A PSO Algorithmic for Data Analysis of Energy Economy Research on Carbon Emission Sustainability Detection

Abstract

In the field of energy economics, accurately analyzing and detecting sustainability in carbon emissions remains a pivotal challenge with profound implications for environmental policy and sustainable development. This study introduces an innovative approach by employing a Particle Swarm Optimization (PSO) algorithm to enhance the accuracy and efficiency of data analysis concerning carbon emission sustainability. By leveraging the inherent capabilities of PSO in navigating complex data landscapes, our methodology provides a nuanced understanding of emission patterns and their sustainability implications. We conducted a series of experiments using real-world energy consumption and carbon emission datasets to validate the effectiveness of our approach. The results demonstrate a significant improvement in detecting sustainable emission trends, with our PSO-based model outperforming traditional analysis methods in terms of precision and computational efficiency. Notably, the algorithm successfully identified key factors influencing emission sustainability, offering insights that could inform more targeted and effective environmental policies. This study not only showcases the potential of PSO algorithms in the realm of energy economy research but also sets a precedent for future investigations into carbon emission sustainability. The findings underscore the importance of adopting advanced computational techniques for environmental analysis, paving the way for more informed decision-making in pursuit of sustainable development.