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

年份	Moran's I	p值	年份	Moran's I	p值
2000	0.017^**	0.016	2010	0.032^***	0.006
2001	0.027^***	0.006	2011	0.031^***	0.009
2002	0.026^***	0.007	2012	0.032^***	0.008
2003	0.031^***	0.003	2013	0.028^**	0.013
2004	0.043^***	0.001	2014	0.027^**	0.014
2005	0.049^***	0.001	2015	0.019^**	0.031
2006	0.046^***	0.001	2016	0.020^**	0.030
2007	0.045^***	0.001	2017	0.013^*	0.057
2008	0.037^***	0.003	2018	0.029^**	0.012
2009	0.035^***	0.004	2019	0.028^**	0.013

Tab. 2 Spatial autocorrelation test of carbon emissions in the Yangtze River Delta urban agglomeration, 2000-2019

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. 3 Verification of spatial panel models
Tab. 4 Estimated results of regression model
Tab. 5 Performance evaluation of different predictive models
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