ADAPTING SUSTAINABILITY METRICS INTO TRADITIONAL MONITORING AND CONTROL SYSTEMS OF CONSTRUCTION PROJECTS: AN EARNED VALUE MANAGEMENT APPROACH

Integration of sustainability in construction projects control systems has emerged as an urgent need due to the growing pressure in the environment and the resources toward development of infrastructure projects globally. Whereas traditional Earned Value Management (EVM) has been embraced to track cost and schedule, the performance, it fails to integrate environmental and resource-efficiency indicators that are becoming more significant in affecting the project outcomes. This paper suggests a comprehensive EVM framework, which incorporates three sustainability measures of Carbon Emissions (CE), Energy Consumption (EC) and Material Waste (MW) in the project performance evaluation. The Multiple Linear Regression (MLR) model used, based on nine months of actual operational history of Malir Expressway project in Karachi, Pakistan which is an ongoing large-scale public infrastructure project. The findings indicate that the variables of sustainability play a significant role in determining the Cost Performance Index (CPI) and Schedule Performance Index (SPI). The suggested model enhances the decision-making process because the project stakeholders can monitor the financial and environmental aspects at the same time. Its methodology is practically applicable to the public infrastructure development and will be a base point to the future development of Sustainable Earned Value Management frameworks.

1. United Nations Environment Programme (UNEP). 2022 Global Status Report for Buildings and Construction. UNEP, 2022. URL: https://www.unep.org/resources/publication/2022-globalstatus-report-buildings-and-construction.

2. De Marco, A., & Narbaev, T. Earned value-based performance monitoring of facility construction projects. Journal of Facilities Management, 11(1), 69–80 (2013). https://doi.org/10.1108/14725961311301475.

3. Aramali, V., Gibson, G.E., el Asmar, M., & Cho, N. Earned Value Management System state of practice: Identifying critical subprocesses, challenges, and environment factors of a highperforming EVMS. Journal of Management in Engineering, 37(4), 04021031 (2021). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000925.

4. Koke, B., & Moehler, R.C. Earned Green Value management for project management: A systematic review. Journal of Cleaner Production, 230, 180–197 (2019). https://doi.org/10.1016/j.jclepro.2019.05.079.

5. Anbari, M.B., Moehler, R.C., & Gholamreza, S.F. Sustainability assessment in construction projects: A sustainable earned value management model under uncertain and unreliable conditions. Environment Systems and Decisions, 44(1), 45–68 (2024). https://doi.org/10.1007/s10669-023-09913-2.

6. Ottaviani, F.M., & De Marco, A. Multiple linear regression model for improved project cost forecasting. Procedia Computer Science, 196, 808–815 (2022). https://doi.org/10.1016/j.procs.2021.12.079.

7. İnan, T., Narbaev, T., & Hazır, Ö. A machine learning study to enhance project cost forecasting. IFACPapersOnLine, 55(10), 3286–3291 (2022). https://doi.org/10.1016/j.ifacol.2022.10.127.

8. Moshood, T.D., Rotimi, J.O.B., & Shahzad, W. Enhancing sustainability considerations in construction industry projects. Environment, Development and Sustainability (2024). https://doi.org/10.1007/s10668-02404946-2.

9. Chen, L., Huang, L., Hua, J., Chen, Z., Wei, L., Osman, A. I., Fawzy, S., Rooney, D.W., Dong, L., & Yap, P.S. Green construction for low-carbon cities: a review. Environmental Chemistry Letters, 21(3), 1627– 1657 (2023). https://doi.org/10.1007/s10311-022-01544-4.

10. Rajabi, S., El-Sayegh, S., & Romdhane, L. Identification and assessment of sustainability performance indicators for construction projects. Environmental and Sustainability Indicators, 15, 100193 (2022). https://doi.org/10.1016/j.indic.2022.100193.

11. Rajabi, S., El-Sayegh, S., & Romdhane, L. Project controls model for sustainable construction projects. Sustainability, 16(20), 9042 (2024). https://doi.org/10.3390/su16209042.

12. Koke, B., & Moehler, R. C. Earned Green Value management for project management: A systematic review. Journal of Cleaner Production, 230, 180–197 (2019). https://doi.org/10.1016/j.jclepro.2019.05.079.

13. Anbari, M.B., Moehler, R.C., & Gholamreza, S.F. Sustainability assessment in construction projects: A sustainable earned value management model under uncertain and unreliable conditions. Environment Systems and Decisions, 44, 45–68 (2024). https://doi.org/10.1007/s10669-023-09913-2.

14. Ottaviani, F.M., & De Marco, A. Multiple linear regression model for improved project cost forecasting. Procedia Computer Science, 196, 808–815 (2022). https://doi.org/10.1016/j.procs.2021.12.079.

15. Narbaev, T., & De Marco, A. An Earned Schedule-based regression model to improve cost estimate at completion. International Journal of Project Management, 32(6), 1007–1018 (2014). https://doi.org/10.1016/j.ijproman.2013.12.005.

16. Narbaev, T., Hazır, Ö., Khamitova, B., & Talgat, S. A machine learning study to improve the reliability of project cost estimates. International Journal of Production Research, 62(12), 4372–4388 (2024). https://doi.org/10.1080/00207543.2023.2262051.

17. Samoilov, A., Narbaev, T., Castelblanco, G., & Mukashev, Y. Evaluating public–private partnership dynamics: The Kazakhstan toll road case. Eurasian Journal of Economic and Business Studies, 68(1), 131–142 (2024). https://doi.org/10.47703/ejebs.v68i1.2024.131.

18. Samoilov, A., Osei-Kyei, R., Kussaiyn, M., Mamyrbayev, A., & Mukashev, Y. Cross-country comparison of risk factors in public–private partnerships in infrastructure development. Sustainability, 16(13), 5712 (2024). https://doi.org/10.3390/su16135712.

19. Yin, R. Case Study Research: Design and Methods. Sage Publications (2018).

20. Project Management Institute (PMI). Practice Standard for Earned Value Management (2nd ed.). PMI (2011).

21. Durdyev, S., Zavadskas, E. K., Thurnell, D., Banaitis, A., & Ihtiyar, A. Sustainable construction across developing countries: Status and future directions. Journal of Cleaner Production, 264, 121–134 (2020). https://doi.org/10.1016/j.jclepro.2020.121–134