Achieving carbon neutrality has become a central policy objective for emerging economies, particularly the BRICS countries-BRICS (Brazil, Russia, India, China, and South Africa) which collectively account for a substantial share of global carbon emissions and energy consumption. The transition toward green energy, rapid technological innovation, and the expansion of green finance mechanisms are increasingly viewed as critical drivers of sustainable development and environmental improvement. However, empirical evidence integrating these three dimensions within a unified analytical framework for BRICS remains limited. This study examines the contribution of green energy transition, technological innovation, and green finance to achieving carbon neutrality in BRICS countries using a Pooled mean group auto regressive distributed Lag (PMG ARDL) framework and Dumitrescu–Hurlin panel causality analysis. The results indicate that green energy transition significantly reduces carbon emissions in both the long run (−0.45) and short run (−5.65), emphasizing the importance of shifting toward renewable energy sources. Technological innovation exerts a significant negative effect in the long run (−0.17), reflecting efficiency gains and cleaner production, although its short-run impact remains insignificant. Similarly, green finance improves environmental quality in the long run (−0.10) by supporting low-carbon investments, while short-run effects are statistically insignificant due to adjustment frictions. Economic growth increases emissions in the long run (0.43), confirming the scale effect, whereas trade openness reduces emissions (−0.87), indicating the role of technology diffusion. The error correction term (−0.76) confirms a strong convergence toward long-run equilibrium. The causality analysis reveals unidirectional causality from green energy transition, technological innovation, and green finance to carbon emissions, while bidirectional causality exists between economic growth and emissions, highlighting a feedback mechanism. Policy implications suggest that BRICS economies should strengthen green financial systems, accelerate renewable energy adoption, promote innovation-driven sustainability, and design growth strategies that decouple economic expansion from environmental degradation.

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.