Global supply chains face increasing disruptions due to biodiversity loss, which undermines vital ecosystem services such as climate regulation, water management and pollination. Despite this growing risk, traditional insurance models often fail to incorporate ecological dimensions, limiting their effectiveness in mitigating the vulnerability of supply chains to ecological changes. This study addresses this gap by developing a structured framework for evaluating insurance models that explicitly consider biodiversity-related risks. The primary contribution lies in the definition of four novel biodiversity-focused insurance models, Biodiversity-Linked Insurance (BLI), Ecosystem-Based Insurance (EBI), Parametric Biodiversity Risk Insurance (PBRI), and Supply Chain Disruption Insurance for Biodiversity Risks (SCDI-BR), and the establishment of twelve comprehensive criteria spanning ecological, financial, operational, and regulatory dimensions. To support model selection, the study introduces a novel multi-criteria decision-making method, the Asymmetric Linear Evaluation using Cosine Similarity Approach (ALECSA), which integrates asymmetric stakeholder preferences and penalty factors. Criteria weights were determined using the Best-Worst Method based on expert evaluations across logistics, insurance, biodiversity and policy sectors. The application of the proposed framework identified EBI as the most effective model, followed by BLI, PBRI and SCDI-BR, and the findings were validated using established MCDM techniques, confirming the robustness of results. Several criteria function as ecological indicators that reflect positive biodiversity impacts, such as risk reduction, alignment with sustainability goals and proactive ecological practices, underscoring the importance of embedding biodiversity considerations into supply chain risk management under climate stress.

Biodiversity-inclusive insurance for supply chains: Assessing positive ecological impacts through a novel MCDM method

Miglietta, Pier Paolo;Valente, Donatella;Porrini, Donatella
2026-01-01

Abstract

Global supply chains face increasing disruptions due to biodiversity loss, which undermines vital ecosystem services such as climate regulation, water management and pollination. Despite this growing risk, traditional insurance models often fail to incorporate ecological dimensions, limiting their effectiveness in mitigating the vulnerability of supply chains to ecological changes. This study addresses this gap by developing a structured framework for evaluating insurance models that explicitly consider biodiversity-related risks. The primary contribution lies in the definition of four novel biodiversity-focused insurance models, Biodiversity-Linked Insurance (BLI), Ecosystem-Based Insurance (EBI), Parametric Biodiversity Risk Insurance (PBRI), and Supply Chain Disruption Insurance for Biodiversity Risks (SCDI-BR), and the establishment of twelve comprehensive criteria spanning ecological, financial, operational, and regulatory dimensions. To support model selection, the study introduces a novel multi-criteria decision-making method, the Asymmetric Linear Evaluation using Cosine Similarity Approach (ALECSA), which integrates asymmetric stakeholder preferences and penalty factors. Criteria weights were determined using the Best-Worst Method based on expert evaluations across logistics, insurance, biodiversity and policy sectors. The application of the proposed framework identified EBI as the most effective model, followed by BLI, PBRI and SCDI-BR, and the findings were validated using established MCDM techniques, confirming the robustness of results. Several criteria function as ecological indicators that reflect positive biodiversity impacts, such as risk reduction, alignment with sustainability goals and proactive ecological practices, underscoring the importance of embedding biodiversity considerations into supply chain risk management under climate stress.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/582886
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