**1. Introduction**

In the fight against climate change, multiple environmental policies arise to guide the markets towards a sustainable future [1]. Such policies aim to steer new investments towards cleaner technology choices. Better energy efficiency, greener heat and power production, electric vehicles and biofuels in the transportation sector-are among the means to reduce emissions [2]. In this paper we concentrate on biofuel-related support policies and how the profitability effect of these policies can be analyzed ex-ante with modern analysis methods.

Green investments are still generally characterized by high costs relative to older technologies and high uncertainty is involved [3,4] (in the power sector many types of renewables are already cheaper than conventional generation [5], but extra costs arise due to their intermittency when the system reliability issues are taken into account [6–8]). For these reasons support mechanisms that are meant to incentivize green investments have been put in place. Many of the support mechanisms are based on simple policies that guarantee profitability by way of providing extra revenue to the investment [1]. Simply providing extra revenue however often leads to a too high subsidy level and consequently may cause policy changes [9,10]. As predictability and a low political-risk environment is crucial in attracting long-term investments, it is important to design policies that address investment risks and uncertainties [11,12] in a way that does not require unexpected and dramatic adjustments. Pre-analysis of the policy effects is important for succeeding in the creation of such policies, thus the issue of using proper analysis-techniques is highlighted.

**Citation:** Ruponen, I.; Kozlova, M.; Collan, M. Ex-Ante Study of Biofuel Policies–Analyzing Policy-Induced Flexibility. *Sustainability* **2022**, *14*, 147. https://doi.org/10.3390/su14010147

Academic Editor: Grigorios L. Kyriakopoulos

Received: 26 November 2021 Accepted: 20 December 2021 Published: 23 December 2021

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The ex-ante analysis of the profitability of complex investment projects and the ex-ante analysis of the profitability effects of specific policies often includes the use of methods that allow for a comprehensive inclusion of the risks and uncertainties that surround the studied cases [13,14]. Methods that underlie modern real option analysis are such methods and real option thinking is a framework that supports the inclusion of uncertainty in the ex-ante profitability analysis context. Real options thinking recognizes and acknowledges the value of flexibility in the face of uncertainty and embraces the thinking that flexibility that is, the ability to steer/change an investment when change takes place, should be built into investments when it can be done in a cost-effective way. This observation has instigated a whole new "world" in investment design, where flexibility is pre-planned into investments in cases where the investment has a high likelihood of facing dramatic enough changes in its environment (markets). These analyses combine the study of uncertainty and flexibility simultaneously. To mention a few typical types of flexibility that allow investment managers to steer investments towards better outcomes when change takes place, we mention an option to delay investment, options related to scaling the size of investments up and down, option to temporarily shut down an investment, and options to change inputs and outputs to/from (typically production) investments [15–18]. Preinvestment planning and testing the effect of construction of flexibility into investments is something that can still be said to be "young" in terms of how widespread it is in the industry, some academic research on the topic exists, see, for example, [19].

Taking this thinking of combining the study of uncertainty and policy-induced flexibility into the world of ex-ante policy evaluation is also new and in the context of supporting policies for green investments it is very new. Some previous academic work, concentrating on renewable energy support mechanisms exists, see, e.g., [10,17,20]. In other words, the "concept" of what we are looking at here is the study of how policies and support mechanisms created to incentivize green investments may be constructed in a way that they include flexibility and thus change, when changes in the "environment in which the policy exists" take place. Furthermore, how the flexibility within the policies affects the investments which the policies are aimed at incentivizing is focal here. It seems rational to expect that similar methods that work for real option analysis (ex-ante analysis of effects of flexibility) for investments work also for ex-ante policy evaluation.

In this vein, in this paper we select two modern analysis techniques used in the analysis and the valuation of flexibility, the pay-off method [21] and (Monte Carlo) simulation based analysis, called "simulation decomposition" [22] and use them to study incentive-policies in the context of biofuels. The reason for selecting these two methods is the fit of these methods to the type of uncertainty that surrounds the context of biofuel-policies [23]. These methods have also previously been used in the analysis of environmental policies [24–26].

To the best of our knowledge this is the first time these techniques are applied in the context of biofuel-policy evaluation. The application of the methods, the analyses, and the obtained results are illustrative, yet helpful in understanding the benefits brought about by using modern analysis methods in the context of ex-ante policy evaluation.

The remainder of the paper is structured as follows. First, we provide a brief overview of the biofuel-policies to introduce the context of the case study. Then we introduce the two methods, the pay-off method and simulation decomposition. We illustrate the use of the methods in the analysis of a biofuel-policy. The discussion and conclusion section summarizes and discusses the results, looks into the comparative performance of the used methods, and outlines implications for policy analysis.
