Search Results (2,126)

The construction industry is a major contributor to environmental degradation, primarily due to the substantial volumes of construction waste (CW) generated on-site. As sustainability becomes a global imperative aligned with the UN 2030 Agenda, identifying and mitigating the root causes of CW is essential. This study adopts a cross-disciplinary approach to explore the drivers of CW and support more effective, sustainable waste reduction strategies. A systematic literature review was conducted to extract 25 key CW source factors from academic publications. These were analyzed using Social Network Analysis (SNA) to reveal their structural relationships and relative influence. The results indicate that the lack of structured on-site waste management planning, accumulation of residual materials, and insufficient worker training are among the most influential CW drivers. Comparative analysis with industry data highlights theoretical–practical gaps and the need for improved alignment between research insights and site implementation. This paper recommends the adoption of tiered waste management protocols as part of contractual documentation, integrating Building Information Modeling (BIM)-based residual material traceability systems, and increasing attention to workforce training programs focused on material handling efficiency. Future research should extend SNA frameworks to sector-specific waste patterns (e.g., pavement or demolition projects) and explore the intersection between digital technologies and circular economy practices. The study contributes to enhancing waste governance, promoting resource efficiency, and advancing circularity in the built environment by offering data-driven prioritization of CW sources and actionable mitigation strategies. Full article

The scientific study of emotion remains fraught with conceptual ambiguity, methodological limitations, and epistemological blind spots. This theoretical paper argues that existing paradigms frequently capture synthetic rather than natural emotional states—those shaped by social expectations, cognitive scripting, and performance under observation. We propose a conceptual framework that distinguishes natural emotion—spontaneous, embodied, and interoceptively grounded—from synthetic forms that are adaptive, context-driven, and often unconsciously rehearsed. These reactions often involve emotional scripts rather than genuine, spontaneous affective experiences. Drawing on insights from affective neuroscience, psychological measurement, artificial intelligence, and neurodiversity, we examine how widely used tools such as EEG, polygraphy, and self-report instruments may capture emotional conformity rather than authenticity. We further explore how affective AI systems trained on socially filtered datasets risk replicating emotional performance rather than emotional truth. By recognizing neurodivergent expression as a potential site of emotional transparency, we challenge dominant models of emotional normalcy and propose a five-step agenda for reorienting emotion research toward authenticity, ecological validity, and inclusivity. This post-synthetic framework invites a redefinition of emotion that is conceptually rigorous, methodologically nuanced, and ethically inclusive of human affective diversity. Full article

The increasing adoption of industrialized offsite construction (IOC) offers substantial benefits in efficiency, quality, and sustainability, yet presents persistent challenges related to data fragmentation, real-time monitoring, and coordination. This systematic review investigates the transformative role of artificial intelligence (AI)-enhanced digital twins (DTs) in addressing these challenges within IOC. Employing a hybrid re-view methodology—combining scientometric mapping and qualitative content analysis—52 relevant studies were analyzed to identify technological trends, implementation barriers, and emerging research themes. The findings reveal that AI-driven DTs enable dynamic scheduling, predictive maintenance, real-time quality control, and sustainable lifecycle management across all IOC phases. Seven thematic application clusters are identified, including logistics optimization, safety management, and data interoperability, supported by a layered architectural framework and key enabling technologies. This study contributes to the literature by providing an early synthesis that integrates technical, organizational, and strategic dimensions of AI-driven DT implementation in IOC context. It distinguishes DT applications in IOC from those in onsite construction and expands AI’s role beyond conventional data analytics toward agentive, autonomous decision-making. The proposed future research agenda offers strategic directions such as the development of DT maturity models, lifecycle-spanning integration strategies, scalable AI agent systems, and cost-effective DT solutions for small and medium enterprises. Full article

Although the sediment–water interface of deep and large reservoirs is recognized as a dominant source of internal phosphorus (P) loading, the quantitative hierarchy of environmental drivers and their interaction thresholds remains poorly resolved. Here, we integrate 512 studies to provide the first process-based synthesis that partitions P release fluxes among temperature, pH, dissolved oxygen, salinity, sediment properties, and microbial activity across canyon, valley, and plain-type reservoirs. By deriving standardized effect sizes from 61 data-rich papers, we show that (i) a 1 °C rise in bottom-water temperature increases soluble reactive P (SRP) flux by 12.4% (95% CI: 10.8–14.0%), with sensitivity 28% lower in Alpine oligotrophic systems and 20% higher in warm monomictic basins; (ii) a single-unit pH shift—whether acid or alkaline—stimulates P release through distinct desorption pathways,; and (iii) each 1 mg L⁻¹ drop in dissolved oxygen amplifies release by 31% (25–37%). Critically, we demonstrate that these drivers rarely act independently: multi-factor laboratory and in situ analyses reveal that simultaneous hypoxia and warming can triple the release rate predicted from single-factor models. We further identify that >75% of measurements originate from dam-proximal zones, creating spatial blind spots that currently limit global P-load forecasts to ±50% uncertainty. To close this gap, we advocate coupled metagenomic–geochemical observatories that link gene expression (phoD, ppk, pqqC) to real-time SRP fluxes. The review advances beyond the existing literature by (1) establishing the first quantitative, globally transferable framework for temperature-, DO-, and pH-based management levers; (2) exposing the overlooked role of regional climate in modulating temperature sensitivity; and (3) providing a research agenda that reduces forecasting uncertainty to <20% within two years. Full article

Sustainable housing is increasingly recognized as a crucial social determinant of health, intersecting environmental sustainability with affordability, safety, and inclusivity to shape population health and equity. This paper reviews the existing literature and presents that integrating sustainable housing into public health frameworks can mitigate health risks, reduce inequities, and promote resilient urban futures. This review paper reframes sustainable housing through a holistic lens, emphasizing its potential to improve health through inclusive design, energy efficiency, green infrastructure, and affordability. Theoretically grounded in the Social Determinants of Health framework, Ecological Systems Theory, Environmental Health Theory, and Life Course Perspective, sustainable housing is shown to influence health outcomes across multiple levels and life stages. Empirical studies further validate these connections, demonstrating improved physical and mental health, particularly among vulnerable populations, when sustainable housing features are implemented. While these benefits span multiple health domains, persistent implementation challenges related to equity, financing, and policy coherence can limit their reach. Equity-centered approaches and cross-sector collaboration are essential to ensure the health benefits of sustainable housing are distributed fairly. Climate-resilient design strategies further underscore the role of housing in protecting communities against growing environmental threats. Furthermore, research priorities are required to strengthen the evidence base, including studies utilizing longitudinal study designs and participatory approaches. The findings of this review call for policy innovations that embed sustainable housing within broader public health and urban development agendas. Full article

This cross-country study examines how higher education institutions collaborate with government, industry, and civil society to promote the European Green Deal and Just Transition initiatives. Framed within the quadruple helix (QH) model, the research investigates emerging partnerships and the integration of green policies across six European countries: Bulgaria, Cyprus, France, Greece, Serbia, and Spain. Special emphasis is placed on the strategic role of universities in advancing the environmental, social, and economic dimensions of sustainability through their initiatives. Drawing on 30 semi-structured interviews with key stakeholders, including local public officials, academics, entrepreneurs, students, and unemployed youth, the study uncovers a growing alignment between academic initiatives and national sustainability agendas. While the extent of policy integration and collaboration varies, the findings underscore the importance of universities in shaping environmental awareness, fostering green innovation, and advancing multi-actor partnerships. The study contributes to the theoretical discourse on the QH model by applying it to the field of green transition policy and offers practical recommendations for enhancing the role of universities in sustainability-oriented governance and education. Full article

Research on corporate failure prediction is focused on increasing the model’s statistical accuracy, most recently via the introduction of a variety of machine learning (ML)-based models, often overlooking the practical appeal and potential adoption barriers in the context of corporate management. This literature review compares ML models with the classic, widely accepted Altman Z-score through a technology adoption lens. We map how technological features, organizational readiness, environmental pressure and user perceptions shape adoption using an integrated technology adoption framework that combines the Technology–Organization–Environment framework with the Technology Acceptance Model. The analysis shows that Z-score models offer simplicity, interpretability and low cost, suiting firms with limited analytical resources, whereas ML models deliver superior accuracy and adaptability but require advanced data infrastructure, specialized expertise and regulatory clarity. By linking the models’ characteristics with adoption determinants, the study clarifies when each model is most appropriate and sets a research agenda for long-horizon forecasting, explainable artificial intelligence and context-specific model design. These insights help managers choose failure prediction tools that fit their strategic objectives and implementation capacity. Full article

This essay examines emotions as a critical, yet underutilized, dimension in marine conservation and ocean sustainability science. Drawing on cognitive neuroscience, social psychology, human geography, and political ecology, we argue that integrating emotional dimensions into research, policy, and practice can enhance both understanding and action toward marine conservation and ocean sustainability. We conceptualize emotions, and explore their experiential and functional implications in marine contexts. Using targeted case examples and theories, we identify both opportunities and challenges for applying emotional insights in research, policy, and practice, including barriers posed by dominant rationality models of human decision-making. We present intellectual pathways as well as research, methodological and policy agendas to integrate emotions into marine conservation research and strategies. Our analysis responds to gaps in the literature and provides actionable recommendations for researchers, policymakers, and practitioners during the UN Decade of Ocean Science for Sustainable Development. Full article

This study presents a systematic literature review and bibliometric analysis focused on female-led nanoenterprises in rural contexts, a marginal yet increasingly relevant category within enterprise research. Despite the growing attention to micro and small businesses, nanoenterprises—defined as unipersonal, informal, low-income productive units—remain underexplored and largely excluded from formal economic frameworks. Using the PRISMA 2020 guidelines with the 10-step B-SLR approach, 12 peer-reviewed articles were selected through a targeted search combining terms such as “nanoenterprise”, “women”, and “rural”. The analysis included citation counts, journal impact, country of origin, and thematic focus. Findings indicate conceptual and geographic fragmentation in existing research, with studies concentrated in Latin America, Asia, and Africa, and focused primarily on commerce, personal services, and subsistence agriculture. Gender emerges as a structural axis, as women face compounded barriers in digital access, credit, and formal recognition. The review reveals a lack of theoretical consolidation, comparative studies, and longitudinal research. This work contributes by articulating the distinct nature of nanoenterprises, proposing a research agenda, and highlighting their role in fostering economic inclusion, resilience, and empowerment among marginalized populations. The results call for inclusive public policies and scholarly frameworks that go beyond traditional models of entrepreneurship. Full article

Geospatial sensing and data-driven technologies (GSDDTs) are playing an increasingly important role in transforming (agro)forestry practices across the Western Balkans 6 region (WB6). This review critically examines the current state of GSDDT application in six WB countries (also known as the WB6 group)—Albania, Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia—with a focus on their contributions to sustainable (agro)forest management. The analysis explores the use of unmanned aerial vehicles (UAVs), light detection and ranging (LiDAR), geographic information systems (GIS), and satellite imagery in (agro)forest monitoring, biodiversity assessment, landscape restoration, and the promotion of circular economy models. Drawing on 25 identified case studies across WB6—for example, ALFIS, Forest Beyond Borders, ForestConnect, Kuklica Geosite Survey, CREDIT Vibes, and Project O₂ (including drone-assisted reforestation in Kosovo*)—this review highlights both technological advancements and systemic limitations. Key barriers to effective GSDDT deployment across WB6 in the (agro)forestry sector and its cross-border cooperation initiatives include fragmented legal frameworks, limited technical expertise, weak institutional coordination, and reliance on short-term donor funding. In addition to mapping current practices, this paper offers a comparative overview of UAV regulations across the WB6 region and identifies six major challenges influencing the adoption and scaling of GSDDTs. To address these, it proposes targeted policy interventions, such as establishing national LiDAR inventories, harmonizing UAV legislation, developing national GSDDT strategies, and creating dedicated GSDDT units within forestry agencies. This review also underscores how GSDDTs contribute to compliance with seven European Union (EU) acquis chapters, how they support eight Sustainable Development Goals (SDGs) and their sixteen targets, and how they advance several EU Green Agenda objectives. Strengthening institutional capacities, promoting legal alignment, and enabling cross-border data interoperability are essential for integrating GSDDTs into national (agro)forest policies and research agendas. This review underscores GSDDTs’ untapped potential in forest genetic monitoring and landscape restoration, advocating for their institutional integration as catalysts for evidence-based policy and ecological resilience in WB6 (agro)forestry systems. Full article

Against the backdrop of the accelerated development of the global digital economy and the deepening advancement of the sustainable development agenda, artificial intelligence (AI) is emerging as the core driving force behind the new round of technological revolution, reshaping the competitive landscape of international trade. Chinese export companies are facing dual pressures from technological barriers imposed by developed countries and cost competition from emerging economies, making traditional development models unsustainable. In this context, exploring how AI technology can promote the sustainable growth of export companies holds significant theoretical and practical significance. This article employs a three-dimensional fixed-effects nonlinear quadratic model to empirically analyze the dynamic relationship between AI adoption and the growth of export companies, based on data from Chinese A-share listed export companies. The analysis results show that AI has a significant dynamic nonlinear effect on the growth of export companies, which is initially inhibitory and then becomes promotional. In the early stages, due to high technology adaptation costs, company growth is somewhat inhibited. However, as the technology matures, AI significantly enhances the company’s innovation capabilities and competitiveness, thereby promoting its long-term sustainable growth. This result remains valid after a series of robustness tests. This effect is significant in non-state-owned enterprises and medium-to-low technology industries, but not in state-owned enterprises and high-technology industries. Three pathways—enterprise efficiency, innovation investment, and levels of digital factor investment—enhance this dynamic effect. Finally, based on the above research findings, this study proposes policy recommendations for enterprises to leverage artificial intelligence technology to promote the growth of export companies. Full article

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

Sustainable transport infrastructure, highlighted in Agenda 21, Rio+20, and the 2030 Agenda, promotes resource efficiency and reduced environmental impact. Integrating circular economy principles into road construction supports these goals. However, limited material traceability and insufficient lifecycle information hinder the effective adoption of circular practices in the sector. Material passports have emerged as an enabling tool to address this gap by systematically documenting detailed data on material composition, environmental impact, lifecycle history, and potential for reuse or recycling. Despite growing adoption in the building sector, their application in road infrastructure remains limited. Therefore, this study aims to develop a material passport framework tailored for road pavements to enhance circularity and promote sustainable material management. A two-phase research method was used; first, a structured desk review identified relevant attributes; second, these attributes were categorized into six key domains and organized across three hierarchical levels: product, layer, and material to reflect pavement system complexity. The proposed framework enables multi-level documentation. Thus, the outcome of this study majorly contributes to advancing circular economy practices and the achievement of sustainable development goals by promoting resource efficiency, sustainable infrastructure, and responsible material use across the pavement lifecycle. Full article

Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article

Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that highlights the multifaceted implications of using LLMs in the context of digital manufacturing. To address this limitation, we conducted a systematic literature review, analyzing 53 papers selected according to predefined inclusion and exclusion criteria. Our descriptive and thematic analyses, respectively, mapped new trends and identified emerging themes, classified into three axes: (1) manufacturing process optimization, (2) data structuring and innovation, and (3) human–machine interaction and ethical challenges. Our results revealed that LLMs can enhance operational performance and foster innovation while redistributing human roles. Our research offers an in-depth understanding of the implications of LLMs. Finally, we propose a future research agenda to guide future studies. Full article