School of Economics and Management, South China Normal University, Guangzhou, Guangdong 510006, PR China.
SCNU 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.
J Environ Manage. 2023 Jul 1;337:117759. doi: 10.1016/j.jenvman.2023.117759. Epub 2023 Mar 21.
The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO 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.
全球碳达峰碳中和目标的确立，对 CO 排放绩效指标（CEPIs）预测和产业结构优化提出了迫切要求。然而，CEPIs 的准确多目标预测仍然是一个棘手的问题。本研究提出了基于多头注意力卷积神经网络（MHA-CNN）的模型，用于准确预测 4 个 CEPIs，并进一步为进一步的产业结构优化提供合理建议。所提出的 MHA-CNN 模型引入了深度学习机制，具有有效的分辨率策略，用于训练模型过拟合、特征提取和自我监督学习，以获取 CEPIs 的适应性。多头注意力（MHA）机制在变量影响权重解释中起着重要作用，有利于 CNN 对 CEPIs 的预测性能。MHA-CNN 模型在使用 8 个影响变量对 CEPIs 进行多目标预测时，表现出优于 CNN 模型和长短期记忆（LSTM）模型的卓越性能，这突出了 MHA 模块在多维特征提取中的优势。此外，基于 MHA 分析的影响变量对 CEPIs 的贡献与地理分布分析具有相对较高的一致性，表明 MHA 模块在变量权重识别和贡献分解方面具有出色的能力。基于 MHA-CNN 比 CNN 和 LSTM 模型更准确的预测结果，第三产业的增加和第一、二产业的减少有利于提高总要素碳排放效率，并进一步提高碳排放效率较低地区的有效能源利用效率。本研究为准确的多目标 CEPIs 预测和进一步的产业结构优化提供了重要的见解，以提高总要素碳排放效率。
