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A Predictive Environmental Assessment Method for Construction Operations: Application to a Northeast China Case Study

Author

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  • Kailun Feng

    (Department of Construction Management, Harbin Institute of Technology, Harbin 150001, China
    Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, Sweden)

  • Weizhuo Lu

    (Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, Sweden)

  • Thomas Olofsson

    (Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, Sweden)

  • Shiwei Chen

    (Department of Construction Management, Harbin Institute of Technology, Harbin 150001, China
    Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, 97187 Luleå, Sweden)

  • Hui Yan

    (School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China)

  • Yaowu Wang

    (Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China)

Abstract

Construction accounts for a considerable number of environmental impacts, especially in countries with rapid urbanization. A predictive environmental assessment method enables a comparison of alternatives in construction operations to mitigate these environmental impacts. Process-based life cycle assessment (pLCA), which is the most widely applied environmental assessment method, requires lots of detailed process information to evaluate. However, a construction project usually operates in uncertain and dynamic project environments, and capturing such process information represents a critical challenge for pLCA. Discrete event simulation (DES) provides an opportunity to include uncertainty and capture the dynamic environments of construction operations. This study proposes a predictive assessment method that integrates DES and pLCA (DES-pLCA) to evaluate the environmental impact of on-site construction operations and supply chains. The DES feeds pLCA with process information that considers the uncertain and dynamic environments of construction, while pLCA guides the comprehensive procedure of environmental assessment. A DES-pLCA prototype was developed and implemented in a case study of an 18-storey building in Northeast China. The results showed that the biggest impact variations on the global warming potential (GWP), acidification potential (AP), eutrophication (EP), photochemical ozone creation potential (POCP), abiotic depletion potential (ADP), and human toxicity potential (HTP) were 5.1%, 4.1%, 4.1%, 4.7%, 0.3%, and 5.9%, respectively, due to uncertain and dynamic factors. Based on the proposed method, an average impact reduction can be achieved for these six indictors of 2.5%, 21.7%, 8.2%, 4.8%, 32.5%, and 0.9%, respectively. The method also revealed that the material wastage rate of formwork installation was the most crucial managing factor that influences global warming performance. The method can support contractors in the development and management of environmentally friendly construction operations that consider the effects of uncertainty and dynamics.

Suggested Citation

  • Kailun Feng & Weizhuo Lu & Thomas Olofsson & Shiwei Chen & Hui Yan & Yaowu Wang, 2018. "A Predictive Environmental Assessment Method for Construction Operations: Application to a Northeast China Case Study," Sustainability, MDPI, vol. 10(11), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3868-:d:178029
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    References listed on IDEAS

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    1. Yu Lay Langston & Craig Ashley Langston, 2008. "Reliability of building embodied energy modelling: an analysis of 30 Melbourne case studies," Construction Management and Economics, Taylor & Francis Journals, vol. 26(2), pages 147-160.
    2. P. D. Hiwase & N. S. Raman & H. V. Hajare, 2018. "Critical factor of large township building and its relevance in environmental audit: a critical evaluation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(1), pages 433-449, February.
    3. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).
    4. Diana Carolina Gámez-García & José Manuel Gómez-Soberón & Ramón Corral-Higuera & Héctor Saldaña-Márquez & María Consolación Gómez-Soberón & Susana Paola Arredondo-Rea, 2018. "A Cradle to Handover Life Cycle Assessment of External Walls: Choice of Materials and Prognosis of Elements," Sustainability, MDPI, vol. 10(8), pages 1-24, August.
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    Cited by:

    1. Kailun Feng & Weizhuo Lu & Shiwei Chen & Yaowu Wang, 2018. "An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming," Sustainability, MDPI, vol. 10(11), pages 1-23, November.
    2. Antonia Rahn & Kai Wicke & Gerko Wende, 2022. "Using Discrete-Event Simulation for a Holistic Aircraft Life Cycle Assessment," Sustainability, MDPI, vol. 14(17), pages 1-31, August.

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