Carbon Price Forecasting for Forest Carbon Markets: Current State and Future Directions

Accurate forecasting of carbon credit prices is increasingly vital for the effective functioning of forest carbon markets, which play a growing role in global climate mitigation strategies. Against this backdrop, the present study conducts a systematic literature review to evaluate the state of carbon price forecasting methodologies, with particular emphasis on their applicability to forest-based carbon credits. The review highlights the predominance of machine learning (ML) and hybrid modeling approaches, which demonstrate enhanced predictive capabilities relative to conventional econometric techniques, particularly in capturing nonlinear dynamics and integrating heterogeneous data sources. However, their predictive power is limited by data scarcity, market opacity, and regulatory volatility. These issues are particularly severe in voluntary forest credit markets. The review identifies a critical research gap. Few studies explicitly model the behavior of forest credit prices. The findings suggest that future research should prioritize the development of policy-sensitive, scenario-based models that incorporate ecological, economic, and regulatory dimensions. While the majority of studies concentrate on compliance carbon markets, the methodological insights and forecasting approaches reviewed are highly relevant for the evolving forest carbon sector, nature-based mitigation strategies, and climate solutions. It also offers guidance for creating more transparent and robust forecasting tools in the forest carbon sector.