Prefabricated buildings are gradually being promoted from pilot demonstration to scale, to combat climate change and improve energy conservation and emission reduction in the building sector. Despite the carbon emission of assembled structures being substantially lower than that of cast-in-place buildings due to the significant reduction in energy demand during the materialization process, there is still a lot of room for improvement. This study looks at the strategy choices made by manufacturers of prefabricated building components in relation to lowering carbon emissions from the standpoint of manufacturing prefabricated building components. By building a dynamic evolutionary game model between two parties, we investigate the evolutionary process of the strategy chosen by prefabricated building component manufacturers and the government, analyzing the evolutionary stability of each side’s strategy choice, and finally using Matlab tools to simulate the effectiveness of the evolutionary stability. The study findings indicate that (1) low-carbon production costs, local government incentives and sanctions, and corporate low-carbon production benefits are the main influencing factors for manufacturers of prefabricated building components to adopt low-carbon production techniques; and that (2) the cost of regulation under low-carbon production methods and the local government performance assessment system are the key elements affecting regulations by the local government. Based on this finding, we suggest corresponding countermeasures in three areas, including investigating new low-carbon technology options for businesses, developing a new carbon emission accounting subsidy mechanism, and improving the regulatory framework of the government, to provide an efficient pathway for the growth of a low-carbon economy.

As an important part of the prefabricated building standard system, the evaluation standards for prefabricated buildings play an important guiding role in the construction and development of prefabricated buildings. However, at present, the national evaluation standards for prefabricated buildings are highly extensive and general, and some of their contents are not well considered, while local standards based on the national standard have been formulated in accordance with the actual situation in their respective regions, which makes the evaluation of prefabricated buildings in various regions more confusing, which also causes differences when calculating the prefabrication rate, restricting the development of prefabricated buildings. In order to promote the construction of a complete evaluation standard system for prefabricated buildings in China, this paper combs through the methods used for the calculation of the prefabrication rate of prefabricated buildings in 28 provincial-level administrative regions in China, conducts a comparative analysis and empirical research on the calculation of the prefabrication rate under different standards in four dimensions, and applies t-tests and simulated annealing algorithms to optimize the projection pursuit model to analyze the differences in the total scores of the prefabrication rate under different standards, as well as the key factors that affect them. The study shows that (1) the same building may have different prefabrication rates under different standards for prefabricated buildings, and the way of calculating the prefabrication rate and the scoring criteria of the evaluation items will result in significant differences in the calculated prefabrication rate. (2) Depending on the value of the optimal projection direction vector, it was found that among the evaluation criteria, the enclosure wall and the internal partition wall have the greatest influence on the total score value, while the main structure has the least influence on the total score value. (3) There are similarities as well as differences in the evaluation criteria of prefabricated buildings in each province. On this basis, by analyzing the reasons for these differences, corresponding suggestions are made for governments needing to formulate or revise local standards.

There are problems with an inadequate quality assurance system and non-standard construction organization and administration while creating prefabricated buildings. There are currently fewer quality assessments employing prefabricated component combinations as the research focus, and the quality evaluation methodology is more subjective. We propose a method for evaluating the quality of prefabricated buildings using an ant colony algorithm and a simulated annealing algorithm to optimize the projection pursuit model: firstly, create a prefabricated building quality index system; secondly, questionnaires were distributed, tested for reliability and validity to avoid the influence of questionnaire subjectivity on the results, and structural equation modeling was used to calculate the weights of the quality influencing factors; thirdly, quantify the quality factors of prefabricated components by using the quality function development method, and construct a quality optimization model for the prefabricated component combinations; fourthly, use the ant colony algorithm to solve the quality optimization model to obtain a set of prefabricated component combinations to satisfy the quality requirements; and lastly, use a simulated annealing to optimize the projected pursuit method for evaluating the quality of prefabricated component combination solutions. The results show that (1) The use of optimization algorithms can successfully avoid the issue of a more subjective evaluation approach and increase the efficiency and accuracy of evaluation. (2) Residential Comfort (RC), Usage Durability (UD) and Structural Reliability (SR) have a substantially negative association, but Residential Comfort (RC) and Installation Stability (IS) have strong positive correlations. (3) Based on the magnitude of the vector of the ideal projection direction of the quality indicators, it was determined that the Installation Stability (IS) indicator had the greatest influence on the evaluation of the program, and the Structural Reliability (SR) indicator had the least influence on the program.