Introduction: Net-zero emissions have become a critical strategic goal for corporations seeking alignment with global sustainability targets. This study aimed to explore the application of Machine Learning (ML) techniques, particularly Bi-LSTM (Bidirectional Long Short-Term Memory), to optimize emission forecasting, carbon offset allocation, and green fund utilization across industrial sectors. Methods: A Bi-LSTM model was trained on industry-specific data from the Steel, Cement, and Aluminum sectors, with key features including emission intensity, production volume, adoption of green technologies, and carbon capture practices. Additional ML models and carbon cycle simulations supported decision-making and comparative analysis. Results: The Bi-LSTM demonstrated superior performance in balancing accuracy, robustness, and consistency, enabling confident long-term emission predictions up to 2050. Incorporating green adoption rates and carbon capture efficiency further highlighted the critical influence of emerging technologies on sustainability outcomes. Discussion: The integration of ML models enhanced transparency in green fund usage and supported optimized investment strategies in sustainable technologies and carbon offsets. The results emphasize the transformative potential of ML in strengthening corporate environmental governance and enabling scalable, data-driven sustainability frameworks. Conclusion: Bi-LSTM emerged as the optimal model for industrial emission forecasting, offering actionable insights to accelerate emission reduction efforts and support corporations in achieving net-zero goals. This research underscores the need for transparent, scalable ML frameworks and highlights future directions, including interoperability with blockchain-based carbon credit systems and circular economy practices, to maximize global impact.
Israel et al. (Tue,) studied this question.