The research delves into the applications of lean management principles into the operational context. From the study, it has been obtained that, the efficiency and quality factor of organizations depends on the various possibilities in decision making process. This has emphasized the imperative approaches in terms of facilitating the terminologies. Employing the primary quantitative method, the study utilizes a survey method which has initiated a data collection method from 55 participants across different industries. The chosen tool for the analysis is SPSS that aligns with the various possibilities generating relevant results. The application of ANOVA, correlation and descriptive factors has acknowledged the nuance interpretation of the findings. The exploration of statistical sources has provided various insights into key variables while the correlation uncovers the relationship between various factors based on product service quality. The study employs with the possibilities that are present in lean management and its core applications in optimizing the organizational shortcomings.

Over the last three decades, the integrated optimization approach to logistics systems has become one of the most important aspects of supply chain management optimization. This approach simultaneously examines the relationships between facility locations, supplier / customer allocation to facilities, transportation route structure, and inventory planning and control. One of the most important issues in logistics decisions is location-routing. In this case, the number and location of facilities, the size of the transport fleet, and the structure of the routes are determined according to the location and characteristics of suppliers and customers. In this paper, a mathematical model of the problem with consideration of product decay time constraints and solving with efficient meta-heuristic methods based on ant colony optimization (ACO) and particle swarm optimization (PSO) is presented. The comparison of the results of the two algorithms shows that the ant colony optimization (ACO) is faster in terms of convergence rate and the number of solution iterations is less than the particle swarm algorithm.

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.