Optimization Algorithm for Emission Reduction Schemes Based on Carbon Footprint Prediction

Hongliang Sun, Feifei Wang, Meng Wang, Jinlan Liu, Qiao Guan

Abstract: Greenhouse gas emissions, especially carbon dioxide, play a critical role in intensifying climate change, a challenge exacerbated by the carbon-intensive operations of businesses and organizations worldwide. As a result, the urgent need to reduce carbon emissions has become a shared priority for corporations and policymakers alike. The research community has shown a growing interest in the precise quantification of emissions, detailed carbon footprint assessments, and the development of effective strategies to mitigate carbon emissions. Yet, the practical execution of these strategies is hampered by significant challenges, including the difficulty of accurately measuring emissions, the impracticality of short-term carbon footprint forecasting, and the inherent uncertainty in the effectiveness of mitigation efforts. Furthermore, existing strategies often fail to balance the need for production efficiency with economic realities, which are crucial for achieving sustainable carbon management. To address these problems, this paper presents a novel research on algorithms that optimize emission reduction plans based on carbon footprint predictions. Our approach is centered on three main components: enhancing carbon emission data completeness, improving carbon footprint predictions, and refining emission reduction strategies through the application of deep learning and optimization techniques. To overcome data granularity and gaps, we introduce a deep learning algorithm that completes carbon emission data sets, utilizing prior physical knowledge to generate training samples and predict missing values. We then propose a deep learning model for carbon footprint prediction that integrates spatial and temporal features, guided by physical knowledge for enhanced accuracy. This model is further refined with transformation modules grounded in physical principles, ensuring a comprehensive consideration of spatial, temporal, and physical insights. Culminating in an optimization algorithm, our approach delivers the most effective emission reduction plans that align with current production efficiency and economic interests. Our experimental findings validate the algorithm’s effectiveness in optimizing emission reduction strategies while incorporating both efficiency and economic perspectives.

External IDs:dblp:conf/icss3/SunWWLG24