Search Results (225)

The integration of digital twin (DT) technology into circular economy (CE) frameworks has emerged as a critical pathway for achieving sustainable and intelligent manufacturing under the Industry 4.0 paradigm. This study addresses the lack of structured guidance for DT adoption in CE strategies by proposing two interrelated frameworks: the Sustainable Digital Twin Maturity Path (SDT-MP) and the Digital Twin Nexus. The SDT-MP outlines progressive stages of DT deployment—from data acquisition and real-time monitoring to AI-enabled decision-making—aligned with CE principles and Industry 4.0 capabilities. The DT Nexus complements this maturity model by structuring the integration of enabling technologies such as AI, IoT, and edge/cloud computing to support closed-loop control, resource optimization, and predictive analytics. Through a mixed-methods approach combining literature analysis and real-world case validation, this research demonstrates how DTs can facilitate lifecycle intelligence, enhance operational efficiency, and drive sustainable transformation in manufacturing. The proposed frameworks offer a scalable roadmap for intelligent circular systems, addressing implementation challenges while supporting Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by promoting digital infrastructure, innovation-driven manufacturing, and environmentally responsible industrial growth. This study contributes to the advancement of digital infrastructure and sustainable circular supply chains in the context of smart, connected industrial ecosystems. Full article

Tourism’s rapid growth has significant and negative effects on the environment, society, and economy. Sustainable tourism practices are essential in order to mitigate these effects. Virtual reality (VR) technologies offer the possibility of implementing sustainable tourism policies by providing immersive experiences that replace real ones. Moreover, VR can be a useful tool for the protection and promotion of cultural and natural heritage. The article discusses the potential directions for sustainable tourism using VR. This technology can reduce the burden on popular tourist sites without losing their value to visitors. Additionally, it can promote less popular destinations in the wider public awareness. A case study of the implementation of a virtual tour at the Pahlavon Mahmud Mausoleum in Khiva (Uzbekistan) is presented. The research method was designed to evaluate the acceptability of VR technology among a convenience sampling of n = 57 Gen Z consumers (university students 20–24 years of age), who completed interviews following their participation in a voluntary virtual walking tour. The research results suggest that VR can be an acceptable and useful tool for implementing sustainable tourism policies in the near future. Another conclusion is that virtual sightseeing should not fully replace onsite tourism. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

Highway accessibility plays a vital role in supporting local economic development, particularly in regions lacking access to sea or river ports. Recognizing the functional transformation of road infrastructure, the Chinese government has made substantial investments in its expansion. Nevertheless, a theoretical gap remains in justifying whether such investments yield significant economic returns. Drawing on the theory of economic externalities, this study investigates the causal relationship between highway development and regional economic growth, and assesses whether highway construction leads to an acceleration in growth rates. Utilizing panel data from 14 Chinese cities spanning 2000 to 2014, the synthetic control method (SCM) is employed to evaluate the economic externalities of highway investment. The results indicate a positive impact on surrounding industries. Furthermore, a growth rate forecasting analysis based on Back-Propagation Neural Networks (BPNNs) is conducted using industrial enterprise data from 2005 to 2014. The growth rate in the treated city is 1.144%, which is close to the real number 1.117%, higher than the number for the weighted control group, which is 1.000%. The findings suggest that the growth rate of total industrial output improved significantly, confirming the existence of positive spillover effects. This not only enriches the empirical literature on transport infrastructure but also provides targeted enlightenment for the sustainable development of urban economy in terms of policy guidance. Full article

Enterprise architecture (EA) provides a strategic foundation for aligning business processes, IT infrastructure, and organizational strategy, enabling firms to navigate uncertainty and complexity. In developing economies, small and medium-sized enterprises (SMEs) face significant challenges in maintaining financial resilience and sustainable growth amidst frequent disruptions. This study investigates how EA-driven change events affect SME financial performance by activating three key adaptive mechanisms: improvisational capability, flexible IT systems, and organizational culture. A novel classification of EA change triggers is proposed to guide adaptive responses. Using survey data from 291 Pakistani SMEs collected during the COVID-19 crisis, the study employs structural equation modeling (SEM) to validate the conceptual model. The results indicate that improvisational capability and flexible IT systems significantly enhance financial performance, while the mediating role of organizational culture is statistically insignificant. This study contributes to EA and sustainability literature by integrating a typology of EA triggers with adaptive capabilities theory and testing their effects in a real-world crisis context. Full article

The latest FAO report indicates that aquaculture accounts for 51% of the global production volume of fish and seafood. However, despite the continuous growth of this activity, there is evidence of the excessive use of groundwater in its production processes, as well as pollution caused by nutrient discharges into surface waters due to the water exchange required to maintain water quality in fishponds. Given this context, the objectives of this study were as follows: (1) to review which emergent and floating plant species are used in constructed wetlands (CWs) for the bioremediation of aquaculture wastewater; (2) to identify the aquaculture species whose wastewater has been treated with CW systems; and (3) to examine the integration of CWs with recirculating aquaculture systems (RASs) for water reuse. A systematic literature review was conducted, selecting 70 scientific articles published between 2003 and 2023. The results show that the most used plant species in CW systems were Phragmites australis, Typha latifolia, Canna indica, Eichhornia crassipes, and Arundo donax, out of a total of 43 identified species. These plants treated wastewater generated by 25 aquaculture species, including Oreochromis niloticus, Litopenaeus vannamei, Ictalurus punctatus, Clarias gariepinus, Tachysurus fulvidraco, and Cyprinus carpio, However, only 40% of the reviewed studies addressed aspects related to the incorporation of RAS elements in their designs. In conclusion, the use of plants for wastewater treatment in CW systems is feasible; however, its application remains largely at the experimental scale. Evidence indicates that there are limited real-scale applications and few studies focused on the reuse of treated water for agricultural purposes. This highlights the need for future research aimed at production systems that integrate circular economy principles in this sector, through RAS–CW systems. Additionally, there is a wide variety of plant species that remain unexplored for these purposes. Full article

The convergence of intelligent computational innovations—exemplified by cognitive intelligence—into the real economy is fundamentally transforming traditional industries and driving high-quality development. As a cornerstone of national economic growth, the energy sector faces mounting pressure to meet demands for green, low-carbon, and sustainable development, particularly under “dual carbon” targets and tightening regulatory frameworks. This study examines how digital transformation in this sector facilitates or impedes carbon emission reduction and green growth. Focusing on five key energy subsectors, including coal mining and processing, a coupling coordination model assesses the interaction between digitalization and greening. Utilizing panel data spanning from 2014 to 2023, the study systematically evaluates the level of digital–green coordination across the sector. The results indicate notable inter-sectoral variation, alongside a consistent upward trend in the overall coupling coordination, reaching moderate to high levels. These findings offer critical strategic insights for policymakers and energy enterprises seeking to harmonize digital innovation with green transition goals. The empirical evidence underscores the potential of next-generation technologies to expedite intelligent system upgrades, embed green development practices, and enhance enterprise-level carbon reduction and sustainability performance. Full article

The construction industry is undergoing a significant transformation due to the increasing influence of digital technology, sustainability requirements, and diverse stakeholder expectations, which highlights the need to update the existing service quality models accordingly. However, the traditional service quality models often fail to address these evolving demands comprehensively. This study systematically reviews 44 peer-reviewed articles to identify the key service quality dimensions and offer clear guidance for future research that can address the complexities of modern construction. The findings reveal that reliability, tangibles, and communication remain the most emphasized dimensions across the reviewed literature, whereas critical areas, such as digital integration, sustainability indicators, and service recovery, are significantly underexplored. This contrast explicitly links the limitations of the classic frameworks to these emerging demands, highlighting their difficulty in accommodating the industry’s growing reliance on real-time data, an environmentally friendly performance, and multi-stakeholder collaboration. Because the construction industry typically contributes 6–10 per cent of the national GDP and underpins wider economic development, inadequate service quality models can propagate cost overruns, productivity losses, and reputational damage across the economy; conversely, improved models enhance project efficiency, and thus support sustained economic growth. This review is limited by its reliance on the Scopus and Web of Science databases, which may exclude relevant regional or non-English studies. Furthermore, many reviewed articles are context-specific, potentially reducing the generalizability of the findings. Despite these limitations, this review offers an evidence-based framework that integrates advanced digital tools, sustainability measures, and diverse stakeholder perspectives. Future studies should demonstrate this framework’s efficacy and applicability in different circumstances. Full article

The export expansion of wood forest products (WFPs) generates substantial socio-economic benefits. Unfortunately, the WFP manufacturing industry frequently experiences challenges in accessing finance and high financing costs. Since profit scramble between financial sector and real economy sectors has become a critical global concern, it is worth investigating how banking sector profits (BSPs) impact WFPs’ export margins, and whether a “financial concession” policy can mitigate or amplify this effect. Drawing on over four million trade records from China’s Customs Database and the United Nations Trade and Business Database, this study quantifies the WFPs’ export margins of 31 provinces in Mainland China to 184 countries during 2007–2022. Then it assesses the effects of regional BSP on the WFPs’ export margins. The results indicate that the extensive, intensive, and quantity margins of WFPs’ export exhibit an overall upward trend with fluctuations, while the price margin has shown steady growth since 2016. Regional BSP has significant negative effects on the extensive, intensive, and quantity margins. The observed upward trend of WFPs’ export margins implies that low BSP may facilitate export growth of WFPs. Further heterogeneity analysis indicates that the BSPs’ negative impact is more pronounced for labor-intensive WFPs’ exports. China’s “financial concession” policy effectively mitigates the BSPs’ adverse effects. Moderation effect analysis demonstrates that a larger number of bank institution outlets, a higher share of rural bank institution outlets, and the development of digital finance significantly reduce the BSPs’ negative effects. Full article

This paper develops a nonlinear optimization model for the optimal allocation of labor and investment resources in a three-sector open economy. The model is based on the Cobb–Douglas production function and incorporates sectoral interdependencies, capital depreciation, trade balances, and import quotas. The resource allocation problem is formalized as a constrained optimization task, solved analytically using the Lagrange multipliers method and numerically via the golden section search. The model is calibrated using real statistical data from Kazakhstan (2010–2022), an open resource-exporting economy. The results identify structural thresholds that define balanced growth conditions and resource-efficient configurations. Compared to existing studies, the proposed model uniquely integrates external trade constraints with analytical solvability, filling a methodological gap in the literature. The developed framework is suitable for medium-term planning under stable external conditions and enables sensitivity analysis under alternative scenarios such as sanctions or price shocks. Limitations include the assumption of stationarity and the absence of dynamic or stochastic features. Future research will focus on dynamic extensions and applications in other open economies. Full article

The rise of knowledge-based economies has positioned higher education institutions as key actors in human capital development, requiring them to engage more actively with labor markets through strategic partnerships. Within this context, university-affiliated science and technology parks (STPs) have evolved into integrated learning environments that support experiential learning and mentorship practices. This study aims to explore the lived experiences of undergraduate students who participated in these processes within an STP in İstanbul, Türkiye. Using a qualitative phenomenological approach, data were collected through semi-structured interviews with 15 students selected via purposive maximum variation sampling. Thematic analysis, supported by MAXQDA 2024, was used to examine the data. Two main themes were identified: (i) talent development through experiential learning and (ii) talent development through mentorship. The findings indicate that students reconstructed theoretical knowledge through real-world applications, developed a clearer professional identity, and gained strategic career awareness. Mentorship provided both technical and psychosocial support, fostering self-confidence, emotional security, and role modeling. This study concludes that STPs play a strategic role in aligning academic learning with employability and institutional talent development goals. These results contribute to broader educational and workforce development discussions and are closely aligned with Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth), highlighting STPs as transformative platforms in higher education. Moreover, this study offers practical implications for aligning higher education with employment systems through structured experiential learning and mentorship practices. Full article

Considering that nowadays the economy plays a crucial role, time series forecasting has become an essential tool across various economic areas and industries. The process of predicting future trends based on historical values in a reliable and accurate manner has generated numerous benefits, such as simplified decision-making processes or strategic planning and reduced risk management. Furthermore, with the advancement made through the use of Artificial Intelligence (AI) methods, time series forecasting has quickly become more precise, adaptive, and scalable, being able to better overcome real-world challenges. In this context, the present paper analyzes the implications of artificial intelligence in time series forecasting by evaluating the scientific articles from the field indexed in Clarivate Analytics’ Web of Science Core Collection database. Through a bibliometric approach, the research identifies key journals, affiliations, authors, and countries, as well as the collaboration networks among authors and countries. It also analyzes the most frequently used keywords and authors’ keywords. The annual growth rate of 23.11% indicates sustained interest among researchers. Prominent journals such as IEEE Access, Energies, Mathematics, Applied Sciences—Basel, and Applied Energy have been the home for the most published papers in this field. Further, thanks to the Biblioshiny library in R, a variety of visualizations have been created, including thematic maps, three-field plots, and word clouds. A comprehensive review of the most cited papers has been performed to highlight the role of AI in time series forecasting. Research results and methods confirmed the versatility of the topics, which have been applied in various fields, such as, but not limited to, finance, energy, climate, and healthcare, and are further discussed. Cutting-edge methodologies and approaches that lead to the transformation of the field of time series analysis in the context of AI are uncovered and discussed through the use of thematic maps. Full article

Cloud Data Centers (CDCs) are an essential component of the infrastructure for powering the digital life of modern society, hosting and processing vast amounts of data and enabling services such as streaming, Artificial Intelligence (AI), and global connectivity. Given this importance, their energy efficiency is a top priority, as they consume significant amounts of electricity, contributing to operational costs and environmental impact. Efficient CDCs reduce energy waste, lower carbon footprints, and support sustainable growth in digital services. Consequently, energy efficiency metrics are used to measure how effectively a CDC utilizes energy for computing versus cooling and other overheads. These metrics are essential because they guide operators in optimizing resource use, reducing costs, and meeting regulatory and environmental goals. To this end, this paper reviews more than 25 energy efficiency metrics and more than 250 literature references to CDCs, different energy-consuming components, and configuration setups. Then, some real-world case studies of corporations that use these metrics are presented. Thereby, the challenges and limitations are investigated for each metric, and associated future research directions are provided. Prioritizing energy efficiency in CDCs, guided by these energy efficiency metrics, is essential for minimizing environmental impact, reducing costs, and ensuring sustainable scalability for the digital economy. Full article

In the context of global warming and the clean energy transition, the rapid development of the digital economy, a highly technology-intensive economic form, has an important impact on the clean energy transition. Examining how the growth of the digital economy has affected the renewable energy transition has broad implications for the creation of national policies, business planning and design, and everyday human behavior. The paper uses a two-way fixed-effect model to empirically investigate the impact of the development of the digital economy on the clean energy transition based on Chinese municipal panel data from 2013 to 2022. It also sorts out the intrinsic mechanism of the digital economy affecting the clean energy transition from a theoretical level. Finally, it tests the indirect effects of technological innovation and upgrading industrial structure using endogeneity analysis and a robustness test. The study finds that (1) digital economy development effectively promotes clean energy transition; (2) the digital economy influences the transformation of renewable energy through two intermediary channels: technological innovation and upgrading of industrial structures; (3) there is geographical variation in how the digital economy affects the growth of the clean energy transformation. Lastly, policy recommendations are provided for boosting investment in digital infrastructure, strengthening the digital technology base, and deepening and broadening the interaction of the digital and real economies. Full article

The evolving macroeconomic landscape, shaped by the global financial crisis and the COVID-19 pandemic, poses significant challenges for economies worldwide. However, Central, Eastern, and Southeastern European (CESEE) countries have demonstrated resilience and rapid recovery during crises, driven by a surge in consumption fueled by domestic credit and robust export growth supported by flexible exchange rates and adaptive monetary policies. Prior to EU accession, substantial foreign direct investment (FDI) during privatization and restructuring facilitated knowledge and technology transfers in CESEE economies. This study examines the interplay of exports, real exchange rates, GDP growth, FDI, inflation, domestic credit, and the human development index (HDI) in the CESEE region from 1995 to 2022, covering the transition period, EU accession, and major crises. Employing a panel ARDL model, we account for asymmetric effects of these variables on exports. The results reveal that GDP, FDI, inflation, domestic credit, and HDI significantly and positively influence exports, with HDI and GDP exerting the strongest effects, underscoring the pivotal roles of human capital and economic growth in enhancing export competitiveness. Conversely, real exchange rate depreciation negatively impacts exports, though non-price factors, such as product quality, mitigate this effect. These findings provide a robust basis for targeted policy measures to strengthen economic resilience and export performance in the CESEE region. Full article