Integrating Machine Learning for Risk Assessment in Renewable Energy Investments in Developing Economies

Renewable energy investments are critical for addressing energy poverty and driving sustainable development in developing economies. However, these investments face significant challenges, including economic volatility, political instability, and environmental complexities, which hinder their successful implementation. This study examines the role of machine learning (ML) models as innovative tools for assessing and mitigating risks associated with renewable energy investments. The study's objectives include analysing the multifaceted risks in renewable energy investments, evaluating the effectiveness of ML techniques, such as predictive analytics, classification models, and neural networks, in risk assessment, and proposing strategies to facilitate their integration into decision-making processes. A qualitative research methodology was adopted, utilizing a comprehensive desk review of existing literature. The findings reveal that ML models enhance the accuracy and efficiency of risk assessments by providing advanced predictive capabilities, improving decision-making, and addressing the complexities of economic, political, and environmental uncertainties. Despite their potential, challenges such as data quality issues, technological barriers, and limited expertise in developing economies remain significant hurdles. The study recommends policy reforms to support ML adoption, targeted capacity-building initiatives to improve ML and data science, and strategic collaborations among governments, private sectors, and international organizations.