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.

Digital twin (DT) technology is recognized for its transformative potential to enhance efficiency in the construction process. However, the full potential of DT in construction practices remains largely unrealised. Moreover, few studies explore how DT success factors affect efficiency improvement (EI), energy optimization (EO), and cost control (CC) in the context of economic strength (ES). The study applied a hybrid research method to examine the impact of key DT success factors on EI, EO, and CC under the moderation of ES. After a critical literature review, five key DT success factors were identified. Then, 490 valid questionnaires were analyzed with the Partial Least Squares Structural Equation Model (PLS-SEM) to assess how success factors affect DT effectiveness. This is complemented using extreme gradient boosting (XGBoost) to assess prediction accuracy and understand which factors most influenced EI, EO, and CC. Research shows that ES exerts a significant positive influence on the relationships between most success factors and performance outcomes. High levels of ES enhance the contribution of success factors to performance in EI, EO, and CC. Resource management (RM) has a strong influence on EI and EO, but a weaker influence on CC; process optimization (PO) has the strongest influence on EO, a moderate influence on CC, and the weakest influence on EI; real-time monitoring (R-Tm) primarily affects EI; sustainable design (SD) has a comprehensive and significant regulatory effect on EI, EO, and CC; and predictive maintenance (PM) has a strong influence on both EI and CC. In practice, it offers practical guidance for implementing DT and supports policy and resource planning for building stakeholders.

As a primary energy consumer and carbon emitter, the construction industry (CI) faces a growing conflict between traditional energy-intensive growth models and global sustainable development goals. To promote the sustainable development of the CI, this study establishes a sequential analytical framework following the logic of “coupling evaluation–driving force identification–causal inference” across 30 developed economies (DE) from 2000 to 2022. Initially, the coupling coordination degree (CCD) between the economic and environmental systems of the CI was evaluated, utilizing the Environmental Kuznets Curve (EKC) to characterize the transition from relative to absolute decoupling. The results show that the economy and the environment in the construction industry (CEECI) for DE is generally high (0.70–0.90). Subsequently, based on Green Innovation Growth (GIG) theory, Panel Data Analysis (PDA) is employed to identify the key drivers of the coupling between the economy and CEECI. The results show that for every 1% increase in per capita GDP, CEECI increases by approximately 0.035; for every 1% increase in science and technology investment (ST Inv), CEECI increases by 0.045; and for every 1 unit increase in building energy use (BEU), CEECI decreases by 0.008. Furthermore, Granger causality analysis (GCA) was used to examine the bidirectional predictive relationship. Furthermore, there is a two-way correlation between GDP and CEECI, and a one-way correlation between CEECI and ST Inv. Overall, our results show that further decoupling requires innovation, not just economic growth; therefore, the CI should optimize its industrial structure, prioritize technological innovation, strengthen lifecycle energy management, and promote coordinated global CI improvement.

Climate warming will have a profound impact on ecosystems, human society and economic development, so mitigating climate warming has become an important challenge facing the world. However, the construction industry is one of the important sources of global carbon emissions, and the use of digital technology had a revolutionary impact on the development of the construction industry. Artificial Intelligence plays an important role in digital technology, and its status and influence are further increasing. However, there is currently no comprehensive explanation of how Artificial Intelligence affects the construction industry’s carbon emissions. This study aims to explore the role of artificial intelligence on carbon emissions in the construction industry from multiple perspectives in the four life cycles of planning, design, operation & maintenance, and tear down. This study also to provide certain guidance for AI's participation in carbon reduction in the construction industry. The search of this study covers the three analysis dimensions of "efficiency improvement", "energy optimization" and "cost control", and analyses the contribution of Artificial Intelligence on carbon emissions in the construction industry by adopting mixed research methods. First, this study systematically analysed 85 publications collected in Scopus and Web of Science databases using PRISMA guidelines. Next, qualitative research method was used where semi-structured interview was use for data collection. Then Analytic Hierarchy Process was used to obtain the research results. Through research, the role of artificial intelligence in carbon reduction in the construction industry is further clarified, and the most obvious role of Artificial Intelligence in each life cycle is efficiency improvement. This research can provide reference and guidance for the use of AI in different life cycles.

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.