 Green investment and asset stranding under transition scenario uncertaintyMaria Flora and Peter Tankov Energy Economics, 2023, vol. 124, issue C Abstract: Risks and opportunities related to environmental transition are usually evaluated through the use of scenarios, produced and maintained by international bodies such as the International Energy Agency. This approach assumes perfect knowledge of the scenario by the agent, but in reality, scenario uncertainty is an important obstacle for making optimal investment or divestment decisions. In this paper, we develop a real-options approach to evaluate assets and potential investment projects under dynamic climate transition scenario uncertainty. We use off-the-shelf Integrated Assessment Model (IAM) scenarios and assume that the economic agent acquires the information about the scenario progressively by observing a signal, such as the carbon price or the greenhouse gas emissions. The problem of valuing an investment is formulated as an American option pricing problem, where the optimal exercise time corresponds to the time of entering a potential investment project or the time of selling a potentially stranded asset. To illustrate our approach, we employ representative scenarios from the scenario database of the Network for Greening the Financial System in two energy-related examples: the divestment decision from a coal-fired power plant without Carbon Capture and Storage (CCS) technology and the potential investment into a green coal-fired power plant with CCS. In both cases, we find that the real option value is very sensitive to scenario uncertainty: the value of the coal-fired power plant is reduced by 25% and that of the green coal investment project is reduced by 7% when the agent deduces the scenario by observing carbon emissions, compared to the setting when the true scenario is known. We also find that scenario uncertainty can lead to considerable delays in the implementation of green investment projects, emphasizing the importance of timely and precise climate policy information. Keywords: Transition risk; Scenario uncertainty; Bayesian learning; Stranded asset; Real options (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:124:y:2023:i:c:s0140988323002712 DOI: 10.1016/j.eneco.2023.106773 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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