Prefabricated buildings and off-site construction are increasingly adopted in modern construction. As one of the most concerning environmental impacts, the embodied carbon emission of prefabricated buildings has been extensively investigated in recent years. Due to the various influencing factors of carbon quantification, such as building characteristics, quantification boundary, emission sources, and quantification methods, no consensus has been reached so far. The impacts of the influencing factors on carbon quantification remain unclear. To fill this gap, this paper provides a systematic review and meta-analysis to comprehensively evaluate the recent research concerning the quantification of the embodied carbon emission of prefabricated buildings. In total, 43 peer-reviewed articles (96 building cases) were screened and analyzed. Twelve influencing factors of embodied carbon quantification have been identified and analyzed to give rise to a synthesized conclusion. The results of the meta-analysis indicated that the embodied carbon emission of prefabricated buildings varied significantly from 26.6 to 1644.4 kgCO₂e/m² in the reviewed literature. The results showed that some of the quantification factors could significantly influence the quantification results, such as the building structure forms, level of prefabrication, type of greenhouse gas considered, and data sources, while some factors have a lesser impact on carbon quantification results, such as the function of the building, quantification methods adopted, quantification tools/software used, and carbon inventory databases applied. The findings of this research provide readers with an in-depth and critical understanding of the quantification of the embodied carbon emission of prefabricated buildings. Research gaps and suggestions for future research are also provided based on the results of this work.

With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet established C&D waste databases and standard prediction methods. This study proposed a method using a weighted combination of the grey theory model (GM) and the autoregressive integrated moving average (ARIMA) model to predict the quantity of urban C&D waste in the future. Based on a case study in Guangzhou, this study compared the prediction results of three prediction models, namely the GM, the ARIMA, and the proposed weighted combined model of the GM and the ARIMA (GM-ARIMA). The results of this study proved that the proposed combined GM-ARIMA model had a better predictive performance than both the separated models. The mean absolute percentage errors (MAPE) of the GM and ARIMA models were 12.11% and 14.26%, respectively, whereas the proposed GM-ARIMA model had a lower MAPE (8.5%). This study found that the generation of C&D waste in Guangzhou will continue to grow steadily. From 2024 to 2035, the quantity of C&D waste is expected to reach 850 million tons cumulatively, with an annual growth rate of 7.1%.

Green construction evaluation systems can assist building stakeholders in scientifically evaluating the carbon emission performance of construction projects. However, most green construction evaluation tools and methods fail to explicitly incorporate construction carbon emission indicators, let alone a quantitative evaluation. Therefore, this study proposes a carbon emission evaluation system based on the entropy–TOPSIS and K-means methods for foundation construction projects. In this study, we innovatively divided the carbon emission of the foundation construction process into three phases, namely, transportation emission, excavation and earthwork emission, and pile work emission, considering their different emission characteristics and reduction difficulties by nature. Different from traditional carbon evaluation methods, the carbon emission of the three phases were evaluated separately against the baseline value obtained from local construction quota. After that, the emission performance of the three phases was weighted and evaluated based on the entropy–TOPSIS method, and then rated via the K-means method. Based on a case study of 19 residential buildings, the weights of the three construction phases were 27.66% (transportation), 42.34% (excavation and earthwork), and 29.99% (pile work). The carbon performance of the 19 cases were rated by the K-means method into four levels: six cases were rated “Excellent”, five were rated “Good”, five were rated “Fair”, and three were rated “Poor”. The proposed method was expected to objectively and scientifically evaluate and rate the carbon emission of the foundation construction process, and provided a theoretical basis for decision makers to identify emission hotspots and formulate specific carbon reduction measures.

Prefabricated buildings have become important in the transformation and upgrading of the construction industry due to their advantages, including high efficiency, energy conservation, low cost, and environmental friendliness. To further promote the wide application of prefabricated construction, the improvement of construction organization design has become an urgent problem to be solved. Therefore, this study developed a new evaluation method for prefabricated construction collaboration. The proposed evaluation system was built based on the combination of knowledge- and data-driven approaches, i.e., a dual-driven evaluation method. The knowledge-driven part of this evaluation system used an evaluation model based on the analytic hierarchy process (AHP), while the data-driven part used a prediction model based on the BO-XGBoost algorithm to verify the validity of the AHP-based model. To demonstrate the effectiveness of the proposed dual-driven evaluation system, we conducted a case analysis using the data of 204 construction cases obtained from digital simulation platform experiments. The results of the AHP-based evaluation model showed that there was a significant disparity in construction collaboration levels in this case study, with a large proportion of low-level collaboration cases. This indicated that there was a lack of proper collaboration in project management, component production, and on-site assembly, reflecting the urgent need for improvement in collaboration efficiency. Regarding the data-driven analysis, the BO-XGBoost prediction model was built based on the AHP-based evaluation results. It was found that the prediction accuracy of the BO-XGBoost model was as high as 98.1%, indicating that the proposed AHP-based model was scientific and effective. Moreover, the BO-XGBoost model was compared with the random forest, support vector machine, and logistic regression prediction models. The BO-XGBoost model outperformed the other three prediction models in terms of accuracy, precision, recall rate, and F1 score. The proposed dual-driven evaluation system provided a new perspective for the scientific evaluation of prefabricated construction collaboration. The findings of this study contributed to enhancing the project management optimization capability of smart construction sites.