Green energy project management: applying industry-specific risk assessment models

Authors

DOI:

https://doi.org/10.47703/ejebs.v68i2.406

Keywords:

Economics and Business, Project Management, Risk Management, Green Energy Projects, Project Efficiency, PMBoK, Kazakhstan

Abstract

By reducing reliance on fossil fuels, green energy projects mitigate climate change by lowering carbon dioxide and other greenhouse gas emissions. They push governments and society to transition to renewable energy production by implementing high-risk green energy projects more effectively. This study evaluates how risk management processes affect the efficiency of green projects in Kazakhstan, identifying critical risk management processes that can increase their success. The methodology is based on data collected from 66 experts in Kazakhstan's green energy sector. Using multilinear regression analysis, the Project Management Body of Knowledge (PMBOK) standard was applied to evaluate the relationship between risk management processes and project efficiency dimensions. The findings show a positive correlation between cost overrun and project performance with the implementation of risk management processes. The statistical significance levels underscore the importance of these findings. The lack of statistical significance for schedule overrun, combined with the low rate of qualitative risk analysis and monitoring among local managers, highlights a deficiency in proactive risk management, leaving projects vulnerable to adverse impacts. These findings impact project management professionals and organizations involved in sustainable energy initiatives, providing valuable insights to enhance their risk management processes. This study paves the way for future research by adding more respondents and using other risk analysis methods, opening new avenues for improving risk management in green energy projects.

How to Cite

Kozhakhmetova, A., & Anarkhan, A. (2024). Green energy project management: applying industry-specific risk assessment models. Eurasian Journal of Economic and Business Studies, 68(2), 153–163. https://doi.org/10.47703/ejebs.v68i2.406

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Published

2024-06-30