Recent research has unveiled a novel hybrid framework, integrating Genetic Algorithms (GA) with Long Short-Term Memory (LSTM) networks, designed to significantly enhance supply chain sustainability. This innovative AI-driven model, leveraging publicly available Carbon Disclosure Project (CDP) reported data, has demonstrated remarkable results, including a 23.67% reduction in total emissions, a 10.98% improvement in operational efficiency, and 100% compliance with environmental regulations.

The framework addresses the critical industry challenge of balancing economic viability with environmental stewardship, moving beyond static analysis methods. The LSTM networks serve as the predictive engine, analyzing historical CDP emissions data to forecast future trends in carbon footprint and resource consumption. This foresight enables businesses to anticipate potential increases in emissions and implement proactive mitigation strategies. Complementing this, the GA component acts as a multi-objective optimizer, navigating complex trade-offs to minimize emissions and operational costs while ensuring adherence to stringent environmental standards.

This approach offers a compelling alternative to traditional, often expensive, real-time data collection methods, such as IoT sensors. By harnessing the structured and standardized datasets from CDP reports, the system provides a cost-effective and highly scalable solution applicable across diverse industries and business sizes, from large corporations to small and medium-sized enterprises. The focus on indirect emissions, which often represent a significant portion of a company's carbon footprint, yielded the most substantial improvements, underscoring the model's effectiveness in optimizing the broader value chain.

The research highlights the framework's ability to dynamically adjust solutions, accounting for the non-linear relationships inherent in sustainability challenges. The multi-objective optimization ensures that sustainability goals are met without compromising economic performance, a crucial factor for widespread industry adoption. As global regulatory landscapes tighten and stakeholder demands for environmental accountability intensify, this hybrid GA-LSTM framework provides a practical, data-driven methodology for organizations to not only meet but exceed their sustainability targets, fostering a more resilient and environmentally responsible supply chain ecosystem.