Investment in Renewables under Uncertainty: Fitting a Feed-in Scheme into ETS.

AuthorBoffa, Federico


The ambitious European targets in terms of renewable energy sources have fuelled a resurgence of interest in the linkages between technological adoption and environmental policy. This trend has gained additional momentum as a result of the changes to the EU ETS mechanism, recently approved in the EU. They prescribe the creation of a Market Stability Reserve (MSR henceforth), expected to start its operations in 2019. MSR will constrain the permits price within a range, thereby reducing price fluctuations. This entails de facto a shift of the ETS system from a pure quantity instrument towards a hybrid between a price (i.e., a tax) and a quantity instrument (Kollenberg and Taschini, 2015).

A prominent concern is whether or not the EU-ETS, as well as the new system emerging with the MSR, could fruitfully overlap with direct support schemes to generation from renewable energy sources (RES-E henceforth). We specifically focus on feed-in schemes, such as feed-in tariffs (FIT henceforth), and feed-in premia (FIP henceforth). Under a FIT, a generator adopting a RES-E technology receives a fixed economic compensation per unit of power that exceeds the average market return, and does not vary with fluctuations in real power prices. FIT represents therefore an insurance against all forms of market price uncertainty. On the other hand, under a FIP, also known in some countries as guaranteed bonus or premium tariff, a generator adopting a RES-E receives a monetary premium on top of the electricity market price. A generator compensated through a FIP is therefore subject to uncertainty on the market price.

While the combination of carbon mitigation policies and of support schemes to renewable energies is now very popular both in Europe and in the United States, the prevailing view among economists is that it is not cost effective (e.g. Bohringer and Rosendahl, 2010, Fischer and Preonas, 2015, Requate 2015). (1)

We develop a theoretical model that extends the existing analysis to account for the key role played by uncertainty. Uncertainty is a defining feature of power generation business. Over the last decade, for example, both electricity consumption and fuel prices exhibited substantial unpre-dicted oscillations. Table 1 shows the discrepancy between the International Energy Agency ten-year forecasts (2000 for 2010) and the actual levels of power generation and of its prices. The IEA overestimated production, while it underestimated prices.

We show that results on the desirability of the overlap crucially depend on the specific type of carbon mitigation policies and on the support scheme to RES-E that are in place. In particular, we consider two alternative carbon mitigation policies, i.e. a pure quantity instrument (ETS), and a carbon tax, which we regard as an extreme case of a MSR. We analyze how each of them interacts with two alternative RES-E support schemes, FIP and FIT. We conclude that, as the EU carbon mitigation policy evolves towards a price-based scheme (as a result of the MSR), its overlap with a FIT may harm RES-E technology adoption. In the post-MSR framework, the overlap with FIP scheme may in fact be better in stimulating RES-E adoption. Our finding rationalizes the EU's decision to promote the phasing out of FIT schemes, in particular for relatively large plants. (2) However, the final word on the choice of the RES-E support schemes belongs to individual Member States, some of which, including France and the UK, still rely on FIT. We predict these countries will experience a decline in RES-E adoption, as a result of the introduction of the MSR, unless they switch to a FIP.

We consider a simple setting, in which two risk neutral firms initially share the same carbon intensive technology. (3) Firms are subject to environmental regulation, and choose whether or not to adopt a RES-E technology. Uncertainty takes the form of shocks on the revenue as well as on the cost side affecting each firm.

We address four potential scenarios, arising from the overlapping of a pure price-based (i.e, carbon tax) or of a pure quantity-based (i.e., cap and trade) carbon mitigation instrument, respectively, with a FIP and with a FIT scheme, which, for the sake of comparison, are assumed to guarantee the same expected return, but differ in terms of the uncertainty they generate.

In our setting, the correlation of the cost shocks is a function of the firms' adoption pattern: if the two firms share the same technology, then they are subject to perfectly correlated cost shocks. If, instead, their technology differs, the correlation of their cost shocks turns out to be weaker. On the revenue side, under a FIP shocks are perfectly correlated across firms, while, under a FIT, revenue is certain for firms adopting the cleaner technology (and revenue shocks are perfectly correlated when no firm adopts the RES-E technology). As a result, both the level of uncertainty, and the degree of correlation across the shocks faced by the firms are affected by the chosen RES-E support scheme, as well as by firms' own technological choice, and firms incorporate this consideration into their adoption decision.

Our article relates to the strand of literature that analyzes the incentives for technology adoption under various pollution control approaches (Milliman and Prince 1989, Parry 1998, Denicolo 1999, Requate 2005). In particular, Requate and Unold (2003) compare a carbon tax and a cap and trade under perfect competition in the regulated sector and no uncertainty. They find that, with initially symmetric firms, taxes tend to induce symmetric adoption, while permits may determine asymmetric adoption. Their result relies on emission permits price reduction that follows from technology adoption, reducing in turn the net benefits from additional adoption. Closer to our paper, Krysiak (2011) emphasizes that technology adoption choices by regulated firms may affect uncertainty, making it endogenous, and profits. Consistently with Krysiak (2011), in our setting as well asymmetric adoption under cap and trade is linked to the exploitation of the positive impact of uncertainty on expected profits when the two firms use different technologies in equilibrium, and are therefore subject to uncorrelated cost shocks. This impact adds to that related to the equilibrium permits price arising in Requate and Unold (2003). Our results on asymmetric RES-E adoption pattern by initially symmetric firms are consistent with Krysiak's. However, we extend Krysiak by including subsidies to RES-E technology that overlap to cap and trade or to carbon taxation, and analyze the effects of the resulting policy mix on the adoption pattern.

The paper is organized as follows: Section 2 introduces our setting. Sections 3 and 4 address the performance of cap and trade and carbon taxation (respectively), in the presence of overlapping FIP or FIT. Section 5 discusses results and policy implications and, finally, Section 6 provides some concluding remarks.


We consider two risk neutral (4) regulated electricity producers, labeled as i and j, facing perfectly elastic demand for their output. Production generates emissions levels [e.sub.i] and [e.sub.j], respectively. The two generators are assumed to be initially endowed with the same traditional emission-intensive fossil-fuel based production technology n, and have to decide whether or not to adopt a renewable technology a, which reduces emissions. There are two sources of uncertainty, a demand shock, reflecting aggregate income fluctuations, and a cost shock. We consider the overlapping of two types of policies, namely a carbon mitigation policy, and a direct support scheme to renewable energies. Carbon mitigation policies effectively impose a price p for each unit of emission that each firm generates. We consider two alternatives: a carbon tax, in which a price for each unit of emissions is set by the regulator, and a cap and trade mechanism, that is, a system of tradable permits (or allowances) which firms have to surrender in proportion to their emissions; in such an arrangement, the regulator sets the total amount of permits (and hence, the total amount of emissions), while the permit price is determined by the market clearing condition, i.e. the intersection between allowances demand and supply.

Direct RES-E support schemes affect the revenue side, by increasing the compensation obtained by the generator for each unit of electricity produced using a renewable source. We compare two alternatives that are equivalent from the viewpoint of the expected revenue: a FIP and a FIT. The FIP increases the expected compensation for the generator, by prescribing a fixed top-up over the market price for electricity, but leaves the generator exposed to uncertainty. The FIT provides the firm with a fixed (certain) compensation per unit of electricity generated, thereby hedging the generator against price fluctuations. We assume that the expected compensation for the generator is the same under the two regimes.

We solve for the Subgame Perfect Nash Equilibrium (SPNE) of a two-stage game. In stage one, before the state of the world is revealed, each firm chooses its technology z = n,a, by deciding whether or not to adopt the renewable technology. Adopting firms incur a fixed cost F. Subsequently, in the interim stage, the state of nature is revealed, and uncertainty is resolved.

In stage two, firms select the optimal amount of final goods production [q.sub.k] and, as a result, their emissions [e.sub.k], for k = i, j; and -k =\k. We assume there is a deterministic link between output and emissions, such that [q.sub.k] = [[alpha].sup.z][e.sub.k], where [[alpha].sup.z] is a productivity per emission parameter. (5) Finally, under cap and trade, the permits market clears at price p.

Each firm's expected cost function in terms of the amount of emissions [e.sub.k] generated by firm k, before the environmental...

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