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<p>As global supply chains face escalating pressure to adopt sustainable practices, the effective integration of Environmental, Social, and Governance (ESG) criteria into supplier selection has become an urgent yet complex challenge, primarily due to prevalent uncertainties and inefficient consensus-building. Addressing this critical gap, this study proposes a novel PT-VRCD-TODIM framework that integrates Probabilistic Linguistic Term Sets (PLTSs), Prospect Theory, and a Variable-Weight Consensus Model. The method dynamically quantifies expert judgments under uncertainty using PLTSs, determines individual weights through evolving social network trust matrices, and streamlines group decision-making via an adaptive consensus process. Validated through an electric vehicle battery recycling case study, the framework demonstrated perfect decision consistency (1.0) while dramatically improving efficiency by reducing consensus iterations from 4 to 5 to merely two. Comparative analyses against TOPSIS and fuzzy MCDM benchmarks confirmed its superiority in ensuring preference stability and robustly modeling risk perceptions. The study contributes three pivotal innovations: a trust-driven dynamic weighting mechanism, prospect theory-enhanced linguistic computations, and a practical, integrated framework for ESG-supplier selection. This work provides a timely and implementable decision-support tool, crucial for advancing sustainable supply chain management in capital-intensive sectors such as electric vehicle manufacturing.</p>

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ESG-Oriented Supplier Selection Under Uncertainty: A TODIM Decision Framework Integrating Probabilistic Linguistic Term Sets, Prospect Theory and Variable-Weight Consensus

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Tianqing Shen,
  • Long Sun

摘要

As global supply chains face escalating pressure to adopt sustainable practices, the effective integration of Environmental, Social, and Governance (ESG) criteria into supplier selection has become an urgent yet complex challenge, primarily due to prevalent uncertainties and inefficient consensus-building. Addressing this critical gap, this study proposes a novel PT-VRCD-TODIM framework that integrates Probabilistic Linguistic Term Sets (PLTSs), Prospect Theory, and a Variable-Weight Consensus Model. The method dynamically quantifies expert judgments under uncertainty using PLTSs, determines individual weights through evolving social network trust matrices, and streamlines group decision-making via an adaptive consensus process. Validated through an electric vehicle battery recycling case study, the framework demonstrated perfect decision consistency (1.0) while dramatically improving efficiency by reducing consensus iterations from 4 to 5 to merely two. Comparative analyses against TOPSIS and fuzzy MCDM benchmarks confirmed its superiority in ensuring preference stability and robustly modeling risk perceptions. The study contributes three pivotal innovations: a trust-driven dynamic weighting mechanism, prospect theory-enhanced linguistic computations, and a practical, integrated framework for ESG-supplier selection. This work provides a timely and implementable decision-support tool, crucial for advancing sustainable supply chain management in capital-intensive sectors such as electric vehicle manufacturing.