Green investments rely crucially on government support, however, the absence of a clear policy framework increases uncertainty in revenue streams. This poses a formidable challenge to firms that must typically determine both the optimal time of investment and the size of a project in the form of installed capacity. For capital intensive projects, such as renewable energy (RE) power plants, such decisions entail considerable risk, since, by installing a large capacity, it may not be possible to recover the investment cost in the case of an unexpected downturn, whereas by installing a small capacity, revenues could be forgone if market conditions suddenly become favourable. Additionally, the inability to contract an investment project after its initial installation due to high cost makes the investment timing and capacity sizing decisions even more crucial. Therefore, we develop an analytical framework in order to determine how such decisions are affected by price and policy uncertainty, in the form of random introduction or retraction of a support scheme, assuming that a project can be completed in either a single or multiple stages. This situation is relevant for both on-- and offshore wind park development, where an area can, and often is, developed in stages. Although the impact of policy uncertainty on investment decisions has been analysed from the perspective of carbon prices and the random introduction of a policy scheme (Blyth et al. 2007; Boomsma and Linnerud 2014), the implications of repeated provisions and retractions of a support scheme on both the optimal investment timing and capacity sizing decisions as well as the optimal investment strategy have not been analysed thoroughly yet. Additionally, while stepwise investment is more preferable than lumpy investment when a firm has discretion over capacity (Chronopoulos et al., 2014), whether the introduction of a subsidy mitigates this effect remains an open question.
Examples that indicate the impact of policy uncertainty on investment and operational decisions are increasing as the structural transformation of the power sector continues. For instance, uncertainty in the introduction of a support scheme delayed more than half of a series of wind power plants in the UK, that had originally been scheduled for operation by March 2016 (The Telegraph, 2013), as well as a $509 million wind farm by AGL Energy Ltd., Australia's largest developer of RE projects (Bloomberg, 2013). Also, in Spain, uncertainty regarding the timing and the size of the reduction in feed-in-tariffs has increased downside risk considerably for both existing and new investors (The Economist, 2011). In addition, the absence of a clear policy framework has also reduced the growth in RE capacity and projections indicate that this reduction will continue over the next years unless policy uncertainty is reduced (IEA, 2014). Despite the crucial impact of policy uncertainty on the evolution of RE projects, its implementation in analytical frameworks for stepwise investment and capacity sizing has been limited, and, therefore, models for predicting the level of RE investment remain underdeveloped. Indeed, although uncertainties for commodities such as electricity, natural gas, and oil are reasonably well known, those pertaining to RE technologies, climate change, and regulatory risk are less well understood. For example, learning curves are necessary to model efficiency improvements in existing technologies, yet may be less well specified for the development of RE technologies, that evolve through several stages, and, therefore, their future development path is likely to be different from their progress in the past (Jamasb and Kohler, 2008). We address this disconnect by assuming that a firm has discretion over both the time of investment and the size of the project and that it can adopt a lumpy or a stepwise investment strategy in the light of random provision or retraction of a support scheme. The latter takes the form of a fixed premium on top of the electricity price, and, as a result, the firm is subject to electricity price uncertainty as is the case with one of the widely implemented support schemes, namely premium feed-in tariff. This policy mechanism has been introduced, for example, in Spain and Portugal, yet, after the financial crisis, tariff levels have been subject to frequent reductions at random points in time. In turn, this has had crucial implications for the viability of private firms. For example, Iberdrola, Spain's biggest power group, reported a 13% decline in profits following a reform of the energy sector that aimed at reducing the tariff deficit (Financial Times, 2014a). Such tariff cuts were also implemented in Portugal, as part of the wider cuts in financial support affecting all electricity producers, in order to reduce the deficit in the generation sector (Wind Power, 2012). Similarly, subsidy cuts in the UK for solar photovoltaic may not only delay the point at which solar could be cost competitive, but also damage broader investor confidence and affect the progress with both deployment and cost reductions (The Guardian, 2015a). Consequently, the contribution of this paper is threefold. First, we develop an analytical framework for stepwise investment under price and policy uncertainty. Second, we analyse how price and policy uncertainty interact to affect the optimal investment timing and capacity sizing decisions as well as the relative value of the two investment strategies, i.e., stepwise and lumpy. Finally, we provide managerial and policy insights based on analytical and numerical results. More specifically, we illustrate how the random provision or retraction of a subsidy impacts not only the time of investment and the size of a project, but also the choice of investment strategy, in terms of lumpy versus stepwise investment. Thus, we derive insights on how policies may be designed not only to incentivise investment in RE projects but also to ensure that the level of investment promotes the viability of decarbonisation targets.
We proceed in Section 2 by discussing some related work. In Section 3, we introduce assumptions and notation and formulate the investment problem under each strategy, i.e., lumpy and stepwise investment, as an optimal stopping-time problem. In Section 4, we analyse the benchmark case of investment and capacity sizing without policy uncertainty and then extend it in Sections 5.1 and 5.2 by allowing for the sudden retraction or provision of a subsidy, respectively. In Section 5.3, we analyse the case of sudden provision of a retractable subsidy, and, in Section 5.4, we allow for infinite provisions and retractions. Section 6 provides numerical examples for each case and illustrates the interaction between price and policy uncertainty in order to enable more informed investment, capacity sizing, and policy decisions. Section 7 concludes the paper and offers directions for further research.
Despite the extensive literature that illustrates the amenability of real options theory to the energy sector (Lemoine, 2010; Rothwell, 2006), analytical formulations of problems that address investment in RE projects typically do not combine crucial features such as policy uncertainty, discretion over capacity, or flexibility for stepwise investment. An empirical approach for analysing the impact of regulatory risk on investment in generation facilities is presented in Walls et al. (2007). They consider regulatory uncertainty with respect to both the timing and pace of restructuring of electricity markets, and find that power plant investment is higher in states that have restructured electricity markets than in states that have taken no restructuring actions. Additionally, they find that greater uncertainty increases the incentive to choose power plant types with lower capital to generating capacity ratios. Blyth et al. (2007) and Kettunen et al. (2011) analyse how a firm's investment propensity is affected by uncertainty in carbon prices. The former find that carbon price uncertainty creates a risk premium for power generation and that the option to retrofit CCS may accelerate investment in a coal power plant, while the latter use a multistage stochastic optimization model and demonstrate how real options valuation yields substantially different results regarding investment propensities compared to conventional economic analysis.
Linnerud et al. (2014) examine how uncertainty in the introduction of RE certificates affects the timing of investments. Their results indicate that while investors with a portfolio of licences act in line with real options theory, i.e., policy uncertainty delays investment rates, investors with a single license act in line with the traditional NPV approach. Boomsma and Linnerud (2014) analyse how investment incentives are affected by the likely termination or revision of a support scheme allowing for electricity and subsidy prices to follow correlated geometric Brownian motions. Their results indicate that, expectations that a support scheme may be terminated, delay investment if it is applied retroactively, but may facilitate investment otherwise. While the aforementioned papers address the impact of various forms of policy uncertainty on a firm's propensity to invest, they ignore both discretion over capacity as well as the flexibility for stepwise investment.
Examples of early work in the area of sequential investment include Majd and Pindyck (1987), who show how traditional valuation techniques understate the value of a project by ignoring the flexibility embedded in the time to build, and Dixit and Pindyck (1994), who develop a sequential investment framework assuming that the project value depreciates exponentially and the investor has an infinite set of investment options. The value of modularity and sequential investment is emphasised in Gollier et al. (2005) and...
Stepwise Green Investment under Policy Uncertainty.
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