A Comprehensive Framework for Evaluating and Predicting Investment Risks in Renewable Energy Projects: A Case Study of Maynak Hydropower Station

Abstract

Abstract This study introduces a holistic analysis framework designed to evaluate and predict the investment risks associated with foreign renewable energy initiatives. The primary objective of this framework is to address the inherent uncertainties that often accompany such projects. To achieve this, we employ the variable weight matter-element extension model to establish the project's fundamental reliability function. Subsequently, we enhance this model using evidence theory to determine the project's risk level and generate risk index measurement results. Additionally, we utilize the GM model for forecasting future project risks. To illustrate the practicality of our approach, we provide a case study focused on the risk assessment and prediction for the Maynak Hydropower Station. Our findings indicate that during 2008, 2014, 2020, and 2022, the project faced a high level of investment risk. Key risk indicators included political instability, policy changes, legislative gaps, cultural risks, exchange rate fluctuations, technical challenges, and management risks. Moreover, from 2023 to 2027, the project's investment risk level moderated, with risk measurement results aligning closely with actual circumstances, thus validating the efficacy and applicability of our model.