The Innovative Solution for Carbon Emission Prediction: Combining Quantum Particle Swarm Optimization and GRU with Dynamic Attention Mechanism

Abstract

Carbon neutrality, also known as net-zero carbon emissions, is a crucial strategy aimed at addressing the global climate crisis by reducing, offsetting, or capturing carbon emissions associated with human activities. Currently, many countries and organizations are setting carbon neutrality targets and taking action to reduce carbon emissions. However, these efforts require accurate carbon emission predictions to ensure that the formulated policies and measures can be successful. Additionally, traditional carbon emission prediction methods often rely on statistical models and historical data, which struggle to cope with the complexity and variability of the climate system. This results in inaccurate predictions in situations such as extreme weather events, impacting policy development and implementation of mitigation measures. To address this issue, we propose an innovative carbon emission prediction method that combines QPSO and GRU, incorporating an attention mechanism. The advantage of the QPSO algorithm is its ability to search for the optimal solution, optimizing the model's hyperparameters by considering global information, thereby improving the model's adaptability and prediction performance. Meanwhile, the GRU takes full account of the temporal nature of time-series data, better capturing dynamic patterns within the data. Experimental results indicate the outstanding performance of the QPSO-GRU model in carbon emission prediction. It can more accurately capture trends and changes in carbon emissions, providing governments and businesses with more reliable data for formulating emission reduction policies and planning carbon neutrality measures. This not only enhances the efficiency of climate change mitigation but also contributes to achieving global carbon neutrality goals.