Flood-vulnerability assessment uses diverse and often incompatible methods, limiting comparison across community, regional, and infrastructure scales and constraining land-use and resilience planning. This systematic review, conducted and reported in accordance with PRISMA 2020, examined quantitative flood-vulnerability methods published from 2000 through 2025, a period spanning the emergence and maturation of contemporary quantification approaches. Scopus was searched on 23 January 2026 and updated on 28 May 2026. Eligible studies were English-language, peer-reviewed articles applying a reproducible procedure to quantify flood-related vulnerability, susceptibility, fragility, damage, loss, disruption, or coping or adaptive capacity; hazard-only and purely conceptual studies were excluded. Of 371 screened records, 144 were included. Data were extracted manually, with reviewer verification, against a standardized coding framework capturing application scale, method family, construct, data source, weighting, validation, uncertainty treatment, and planning relevance, yielding 217 harmonized indicators across 226 indicator–component assignments; where source studies left components undefined, indicators were assigned by structural position within the study’s own model, and otherwise by reviewer consensus. Community-scale studies relied mainly on composite socioeconomic indices; regional-scale studies combined geospatial data with multicriteria decision analysis; infrastructure-scale studies used fragility, probabilistic, hydraulic, and network-performance models, exemplified by Monte Carlo–derived fragility curves and Manning-based hydraulic models. Inconsistent construct definitions, limited validation, weak treatment of indicator interactions, and low transferability, the last arising from locally calibrated weights, census-bounded indicator availability, and damage models encoding local construction typologies, constrained comparison. Principal limitations are reliance on one database, restriction to English-language literature, and the absence of a common effect measure, unreported variance estimates, and methodological heterogeneity, which jointly precluded meta-analysis. For municipal managers and territorial planners, the consolidated inventory offers an immediately usable instrument: it allows existing local vulnerability metrics to be screened against a comprehensive reference set, component-level indicators measurable from current municipal GIS, census, and cadastral data to be selected, and those indicators to be embedded directly in floodplain overlay districts, zoning provisions, and stormwater requirements, provided they are locally calibrated before adoption.
The traditional value of beekeeping lies in the production of honey, other bee products, and the provision of pollination services. In recent decades, beekeepers have evolved from mere service providers into ecological stakeholders who shape sustainable land use management and ecological restoration. Through an anonymous questionnaire, we investigated the socio-cultural perceptions of Estonian beekeepers as indicators of their stakeholder potential on two operation scales. We observed that Estonian beekeepers balance economic orientation with hobby activity, and these proportions vary according to operation size. Beekeepers reported that they are considered positive members of their communities, often with deep-rooted family ties. Estonian beekeeping suffers from a ‘myth gap’ because beekeepers have weak connections to national culture and heritage; these assets could otherwise be leveraged in product marketing through ‘heritage branding’. The stakeholder potential of beekeepers is limited by their subordinate position in interactions with landowners and land managers; however, their motivations, constraints, and options to act as efficient stakeholders differ between small-scale and large-scale holdings. We conclude that large-scale beekeepers have greater potential to act as stakeholders in nudging land users towards sustainability. We suggest that beekeepers move beyond opportunistic practices, activate their social networks, and integrate local cultural traditions to advocate for sustainable land use and guide pollinator-friendly ecological restoration.
Maritime decarbonisation requires fuel-consumption models that are accurate, physically plausible, and suitable for operational decision support. We develop a three-layer interpretable machine-learning framework combining predictive validation, physical-plausibility auditing, and operational aggregation. Using 489,620 speed-stable cruising observations from 21 vessels, the predictive layer separates record-level interpolation from temporal and cross-vessel transfer; RMSE increases by about threefold under stricter validation, showing that random splits overstate transferability. The explanatory layer audits SHAP-derived responses against pre-specified physical expectations and empirical support. Speed responses are strongly monotonic and nonlinear, the primary wave perturbation is not confirmed under the locked C4 rule, and the rudder response is empirically unidentifiable. The operational layer evaluates speed-reduction sensitivity using an observation-period main-engine CII proxy. Fixed-time responses are heterogeneous across vessel groups and reproduced across XGBoost, LightGBM, and Random Forest models; deeper perturbations progressively leave multivariable training support. Paired ERA5 controls retain the principal transfer and wave-audit conclusions but do not remove pathway–vessel-type confounding. These findings are local, model-based sensitivities in the studied 21-vessel sample and do not estimate a statutory annual CII or establish causal vessel-type mechanisms. The framework provides an auditable validation protocol for maritime ITS; applicability beyond shipping requires independent validation.
Long-term deformation of urban road networks can compromise infrastructure performance and requires spatially continuous methods for identifying segments that warrant targeted monitoring. This study integrates Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR), geotechnical data, multivariate k-means clustering, and GIS road-network analysis to evaluate roadbed deformation susceptibility in Daegu, South Korea. A total of 184 ascending Sentinel-1 SLC images acquired between June 2018 and December 2025 yielded 32,590 roadbed persistent scatterers (PS) and 247,734 surrounding-ground PS. Because a single ascending geometry was used, all measurements represent relative line-of-sight (LOS) motion rather than absolute vertical displacement. Roadbed relative LOS velocities ranged from −8.63 to + 4.87 mm/yr. Roadbed and surrounding-ground velocities correlated moderately within a 200 m neighborhood (r = 0.59), while percentile- and exceedance-based comparisons showed slightly heavier deformation tails on the roadbed. Susceptibility was assessed by clustering relative LOS velocity, SPT-derived VS30, and effective soil depth for 31,263 PS with complete data. The three resulting classes were defined by combinations of deformation and subsurface conditions rather than by velocity thresholds. The critical class exhibited the most negative mean relative LOS velocity (−0.60 mm/yr), lower mean VS30 (382 m/s), and relatively deep soil conditions, whereas the concern and stable classes had similar mean LOS velocities but were differentiated primarily by their subsurface profiles. Of the approximately 883.7 km major road network, 668.8 km was classified, comprising 161.8 km (24.2%) critical, 284.1 km (42.5%) concern, and 222.8 km (33.3%) stable roads, while 215.0 km remained unclassified because of insufficient PS coverage. Comparison with 19 documented ground-subsidence incidents yielded critical-class enrichment ratios of 0.34–0.49, but the limited sample was insufficient to either confirm or reject the susceptibility classification; 14 of the 19 incidents were associated with abrupt underground-utility or construction-related failures rather than gradual deformation processes. The proposed framework can support targeted monitoring, preventive maintenance, and sustainable urban road management.
With the rapid development of artificial intelligence and digital tools, the digital economy has become an important driving force for socioeconomic development, and digital skills have become a crucial capability for achieving common prosperity. Based on 804 pieces of micro-survey data from agricultural and pastoral areas in Xizang, this study explores common prosperity from the objective material dimension and subjective spiritual dimension, starting with the economic effect and social effect of digital skills. It constructs a dual-path analysis framework of “digital skills–economic behavior–common prosperity” and “digital skills–social psychology–common prosperity” to theoretically analyze and empirically test the logical relationship and mechanism of action of digital skills on the common prosperity of agricultural and pastoral households in underdeveloped regions. The research indicates that digital skills significantly promote the common prosperity of agricultural and pastoral households in underdeveloped regions, with a greater impact on spiritual common prosperity than material common prosperity. Heterogeneity analysis reveals that the promotion effect of digital skills on common prosperity is more pronounced in villages located in relatively economically developed agricultural areas, indicating the existence of certain digital poverty and a digital divide. The mechanism analysis shows that digital skills affect common prosperity through two paths: non-agricultural employment in economic behavior and fairness perception in social psychology. Therefore, it is necessary to improve digital infrastructure in agricultural and pastoral areas and enhance the digital skills of agricultural and pastoral households in underdeveloped regions; improve the information release and transmission mechanism of digital platforms to promote non-agricultural employment; enhance digital social inclusiveness and digital cultural regulation to improve fairness perception; and leverage the empowering and converging effects of the digital economy, harness the economic effect and social effect of digital skills, and enhance both material and spiritual common prosperity to achieve comprehensive common prosperity.
