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Risk Assessment Models to Improve Environmental Safety in the Field of the Economy and Organization of Construction: A Case Study of Russia

Author

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  • Arkadiy Larionov

    (Department of Economics and Management in Construction, Moscow State University of Civil Engineering, 129337 Moscow, Russia)

  • Ekaterina Nezhnikova

    (Department of National Economy, Peoples’ Friendship University of Russia (RUDN University), 117198 Moscow, Russia)

  • Elena Smirnova

    (Department of Technosphere Safety, Saint Petersburg State University of Architecture and Civil Engineering, 190005 Saint Petersburg, Russia)

Abstract

This article assesses risks in order to substantiate the economic and organizational efficiency of housing and industrial construction. This topic is relevant because it is necessary for sustainable development. In Russia, environmental safety in construction and housing, as well as communal services, is poorly developed and not regulated by the legal system. As building construction, housing, and communal services should be based on environmental safety, this topic requires rapid development. Methods related to quantifying environmental risk and making decisions under conditions of uncertainty were studied. A quantitative risk assessment was performed using the Monte Carlo method for pessimistic and optimistic options to prevent environmental damage. The model reproduced the distribution derived from the evidence-based fit. The results of sensitivity analysis are also presented to prove the hypothesis. The selection of the most appropriate probability density functions for each of the input quantities was implemented through settings in a computer program. The simulation modeling results clearly illustrate the choice of the general principle of assessment and the adoption of the optimal decision. In conditions of uncertainty, the decision to choose the optimistic options with high cost (to maintain the reliability of the technical system) but less risk plays a decisive role in the future environmental safety strategies of construction projects. The Monte Carlo method is preferable for environmental impact assessments. In the future, the amended methodology can be applied to raise environmental safety in the field of construction.

Suggested Citation

  • Arkadiy Larionov & Ekaterina Nezhnikova & Elena Smirnova, 2021. "Risk Assessment Models to Improve Environmental Safety in the Field of the Economy and Organization of Construction: A Case Study of Russia," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13539-:d:697060
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    References listed on IDEAS

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    1. Guoyu Chen & Yong Wan & Huanhuan Lin & Houlin Hu & Guixiong Liu & Yanhua Peng, 2021. "Vertical tank capacity measurement based on Monte Carlo method," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-18, April.
    2. Eugene A. Rosa, 1998. "Metatheoretical foundations for post-normal risk," Journal of Risk Research, Taylor & Francis Journals, vol. 1(1), pages 15-44, January.
    3. Stanley Kaplan & B. John Garrick, 1981. "On The Quantitative Definition of Risk," Risk Analysis, John Wiley & Sons, vol. 1(1), pages 11-27, March.
    4. Roy L. Smith, 1994. "Use of Monte Carlo Simulation for Human Exposure Assessment at a Superfund Site," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 433-439, August.
    5. Stoica, George, 2006. "Relevant coherent measures of risk," Journal of Mathematical Economics, Elsevier, vol. 42(6), pages 794-806, September.
    6. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
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    Cited by:

    1. Nasanjargal Erdenekhuu & Balázs Kocsi & Domicián Máté, 2022. "A Risk-Based Analysis Approach to Sustainable Construction by Environmental Impacts," Energies, MDPI, vol. 15(18), pages 1-21, September.
    2. Andrea Senova & Alica Tobisova & Robert Rozenberg, 2023. "New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    3. Sabriye Topal & Emine Atasoylu, 2022. "A Fuzzy Risk Assessment Model for Small Scale Construction Work," Sustainability, MDPI, vol. 14(8), pages 1-17, April.

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