Carbon peak prediction for Yangtze River Delta urban agglomeration based on spatially embedded GA-LSTM model
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.
carbon peakpath simulationspatial correlationGA-LSTM modelYangtze River Delta urban agglomeration
万方数据
维普
