AIベースのバイオ燃料生産最適化：劣化土地を活用した気候変動緩和、グリーンファイナンス動員、国連SDGs達成のための戦略

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AIとバイオ燃料の統合研究の最新動向とABLRフレームワークを提供し、今後の研究の方向性を示す。

🏢実務担当者:劣化土地でのバイオ燃料プロジェクトの収益性とグリーンファイナンス活用の具体的な指標を提供する。

🏛政策担当者:土地劣化対策と再生可能エネルギー政策を統合し、グリーンファイナンス動員のための政策設計に示唆を与える。

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green finance for sustainable transitions has created a rare policy window in which AI-optimized biofuel production on degraded lands can simultaneously serve multiple imperatives. This study presents a comprehensive secondary data analysis of AI-based optimization frameworks for deploying biofuel production systems on degraded lands, integrating an explicit green finance dimension that has been largely absent from prior synthesis literature. Drawing on 152 peer-reviewed studies and authoritative datasets from FAO, IEA, IRENA, UNCCD, the Green Climate Fund (GCF), and the World Bank, we analyze machine learning, deep learning, reinforcement learning, and hybrid AI architectures applied to feedstock selection, soil remediation, yield prediction, supply-chain logistics, and green finance risk-return optimization. Our findings reveal that AI-optimized biofuel systems on degraded lands recover 75-94% of prime-land bioenergy yields, sequester 8.3-10.5 t CO2e ha-1 over 30 years, reduce lifecycle GHG emissions by 55-88%, and generate internal rates of return of 9-22% when green finance instruments are systematically integrated. Green bonds, Article 6 carbon credits, GCF concessional finance, and blended finance structures are identified as the most impactful instruments, collectively capable of reducing project risk scores by 30-45% and expanding the investable universe of degraded-land biofuel projects by an estimated 340%. We develop the AI-Biofuel-Land Restoration (ABLR) conceptual framework with explicit green finance routing pathways and identify critical policy enablers for global deployment. This study advances the evidence base for policy-makers, investors, researchers, and development practitioners working at the intersection of artificial intelligence, bioenergy, green finance, and sustainable land management.

• openalex https://doi.org/10.3390/pr14172823first seen 2026-09-03 05:00:23
• scopus https://api.elsevier.com/content/abstract/scopus_id/105050075233first seen 2026-09-17 05:43:29 · last seen 2026-09-21 05:39:46