Green logistics management is an integral part of sustainable supply chain management, focusing on minimizing the environmental issues aroused in logistics operations. Moreover, with the escalating concern about climate changes and carbon emissions, organizations are challenged to implement analytics-based methods to enhance sustainability indices. The proposed research focuses on carbon emissions prediction in the logistics area of the Western Maharashtra region using the Hybrid LSTM-XGBoost approach.

Contrary to previous regression methods, the novel LSTM-XGBoost framework applies deep learning to identify temporal relationships and machine learning for efficient utilization of categorical variables, thus imbibing a novel modelling mechanism for green logistics. This study uses past logistics-related data for Western Maharashtra in terms of fuel consumption, type of vehicles, payload, route characteristics, and weather to build a robust model for predicting carbon emissions. This dataset has been generated from various governmental reports and industry-related publications.

The findings suggest that variables such as route planning options, choice of vehicles, fuel efficiency, and government policies impact carbon emissions in the logistics industry of Maharashtra. The hybrid model shows better predictive analytic results by registering lower MAE and RMSE scores than traditional models.

In addition, the report sheds light on critical factors surrounding AI-driven sustainability solutions, specifically related to data availability, the efficacy of regulation, and infrastructure requirements within the region. In conclusion, the proposed model becomes a real decision-making tool for logistics corporations aiming to optimize operations and policymakers intending to adopt AI-driven elements within environmental policies. By integrating AI-driven models within logistics management, corporations assist in supporting global sustainability policies and improve logistical operations. This study significantly contributes to green logistics and methods intending to mitigate carbon emissions, enhancing intelligent, green, and eco-friendly solutions for traffic systems.