Search in Publications
Found 5152 publications
Empowering circular economy transformation through immersive digital open innovation
Publication Name: Journal of Innovation and Knowledge
Publication Date: 2025-11-01
Volume: 10
Issue: 6
Page Range: Unknown
Description:
Despite broad support for sustainability, the transition from linear to circular economic models remains slow and fragmented across industries. Digital technologies such as the metaverse present new opportunities for enabling circular economy (CE) practices, yet their strategic integration within organizational systems is not fully understood. To address this gap, this study proposes and empirically validates a model that explains how metaverse adoption influences CE implementation through the mediating role of Immersive Knowledge Co-Creation (IKCC), and the moderating effect of Open Eco-Innovation Capability (OEIC). Grounded in the knowledge-based view (KBV), interactive learning theory, and dynamic capabilities theory, the research develops a process-driven and capability-oriented framework to explore the mechanisms and boundary conditions for digital-enabled circular transformation. Empirical data were collected from 220 respondents in Germany's advanced manufacturing sector and analyzed using partial least squares structural equation modeling. The results confirm that IKCC significantly mediates the relationship between metaverse adoption and CE implementation, and that this mediated relationship is stronger in firms with high OEIC. These findings contribute to the growing body of literature on digital innovation in the CE by highlighting the critical role of immersive collaboration and organizational readiness in driving circular processes.
Open Access: Yes
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
Analysis of Success Factors in Construction Projects under Consideration of Sustainability: A Literature and Legal Approach
Publication Name: Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
Publication Date: 2025-11-01
Volume: 17
Issue: 4
Page Range: Unknown
Description:
Success of construction projects will be affected by changing law and policy. In construction, where much money is at stake, risks must be calculated very carefully to finish the construction project with success. Nowadays, social impact of buildings, engineering ethics, and, in particular, environmental sustainability become increasingly important topics in construction, driven by current law and policy, especially in the European Union. The aim of the recently started research project is to find out if and how risks resulting from current changes in law and policy, with focus on sustainability, will be considered in construction project management. In the first step, the authors reviewed legal regulations as well as political considerations, and analyzed relevant research literature. The results are summarized in this paper. They can be a basis for a deeper risk analysis and the elaboration of solutions for how to deal with these challenges in construction contract design and project management. During their research, the authors studied relevant current European and German law and policy as well as scientific articles from the databases Web of Sciences and Scopus. The conclusion from the detailed literature analysis is that there is a need for more and deeper research to identify risks for sustainable construction projects resulting from law and policy and for developing strategies to manage them.
Open Access: Yes
Climate resilience through finance: The divergent roles of institutions and markets
Publication Name: Finance Research Letters
Publication Date: 2025-11-01
Volume: 85
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the divergent roles of financial institutions and financial markets in shaping climate resilience, with a focus on lower- and middle-income countries. Using a panel dataset spanning 1996–2021, we employ fixed effects models with Driscoll–Kraay standard errors and instrumental variable (IV-2SLS) techniques to address cross-sectional dependence, heteroskedasticity, and endogeneity. Our findings reveal that well-developed financial institutions, such as commercial and development banks, have a consistently positive and significant impact on climate resilience, especially when supported by strong governance frameworks. Interaction effects show that governance quality, particularly regulatory quality and control of corruption, significantly amplifies the effectiveness of financial institutions. Conversely, the role of financial markets appears more complex and context-dependent: in the absence of robust governance, financial markets can exhibit negative or neutral effects on resilience outcomes. These results underscore the importance of institutional quality in determining whether financial development supports or hinders climate adaptation. The study offers actionable insights for policymakers seeking to leverage finance for climate-resilient development in emerging economies.
Open Access: Yes
Multi-period natural gas pipeline scheduling optimisation integrated with LNG cold energy cascade utilisation
Publication Name: Sustainable Energy Technologies and Assessments
Publication Date: 2025-11-01
Volume: 83
Issue: Unknown
Page Range: Unknown
Description:
Liquefied Natural Gas (LNG), as a vital form of natural gas resources, has exhibited a steadily increasing trend in global production and trade volumes. LNG terminals are facing the challenge of how to recover and utilise cold energy in a safe and efficient regasification process, while coordinating with the natural gas pipeline network transport scheduling. This study proposes an integrated regulation and collaborative optimisation approach for natural gas pipeline networks and LNG cold energy cascade utilisation systems. For natural gas pipeline network systems, P-Graph develops multi-period gas-electric interconnected supply chain network to optimise resource allocation. For the LNG cold energy cascade utilisation system, a dual Organic Rankine Cycle (ORC) framework for both power generation and refrigeration is developed, as well as thermodynamic analysis and heat integration techniques are applied to optimise system efficiency. Using a coastal LNG terminal in Zhejiang, China, as a case study, when the LNG regasification flow rate is 62.46 t/h, cold energy generates electricity of 2,335.94 kW and air-conditioning cooling load of 1,651.5 kW, system efficiency reaches 44.75 %. The peak regulation and gas storage effect of LNG is significant, which helps to alleviate that energy shortage in the region, and the coupled system of LNG and natural gas pipeline network improves energy utilisation efficiency and economic benefits for LNG industry chain.
Open Access: Yes
Straw mulching optimized the root and canopy structure of soybean by reducing the topsoil temperature before blooming period
Publication Name: Field Crops Research
Publication Date: 2025-11-01
Volume: 333
Issue: Unknown
Page Range: Unknown
Description:
Context: The soybean seed yield in the Huang-Huai-Hai (HHH) region is challenged by high temperatures before blooming. Straw mulching can act to reduce topsoil temperature. However, little is known about whether changes in topsoil temperature contribute to the optimization of soybean root and canopy structure and, ultimately, yield. Objective: The aim of this study is to investigate the effects of straw mulching on soybean topsoil temperature, root growth, and canopy structure in the HHH region, China. Methods: A randomized block design was adopted (2020–2023) in the field, including three straw treatments: straw removing (SR), straw mulching (SM), and straw crushing (SC). Topsoil temperature, root morphology, leaf area index (LAI), light transmittance, canopy photosynthesis, dry matter accumulation, and seed yield of soybean under different treatments were measured. Furthermore, the test results were validated by pot experiment (LT: topsoil cooling, CT: topsoil non-cooling) in 2024. Results: Before soybean blooming, the highest topsoil temperature was 28.47℃ in SR, followed by 27.47℃ in SC and 26.95℃ in SM. Compared to SR and SC, the root length, root surface area, root volume and root dry weight of SM increased by an average of 26.04 %, 27.79 %, 29.13 % and 38.82 %, respectively. Soybean root dry matter weight was significantly positively correlated (P < 0.01) with the LAI and above-ground dry matter accumulation. Compared to SR and SC, Fv/Fm, Y(II), and ETR under SM treatment increased by 8.38 %, 7.94 %, and 7.73 %, respectively. Y(II) of the LT treatment was also significantly (P < 0.05) increased by 17.53 % compared to CT. Among the three treatments, soybean canopy photosynthetic rate and seed yield under SM treatment were, on average, significantly increased by 9.97 %, and 11.87 %, respectively. Furthermore, we identified the LAI characteristics of high-yield soybean canopy: 2.22 0.62 in the lower layer. Conclusion and implications: These findings imply that regulating topsoil temperature through straw mulching optimizes root and canopy development, improving soybean yield. This study provides insights into mitigating heat stress and enhancing sustainable soybean production in warm climates.
Open Access: Yes
Design constraints of a forerunner UAV in safety improvement of first responders
Publication Name: Transportation Research Interdisciplinary Perspectives
Publication Date: 2025-11-01
Volume: 34
Issue: Unknown
Page Range: Unknown
Description:
Unmanned Aerial Vehicles (UAVs) are already in use by emergency services. Drones are becoming faster and more reliable, making them suitable for safety-critical tasks. A forerunner UAV can fly ahead of an emergency ground vehicle (EGV). It can make a decision on the traffic situation at the next intersection to notify the EGV driver if other vehicles have given the right of way or not. This notification service can increase the speed of the EGV and prevent crashes that may occur due to the driver's obstructed view. A forerunner drone for slow-speed EGV (10 m/s) has already been developed and successfully tested; however, the applicability of current drone technology to the forerunner task at higher speeds was not analyzed. This paper identifies the key parameters of the task itself and the forerunner system in a general case, and gives the main tradeoff inequalities of these design parameters. The requirements and limitations of a forerunner drone are investigated within a reasonable parameter space, and the most relevant configurations are tested in dynamic simulation using optimized velocity profiles for the drone. Software-in-the-loop tests were also performed in a complete city simulation. The results indicate that the forerunner task is solvable with current fastest or near-future drone technology if the route of the EGV is known to the UAV control.
Open Access: Yes
Enhancing lake water level forecasting with attention-based LSTM: a data-driven approach to hydrology and tourism dynamics
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-11-01
Volume: 16
Issue: 11
Page Range: Unknown
Description:
In recent decades, freshwater lakes in the Northern Hemisphere have faced significant challenges, including severe water shortages and increased stormwater discharges. As a result, accurate forecasting of lake water levels has become essential for effective water resource management, flood mitigation, and ecological sustainability—all of which are interconnected with dynamics in tourism within freshwater basins. This study evaluates the performance of an Attention-based Long Short-Term Memory (LSTM) model compared to a standard LSTM for predicting lake water levels over 5-day and 30-day intervals, utilizing five different input combinations at one of Hungary's popular tourist destinations Lake Velence. The results demonstrate that the Attention-based LSTM consistently outperforms the standard LSTM, particularly in long-term forecasting, as it effectively captures relevant temporal dependencies and reduces error accumulation. Additionally, a Pearson correlation analysis was performed to examine the relationship between guest nights and environmental factors, including lake water level, precipitation, temperature, and evapotranspiration. The findings reveal a strong correlation between guest nights and both temperature and evapotranspiration, while the associations with lake water level and precipitation are relatively weak. This indicates that climate conditions, rather than hydrological variations, primarily drive visitor numbers. The study highlights the importance of integrating advanced machine learning models in hydrological forecasting and tourism planning, providing valuable insights for sustainable water management and climate-adaptive tourism strategies.
Open Access: Yes
Decision support for sustainable circular food supply chain in Iran: A fuzzy multi-criteria approach
Publication Name: Computers and Industrial Engineering
Publication Date: 2025-11-01
Volume: 209
Issue: Unknown
Page Range: Unknown
Description:
As an interconnected network, the food supply chain links multiple actors across production, processing, distribution and consumption. While it plays a vital role in ensuring food security, safety and economic resilience, the sector also faces growing challenges related to its environmental impact and long-term sustainability. Addressing these issues requires a systemic shift toward sustainable circular supply chain models that support net-zero objectives, decarbonization pathways, and ecosystem-wide coordination. This study aims to explore the key factors influencing sustainable circular supply chain management (SCSCM) across five major sectors of the food industry in Iran: grain, dairy, meat, sugar and carbohydrate products. By incorporating the concept of dynamic capabilities into the supply chain context, this study underscores the importance of organizational adaptability and innovation in facilitating the transition toward circular, low-emission supply chains. A snowball-based literature review revealed a lack of prioritization frameworks tailored to the food industry in Iran. To address this gap, the fuzzy Delphi method (FDM) was used to identify critical factors, followed by the fuzzy analytic network process (FANP) to evaluate and rank them based on expert judgment. The findings indicate that supplier facilities, trade credit, supplier risk management, environmental policy and environmental costs are the five most critical enablers of circular and sustainable transformation within the food supply chain. These identified factors offer a foundation for policymakers and industry leaders to design long-term, ecosystem-oriented strategies that enable systemic change and accelerate progress toward net-zero goals within the sector.
Open Access: Yes
