hunan University, Changsha, 410082, China.
School of Public Finance & Public Administration, Jiangxi University of Finance and Economics, Nanchang, 330013, China.
Environ Sci Pollut Res Int. 2024 Jan;31(4):5699-5715. doi: 10.1007/s11356-023-31574-6. Epub 2023 Dec 21.
Existing studies on urbanization and carbon emissions are mostly based on a single pathway and lack the support of a theoretical framework. This study innovatively integrates Grossman's perspective of environmental effects analysis to develop a new framework to interpret the mechanism of multidimensional urbanization (MU) and carbon emissions (CEs). We first explored the spatial effects of MUs and CEs in the Yangtze River Delta urban agglomeration (YRDUA) and then introduced the "population-land-economic" urbanization variables into the S-STIRPAT model to determine the impact mechanisms of each factor on CEs under different urbanization dimensions. The results show that the spatiotemporal development patterns of MUs and CEs overlap to some extent. The Shanghai-Nanjing line is a high-value area of urbanization with different dimensions, as some edge cities are in low-value areas. However, there are local differences in the different dimensions of urbanization, e.g., population urbanization in the southern area is in a high-value area. CEs show a core-edge structure of "high in the center and low in the north and south". All factors, except for population urbanization, affect CEs locally, and their spillover effects are all positive, except for energy intensity, which has a negative influence on CEs in neighboring regions. Land urbanization has the largest positive impact on CEs, with a total effect coefficient of 0.409; economic urbanization has a coefficient of 0.195, and population urbanization has a coefficient of only 0.070. The findings can help to maximize urbanization growth while minimizing harmful environmental externalities.
现有关于城市化和碳排放的研究大多基于单一路径，缺乏理论框架的支持。本研究创新性地整合了 Grossman 的环境影响分析视角，构建了一个新的框架来解释多维城市化（MU）和碳排放（CEs）的机制。我们首先探索了长三角城市群（YRDUA）中 MU 和 CEs 的空间效应，然后将“人口-土地-经济”城市化变量引入 S-STIRPAT 模型，以确定在不同城市化维度下各因素对 CEs 的影响机制。结果表明，MU 和 CEs 的时空发展模式在一定程度上重叠。沪宁线是城市化不同维度的高值区，一些边缘城市处于低值区。然而，城市化的不同维度存在局部差异，例如，南部地区的人口城市化处于高值区。CEs 呈现出“中心高、南北低”的核心-边缘结构。除人口城市化外，所有因素都对 CEs 产生局部影响，其溢出效应均为正，除能源强度对相邻地区的 CEs 有负影响外。土地城市化对 CEs 的正面影响最大，总效应系数为 0.409；经济城市化的系数为 0.195，人口城市化的系数仅为 0.070。这些发现有助于在实现城市化增长的同时，最大限度地减少有害的环境外部性。
