Search Results (4,065)

Background: Multi-objective optimization (MOO) is widely used in engineering design and energy systems to represent trade-offs through Pareto fronts. Yet practical deployment requires moving from a non-dominated set to an implementable preferred design, and this decision step is often treated implicitly. Many studies equate decision support with improved Pareto front generation or visualization, while decision-maker preferences are assumed, weakly specified, or not elicited from stakeholders. Methods: A two-phase scoping evidence synthesis with PRISMA-informed reporting was adopted to map the literature and synthesize explicit Pareto-front decision-support mechanisms. Phase 1 produced a broad evidence map of how Pareto-front decision support is framed and clustered studies by primary contribution, while Phase 2 conducted a focused synthesis of explicit Pareto-front decision-support methods using refined searches in Scopus and SpringerLink. Results: Phase 1 mapped 46 studies; only 10 reported an explicit reproducible Pareto front solution-selection mechanism. Phase 2 included 17 studies and identified four method families: post hoc scoring and ranking, compromise aggregation, interactive preference-guided exploration, and preference elicitation and learning. Conclusions: The literature remains dominated by Pareto front generation and exploration rather than reproducible final solution selection; future work should strengthen preference elicitation, transparency, sensitivity analysis, and uncertainty-aware recommendation stability. Full article

Systems that farm non-typical (wild) species for human consumption are on the rise globally, in contrast to more typical livestock production. In some instances, wildlife farming may arguably help alleviate poverty, provide sustainable animal protein, and be a useful strategy for conservation through reducing wildlife poaching or breeding some animals on farms for reintroduction. However, it is unclear whether farming non-typical species within variable and often unregulated systems truly offers these benefits or outweighs the costs including animal welfare implications, public health concerns, and normalising or intensifying the consumption of wild animals. A previous study proposed a sentiocentric ethical decision-making framework for the farming of wild species. In the present study we invited academic ‘key informants’ with specialised knowledge about farming non-typical species to pilot the framework via an online survey using a species of their choice and requested their feedback on its strengths and weaknesses. Thirteen respondents applied ten different mammalian, reptilian, insect, and avian species to the framework, spanning all continents. Ultimately, the framework outcome for 11 appraisals was that the chosen species may be suitable for farming. However, erroneous responses were likely in places, and there was some uncertainty over definitions of framework terminology. We publish resultant amendments to the ethical framework to clarify meaning and suggest that it can be applied proactively or reactively by different stakeholders (e.g., governments, businesses, and NGOs). We reflect our informants’ views, acknowledging the need to solicit expertise from additional stakeholders (e.g., farmers) and the role of cultural significance and rural communities when considering farming non-typical species. Full article

The integration of environmental, social and governance (ESG) criteria into corporate and financial decision-making has become one of the most significant transformations in today’s financial markets. Growing regulatory pressure, stakeholder expectations and increased awareness of sustainability challenges have led companies and investors to incorporate ESG considerations into strategic and investment decisions. Despite the rapid spread of ESG practices, the academic literature presents conflicting and sometimes contradictory evidence regarding their economic implications and practical effectiveness. This article provides a review of the literature on the main academic contributions to ESG integration, focusing on three key dimensions: the economic benefits associated with ESG practices, the methodological and credibility challenges relating to ESG measurement, and the organisational and technological factors that enable effective ESG implementation. The findings indicate that ESG integration is generally associated with positive organisational outcomes, including improved financial performance, lower cost of capital, greater stakeholder trust and a reduction in firm-specific risk. However, the realisation of these benefits is not automatic and depends to a large extent on the credibility of ESG practices and information. Rather than endorsing the widely held view that ESG criteria are inherently capable of creating value, the analysis shows that the value-creating effect of ESG criteria depends crucially on the credibility of ESG practices and the quality of their implementation. The literature highlights significant methodological challenges, including rating divergence, the lack of standardised metrics, methodological opacity and the growing risk of greenwashing, which can undermine the reliability of ESG information. This paper proposes an deductive conceptual framework in which ESG effectiveness emerges from the interaction between value creation mechanisms, credibility constraints, and enabling organisational and technological factors. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

This study evaluates the adoption and efficacy of the 5th Dimension Building Information Modelling (5D-BIM) as a cost dimension for mega rail projects, extending the discussion beyond just technological implementation to consider broader policy and practical implications. The purpose of this article is to understand the governance context of 5D-BIM implementation for rail and transport projects and evaluate the effectiveness of the 5D-BIM framework as currently applied by conducting semi-structured interviews with key stakeholders. Drawing on semi-structured interviews with 22 stakeholders across government, industry, and technology providers, the research examines current 5D-BIM practices. While the primary focus of the research is 5D BIM implementations within the state of Victoria, Australia, which is currently experiencing a surge in rail projects, interviews were also conducted with additional stakeholders from international rail projects for context. The findings reveal fragmented adoption, varying levels of organisational maturity, and significant policy and implementation gaps, particularly in the role of government as the primary client of transport infrastructure. The results of the interviews emphasise the centrality of government and regulatory context in driving the adoption and implementation of 5D-BIM as the primary client of transportation infrastructure and identify actionable recommendations for policymakers and practitioners towards a more integrated approach to 5D-BIM in mega rail projects. While 5D-BIM demonstrates clear benefits in enhancing cost estimation, coordination, and decision-making, its effectiveness is constrained by the absence of clear standards, limited BIM literacy, and inconsistent regulatory guidance. This study provides one of the first empirical validations of the 5D-BIM governance framework, demonstrating that its success is driven less by technological capability and more by policy alignment, standardisation, and institutional leadership. 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

Background: One form of domestic and family violence (DFV) is financial abuse, which involves a person manipulating another person’s access to finances, assets, and financial decision-making. The aim of this study was to understand the perceived role of Australian financial institutions, government bodies and other key stakeholders in the prevention, early identification and resolution of financial abuse in small businesses. Methods: A single workshop was conducted in Melbourne, Australia, in November 2024. Representatives from five stakeholder groups were invited to participate: (i) Australian regulated financial institutions; (ii) Australian unregulated commercial lenders; (iii) government bodies; (iv) small business professional services organisations and their peak bodies; and (v) industry and representative bodies. Results: Four main themes were generated relating to the prevention, early identification and resolution of financial abuse in small businesses: (1) shining a light on financial abuse; (2) detecting and revealing red flags; (3) business lending practices create vulnerability; and (4) building a collective response. Conclusion: Whilst institutions demonstrate inherent potential for addressing family violence and financial abuse within small business contexts, realising this capacity requires substantial investment in education, contextual literacy development, collective responses and structural and legislative reform. Full article

Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions, a share expected to grow without effective technological and regulatory intervention. Recent policy developments, including the IMO Revised GHG Strategy (2023), the extension of the EU Emissions Trading System to maritime transport, and the FuelEU Maritime Regulation, require ports and shipping stakeholders to evaluate multiple decarbonization technologies under complex and often conflicting constraints. These decisions involve trade-offs across economic, technical, environmental, social, and cyber–physical security dimensions, which are not adequately addressed by conventional decision-support tools. This paper introduces XAI–MCDA-HoDEM, an explainable multi-criteria decision framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and SHAP-based explainability. The framework explicitly incorporates cyber–physical security as a core evaluation criterion and provides transparent, criterion-level explanations of decision outcomes. Using real-world data, the methodology is demonstrated through an illustrative case study and empirically validated at the Port of Rotterdam. Results show stable and robust rankings, alignment with observed port decarbonization strategies, and improved interpretability of decision drivers. The proposed framework supports transparent, policy-relevant decision-making for the maritime energy transition. Full article

Intelligent automation is a core component of Industry 4.0, enabling artificial intelligence (AI) systems to support or execute operational and managerial decisions in real time. In high-risk industrial environments such as mining and metallurgy, real-time decision-making improves efficiency but also raises critical challenges related to trust, explainability, human oversight, and institutional accountability. This study proposes PRIME–INSPECT, a two-layer socio-technical framework designed to support trustworthy AI-driven real-time decision-making. The PRIME (predict, regulate, interpret, mitigate, execute) layer formalizes the operational decision flow, embedding control mechanisms, uncertainty quantification, and explainability into the automation pipeline. The INSPECT (integrity, navigability, supervisory control, policy maturity, ethical compliance, collaboration, trust calibration) layer defines the organizational and governance conditions required for safe deployment. The framework is conceptually developed through a structured literature synthesis and supported by exploratory empirical grounding through stakeholder perceptions from IT and top management participants, alongside an illustrative industrial use case intended to demonstrate conceptual applicability rather than engineering performance validation. The findings highlight the importance of aligning operational AI processes with institutional safeguards to support calibrated trust and responsible automation. The empirical component is intended to provide conceptual and organizational grounding of framework dimensions rather than quantitative validation of predictive performance. PRIME–INSPECT provides a structured architecture for designing and governing AI-enabled real-time decision systems in high-risk industrial contexts. Full article

Coal-fired power units remain integral to electricity supply in many regions while facing increasingly stringent environmental expectations. Bridging reliable generation with sustainability requires more than end-of-pipe controls; it demands continuous intelligence embedded in plant operations. This study introduces an industry-oriented monitoring framework that transforms historical operational records into actionable foresight, enabling on-the-fly orchestration of combustion conditions to anticipate sulfur oxide (SOx) concentrations. Leveraging 919 empirical data points collected in 2019 from Unit 8 of the Taichung Thermal Power Plant, the framework integrates robust data governance, targeted feature curation, and a neural network-based analytics core. Eight process variables—sulfur content, coal feed rate, fixed carbon, grinding rate, calorific value, excess air, air flow, and boiler efficiency—emerge as the most influential drivers through systematic selection and feature importance attribution. The resulting forecasting module exhibits near-perfect alignment with observed emissions (R² = 0.99), enabling near-real-time guidance for setpoint adjustments and facilitating compliance strategies under varying load and fuel-quality conditions. Beyond accuracy, the system is architected for scalability and portability, aligning with Industry 4.0 paradigms by coupling continuous sensing, data-driven decision support, and stakeholder transparency. By reframing emission oversight as a proactive, intelligent service rather than a static reporting function, the proposed approach advances operational resilience, regulatory compliance, and community trust, with direct implications for resource efficiency and circular economy initiatives across heavy industry. The framework reduces potential SOx emissions and improves energy utilization efficiency under varying operational conditions. This approach contributes to environmental sustainability by enabling proactive emission reduction and cleaner production practices. It supports regulatory compliance and aligns with global sustainability goals, including SDG 7 and SDG 13. Full article

Stakeholders in the tourism system, recognizing the importance of digitalization and the adoption of modern technological solutions, are increasingly integrating artificial intelligence into their operations, including the use of autonomous robots. These initiatives should primarily aim to enhance the customer experience by simplifying and streamlining procedures, while allowing tourism employees to devote more attention to guests. Nautical tourism is a specific form of tourism in which the Republic of Croatia is a global leader; however, it also faces a shortage of qualified staff. Marinas, as the most significant stakeholders within the nautical tourism sector, are the first to invest in the development of innovative solutions. In addition to reviewing the theoretical framework, this paper emphasizes primary research. The aim of the research was to examine the attitudes and habits of nautical tourism guests regarding the adoption of new technologies in marinas, as well as their willingness to use autonomous robots. Given the decision to develop and implement autonomous robots in business operations, the research was conducted among users of nautical services in one of the most modern nautical tourism ports in Croatia and the Mediterranean. A structured online questionnaire was used. The results indicate users’ readiness for the immediate adoption of autonomous robots in certain services, providing a direct incentive for stronger implementation of similar solutions among other stakeholders. This research also suggests that Croatia has the potential to become a technological hub for smart nautical tourism. Full article

Peer-to-peer car-sharing (P2PCS) is a mobility solution in which vehicle owners make their cars available through digital platforms that mediate booking, access, and fleet management. Although P2PCS can offer theoretical advantages in terms of resource efficiency and reduction in private ownership, its market scaling is critically dependent on users’ perceived value. In this context, the present study investigates systemic determinants of scalability in the P2PCS market using a Mixed-Methods Research (MMR) approach, combining a literature review, expert survey, focus-group interviews, Interpretive Structural Modeling (ISM), and cross-impact matrix analysis (MICMAC). This combined approach allows for the identification of barriers and their position within a hierarchical structure of interrelationships, influence, and dependence. The most influential determinants are emotional attachment to private car ownership, legal and regulatory uncertainty, stakeholder misalignment, infrastructure constraints, and ambiguous insurance coverage. By analyzing the hierarchical interrelationships between these determinants and their respective driving and dependence powers, experts in a focus group suggested a set of targeted measures to address the P2PCS system of barriers. Proposed measures include regulatory sandboxes to safely test innovative business models, modular and usage-based insurance products, the promotion of public–private partnerships for shared mobility infrastructure, the advancement of digital interoperability across smart mobility services, and communication campaigns to foster sustainable travel behaviors. Collectively, the findings provide a decision-support framework for policymakers, P2P platform operators, and municipalities, illustrating how the Mixed-Methods Research approach can deepen understanding of the socio-technical interdependencies that shape the effectiveness and scalability of emerging mobility solutions such as peer-to-peer car-sharing. Full article

Marine oil spills pose critical challenges to environmental sustainability and socioeconomic stability. Taking four pivotal cases as the entry point, this study uses comparative case analysis, semi-structured stakeholder interviews, policy analysis and international gap comparison to systematically analyze the evolution of marine oil spill governance in the Taiwan region of China over two decades, aiming to identify systemic gaps and propose actionable reforms. By integrating and explicitly detailing these multiple methodologies, this research not only identifies but also systematically examines the Taiwan region of China’s unique challenges as a non-UN-member entity navigating international conventions like the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto (MARPOL 73/78). Key findings reveal persistent issues in decision-support tools, fragmented inter-agency coordination, and legal inadequacies in compensation mechanisms. The study’s novelty lies in its rigorous synthesis of localized case-driven insights compared with global best practices, proposing a concrete, phased model for a unified task force and context-aware, data-driven contingency plans to enhance real-time response efficiency. It further advocates for pragmatic steps to align the Taiwan region of China’s Marine Pollution Control Act with international standards while critically addressing the transboundary collaboration barriers imposed by its political status, exploring potential pathways through sub-national and regional partnerships. Notably, the 2023 Angel Container case underscores the urgency of modernizing enforcement capacities and integrating advanced technologies. By bridging gaps in governance, legal accountability, and practical international engagement, this research not only advances the Taiwan region of China’s preparedness but also offers a nuanced and adaptable blueprint for coastal regions facing similar geopolitical and environmental constraints. Its recommendations hold significant implications for global marine pollution management, emphasizing the interplay of policy innovation, technological adoption, and pragmatic cross-jurisdictional cooperation. Full article

The transition to a circular economy (CE) is a critical challenge for the urban built environment, particularly within the Building and Construction sector. This study explores the application of circularity micro-indicators to assess the use of plastic materials in two Portuguese construction approaches: traditional (CS1) and modular (CS2). A set of four relevant micro-indicators—Material Circularity Indicator (MCI), Recycled Content Performance Indicator (RPI), Circular Design Guidelines (CDG), and Circular Economy Implementation Potential (CEIP)—was selected to evaluate material circularity at the product and system levels. The novelty of this study lies in selecting and applying the most relevant circularity indicators from the literature to plastic products in construction, providing the first practical demonstration of their use and offering actionable tools to support stakeholders in material selection, design decisions, and circularity assessment. Findings show that modular construction demonstrates a stronger alignment with CE principles compared to traditional methods, although both cases present low overall levels of circularity. For instance, the Material Circularity Indicator (MCI) ranged from 8.4% in the traditional building (CS1) to 15.2% in the modular building (CS2), quantitatively illustrating the circularity potential of modular construction. Key strategic opportunities for improvement include design for disassembly, the elimination of toxic or scarce materials, improved recyclability of plastic components, and the integration of on-site material separation and recovery zones. Strengthening waste management systems is also essential to enhance the quality and reliability of recycled plastic materials. The study highlights the value of micro-indicators as practical tools to support evidence-based decision-making in material selection, design strategies, and circular construction planning. By guiding more sustainable practices in urban construction, these indicators can play a pivotal role in accelerating the sector’s transition toward a circular and resource-efficient economy. Methodologically, the study adopts a collaborative case-study approach with an industry partner, involving brainstorming sessions with experts in CE and circularity indicators to select representative buildings, and identify indicators from the literature based on data availability and relevance for construction plastic products, which are then applied to real project data, complemented by exploratory improvement scenarios. Full article

Participatory housing design is widely associated with social sustainability because it can support community-building, strengthen user acceptance, and foster long-term place attachment. At the same time, participatory processes are organisationally demanding, as they involve multiple stakeholder groups, frequent design iterations, and the need to communicate spatial and technical implications to non-professional participants. While recent research has examined BIM in housing, collaboration, and digital participation, fewer studies begin with empirically documented workflow bottlenecks in non-BIM participatory housing projects and translate them into actionable BIM-supported strategies. This study addresses this gap by examining (1) recurring process-related challenges in participatory housing design conducted without BIM-based workflows, (2) BIM-supported workflows that could realistically mitigate these challenges, and (3) the implications for socially sustainable practice. A qualitative research design is applied to two participatory multi-family housing projects in Vienna, Austria. The cases were reconstructed from earlier semi-structured interviews with project architects and complemented by a follow-up structured questionnaire to validate key process aspects. Two independent BIM experts then interpreted the empirically identified challenges and proposed BIM-based responses. The results indicate that the most persistent difficulties are procedural rather than formal, centring on iteration and variant management, decision traceability, communication with lay participants, and coordination under time pressure. Expert interpretations suggest that BIM can strengthen participatory workflows through CDE-based information governance, structured issue and decision tracking, curated option management, and improved visual communication, while also introducing constraints related to costs, training, interoperability, organisational readiness, and potential cognitive overload. Overall, the paper positions BIM as a socio-technical infrastructure that can enhance procedural justice and transparency when embedded within carefully moderated participatory workflows. Full article