Determining Cost and Causes of Overruns in Infrastructure Projects in South Asia

Jelena M. Andrić (), Shuangyu Lin, Yuan Cheng and Bin Sun
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Jelena M. Andrić: Department of Civil Engineering, Design School, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Shuangyu Lin: Department of Civil Engineering, Design School, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Yuan Cheng: Department of Financial and Actuarial Mathematics, School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
Bin Sun: Department of Civil Engineering, Design School, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

Sustainability, 2024, vol. 16, issue 24, 1-22

Abstract: Sustainable development is supported by infrastructure projects that have a long-term impact on economic development, societies, and the environment. In this paper, the aim is to estimate the cost performance, investigate the best-fit function for modeling the correlations between cost overruns and three variables, and identify the root causes of overruns in South Asian infrastructure projects. In the past, linear regression analysis has been utilized to model the correlations between cost overruns and project size, implementation period, and time overruns. Modeling these correlations requires the study and application of other regression functions. A database of 138 infrastructure projects from the South Asian region is established from the collected data. A methodology based on mixed methods for qualitative and quantitative data analysis is developed to achieve the aims of the paper. A mixed method encompasses a probabilistic and statistical approach alongside machine learning as quantitative methods and employs content analysis facilitated by NVivo v. 11 software as a qualitative method. Based on the results, the average cost overrun in infrastructure projects in South Asia is 3.3%. The random forest regression function, a machine learning technique, is tested as the most suitable function for modeling the impact between cost overruns and other variables compared to the linear and quadratic regression functions. The practical application is to support project stakeholders in the process of cost estimation during the decision-making phase of the project, to predict overruns in future infrastructure projects using machine learning techniques such as random forest regression, and to contribute to overall sustainable development.

Keywords: cost overruns; sustainable development; infrastructure; mixed methods; probabilistic and statistical approach; machine learning; content analysis; NVivo; South Asia (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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