Systematic review synthesizes a 40-indicator sustainability framework for passenger mobility systems, highlighting unified environmental, economic, and social evaluation methods.
Passenger transport is a major contributor to greenhouse gas emissions, urban air pollution and traffic-related economic costs in Europe, while unequal access to affordable mobility reinforces social inequalities. Addressing these pressures requires assessment approaches that integrate environmental, economic and social dimensions at the level of whole mobility systems, rather than individual technologies or modes. However, existing Life Cycle Sustainability Assessment (LCSA) applications in transport are fragmented, often focusing on single modes and lacking standardised indicator sets or methodological guidance for system-level decision-making. This study conducts a systematic literature review, following the ROSES (RepOrting standards for Systematic Evidence Syntheses) guidelines, of 61 peer-reviewed studies and key handbooks on LCA, S-LCA, LCC and LCSA applied to passenger land-based mobility systems. We identify and characterise the impact categories, indicators and methodological choices used across the three sustainability dimensions, applying selection criteria based on frequency of use, measurability, data availability and relevance to mobility-system assessment. Results are synthesised into a conceptual LCSA framework distinguishing direct on-site from indirect life cycle impacts and organising indicators by stakeholder group. The final framework comprises 40 indicators (14 environmental LCA indicators, 5 LCC cost categories, 21 S-LCA subcategories), operationalisable with existing databases and statistics. By linking system-level life cycle thinking with stakeholder-oriented social indicators, the framework offers policymakers and researchers a consistent, transparent basis for evaluating trade-offs currently missing from the field, advancing the methodological foundation for LCSA in transport planning and supporting future case studies and aggregation approaches for policy decision-making.
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Boom et al. (2026) studied this question.
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