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<p>This study investigates the integration of Multi-Criteria Decision Analysis (MCDA) and Cost-Benefit Analysis (CBA) through alternative scaling methodologies, focusing on the role of Likert-type, bipolar, and symmetric scales in infrastructure evaluation. A key methodological concern addressed is the positive bias introduced by conventional Likert scales, which may distort composite evaluations that blend subjective stakeholder judgments with monetary valuations. To mitigate this, the research explores the application of bipolar and symmetric scoring systems, both ranging from [-1 to 1], which enable the representation of both positive and negative performance across criteria. To address these scaling conflicts, this study employs a hybrid aggregation framework for integrating MCDA and CBA to control the weighting of qualitative and quantitative dimensions. Unlike conventional approaches that treat CBA as merely one among many criteria within MCDA, this study positions CBA as the central evaluative component and incorporates MCDA at a predefined calibrated weight, enhancing methodological clarity and preserving the analytical strength of economic evaluation. The framework is applied to a massive and highly debated infrastructure planning initiative: the selection and evaluation of long-term capacity expansion alternatives for Lisbon Airport. Through this large-scale case study, three MCDA scaling approaches are compared and examined for their effects on final rankings, stakeholder sensitivity, and evaluation robustness. The findings demonstrate that bipolar and symmetric scales improve reliability by highlighting option-specific trade-offs and sensitivities that may remain obscured under traditional methods. This reinforces the importance of using appropriate scaling methods and hybrid evaluation models that preserve economic rigor while capturing stakeholder perspectives. The proposed approach contributes to more transparent, balanced, and context-sensitive decision support systems, ultimately supporting more effective and equitable infrastructure investment strategies.</p>

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Scaling challenges in multi-criteria decision analysis in a hybrid approach with cost-benefit analysis for infrastructure investment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月19日

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  • Ali Shoaei,
  • Vitor Sousa,
  • Carlos Oliveira Cruz

摘要

This study investigates the integration of Multi-Criteria Decision Analysis (MCDA) and Cost-Benefit Analysis (CBA) through alternative scaling methodologies, focusing on the role of Likert-type, bipolar, and symmetric scales in infrastructure evaluation. A key methodological concern addressed is the positive bias introduced by conventional Likert scales, which may distort composite evaluations that blend subjective stakeholder judgments with monetary valuations. To mitigate this, the research explores the application of bipolar and symmetric scoring systems, both ranging from [-1 to 1], which enable the representation of both positive and negative performance across criteria. To address these scaling conflicts, this study employs a hybrid aggregation framework for integrating MCDA and CBA to control the weighting of qualitative and quantitative dimensions. Unlike conventional approaches that treat CBA as merely one among many criteria within MCDA, this study positions CBA as the central evaluative component and incorporates MCDA at a predefined calibrated weight, enhancing methodological clarity and preserving the analytical strength of economic evaluation. The framework is applied to a massive and highly debated infrastructure planning initiative: the selection and evaluation of long-term capacity expansion alternatives for Lisbon Airport. Through this large-scale case study, three MCDA scaling approaches are compared and examined for their effects on final rankings, stakeholder sensitivity, and evaluation robustness. The findings demonstrate that bipolar and symmetric scales improve reliability by highlighting option-specific trade-offs and sensitivities that may remain obscured under traditional methods. This reinforces the importance of using appropriate scaling methods and hybrid evaluation models that preserve economic rigor while capturing stakeholder perspectives. The proposed approach contributes to more transparent, balanced, and context-sensitive decision support systems, ultimately supporting more effective and equitable infrastructure investment strategies.