Search Results (277)

Background: Circular supply chains (CSC) have emerged as a strategic response to sustainability challenges, while adoption remains uneven. Supplier selection is a key driver of effectiveness, shaped by organizational capabilities, institutional support, and leadership. This study develops a structured framework for circular supplier selection (CSS) using a hybrid multi-criteria decision-making approach, addressing fragmented research and strengthening the link between methodological innovation and practice. Methods: The proposed framework integrates fuzzy DEMATEL, the Best-Worst Method (BWM), and the Analytic Hierarchy Process (AHP) within MCDM. Fuzzy DEMATEL identifies cause-and-effect relationships among criteria, distinguishing net causes from net effects. The most influential and dependent criteria serve as anchors for the BWM weighting, followed by AHP to evaluate sub-criteria and alternatives. Results: Environmental governance emerged as the most influential driver in the causal analysis, while circular performance received the highest weight in BWM. The final AHP evaluation ranked Alternative 5 as the most suitable, followed by A9 and A3, confirming the framework’s ability to deliver consistent, actionable insights for circular supplier selection. Conclusions: This integration enables a more granular and robust evaluation of supplier strategies within CSC, reinforcing their role in accelerating sustainability transitions. It establishes a structured framework for CSS, highlighting CSS performance and upstream supply chain decision-making. Full article

Background: The transition from linear to circular supply chains (CSC) is critical for advancing sustainability, resilience, and resource efficiency, while supporting the UN Sustainable Development Goals (SDGs). However, existing studies rarely integrate internal operational performance with external PESTEL factors under the Benefits, Opportunities, Costs, and Risks (BOCR) perspective, limiting the ability to prioritize circular strategies holistically. Methods: This study develops a decision-making framework that combines the Best-Worst Method (BWM) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), enabling reliable prioritization of interdependent sustainability criteria. Results: A case analysis in the chemical industry demonstrates the applicability of the framework, enhancing transparency and reducing subjectivity in CSC evaluation. Findings highlight quality as the key operational attribute and social as the dominant PESTEL dimension, reinforcing the integration of internal and external factors toward SDG-oriented strategies. Conclusions: The study contributes theoretically by bridging operational and contextual dimensions in CSC evaluation under the BOCR perspective, and methodologically by advancing hybrid MCDM applications to address uncertainty. Managerially, the framework provides a structured tool for aligning circular supply chain strategies with organizational objectives and SDGs, supporting decision-making that strengthens environmental sustainability, stakeholder legitimacy, and resilience. Full article

Urbanization can act as a powerful ecological filter, restructuring biodiversity through species loss, replacement, and altered resource pathways. While urban green spaces (UGS) are recognized as potential biodiversity refuges, the effectiveness and mechanisms for conserving insect diversity across the urban-to-natural gradient remain poorly understood. Here, we combine full-season Malaise trapping (April–November) with MinION-based DNA barcoding to test two predictions about how urbanization reshapes Diptera communities across five sites in Haidian District, Beijing, ranging from residential areas and urban parks to a nearby shallow mountain reserve (BWM). Based on 5528 barcoded individuals, we identified 686 putative species from 39 families. As predicted, β-diversity between urban and mountain sites was overwhelmingly driven by species turnover rather than nestedness, demonstrating that cities do not simply receive subsets of the surrounding fauna but actively reassemble communities. This filtering effect was, however, trophic-guild specific. Detritivores showed the highest replacement, consistent with a shift from natural to anthropogenic resource subsidies, while predators/parasitoids exhibited significant nested loss, aligning with their hypothesized sensitivity at higher trophic levels. Vegetation structure further clarified these patterns: vegetation density, not plant species richness, was the primary bottom-up driver for herbivore and predator/parasitoid diversity, whereas detritivores were decoupled from living plant biomass. These findings demonstrate that urban and near-natural habitats maintain distinct species pools via guild-specific assembly pathways, highlighting the need for guild-specific conservation strategies for urban biodiversity conservation. Extending beyond compositional analysis, we propose a temporal-abundance framework, classifying species by persistence and abundance, as a diagnostic tool for assessing ecological integrity and guiding conservation in urbanizing landscapes. Full article

This study develops a GIS-based multicriteria decision analysis framework to assess the territorial suitability of onshore wind energy in Colombia. The proposed approach combines technical and socio-environmental suitability modelling with territorial interpretation based on conflict and governance, moving beyond conventional siting models focused mainly on wind resource availability and infrastructure proximity. The technical assessment included wind speed, wind power density, terrain slope, land cover, land use, and proximity to electrical grids, main roads, settlements, and water bodies. In addition, a National Conflict Index and a National Governance Index were constructed to represent broader territorial conditions that may affect project implementation. Quantitative variables, including wind speed, wind power density, terrain slope, and distance-based criteria, were transformed onto a common suitability scale using linear fuzzy membership functions, whereas qualitative variables, including land cover and land use, were incorporated through categorical reclassification. The National Conflict Index and National Governance Index were first constructed using CRITIC to obtain objective weights for their internal variables. Subsequently, the final onshore wind suitability criteria were weighted through the linear Best–Worst Method based on expert judgment. The standardized suitability layers and corresponding BWM-derived weights were integrated through weighted spatial overlay to generate a national suitability map, while the conflict and governance indices were used to interpret the territorial conditions associated with the resulting suitable areas. The results show a highly selective territorial pattern, with the most favorable areas concentrated mainly in La Guajira (1286.09 km²) and Cesar (574.45 km²), and more fragmented secondary opportunities in Nariño, Boyacá, Norte de Santander, Cundinamarca, Atlántico, and Magdalena. Three territorial intervention scenarios were identified: priority intervention, complementary or selective development, and low relative priority. The main contribution of the study is the articulation of a BWM-weighted technical and socio-environmental suitability model with CRITIC-based conflict and governance indices, offering a replicable framework to support strategic planning and public policy decisions for onshore wind deployment in Colombia. Full article

This study proposes an intelligent decision-support framework for alternative selection in complex construction projects, where evaluation processes are affected by uncertainty, multiple decision-makers, and interdependent criteria. The framework integrates the fuzzy group best–worst method with fuzzy TOPSIS into a unified structure that explicitly captures cross-criterion influence effects. First, triangular fuzzy judgments from multiple experts are used to derive criterion weights, while interdependencies among criteria are represented through a fuzzy influence-intensity matrix and incorporated into fuzzy nonlinear optimization models. This process enables the systematic estimation of both independent and interdependency-adjusted criterion weights. Second, the resulting weights are used in a fuzzy ranking procedure to evaluate alternatives according to their relative closeness to fuzzy ideal solutions. To enhance transparency, reproducibility, and practical usability, the proposed method is implemented in Python as an automated computational workflow for decision analysis. Its applicability is demonstrated through a real-world case study on access platform system selection for mechanical, electrical, and plumbing installation in an airport terminal subject to safety, productivity, workspace, and elevation-related constraints. The results show that explicitly modeling criterion interdependencies provides a more realistic evaluation structure and enhances the robustness and reliability of alternative selection in complex construction management contexts. Full article

The 6 February 2023 Kahramanmaraş earthquakes highlighted the importance of emergency assembly areas for disaster response, evacuation safety, and urban resilience in earthquake-prone cities. Although GIS-based multi-criteria decision-making approaches are widely used to assess spatial suitability, relatively few studies integrate suitability, capacity adequacy, and accessibility within a single framework, particularly in cities directly affected by the 2023 earthquakes. This study evaluates emergency assembly areas in Malatya, Türkiye, using an integrated GIS–Best–Worst Method (BWM) framework. Nine criteria—geology, population density, building density, elevation, slope, distance to roads, distance to rivers, distance to fault lines, and distance to buildings—were weighted based on the judgements of 15 experts involved in Provincial Disaster Risk Reduction Plan (İRAP) processes. The BWM results show that geology and distance to fault lines received the highest weights, whereas distance to roads had the lowest weight. The spatial analysis indicates that highly suitable areas are concentrated mainly in the city centre, while several peripheral neighbourhoods are constrained by geological, topographical, and accessibility-related factors. Existing official emergency assembly areas cover only 27.9% of the population and are located in 13 of 88 neighbourhoods. Estimated access times range from 0 to 5 min in central areas to 10–15 min, or beyond effective service coverage, in peripheral neighbourhoods. Although integrating parks and green spaces substantially increases potential capacity, it does not fully eliminate neighbourhood-level inequalities. The findings provide a spatial decision-support framework for emergency planning in earthquake-prone cities. Full article

Modular construction advances sustainability and is reshaping designer competencies, making workforce development critical to industry transition. Existing competency models rely mainly on expert interviews and Delphi methods, offering limited quantitative evidence on role-specific labor-market demands, causal relationships among competencies, or experience-based perceptual differences. This study presents a preliminary, data-driven competency-mapping study for modular construction designers by integrating BERTopic, Ward clustering, CVR, Bayesian BWM, and Fuzzy DEMATEL. Applied to 243 job postings from six countries, the text-mining stage identifies a candidate competency structure of 3 domains, 9 categories, and 36 performance statements. This candidate structure was then examined through an exploratory survey of 30 Korean respondents. The results suggest that Codes and Compliance represents the most clearly recognized high-consensus competency area within this local validation sample, whereas Modular Construction shows an indicative experience-related divergence in perceived causal position. Given the small and uneven subgroup sample and the formative state of Korea’s modular construction industry, the findings should be interpreted as preliminary evidence rather than as a validated competency framework or a confirmed expert–novice model. The study contributes a reproducible mixed-method workflow, a candidate competency map, and an illustrative maturity prototype for future validation and refinement. Full article

Conventional multi-criteria decision-making approaches for battery energy storage system (BESS) dispatch evaluation treat regulatory and policy conditions as compensable criteria within a single aggregate score. This becomes problematic when institutional admissibility functions as a prerequisite for deployment rather than a tradeable attribute. This study aims to develop and test a sequential gate framework. The methodological contribution lies in the evaluation architecture itself: the framework distinguishes sequential admissibility gating from conventional compensatory Multi-Criteria Decision-Making (MCDM). Deployment feasibility is conceptualized as a layered construct in which regulatory admissibility defines the feasible solution space and technical performance differentiates among admissible options. The framework integrates systematic literature screening, quantitative policy and regulatory assessment, and technical ranking using a hybrid Best-Worst Method, Entropy weighting, and TOPSIS approach. A Danish case study covering twelve dispatch strategies compares the proposed sequential design with two flat alternatives. The results show that the evaluation architecture materially affects outcomes: sequential gating excludes an institutionally incomplete strategy and reorders the upper tier by removing compensatory policy effects. Coordinated multi-BESS control at Electric Vehicle charging parks achieves the highest combined feasibility (closeness coefficient 0.891, ranked 1st), while mobile BESS is excluded by the admissibility gate. The sequential design reorders the upper tier relative to flat MCDM, with S4 and S6 rising and S2 and S10 falling once policy compensation is neutralized after the gate. The top-ranked strategy remains robust across sensitivity analysis, Monte Carlo simulation, score perturbation, and VIKOR cross-validation. The framework is presented as an analytical pre-simulation screening tool rather than a validated implementation instrument; external validation against real deployment outcomes is identified as a priority for future research. The framework provides a structured, decision-consistent approach for evaluating deployment feasibility in regulated energy systems. Full article

As a core component of power system reform, the transmission and distribution price reform plays a critical role in optimizing the grid regulation model and promoting efficient allocation of power resources by establishing an independent pricing mechanism based on “permitted cost plus reasonable return”. This study evaluates the provincial transmission and distribution price reform effect in China. First, an evaluation index system is constructed from four dimensions, namely, economic efficiency, security guarantee, market mechanism and social welfare. Second, a comprehensive evaluation model is developed using a multi-attribute decision-making model consist of the Best–Worst Method (BWM), entropy weight method (EWM) and cloud model. Of these, the BWM and EWM are employed to determine the indicator weights, and the cloud model is utilized to rank the transmission and distribution price reform effect. Third, an empirical assessment and analysis are conducted on three typical provinces in China. Empirical analysis reveals significant regional heterogeneity in reform effectiveness. Based on the comprehensive cloud expectation (Ex) values, Province B (eastern coastal) ranks first with an Ex of 82.10 (on a 0–100 scale), falling into the “good” grade; Province C (northern) ranks second with an Ex of 81.05, also “good”; and Province A (central-western) ranks third with an Ex of 78.70, likewise “good”. Province B’s leading position is attributed to synergistic outcomes in cost control, market vitality, and social welfare. The study can provide references for the sustainable development of electric power companies and the electricity industry. Full article

Background: The global transition to low-carbon energy systems has intensified the need for circular approaches in energy supply chains, yet studies on second-life EV battery ecosystems in emerging economies remain fragmented between barrier prioritization and efficiency assessment. Methods: This study addresses this gap by integrating the Best–Worst Method (BWM) and Data Envelopment Analysis (DEA) to connect subjective expert-based prioritization with objective efficiency benchmarking. Using expert panel inputs and scenario-based circular energy configurations representing emerging economy conditions, the results indicate that technical barriers (28.4%) and economic barriers (24.9%) dominate the priority structure, with battery performance uncertainty and high initial investment as the most critical constraints. Results: DEA results show that configurations with formal reverse logistics and certification mechanisms achieve frontier efficiency (θ = 1.000), whereas fragmented informal configurations exhibit the lowest efficiency (θ = 0.712). High-tech configurations with weak regulation demonstrate that technological investment alone is insufficient without institutional development. Conclusions: The novelty lies in developing a context-sensitive BWM–DEA framework that embeds barrier priorities into efficiency evaluation, an approach rarely explored in prior circular supply chain research. The study provides a holistic decision-support tool for policymakers and industry stakeholders seeking to accelerate circular energy transitions in emerging economies. Full article

Environmental, social, and governance (ESG)-oriented green transformation is reshaping the competency requirements for management consultants, particularly in Taiwan, where firms face growing pressure to disclose sustainability information, manage carbon, and comply with regulations. Yet limited research has systematically identified and prioritized the competencies required of consultants supporting this transformation. This study develops an integrated competency-prioritization framework using expert content validation, the best–worst method (BWM), and the technique for order preference by similarity to ideal solution (TOPSIS). An initial pool of 150 competency items was generated from a structured literature review and refined by six experts using a strict one-strike rule, yielding 48 validated competencies. Survey data from 54 professionally relevant respondents were then analyzed to estimate competency weights and rankings. The results show that communication and advocacy capability was the leading dimension, with a weight of 37.1206%. At the item level, the highest-ranked competency was the ability to negotiate with stakeholders and build consensus on sustainability initiatives. The study contributes a BWM–TOPSIS-based competency prioritization framework and offers practical guidance for consultant selection, capability assessment, training design, and professional development in ESG-oriented green transformation contexts. Full article

Food Loss and Waste (FLW) remain major challenges for global food security, environmental sustainability, and economic stability, with nearly one-third of food produced each year being lost or wasted. Although many technologies exist to mitigate FLW, they are often assessed separately, making it difficult for decision-makers to compare options and select solutions suited to specific contexts. This research introduces an explainable decision support system (XDSS) that helps prioritise FLW mitigation strategies while accounting for uncertainty in stakeholder preferences. The proposed framework combines the Best–Worst Method (BWM) with Stochastic Multi-criteria Acceptability Analysis for Group Decision-Making (SMAA-2) to produce transparent and uncertainty-aware rankings. It evaluates one hundred FLW mitigation strategies across five contextual criteria: geographic fit, product category, food supply-chain stage, stakeholder role, and technology type. Rather than producing a single fixed ranking, the system generates probabilistic rank-acceptability profiles that indicate the likelihood of each strategy performing well under different preference conditions. Illustrative scenarios demonstrate that the framework can translate qualitative user preferences into robust prioritisation outcomes, with leading alternatives achieving first-rank-acceptability levels between 62% and 74%. These results indicate that the system can support clearer and more flexible decision-making when preferences are incomplete, inconsistent, or uncertain. Although the current results are based on simulated structured cases, the proposed XDSS provides a transparent methodological foundation for future real-world validation and operational deployment. The framework offers practical value for selecting FLW technologies and for policy planning, contributing to more sustainable food systems and supporting progress toward SDG 12.3. Full article

This study addresses the issue of low utilization rates of kitchen waste recycling facilities in urban communities in China, caused by insufficient resident willingness to participate and sustain use, which impacts the efficiency of urban sustainability. A system design model based on Affordance Theory (BIATM) is proposed to connect behavioral insights with design and decision-making tools to improve sustainability outcomes at the local level. This model includes three components: demand classification identification based on Affordance Theory and the Best-Worst Method (BWM), hierarchical relationship analysis of demand based on Interpretive Structural Modeling (ISM), and design transformation based on Affordance Theory. Unlike prior approaches that apply affordance theory as a post hoc interpretive lens, this study operationalizes it as a generative design tool integrated with quantitative demand analysis. The results indicate that “Incentive Feedback” is a key factor directly influencing sustained participation behavior, while factors related to hygiene anxiety and operational burden are fundamental constraints to residents’ use of community kitchen waste recycling facilities. This suggests that the explicit needs expressed by residents may not always be the primary drivers of behavior initiation and continuity. To promote sustained participation, design should prioritize meeting deeper behavioral prerequisites and maintain participation through superficial feedback and incentive mechanisms. Based on this analysis, three design proposals for community kitchen waste management facilities are presented, evaluated, and optimized using the TOPSIS method. The study concludes that combining demand weight identification, hierarchical relationship analysis, and Affordance Theory translation effectively supports the identification, design generation, and optimization of community kitchen waste management facilities, providing a methodological reference for the sustainable design of public service facilities and practical pathways for promoting resident participation in community resource recycling. Full article

Sustainable forklift technologies have become essential in modern industrial logistics due to increasing environmental regulations, rising energy costs, and heightened occupational safety requirements. Given the complexity and variety of sustainable forklift options, selecting the most appropriate one has become a critical multi-criteria decision-making (MCDM) problem for companies. This study aims to determine the most appropriate sustainable forklifts by considering multiple qualitative and quantitative criteria that play a critical role in the forklift selection process of companies. To this end, meetings are conducted with managers possessing expertise in sustainability and logistics at companies operating in Turkey. Based on these insights, ten forklift alternatives and six evaluation criteria are identified. This is the first time, in this study, sustainability criteria such as sustainability in occupational health and safety, sustainability in agility, sustainability in ergonomics, durability and material sustainability, sustainability in load lifting capacity and sustainability in price are incorporated into the evaluation. To the best of our knowledge, no study in existing literature has specifically focused on sustainable forklift selection, incorporating the comprehensive sustainability-oriented criteria considered in this study. The Analytic Hierarchy Process (AHP) is employed to determine the weight of each criterion. Subsequently, forklift alternatives are ranked using the Multi-objective Optimization by Ratio Analysis (MOORA) ratio approach, the Additive Ratio Assessment (ARAS), and the Elimination and Choice Translating Reality (ELECTRE) methods. Moreover, weights derived based on different subjective and objective weighting schemes, specifically FUCOM, BWM, and Entropy, as well as the resulting ranking outcomes are comparatively examined to assess the impact of varying weighting structures on the robustness and consistency of the final decision results. The proposed methodology is applied within manufacturing and logistics companies in Turkey to assess its practical effectiveness. As a result of this study, the most appropriate sustainable forklifts for the companies are identified. Furthermore, the outcomes of the applied methods yield consistent/similar results. The results emphasize that managers should place greater importance on the criteria of sustainability in occupational health and safety—identified as the most critical factor—followed by durability and material sustainability, and sustainability in load lifting capacity when selecting forklifts. Sensitivity analyses indicate that the method yields consistent and effective results. Moreover, it demonstrates the robustness and accuracy of the forklift evaluations. In this context, this study serves as a guided reference for companies in the selection of sustainable forklifts. Full article

The rapid expansion of air cargo transportation has necessitated fleet expansion to meet growing demand. Due to the high capital costs associated with new aircraft acquisitions, attention has increasingly shifted toward used aircraft as a cost-effective alternative. However, selecting an appropriate used aircraft from a range of heterogeneous options is a critical multi-criteria decision-making challenge. To address this issue, this study introduces an integrated decision-making framework for used aircraft selection by combining the technique for order preference by similarity to ideal solution (TOPSIS) and the best–worst method (BWM) in a hesitant fuzzy environment. First, in response to the limitations of existing distance measures, a novel distance measure for hesitant fuzzy sets (HFSs) is proposed that explicitly incorporates the hesitation degree to better capture uncertainty. Subsequently, this measure is incorporated into a modified hesitant fuzzy TOPSIS (M-HFTOPSIS) to enable a more precise evaluation of alternatives. The hesitant fuzzy BWM (HFBWM) is employed to calculate criteria weights, and the proposed M-HFTOPSIS is used to rank the alternatives. A case study involving ten criteria from technical, economic, and environmental perspectives is conducted to validate the effectiveness of the proposed method. Comparative results demonstrate that the proposed approach provides reasonable and reliable outcomes and that the enhanced HFS distance measure effectively models the differences between hesitant fuzzy sets. Full article