4.8 Article

A holistic risk management framework for renewable energy investments

Journal

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 160, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2022.112305

Keywords

Renewable energy investment risks; Renewable energy risk assessment methods; Multicriteria decision analysis; System dynamics; Agent-based modelling; Monte Carlo simulation; Developing countries; Sub-sahara africa countries; Holistic risk management framework; Analytical hierarchy process; Analytical network process

Funding

  1. Petroleum Trust Development Fund (PTDF) Nigeria

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This paper reviews risk assessment and mitigation methods for renewable energy projects in developing countries, with a focus on Sub-Saharan African countries. The study found that both semi-quantitative and qualitative methods were used for risk analysis, with a focus on technical and economic risks. Additionally, a comprehensive investor risk management framework was introduced.
Private investments are critical enablers to achieving energy access for over 770 million people worldwide. Despite decreasing capital costs, investments in renewable energy (RE) projects in developing countries are low due to unattractive risk-return profiles. Through understanding key risks drivers and their interactions, actionable insights can be drawn to mitigate investment risks, making energy more accessible. This paper reviews RE risks and methods used for risk assessment and mitigation for developed and developing countries with a focus on Sub-Saharan Africa countries (SSA). The review finds that while risk analysis and evaluation mainly employed semi-quantitative multicriteria decision analysis (MCDA) and system dynamics (SD) methods for developing countries, qualitative methods were used to identify mitigations. The methods assessed technical and economic risks at a minimum, while MCDA and SD methods can assess social, political, and policy risks. The efficacies of mitigations were tested using SD and quantitative methods such as agent-based modelling and Monte Carlo simulation. The paper further introduces a 'holistic multi-dimensional investor risk management framework' which can be used to identify actions to improve investment risks in a structured manner. The framework addresses four fundamental limitations observed in the existing literature, recognising that RE risks are complex and involve multidisciplinary perspectives having interactions and feedbacks with other risks, actors, and their actions. This review provides a valuable reference to investors, policymakers, and researchers, providing a catalogue of risks, methods deployed in literature, including a framework to identify impactful actions to improve risk levels.

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