Behavioral and Uncertainty-Aware Decision Support for Sustainable Technology and Policy

Sustainable technology and policy decisions require balancing economic, environmental, social, and institutional objectives under uncertainty. Conventional optimization and multi-criteria decision-making models often assume stable preferences, complete information, and fully rational behavior. In practice, stakeholders evaluate gains and losses relative to reference points, react asymmetrically to risk, and express judgments with ambiguity, hesitation, or incomplete data. At the same time, AI and data-driven analytics expand decision-support capacity but raise questions about interpretability, robustness, and stakeholder trust. Integrating behavioral insights with uncertainty-aware modeling can therefore produce more realistic and transparent support for cleaner production, circular economy, low-carbon energy, sustainable logistics, resilient infrastructure, materials selection, and environmental governance.This Research Topic aims to advance behavioral and uncertainty-aware decision support for sustainable technology selection and policy design. It seeks theoretical, methodological, and empirical contributions that model bounded rationality, reference dependence, loss aversion, regret, ambiguity, and heterogeneous stakeholder preferences. Particular attention will be given to integrations of behavioral decision theory with multi-criteria decision-making, multi-objective optimization, artificial intelligence, machine learning, simulation, data envelopment analysis, life-cycle assessment, and robust or stochastic analysis. Contributions should move beyond purely formulaic extensions by explaining why the proposed model is appropriate, how results are validated, and what actionable sustainability insights are generated. Comparative studies, sensitivity and robustness analyses, reproducible computational workflows, and real-world applications are especially encouraged. The collection will connect methodological innovation with credible decision support for organizations, industries, communities, and policymakers navigating sustainability transitions.We welcome Original Research, Methods, Review, Systematic Review, Perspective, Policy and Practice Review, Brief Research Report, and Technology and Code articles. Themes include:- behavioral MCDM and multi-objective decision-making;- prospect, regret, reference-dependent, and bounded-rationality models;- fuzzy, spherical fuzzy, neutrosophic, rough, probabilistic, robust, and stochastic approaches;- AI-enabled, explainable, and human-in-the-loop decision support;- group, participatory, and consensus-based sustainability decisions;- efficiency and sustainability benchmarking using DEA and related models;- sensitivity, uncertainty, validation, and reproducibility; and- applications in cleaner production, circular economy, energy, logistics, manufacturing, materials, urban systems, infrastructure, and environmental policy.Papers should make a clear methodological or applied contribution and explicitly demonstrate relevance to sustainable development. Topic editors: Nhat-Luong Nhieu Source: https://www.frontiersin.org/research-topics/85406/behavioral-and-uncertainty-aware-decision-support-for-sustainable-technology-and-policy