Explore the vast range of titles available.

This paper explores the uncertainty of expected returns by adopting the real options analysis method for the financial evaluation of renewable energy projects in Brazil. Energy transition is key to meeting climate targets, and real options analysis can play a pivotal role in evaluating renewable energy projects to meet those targets. The impact of the volatility of the chosen variables on the viability of the project is studied using Monte Carlo simulation in the R software. The results indicate that the lower the option value the higher the volatility of the project, leading to lower likelihood of the project being financed. The resulting model represents a simple instrument that can be incorporated in larger modelling frameworks (e.g., agent-based simulation) to assess the impact of real option analysis on different markets and environmental and socio-political conditions. These findings represent a strong case for the adoption of systems modelling to inform policy to support global energy transition, as the application of this method can make a renewable energy project financially more attractive in comparison to those relying on carbon intensive energy sources.

Incorporating debt in a dynamic real options framework, we show that underinvestment stems from truncation of equity's horizon at default. Debt overhang distorts both the level and composition of investment, with underinvestment being more severe for long-lived assets. An empirical proxy for the shadow price of capital to equity is derived. Use of this proxy yields a structural test for debt overhang and its mitigation through issuance of additional secured debt. Using measurement error-consistent GMM estimators, we find a statistically significant debt overhang effect regardless of firms' ability to issue additional secured debt.

Technology valuation, especially in the early growth stages of New TechnologyBased Firms (NTBFs), is one of the most critical challenges that most often hinders investors and entrepreneurs' deals in the Venture Capital (VC) financing process. It is clear that uncertainties arising from the likelihood of implementing public policies could significantly affect the volatility of NTBFs' cash flows in the field of cleaner production. Commonly, these types of technologies require public supportive policies for achieving success. Consequently, technology valuation is more challenging and traditional valuation methods are not suitable anymore because of the definitive assumption of cash flow and disregard for investors' flexibility and uncertainty. Therefore, this study proposes a method to perform the technology valuation of firms during all their growth stages by introducing a framework based on decision tree and real options analysis. Furthermore, unlike previous papers that have utilized the compound options, the option to choose approach was used to consider investors' flexibility. Then, the proposed framework was supported by a case study, which was conducted to verify and validate it. Finally, the conclusion section discusses the contributions and limitations of the study and provides directions for future research.

Adaptation decision‐scientists increasingly use real‐option analysis to consider the value of learning about future climate variable development in adaptation decisions. Toward this end learning scenarios are needed, which are scenarios that provide information on future variable values seen not only from today (as static scenarios), but also seen from future moments in time. Decision‐scientists generally develop learning scenarios themselves, mostly through time‐independent (stationary) or highly simplified methods. The climate learning scenarios thus attained generally only poorly represent the uncertainties of state‐of‐the‐art climate science and thus may lead to biased decisions. This paper first motivates the need for learning scenarios by providing a simple example to illustrate characteristics and benefits of learning scenarios. Next, we analyze how well learning scenarios represent climate uncertainties in the context of sea level rise and present a novel method called direct fit to generate climate learning scenarios that outperforms existing methods. This is illustrated by quantifying the difference of the sea level rise learning scenarios created with both methods to the original underlying scenario. The direct fit method is based on pointwise probability distributions, for example, boxplots, and hence can be applied to static scenarios as well as ensemble trajectories. Furthermore, the direct fit method offers a much simpler process for generating learning scenarios from static or “ordinary” climate scenarios. Plain Language Summary Many climate change adaptation decisions require large investments in infrastructure (e.g., dikes), while at the same time future projections about critical variables (e.g., sea level rise) are highly uncertain. Decision‐scientists address these challenges with methods based on flexibility and staged decision‐making. For example, a coastal decision‐maker could implement a dike with a wider foundation, and, if necessary, upgrade the dike height in the future. The decision‐maker will learn by observing future sea level rise if higher dike protection levels are actually necessary in the future. In order to assess whether it is economically beneficial to wait for future learning through observations, and thus to justify additional expenses for flexible infrastructure investments, learning scenarios are required. Learning scenarios provide projections of critical variables seen from today and from future moments in time. For example, learning scenarios of sea level rise contain sea level rise projections seen from 2050 onward, depending on a certain amount of sea level rise observed until 2050. In this paper, we provide a simple example to illustrate coastal decision‐making with a learning scenario, propose a new method to generate learning scenarios, and apply this method to generate sea level rise learning scenarios. Key Points We show how climate learning scenarios can be applied for improving and justifying investments in flexible long‐lasting infrastructure We develop sea level rise learning scenarios based on Intergovernmental Panel on Climate Change sixth Assessment Report using a novel method termed direct fit Our new method reduces the average deviation of learning scenarios from the original data by 83% compared to standard methods

Adaptive decision‐making allows decision‐makers to plan long‐term coastal infrastructure under uncertain sea level rise projections. To date, economic assessments of adaptive decision‐making that take into account future learning about sea level rise uncertainty are rare and the existing ones have relied on simple quantification of future learning not validated against sea level science. To address this gap, we develop an economic adaptive decision‐making framework that takes into account future learning about sea level rise uncertainty and apply it to a coastal case study in Lübeck, Germany, to answer the question of how adaptation to sea level rise can be improved through adaptive adaptation pathways as opposed to non‐adaptive pathways. To address this question, we use a Markov decision process to formulate the stochastic optimization problem. We quantify future learning about sea level rise uncertainty through sea level rise learning scenarios based on and validated against the latest scenarios of the Intergovernmental Panel on Climate Change. Our case study results show that the city of Lübeck is currently under‐protected against storm surges and that immediate adaptation actions are advisable in the face of future sea level rise. We find that adaptive adaptation pathways, in contrast to non‐adaptive pathways, generate sea level rise thresholds for adaptation actions that are similar across climate change scenarios and can reduce expected costs up to 1.8%. Plain Language Summary Climate change is causing sea levels to rise as land ice melts under higher air temperatures. By 2100, sea levels are projected to rise between 28 and 101 cm, depending on the extent of future global warming. This will increase the risk of coastal flooding in the future and require adaptation actions. Planning for coastal adaptation to sea level rise is challenged by long‐lived protective infrastructure and uncertain projections of sea level rise. Adaptive decision‐making methods that specifically incorporate future learning about the uncertainty of sea level rise can address this challenge. For example, observing 30 cm of sea level rise in 2060 will lead to different projections from 2050 onwards and require different adaptation actions than observing 70 cm of sea level rise in 2060. To date, economic adaptation studies that account for future learning about sea level rise uncertainty are rare, and those that do exist have relied on simple methods. We develop a decision framework to address this research gap and apply it to the city of Lübeck on the Baltic Sea in Germany. Our results show that the city of Lübeck is currently under‐protected against storm surges and that immediate adaptation actions are advisable from an economic perspective. Key Points We develop adaptive adaptation pathways that incorporate learning about sea level rise uncertainty based on future observations Adaptive adaptation pathways generate sea level rise thresholds for adaptation actions that can be similar across climate change scenarios Adaptive adaptation pathways can reduce expected costs compared to non‐adaptive pathways by up to 1.8% in our study

For adaptation to the changing climate, planning of new water infrastructures should be carefully evaluated by either “robust” or “adaptive” decision making methods. For this purpose, a new economic feasibility analysis framework has been developed using real option analysis that can reflect “robust” and “adaptive” perspectives in decision making. To reflect uncertainty in climate (“robust”), the probabilities of drought occurrences are estimated by the results of dam storage simulation. To provide flexibility in decision making (“adaptive”), three different types of real options are used as a form of a decision tree. By re-evaluating economic feasibility of the Boryeong Dam conduit project, it is found that the “abort” option can be the best choice for minimal economic loss on the project. Further, more conditions for maximizing economic feasibility on the project are addressed from the sensitivity analysis. It is found that the “invest” option would be more economically feasible than “abort” option, when the probability of severe drought increases by approximately 20%. Thus, though the Boryeong Dam conduit project is not economically feasible for now, it might be an appropriate infrastructure if it is constructed in the future, when the probability of drought occurrence increases.

With concerns regarding global climate change increasing, recent studies on adapting to nonstationary climate change recommended a different planning strategy that could spread risks. Uncertainty in global climate change should be considered in any decision-making processes for flood mitigation strategies, especially in areas within a monsoon climate regime. This study applied a novel planning method called real option analysis (ROA) to an important water resources planning practice in Korea. The proposed method can easily be applied to other watersheds that are threatened by flood risk under climate change. ROA offers flexibility for decision-makers to reflect uncertainty at every stage during the project planning period. We successfully implemented ROA using a binomial tree model, including two real options—delay and abandon—to evaluate flood mitigation alternatives for the Yeongsan River Basin in Korea. The priority ranking of the four alternatives between the traditional discount cash flow (DCF) and ROA remained the same; however, two alternatives that were assessed as economically infeasible using DCF, were economically feasible using ROA. The binomial decision trees generated in this study are expected to be informative for decision-makers to conceptualize their adaptive planning procedure.

The traditional net present value approach to investment in microgrid assets does not take into account the inherent uncertainties in fuel prices, cost of technology, and microgrid load profile. We propose a real option approach to microgrid investment, which includes solar photovoltaic (PV) and gas-fired generation assets. Likewise the (n, m) exchange literature in real option analysis, we examine cases with interdependency and independency of fuel price and the cost of PV technology. This work, however, makes a major contribution by the way of introducing a new parameter, which is defined as the elasticity of the option value to prices and is used in the formulation of closed form solutions. We further extend the (1, 1) exchange problem here to include operational flexibility of microgrid, such that optimal switching between investment, suspension and re-activation can be examined.

As decarbonisation progresses and conventional thermal generation gradually gives way to other technologies including intermittent renewables, there is an increasing requirement for system balancing from new and also fast-acting sources such as battery storage. In the deregulated context, this raises questions of market design and operational optimisation. In this paper, we assess the real option value of an arrangement under which an autonomous energy-limited storage unit sells incremental balancing reserve. The arrangement is akin to a perpetual American swing put option with random refraction times, where a single incremental balancing reserve action is sold at each exercise. The power used is bought in an energy imbalance market (EIM), whose price we take as a general regular one-dimensional diffusion. The storage operator's strategy and its real option value are derived in this framework by solving the twin timing problems of when to buy power and when to sell reserve. Our results are illustrated with an operational and economic analysis using data from the German Amprion EIM.

Research summary: When war occurs in a country, some foreign multinational enterprises (MNEs) stay on, while others flee. We argue that MNE responses to external threats depend on the firm's vulnerability, which we decompose into exposure (proximity to threat), at-risk resources (potential for loss), and resilience (capacity for coping). We test the independent and interactive effects of these dimensions using a geo-referenced sample of 1,162 MNE subsidiaries in 20 war-afflicted countries between 1987 and 2006. We find that highly valuable resources can become liabilities when exposed to harm, and the best way to cope with external threats may be to exit. Our findings extend the resource-based view and real options theory by demonstrating the bounded value of resources and options in the face of environmental contingencies. Managerial summary: A recent survey of multinational enterprise (MNE) executives revealed that 30 percent of the respondents believed that their firms were exposed to collateral damage from war, with more than 90 percent expecting risks to rise. Yet, 25 percent of the executives indicated that their firms had no continuity plan. Our study of MNEs in war-afflicted countries highlights the costs of not having a response strategy in place. We find that, in war zones, otherwise highly valuable locations and resources can become sources of vulnerability that prompt early withdrawal from a host country. Our work further highlights the value of real options thinking—where structural solutions such as building redundancy into a portfolio of options may exist in advance of problems—for navigating hostile environments.