AI-driven Optimization and Forecasting for E-commerce Logistics and Low Carbon emission for Green Economy

These days, the logistics industry has experienced remarkable growth as global economic integration and cross-border e-commerce trade accelerate. However, this growth has brought about significant ecological and environmental challenges. This study analyzes the connection between China's cross-border e-commerce logistics market and the low-carbon economic system. The objective is to reduce carbon emissions in the logistics industry, minimize regional pollutant emissions, and bridge the carbon emission gap across different regions. The research employs Genetic Algorithm optimization to strike a trade-off between cross-border trade and carbon emissions. Eviews 6.0 software is used to analyze the relationship between the cross-border e-commerce logistics industry and the rate of low-carbon economic growth, represented by the carbon emission intensity index. Different trade volume combinations for each region in China are explored and evaluated based on their corresponding carbon emissions. The goal is to minimize carbon emissions while maintaining the same trade level. Additionally, an AI-based predictive model, combining the Genetic Algorithm and Autoregressive Integrated Moving Average (ARIMA) forecasting, is employed to validate the optimization results. The Genetic Algorithm provides optimal points for low carbon emissions, and the ARIMA forecast predicts the optimal solution for the following year. The findings indicate that the optimized solution significantly reduces carbon emissions compared to the original values.