Search Results (328)

Given the construction industry’s significant contribution of approximately 39% of global CO₂ emissions, implementing effective carbon reduction strategies is becoming increasingly critical. In this context, Internet of Things (IoT) technologies present promising solutions for monitoring and reducing emissions. However, there is a lack of comprehensive understanding regarding specific IoT applications, implementation barriers, and opportunities for carbon reduction in construction practices. This study investigates the role of IoT in reducing carbon emissions in the construction industry. Following PRISMA guidelines, this study analyzed bibliometric data from Scopus and Web of Science databases using VOSviewer for science mapping visualization. Content analysis was conducted on 17 carefully selected articles to identify key research topics and applications. The analysis identified four mainstream application areas: (1) IoT-based smart monitoring systems for carbon emissions, (2) energy efficiency and management applications, (3) sustainable construction implementation frameworks, and (4) smart cities and other built environment applications. Key findings highlight growing research interest in IoT applications for sustainable construction, with China, the United States, and the United Kingdom leading collaborative efforts. Despite demonstrated carbon reduction potential, significant implementation barriers exist, including technical limitations, organizational resistance, skill gaps, and economic constraints. Key opportunities include Artificial Intelligence (AI) integration, Building information modeling (BIM)-IoT synergies, energy prosumer models, and standardization frameworks. This study provides the first focused review of IoT applications specifically targeting carbon reduction in construction, highlighting a critical technology-practice gap where organizational factors frequently outweigh technological barriers. A proposed socio-technical integration framework in this study bridges technical and organizational elements to overcome adoption barriers. Full article

Tin mining has played a central role in Indonesia’s economy, particularly in the Bangka Belitung Islands, but it has also caused severe environmental and socio-economic impacts. This study aims to critically review the consequences of tin mining and evaluate reclamation initiatives through a narrative literature review of scientific publications, technical reports, and policy documents. The findings show that open-pit mining has led to deforestation, biodiversity loss, environmental degradation, and decreased soil fertility, while weak governance has fueled illegal mining and social conflicts. Rehabilitation strategies such as reforestation, agroforestry, aquaculture, and soil improvement have the potential to restore or reclaim degraded land and are proposed as a viable pathway to balance ecological improvement with socio-economic development. This study focuses on its interdisciplinary approach, integrating ecological, socio-economic, and institutional perspectives to propose a comprehensive, ecosystem-based framework for post-mining reclamation. By highlighting both challenges and opportunities, the study provides actionable insights for policymakers, mining companies, and local communities. Full article

Forest fire risk assessment methodologies vary considerably, presenting challenges for adaptation to specific local contexts. This study provides a systematic analysis of forest fire assessment approaches across the Mediterranean basin, American, African, and Asian regions through a comprehensive review of 112 peer-reviewed studies published from 2015 to 2025. Statistical significance testing (Chi-square tests, p < 0.05) confirmed significant regional variation in methodological preferences and indicator usage patterns. Key findings revealed that Multi-Criteria Decision Analysis dominates the field (44% of studies, n = 49), with Analytical Hierarchical Process being the most utilized method (39 studies). Machine learning approaches represent 25% (n = 28), with Random Forest leading significantly (22 applications). The analysis identified 67 indicators across seven major categories, with topographic factors (slope: 105 studies) and anthropogenic indicators (road networks: 92 studies) showing statistically significantly highest usage rates (p < 0.001), representing a statistically significant critical gap in vulnerability assessment (p < 0.01). Organizational factors remain severely underrepresented (a maximum of 14 studies for any factor), representing a statistically significant critical gap in risk assessments (p < 0.01). Statistical analysis revealed that while Mediterranean approaches excel in integrating historical and cultural factors, American methods emphasize advanced technology integration, while Asian approaches focus on socio-economic dynamics and land-use interactions. This study serves as a foundation for developing tailored assessment frameworks that combine remote sensing analysis, ground-based surveys, and community input while accounting for local constraints in data availability and technical capacity. The study concludes that effective forest fire risk assessment requires a balanced integration of global best practices with local environmental, social, and technical considerations, offering a roadmap for future forest fire risk assessment approaches in different regions worldwide. Full article

Taking Renhezhuang in Yongtai, Fujian, as a case study, this paper explores a research framework that integrates historical records with architectural remains under technological support. Renhezhuang not only preserves relatively complete partition documents but also retains a clear spatial configuration, providing ideal conditions for comparative analysis between textual records and physical structures. By employing space syntax, daylighting simulation, and ventilation modeling, this study links the logic of property allocation recorded in documents with the spatial organization of the building. The results reveal that the distribution of living units in Renhezhuang did not pursue strict equality; rather, it achieved balance through differentiated allocation, reflecting the coordination mechanisms of a traditional lineage society under limited resources. This study not only proposes a methodological framework that integrates textual evidence, spatial remains, and technical tools, but also underscores the sociological significance of built heritage as an entry point for understanding social order. The findings offer valuable insights for both heritage conservation and the study of socio-spatial dynamics. Full article

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs. Full article

This study investigates the paradoxical water security challenges in western Chilean Patagonia, where the regional abundance of water resources masks significant vulnerabilities of drinking water systems. We conducted an integrated socio-hydrological analysis over rural (APR) and urban (APU) drinking water systems, which provide water to approximately 846,000 people. We georeferenced 343 drinking water intake points, from which 51.6% are sourced from groundwater, and 45.8% from surface waters (2.6% other sources). An eco-hydrological characterization was conducted on the 147 watersheds supplying water to the surface intake points. Watersheds were characterized by their main hydrological, morphological, and land cover features, as well as by their level of anthropization (AI) and water stress index (WSI). Social dimensions were captured through structured interviews with 117 APR directorate leaders regarding their perceptions of infrastructure, governance, climate change, and local water management challenges. Our findings suggest that water availability in Patagonia creates a mirage of water security. AI and WSI indicate high variability in the status of water sources, with 25% of watersheds showing high levels of anthropization and 33% with medium to high levels of water stress, making it relevant to explore the results through a combination of hydroclimatic, longitudinal, and latitudinal gradients. A novel analysis linking WSI and AI to governance perceptions was conducted, finding significant inverse correlations between WSI and both technical capacity and users’ participation. Despite the region’s evident abundance of water resources, rural communities consistently express concerns regarding supply sustainability, infrastructure deficiencies, insufficient technical support, and climate change risks to current and future water availability, all of which constrain water security in Chilean Patagonia. Full article

As an integrated socio-technical system linking information technology with industrial infrastructure, the Industrial Internet is increasingly central to urban digital transformation. However, current research largely centers on national or sectoral scales, lacking systematic analysis at the city level—particularly regarding system structure, enabling mechanisms, and region-specific pathways. This study takes Dalian, a city with a strong industrial base and urgent digital transformation needs, leveraging the Industrial Internet Development Index (IIDI), employing a “system structure–mechanism–pathway” analytical framework, we conducted a comprehensive assessment of the spatiotemporal relationship between industrial structure and Industrial Internet performance in Dalian from 2020 to 2022. The study finds that, during the research period, Dalian’s Composite IIDI increased from 0.31 to 0.65, with substantial improvements in platform infrastructure, resource coordination, and data application capacity—providing key support for enterprise digitalization and intelligent consumption. A strong correlation (R² = 0.85) between industrial structure and Industrial Internet performance underscores the structural foundation’s critical role. However, comparative analysis reveals that Dalian still faces structural deficiencies in platform openness, international interface integration, and ecosystem synergy. The study introduces a systemic pathway for empowering Industrial Internet capabilities and offers actionable insights for policymakers seeking to foster regionally adapted digital transformation. Full article

Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness. Full article

This paper introduces a novel multi-criteria evaluation system for railway sidings, designed to support sustainable freight transport and strategic infrastructure planning. The methodology is original because it integrates six criteria—technical, economic, operational, environmental, legal, and socio-economic—into a transparent scoring model based on the Analytic Hierarchy Process (AHP). This structured approach reduces subjectivity, ensures replicability, and enables the evidence-based prioritization of infrastructure investments. Policy relevance is emphasized by aligning the model with EU decarbonization targets and national railway development strategies, providing actionable guidance for decision-makers. The framework was applied to the SCP Mondi siding as a case study, achieving an Overall Quality Index (OQI) score of 4.175, categorizing it as a high-performing siding. Sensitivity analysis of weighting factors confirmed the model’s robustness and adaptability across different contexts. These results highlight the framework’s practical value in optimizing resource allocation, revitalizing underused infrastructure, and accelerating the modal shift to rail. Full article

This study addresses the limitations of traditional nighttime light remote sensing data in ground object feature recognition and carbon emission monitoring by proposing a fusion framework based on Nonsubsampled Contourlet Transform (NSCT) and Intensity-Hue-Saturation (IHS). This framework successfully generates a high-resolution color nighttime light remote sensing imagery (color-NLRSI) dataset. Focusing on Guangzhou, an important city in the Guangdong-Hong Kong-Macao Greater Bay Area, the study systematically analyzes the spatiotemporal coupling mechanism between urban form evolution and carbon footprint by integrating multiple remote sensing data sources and socio-economic statistical information. Key findings include: (i) The color-NLRSI dataset outperforms traditional NPP-VIIRS data in built-up area extraction, providing more accurate spatial information by refining urban boundary recognition logic. (ii) Spatial correlation analysis reveals a remarkably strong positive relationship between built-up area expansion and carbon emissions, with the correlation coefficient for numerous districts exceeding 0.9. High-density built-up areas are strongly associated with a carbon lock-in effect, hindering low-carbon transformation efficiency. (iii) Geographically Weighted Regression analysis demonstrates that in population-polarized regions, the impact coefficient of built-up area expansion on carbon emissions is notably high at 0.961. This factor’s association (22.43%) surpasses economic development (10.34%) and urbanization rate (14.91%). The established “data fusion—dynamic monitoring—mechanism analysis” technical system, which generates a novel high-resolution color-NLRSI dataset and reveals a distinct ‘core-periphery’ heterogeneity pattern in Guangzhou, demonstrating that urban expansion is the dominant driver of carbon emissions. This approach offers a scientific basis for tailored urban low-carbon development strategies, spatial optimization, and enhanced precision in carbon emission monitoring. Full article

This article explores the converging challenges and opportunities at the intersection of underwater cultural heritage, digital archaeology, and participatory science. In an era of accelerated climate change, data fragmentation, and rapid technological advancement, underwater archaeology is being reshaped by the rise of generative artificial intelligence (GAI), FAIR (Findable, Accessible, Interoperable, and Reusable) data governance, and the growing role of public archaeology. We argue for an ethical and epistemologically inclusive framework that recognizes the importance of co-authorship, data transparency, and multisensory narratives in interpreting submerged sites. Drawing on case studies from Latin America and Europe, this article demonstrates how socio-technical networks, collaborative models, and culturally sensitive ontologies offer a pathway toward a decolonized, accessible, and sustainable archaeology. This paper concludes with recommendations for integrated public policy and citizen-driven heritage protection, highlighting digital archaeology’s transformative potential in the Anthropocene. Full article

Blockchain is described as a technology of trust. Its design relies on cryptography, decentralization, and immutability to ensure secure and transparent transactions. Yet users frequently report confusion, frustration, and skepticism when engaging with blockchain applications. This tension is the blockchain trust paradox: while trust is engineered into the technology, trust is not always experienced by its users. Our article examines the paradox through three theoretical perspectives. Socio-Technical Systems (STS) theory highlights how trust emerges from the interaction between technical features and social practices; Technology Acceptance models (TAM and UTAUT) emphasize how perceived usefulness and ease of use shape adoption. Ostrom’s commons governance theory explains how legitimacy and accountability affect trust in decentralized networks. Drawing on recent research in experience design, human–computer interaction, and decentralized governance, the article identifies the barriers that undermine user confidence. These include complex key management, unpredictable transaction costs, and unclear processes for decision-making and dispute resolution. The article offers an integrated framework that links engineered trust with experienced trust. Seven propositions are developed to guide future research and practice. The conclusion argues that blockchain technologies will gain traction if design and governance evolve alongside technical protocols to create systems that are both technically secure and trustworthy in experience. Full article

The study investigates the disconnect between formal urban planning standards and experiential walkability outcomes in Viranşehir, a planned neighborhood in Mersin, Türkiye. Although the area complies with national regulations on the provision of public services, it exhibits systemic limitations, including car-oriented street layouts, fragmented pedestrian networks, and underutilized public spaces. Employing a mixed-methods case study, the research integrates archival sources (aerial imagery, zoning plans, satellite data) with field observations to assess pedestrian environments. A light coding of sidewalk continuity, crossings, and edge conditions indicates that many streets are bounded by extensive inactive walls, protected crossings are absent along critical routes such as the school–park axis, and sidewalks are frequently narrow, obstructed, or discontinuous. These built-form features undermine safety, comfort, and social interaction despite formal regulatory compliance. The findings demonstrate how grid-pattern street systems prioritize vehicular mobility, while gated developments restrict permeability and diminish everyday encounters. In response, the study proposes a hierarchy of interventions: immediate measures such as school streets, protected crossings, and traffic calming, followed by medium- to long-term strategies including shaded seating, sidewalk widening, and participatory design guidelines. By linking statutory standards with lived experience, the paper conceptualizes walkability not only as a technical planning requirement but also as a socio-cultural right, offering transferable insights for the creation of more inclusive urban environments. Full article

The main objective of this paper is to examine the factors influencing investor intention to adopt robo-advisory services in Saudi Arabia, with a particular focus on sustainability and platform interface quality (PIQ) within a socio-technical framework. Drawing on the Diffusion of Innovation (DOI), Technology Acceptance Model (TAM), Value-Based Adoption Model (VAM), and Trust theory, the research integrates constructs such as Knowledge about Robo-Advisors (KRA), PIQ, Green Perceived Value (GPV), and Perceived Trust (PT). Data were collected through a structured questionnaire targeting financially active individuals, with 387 valid responses analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that KRA significantly influences Intention to Use Robo-Advisors (IURA) both directly and indirectly, through GPV and Relative Advantage (RA), with only marginal support observed for Perceived Usefulness (PU). PIQ strongly influences perceived ease of use (PEOU) and PU, contributing to IURA, while PT significantly moderates the effects of KRA and PIQ. Multi-group analysis (MGA) further highlights heterogeneity across age, education, and investment groups, underscoring the contextual nature of adoption. The study highlights the critical role of PT, PIQ, and GPV alignment in investor decision-making when engaging with robo-advisory platforms. It offers theoretical contributions by extending traditional adoption models through the inclusion of green value and interface quality, and practical implications for FinTech developers and policymakers aiming to build inclusive, trustworthy, and environmentally aligned robo-advisory platforms. Full article

This paper explores the potential of Electric Vehicle (EV) Vehicle-to-Home (V2H) technology to enhance household resilience during extreme weather events, integrating socio-economic, technical, and human rights perspectives. V2H technology enables EVs to provide backup power during outages, offering a promising solution for disaster preparedness and response. However, widespread adoption of this technology faces barriers shaped by socio-economic disparities, including income, housing, education, and access to infrastructure, as well as human decisions related to EV ownership, V2H utilization, and evacuation behaviors. To investigate these challenges, this study adopts a qualitative review of existing literature and policy frameworks, critically analyzing how social vulnerabilities and adoption barriers influence the effectiveness of V2H in improving household-level disaster resilience. The findings indicate that while V2H technology can significantly support disaster resilience, its benefits are contingent on equitable access, affordability, and public awareness. To maximize its potential, various public and private stakeholders must adopt equity-driven strategies that align technological innovation with socio-economic inclusion. This paper highlights the need for cross-sector collaboration to ensure V2H systems reach underserved and marginalized communities, advocating for policies that prioritize both technological advancement and distributive justice. Full article