文献类型：期刊文献

英文题名：Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces

作者：Hong, Siting[1];Fu, Ting[1];Dai, Ming[1]

机构：[1]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524088, Peoples R China

年份：2025

卷号：17

期号：5

外文期刊名：SUSTAINABILITY

收录：SSCI(收录号：WOS:001443490400001)、SCI-EXPANDED(收录号：WOS:001443490400001)、、Scopus(收录号：2-s2.0-86000718578)、WOS

基金：This research was funded by the National College Students Innovation and Entrepreneurship Training Program, grant number 202410566025; the Guangdong Province College Students Innovation and Entrepreneurship Training Program, grant number 202410566045; the Guangdong Ocean University Undergraduate Innovation Team Project, grant number CXTD2023014; the Guangdong Basic and Applied Basic Research Foundation, grant number 2023A1515011326; and the program for scientific research start-up funds of Guangdong Ocean University, grant number 060302102101.

语种：英文

外文关键词：carbon emissions; driver selection; GM-SVR; PEST-SWOT; SHAP

外文摘要：With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis-strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure.