AI-Based Optimization for Biofuel Production: Strategies for Utilizing Degraded Land for Climate Change Mitigation, Green Finance Mobilization, and Achieving United Nations Sustainable Development Goals

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🔬研究者:Provides a comprehensive synthesis of AI applications in biofuel production and green finance integration, identifying research gaps and a new conceptual framework (ABLR).

🏢実務担当者:Offers actionable insights on combining AI optimization with green finance instruments to de-risk and scale biofuel projects on degraded lands.

🏛政策担当者:Highlights policy enablers for AI-biofuel-land restoration, including Article 6 carbon credits and blended finance, to support climate and SDG goals.

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/10.20944/preprints202605.0940.v1first seen 2026-06-03 04:46:25 · last seen 2026-06-13 04:30:36