Search Results (117)

Green and Sustainable Remediation (GSR) has become a guiding framework for selecting remediation solutions for contaminated sites. However, in practice, there is a lack of quantitative decision support tools that can reflect the multi-dimensional environmental, social, and economic objectives of GSR. To address this, a GSR alternative decision-making model was developed, integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Entropy Weight method for weighting, combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for ranking. A preference coefficient was introduced to simulate four typical decision-making scenarios: balanced-preference, health-sensitive, economy-priority, and low-carbon constraint scenarios. Empirical analysis was conducted using three remediation alternatives for a complex contaminated site in Jiangsu Province, China. The results indicate that the optimal alternative selection is highly dependent on decision preferences: under the balanced scenario and low-carbon constraint scenario, Alternative 1 (Cement Kiln Co-processing, CKC) is optimal; under the health-sensitive scenario and economy-priority scenario, Alternative 3 (Ex situ Solidification/Stabilization + Ex situ Thermal Desorption, ESS + ESTD) is optimal. Furthermore, uncertainty analysis demonstrates the robustness of the proposed model. Full article

The rapid growth of Digital Financial Services (DFSs), including what is occurring in Indonesia, necessitates evaluation methods that are capable of objectively and systematically handling multiple assessment criteria. Therefore, this study aimed to propose a hybrid FSS–CRITIC–TOPSIS framework for selecting optimal DFSs. Fuzzy soft sets (FSSs) were used to model uncertainty and subjectivity in criterion assessments. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method determined the weights objectively based on the degree of contrast and inter-criteria correlation. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank the alternatives based on the closeness to the ideal solution. The incorporation led to a formally defined decision operator, τ, which mapped FSS to complete preference orderings while ensuring provable stability and strong discriminative properties. The framework was applied to five major Indonesian digital wallets, namely ShopeePay, GoPay, OVO, LinkAja, and DANA, as well as being evaluated across five criteria. This framework identified DANA as the optimal alternative, with a score of 0.9282, followed by ShopeePay (0.8354) and GoPay (0.6958). Comparative analysis with other methods showed a near-perfect ranking correlation (ρ = 0.9−1) with a more proportional score distribution and ranking results that reflected actual conditions. Sensitivity analysis also confirmed robustness, with ranking changes remaining logically consistent underweight variations. In conclusion, the FSS-CRITIC-TOPSIS framework provided an effective, mathematically rigorous method for multi-criteria decision-making (MCDM) under uncertainty, which applied to digital wallet selection as well as potential extension to broader evaluation contexts supporting SDGs 8, 9, and 10. Full article

Sustainable competitiveness has increasingly become an important policy objective for OECD countries, as economic performance is expected to be balanced with environmental protection, social well-being, and effective governance structures. The aim of this study is to evaluate and compare the sustainable competitiveness performance of OECD countries from a holistic perspective. In the analysis, six criteria reflecting the main dimensions of global sustainable competitiveness were considered. Criterion weights were calculated using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, an objective weighting technique that does not rely on subjective judgments. These weights were then integrated with the CoCoSo (Combined Compromise Solution) method to rank the sustainable competitiveness performance of countries. In the final stage, a clustering analysis was applied to group OECD countries exhibiting similar sustainability characteristics. The findings indicate that natural capital emerges as the most influential dimension within the evaluation framework. According to the ranking results, Finland, Sweden, Lithuania, Denmark, and Estonia are positioned among the countries with the highest sustainable competitiveness performance. The results reveal noticeable differences across OECD countries, demonstrating that environmental, social, economic, and governance-related dimensions affect country performance in distinct ways. A direct comparison with the 2025 Global Sustainable Competitiveness Index shows a strong but not perfect association between the two rankings (Spearman’s ρ = 0.977), indicating structural consistency alongside meaningful mid-ranking shifts. Furthermore, the clustering results enable the identification of country groups sharing relatively similar sustainability profiles. Overall, the study contributes methodologically to the sustainable competitiveness literature by integrating objective weighting, multi-criteria decision-making, and clustering analysis within a unified analytical framework, while also offering insights for comparative policy evaluation. Full article

Safety production constitutes a core pillar of operational management for power grid enterprises. Assessing the safety production risks of target entities in mergers and acquisitions (M&A) is a prerequisite for strengthening safety governance, and it holds significant value for elevating the safety levels of power grids, equipment, and personnel. To address the issues of inconsistent assessment dimensions and over-reliance on empirical judgment in safety production risk evaluation during power grid M&A activities, this paper proposes an assessment model that integrates due diligence information with hybrid multi-attribute decision-making (MADM). By systematically identifying safety production risk factors throughout the M&A process, an indicator system encompassing four dimensions—physical constraints, management systems, historical performance, and dynamic adaptability—is established. A game-theoretic approach is adopted to combine the Level-Based Weight Assessment (LBWA) method and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method for subjective–objective integrated weighting. Additionally, grey relational analysis (GRA) is introduced to refine the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) algorithm, enabling quantitative evaluation of risk levels. Case analysis results demonstrate that the proposed model can effectively distinguish risk discrepancies across different M&A scenarios with rational weight allocation for key indicators. Compared with traditional methods, it maintains ranking consistency while exhibiting higher discrimination efficiency, thus providing a scientific and effective risk assessment tool for power grid enterprises’ M&A decision-making. Full article

Against the backdrop of China’s dual carbon goals, understanding how terrain complexity affects the decoupling linkage between infrastructure investment and carbon emissions is crucial for developing differentiated low-carbon strategies. This study focuses on Sichuan Province, a region characterized by significant topographical heterogeneity, to investigate how terrain constraints influence carbon emission decoupling. We construct a Terrain Constraint Index (TCI) using three indicators (Digital Elevation Model (DEM), Coefficient of Variation of elevation (CV), and Terrain Position Index (TPI)) weighted by a game theory-based combination of entropy and Criteria Importance Through Intercriteria Correlation (CRITIC) methods and employ the Tapio decoupling model combined with group comparison analysis to examine the correlation between terrain complexity and decoupling performance. The key findings are as follows. (1) The TCI exhibits a “high in the west, low in the east” spatial pattern, ranging from 0.151 (Zigong) to 0.591 (Ya’an), with five distinct terrain complexity levels identified. (2) During 2001–2021, good decoupling states (strong + weak decoupling) accounted for 76.8% of all observations, indicating overall improvement in carbon emission efficiency. (3) A monotonic negative association is observed between terrain complexity and decoupling performance: the good decoupling ratio decreases from 82.5% in Low TCI regions to 62.5% in Very High TCI regions, with Mann–Whitney tests showing suggestive differences (raw p < 0.05, though not significant after Bonferroni correction). (4) Average decoupling elasticity increases from 0.182 in Very Low TCI regions to 0.705 in Very High TCI regions, demonstrating that higher terrain complexity is associated with worse decoupling outcomes. (5) Geodetector analysis reveals that infrastructure investment has the highest explanatory power (q = 0.401, p < 0.01), and the interaction between terrain factors and investment shows significant nonlinear enhancement effects (q = 0.544–0.830). These findings suggest that terrain complexity is associated with worse carbon emission decoupling, plausibly through affecting infrastructure investment efficiency, and point to the need for differentiated low-carbon strategies for regions with varying topographical conditions. Full article

Effective management of Environmental, Social, and Governance (ESG) practices within the framework of transparency and accountability in businesses is crucial for enhancing their compliance capacity in the face of regulatory pressures and contributing to the early detection of environmental and social risks. This study aims to evaluate the ESG disclosure-based performance of businesses operating in the textile, clothing, and leather sectors in Turkey by examining their ESG indicators from a transparency and accountability perspective. The CRITIC (Criteria Importance Through Intercriteria Correlation) method was used to determine the relative importance levels of the indicators, while the MABAC (Multi-Attributive Border Approximation Area Comparison) and COPRAS (Complex Proportional Assessment) methods were used to rank the performance of businesses within the framework of these indicators. Finally, clustering analysis was used to classify businesses with similar characteristics. The findings show that corporate governance principles are the most important indicator, and that Kordsa Teknik Tekstil A.Ş. and Söktaş Tekstil Sanayi ve Ticaret A.Ş. exhibit a significant and positive difference in terms of transparency and accountability in their ESG practices compared to other businesses. The combined use of CRITIC, MABAC, COPRAS, and cluster analysis offers an innovative, robust decision-making approach and holistic methodological integration for assessing ESG disclosure-based performance in the context of transparency and accountability for businesses. Full article

A representative cluster-based model of the batch process of ethanol production by Kluyveromyces sp. is proposed. Experimental data from fermentation processes of 17 different strains of K. marxianus are used; each of them potentially exhibits different metabolic and kinetic behavior. Three algorithms for clustering are applied. Two modifications of Principal Component Analysis (PCA)—hierarchical clustering and k-means clustering; and InterCriteria Analysis (ICrA) are used to simplify a large dataset into a smaller set while preserving as much information as possible. The experimental data are organized into two main clusters. As a result, the most representative fermentation processes are identified. For each of the fermentation processes in the clusters, structural and parameter identification are performed. Four different structures describing the specific substrate (glucose) consumption rate are applied. The best structure is used to derive the representative model using the data from the first cluster. Verification of the derived model is performed using experimental data of the second cluster. Model parameter identification is performed by applying an evolutionary optimization algorithm. Full article

Gait analysis is a non-invasive, cost-effective method for detecting subtle motor changes in neurodegenerative disorders. This study uses an exploratory approach to identify temporal–kinetic gait feature relationships specific to amyotrophic lateral sclerosis (ALS) and Huntington (HUNT) and Parkinson (PARK) disease versus healthy controls (CONTROL) using recent advances in InterCriteria Analysis (ICrA). The novelty lies in the (i) comprehensive temporal–kinetic feature set, (ii) use of ICrA to characterize inter-feature coordination patterns at population and disease-group levels and (iii) interpretation in a neuromechanical context. Forty-one temporal/kinetic features were extracted from left/right leg ground reaction force and rate-of-force-development signals, considering laterality, gait phase (stance, swing, double support), magnitudes, waveform correlations, and inter-/intra-limb asymmetries. The analysis included 14,580 steps from 64 recordings in the Gait in Neurodegenerative Disease Database: 16 CONTROL (4054 steps), 13 ALS (2465), 20 HUNT (4730), 15 PARK (3331). Sensitivity analysis identified strict consonance thresholds (μ ≥ 0.75, ν ≤ 0.25), selecting <5% strongest inter-feature relations from 820 feature pairs: population level (16 positive, 14 negative), group-level (15–25 positive, 9–14 negative). ICrA identified group-specific consonances—present in one group but absent in others—highlighting disease-related alterations in gait coordination: ALS (15/11 positive/negative, disrupted bilateral stride coordination, prolonged stance/double-support, decoupled stride/cadence, desynchronized force-generation patterns—reflecting compensatory adaptations to muscle weakness and instability), HUNT (11/7, severe temporal–kinetic breakdown consistent with gait instability—loss of bilateral coordination, reduced swing time, slowed force development), PARK (1/2, subtle localized disruptions—prolonged stance and double-support intervals, reduced force during weight transfer, overall coordination remained largely preserved). Benchmarking vs. Pearson correlation showed strong linear agreement (R² = 0.847, p < 0.001), confirming that ICrA captures dominant dependencies while moderating the correlation via uncertainty. These results demonstrate that ICrA provides a quantitative, interpretable framework for characterizing gait coordination patterns and can guide principled feature selection in future predictive modeling. Full article

Equipment failure is the leading cause of industrial operational disruption, with unplanned downtime accounting for up to 11% of manufacturing revenue, highlighting the need for effective proactive maintenance strategies, such as protective sensors that can detect potential failures in critical equipment before a functional failure occurs. However, sensors are also subject to hidden failures themselves, requiring periodic failure-finding inspections. This study proposes a novel integrated multimethodological approach combining discrete event simulation, Monte Carlo, optimization, risk analysis, and multicriteria decision analysis methods to determine the optimal inspection period for protective sensors subject to hidden failures. Unlike traditional single-objective models, this approach evaluates alternative inspection periods based on their risk-informed overall values, considering multiple conflicting key performance indicators, such as maintenance costs and equipment availability. The optimal inspection period is then selected considering uncertainties and the intertemporal, intra-criterion, and inter-criteria preferences of the organization. The approach is demonstrated through a case study at the leading Portuguese electric utility, replacing previous empirical inspection standards that did not consider economic costs and uncertainties, supported by an open, transparent, auditable, and user-friendly decision support system implemented in Microsoft Excel using only built-in functions and modeled based on the principles of probability management. The results identified an optimal inspection period of 90 h, representing a risk-informed compromise distinct from the 120 h interval suggested by cost minimization alone, highlighting the importance of integrating organizational preferences into the decision process. A sensitivity analysis confirmed the robustness of this solution, maintaining validity even as the organizational weight for equipment availability ranged between 35% and 82%. The case study shows that the proposed approach enables the identification of inspection intervals that lead to quantitatively better maintenance cost and availability outcomes compared to empirical inspection standards. Full article

Feedstock quality has been proven to be the single variable that most affects fluid catalytic cracking (FCC) unit performance, but catalyst characteristics have also been reported in the literature to have a considerable effect on cracking process performance. How these two main variables of the FCC process complement each other in the search for ways to optimize the performance of the FCC unit is the subject of current research. Twenty-one feedstocks with K_W-characterizing factors ranging from 11.08 to 12.06, Conradson carbon contents ranging from 0.05 to 12.8 wt.%, and nitrogen contents ranging from 800 to 3590 ppm (wt/wt) (basic nitrogen from 172 to 1125 ppm (wt/wt)) were cracked on 21 catalysts with micro-activity between 67% and 76% (wt/wt) in a laboratory-based advanced catalytic evaluation (ACE) unit at a reaction temperature of 527 °C, catalyst–to-oil ratios between 3.5 and 12.0 wt/wt, and a catalyst time on stream of 30 s. Some of the feeds and catalysts tested in the laboratory FCC ACE unit were also examined in a commercial short-contact-time FCC unit resembling a UOP side-by-side design. It was found that conversion can be very well predicted in both the laboratory ACE and the commercial FCC units using multiple linear correlations developed in this work from information about the following feed properties: K_W-characterizing factor, nitrogen content, and micro-activity of the catalyst. The coke on the catalyst that controls the catalyst-to-oil ratio and the regenerator temperature in the commercial FCC unit could be calculated using the correlations developed in this work for the laboratory ACE and commercial FCC units, based on feed characteristics and catalyst micro-activity. Due to the greater slope of the Δ coke/Δ micro-activity dependence observed in the ACE FCC unit, the more active catalysts show weaker results compared to the less active catalysts at a constant coke yield. In contrast, catalysts with higher activity are preferable for operation in the commercial FCC plant because they provide higher conversion at the same coke yield due to the lower slope of the Δ coke/Δ micro-activity relationship. Full article

The symmetrical relationship between affective semantics and form bionics creates new possibilities for tea packaging. This study proposes a biologically inspired workflow for tea packaging design, effectively integrating natural forms, affective semantics, and sustainability assessment. First, ten natural forms suitable for bionic design were collected. The Affinity Diagram (AD) method was adopted based on evaluations from 20 consumers and tea merchants, yielding nine effective semantic and sustainability evaluation systems. Then, 10 domain experts scored the affective semantics, and the indicator weights were determined via the Precedence Chart (PC) method. The Quality Function Deployment (QFD) method was used to construct a relationship matrix between natural forms and affective semantics, identifying prioritized natural forms. Three biomimetic tea packaging designs were developed based on the three selected priority forms. Subsequently, the Criteria Importance Through Intercriteria Correlation (CRITIC) method calculated the objective weights of sustainability indicators. These weights were combined with Grey Relational Analysis (GRA) for comprehensive ranking to determine the optimal packaging scheme. The results show that stylish design (P1) has the highest weight among affective semantics, while low resource consumption (Q1) ranks first in sustainability evaluation indicators. Bamboo joint packaging was selected as the optimal design solution in the comprehensive ranking. This design process provides a methodological framework for tea packaging design, integrates biological bionics with affective semantics, and demonstrates potential for cross-category applications. Full article

Renewable energy communities are emerging as key players in the sustainable energy transition, yet there is a lack of systematic approaches for evaluating the digital tools that support their development and operation. This study proposes a comprehensive methodology for assessing tools for supporting renewable energy communities, based on a system of key performance indicators and the multi-criteria decision analysis framework method. Twenty-three specific sub-criteria were defined and scored for each tool, and a weighted sum model was applied to aggregate performance. To ensure robust comparison, criteria weights were derived using both expert judgement (pairwise comparisons of ranking and analytical hierarchy process) and objective data-driven methods (the entropy-based method and the criteria importance through intercriteria correlation weighting method). The framework was applied to a diverse sample of contemporary renewable energy community’s tools, including open-source, commercial, and European Union project tools. Key findings indicate that some of the tools have shown noticeable rank shifts between expert-weighted and data-weighted evaluations, reflecting that expert opinions emphasize technical and operational features while objective variability elevates environmental and economic criteria. This assessment enables stakeholders to compare energy community tools based on structured criteria, offering practical guidance for tool selection and highlighting areas for future improvement. Full article

Environmentally friendly (green) stock investment has evolved into a global trend over the past few decades, including in the Indonesian capital market. However, the process of selecting sustainability-oriented stocks involves various complex criteria that are often qualitative, subjective, and uncertain. Therefore, an analytical tool is needed to support the decision-making process more adaptively and objectively. This study proposes the Criteria Importance Through Inter-criteria Correlation–Weighted Fuzzy Soft Set (CRITIC-WFSS) integration model, a decision-making method that combines WFSS with the objective, data-driven weighting mechanism of the CRITIC method. In the proposed model, parameter weights are determined by considering data variation (standard deviation) and inter-criteria correlation, ensuring that more discriminative and informative parameters receive higher weights. The model was applied to data on environmentally friendly stocks in the SRI-KEHATI Index, obtained from the Indonesia Stock Exchange (IDX) official website, to evaluate and identify stocks with optimal performance. The model’s performance is evaluated through a comparative study with the AHP-WFSS and Entropy–WFSS methods, complemented by a sensitivity analysis. The results show that UNVR ranked highest with a perfect score of 1, indicating an optimal balance between financial performance and sustainability. Furthermore, a comparative study demonstrated that CRITIC-WFSS can generate rankings that are more reliable, appropriate, and logical than those generated by two comparison methods. Meanwhile, the results of the sensitivity analysis indicate that the CRITIC-WFSS model demonstrates strong robustness to variations in input parameters, ensuring stable rankings. The model shows significant potential to support more accurate and transparent investment decision-making by generating consistent stock rankings based on a balanced integration of financial, and sustainability (environmental, social, and governance (ESG)) aspects. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth). Full article

Rapid progress in sustainable and intelligent transportation has intensified interest in smart airport initiatives, driven by the need to support environmentally responsible and technology-enabled aviation development. As complex sociotechnical subsystems of smart aviation, smart airports integrate advanced digital, operational, and organizational technologies to enhance efficiency, resilience, and passenger experience. With increasing emphasis on such transformations, multiple strategic development plans have emerged, each with distinct priorities and implementation pathways, which necessitates a rigorous and transparent evaluation mechanism to support informed decision-making under uncertainty. This study proposes an integrated spherical fuzzy multi-criteria decision-making (MCDM) framework for assessing and ranking smart airport development plans. Subjective expert judgments are modeled using spherical fuzzy sets, allowing for the simultaneous consideration of positive, neutral, and negative membership degrees to better capture linguistic and ambiguous information. Expert importance is determined through a hybrid weighting scheme that combines a social trust network model with an entropy-based objective measure, thereby reflecting both relational credibility and informational contribution. Criterion weights are computed through an integrated approach that merges criteria importance through the inter-criteria correlation (CRITIC) method with the stepwise weight assessment ratio analysis (SWARA) method, balancing data-driven structure and expert strategic preferences. The weighted evaluations are aggregated using a spherical fuzzy extension of the combined compromise solution (CoCoSo) method to obtain the final rankings. A case study involving smart airport development planning in China is conducted to illustrate the applicability of the proposed approach. Sensitivity, ablation, and comparative analyses demonstrate that the framework yields stable, discriminative, and interpretable rankings. The results confirm that the proposed method provides a reliable and practical decision support tool for smart airport development and can be adapted to other smart transportation planning contexts. Full article

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three key dimensions, to-metro accessibility, by-metro accessibility, and land use accessibility, which are further refined into 14 secondary indicators for detailed analysis. A Combination Weighting Method (CWM) is then introduced, integrating the Analytic Hierarchy Process (AHP) for subjective weighting and the Criteria Importance Through Intercriteria Correlation (CRITIC) method for objective weighting, with their integration optimized through Game Theory. Subsequently, the overall accessibility of metro stations is evaluated and ranked using a hybrid Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The proposed method is applied to Wuhan, China, to demonstrate its effectiveness and applicability. Results show that the CWM-GRA-TOPSIS model, by balancing objectivity and practical relevance, provides a more reliable and systematic approach for identifying accessibility disparities and formulating targeted improvement strategies for urban metro systems. Full article