Search Results (446)

Urban flood vulnerability is increasingly shaped by the interaction between climate change, urbanization, and spatial planning practices, particularly in Mediterranean metropolitan areas. This study develops an integrated GIS–AHP framework to assess the susceptibility component of flood vulnerability in the urban area of Thessaloniki, Greece. Using open-access geospatial data, ten indicators representing soil, hydrological, and environmental conditions are derived and spatially analyzed. The Analytic Hierarchy Process (AHP), based on expert judgment, is applied to estimate the relative importance of these indicators and to support their integration into a composite flood susceptibility index. The results reveal strong spatial heterogeneity, with high susceptibility concentrated in low-lying, densely urbanized areas and zones near drainage pathways. Among the examined factors, the Topographic Wetness Index emerges as the most influential, highlighting the persistent role of terrain-driven hydrological processes even in highly built environments. The proposed framework provides a transparent and transferable methodology for identifying flood-prone areas and supports evidence-based urban planning and climate resilience strategies. The findings contribute to the broader discussion on vulnerability and resilience in urban systems by linking spatial analysis with decision-support tools in a policy-relevant context. Full article

Rapid urbanization has intensified artificial light at night (ALAN) and reduced access to natural dark sky environments. Dark sky parks provide a potential spatial approach for nighttime environmental protection, ecological conservation, and astronomical recreation. This study develops a constraint-based suitability assessment framework for urban dark sky park site selection and applies it to Wuhan, China. Multi-source geospatial data were integrated into a 1 km × 1 km evaluation grid. The AHP–Delphi method was used to determine indicator weights, while land cover constraints were introduced to exclude artificial surfaces from candidate evaluation areas. Weighted overlay analysis, sensitivity analysis, continuous patch screening, and dark sky quality verification were then conducted. The results show that (1) artificial light visibility (ALV) and cloudless days (CVD) are the most important indicators, with weights of 0.328 and 0.250, respectively; (2) 29.38% of the evaluation units are classified as most suitable or more suitable; (3) the spatial pattern of highly suitable areas remain relatively stable, with Jaccard overlap rates of 73.65% and 87.09% under alternative weighting scenarios; and (4) continuous patch screening identifies Caidian and Yangda as priority candidate areas. Further verification using the Bortle Scale, a nine-level classification of night darkness, shows that the Caidian patch reached Bortle class 4 and National Astronomical Observatories (NAOC) dark sky class 1, indicating stronger practical feasibility for dark sky park development. The proposed framework provides a methodological reference for integrating dark sky protection, land use feasibility, and urban planning in metropolitan regions. Full article

This study addresses the high degree of subjectivity and the supply–demand imbalance in wetland park science communication. Taking the Nanjing Longpao Yangtze River Provincial Wetland Park as a case study, it develops a data-driven, sustainability-oriented design framework integrating NVivo (qualitative data analysis software), the Analytic Hierarchy Process (AHP), the KANO model, Artificial Intelligence Generated Content (AIGC), and Fuzzy Comprehensive Evaluation (FCE) into a closed-loop methodology. A wetland database was constructed through field research and a literature review. User interviews (N = 73) were analysed using NVivo to distill core themes. AHP, based on 15 expert pairwise comparisons, identified Distinctive Plants (global weight 0.314) and Rare Bird Species (0.275) as priority design elements. The KANO model classified 20 science topics into Must-be (N = 5), One-dimensional (N = 2), and Attractive (N = 8) attributes. AIGC was then employed to generate a three-module promotional brochure (wetland flora, avifauna, and culture). FCE with 60 valid respondents confirmed all three modules achieved an ‘Excellent’ rating, validating the framework’s effectiveness. This study provides a replicable, sustainable design paradigm for wetland science communication. Full article

Amid accelerating global environmental change, assessing ecological vulnerability is critical for sustainability science. Focusing on the Yellow River Source Region (YRSR)—a key water source and ecological shield in China—this study develops an integrated assessment system based on the “Pressure–State–Response” (PSR) framework, incorporating 29 indicators. A combined weighting approach integrating analytic hierarchy process (AHP) with entropy-based objective weighting characterizes the spatiotemporal patterns, drivers, and future trajectories of ecological vulnerability. Key findings reveal: (1) heterogeneous warming–wetting trends with stronger humidification in the south and relative stability in the north drive divergent hydrological responses, highlighting the limitations of single-climate metrics in explaining vulnerability dynamics; (2) vulnerability patterns are primarily shaped by climatic factors—especially temperature and potential evapotranspiration—with anthropogenic pressures serving as secondary modulators, reinforcing the foundational role of thermal and moisture regimes in alpine ecosystem resilience; and (3) scenario projections consistently identify the northeast as a persistently high-vulnerability zone, yet show that balanced socioeconomic development can reconcile ecological protection with development needs. Based on these insights, a four-tier ecological zoning scheme and a governance framework comprising three strategies—strict conservation, adaptive regulation, and sustainable utilization—are proposed. This work offers actionable scientific guidance for tailored ecological conservation in the YRSR and contributes methodological advancements for vulnerability assessment and adaptive management of high-elevation ecosystems globally. Full article

Rapid urbanization and increasing landscape fragmentation pose significant threats to ecological connectivity, creating a need for integrative decision support approaches in sustainable spatial planning. This study presents a systematic review of ecological network optimization studies published between 2005 and 2025, following the PRISMA protocol. A total of 78 peer-reviewed studies were analyzed to identify methodological trends, recurring limitations, and research gaps in the assessment of structural and functional connectivity. Based on the gaps identified through the systematic review, this study proposes a conceptual Sustainable Spatial Decision Support System (S-SDSS) framework that integrates Morphological Spatial Pattern Analysis (MSPA), Multi-Criteria Evaluation (MCE/AHP), Minimum Cumulative Resistance (MCR), Least-Cost Path (LCP), and Gravity Modeling (GM) within a unified analytical structure. The review findings reveal a clear shift from single-method applications toward integrated multi-model approaches that better represent ecological processes and improve corridor prioritization. The proposed framework synthesizes the complementary strengths of these established methods to support evidence-based ecological network planning. The framework operates as a hybrid structure that combines a sequential analytical workflow with a unified typological classification system, generating Hybrid Ecological Typologies (T1–T5) as planning-oriented outputs that cannot be produced by any individual method alone. The proposed S-SDSS offers a transferable and policy-relevant conceptual basis for ecological network optimization, supporting green infrastructure planning, biodiversity conservation, and long-term landscape resilience across multiple spatial scales. Full article

Background: Multi-criteria decision analysis (MCDA) has become a dominant approach for glacier hazard susceptibility mapping, widely used to support risk management and climate adaptation planning. However, despite its widespread adoption, the role of MCDA outputs remains conceptually ambiguous: hazard classifications are often interpreted as predictive representations of risk, even though they are derived from preference-dependent decision models. This raises a critical but underexamined question regarding the reliability of MCDA-based glacier hazard assessments. This issue becomes particularly relevant in the current transition toward data-driven and artificial intelligence (AI)-based approaches for hazard modelling, where similar challenges of interpretability, validation, and reliability arise. Methods: To address this issue, we conducted a systematic literature review following the PRISMA 2020 protocol, analysing peer-reviewed studies published between 2015 and 2025. After screening 571 records, 60 studies were included. Data were extracted using a structured framework and synthesised through quantitative descriptive analysis and qualitative assessment of modelling practices, including method selection, criteria weighting, uncertainty treatment, validation, and geographical distribution. This study conducts a structured methodological audit—not a catalogue—of multi-criteria decision analysis (MCDA) applications in glacier hazard susceptibility mapping. Results: The analysis reveals a consistent methodological pattern. The Analytic Hierarchy Process (AHP) dominates current practice (36/60 studies, 60%), typically implemented through GIS-based weighted overlay with expert-derived weights. Critically, 80% of studies (48/60) derive criteria weights exclusively from expert judgement, with no data-driven calibration or sensitivity testing of subjective inputs. This epistemic reliance on unstructured or semi-structured expert elicitation, presented without robustness analysis, forms a central concern of this review. Moreover, empirical validation is limited: only 21/60 studies (35.0%) report quantitative performance metrics. Uncertainty and robustness analyses are rarely conducted, and most studies rely on single-model configurations without comparative evaluation. Despite these limitations, the resulting hazard maps are frequently presented as objective spatial predictions. The evidence base is also geographically concentrated, with 48/60 studies (80.0%) located in High Mountain Asia. Conclusions: The findings indicate a systematic mismatch between how MCDA-based hazard maps are constructed and how they are interpreted. In most cases, MCDA functions as a decision-structuring framework rather than a validated predictive model, yet its outputs are commonly treated as predictive evidence. This gap has important implications for the use of such models in risk management and climate adaptation, particularly in the emerging context of AI-driven hazard modelling, where issues of model validation, interpretability, and reliability become even more critical. Advancing the field requires explicit validation against observed events, systematic robustness and sensitivity analysis, transparent uncertainty modelling, and comparative evaluation of alternative or hybrid decision frameworks. Full article

Operational Technology (OT) and Industrial Control Systems (ICSs) along the NATO-EU eastern flank face escalating hybrid threats, yet existing cyber-resilience metrics remain IT-centric, lacking OT-specific constraints and geopolitical exposure dimensions. This paper presents a Design Science Research contribution: the development and simulation-based feasibility demonstration of two interconnected artefacts. The first is the AI-enabled Cyber-Resilience Index (ACRI)—a composite 0–100 metric operationalized through 16 indicators across four domains (detection performance, operational continuity, governance maturity, supply-chain risk), aggregated as a three-term convex combination of capability domains with a linear subtractive supply-chain exposure penalty, weighted via AHP-based illustrative sector-reference profiles. The second is the Unified Compliance Framework (UCF), a structured R → C → E → SLO mapping linking 47 atomic regulatory requirements (NIS2, DORA, CER, AI Act, CRA) to standards (IEC 62443, ISO/IEC 27001) and auditable evidence artifacts, with a Continuous Assurance Loop operationalizing continuous control monitoring. Feasibility is demonstrated through digital twin simulation under three OT-representative threat scenarios (energy SCADA APT, railway supply-chain compromise, manufacturing ransomware). Results in simulated environments show ACRI improvement from Moderate-Risk baselines (45–61) to Adequate-Resilience thresholds (65–73); the proposed federated autoencoder–LSTM detector attains a composite $D_{perf}$ of 0.883 versus 0.510 for a static ±3$\sigma$ threshold baseline (a 73% relative improvement at the domain level). Sensitivity analysis confirms classification robustness ($\pm 7.3\%$ weight perturbation; coefficient of variation below 9.1% across 10,000 Monte Carlo iterations). Critical limitations are explicit: simulation-only evidence ($n=12$ scenario instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. instances), illustrative (non-empirical) AHP weights, no operational field validation, and limited inferential statistical power. The contribution is positioned as a proof-of-concept design artifact establishing methodological foundations for OT-centric resilience assessment and compliance-to-engineering traceability, not as a field-validated operational system. Full article

This paper proposes a robust multi-criteria decision-making (MCDM) framework for evaluating and selecting Point-of-Sale (POS) systems in the context of Retail 5.0, where decisions involve multiple criteria and inherent uncertainty. The approach integrates entropy-based objective weighting with fuzzy AHP for subjective assessment, while incorporating Z-number theory to explicitly account for decision-makers’ confidence. Unlike conventional methods that assume equal importance between subjective and objective components, the proposed framework introduces a confidence-adjusted integration mechanism, in which Z-numbers are used to dynamically modulate the influence of subjective judgments based on their reliability. This enables a more balanced and context-sensitive weighting process that better reflects both data characteristics and human uncertainty. The contribution of this study is twofold: methodologically, it develops a reliability-driven integration framework that enhances the robustness and credibility of criteria weighting; practically, it demonstrates the applicability of the approach through a real-world POS system selection case. The results confirm that the proposed method provides more stable and informative decision outcomes, highlighting its effectiveness in complex decision-making environments. Full article

Natural hazards and their negative impacts on assets are increasing because of a variety of causes, including climate change, population expansion, and urbanization. Moreover, several areas are susceptible to multiple hazards that interact spatially and/or temporally, necessitating a multi-hazard assessment to adequately mitigate their effects. The goal of this study is to investigate the direct monetary losses produced by the simultaneous interaction of two independent hazards in Lisbon’s city centre, i.e., earthquake and pluvial flood. Seismic hazard has been assessed in terms of macro-seismic intensity, while flood scenario allows for the prediction of water depth for different return periods through a hydrologic-hydraulic model in HEC-RAS software. The seismic and flood vulnerability of the urban investigated compound was evaluated through MCDM methodology—specifically, AHP and TOPSIS methods. A framework for multi-hazard analysis was subsequently developed, explicitly accounting for the interaction between the two hazards and their joint occurrence probabilities based on historical data from the case study area. The results demonstrate that multi-hazard losses are 108 M€ for a 2-year return period and 232 M€ for a 475/500-year scenario, emphasizing that floods contribute more across all return periods in the research area; however, for longer return periods, the earthquake contribution increases significantly. Full article

This manuscript creates a framework for decision support based on green urbanism to direct the sustainable development of Gamasa, an Egyptian seaside city. The paper aims to convert the concepts of green urbanism into a multi-criteria evaluation that can support strategic urban development and prioritize spatial interventions. Sustainable mobility, green and blue infrastructure, energy and resource efficiency, urban form and density, social livability and public space quality, and governance and implementation feasibility are the six dimensions that are defined. These dimensions are derived from international sustainability literature and tailored to Gamasa’s particular challenges. The study’s methodology combines a multi-criteria decision-making approach based on the AHP with spatial analysis of land use, street hierarchy, building shape, and green space distribution. Weights for these dimensions are determined by expert-based pairwise comparisons, which are backed by a SWOT analysis. To prioritize priority zones for green transformation, the weighted framework is applied to four important urban areas: residential districts, a large urban park, the waterfront, and the main urban corridor. The top priorities, according to the results, are climate-responsive coastal design, increased green and blue infrastructure, and sustainable transportation. For quickly urbanizing coastal cities, the method demonstrates how the AHP operationalizes green urbanism into quantifiable, context-sensitive goals. Full article

Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions, a share expected to grow without effective technological and regulatory intervention. Recent policy developments, including the IMO Revised GHG Strategy (2023), the extension of the EU Emissions Trading System to maritime transport, and the FuelEU Maritime Regulation, require ports and shipping stakeholders to evaluate multiple decarbonization technologies under complex and often conflicting constraints. These decisions involve trade-offs across economic, technical, environmental, social, and cyber–physical security dimensions, which are not adequately addressed by conventional decision-support tools. This paper introduces XAI–MCDA-HoDEM, an explainable multi-criteria decision framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and SHAP-based explainability. The framework explicitly incorporates cyber–physical security as a core evaluation criterion and provides transparent, criterion-level explanations of decision outcomes. Using real-world data, the methodology is demonstrated through an illustrative case study and empirically validated at the Port of Rotterdam. Results show stable and robust rankings, alignment with observed port decarbonization strategies, and improved interpretability of decision drivers. The proposed framework supports transparent, policy-relevant decision-making for the maritime energy transition. Full article

The rapid integration of UAVs (Unmanned Aerial Platforms) introduces new operational capabilities but also raises critical challenges. This paper presents a quantitative risk assessment approach for evaluating the risks related to drone-assisted systems. The methodology combines established standards with the principles of the multi-criteria hierarchy concept. First, a qualitative analysis is performed to identify and register the required risk elements. Following this, a hierarchical model is developed to model the dependencies between systems’ components, environmental factors, structural limitations, and operational uncertainties. An AHP-based (Analytic Hierarchy Process) process is applied to enable elements quantification. To demonstrate the applicability and feasibility of the proposed methodology, two different drone-assisted systems are examined, showcasing their effectiveness in evaluating critical risk elements and computing cumulative risk contribution to quantify and prioritize potential risk events. The results indicate the significance of the methodology in ranking the verified risk elements and identifying those that made the greatest contribution to system failure. As revealed, power- and weather-related elements are among the most significant contributors to performance deterioration. In addition, operator-related factors significantly contribute to the system’s overall functional performance, especially when it is manually controlled. Finally, a comparative analysis underscores the sensitivity of risk ranking to variations in AHP scoring. Full article

While the circular economy offers a new perspective for achieving sustainability goals, digital technologies have become key enablers of this transformation. However, few studies in the literature address the identification and prioritisation of critical success factors for digitalisation that support the transition to a circular economy, particularly for agri-food SMEs operating in developing countries. This study proposes an integrated PESTEL-based and Spherical Fuzzy AHP (SF-AHP) framework to identify and prioritise critical success factors for digitalisation in the circular economy transition of agri-food SMEs. First, the literature-derived critical success factors were identified and structured according to the PESTEL framework. The TOE framework was then employed as a theoretical lens to interpret these factors at the firm level in terms of technological, organisational, and environmental dimensions. A five-member expert panel evaluated the factors in the context of Türkiye, and their relative importance was analysed using a weighted SF-AHP approach. Quantitative results reveal that ‘Data analytics to boost agricultural output’ is the most significant factor (w = 0.128), followed by ‘High investment costs’ (w = 0.123) and ‘Efficient technology for the CE process’ (w = 0.114). To ensure the robustness of the findings, a comparative analysis was performed; the results revealed a strong alignment between SF-AHP and Fuzzy AHP (r = 0.986), as well as a high degree of consistency with AHP (r = 0.910), validating the methodological stability of the proposed framework. This study contributes to the identification of strategic priorities for digitalisation in the transition to a circular economy among agri-food SMEs in developing countries and provides policymakers and practitioners with a guiding framework. Full article

The growing deployment of unmanned aerial vehicles (UAVs) in energy-constrained applications has highlighted the need for appropriate energy consumption models. However, selecting between physics-based (white-box) and data-driven (black-box) modeling paradigms remains a largely implicit process. Researchers often navigate undocumented trade-offs among required predictive accuracy, empirical data availability, and access to aerodynamic testing infrastructure without a formalized structure. This study proposes a two-stage decision-making framework to formalize UAV energy model selection. In the first stage, a qualitative decision tree is inductively derived from a corpus of 23 recent studies, explicitly mapping infrastructural and informational constraints to five distinct modeling regimes. In the second stage, the Analytic Hierarchy Process (AHP) is applied to quantitatively evaluate the feasible alternatives based on context-specific criteria: accuracy, interpretability, development cost, and customization adaptability. The structural logic of the framework is evaluated against an independent set of 24 holdout studies, demonstrating a high degree of consistency between the framework’s recommendations and the methodologies employed in the literature. Furthermore, the quantitative AHP scoring introduces “fallback flexibility,” enabling researchers to mathematically identify alternative modeling strategies when primary experimental conditions are compromised. Supported by an open-source Python graphical interface, this framework aims to reduce methodological ambiguity and support more structured, reproducible model selection in UAV energy research. Full article

This study develops a human-centered Artificial Intelligence (AI) framework enabling rapid ecodesign prioritization for Ecodesign for Sustainable Products Regulation (ESPR) compliance while demonstrating Large Language Model (LLM) integration in sustainability strategy. A four-stage hybrid methodology combining LLM-assisted action identification (30 ESPR-aligned interventions) with multi-criteria decision analysis with analytic hierarchy process (MCDA-AHP) is developed. Expert validation addressed LLM-driven interventions’ limitations with practitioners evaluating AI suggestions based on the value chain context. The framework applied to two Italian building-integrated photovoltaic (BIPV) small-medium enterprises (SMEs) demonstrated strategic differentiation based on feasibility vs. desirability vs. affordability, producing systematically different action portfolios within regulation-aligned aggregate structures. Sensitivity analysis showed 100% priority stability under ±10% AHP variations for priority one, three, and four actions and 82% for priority two actions, validating framework robustness. The framework provides empirical evidence for augmentation-not-automation in AI-assisted strategic planning, contributing a replicable methodology for responsible LLM integration across manufacturing sectors. Results demonstrate that combining AI synthesis efficiency with human contextual judgment enable regulation-aligned, business-model-specific sustainability strategies. Full article