AI-Supported Forecasting of Biomass Availability under Changing Environmental and Resource Conditions

Reliable forecasting of biomass availability is essential for sustainable bioenergy planning, climate mitigation, and efficient resource management. Biomass production is influenced by complex interactions among climate variability, land use, management practices, and socioeconomic drivers, which limits the effectiveness of conventional empirical and process-based models. This study reviews recent advances in artificial intelligence (AI) and machine learning approaches for biomass availability forecasting under dynamic environmental and resource conditions. Emphasis is placed on models that integrate multi-source data, including remote sensing, field observations, climate records, management inputs, and socioeconomic indicators. The reviewed literature shows that AI-based methods capture nonlinear and spatiotemporal relationships more effectively than traditional approaches, resulting in improved prediction accuracy, scalability, and adaptability across regions. Ensemble, hybrid, and probabilistic frameworks further support uncertainty-aware forecasting, which is critical for policy formulation and industrial decision-making. From a sustainability perspective, AI-supported biomass forecasting contributes directly to several United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). By supporting informed decision-making, resilient biomass supply chains, and risk-aware planning, AI-based forecasting frameworks provide a practical pathway toward sustainable and climate-resilient bioenergy systems.