Sustainable construction requires effective control over both economic efficiency and environmental performance, particularly in terms of construction costs and greenhouse gas (GHG) emissions. However, current practices often lack real-time, data-driven frameworks to dynamically assess the economic and environmental impacts of ongoing construction activities. This study proposes a vision-based system that enables real-time monitoring, assessment, and management of construction costs and GHG emissions, contributing to low-carbon and resource-efficient construction practices. Three core contributions are presented: (1) a comprehensive real-world construction resource dataset covering 12 object categories is developed to address the scarcity of domain-specific training data; (2) a modular visual analysis framework is designed to automatically translate surveillance video data into quantitative cost and GHG emission metrics in real time; and (3) an earned value management-based deviation diagnosis mechanism is integrated to benchmark actual performance against budgeted expectations at the resource and activity level, enabling targeted interventions during ongoing construction. Experimental validation on a real-world construction project demonstrates that the system achieves high detection accuracy (mAP@0.5 = 92.5%), rapid processing (714 FPS), and near-instantaneous output (within 5 s), significantly reducing manual effort while enhancing decision-making. The proposed method represents a practical step toward integrating intelligent visual sensing with sustainable construction management, offering a practical tool for smart and green transformation of the construction industry.

Excessive carbon emissions pose a major barrier to sustainable urban development; however, neighborhood-scale assessments are often hindered by data scarcity and low resolution, limiting the ability to explain how human activities and environmental factors jointly shape emissions. This study develops a neighborhood-scale carbon emissions simulation framework that integrates unmanned aerial vehicle-based remote sensing, computer vision, geographic information system techniques, and field surveys to acquire multi-source data. A multi-agent system was constructed to represent human behavior, built environments, and climate conditions as interacting agents with heterogeneous attributes and behavioral rules, enabling interaction-driven emissions estimation. A case study in Nanjing, China, estimated annual carbon emissions of 2.61 x 108 kg CO2e, corresponding to a land-based emission intensity of 106.53 kgCO2e/m2. The results reveal detailed spatiotemporal emission patterns and pronounced seasonal variability: buildings dominate emissions, mobility contributes 1.6%, and open spaces act as a net sink, offsetting over 2% of neighborhood emissions. Seasonally, summer emissions were approximately 40% higher than those in the transitional seasons, primarily driven by extended cooling operations and climate-sensitive behavioral shifts. The proposed approach advances explanatory power and computational efficiency in neighborhood-scale carbon accounting, providing a practical basis for low-carbon neighborhood management and sustainable urban development.

Public-Private Partnerships (PPPs) have been a critical vehicle in procuring large-scale infrastructure projects worldwide, offering benefits of innovation and risk sharing. Despite its significant contribution, the arrangements of transport PPPs are more complex, rendering the projects being vulnerable to extensive risks that threaten the service performance of the assets. Therefore, this paper aims to investigate the key risks and their interaction in relation to the services of transport PPPs through the case studies of Canberra Capital Metro and Sydney Metro, Australia, in line with the following research questions: (1) What are the key risks relating to transport PPPs? and (2) How are these risks interacted with one another as a network in determining the asset's service performance? Using the social network analysis, the empirical evidence highlights a sequence of risk factors covering not only those 'traditional' concerns such as 'budget/schedule overruns' but also some 'interesting' issues like 'integration with existing transport systems', 'uncertainty of acquiring accreditation' and 'update of ticketing system'. This research steps into a key concern on service quality of transport infrastructure assets, further expanding the knowledge in managing and facilitating PPPs.

With the growing stock of aged non-industrial buildings, structural health assessment is critical to ensuring occupant safety and enabling safety risk management during urban regeneration. Existing structural health appraisal practices rely on extensive field investigations, experimental testing, and complex structural analysis, which are time-consuming and difficult to scale to large building stocks. To address this challenge, this study proposes a two-stage structural health assessment approach for rapid screening of aged non-industrial buildings. Unlike conventional end-to-end machine learning (ML) approaches that directly predict overall building condition using a unified model across different building types, the proposed approach employs structure-specific ML models to capture heterogeneous deterioration characteristics at the subsystem level. These predictions are aggregated into the overall building structural health condition through rule-based constraints aligned with the hierarchical appraisal logic prescribed in professional standards. Specifically, feature sets are constructed from historical appraisal reports, with features selected using correlation analysis and recursive feature elimination with cross-validation. Six ML algorithms are evaluated, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM). LightGBM performs best for brick masonry buildings, whereas XGBoost performs best for concrete frame buildings, confirming the necessity of structure-specific modeling. Shapley additive explanations (SHAP) analysis shows that aging- and deformation-related features dominate brick masonry assessment, whereas deformation- and damage-related indicators are more influential for concrete frame buildings. The proposed approach supports rapid and accurate structural health assessment of large aged non-industrial building stocks in urban regeneration.

Patient satisfaction is a central indicator of healthcare quality and a key outcome in outpatient care. The waiting room, as the first point of contact, strongly shapes patients’ comfort, expectations, and perceptions of service quality. Yet most existing studies remain confined within single disciplinary domains rather than integrated relationships among them. This lack of cross-disciplinary synthesis limits theoretical understanding and practical guidance. A systematic review is therefore needed to develop a comprehensive framework for improving outpatient experiences and supporting evidence-based design. A mixed-methods systematic review was conducted following PRISMA 2020 guidelines to identify major research themes and determinants of patient satisfaction in outpatient waiting rooms. Literature was retrieved from Scopus (2010–2024) using a modified PICO framework adapted for environmental and experiential variables. Quantitative bibliometric mapping analyzed keyword co-occurrence and thematic evolution across three periods, complemented by qualitative coding and thematic synthesis to interpret mechanisms and integrate findings. This review included 125 studies, 113 research articles and 12 reviews (9.6

With the increasing prevalence of digital technology, the inherent influencing mechanisms by which digital empowerment fosters rural environmental governance remain a critical area of inquiry. However, existing research neglects the dual effects of digital technology on rural environmental governance from the aspect of stakeholders’ engagement. To address this gap, this study develops an integrated framework to investigate not only the direct impact of digital empowerment on rural environmental governance but also the mediating roles of stakeholders’ engagement and governance mechanisms, alongside the moderating role of perceived technology anxiety. Grounded in theoretical frameworks and extensive literature reviews, this study analyzes data from Jiangsu province in 2025 using an Ordinary Least Squares (OLS) model. The baseline regression results reveal that digital empowerment significantly promotes rural environmental governance, even after endogeneity analysis. Moreover, the results of the mediation effect show that digital empowerment enhances the level of rural environmental governance by accelerating stakeholders’ engagement and improving governance mechanisms. Furthermore, the moderating effect results imply that perceived technology anxiety may inhibit the negative effect of digital empowerment on rural environmental governance. Additionally, regional disparities exist in the influences of digital empowerment on rural environmental governance, with rural areas in Southern Jiangsu exhibiting a more pronounced effect compared to other regions.

The prominence of Energy Performance Contracting (EPC) has grown particularly dramatically in recent years, prompting countries to enact policies aimed at fostering wider EPC adoption and backing up the growth of Energy Service Companies (ESCOs). However, the effectiveness of these overarching policies and their varied policy instruments remains a topic of uncertainty. This study aims to design an analytical framework for evaluating EPC policy power and examine the effectiveness of different policy instruments to ESCO industry growth through empirical analysis, focusing on China and the United States—two leading ESCO markets. Comparative analysis was conducted in three development periods based on the results of quantitative analysis of policy power. Multiple linear regression and ARDL model are applied to examine how policy power influences ESCO industry growth in both countries, followed by the use of the LMG method to evaluate the relative importance of selected drivers. The findings underscore the preeminent role of policy promotion in propelling the development of the ESCO industry in both countries. Additionally, the LMG technique is employed to assess the individual contribution of command-and-control (CAC), economic incentive (EI), and exhortatory instruments (EXI) in shaping industry outcomes. The impact of these policy types on the ESCO industry varies significantly, with EXI exerted most pronounced impact in China and CAC in US. In terms of the results, suggestions are proposed for policymakers to effectively promote ESCO development.

The increasing integration of robotics in the industrial and construction sectors has significantly enhanced productivity and efficiency. However, the rapid adoption of human-robot collaboration (HRC) introduces unique safety challenges, particularly regarding workers' improper use of personal protective equipment (PPE). These issues, if not effectively addressed, can compromise safety and impede the widespread adoption of robotic technologies. This study proposes an automated framework that combines semantic scene graph generation and named entity recognition (NER) to detect unsafe behaviors in real time across diverse HRC scenarios. The method employs encoder-decoder-attention (EDA) architectures and natural language processing techniques to transform unstructured visual data into structured textual descriptions. By integrating domain-specific safety knowledge, the framework enables accurate hazard inference and actionable insights for safety management. For HRC image understanding, the EDA achieved superior results with a convergence loss of 1.089, 99.1% accuracy, and a Bilingual Evaluation Understudy (BLEU)-4 score of 0.8905. Then the detection precisions for entities like human, behavior, unsafe behavior, and robot are 0.84, 0.81, 0.78, and 0.79, respectively, with an overall mean precision of 0.81. Experimental results confirm the robustness and scalability of the proposed approach within HRC contexts, particularly in detecting unsafe behaviors arising from the improper or missing use of PPE. This study contributes to the development of intelligent safety monitoring systems, offering practical solutions to enhance safety compliance and foster sustainable HRC in complex industrial environments.

Highway projects that harness digital technologies during operation (known as digital highway projects; DHPs) can stimulate economic growth, but limited efforts have been made to fully unravel this mechanism. To address this void, this study examined the impact of DHPs on the economic growth. Specifically, under the auspices of the regional competitiveness theory, the development level of DHPs, transportation demand, new factor endowments, and related and supporting industries were identified and measured first, and their impact on economic growth was then unearthed using data from 11 operational DHPs and a partial least squares structural equation modeling (PLS-SEM) framework. It was observed, from the perspective of stakeholders of our DHPs, that DHPs increase transportation demand, which in turn has a positive effect on a new factor endowment (i.e., data flow) and the development of related and supporting industries, with the former (beta = 0.859, p < 0.001) being impacted more than the latter (beta = 0.363, p < 0.001). In addition, new factor endowment has a statistically significant impact (beta = 0.666, p < 0.001) on the economy, while the development of related and supporting industries is insignificant (beta = 0.095, p > 0.05). Finally, although DHPs promote economic growth, this path can be mediated by increased transportation demand and subsequently by the new factor endowment including data flow. As such, this study further develops the regional competitiveness theory in the context of DHPs and provides new empirical evidence on the 'transportation induced demand' effect and the growth theory. Practically, this study arms policymakers with a better understanding of how DHPs influence the regional economy, and offers effective and targeted recommendations for managing these projects.

This study employs a mediating effect model and the Generalized Method of Moments (GMM) approach to examine the direct effect of ICT on the economy and the mediating role of data flow in the ICT – economic growth nexus. The results indicate that ICT significantly enhances data flow intensity, which in turn promotes economic growth. Moreover, both the direct effect of ICT and the mediating role of data flow are more pronounced in developed regions compared to underdeveloped areas. Further analysis shows that with the implementation of the policy, the mediating effect of data flow shifted from being insignificant (2006–2010) to significant (2011–2019). This study contributes to the understanding of the digital divide, highlighting potential drivers such as disparities in ICT infrastructure and data flow inequality. The government should develop tailored ICT development policies based on the region’s economic level to fully harness the benefits of digitalization. First published online 18 May 2026

In the intelligent recognition of construction drawings, architectural components significantly vary in size, and small objects are easily overlooked. To address these two challenges, this paper proposes a multi-scale feature fusion method based on channel and spatial attention mechanisms. Specifically, the method constructs a hybrid feature pyramid structure (ArchFuse-Net) to enhance multi-scale feature representation. Subsequently, a small-object perception module is introduced to improve the recall rate of small components. Finally, the bounding box regression loss function and post-processing mechanisms are refined to boost detection accuracy and localization precision. Experimental results demonstrate that the proposed method effectively improves overall detection accuracy across varying sizes and significantly boosts the recall of small objects, providing a robust solution for architectural drawing analysis.

As eldercare service platforms (ECSPs) are becoming increasingly popular due to the rapidly ageing population, using only a single pricing strategy has resulted in a mismatch between bilateral users (providers and consumers) in these platforms. To address these challenges, multiple innovative pricing strategies, considering the characteristics of the consumers, service providers, governments, and the private sector should be formulated by governments. Therefore, based on social welfare maximisation, this study develops a concession pricing model for ECSPs that combines different government subsidy methods, platforms' service quality, network externalities, and price elasticity. The results suggest that ECSPs should set a higher price for the group with more significant network externalities and a lower price for the group with more negligible network externalities. The optimal concession price decreases with service quality, while the size of bilateral users increases with service quality. It is further found out that governments should prioritise subsidising eldercare services with a low price elasticity of demand to improve subsidy efficiency, and that their subsidies can expand the size of bilateral users, decrease the concession prices, and improve social welfare. This study contributes to the body of knowledge of public services by proposing a concession pricing model for ECSPs, adding further evidence to the discussion about social and health services in platform. In practice, the findings would be of utmost interest to governments and the private sector that need to make more informed pricing decisions to improve the efficiency of eldercare services provision and maximise platform profit.

Tower crane operations in construction are inherently hazardous due to complex and dynamic site environments. Enhancing operators' perceptual and cognitive capabilities is essential for ensuring safety and improving situational awareness. This paper presents an integrated framework that combines an improved YOLOv8 model with a Knowledge Graph (KG)-enhanced large language model to achieve proactive and intelligent safety management. The improved YOLOv8 incorporates attention-based optimization to improve detection accuracy for small targets in tower crane perspectives. A domain-specific safety KG is constructed to represent critical entities, relationships, and operational contexts, and is aligned with a fine-tuned GPT model, enabling semantic reasoning and context-aware hazard interpretation. The integrated system links visual perception with structured knowledge reasoning to provide real-time and interpretable safety feedback. This approach enhances the perception, understanding, and decision-making capabilities of tower crane operators, transforming safety management from reactive monitoring to proactive and intelligent control in complex construction environments.

Neighborhoods consume large amount of energy and generate substantial pollutant emissions worldwide, and assessment of the environmental impacts (EIs) has garnered particular attention. Nevertheless, the traditional life cycle assessment (LCA) method fails to account for the local variability in non-homogeneous systems due to spatiotemporal dynamics and interactions. Hence, this study integrated the multi-agent system (MAS) with the dynamic LCA (DLCA) model to develop an intelligent EIs assessment model at the neighborhood scale from an interactive and dynamic perspective. MAS was employed to simulate the interactions in neighborhoods and generate foreground elementary flow data considering dynamics. DLCA provides an impact assessment framework. The proposed model was applied to a university campus to demonstrate its operability, and the enhancement effectiveness of integrating MAS with DLCA can be observed. The interactions among the climate, people, and built environment agents on the campus were simulated.10 temporal dynamic factors and four kinds of case-specific dynamic parameters were considered. The effects of several optimization strategies were simulated, and valuable directions, like raising energy conservation awareness, regulating public devices, and improving the campus layout, were proposed. This study established a comprehensive integrated MAS-DLCA model at the neighborhood scale, providing a methodology and practical application process for future studies. It can be used to promote green neighborhood management and sustainable city practices.

AIMS:This study investigates the impact of the hospital environment on nurse job productivity in the post-pandemic era, with a focus on the moderating role of occupational calling, based on the person-environment-occupation-productivity (PEOP) theory. DESIGN:A mixed-methods approach was employed, combining two-stage quantitative surveys and qualitative interviews. METHODS:In April 2022, 230 nurses from 11 Chinese public hospitals participated in a two-stage quantitative survey. Additionally, qualitative interviews were conducted with 10 nurses and 2 physicians. Quantitative data were analysed using partial least squares structural equation modelling (PLS-SEM), while qualitative data were analysed through Colaizzi's method to identify themes. To ensure the validity and reliability of the mixed-methods design, the study adhered to the Mixed Methods Appraisal Tool (MMAT) guidelines. Both sets of data were used to evaluate the relationships between hospital environments, job productivity, and occupational calling. RESULTS:The study found significant correlations between the hospital's indoor, spatial and sanitary environments and nurses' job productivity. Additionally, the research revealed that occupational calling moderates the relationship between indoor and spatial environments and job productivity to varying extents. However, occupational calling does not significantly moderate the impact of the sanitary environment on job productivity. CONCLUSION:This study provides insights into the transformative effects on hospital environments in the post-pandemic era, emphasising the importance of combining personal intrinsic and environmental extrinsic factors to boost nursing productivity. It proposes strategies for optimising hospital indoor, spatial, sanitary environments and enhancing nurses' occupational calling, providing practical, theoretical and educational insights to healthcare policymakers and practitioners. PATIENT OR PUBLIC CONTRIBUTION:There was no patient or public contribution in this study, as the focus was on nurses.

Understanding public emotional and behavioral response is critical for adaptive disaster management. Integrating natural language processing (NLP), econometric, and social psychological models, this study establishes an emotion-behavior framework to analyze multidimensional interactions between public and government responses. Using social media data from “7.20” Zhengzhou Rainstorm, we reveal distinct emotional drivers: social support offering (SSO) thrived on positive emotions, yet help seeking (HSK) correlated with fear, deviance (DEV) driven by anger, and avoidance & venting (A&V) sustained by multiple negative emotions. Public behavior patterns shifted from pre-disaster instrumental aid to fear-driven avoidance coupled with emotional aid arising during crises, eventually evolving into intensified post-disaster resource competition. Government strategies show asymmetric impacts on these behaviors. Most strategies yielded instantly positive effects on SSO, while all strategies responded passively to HSK and some had time-lag or limited effects in mitigating A&V and DEV. The findings advocate integrating psychosocial factors into emergency strategies, with emphases on prioritizing proactive community engagement to sustain social cohesion, embedding psychological support mechanisms, and enforcing transparent resource governance to redirect emotions like fear, anger, and sadness. This approach advances urban resilience by highlighting that adaptive climate defenses requires aligning policy interventions with community-driven collaboration and emotion-driven public behavioral dynamics.

Urban communities are fundamental units in addressing carbon mitigation, yet the patterns of carbon metabolism remain largely unexplored due to limited data and less-integrated methods. This study unveils the carbon metabolism dynamics of a Chinese mixed-function community from 2013 to 2022 by integrating bottom-up carbon accounting, spatiotemporal analysis, and ecological network analysis (ENA). We investigate carbon stocks and flows, sectoral characteristics, and intersectoral interactions at a refined spatial level in the 1.6 km2 community over the decade. Results show that the embodied carbon accumulates to 1.64 Mt with community construction. The operational emissions rise by 26 %-40,308 t in 2022 across three stages: population aggregation, service concentration, and saturation. The disproportionate contributions of community to urban population, land area, material stocks, and carbon emissions emphasize that community carbon metabolism cannot be regarded as a scaled-down version of urban carbon metabolism. Population growth, service expansion, and policy interventions impact the sectoral emissions over time. Households (53 %) and services (31 %) sectors contribute most, driven by rising carbon-related inflows. Spatially, mixed-use buildings exhibit the highest carbon intensities (0.63-0.73 t CO2/m2 from 2013 to 2022) due to vertical energy stackings in high-rises. ENA reveals that energy and construction sectors dominate network control allocation. Mutualism increased from 25.0 % to 28.6 %, while competition declined from 14.3 % to 10.7 %, demonstrating the enhanced system synergism through intensified circular carbon exchanges. These findings offer mechanism-based insights and benchmarks into carbon metabolism dynamics in evolving communities, informing sector-specific climate policies for sustainable community development in China and beyond.