Search Results (2,674)

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water). Full article

Vegetation degradation in arid and semi-arid regions is intensifying due to rising temperatures, declining rainfall, soil exposure, and persistent human pressures. Drylands cover over 41% of the global land surface and support nearly two billion people, making their degradation a major environmental and socio-economic concern. However, many remote sensing and GIS-based assessment approaches remain fragmented and difficult to reproduce. This study proposes a Computational Sustainability Framework for vegetation degradation assessment that integrates multi-source satellite data, biophysical indicators, automated geospatial preprocessing, and the Analytical Hierarchy Process (AHP) within a transparent and reproducible workflow. The framework comprises four phases: data preprocessing, indicator extraction and normalization, AHP-based modeling, and spatial classification with qualitative validation. The framework was applied to the Al-Khunfah and Harrat al-Harrah Protected Areas in northern Saudi Arabia using multi-source datasets for the January–April 2023 period, including Sentinel-2, Landsat-8, CHIRPS precipitation, ESA-CCI land cover, FAO soil data, and SRTM DEM. High degradation zones were associated with low NDVI (<0.079), high BSI (>0.276), and elevated LST (>49 °C), whereas low degradation areas were concentrated near wadis and relatively more fertile soils. Overall, the proposed framework provides a scalable and interpretable tool for early-stage vegetation degradation screening in arid environments, supporting the prioritization of areas for ecological investigation and restoration planning. Full article

Crowdsensing data serves as a crucial resource for supporting spatiotemporal applications and services. However, its inherent heterogeneity and quality uncertainty present significant challenges for data usability assessment: the evaluation methods are difficult to standardize due to the diverse types of data; assessment dimensions are predominantly confined to internal quality attributes; and a comprehensive framework for data usability evaluation remains lacking. To address these challenges, this study proposes an innovative, multi-layered usability assessment framework applicable to six major categories of crowdsensing data: specialized spatial data, Internet of Things (IoT) sensing data, trajectory data, geographic semantic web, scientific literature, and web texts. Building upon a systematic review of existing research on data quality and usability, our framework conducts a comprehensive evaluation of data efficiency, effectiveness, and satisfaction from dual perspectives—data sources and content. We present a complete system comprising primary and secondary indicators and elaborate on their computation and aggregation methods. Indicator weights are determined through the Analytic Hierarchy Process (AHP) and expert consultations, with sensitivity analysis performed to validate the robustness of the framework. The practical applicability of the framework is demonstrated through a case study of constructing a spatiotemporal knowledge graph, where we assess all six types of data. The results indicate that the framework generates distinguishable usability scores and provides actionable insights for improvement. This framework offers a universal standard for selecting high-quality data in complex decision-making scenarios and facilitates the development of reliable spatiotemporal knowledge services. Full article

Modern construction projects face persistent challenges with cost overruns and fragmented management across disconnected service phases. Whole-Process Engineering Consulting (WPEC) addresses these issues by integrating investment decision-making, design, supervision, and cost management into a unified delivery framework. Therefore, this study aims to develop a WPEC competitiveness influencing factor system to identify the key influencing factors and the impact pathways. Firstly, a WPEC competitiveness framework comprising five dimensions and 28 factors is developed. Secondly, the Analytic Hierarchy Process (AHP) is applied to calculate factor weights based on 225 questionnaires. Then, the multi-level structural model is constructed based on Interpretative Structural Modeling (ISM) to identify the critical impact pathways. Finally, BZ Consulting Enterprise was selected as a case study to verify the rationality and practical value. The results show that the Corporate Full-Service Consulting Capability and Corporate Foundational Resources are identified as the core pillars, in addition to highlighting three key pathways—resource-integration drive, legacy-capability transfer, and service-awareness transformation—all of which link foundational drivers to market performance. Theoretically, this study introduces a systematic analytical framework for WEPC by mapping its competitiveness factors into the multi-level structural model. Practically, it enables enterprises to assess their transition readiness and formulate targeted strategies to secure a sustainable competitive advantage in the integrated consulting market. Full article

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

Ecological restoration of abandoned mines with high and steep slopes is challenging due to their steepness, water scarcity, and lack of soil, and restoration effects vary with applied techniques. This study aims to assess the ecological restoration effectiveness of restored steep bare rock slopes in Jiangsu Province. The restoration status of steep bare rock slopes was assessed through field surveys, with corresponding methods for vegetation data collection and soil sample analysis subsequently selected. An evaluation system consisting of 14 evaluation indicators from 3 aspects was established using the analytic hierarchy process (AHP). Based on the on-site investigation results and test data, the evaluation criteria and evaluation intervals for the five evaluation levels of “optimum, excellent, good, medium, and poor” have been determined. After obtaining the weights of each indicator, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to establish a normalized matrix of evaluation indicators, calculate the weighted decision matrix, and determine the ideal solution. The results showed that (1) the proportion of ecological restoration effects in the excellent–optimum, good–excellent, and medium–good was 30%, 43.3%, and 26.7%, respectively, which is consistent with the on-site investigation results; (2) the on-site investigation and evaluation results indicate that the ecological restoration effect of steep bare rock slopes is easily affected by the slope’s soil and water conservation capacity; (3) the weights of each indicator layer are slope ecosystem stability > vegetation > soil, with a maximum value of 0.443, indicating that ecosystem stability is the main factor affecting the ecological restoration effect of mines. This evaluation system is based on on-site investigations and indoor test results, and objectively and effectively evaluates the ecological restoration effect of steep bare rock slopes through qualitative evaluation and quantitative analysis. The methodology demonstrates high applicability and reliability for steep bare rock slopes, thereby serving as a valuable reference for selecting and evaluating the efficacy of ecological restoration technologies in similar environments. Full article

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. Full article

Flood risk has been increasing due to the effects of climate change, frequent rainfall, and urbanization. Therefore, flood risk assessments in urban areas are important issues for the mitigation of flood disaster and sustainable development. Although there has been an increase in studies on flood risk, there remains a scarcity of research examining the effects of rainfall at different return periods on flood risk under non-stationary conditions in Geographic Information System (GIS) - and multi-criteria decision-making model (MCDM)-based flood risk assessments. To address this gap, this study integrated MCDM-based flood hazard mapping techniques with rainfall quantiles calculated for different return periods under non-stationary conditions to identify and prioritize flood risk areas in Izmir, Türkiye. Firstly, to analyze the current flood risk, the Analytical Hierarchy Process (AHP) was integrated into the GIS and the VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) approach was used to determine the flood risk priority of 165 points. The results showed that Buca, Menderes, Bornova, Kemalpaşa, Çeşme, Torbalı, Menemen, Seferihisar, and Çiğli were identified as high-flood-risk areas. The VIKOR results indicate that the highest-flood-risk points are R91 (Çeşme), R153 (Buca), and R93 (Çeşme). For a thorough flood risk assessment, the rainfall estimates obtained with the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) at 10-, 20-, 50-, and 100-year return levels under non-stationary conditions were re-weighted with AHP and were incorporated into the hazard criteria, and flood risk analyses were performed for four scenarios. The results showed that as return periods increase, high-risk areas expand, while low-risk areas shrink. Specifically, the proportion of very-low-risk areas declined from 15.12% for the 10-year return period to 13.92% for the 100-year return period, whereas the proportion of very-high-risk areas increased from 6.73% to 7.53% over the same return period levels. VIKOR results, unlike the VIKOR findings for the current case, revealed that points R55, R56, and R54 in Kemalpaşa had the highest flood risk in four scenarios. Full article

With the expansion of urban scale, forests and water areas have suffered a reduction. This reduction has resulted in insufficient carbon sequestration capacity. Strengthening environmental protection, especially enhancing the function of carbon sinks, is of great significance to the ecologically friendly development of the region. This study aims to clarify the distribution of regional ecological vulnerability and carbon storage capacity, and proposes a scientifically optimized ecological functional zoning plan. Specifically, we conducted a comprehensive assessment of land use and zoning in Huinan County by integrating ecological sensitivity with the InVEST model. First, based on the DPSIRM model, we evaluated the weights of ecological sensitivity influencing factors by combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). Using ArcGIS, we overlaid these factors with their respective weights to obtain the distribution of overall ecological sensitivity. Referencing relevant literature, we classified Huinan County’s ecological sensitivity into five categories. These categories include insensitive areas, low-sensitivity areas, medium-sensitivity areas, high-sensitivity areas, and extremely sensitive areas. Second, the carbon sequestration capacity of this region was visualized using the InVEST model to analyze Huinan County’s carbon storage potential. Finally, using the ArcGIS spatial overlay, we combined sensitivity levels with carbon storage zones. Based on varying degrees of ecological sensitivity and carbon storage distribution, we established five ecological conservation zones. These five ecological protection zones were: ecological buffer zone, restoration zone, stabilization zone, potential zone, and fragility zone. We implemented differentiated measures tailored to distinct regions, thereby advancing ecological restoration and sustainable development. This study provides a policy basis for ecological restoration in Huinan County and offers a replicable framework for ecological conservation in urbanized areas. Consequently, it holds practical significance for enhancing landscape multifunctionality and resilience. Full article

Greenhouse gas emissions from the energy sector are the main driver of global warming, which has led to an increase in the average surface temperature of the Earth by more than 1 °C above pre-industrial levels. Responding to the urgent need for energy transition, the countries of the European Union have set themselves the goal of achieving climate neutrality by 2050. The main objective of this article is to comprehensively assess the progress of decarbonization in the 27 European Union countries between 2004 and 2024, using an advanced multi-criteria model. The study used the quantitative Analytical Hierarchy Process (AHP) method to construct a multidimensional decision-making model. Eight energy technologies were evaluated through the prism of 13 criteria grouped into three pillars of sustainable development: economic (including technical), environmental, and social. Based on the weights of each criterion, estimated by a group of experts, a synthetic decarbonization index (DI) was calculated for each technology. In the next stage, a cumulative decarbonization index (CDI) was formulated for each country, reflecting the structure of its energy mix. The analysis revealed a fundamental divergence between conventional and zero-emission technologies. Renewable sources and nuclear energy have the highest positive impact on decarbonization (highest DI): hydropower (27.5), nuclear (20.7), wind (20.3). The lowest, unfavorable values of the index are characteristic of fossil fuels: oil (3.6), coal (3.9), and gas (4.8). The average cumulative decarbonization index (CDI) for the EU-27 rose from 14.0 in 2004 to 26.4 in 2024, demonstrating the effectiveness of the EU’s common policy. The leaders of the transition are countries with diversified, green mixes, such as Luxembourg (CDI = 40.4), Lithuania (CDI = 39.6), Portugal (38.5), Austria (36.9), and Spain (33.6). Despite starting from the lowest level in 2004 (CDI = 5.2), Poland recorded one of the most dynamic increases in 2024 (CDI = 17.7), mainly due to a reduction in the share of coal from 93% to 53.5%. The analysis confirms the effectiveness of the EU’s common climate and energy policy and demonstrates the usefulness of the methodology presented for a comprehensive assessment of the decarbonization process. The results indicate the need to further increase the share of zero-emission energy sources in the energy mix in order to achieve the objectives of the European Green Deal. The varying pace of transformation among Member States requires an individualized approach and support for countries with a historical dependence on fossil fuels. Full article

Global warming is modifying precipitation patterns, and hence increasing the hazards of severe and extended rainstorms. Addressing the gap in integrating economic and environmental assessments into urban stormwater management—a key challenge in urban water resource analysis—this study utilizes the analytical hierarchy process (AHP) and SUSTAIN model to identify and evaluate low-impact development (LID) stormwater management strategies, assessing their impacts on runoff volume, peak flow reduction, chemical oxygen demand (COD), and suspended solids (SS) across four planning scenarios under five rainfall recurrence intervals, culminating in a cost–benefit analysis to ascertain the optimal scenario. The reduction rates for COD and SS varied from 41.85% to 87.11% across different scenarios, with Scenario Three (RM03) demonstrating the highest efficacy in pollutant management. (The four labels RM01–RM04 are used throughout the text to represent the four scenarios) Implementing the best plan may result in a reduction of yearly carbon emissions of 189.70 metric tons, with emissions from the operational load of the drainage network and COD pollution treatment potentially decreasing by 2.44% and 2.06%, respectively, indicating an overall annual reduction of 85.46%. This approach not only mitigates urban rainwater and flooding issues but also prevents resource wastage, optimizes resource utilization and benefits, offers a scientific foundation for urban construction and planning, and serves as a reference for sponge city development in other regions. Full article

Floods rank among the most frequent and destructive natural hazards, threatening ecosystems, human settlements, and national economies. This study delineates flood-prone areas across Kosovo by developing a national-scale Flood Risk Database (FRDB) and a comprehensive mapping framework integrating Geographic Information Systems (GIS), Multi-Criteria Decision-Making (MCDM), and the Analytical Hierarchy Process (AHP). Eight hydrological and topographic conditioning factors—slope, elevation, flow accumulation, distance to rivers, land use/land cover, soil type, precipitation, and drainage density—were analyzed. AHP was employed to assign factor weights based on their relative influence on flood susceptibility, while MCDM aggregated these weighted spatial layers to generate a national flood risk map. Model validation, based on historical flood points, achieved an AUC of 0.909, confirming its high predictive accuracy. The resulting flood risk map classifies Kosovo’s territory into five risk levels: very high (0.56%), high (14.44%), moderate (36.68%), low (46.46%), and very low (1.88%). This research provides the first systematic national-scale FRDB for Kosovo, offering a reliable scientific basis for flood management, spatial planning, and climate resilience policy. Full article

Understanding consumer preferences is vital for rational decision-making in the agri-food sector and for effective product development. This study examines two comparative evaluation methods, Best–Worst Scaling (BWS) and Pairwise Comparison via the Analytic Hierarchy Process (AHP), focusing on the sensory attributes of coffee. The objective is to explore which attributes influence the preferences of students from the International Hellenic University in Sindos and assess the effectiveness of each method in capturing these preferences. Primary data were collected through structured questionnaires where participants ranked six attributes: taste, aroma, aftertaste, body, acidity and intensity. Taste emerged as the most significant attribute across all methods. However, discrepancies in the ranking of the remaining attributes revealed methodological differences. This research contributes to the applied evaluation of qualitative attributes in coffee and proposes the combined use of BWS and AHP for a more comprehensive understanding of consumer behavior. Full article

Background: Inventory record inaccuracy (IRI) causes discrepancies between physical and digital inventories, leading to production delays and customer dissatisfaction. Cycle counting, in this context, is a common corrective action. Pareto-based ABC analysis is widely used to decide which items to inspect, but it often oversimplifies inventory decisions, and recent studies suggest that multi-criteria decision-making (MCDM) and machine learning (ML) may offer more effective solutions. Methods: This study applies the analytic hierarchy process (AHP) method, combined with K-means (AHP-K), to classify stock-keeping units (SKUs) into three groups with distinct counting policies. A selection procedure is then applied to identify an optimal ML algorithm and compare its classification with the original AHP-K results; each model in this phase is trained on a subsets of 100 SKUs. A Veto method is also introduced to improve output consistency for both AHP-K and the best ML method, and a comparative cost evaluation is presented. Results: The ML-AHP-K-Veto classification achieves over 90% accuracy. Analysis of a dataset of 12,863 SKUs from a mechanical manufacturing company shows minimal cost differences between ML-based and MCDM classifications, but significant differences compared to Pareto-based costs. Conclusions: ML can effectively address IRI, supporting the development of pure ML applications, including decision-maker (DM) preferences, to manage cycle counting strategies. Full article