Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

• Published in: The Science of the total environment 2024-01, Vol.906, p.167692-167692, Article 167692
• Main Authors: Jiang, Hongtao, Yin, Jian, Wei, Danqi, Luo, Xinyuan, Ding, Yi, Xia, Ruici
• Format: Article
• Language: English
• Subjects: BP neural network; carbon; climate change; environment; Geographical detector; Industrial carbon emission efficiency; information technology; macroeconomics; Multi-objective particle swarm optimization; Multi-scale geographically weighted regression; prediction; spatial variation; Spatially constrained multivariate clustering
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2023.167692

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial constraints regionalization and multi-objective machine learning to predict future industrial carbon emission efficiency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development. [Display omitted] •MOPSO-BP is a reliable tool for predicting the future trend of ICEE.•The 285 cities were classified into four ICEE regions by spatial constraints.•In regions other than APR, cities meet challenges in achieving super efficiency.•The improvement strategies for ICEE are based on geographical detector and MGWR.•Proposing low-carbon development recommendations at regional and urban scales.