Climate risk management increasingly relies on social vulnerability assessments to inform adaptation, yet systematic evidence on assessment quality, validation, and policy actionability remains limited. This structured review evaluates 41 quantitative studies (2003–2025), examining indicator selection, validation strategies, spatial scales, and methodological transparency. Analysis reveals four critical technical barriers constraining assessment quality. First, the “Data Wall”—predominant reliance on static census datasets (66%, n = 27)—limits temporal resolution and systematically excludes tourists, seasonal workers, and mobile populations despite their disproportionate exposure in many adaptation contexts. Second, residential bias pervades indicator selection (88%, n = 36), privileging resident vulnerability whilst neglecting functional populations in tourism, commerce, and transport sectors. Third, a “rigor gap” emerges: only 29% (n = 12) conduct systematic validation, 15% (n = 6) employ sensitivity analysis, and 24% (n = 10) report uncertainty quantification, undermining metric credibility despite increasingly sophisticated statistical techniques. Fourth, scalar mismatch characterises 61% (n = 25), where administrative units serve as analysis scales despite adaptation interventions requiring infrastructure-specific resolution. These structural limitations reveal that vulnerability metrics, whilst conceptually advancing beyond hazard-centric approaches, remain methodologically constrained by data availability and disciplinary conventions. Addressing these barriers requires: (1) integrating dynamic datasets capturing functional populations, (2) institutionalising validation protocols, (3) developing multi-scale assessment frameworks, and (4) enhancing uncertainty communication. This review provides indicative empirical evidence on vulnerability assessment limitations, informing both methodological advancement and operationalisation in adaptation policy. Given the modest sample size (n = 41) and reliance on associational analyses, findings should be interpreted as indicative patterns warranting further empirical verification rather than as definitive causal claims.
Armas et al. (Fri,) studied this question.