The development of Green Transport Corridors (GTCs) is an important strategy to help a region achieve more sustainable solutions. When such GTCs are implemented, multimodal supply chains and environmentally-friendly alternatives for freight transportation through economically relevant hubs and long-distance routes can be facilitated. Based on previous efforts for evaluating single routes, this paper aimed to propose a single multi-criteria Logistics Composite Index (LCI), constructed based on a set of Key Performance Indicators (KPIs), to evaluate the efficiency of GTCs considering the integration of multiple transport modes (highways, railways, and waterways). This approach consists of a dual-step procedure, applying a Network Equilibrium Model (NEM) and Data Envelopment Analysis (DEA). It was applied to Brazilian agricultural bulk transport export corridors, considering the existing and planned infrastructure in the harvest year of 2018/2019. In general, the best indexes were those from corridors considering planned railways. Specifically, the best index was from a corridor from the Northeast region. The second was from the South. The third was from the North (Amazon), and was one of the few corridors with adequate waterways. This approach is useful for decision-makers to determine the most efficient corridors as well as for policy-makers to guide infrastructure investments and address public policies.

Background: Effective decision-making in supply chain contexts requires understanding how criteria interact to shape rational and transparent decision structures. This study investigates how behavioral aspects influence the structuring of decision-making logic and the interdependencies between key criteria in supply chain contexts. Methods: Using Fuzzy DEMATEL, the research models the interactions between five core criteria —classification, definition, specification, decision, and action feedback—based on inputs from experienced professionals in a global chemical company. The approach enables mapping of causal influences while accounting for subjectivity and uncertainty in expert judgments. Results: The analysis identified specification, definition, and action feedback as causal criteria, with classification and decision being primarily influenced by them. The modeling process supported clearer prioritization and revealed how expert-based interactions can reduce decision biases. Conclusions: This study demonstrates how structuring decision-making logic through causal modeling enhances clarity and reduces subjectivity. The findings contribute to the development of decision support tools applicable across strategic supply chain contexts, offering practical implications for professionals seeking to improve decision transparency and effectiveness.