人工知能技術を用いた植林プロジェクトの炭素吸収リターン予測と財務的実現可能性の評価

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🔬研究者:Provides a novel hybrid AI framework for integrating ecological and economic modeling, with insights into feature importance and probabilistic forecasting.

🏢実務担当者:Offers a tool for assessing afforestation project viability, enabling better investment decisions and carbon credit portfolio optimization.

🏛政策担当者:Highlights the importance of dynamic carbon price thresholds and AI-driven monitoring for effective carbon market design and climate policy.

This study develops and evaluates an artificial intelligence framework for forecasting carbon sink returns and assessing the financial viability of afforestation projects. While naturebased solutions for carbon sequestration are increasingly recognized as critical for climate mitigation, investment in afforestation remains constrained by uncertainty surrounding both the physical carbon uptake and the economic returns from carbon credits. Traditional models rely on linear assumptions and static parameters that fail to capture the complex, non-linear interactions between ecological, climatic, and market variables. Drawing on advances in machine learning for environmental prediction, this research constructs a hybrid model integrating random forest regression, long short-term memory networks, and gradient boosting algorithms. The model is trained on a multi-dimensional dataset comprising satellite-derived vegetation indices, soil carbon measurements, historical carbon credit prices, regional climate projections, and project-level cost data from afforestation initiatives across tropical and temperate zones. A key methodological innovation is the incorporation of a feature importance analysis to identify the dominant drivers of carbon sink variability and financial return, revealing that soil moisture dynamics, temperature anomalies, and carbon credit price volatility exert the strongest influence. The forecasting framework generates probabilistic projections of carbon sequestration rates and net present value under multiple climate and market scenarios over 30-year project horizons. Results indicate that afforestation projects in humid tropical regions exhibit the highest median carbon sink returns but also the greatest downside risk from drought-induced mortality, while temperate projects demonstrate lower but more stable financial returns. The analysis further demonstrates that machine learning models substantially outperform conventional carbon accounting methods in predicting year-to-year carbon flux variability, reducing forecast error by over 40 percent. Financial viability assessments reveal that only projects achieving carbon credit prices above a dynamic threshold-which varies by region and project design-yield positive risk-adjusted returns. The study concludes that AI-driven forecasting can reduce investment uncertainty, enable dynamic portfolio optimization, and unlock private capital for afforestation by providing transparent, data-driven evidence of both ecological impact and financial performance. This research contributes a robust, scalable methodology for integrating ecological and economic modeling, with direct implications for carbon market design, project financing, and climate policy.

• openaire https://doi.org/10.2139/ssrn.6912281first seen 2026-09-01 05:02:11 · last seen 2026-09-21 04:33:39