Study of Spatial and Temporal Characteristics and Influencing Factors of Net Carbon Emissions in Hubei Province Based on Interpretable Machine Learning

Junyi Zhao, Bingyao Jia, Jing Wu () and Xiaolu Wu
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Junyi Zhao: School of Urban Design, Wuhan University, Wuhan 430072, China
Bingyao Jia: School of Urban Design, Wuhan University, Wuhan 430072, China
Jing Wu: School of Urban Design, Wuhan University, Wuhan 430072, China
Xiaolu Wu: School of Urban Design, Wuhan University, Wuhan 430072, China

Land, 2025, vol. 14, issue 6, 1-19

Abstract: Carbon emissions from global warming pose significant threats to both regional ecology and sustainable development. Understanding the factors affecting emissions is critical to developing effective carbon neutral strategies. This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. The results are interpreted using Shapley additive explanation (SHAP), revealing dynamic factor impacts. Our findings include the following. (1) Between 2000 and 2020, net carbon emissions in Hubei increased threefold, with emissions from construction land rising by approximately 7.5 times over the past two decades. Woodland, a major carbon sink, experienced a downward trend. (2) Six key factors are population, the normalized difference vegetation index (NDVI), road density, PM 2.5 , the degree of urbanization, and the industrial scale, with only the NDVI reducing emissions. (3) Net carbon emissions displayed significant spatial differences and aggregation and are mainly concentrated in the central urban areas of Hubei Province. Overall, this study evaluates various regression models and identifies the primary factors influencing net carbon emissions. The net carbon emission map we have developed can visually identify and locate high-emission hotspots and vulnerable carbon sink areas, thereby providing a direct basis for provincial land use planning.

Keywords: machine learning; net carbon emissions; Shapley additive explanation (SHAP) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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