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长三角城市群碳达峰路径模拟研究

Carbon peak prediction for Yangtze River Delta urban agglomeration based on spatially embedded GA-LSTM model
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摘要 城市群是构建国内大循环、促进区域协调发展的重要平台。城市群一体化深入发展的现实背景下,其内部空间网络特性决定了单一城市碳达峰路径不可避免地受到邻近城市影响。为此,本文以中国一体化程度较高的长三角城市群为研究对象,构建基于地理—经济复合维度的空间权重矩阵,应用空间计量模型考察该城市群碳排放空间关联效应,进一步构建空间嵌入式GA-LSTM模型以对长三角城市群碳达峰路径进行动态模拟。实证结果显示:①若考虑长三角城市群空间关联效应,部分城市碳达峰时点提前且多数城市峰值水平降低,表明空间关联效应能有效优化城市群碳排放空间格局,但其达峰后排放态势并未受到显著影响。②基准情景下,除苏州、亳州以外城市均能于2030年前顺利实现碳达峰,其中安徽省多数城市在2019年后保持碳排放稳中有降,江苏省及浙江省部分城市达峰后的碳排放下降态势相对缓慢,而上海、南通在早期实现达峰后其碳排放却呈缓慢增加的反弹趋势。③绿色情景下,长三角城市群整体碳排放于2019年后呈平稳下降趋势,有效逆转了基准情景下的惯性增长,并且其内部城市在碳达峰时点、峰值水平以及达峰后态势方面均呈显著改善趋势,有助于形成互为促进的区域减排合力。 Urban agglomerations serve as crucial platforms for constructing substantial domestic circulation and fostering harmonious regional development in China.Given the evolution of the integrated development of urban agglomerations,the characteristics of their internal spatial networks inevitably lead to the carbon peak paths of individual cities being influenced by their proximate counterparts.Consequently,this study focused on the Yangtze River Delta urban agglomeration,which boasts a high degree of integration within China,constructed a spatial weight matrix based on composite geographic and economic dimensions,applied a spatial econometric model to analyze the spatial correlation of carbon emissions in this urban agglomeration,and further applied the spatially embedded Genetic Algorithm-Long Short-Term Memory(GA-LSTM)model to simulate dynamically the peak paths of carbon emissions in this urban agglomeration.The empirical results revealed several important findings:(1)Considering the spatial correlation effects of the urban agglomeration,the carbon peaks of several cities occur sooner than expected,and most cities experience a reduction in their peak level,indicating that the spatial correlation effect can effectively optimize the spatial pattern of carbon emissions.However,the post-peak emission dynamics of these cities are not significantly affected.(2)In the baseline scenarios,with the exception of Suzhou(Jiangsu)and Bozhou,all cities attain their carbon peak by 2030,with most cities in Anhui province maintaining a steady decrease in carbon emissions after 2019,some cities in Jiangsu and Zhejiang provinces experiencing a relatively slow decrease in carbon emissions after reaching the peak,and Shanghai and Nantong showing a rebound trend of slow increase in carbon emissions after reaching their peak at an early stage.(3)Under the green scenarios,the total carbon emissions from the Yangtze River Delta urban agglomeration follow a steady downward trend since 2019,effectively reversing the inertial growth under the baseline scenarios,and the cities within the urban agglomeration show significant improvement in the time to peak,peak level,and post-peak situation,which contributes to a synergistic emission reduction pattern.
作者 石常峰 俞越 姚潇 庞庆华 SHI Changfeng;YU Yue;YAO Xiao;PANG Qinghua(School of Economics and Finance,Hohai University,Changzhou 213200,Jiangsu,China;College of Information Science and Engineering,Hohai University,Changzhou 213200,Jiangsu,China)
出处 《地理学报》 EI CSSCI CSCD 北大核心 2024年第11期2895-2914,共20页 Acta Geographica Sinica
基金 教育部人文社会科学研究基金项目(22YJAZH086)。
关键词 碳达峰 路径模拟 空间关联 GA-LSTM模型 长三角城市群 carbon peak path simulation spatial correlation GA-LSTM model Yangtze River Delta urban agglomeration
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