Research on carbon emission prediction and economic policy based on TCN-LSTM combined with attention mechanism

• Published in: Frontiers in ecology and evolution Vol. 11
• Main Authors: Wei, Xiaoyan, Xu, Ying
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 13.10.2023
• Subjects: attention mechanism; carbon emission prediction; environmental issues; LSTM; TCN
• ISSN: 2296-701X; 2296-701X
• DOI: 10.3389/fevo.2023.1270248

Introduction In the face of increasingly severe global climate change and environmental challenges, reducing carbon emissions has become a key global priority. Deep learning, as a powerful artificial intelligence technology, has demonstrated significant capabilities in time series analysis and pattern recognition, opening up new avenues for carbon emission prediction and policy development. Methods In this study, we carefully collected and pre-processed four datasets to ensure the reliability and consistency of the data. Our proposed TCN-LSTM combination architecture effectively leverages the parallel computing capabilities of TCN and the memory capacity of LSTM, more efficiently capturing long-term dependencies in time series data. Furthermore, the introduction of an attention mechanism allows us to weigh important factors in historical data, thereby improving the accuracy and robustness of predictions. Results Our research findings provide novel insights and methods for advancing carbon emission prediction. Additionally, our discoveries offer valuable references for decision-makers and government agencies in formulating scientifically effective carbon reduction policies. As the urgency of addressing climate change continues to grow, the progress made in this paper can contribute to a more sustainable and environmentally conscious future. Discussion In this paper, we emphasize the potential of deep learning techniques in carbon emission prediction and demonstrate the effectiveness of the TCN-LSTM combination architecture. The significant contribution of this research lies in providing a new approach to address the carbon emission prediction problem in time series data. Moreover, our study underscores the importance of data reliability and consistency for the successful application of models. We encourage further research and application of this method to facilitate the achievement of global carbon reduction goals.