Acta Geographica Sinica

Carbon peak prediction for Yangtze River Delta urban agglomeration based on spatially embedded GA-LSTM model

SHI Changfeng, YU Yue, YAO Xiao, PANG Qinghua

Acta Geographica Sinica, 2024, 79(11): 2895-2914. DOI: 10.11821/dlxb202411013

Tab. 5 Performance evaluation of different predictive models

Other figure/table from this article

Fig. 1 Flowchart of spatially embedded GA-LSTM model
Fig. 2 Structure of spatially embedded LSTM model
Fig. 3 Comparison of city carbon emission trends from two data sources
Tab. 1 Scenario variables setting for Shanghai
Tab. 2 Spatial autocorrelation test of carbon emissions in the Yangtze River Delta urban agglomeration, 2000-2019
Tab. 3 Verification of spatial panel models
Tab. 4 Estimated results of regression model
Tab. 6 Metrics for ablation experiments based on spatially embedded GA-LSTM model
Fig. 4 Comparison of prediction results based on spatially embedded GA-LSTM model and GA-LSTM model
Fig. 5 Historical evolution and prediction results of carbon emissions in the Yangtze River Delta urban agglomeration
Fig. 6 Peaking time and spatial distribution of carbon emissions in the Yangtze River Delta urban agglomeration
Fig. 7 Comparison of prediction results based on spatially embedded GA-LSTM model under baseline and green scenarios