气候变化领域集成服务门户(CCPortal): Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China

CCPortal

DOI	10.1016/j.apenergy.2024.122819
	Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China
	Luo, Haizhi; Wang, Chenglong; Li, Cangbai; Meng, Xiangzhao; Yang, Xiaohu; Tan, Qian
发表日期	2024
ISSN	0306-2619
EISSN	1872-9118
起始页码	360
卷号	360
英文摘要	Carbon emissions are a significant factor contributing to global climate change, and their characterization and prediction are of great significance for regional sustainable development. This study proposes a novel carbon emission characterization and prediction model based on interpretable machine learning and land use. It does not rely on socio-economic indicators, thus enabling carbon emission predictions after the decoupling effect. It can also reflect spatial distribution characteristics of carbon emissions, and demonstrates high accuracy and interpretability. The Yangtze River Delta (YRD) region serves as the application case for the model. Utilizing GISKernel Density for land -use subdivision and Optimized Extra Tree Regression, the model achieves high precision (R2 = 0.99 for training, R2 = 0.86 for testing). Shapley Additive exPlanations (SHAP) model was employed to interpret the model, revealing the impact curves of different land areas on carbon emissions. Optimized Land Expansion Analysis Strategy (Opti-LEAS) and Cellular Automaton based on Multiple Random Seeds (CARS) models simulated land use under baseline scenarios, confirming an overall accuracy exceeding 85%. The total carbon emissions in the YRD in 2030 are projected to reach 1580.70 million tons, with Shanghai leading at 223.84 million tons, followed by Suzhou at 172.20 million tons. County -level carbon emissions were characterized, and a spatial econometrics model was employed to reveal the spatial distribution characteristics of future carbon emissions, indicating a clustering effect (Moran's I = 0.6076). As industrial land disperses, clustering shifts towards regional centers, with areas like Wuzhong District identified as 99% confident carbon emission hotspots.
英文关键词	Land use; Carbon emission; Interpretable machine learning; China; Multi-scale characterization and prediction
语种	英语
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:001183369300001
来源期刊	APPLIED ENERGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300837
作者单位	Xi'an Jiaotong University; Chongqing University; Guangdong University of Technology
推荐引用方式 GB/T 7714	Luo, Haizhi,Wang, Chenglong,Li, Cangbai,et al. Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China[J],2024,360.
APA	Luo, Haizhi,Wang, Chenglong,Li, Cangbai,Meng, Xiangzhao,Yang, Xiaohu,&Tan, Qian.(2024).Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China.APPLIED ENERGY,360.
MLA	Luo, Haizhi,et al."Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China".APPLIED ENERGY 360(2024).