6D building information modeling (6D BIM) has been proposed as a sustainability dimension in BIM technology. It serves as a life-cycle sustainability assessment tool and an important method for decision makers and designers to make informed decisions in sustainable design and facility management. However, there is currently a lack of comprehensive evaluation of the key influencing factors of 6D BIM in residential Building-Integrated Photovoltaic (BIPV) systems. Previous studies have typically examined BIPV performance or BIM-based sustainability workflows in isolation, lacking a comprehensive analysis of how 6D BIM enhances BIPV sustainability throughout the building lifecycle. This study aims to identify the core influencing factors of 6D BIM in BIPV systems and analyze their impact on sustainability performance indicators. To this end, this study used a systematic literature review method to retrieve research literature related to 6D BIM and BIPV from the past decade (2014-2024) from Scopus and Web of Science databases and applied the PRISMA method to screen and analyze 111 highly relevant literature. The results show that the keywords of BIPV-related research have been continuously updated in the past decade, showing the rapid development of 6D BIM in the field of sustainable buildings and its potential in building energy management. Based on the sustainability of BIPV, the study identified five key factors that affect the application of BIPV in sustainable systems: economic and cost factors, policy and social factors, geographical and meteorological factors, design and performance factors, and operation and maintenance factors. In addition, according to the sustainable performance of BIPV, 6D BIM is used as an enhancement factor to improve the sustainability of BIPV. The results of this study provide new insights into the application of 6D BIM in the field of BIPV and provide a scientific basis for stakeholders to develop more efficient implementation strategies.

With the rapid development of China's property industry, residential construction projects face significant schedule delays due to inadequate progress management. It makes it difficult for enterprises to realize the return of funds on time, causing economic losses and reputational damage which brings serious economic losses and loss of corporate reputation. This study aims to identify and prioritize delay factors and propose effective control measures. Qualitative research methods, including semi-structured interviews, are used to collect data from the Yun Yue Dong Fang residential project in China, a case study, and analyze the influencing factors of project schedule management. Like construction, design, materials, finance, communication, non-human elements, planning, and supervision. Results show construction factors (particularly execution efficiency, funding timeliness, and resource allocation) dominate, as evidenced by their highest word frequency in interview coding. Followed by client factors, while the factors related to the design teams, as well as the factors related to the raw material and equipment suppliers have less impact. The study proposes strengthening control through integrated management systems and real-time monitoring, validated by the case study, to mitigate delays. The results of the study provide actionable insights for optimizing schedule management in residential projects as well as the project schedule management of residential development enterprises and enhancing enterprise performance.

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively.