Search Results (155)

China–Iraq international energy cooperation on natural gas constitutes a critical component of energy diplomacy under the Belt and Road Initiative. This study develops a multidimensional analytical framework encompassing geopolitical, economic–market, legal–policy, resource–technology, social–environmental, and bilateral–institutional dimensions. Subsequently, an integrated fuzzy DEMATEL-ISM model is employed to investigate the hierarchical structure and transmission pathways of influence among these factors. The findings reveal that the multiple factors can be classified into four clusters (driving, linkage, independent, and dependent), each exhibiting distinct roles in system evolution. Meanwhile, the model identifies a six-tier hierarchical structure, with directed pathways transmitting from deep-rooted factors through intermediate nodes to surface-level outcomes. Surface-level factors occupy the upper tier and directly shape cooperative performance, while intermediate-level factors act as transmission conduits that relay and transform deeper influences. Deep-level factors, including great-power rivalry, resource endowment, and market demand, form the system’s foundational layer. They remain immune to influence from upper tiers and thus require strategic governance to fundamentally ensure enduring cooperation sustainability. Consequently, policy priorities should center on deep-level drivers, complemented by targeted adjustments to intermediate and surface factors. This study offers a novel multi-level structural analytical lens and provides actionable policy recommendations to enhance cooperative resilience under the Belt and Road framework. Full article

This paper systematically examines the core of new quality productive forces underpinning the sustainable development of the low-altitude economy industry. Based on the Triple Bottom Line theory, it analyzes the interrelationships among various factors of new quality productive forces and reveals the underlying mechanism through which new quality productive forces drive sustainable development in this sector. By employing an integrated Fuzzy Decision-Making Trial and Evaluation Laboratory—Interpretive Structural Modeling (Fuzzy-DEMATEL-ISM) approach, this study establishes a hierarchical structure for sustainable development in the low-altitude economy industry grounded in the logical framework of new quality productive forces. The findings indicate that: (1) the strategic origin layer represents the prerequisite and most critical task for the emergence of the low-altitude economy industry; (2) the technological breakthrough layer constitutes the core competitive strength enabling its development; (3) the resource guarantee layer provides essential resource support for operational sustainability; (4) the institutional synergy layer enhances industrial efficiency through coordinated governance; and (5) the value realization layer signifies the ultimate form of industrial evolution. These research outcomes offer significant forward-looking insights for advancing sustainable development in the low-altitude economy industry. Full article

Pharmaceutical cold chains operate under tightly coupled compliance, operational, and sustainability requirements, yet managers still face a practical challenge when deciding which improvement initiatives should be implemented first under limited resources and uncertain expert judgment. This study develops an integrated multi-criteria decision framework to prioritize pharmaceutical cold chain improvement initiatives by combining fuzzy DEMATEL and fuzzy TOPSIS. Thirteen evaluation criteria were derived from the literature and organized into three clusters covering risk, operational performance, and sustainability, while nine implementable initiatives were evaluated by a cross-national panel of pharmaceutical cold chain experts. Fuzzy DEMATEL was used to model causal interdependencies among the criteria and to derive structurally informed weights, and fuzzy TOPSIS was then applied to rank the initiatives. The results show that monitoring reliability, handling and process compliance, deviation management capability, and traceability event quality act as the main upstream drivers in the system. In the resulting prioritization, handling procedure redesign and targeted training, followed by formal excursion management, emerged as the highest priority initiatives. Packaging qualification, monitoring governance, and interoperable event capture formed the next tier. Sensitivity analysis showed that the leading priorities remained stable under plausible weight changes, supporting the robustness of the proposed framework. This study moves beyond method combination by linking expert perceived interdependencies among pharmaceutical cold chain risk, performance, and sustainability criteria to a sequenced portfolio of implementable initiatives. It contributes a theory-informed and operationally interpretable prioritization framework while recognizing that the inferred influence structure reflects structured expert judgement rather than externally validated operational causality. Full article

This study investigates the key drivers and barriers influencing the adoption of electric vehicles (EVs) in urban freight logistics, using Lithuania as a case study. An integrated methodological framework combining Delphi, Fuzzy logic, DEMATEL, and System Dynamics is applied to identify critical factors and analyse their interdependencies. Four main drivers are identified: infrastructure, acquisition costs, technological development, and policy measures. Expert evaluations are transformed into fuzzy values to quantify factor importance, which are then incorporated into a dynamic simulation model to assess EV adoption and CO₂ emission trends. In addition to baseline scenarios, extreme scenario analysis is conducted to evaluate system sensitivity to economic, technological, and policy changes. The results reveal strong nonlinear relationships between factors and highlight the importance of their balanced development. The findings suggest that rapid EV adoption in urban logistics requires a coordinated approach integrating infrastructure expansion, financial incentives, technological progress, and policy support. The study provides practical insights for policymakers and logistics companies aiming to accelerate sustainable urban transport transitions. Full article

This study examines the characteristics of Group Multi-Attribute Decision Making (GMADM), including highly ambiguous information, divergent expert opinions, and bounded rationality among decision-makers. From the perspective of symmetry modeling and bias control, we propose an adaptive decision-making framework based on Spherical Fuzzy Sets (SFS). First, a spherical fuzzy quantification system for online reviews is constructed to map multi-source asymmetric information within reviews to Spherical Fuzzy Numbers. Second, an adaptive expert weighting mechanism is developed that integrates individual expert performance with the level of group consensus, dynamically adjusting weights to suppress the asymmetric interference of outlier opinions. Subsequently, we design the Credibility-based Spherical Weighted Arithmetic Mean (CSWAM) to preserve the dominance of expert judgments in a nonlinear manner and construct the Spherical Fuzzy Score function with Adaptive Hesitation Penalty (HP-SC) to ensure robustness and non-negativity in the defuzzification process. Furthermore, we extend DEMATEL and TODIM to the SFS environment, constructing a comprehensive evaluation model that captures causal relationships among attributes and asymmetric information, such as decision-makers’ loss aversion. Finally, empirical results from online movie review usefulness rankings demonstrate that this model can accurately identify and mitigate asymmetric information biases while maintaining decision symmetry equilibrium and exhibiting higher ranking stability. Full article

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Driven by China’s “dual carbon” (carbon peak and carbon neutrality) goals and the national strategy of new-type urbanization, prefabricated construction has emerged as a pivotal pathway toward industrialized and sustainable development in the construction sector—leveraging its distinctive advantages in construction efficiency, cost optimization, environmental performance, and design adaptability. Nevertheless, the inherently sequential and interdependent nature of the full construction process—encompassing off-site component manufacturing, logistics transportation, and on-site assembly—introduces pronounced cross-stage risk transmission mechanisms, with prefabricated components serving as critical risk carriers. Such transmission dynamics significantly impede the scalable and safe deployment of prefabricated construction. To date, scholarly efforts on construction safety in prefabricated buildings have predominantly addressed isolated, stage-specific risks, falling short in quantitatively modeling the coupled propagation of risks across stages, accommodating epistemic uncertainties and latent (i.e., unknown or unobserved) risks, and informing targeted, evidence-based mitigation strategies. To bridge this gap, this study develops a rigorous quantitative framework for assessing cross-stage risk transmission in prefabricated construction safety. Specifically, it aims to (i) uncover the structural patterns and driving mechanisms underlying inter-stage risk propagation; (ii) reduce the likelihood of safety incidents throughout the construction life cycle; and (iii) deliver actionable theoretical insights and methodological guidance for practitioners and policymakers. Methodologically, we first conduct a systematic identification of safety-critical risk factors and establish a hierarchical risk indicator system comprising three first-level dimensions and twenty second-level indicators. Second, using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, causal relationships among risk factors are clarified, while incorporating the Leaky Noisy-or Gate (LNOG) extended model to account for unknown risks. Risk data are processed using triangular fuzzy functions, and a Bayesian network (BN) topology diagram is constructed via the GeNIe 5.0 platform, forming a DEMATEL-LNOG-BN-based model for assessing cross-phase risk transmission. Finally, applying the model to an actual project—”a prefabricated construction project in Shanghai”—the study conducts a cross-phase risk transmission analysis. Through forward probability inference, backward causality tracing, sensitivity analysis, and pathway decomposition, sensitivity comparisons are performed under different LNOG unknown risk parameters. Results are compared with those from the traditional DEMATEL-BN model to validate the stability and consistency of high-sensitivity risk factor identification, comprehensively verifying the applicability and predictive reliability of the proposed DEMATEL-LNOG-BN model. The study quantitatively reveals the progressive diffusion and amplification mechanisms of risks across the production–transportation–assembly process, providing scientific support and practical reference for precise safety risk prevention, critical node control, and the optimization of management systems in prefabricated construction sites. 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

Port and shipping enterprises face significant uncertainty in making effective sustainable investment decisions to meet pressing carbon reduction targets. This study addresses this challenge by developing a fuzzy multi-method framework to identify and prioritize pivotal factors that guide sustainable investments. An evolutionary game model simulates the influencing factors, while the triangular fuzzy number (TFN) and evidential reasoning (ER) algorithm assess their importance and operability. The decision-making trial and evaluation laboratory (DEMATEL) method further refines these assessments. Finally, the Bayesian probability method corrects the posteriori probability, providing a comprehensive ranking. The results reveal that low-carbon technology is the most critical driver of sustainable investment, whereas environmental factors consistently rank the lowest in importance. This methodology aids ports and shipping enterprises in making sustainable investment decisions to reduce carbon emissions. Full article

Runway incursions (RIs) have emerged as a major threat to airport surface safety, driven by the coupled influence of human, equipment, environmental, and management factors. Conventional assessment methods struggle to simultaneously capture the fuzziness of expert linguistic judgment and the randomness of operational conditions. This study proposes an integrated DEMATEL–Cloud–TOPSIS framework for runway incursion risk assessment and validates it at Chengdu Tianfu International Airport. A hierarchical indicator system comprising 24 indicators across four dimensions—Human (H), Equipment (M), Environment (E), and Management (G)—was constructed from 90 RI cases collected between 2018 and 2023. DEMATEL quantified inter-indicator causal dependencies and DEMATEL-derived weights; the Cloud model translated linguistic expert judgments into digital characteristics (Ex, En, He); and TOPSIS produced relative closeness coefficients for risk ranking. Human, equipment, and environmental risks are all at a medium-risk, while management risk is at a low-risk, but significant differences still exist. Management achieved the highest closeness ($C_i$ = 0.6322) and Environment the lowest ($C_i$ = 0.5096). At the indicator level, ATC Instruction Accuracy (H1) exhibited the greatest operational maturity ($C_i$ = 0.9119), whereas Unclear Crew Coordination (H6) showed the lowest relative closeness ($C_i$ = 0.0156), followed by Aircraft Equipment (M5) ($C_i$ = 0.0195). Meanwhile, Runway Configuration Complexity (E2) remained a weak structural factor within the Environmental dimension ($C_i$ = 0.1502). The framework provides an interpretable, quantitative basis for targeted safety management at complex hub airports. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

Tangyue Village in Huizhou, China, is renowned for its monumental Bao-family archway complex and well-preserved ancestral halls, which host and memorial activities embodying rich clan traditions and regional cultural identity. However, these traditional spaces face contemporary challenges, including functional obsolescence, high energy consumption, and limited sustainability. Focusing on the memorial spaces of Tangyue Village, this study explores low-carbon design strategies for their renewal by developing a comprehensive research framework that integrates multi-stakeholder demand analysis, weighting evaluation, case-based design, and performance verification. Initially, user needs were identified through semi-structured interviews and behavioral observations, followed by the application of the Fuzzy Kano (FKANO) model to classify and filter these requirements. Subsequently, a multi-level evaluation system was established, encompassing low-carbon performance, spatial functionality, cultural continuity, and community participation. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach combined with the entropy weight method was then employed to determine the relative importance of each indicator. The results indicate that the organization of memorial spaces, the application of low-carbon materials, rainwater harvesting, and spatial accessibility represent key design priorities. Space syntax simulations conducted via DepthmapX further demonstrate that the optimized design significantly improves spatial accessibility, permeability, and vitality while enhancing the overall low-carbon performance. Ultimately, this study proposes practical low-carbon renewal strategies for memorial spaces in traditional settlements, offering a systematic approach that balances cultural heritage preservation with environmental sustainability. Full article

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article