气候变化领域集成服务门户(CCPortal): Carbon emission causal discovery and multi-step forecasting for global cities

CCPortal

DOI	10.1016/j.cities.2024.104881
	Carbon emission causal discovery and multi-step forecasting for global cities
	Liang, Xuedong; Li, Xiaoyan
发表日期	2024
ISSN	0264-2751
EISSN	1873-6084
起始页码	148
卷号	148
英文摘要	The increasing threat of global climate change is primarily caused by rising carbon emissions, with cities acting as significant contributors. This study bridges two vital gaps in urban carbon neutrality research: unraveling the causal dynamics of carbon emissions within urban networks and forecasting emission trends. This study proposes a reinforcement learning-based causal discovery algorithm, progressively deciphering the complex causal relationships in global urban emissions, and facilitating the creation of directed acyclic causal graphs. Furthermore, a hyperbolic graph neural network-based forecasting algorithm is introduced, through integrated fusion curvature to improve the information transfer between cities, for predicting global urban emission trends. A comparative analysis positions these innovative algorithms against leading methods, using emission data from thousands of cities for predictions one, five, and ten steps ahead. The experiment employs prediction error metrics, Taylor statistics, the Diebold-Mariano test, and the ablation analysis for validation. Results reveal proposed causal discovery algorithm effectively identifies the causality of carbon emissions among cities, while the forecasting algorithm leads other competing models across all prediction ranges. Based on the effectiveness of the algorithms, this study decodes the significant nature of the global urban carbon emission network, offering policy insights for collaborative carbon mitigation in cities worldwide.
英文关键词	Causal discovery of carbon emissions; Artificial intelligence supporting carbon; neutrality; Reinforcement learning; Low-carbon cities; Data-driven urban governance
语种	英语
WOS研究方向	Urban Studies
WOS类目	Urban Studies
WOS记录号	WOS:001199436300001
来源期刊	CITIES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295496
作者单位	Sichuan University; Hong Kong Polytechnic University
推荐引用方式 GB/T 7714	Liang, Xuedong,Li, Xiaoyan. Carbon emission causal discovery and multi-step forecasting for global cities[J],2024,148.
APA	Liang, Xuedong,&Li, Xiaoyan.(2024).Carbon emission causal discovery and multi-step forecasting for global cities.CITIES,148.
MLA	Liang, Xuedong,et al."Carbon emission causal discovery and multi-step forecasting for global cities".CITIES 148(2024).