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A DPSIR Framework to Evaluate and Predict the Development of Prefabricated Buildings: A Case Study

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  • Fanrong Ji

    (School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Zhaoyuan Luo

    (School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Xiancun Hu

    (School of Design and the Built Environment, University of Canberra, Canberra 2601, Australia)

  • Yunquan Nan

    (College of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China)

  • Aifang Wei

    (School of Design and the Built Environment, University of Canberra, Canberra 2601, Australia)

Abstract

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.

Suggested Citation

  • Fanrong Ji & Zhaoyuan Luo & Xiancun Hu & Yunquan Nan & Aifang Wei, 2023. "A DPSIR Framework to Evaluate and Predict the Development of Prefabricated Buildings: A Case Study," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14264-:d:1248644
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    References listed on IDEAS

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    1. Bartosz Radomski & Tomasz Mróz, 2021. "The Methodology for Designing Residential Buildings with a Positive Energy Balance—General Approach," Energies, MDPI, vol. 14(15), pages 1-16, August.
    2. Yinghui Song & Junwu Wang & Feng Guo & Jiequn Lu & Sen Liu, 2021. "Research on Supplier Selection of Prefabricated Building Elements from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 13(11), pages 1-24, May.
    3. Chaofeng Shao & Xiaogang Tian & Yang Guan & Meiting Ju & Qiang Xie, 2013. "Development and Application of a New Grey Dynamic Hierarchy Analysis System (GDHAS) for Evaluating Urban Ecological Security," IJERPH, MDPI, vol. 10(5), pages 1-25, May.
    4. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    5. Bartosz Radomski & Tomasz Mróz, 2021. "The Methodology for Designing Residential Buildings with a Positive Energy Balance—Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.
    6. He Wang & Yinqi Zhang & Weijun Gao & Soichiro Kuroki, 2020. "Life Cycle Environmental and Cost Performance of Prefabricated Buildings," Sustainability, MDPI, vol. 12(7), pages 1-19, March.
    7. Cavoli, Clemence, 2021. "Accelerating sustainable mobility and land-use transitions in rapidly growing cities: Identifying common patterns and enabling factors," Journal of Transport Geography, Elsevier, vol. 94(C).
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