Journal of environmental management

1095-86303372023Jul01Journal of environmental managementJ Environ ManageAccurate multi-objective prediction of CO₂ emission performance indexes and industrial structure optimization using multihead attention-based convolutional neural network.11775911775910.1016/j.jenvman.2023.117759S0301-4797(23)00547-9The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO₂ emission performance indexes (CEPIs) and industrial structure optimization. However, accurate multi-objective prediction of CEPIs is still a knotty problem. In the present study, multihead attention-based convolutional neural network (MHA-CNN) model was proposed for accurate prediction of 4 CEPIs and further provided the rational suggestions for further industrial structure optimization. The proposed MHA-CNN model introduces deep learning mechanism with efficient resolution strategies for training model overfitting, feature extraction, and self-supervised learning to acquire the adaptability for CEPIs. Multihead attention (MHA) mechanism plays important roles in influence weight interpretation of variables to facilitate the prediction performance of CNN on CEPIs. The MHA-CNN model presented its overwhelmingly superior performance to CNN model and long short-term memory (LSTM) model, two frequently-used models, in multi-objective prediction of CEPIs using 8 influence variables, which highlighted advantages of MHA module in multi-dimensional feature extraction. Additionally, contributions of influence variables to CEPIs based on MHA analyses presented relatively high consistency with the geographical distribution analyses, indicating the excellent capacity of the MHA module in variable weights identification and contribution dissection. Based on the more accurate prediction results by MHA-CNN than those by CNN and LSTM model, the increase in the tertiary industry and the decreases in the first and secondary industries are conducive to improvement of total-factor carbon emission efficiency and further enhancement of effective energy utilization in regions with inefficient carbon emissions. This study provides insights towards the critical roles of the proposed MHA-CNN model in accurate multi-objective prediction of CEPIs and further industrial structure optimization for improvement of total-factor carbon emission efficiency.Copyright © 2023. Published by Elsevier Ltd.WuFengerFSchool of Economics and Management, South China Normal University, Guangzhou, Guangdong 510006, PR China.HeJiaanJSCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China.CaiLiangyuLGuangdong Guangya High School, Guangzhou, Guangdong 510145, PR China.DuMinzheMSchool of Economics and Management, South China Normal University, Guangzhou, Guangdong 510006, PR China. Electronic address: minzhe_du@126.com.HuangMingzhiMSCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, PR China. Electronic address: mingzhi.huang@m.scnu.edu.cn.engJournal Article20230321