Search Results (472)

Electrochemical reduction of carbon dioxide (CO₂RR) to methanol represents a promising approach for sustainable methanol production. Despite this potential, current technological limitations constrain both economic viability and environmental benefits. This research introduces a solar-driven multigeneration system that integrates CO₂RR to enable the coproduction of electricity and green methanol. A comprehensive energy integration analysis was conducted, alongside a combined techno-economic, energy-efficiency, and environmental (3E) assessment. Multiobjective optimization was conducted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). For solution selection, the analytic hierarchy process (AHP) was integrated with the order preference by similarity to ideal solution (TOPSIS) methodology. Results indicate that the integrated system achieves a 4.2% reduction in total utility consumption. The optimal levelized cost of methanol (LCOM), net specific carbon emissions (Net_SCE), and energy efficiency (η_EN) are USD 0.526/kg, −1.16 kg CO₂SCE/kg CH₃OH, and 6.52%, respectively. LCOM decreases by 30.6% compared to the initial system, Net_SCE increases by 3.44%, and η_EN improves by 5.84%. Under optimal operating conditions, CH₃OH production capacity and grid power consumption reach 45.27 tons/day and 475.83 MWh/day, respectively. The system does not currently meet the commercial threshold and becomes economically viable only if the electricity price exceeds USD 0.223/kWh. This study provides a valuable reference for future research in system-level integration of CO₂RR and multiobjective solution selection. Full article

Additive manufacturing enables lightweight sandwich structures with complex cellular cores, but the selection of material and process settings typically involves trade-offs among mechanical performance, cost, and sustainability. This study proposes an integrated multi-criteria decision-making framework to identify the most suitable configuration for Fused Filament Fabrication (FFF) sandwich structures featuring a gyroid triply periodic minimal surface (TPMS) core. Eight alternatives are evaluated by combining two materials (PLA and PLA–Flax biocomposite) with two extrusion temperatures (200 °C and 220 °C) and two infill densities (20% and 30%). Mechanical performance is represented by flexural strength obtained from three-point bending tests reported in a previously published experimental campaign, while economic and environmental indicators are quantified through material cost and printing energy consumption, respectively. Criteria weights are derived using the Analytic Hierarchy Process (AHP) based on expert judgment and consistency-ratio verification, and the alternatives are ranked using the TOPSIS method. The results highlight a clear dominance of PLA-based configurations under the adopted weighting scheme, with PLA printed at 200 °C and 20% infill emerging as the best compromise solution. PLA–Flax options are penalized by higher material cost, higher printing-process energy demand, and lower flexural strength in the investigated conditions. The proposed AHP–TOPSIS workflow supports transparent, data-driven selection of AM process–material combinations for gyroid sandwich structures, and it can be readily extended by including additional sustainability metrics (e.g., CO₂-equivalent) and application-specific constraints. A sensitivity analysis under alternative weighting scenarios further confirms the robustness of the obtained ranking. Full article

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

Intangible cultural heritage faces several challenges, including a fragile transmission system, disconnection from modern life, and poor market adaptability. This study takes the Jingsha tenon-and-mortise woodcarving, an important example of Chinese intangible cultural heritage, as a case study to address the issue of the disconnection between traditional craftsmanship and contemporary demands. Methods: A sustainable development model based on user–AIGC–craftsman collaboration is proposed. The research integrates Kano Model and Analytic Hierarchy Process (AHP) based demand analysis, AIGC-generated design solutions, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) evaluation, and Cursor and MCP 3D modeling technologies. The results indicate that this approach reduces design confirmation time from three days to one, minimizes material waste through precise size specifications, and achieves high user satisfaction. The study demonstrates that combining user-centered design with AI-assisted craftsmanship creates a balanced pathway for the sustainability of intangible cultural heritage, while addressing issues of cultural preservation, economic feasibility, and resource efficiency. This tripartite model offers a replicable framework for the sustainable development of traditional crafts globally. Full article

The increasing complexity of contemporary urban systems necessitates decision-making frameworks capable of systematically integrating multidimensional sustainability considerations into policy evaluation processes. While existing urban sustainability assessment approaches predominantly focus on isolated environmental or socio-economic indicators, they often lack methodological coherence and direct applicability to operational decision-making. This study proposes a multi-layer sustainability indicator framework explicitly designed to support evidence-based urban decision-making under conditions of uncertainty, institutional constraints, and competing policy objectives. The framework integrates environmental, economic, social, and institutional dimensions of sustainability into a structured decision-support architecture. Methodologically, the study employs a two-stage approach combining expert-based weighting techniques (Analytic Hierarchy Process and Best–Worst Method) with multi-criteria decision-making methods (TOPSIS and VIKOR) to evaluate and rank alternative urban policy scenarios. The proposed framework is empirically validated through an urban case study, demonstrating its capacity to translate abstract sustainability indicators into comparable decision outcomes and policy priorities. The results indicate that the integration of multi-layer indicator systems with formal decision-analysis tools enhances transparency, internal consistency, and strategic coherence in urban governance processes. By bridging the gap between sustainability measurement and decision implementation, the study contributes to the advancement of urban governance scholarship and provides a replicable analytical model applicable to cities facing complex sustainability trade-offs. Full article

Selecting a sustainable healthcare waste treatment method is a complex multi-criteria problem influenced by environmental, economic, social and technological factors. This study addresses key gaps in the literature by proposing an intuitionistic fuzzy AHP–TOPSIS framework that explicitly models cognitive uncertainty and expert hesitation, while demonstrating its application through a real-world case study in Adana, Türkiye. In contrast to prior studies utilizing fewer criteria, our framework evaluates four treatment alternatives—incineration, steam sterilization, microwave, and landfill—across 17 comprehensive criteria that directly integrate circular economy principles such as resource recovery and energy efficiency. The results indicate that steam sterilization is the most sustainable option, demonstrating superior performance across environmental, economic, social, and technological dimensions. A 15-scenario sensitivity analysis ensures ranking resilience across varying decision contexts. Furthermore, a systematic comparative analysis highlights the methodological advantages of the proposed framework in terms of analytical granularity and robustness compared to existing models. The study also offers step-by-step operational guidance, creating a transparent and policy-responsive decision-support tool for healthcare waste management authorities to advance sustainable practices. Full article

In the context of the “Dual Carbon” goals, achieving synergistic development between digitalization and green transformation in the coal power industry is essential for ensuring a just and sustainable energy transition. The core scientific problem addressed is the lack of a robust quantitative tool to evaluate the comprehensive performance of diverse transition scenarios in a complex environment characterized by multi-objective trade-offs and high uncertainty. This study establishes a sustainability-oriented four-dimensional performance evaluation system encompassing 22 indicators, covering Synergistic Economic Performance, Green-Digital Strategy, Synergistic Governance, and Technology Performance. Based on this framework, a Fuzzy DEMATEL–MultiMOORA–Borda integrated decision model is proposed to evaluate seven transition scenarios. The computational framework utilizes the Interval Type-2 Fuzzy DEMATEL (IT2FS-DEMATEL) method for robust causal analysis and weight determination, addressing the inherent subjectivity and vagueness in expert judgments. The model integrates MultiMOORA with Borda Count aggregation for enhanced ranking stability. All model calculations were implemented using Matlab R2022a. Results reveal that Carbon Price and Digital Hedging Capability (C13) and Digital-Driven Operational Efficiency (C43) are the primary drivers of synergistic performance. Among the scenarios, P3 (Digital Twin Empowerment and New Energy Co-integration) achieves the best overall performance (score: 0.5641), representing the most viable pathway for balancing industrial efficiency and environmental stewardship. Robustness tests demonstrate that the proposed model significantly outperforms conventional approaches such as Fuzzy AHP (Analytic Hierarchy Process) and TOPSIS under weight perturbations. Sensitivity analysis further identifies Financial Return (C44) and Green Transformation Marginal Economy (C11) as critical factors for long-term policy effectiveness. This study provides a data-driven framework and a robust decision-support tool for advancing the coal power industry’s low-carbon, intelligent, and resilient transition in alignment with global sustainability targets. Full article

Marine traffic poses a significantly increasing threat to the marine environment, especially marine megafauna species, due to collisions between large vessels and marine organisms that most frequently result in mortality. The adoption of mitigation methods for collisions is critical to avoid population declines. Selecting the optimal mitigation method depends on a set of criteria and is best assessed using decision-making tools. The current study reviewed the use of decision-making tools for marine traffic applications and discusses the existing gap regarding environmental applications (especially considering the impact on marine biodiversity). Furthermore, the authors propose a method for estimating hotspots of marine traffic that may overlap with marine biodiversity foraging grounds, and the structure for a decision-making tool for mitigating collisions and conserving the marine environment. Full article

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence. Full article

The purpose of this study is to determine the optimal location for siting an onshore wind farm on the island of Skyros, thereby maximizing performance and minimizing the project’s environmental impacts. Seven evaluation criteria are defined across various sectors, including environmental and economic sectors, and six criteria weighting methods are applied in combination with four multicriteria decision-making (MCDM) ranking methods for suitable areas, resulting in twenty-four ranking models. The alternatives considered in the analysis were defined through the application of constraints imposed by the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD RES), complemented by exclusion criteria documented in the international literature, as well as a minimum area requirement ensuring the feasibility of installing at least four wind turbines within the study area. The correlations between their results are then assessed using the Spearman coefficient. Geographic information systems (GISs) are utilized as a mapping tool. Through the application of the methodology, it emerges that area A9, located in the central to northern part of Skyros, is consistently assessed as the most suitable site for the installation of a wind farm based on nine models combining criteria weighting and MCDM methods, which should be prioritized as an option for early-stage wind farm siting planning. The results demonstrate an absolute correlation among the subjective weighting methods, whereas the objective methods do not appear to be significantly correlated with each other or with the subjective methods. The ranking methods with the highest correlation are PROMETHEE II and ELECTRE III, while those with the lowest are TOPSIS and VIKOR. Additionally, the hierarchy shows consistency across results using weights from AHP, BWM, ROC, and SIMOS. After applying multiple methods to investigate correlations and mitigate their disadvantages, it is concluded that when experts in the field are involved, it is preferable to incorporate subjective multicriteria analysis methods into decision-making problems. Finally, it is recommended to use more than one MCDM method in order to reach sound decisions. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

Monitoring the technical condition of conveyor belts is essential for the reliable and safe operation of pipe belt conveyors. Integrating passive UHF RFID tags directly into the belt structure enables continuous monitoring of belt circulation, elongation, and splice condition without interrupting operation. This study aimed to verify the technical feasibility of such an approach, optimize the RFID system architecture, and experimentally evaluate the impact resistance of tags vulcanized into a rubber–textile conveyor belt. A multicriteria decision-making approach (AHP and TOPSIS) was used to select a suitable UHF antenna and mounting system for the experimental pipe conveyor TMEL, resulting in the choice of a circularly polarized Alien ALR-8698 patch antenna and a fully adjustable portal-type holder. Impact tests on an S 250/2 RA belt with integrated RFID tags showed that all tags remained functional up to complete mechanical failure of the specimens, even under direct impact, with maximum impact forces of 6–12 kN depending on specimen width. The integration of RFID tags did not introduce a critical weakening of the load-bearing belt structure, confirming that RFID is a robust and suitable complement for intelligent condition monitoring of pipe conveyors. Full article

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