Accurate multi-objective prediction of CO2 emission performance indexes and industrial structure optimization using multihead attention-based convolutional neural network. (1st July 2023)

• Record Type: Journal Article
• Authors: Wu, Fenger; He, Jiaan; Cai, Liangyu; Du, Minzhe; Huang, Mingzhi
• Is Part Of: Journal of environmental management. Volume 337(2023)
• Publication Date: 2023-07-01
• Subjects: Carbon emission -- Multi-objective prediction -- Convolutional neural network -- Multihead attention mechanism -- Industrial structure optimization; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2023.117759
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: The 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. Highlights: The MHA–CNN model was proposed for multi-objective prediction of carbon emission. The MHA module was employed to dissect the contributions of influence variables. The industrial structure was optimized based on TFCE prediction using MHA–CNN. MHA–CNN showed its superior performance to CNN and long short-term memory model. The increase in tertiary industry proportion is conducive to the TFCE improvement.