Search Results (72)

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article

Sustainability reporting has evolved into a multidimensional field encompassing Environmental, Social, and Governance (ESG) disclosure, integrated reporting (IR), and circular economy (CE) practices. This study aims to map the intellectual and thematic landscape of sustainability reporting research over the past decade, with a focus on sectoral differentiation. Drawing on bibliometric analysis of 1611 scientific articles indexed in Scopus, this research applies co-word analysis, thematic mapping, and bibliographic coupling to identify prevailing trends, conceptual clusters, and knowledge gaps. The results reveal a clear progression from fragmented debates toward a more integrated discourse combining ESG, IR, and CE frameworks. In the real economy, sustainability reporting demonstrates a mature operational focus, supported by standardized frameworks and extensive empirical evidence. In contrast, the banking sector exhibits emerging engagement with sustainability disclosure, while the public sector remains at an earlier stage of conceptual and practical development. Despite the increasing convergence of research streams, gaps persist in linking reporting practices to tangible sustainability outcomes, integrating digital innovations, and addressing social dimensions of circularity. This study concludes that further interdisciplinary and sector-specific research is essential to advance credible, comparable, and decision-useful reporting practices capable of supporting the transition toward sustainable and circular business models. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

This paper presents a near real-time implementation of the Karhunen–Loève Transform (KLT) for Radio Frequency Interference (RFI) mitigation in microwave radiometry. KLT is a powerful, data-adaptive technique capable of adjusting to various signal types by estimating the covariance matrix of the incoming signal and segmenting its eigenvectors to form an effective RFI basis. In this paper, the KLT is evaluated with real signals in laboratory conditions, aiming to characterize its performance in realistic conditions. To that effect, the dual Rx/Tx capability of a Pluto SDR is used to generate and capture RFI. The main mitigation metrics are computed for the KLT and other commonly used mitigation methods. In addition, while previous studies have shown the effectiveness of offline processing of recorded I/Q data, real-time mitigation is often necessary. Given the computational cost of eigendecomposition, this work introduces a low-complexity solution using the “economy covariance” approach alongside asynchronous covariance decomposition. The proposed implementation, realized within the GNU Radio framework, demonstrates the practical feasibility of real-time KLT-based mitigation and underscores its potential for improving signal integrity in digital radiometers operating under dynamic RFI conditions. Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital twins can enhance progress toward the goals the circular economy sets to identify inefficiencies, predict maintenance needs, and optimize the use of resources. This integration is a promising industry approach that will introduce more sustainable operations and maintain economic viability. Full article

The construction industry is one of the largest contributors to the world economy. However, the level of automation and digitalization in the construction industry is still at its infancy in comparison with other industries due to the complex nature and the large size of construction projects. Meanwhile, construction projects are prone to cost overruns and schedule delays due to the adoption of traditional progress monitoring techniques to retrieve progress on-site, having indoor activities participating with an accountable ratio of these works. Improvements in deep learning and Computer Vision (CV) algorithms provide promising results in detecting objects in real time. Also, researchers have investigated the probability of using CV as a tool to create a Digital Twin (DT) for construction sites. This paper proposes a model utilizing the state-of-the-art YOLOv8 algorithm to monitor the progress of bricklaying activities, automatically extracting and analyzing real-time data from construction sites. The detected data is then integrated into a 3D Building Information Model (BIM), which serves as a DT, allowing project managers to visualize, track, and compare the actual progress of bricklaying with the planned schedule. By incorporating this technology, the model aims to enhance accuracy in progress monitoring, reduce human error, and enable real-time updates to project timelines, contributing to more efficient project management and timely completion. 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 built environment plays a critical role in achieving climate neutrality, yet the construction sector continues to contribute significantly to carbon emissions and resource depletion. This study evaluates the experimental phase of the New European Bauhaus Academy (NEBA) Alliance pilot project, which aims to support sustainable transformation in the built environment through the integration of circular economy principles, adaptive reuse, and nature-based solutions. Conducted at the Lodz University of Technology, the pilot study involved interdisciplinary modules combining Building Information Modeling (BIM), urban regeneration strategies, and sustainable material use. A mixed-methods approach was employed, including structured surveys and qualitative analysis of student projects, to assess the effectiveness of these interventions. The results indicate that the pilot project successfully enhanced the participants’ understanding of sustainable design practices and their application in real-world architectural and urban contexts. Participants demonstrated increased competence in using digital tools for low-carbon design and in proposing regenerative solutions for existing urban fabric. The findings suggest that targeted, design-led initiatives can contribute meaningfully to the transformation of the built environment, aligning with the goals of the European Green Deal and the New European Bauhaus. This study offers a replicable model for embedding sustainability into professional practice through applied, context-sensitive strategies. Full article

Ancient paintings are an intangible window to the economy, politics, and customs of the past. Their characteristics have evolved or were made obsolete, with only limited contemporary connections remaining. This research aims to preserve and to interact with characters in 2D paintings to evolve their cultural identity through combining AI and AR. The scope of this research covers traditional Chinese paintings archived by the National Palace Museum in digital collections, mainly “New Year’s Market in a Time of Peace”. About 25 characters were used for training and 3D reconstruction in RODIN^®. The models were converted into Augment^® and Sketchfab^® platforms as reactivated AR characters to interact with new urban fabrics and landscapes. Stable Diffusion^® and RODIN^® were successfully integrated to perform image training and reconstruct 3D AR models of various styles. As a result, interactions were conducted in two ways: in a mixed context with mixed characters in a painting and in a familiar context in the real world with mixed characters. It was found that AR facilitated the interpretation of how the old urban fabric was arranged. Using AI and AR is a current issue. Combining AI and AR can activate ubiquitous preservation to perform recursive processing from diffused images in order to reconstruct 3D models. This activated heritage preservation method is a reasonable alternative to redefining intangible subjects with a new and evolved contemporary cultural identity. Full article

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals. Full article

As global economic development advances, the constraints of traditional growth paradigms, particularly the escalating challenge of environmental pollution, have become increasingly evident. In this context, the deep integration of the digital and real economies (IDE) has emerged as a promising approach to sustain economic expansion while addressing environmental concerns. Drawing on panel data from 30 Chinese provinces throughout 2008–2022, this study employs the entropy weight method and the coupling coordination degree model to quantify the levels of IDE and pollution. A two-way fixed-effects regression framework is then applied to assess the relationship between IDE and environmental pollution and to uncover potential mediating mechanisms. The principal findings are as follows: (1) The integration level of the digital and real economies has a suppressive effect on environmental pollution, with this effect exhibiting significant regional heterogeneity. (2) The deep IDE facilitates the optimization of the industrial structure (IS) and the reduction in energy consumption through two intermediary channels, leading to a marked improvement in environmental quality. (3) The industrial structure exhibits a threshold effect within the mechanism, with its influence on pollution levels displaying a nonlinear model characteristic of increasing marginal effect. These results enrich the interdisciplinary nexus of environmental studies and the digital economy, offering a scientific basis for policymaking and contributing to China’s dual-carbon objectives and the global sustainability transition. Future research may explore the differentiated impacts of digital convergence under diverse policy regimes and identify strategies to maximize its environmental benefits. Full article

Polyvinyl chloride (PVC) recycling poses significant engineering challenges and opportunities, particularly regarding material integrity, energy efficiency, and integration into circular manufacturing systems. This systematic review evaluates recent advancements in mechanical innovations, tooling strategies, and intelligent technologies reshaping PVC recycling. An emphasis is placed on machinery-driven solutions—including high-efficiency shredders, granulators, extrusion moulders, and advanced sorting systems employing hyperspectral imaging and robotics. This review further explores chemical recycling technologies, such as pyrolysis, gasification, and supercritical fluid extraction, for managing contamination and additive removal. The integration of Industry 4.0 technologies, notably digital twins and artificial intelligence, is highlighted for its role in predictive maintenance, real-time quality assurance, and process optimisation. A combined PRISMA approach and ontological mapping are applied to classify technological pathways and lifecycle optimisation strategies. Critical engineering constraints—including thermal degradation, additive leaching, and feedstock heterogeneity—are examined alongside emerging innovations, like additive manufacturing and microwave-assisted depolymerisation, offering scalable, low-emission solutions. Regulatory instruments, such as REACH and Extended Producer Responsibility (EPR), are analysed for their influence on machinery compliance and design standards. Drawing from sustainable manufacturing frameworks, this study also promotes energy efficiency, eco-designs, and modular integration in recycling systems. This paper concludes by proposing a digitally optimized, machinery-integrated recycling model aligned with circular economy principles to support the development of future-ready PVC reprocessing infrastructures. This review serves as a comprehensive resource for researchers, practitioners, and policymakers, advancing sustainable polymer recycling. Full article

The digital economy (DE) and real economy (RE) are dual pillars of the modern economic system. The deep integration of the digital economy and real economy (IDR) has emerged as a pivotal strategic trend. IDR not only can enhance international competitiveness but also contributes to sustainable development goals. This work collects DE and RE data from 30 provinces in China between 2012 and 2022. The entropy weight method and the coupling coordination degree (CCD) model are employed to measure the level of IDR. Furthermore, the Dagum Gini coefficient, Kernel density estimation, the spatial autocorrelation model, and the geographically and temporally weighted regression (GTWR) model are utilized to analyze the spatial–temporal evolution and influencing factors of CCD. The following conclusions are drawn: (1) During the study period, CCD shows an upward trend, but the value is relatively low. (2) There are significant spatial differences in CCD, and the inter-regional difference is the primary cause. (3) The regional differences in CCD are continuously widening. (4) CCD shows an obvious global spatial agglomeration feature, and the spatial agglomeration degree of CCD has been enhanced from 2012 to 2022. (5) The policy intensity, digital infrastructure, industrial structure, human capital, technological innovation, and market environment have significant impacts on CCD. The obtained findings provide important theoretical support for the coordinated development of DE and RE. Full article