Prefabricated building construction is an important method of enhancing construction productivity and promoting sustainable development in the construction industry. Evaluating and predicting the development performance of prefabricated buildings will contribute to identifying and implementing the most effective responses to promote prefabricated building technologies. Based on the Drives–Pressures–States–Impacts–Responses (DPSIR) framework, 14 evaluation indexes are determined to evaluate the development level of prefabricated buildings. The entropy weight method was used to determine the weight of the evaluation index, and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method with improved grey correlation was applied to comprehensively evaluate the trend of the index. The grey model (GM(1,1)) was developed to predict the development trend of prefabricated buildings. The development of prefabricated buildings in Shandong province, China, is employed as a case to apply the developed method and investigate development experiences. The results demonstrate that the case has achieved significant progress and has great potential in promoting the use of prefabricated buildings. The development recommendations include developing a policy and regulation system, strengthening a prefabricated building talent pool, and enhancing the investment in technological innovation. This study innovatively formulated the evaluation and prediction system based on the DPSIR, TOPSIS and GM(1,1) models, which could be used for evaluating development performance between social and environmental factors among various cause-effect relationships.

Purpose: This paper presents a developed spatial-temporal analysis framework for the case of investigating the business performance of construction consultancy services (CCS) in China.Design/methodology/approach: The spatial-temporal analysis is based on the data envelopment analysis (DEA) technique. The spatial analysis follows the DEA results under a contemporaneous benchmark technology and a virtual decision-making unit, consisting of ranking analysis, cluster analysis and variation analysis. The temporal analysis is reliant on the DEA results under a global benchmark technology and the time value of money, including trend analysis and driving force analysis containing pure technical and scale efficiency factors.Findings: Three CCS types in China are investigated, including engineering survey and design, construction supervision and procurement agency. The performance rank order and cluster classifications are mainly related to economic development levels. Engineering survey and design demonstrates the best performance and higher imbalances; however, construction supervision and procurement agency illustrate lower performance and imbalances. Scale efficiency significantly promotes business performance, whereas pure technical efficiency plays an inconspicuous role.Practical implications: The CCS promote technical efficiency by developing their service and innovation levels. The service of engineering survey and design registered in Beijing, Shanghai and Guangdong is recommended for entering the service market in China.Originality/value: The spatial-temporal analysis framework was developed, which is generic and provides a pathway to measure, compare and assess performance comprehensively. The CCS business performance is firstly measured.

This research presents a comprehensive analysis of green development policies in the building industry in New South Wales (NSW), Australia, examining their evolution and development over the past two decades. The research adopts a structured methodology comprising a policy review to identify relevant policy documents, content analysis to trace the policy framework, and SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis to evaluate the policy development, and then generate evidence-based recommendations. As the first comprehensive assessment of green development policy in the Australian building industry, the study proposes targeted policy recommendations based on analyzing the SWOT factors, including policy for the non-residential sectors and construction phase, education and training, financial support and incentives, and innovation and technology adoption. The insights offer guidance for policymakers to strengthen policy integration and accelerate the transition toward a low-carbon building industry.