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

• Published in: Applied energy 2024-04, Vol.360, p.122819, Article 122819
• Main Authors: Luo, Haizhi, Wang, Chenglong, Li, Cangbai, Meng, Xiangzhao, Yang, Xiaohu, Tan, Qian
• Format: Article
• Language: English
• Subjects: Carbon emission; China; Interpretable machine learning; Land use; Multi-scale characterization and prediction
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2024.122819

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 GIS-Kernel 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. •A novel model utilizes land use for carbon emission prediction has been proposed;•The model provides specific carbon reduction strategies due to its interpretability;•The advantages of the model for multi-scale CO2 emission prediction are elucidated;•Yangtze River Delta region, China was chosen as the model application case.