As cities grow denser, renewing old communities has become vital to improving urban functions and achieving high-quality development. However, institutional, economic, social, and technical factors intertwine, constraining the renewal process and limiting its effectiveness. Therefore, this study aims to identify and analyze the barriers to the Renewal of Old Residential Communities (RORCs). Twenty-eight barriers were identified through a literature review, questionnaires, and face-to-face interviews with community residents and professionals involved in RORCs. Based on the statistical results of 183 valid survey responses, this study identified the top five barriers to RORCs as follows: insufficient renewal funding, divergent resident demands and opinions, conflicts of interest among stakeholders, high renewal costs, and complex property rights. The 28 barriers to RORCs were further extracted into seven latent factors based on exploratory factor analysis (EFA): (1) Policy and Planning Deficiencies, (2) Infrastructure and Historical Legacy Issues, (3) Resident Participation and Community, (4) Spatial and Physical Limitations, (5) Consensus Execution and Management Inadequacies, (6) Economic and Financial Constraints, and (7) Property Rights Complexity and Social Structure. Through an in-depth interpretation of these groups, this study enhances the understanding of the systemic barriers to RORCs and provides practical insights for policymakers and practitioners to prioritize interventions and formulate integrated, sustainable renewal strategies suited to high-density urban contexts.

This study presents an in-depth investigation into the intersection of Building Information Modeling (BIM) and green building practices within China’s rapidly evolving construction industry. As China intensifies its efforts to mitigate environmental impacts and promote sustainable growth, the integration of BIM into green building practices emerges as a crucial area of study. A qualitative research method was adopted in this research. In the first step, utilizing semi-structured interviews with a diverse array of industry professionals, this research provides nuanced insights into the current state and prospects of BIM in the green building landscape. Secondly, thematic analysis is used to formalize the views and points from interviewees. Finally, a novel conceptual framework is proposed, addressing these challenges through technological innovation, supportive policies, cultural and educational reform, economic incentives, and collaborative dynamics. This study contributes a systematic approach to amalgamate BIM with sustainable construction, offering insights for industry professionals and policymakers to promote environmentally conscious building practices in China.

Globally, monitoring productivity, occupational health, and safety of construction workers has long been a significant concern. To address this issue, there is an urgent need for efficient methods to continuously monitor construction sites and recognize workers’ actions in a timely manner. Recently, advances in electronic technology and pose estimation algorithms have made it easier to obtain skeleton and joint trajectories of human bodies. Deep learning algorithms have emerged as robust and automated tools for extracting and processing 3D skeleton information on construction sites, proving effective for workforce action assessment. However, most previous studies on action recognition have primarily focused on single-stream data, which limited the network’s ability to capture more comprehensive worker action features. Therefore, this research proposes a Spatial-Temporal Multi-Feature Network (STMF-Net) designed to utilize six 3D skeleton-based features to monitor and capture the movements of construction workers, thereby recognizing their actions. The experimental results demonstrate an accuracy of 79.36%. The significance of this work lies in its potential to enhance management models within the construction industry, ultimately improving workers’ health and work efficiency.

Purpose: The construction industry is now experiencing a thorough transformation through digital technologies, especially with building information modeling (BIM). Despite significant BIM advantages, most construction projects suffer from low BIM performance due to the fragmented BIM use mode. To facilitate lifecycle-integrated BIM implementation, this study demonstrates that introducing BIM lifecycle service providers (BLSPs) is feasible and offers significant improvements in terms of BIM benefits. Hence, this study proposes a customized framework to select BLSPs. Approach: This study utilized both qualitative and quantitative methods. It first adopted semi-structured interviews as part of the qualitative method to deduce the initial criteria for BLSPs’ selection. 30 interviews were conducted iteratively with managers proficient and experienced in selecting BLSPs, through which 25 initial criteria were identified. Then, as the basis of the applied quantitative method, a questionnaire survey was used to evaluate these criteria by determining the critical ones, identifying the latent factor groupings, and assigning criteria weights. Subsequently, an assessment framework was established. Finally, the study was in favor of eight construction projects, highlighting the practicality and validity of the framework. Findings: The results depicted that project BIM service capability is a primary factor for BLSPs’ selection. Within this factor, several specialized criteria need to be considered, such as “boundary spanning competence of the BIM manager” and “BIM service plans with lifecycle cognition.” Meanwhile, “past innovative BIM service practices” and “BIM research and development (R&D)” that originate in corporate innovation capacity were emphasized when selecting BLSPs. Furthermore, for holistic assessment and recognizing the peculiarities of digital BIM service, the study found that criteria like “Privacy and security” and “Backup system” are required, which demonstrate BIM service reliability. Originality/value: This study expands on the conventional partner selection frameworks in the construction sector and thus defines and validates a tailored one for BLSPs’ selection. Moreover, drawing such a reference solution from the framework, the study enables the selection of appropriate BLSPs for clients.

Despite the growing use of collaborative robots in high-rise construction, ensuring safe human–robot collaboration (HRC) in hazardous environments remains a critical challenge. Addressing the gap that previous studies optimized human, robot, or task factors in isolation without a systemic coordination perspective, this study develops and empirically validates a Human–Task–Robot Alignment (HTRA) framework to explain how alignment mechanisms enhance safety performance in the construction of HRC. Data from 84 high-rise HRC projects were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that HTRA serves as a pivotal mechanism for safety improvement, where Human–Robot Alignment (HRA) and Task–Human Alignment (THA) exert stronger effects on bilateral safety perception than Task–Robot Alignment (TRA), underscoring the centrality of human factors at the current stage of HRC development. Moreover, this study identifies a sequential cognitive–behavioral path from safety perception to safety intention and safety behavior performance, explaining how alignment enhances safety performance. Standardized HRC guidelines further strengthen HRA, facilitating safer and more efficient collaboration. This study extends Task–Technology Fit theory to a triadic human–task–robot context and advances the concept of bilateral safety, providing theoretical and managerial guidance for developing next-generation safe collaboration systems in construction.

The coordinated development of artificial intelligence (AI) and high-quality energy development (HED) is essential for advancing digital transformation and energy transition. However, existing research primarily explores the unidirectional impact of AI on HED, neglecting their bidirectional relationship. Drawing on provincial data from China during 2012–2022, this study examines the coupling coordination between AI and HED (AHCC) and its spatiotemporal differentiation and driving factors. Results show that although both AI and HED advanced steadily, AI started from a lower base and remained below HED, and their spatial distributions were mismatched. The national average AHCC improved from mild imbalance to marginal imbalance, but large regional disparities persisted. Only several provinces—six in the east and two in the west—entered coordination stages, with most remaining in imbalance. Intensifying spatial autocorrelation of AHCC indicates that strong or weak regions are increasingly locked into self-reinforcing trajectories, making balanced regional development difficult. Regression results indicate that technological innovation, economic development level, and industrial structure upgrading significantly promote AHCC, whereas environmental regulation, urbanization level, and government intervention inhibit it. These effects exhibit pronounced spatiotemporal heterogeneity. This study enriches the theoretical understanding of AHCC and provides empirical evidence to inform coordinated digital and energy transition policies.