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A DPSIR Framework to Evaluate and Predict the Development of Prefabricated Buildings: A Case Study
Summary
This paper is not directly about microplastics — it applies the DPSIR (Drivers-Pressures-States-Impacts-Responses) framework combined with TOPSIS and grey prediction models to evaluate and forecast the development of prefabricated building construction in China's Shandong province.
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.
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