The research, led by Wen-Kuo Chen of the National Kaohsiung University of Science and Technology together with Venkateswarlu Nalluri of Chaoyang University of Technology, Sai Manideep Appana and Yogesh Vinayak Hadole of Vignan’s Foundation for Science, Technology and Research, and Madhavaiah Chendragiri of Pondicherry University, combines two established decision-science techniques into a single analytical framework. The team first used Interpretive Structural Modelling, or ISM, to map how the many determinants of rural quality of life influence and depend on one another. They then applied the Fuzzy Technique for Order Preference by Similarity to Ideal Solution, known as Fuzzy TOPSIS, to rank those determinants while explicitly accounting for the uncertainty and subjectivity that inevitably color expert judgments about social priorities.

The methodological pairing matters because neither technique alone can answer the question the researchers posed. ISM excels at revealing hierarchy: it takes a set of criteria identified through an extensive literature review and, through structured pairwise judgments, determines which factors are foundational drivers that shape everything else and which are surface outcomes that depend on deeper variables. Fuzzy TOPSIS, by contrast, is a ranking engine. It compares each candidate factor against an idealized best case and a worst case, calculating how close each one sits to the ideal. By replacing crisp numerical judgments with fuzzy sets, the technique absorbs the vagueness inherent in statements like one criterion is somewhat more important than another, producing rankings that are more robust to the imprecision of human assessment.

Applied to rural India, the integrated model produced a striking result. Three critical areas rose above the rest for enhancing quality of life in rural communities: cultural preservation, which corresponds to SDG 11 on sustainable cities and communities; transparency and accountability, which corresponds to SDG 16 on peace, justice and strong institutions; and agricultural innovation, which corresponds to SDG 2 on zero hunger and SDG 9 on industry, innovation and infrastructure. The finding challenges the intuitive assumption that income growth or basic service delivery must always come first. Instead, the analysis suggests that protecting local cultural identity, ensuring honest and responsive governance, and modernizing farming practices form the most powerful levers for improving daily life in the countryside.

Cultural preservation ranking at the top is perhaps the most unexpected of the three. Development economics has long treated cultural heritage as a luxury concern, something to address after material needs are met. But the ISM hierarchy indicates that cultural continuity functions as a foundational element of rural wellbeing rather than a decorative one. Villages that maintain their traditions, languages and community institutions tend to retain social cohesion, and that cohesion underpins collective action on everything from water management to school attendance. The alignment with SDG 11 signals that sustainable communities, in the sense the United Nations intended, depend on more than infrastructure; they depend on the intangible fabric that makes people want to stay, invest and participate in the places they live.

Transparency and accountability, mapped to SDG 16, occupy a similarly structural position in the model. Rural development programs in India are vast, involving subsidies, employment guarantees, housing schemes and food distribution networks, and their effectiveness hinges on whether resources actually reach intended beneficiaries. When governance is opaque, leakage erodes even well-funded initiatives, and trust between citizens and institutions collapses. The study’s ranking suggests that strengthening accountability mechanisms is not merely an administrative nicety but a precondition for progress across the entire spectrum of rural quality of life determinants. In the ISM hierarchy, governance quality sits among the variables that other factors depend upon, meaning improvements here cascade outward through economic and social outcomes.

Agricultural innovation, linked to SDG 2 and SDG 9, completes the top tier. Farming remains the economic backbone of rural India, and the gap between traditional practices and available modern techniques represents one of the largest untapped reservoirs of productivity in the country. Innovations in seeds, irrigation, mechanization and digital market access can raise incomes directly while also reducing vulnerability to climate shocks. The model’s placement of agricultural innovation among the highest-ranked determinants reflects its dual role: it is simultaneously an economic driver that lifts household welfare and an infrastructure challenge that requires investment in research, extension services and rural connectivity. Its coupling of SDG 2 and SDG 9 in the analysis underscores that hunger eradication and technological upgrading are two faces of the same rural transformation.

Beyond the headline rankings, the combined application of ISM and Fuzzy TOPSIS offers something the study argues is genuinely new: a thorough analytical approach that permits an in-depth examination of the intricate interconnectedness of various socio-economic elements. The researchers organized the quality of life criteria into social, economic, environmental and government categories, and the appendices to the paper document the level partitioning process for each, showing how the criteria stratify into hierarchical layers of drivers and dependent outcomes. This layered structure gives policymakers a roadmap that is more informative than a simple priority list. It tells them not only what to work on first but also which interventions will naturally reinforce others once foundational variables improve.

The practical implications extend to how rural development projects are evaluated and aligned with the Sustainable Development Goals. The techniques adopted, the authors argue, are effective tools for assessing rural development initiatives and ensuring they contribute to a more inclusive and fair growth path for India’s rural territories. A district administrator deciding between investments in cultural programming, grievance redress systems or agricultural extension could, in principle, apply the same hybrid framework locally, feeding in expert judgments specific to that region and obtaining a defensible ranking tailored to local conditions. The approach is transparent, reproducible and computationally light, which makes it feasible for use in settings where sophisticated modelling resources are scarce.

The study, which received no external financial support and was published open access under a Creative Commons license, ultimately fulfills its central aim of prioritizing the key challenges in enhancing quality of life in rural India through the Sustainable Development Goals. Its message for the global development community is that the path to better rural lives runs through cultural vitality, clean governance and farm innovation as much as through conventional economic metrics. As the 2030 deadline for the SDGs approaches, research of this kind offers a reminder that sustainable development is not a checklist of isolated targets but a web of mutually reinforcing conditions, and that identifying the strongest threads in that web is the first step toward pulling the whole structure upward.

Subject of Research: Development studies

Article Title: Prioritizing quality of life determinants for sustainable development goals in rural India: an integrated ISM-fuzzy TOPSIS approach

Article References: Chen, W.-K., Nalluri, V., Appana, S. M., Hadole, Y. V., & Chendragiri, M. (2026). Prioritizing quality of life determinants for sustainable development goals in rural India: an integrated ISM-fuzzy TOPSIS approach. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09249-7

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09249-7

Keywords: quality of life, rural India, sustainable development goals, ISM, fuzzy TOPSIS, cultural preservation, transparency and accountability, agricultural innovation, development studies, science technology and society, Prioritizing, quality

The research, published in Clean Technologies and Environmental Policy, responds to a problem that has frustrated policymakers across the developing world. Biofuel supply chains in emerging economies are constrained by fragmented governance, technological gaps, and competing sustainability priorities, and interventions aimed at one part of the chain often fail because deeper, upstream factors remain untouched. The team, led by Ilyas Masudin together with Rangga Primadasa and Dian Palupi Restuputri, set out to bring analytical order to this complexity by combining three established modeling techniques into a single, evidence-based pipeline for decision-makers.

The first technique, Interpretive Structural Modeling, or ISM, is a method for converting expert judgment about how factors influence each other into a hierarchical map. The researchers assembled a six-member multidisciplinary panel of domain experts who worked through structured questionnaires and Delphi-style consultations, assessing whether each of the seventeen decarbonization factors shaped, or was shaped by, every other factor. From these pairwise judgments, the team constructed a reachability matrix and distilled it into a layered structural model showing which factors sit at the base of the system, exerting influence from below, and which sit at the top, shaped by everything beneath them.

Complementing the structural model, the team applied MICMAC analysis, whose French name translates as cross-impact matrix multiplication applied to classification. This technique computes two scores for every factor: driving power, which measures how strongly a factor influences the rest of the system, and dependence power, which measures how strongly the factor is itself influenced. Plotting these scores sorts the seventeen factors into four families. Autonomous factors are relatively isolated; dependent factors are passive consequences of the system; linkage factors are both influential and unstable, amplifying whatever happens around them; and independent factors are the true engines of change, shaping the system while remaining largely beyond its control.

The structural analysis produced an unambiguous verdict about where the system’s leverage lies. Regulatory and policy support, designated CF7 in the study, and carbon pricing, designated CF8, each exhibited the maximum possible driving power of 17, meaning they influence every other factor in the framework while depending on none. In structural terms, these governance instruments are the roots of the entire decarbonization effort: strengthen them, and the effects cascade upward through technology adoption, financing, feedstock logistics, and stakeholder engagement. This finding aligns with a substantial body of literature showing that biofuel industries flourish or wither largely according to the policy environment that surrounds them.

Yet the study’s most consequential result emerged from its third component, Fuzzy VIKOR, a multi-criteria optimization technique that ranks alternatives by their closeness to an ideal compromise solution while explicitly handling the uncertainty inherent in human expert judgment. By encoding expert assessments as fuzzy numbers rather than crisp values, the method acknowledges that real-world evaluations are rarely precise. When the seventeen factors were ranked through this compromise-based lens, the ordering diverged sharply from the structural hierarchy. Technological maturity, CF13, emerged as the top-ranked priority with a VIKOR index Q of 0.000, the best possible compromise score, followed jointly by production process energy efficiency, CF4, and regulatory and policy support, CF7, each at Q equal to 0.274.

This divergence between the two lenses is the study’s central originality claim, and the authors argue it is a perspective absent from prior biofuel supply chain research. The structural analysis says that policy and carbon pricing are the systemic drivers whose improvement unlocks everything else. The compromise ranking says that technological maturity is the urgent binding constraint, the factor whose current inadequacy most severely holds back overall decarbonization performance relative to an ideal state. Both statements are true at once, and neither alone is sufficient. A government that pours resources into carbon pricing while the underlying conversion technologies remain immature may find its policies have nothing to grip; conversely, perfecting technologies without driving policy change leaves the system’s root causes untouched.

For emerging economies, the practical implications are considerable. The framework offers what the authors describe as a transparent, actionable tool to sequence interventions, optimize resource allocation under uncertainty, and foster stakeholder consensus on decarbonization roadmaps. Because the ISM layer reveals interdependencies, planners can identify which investments will propagate benefits through the chain; because the MICMAC layer classifies factors by driving and dependence power, planners can distinguish levers from symptoms; and because the Fuzzy VIKOR layer produces a compromise ranking robust to judgment uncertainty, planners can defend their sequencing choices to ministries, investors, and communities. The synthesis also demonstrates that effective decarbonization must concurrently address technological, governance, and socio-economic linkages to align emission reductions with broader circular economy and sustainability goals.

The study arrives amid intensifying global scrutiny of biofuels’ climate credentials. Life cycle assessments have shown that the carbon arithmetic of liquid biofuels depends heavily on feedstock choices, land use change, and production energy, while food-feed-fuel competition remains a persistent concern in biomass-constrained regions. In this context, a framework that helps emerging economies prioritize the factors that genuinely determine supply chain emissions, rather than spreading scarce resources across every plausible intervention, addresses a real and growing need. The authors report that the expert panel’s judgments were anonymized and obtained with verbal informed consent, and that no funding was received for the work.

The researchers acknowledge the inherent limits of expert-based modeling, and the framework is designed to be rerun as conditions evolve: as technologies mature, as carbon markets deepen, and as governance capacity strengthens, the interdependency structure and the compromise rankings can be recomputed to reflect the new reality. Data from the study will be made available on request. For now, the message for decision-makers in biofuel-producing developing nations is twofold: respect the structural roots of the system in policy and carbon pricing, but recognize that the most urgent bottleneck today is the maturity of the technologies themselves, and plan accordingly.

Subject of Research: A hybrid multi-criteria decision-making framework for prioritizing decarbonization factors in biofuel supply chains in emerging economies

Article Title: A hybrid ISM-MICMAC-Fuzzy VIKOR framework for decarbonizing biofuel supply chains in emerging economies

Article References: Masudin, I., Primadasa, R., & Restuputri, D. P. (2026). A hybrid ISM-MICMAC-Fuzzy VIKOR framework for decarbonizing biofuel supply chains in emerging economies. Clean Technologies and Environmental Policy, 28(10), Article 250. https://doi.org/10.1007/s10098-026-03601-w

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03601-w

Keywords: biofuel supply chain, decarbonization, ISM, MICMAC, Fuzzy VIKOR, multi-criteria decision-making, emerging economies, technological maturity, carbon pricing, policy support, circular economy, sustainable energy