Supplementing an emissions tax by a feed-in tariff for renewable electricity to address learning spillovers

Energy Policy 61 (2013) 635–641

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Supplementing an emissions tax by a feed-in tariff for renewable electricity to address learning spillovers Paul Lehmann n Helmholtz-Centre for Environmental Research – UFZ, Department of Economics, Permoserstr. 15, D-04318 Leipzig, Germany

H I G H L I G H T S








Learning spillovers may warrant a combination of emissions and technology policies. A revenue-neutral feed-in tariff can correct learning spillovers efficiently. Optimal implementation of this approach is tedious in practice though. Tariff and emissions tax have to be differentiated and adapted continuously. Eventual policy decision depends on institutional constraints and economy-wide effects.

art ic l e i nf o

a b s t r a c t

Article history: Received 25 June 2012 Accepted 18 June 2013 Available online 19 July 2013

In the presence of learning spillovers related to renewable energy technologies, an optimal strategy to mitigate climate change should complement an emissions tax by a subsidy for renewables. This article addresses the question how such subsidy should be designed. It is shown that the widely-used approach of a revenue-neutral fixed feed-in tariff can yield an optimal outcome under restrictive conditions only. It has to be adapted continuously as the electricity price changes. Moreover, funding the tariff by a surcharge on the electricity price has important implications for the design of the emission tax. The optimal tax rate has to be below the Pigovian level, differentiated across fossil fuels and adapted over time as the patterns of technological development change. These requirements may pose a formidable challenge for practical decision-making. However, it is important to point out that the eventual choices made with respect to the design and funding of a feed-in tariff have to be based on a careful and more comprehensive policy assessment, including, inter alia, economic effects beyond the electricity sector and existing institutional constraints. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Emissions tax Feed-in tariff Technological change

1. Introduction When a pollution externality is coupled with knowledge spillovers, an optimal policy mix includes an emissions tax to control pollution and a subsidy to promote technology development (Bennear and Stavins, 2007; Jaffe et al., 2005; Lehmann, 2012). This article sheds particular light on the optimal design of these climate policy instruments when there are knowledge spillovers related to learning-by-doing with renewable energy technologies. Learning-by-doing implies that the unit cost of a product decreases with increasing cumulative investment, cumulative production and market growth due to experience producers gain (Argote and Epple, 1990; Arrow, 1962; Neuhoff, 2008). There is strong evidence of learning-by-doing for renewable energy

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technologies (Ek and Söderholm, 2010; IEA, 2010; Isoard and Soria, 2001; Junginger et al., 2010; Kahouli-Brahmi, 2009; Klaassen et al., 2005; Neij, 2008; Söderholm and Klaassen, 2007), even though there is little consensus on the actual size of learning rates for specific technologies (Lindman and Söderholm, 2012). It is usually argued that learning-by-doing is decisive for bringing down the cost of emissions policies, such as carbon taxes or emissions trading schemes (Castelnuovo et al., 2005; Edenhofer et al., 2005; Gerlagh and Lise, 2005). However, learning-by-doing may be hampered by knowledge spillovers. In this case, firms benefit from experiences made by competitors without having invested in a costly learning process and without compensating the learning firm. Consequently, investments in learning are suboptimal, i.e. the chosen output is too low to stimulate efficient levels of cost reduction (Jaffe et al., 2005). Knowledge spillovers have been found to be significant for learning-by-doing with nonrenewable energy technologies (Lester and McCabe, 1993; Zimmerman, 1982) and in the manufacturing sector in general

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(Argote and Epple, 1990; Irwin and Klenow, 1994). Empirical evidence on learning spillovers associated with renewable energy technologies is still limited, relying primarily on anecdotal observations (Hansen et al., 2003; IEA, 2000; Junginger et al., 2005; Neij, 1999). The only econometric analysis is provided by Braun et al. (2010). Based on patent data, they show that innovation in wind and solar technologies is strongly driven by knowledge spillovers. Thus, it seems fair to assume that learning spillovers may also hamper the development of renewable energy technologies. Consequently, there is a rationale for combining an emissions policy with a support scheme for renewable energy technologies in order to mitigate climate change efficiently. This basic reasoning notwithstanding, the details of policy design have a decisive effect on the actual efficiency of a policy mix. This paper aims at discussing how different characteristics of policy design may affect the overall efficiency of the policy mix in addressing a pollution externality and learning spillovers simultaneously. This understanding is necessary to provide an applied analysis which yields useful policy recommendations for the real world. The article addresses the specific question under which conditions a feed-in tariff can efficiently supplement an emissions tax. A particular focus is on possible interactions between both policies. Feed-in tariffs are the most common approach to promote renewable energy technologies (Madlener and Stagl, 2005).1 Their basic characteristic is that they require grid operators to buy renewable electricity at a legally set price per unit of electricity generated. Nevertheless, feed-in tariff schemes may differ in two important respects (Couture and Gagnon, 2010; European Commission, 2008; Klein et al., 2008). (1) How strong are feedin tariffs related to the dynamics of the electricity market? Most European countries, but also the states of Ontario in Canada and Minnesota in the United States, have chosen fixed tariffs. With this approach, the remuneration per unit of electricity is irrespective of the prevailing market price of electricity. In contrast, fewer countries, including Spain, Denmark and the Netherlands, have opted for a premium tariff. Here, a premium is paid in addition to the electricity price.2 (2) How is the feed-in tariff funded? Most countries have implemented a revenue-neutral system which recovers tariff expenditures by a surcharge on the electricity price. Thus, tariff costs are borne by electricity consumers. Alternatively, the tariff may also be funded by a general government subsidy. This approach, which has been implemented in the Netherlands, distributes the burden to all tax payers. This article analyzes the performance of the most common feed-in tariff approach, a revenue-neutral fixed tariff, in the climate policy mix. It also highlights relevant differences compared to a governmentfunded premium tariff. The article uses a straightforward analytical partial-equilibrium model of the electricity sector. Model results show that, in theory, a climate policy mix can be designed efficiently with a revenueneutral fixed tariff. The fixed tariff has to be set equal to the sum of the prevailing electricity price and the knowledge spillover effect. This implies that the tariff has to be adapted continuously as the electricity price changes. This is different to a premium tariff which can be determined irrespectively of the electricity price. The analysis also reveals that funding a feed-in tariff by a surcharge on the electricity price has important implications for the design of the emissions policy. The optimal emissions tax has to be set below the Pigovian level and be differentiated across fossil fuels. Both effects are due to the fact that the surcharge can be understood as an implicit heterogeneous emissions tax. The emissions 1 Alternative approaches include tradable green certificates and tender-based systems (for an overview, see Madlener and Stagl, 2005). 2 In fact, Spain allows operators to choose between a fixed and a premium tariff (Couture and Gagnon, 2010).

tax has to be adjusted continuously as the total level of feed-in tariffs and the resulting surcharge change. These requirements would not apply to a policy mix with a government-funded tariff under which the tax could be fixed at the level of the marginal damage from emissions. These results indicate that getting policy design right is a challenging task when a revenue-neutral fixed tariff is in place. However, it is also pointed out that the eventual choices made with respect to the design and funding of a feed-in tariff have to be based on a more comprehensive policy assessment, which would go beyond the scope of this article. It would be necessary, for example, to consider carefully economic effects beyond the electricity sector and existing institutional constraints. This paper adds to the debate on rationales for implementing support schemes for renewable electricity in the presence of an emissions policy (for overviews, see Gawel et al., 2013; Lehmann and Gawel, 2013). Rationales are manifold, including, inter alia, the imperfect internalization of pollution externalities under existing emissions policies (Fischer, 2008; Kalkuhl et al., 2013), subsidies to non-renewable energy sources (Lehmann and Gawel, 2013), policyinduced uncertainties (Ulph and Ulph, 2010) and, more generally, technological and institutional path dependencies (Acemoglu et al., 2012; Neuhoff, 2005) – all of which may eventually result in a carbon lock-in of the electricity sector (Lehmann et al., 2012; Unruh, 2000). However, the most straightforward economic justification for implementing a support scheme for renewables is a failure in the technology market. In this respect, existing policy studies have particularly addressed the situation of a pollution externality combined with knowledge spillovers related to research and development (R&D). Parry (1995) shows that in the case of R&D spillovers, the optimal tax rate has to be higher than the Pigovian tax rate. However, his conclusion is contingent on the assumption that all emissionreducing investments carry the same potential for innovation. Otherwise, first-best levels of abatement and R&D can only be attained by a policy mix of an emissions policy and a subsidy to R&D expenditures (Bosetti et al., 2008; Bye et al., 2011; Fischer, 2008; Golombek et al., 2010; Golombek and Hoel, 2006; Goulder and Schneider, 1999; Katsoulacos and Xepapadeas, 1996; Massetti and Nicita, 2010). For learning spillovers, Rosendahl (2004) demonstrates that a costeffective emissions tax has to be higher than in the absence of knowledge externalities (and potentially differentiated by technologies). However, a superior outcome can be attained by implementing an additional technology policy. Depending on how the learning process is modeled, the optimal strategy is to subsidize renewable electricity generation (Fischer and Newell, 2008; Kalkuhl et al., 2012), renewable generation capacity installed (van Benthem et al., 2008), investments in renewable generation capacity (Kverndokk and Rosendahl, 2007), or the output of producers (rather than operators) of renewable energy plants (Bläsi and Requate, 2010). Thus, it can generally be argued that a feed-in tariff, which is paid per kilowatthour, is only a second-best policy to address learning spillovers in most settings. However, not much attention is paid to differences in the design of a feed-in tariff. Those studies explicitly analyzing a feedin tariff focus on a premium tariff and usually assume that it is funded by the government (Bläsi and Requate, 2010; Fischer and Newell, 2008). Only Kalkuhl et al. (2012) examine a revenue-neutral premium tariff and highlight corresponding distortions of fossil fuel use. However, they do not discuss how these could be corrected. Thus, the policy implications of two characteristics of feed-in tariff design remain insufficiently researched: (1) the payment of a fixed tariff instead of a premium tariff and (2) the funding of the tariff by a surcharge on the electricity price rather than by government expenditures. These aspects will be studied in this article. The combination of an emissions policy with support schemes for renewable energy technologies is also analyzed by another research strand which employs a primarily static perspective. It addresses interactions between an emissions trading scheme and a feed-in

P. Lehmann / Energy Policy 61 (2013) 635–641

tariff (Abrell and Weigt, 2008; Frondel et al., 2008; Traber and Kemfert, 2009) or tradable green certificates (Amundsen and Mortensen, 2001; Böhringer and Rosendahl, 2010; Jensen and Skytte, 2003; Morthorst, 2001; Unger and Ahlgren, 2005). The basic reasoning of these studies is that the promotion of renewable energy sources does not affect the overall level of emissions, as this is set by the cap of the emissions trading scheme. Rather, it drives down the emissions permit price and increases the overall cost of abatement. However, these studies do not take knowledge spillovers into account. Consequently, they neglect possible benefits of the policy mix as well as implications for policy design in a dynamic context – which are addressed in this article. The paper is organized as follows. Section 2 introduces the model. Section 3 highlights the conditions of the social optimum. Section 4 analyzes the policy mix of an emissions tax with a revenue-neutral fixed tariff. Section 5 concludes.

2. Model The model structure used in this article is primarily based on Fischer and Newell (2008). The main extension refers to the assumptions regarding the design of the policy to support renewable energy technologies, which will be introduced later on. The analysis uses a stylized partial equilibrium model of the electricity sector. The electricity sector encompasses two subsectors. One sector employs renewable energy sources, which are carbon-free. The other sector uses fossil fuels to generate electricity and produces emissions of carbon dioxide. Both sectors are perfectly competitive and produce an identical output: electricity. Any electricity generated from renewable energy sources substitutes marginal fossil-fuel production.3 The model has two periods. Electricity generation, consumption and emissions occur in both periods. Firms take the electricity price as given not only in the first period but also in the second period. Moreover, they are assumed to have perfect foresight regarding the price in period 2. There is discounting at rate δ between periods. Social and private discounting rates are assumed to be identical. The renewable sector consists of n identical firms, each of which produces an electricity output qt in period t.4 The production costs are given as in Bläsi and Requate (2010). Production cost in period 1 is C 1 ðq1 Þ. Production cost in period 2 is a function of output in period 2 and the total level of learning (or experience) L in period 1, i.e. C 2 ðq2 ; LÞ. Total learning depends on the output of the firm under consideration (private learning) and the output of all other identical firms in the sector (learning spillovers): L ¼ q1 þ ρðn1Þq1 .5 The spillover rate ρ indicates to which extent a firm can benefit from the experience made by other firms. Production costs in each period are increasing and convex in output, i.e. C tqt 40 and

637

C tqt qt 4 0. Production costs in period 2 are declining and convex in learning: C 2L o 0 and C 2LL 4 0. Learning also reduces marginal production cost in period 2, i.e. C 2q2 L o0. Moreover, production costs in period 2 are assumed to be convex overall, which requires that C 2LL C 2q2 q2 ðC 2q2 L Þ2 4 0. Subscripts qt and L denote partial derivatives with respect to the subscripted variable. The fossil-fuel sector may choose between a technology using a carbon-intensive fossil fuel x, e.g. coal, and a technology using a low-carbon fuel y, such as natural gas. The total output of electricity produced in the emitting sector in period t is simply f t ¼ xt þ yt . Emissions with each technology are assumed to be fixed at the rates μx and μy , respectively. That is, fuel switching is the only means to reduce emissions in the emitting sector. Other measures, such as improvements in generation efficiency or carbon capture and storage are neglected here. Total emissions from the emitting sector in period t are Et ¼ μx xt þ μy yt . Production costs of the technology using the carbon-intensive fuel, Gt ðxÞ, are assumed to be increasing in output and strictly convex in each period, i.e. G′t 4 0 and G″t 4 0. The same applies for the production costs of the low-carbon technology, K t ðyÞ. Notably, as in Fischer and Newell (2008), learning-by-doing is assumed for the relatively immature renewable energy technologies but not for the relatively mature fossil-fuel technologies.6 Total output of the electricity sector in period t is the sum of electricity generated in the fossil-fuel sector and the renewables sector: Q t ¼ f t þ nqt . In equilibrium, electricity output equals electricity demand. The inverse demand function can then be given by pt ¼ P t ðQ t Þ, where pt is the market price for electricity in period t. This function is downward sloping, i.e. P′t ðQ t Þ o 0. Carbon dioxide emitted by the fossil-fuel sector in period t produces damage to society, which depends on the overall level of emissions: Dt ðEt Þ. Damage is assumed to be increasing and convex in emissions, i.e. D′t 4 0 and D″t ≥0. Social welfare W over the two periods under consideration is given by Z Q1 W¼ P 1 ðQ 1 ÞdQ 1 nC 1 ðq1 ÞG1 ðx1 ÞK 1 ðy1 ÞD1 ðE1 Þ 0 "Z 2 # þδ

Q

0

P 2 ðQ 2 ÞdQ 2 nC 2 ðq2 ; LÞG2 ðx2 ÞK 2 ðy2 ÞD2 ðE2 Þ

ð1Þ

Thus, social welfare computes as the sum of consumer surplus and firm revenues net of production costs with renewable energy sources, coal and natural gas and the environmental damage from emissions in the first-period, and the same values discounted for period 2. As this is a partial equilibrium model, indirect costs and benefits which may arise outside the electricity sector (e.g. on labor and capital markets) due to changes in electricity prices and/or government budget spending and raising are not considered here.

3. The social optimum 3

This model abstracts from nuclear and hydro as important further energy sources currently used. However, these sources are carbon-free and employed to generate base load electricity. Their output can be assumed to be fixed in the presence of emission policies and support schemes for renewable energy sources. Thus, integrating them into the model would not change the analytical results. 4 The number of firms in the renewable sector is assumed to be constant in this model. Bläsi and Requate (2010) allow for firm entry and show that with learningby-doing spillovers an additional policy is necessary to attain a socially optimal outcome. When deciding about market entry, firms do not consider that their entry produces a benefit to other market participants in terms of learning-by-doing. Market entry will be suboptimal in the absence of regulation and needs to be stimulated by an entry premium. 5 In fact, the extent of learning-by-doing and related spillovers may actually be a function of cumulative capacity installed or the cumulative output of producers of renewable energy plants, rather than renewable electricity generation (see studies cited in the Introduction). These aspects are neglected as they would not affect the comparison of different types of feed-in tariffs.

The social planner maximizes welfare with respect to electricity generation from renewable energy sources, coal and natural gas in both periods, qt , xt , and yt . The resulting first-order conditions are P 1 ðQ 1 Þ ¼ C 1q1 ðq1 Þ þ δ½C 2L ðq2 ; LÞ þ C 2L ðq2 ; LÞρðn1Þ

ð2Þ

6 Learning-by-doing effects (and related spillovers) in the fossil-fuel sector are neglected because they are relatively less important and would unnecessarily complicate the analysis. In fact, such enhanced analysis would reveal that an additional policy is needed to promote learning-by-doing in the presence of spillovers in the fossil-fuel sector. Such policy may be required in particular once new promising but immature technologies – such as carbon capture and storage – are to be adopted in the fossil fuel sector.

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P 2 ðQ 2 Þ ¼ C 2q2 ðq2 ; LÞ

ð3Þ

P t ðQ t Þ ¼ G′t ðxt Þ þ D′t ðEt Þμx

ð4Þ

P t ðQ t Þ ¼ K′t ðyt Þ þ D′t ðEt Þμy

ð5Þ

Condition (2) implies that the socially optimal electricity output of firms in the renewable sector is attained in period 1 when the marginal willingness to pay for another unit of electricity (represented by the market price) equals the sum of marginal production costs in period 1 and the discounted marginal reduction of production costs in period 2 due to learning experienced by all firms in period 1. Condition (3) highlights that firms in the renewable sector should produce electricity in period 2 until their marginal production costs equal the marginal willingness to pay. Conditions (4) and (5) represent the common first-order conditions requiring the emitting fossil-fuel sector to generate electricity from coal and natural gas in period 1 and 2 until the sum of marginal production cost and marginal environmental damage of either technology equals the willingness to pay.

4. Evaluation of the policy mix 4.1. Policy assumptions In order to address the learning spillover, a feed-in tariff is implemented for the non-emitting renewable energy sector. Producers of renewable electricity receive a fixed tariff s1 per unit of electricity produced in period 1. Thus, remuneration for renewable electricity in period 1 depends on the tariff only, not on the prevailing electricity price. In period 2, when no learning occurs, electricity from renewable energy sources is assumed to compete with electricity from fossil-fuels at the market price. The fixed tariff includes an implicit subsidy which amounts to the difference between the tariff and the actual electricity price, s1 p1 . The tariff is designed to be revenue-neutral for the government. Grid operators buying renewable electricity are allowed to spread the financial burden resulting from the implicit subsidies across all electricity customers. This results in a uniform surcharge ϕ1 on the electricity price in period 1. Revenues from raising the surcharge have to equal the expenditures on tariffs paid for renewable electricity net of the prevailing electricity price, i.e. ϕ1 Q 1 ¼ ðs1 p1 Þnq1 . Thus, the surcharge on the electricity price computes as the implicit subsidy granted under a fixed tariff times the share of renewable electricity in total electricity supply ϕ1 ¼ ðs1 p1 Þ

nq1 Q1

:

ð6Þ

In a competitive market, electricity producers take this surcharge as given. Thus, it is similar to an output tax. However, the surcharge only affects production choices of fossil-fuel generators. This is because renewable generators benefit from the obligation of grid operators to purchase renewable electricity preferentially. This implies that any reduction in electricity demand resulting from a price increase induced by the surcharge has to be borne by fossil-fuel generators only. In addition to the surcharge, fossil-fuel producers have to pay a tax rate τt per unit of emission.

The profit maximization problem for firms in the renewables sector writes as follows: qt

C 1q1 ðq1 Þ ¼ s1 δC 2L ðq2 ; LÞ

ð8Þ

C 2q2 ðq2 ; LÞ ¼ p2

ð9Þ

In period 1, firms in the renewables sector produce until their marginal costs of electricity generation equal the sum of the fixed tariff and the discounted reduction in production costs in period 2 which can be appropriated by the firm (note that the term δC 2L ðq2 ; LÞ is overall positive). Optimal production in period 2 is only determined by the market price of electricity. The optimal feed-in tariff in period 1 can be derived by equating conditions (8) and (2) s1 ¼ p1 δC 2L ðq2 ; LÞρðn1Þ

ð7Þ

superscript R denotes the renewables sector. Note that, by assumption, firms only consider the effect of private learning on

ð10Þ

The optimal fixed tariff has to be set equal to the sum of the market price for electricity and the marginal gain in period 2 from learning in period 1, i.e. the spillover effect, which is not considered by the firms in the renewable sector. This means in turn that when learning is purely private, i.e. when ρ¼ 0, the fixed tariff has to equal the electricity price. Thus, in this case, the additional promotion of renewable energy sources cannot be justified on efficiency grounds. Compared to an optimal premium tariff, the optimal fixed tariff is higher by the electricity price. This is straightforward as the premium tariff is paid in addition to the electricity price. The electricity price itself is a function of a variety of variables, such as the prices of crude oil and coal, that are exogenous to the partial equilibrium model of the electricity sector. Variations in these variables, and thus in the electricity price, require changes in the level of the fixed tariff. Thus, the optimal fixed tariff has to be adapted continuously. This dynamic requirement is the major difference between a fixed tariff and a premium tariff. If a fixed tariff is set with respect to some past electricity price, it may bring about an inefficient level of output in the renewable sector. If the electricity price increases (decreases), electricity generation from renewable energy sources will be too low (high). Eq. (10) also reveals that if renewable technologies differ with respect to the marginal gains from learning, C 2L ðq2 ; LÞ, the spillover rate, ρ, or the number of adopting firms, n1, a differentiation of feed-in tariffs can be justified on efficiency grounds. Moreover, the tariff has to be decreased over time as technologies become more mature (because the marginal gain from learning declines), and increased with a growing number of adopters (because the overall level of the spillover effect increases). These requirements would similarly apply for a premium tariff. 4.3. The optimal emissions tax Firms using fossil fuels for electricity generation face the following maximization problem max π F ¼ p1 ðx1 þ y1 ÞG1 ðx1 ÞK 1 ðy1 Þϕ1 ðx1 þ y1 Þτ1 ðμx x1 þ μy y1 Þ xt ;yt

þδ½p2 ðx2 þ y2 ÞG2 ðx2 ÞK 2 ðy2 Þτ2 ðμx x2 þ μy y2 Þ

4.2. The optimal fixed tariff

max π R ¼ s1 q1 C 1 ðq1 Þ þ δ½p2 q2 C 2 ðq2 ; LÞ

production costs in period 2 but not that of learning spillovers to other firms, i.e. L ¼ q1 at the private level (Bläsi and Requate, 2010). The resulting first-order conditions for firms in the renewables sector are

ð11Þ

superscript F denotes the fossil-fuel sector. The resulting first-order conditions for optimal electricity generation from coal and natural gas in both periods are p1 ¼ G′1 ðx1 Þ þ ϕ1 þ τ1 μx

ð12Þ

p2 ¼ G′2 ðx2 Þ þ τ2 μx

ð13Þ

P. Lehmann / Energy Policy 61 (2013) 635–641

ð14Þ

p2 ¼ K′2 ðy2 Þ þ τ2 μy

ð15Þ

Conditions (12) through (15) can be interpreted as the inverse supply curves for electricity from the fossil-fuel sector. When deciding about its output in period 1, the fossil-fuel sector produces until the sum of marginal production costs, the surcharge per unit of output produced and the emission costs per unit of output equal the market price of electricity (conditions (12) and (14)). Producers' choices in period 2 only depend on marginal production and emission costs (conditions (13) and (15)). As in Fischer and Newell (2008), an interior solution is assumed, i.e. no fuel is completely driven out of the market. Comparing conditions (12) to (15) with conditions (4) and (5) gives the optimal set of taxes τ1x ¼ D′1 

ϕ1 μx

ð16Þ

τ1y ¼ D′1 

ϕ1 μy

ð17Þ

τ2 ¼ D′2

ð18Þ

The optimal emissions tax in period 2 has to be set equal to the marginal damage from emissions, i.e. at the Pigovian level (Eq. (18)). This result is straightforward since no additional policy affects the behavior of the fossil-fuel sector in that period. It is in line with the outcome derived by previous studies for the combination of an emissions tax and a government-funded premium tariff. However, major differences from these analyses result with respect to the optimal emissions tax in period 1. Eqs. (16) and (17) show that funding a feed-in tariff endogenously by a surcharge on the electricity price rather than by general government expenditures has three important implications for the optimal design of the tax in period 1. Firstly, the optimal emissions tax rate in period 1 has to be below the marginal damage of emissions. This is because the surcharge on the electricity price results in a reduction of electricity consumption and production. Thus, emissions from electricity generation are reduced as well. As a consequence, the emissions tax that is required to attain the socially optimal level of emissions must be lower than in the absence of the surcharge. It has to be reduced such that the overall emission reduction effect induced by the tax and the surcharge corresponds to that of a Pigovian tax. This finding also implies that a feed-in tariff funded by a surcharge on the electricity price may lose its appeal of revenue neutrality. If the marginal damage from emissions D′1 is very low, or if the total of feed-in tariffs paid and, thus, the surcharge ϕ1 are very high, the optimal emissions tax may turn out to be negative. In this case, additional governmental funds are needed to compensate for the excessive emissions reduction induced by the surcharge. Secondly, the emissions tax in period 1 has to differentiate between fossil fuels. Emissions from electricity generation with an emissionintensive fuel (coal) have to be taxed at a higher rate than emissions from combusting a low-emission fuel (natural gas). This is due to the fact that the surcharge – as any output tax – reduces electricity output from fossil fuels irrespectively of the emissions related to different types of fuels. It imposes a higher implicit emissions tax, ϕ1 =μi , on low-emission fuels than on emission-intensive fuels. Consequently, abatement choices in the fossil-fuel sector are distorted whenever a uniform emissions tax (even if it is reduced below the marginal damage from emissions) is combined with a surcharge on the electricity price. The same level of emission reduction can be achieved at less cost by increasing electricity generation from low-emission

70 Tax rate (Euro/ton CO2)

p1 ¼ K′1 ðy1 Þ þ ϕ1 þ τ1 μy

639

60 50 40 30 20 10 0 0.00

Pigovian tax rate Tax rate for coal Tax rate for natural gas 0.50

1.00 1.50 2.00 Surcharge (Eurocent/kWh)

2.50

Fig. 1. Differentiation of emissions tax rates for coal- and natural gas-fired electricity generation as a function of the surcharge on the electricity tax.

fuels at the expense of generation with emission-intensive fuels. To bring about this correction, the optimal emissions tax in the presence of the surcharge has to be lower for the low-emission fuel than for the emission-intensive fuel. These implications can be illustrated using a simple numerical example. The average emission intensity of coal- and natural gas-fired electricity generation worldwide in 2007 amounted to 907 and 387 g CO2/kWh, respectively (IEA, 2009).7 A rough estimate of the marginal damage per ton of CO2 may be around 70 Euro (Downing et al., 2005). Using Eqs. (16) and (17), the corresponding tax rates can be easily computed for different levels of the surcharge (see Fig. 1). In Germany, the surcharge was 2.05 Eurocent/kWh in 2010 (BMU, 2011). This would imply tax rates for coal- and natural gas-fired electricity of 47 and 17 Euro/ton of CO2, respectively. The differences between these tax rates and the Pigovian level correspond to the implicit emissions tax rates which are imposed on each type of fuel by the surcharge. A third implication of the surcharge becomes obvious when the equations for the optimal tax rate in period 1 are further developed. Substituting Eq. (10) into (16) using Eq. (6), the optimal tax rate for emission-intensive electricity generation can be rewritten τ1x ¼ D′1 þ

½δC 2L ðq2 ; LÞρðn1Þðnq1 =Q 1 Þ μx

ð19Þ

The optimal tax rate in period 1 for fossil-fueled electricity generators with low emissions, τ1y , writes accordingly. Eq. (19) reveals that with a surcharge, the optimal tax rate cannot be set once for all but has to be adapted continuously. First of all, this is attributable to the requirements regarding an optimal feed-in tariff. As it has been highlighted above, the tariff has to be adapted as technologies become more mature and as the number of adopters increases. Consequently, both parameters also determine the level of the surcharge and, thus, the level of the tax rate. Moreover, the optimal tax rate has to decrease as the share of renewable energies in total electricity supply increases. This implication is due to the design of the surcharge. In contrast to the optimal fixed tariff, however, the tax rate does not depend on the electricity price. A price increase requires higher feed-in tariff and implies a lower surcharge. Both effects cancel out. Similarly, the electricity price would neither affect the level of the tax in the presence of a premium tariff. This is because in this case, neither the feed-in tariff nor the surcharge would depend on the electricity price. 7 In fact, emission rates do not only vary with fuels but also with technologies employed for electricity generation. Thus, the tax does not only have to be differentiated with respect to fuels but also with respect to technologies. This issue is neglected in this article where a fixed emissions rate is assumed for each type of fossil fuel.

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5. Conclusion In the presence of a pollution externality coupled with knowledge spillovers related to learning-by-doing, a policy mix is needed to stimulate efficient levels of abatement and technology development. Regarding climate change, this case of multiple market failures may justify the combination of an emissions policy with a support scheme for renewable energy technologies. This general rationale notwithstanding, a decisive question for practical decision-making is how the policy mix should be designed in detail. The focus of this article is on whether and how a feed-in tariff for renewable electricity generation can efficiently complement an emissions tax. Economic theory suggests that an efficient policy mix should include a Pigovian tax to address the pollution externality and an output subsidy for renewable energy technologies to correct for learning spillovers. An output subsidy corresponds to the concept of a government-funded premium tariff, an approach that is implemented in only few countries. More popular are revenueneutral fixed tariffs. It is shown in this article that an efficient policy mix can also be designed with this approach. However, the requirements for optimal policy design become more complex. Paying a fixed tariff rather than a premium tariff implies that the tariff has to be adapted continuously as the electricity price changes. Moreover, funding the tariff by a surcharge on the electricity price rather than by general government expenditures has three important implications for the optimal design of the emissions tax: (1) the tax rate has to be below the Pigovian level, (2) it has to be differentiated across fossil fuels, and (3) it has to be modified continuously over time as technologies become more mature and as the number of technology adopters and the share of renewable energies in total electricity generation increase. These requirements for optimal taxation are due to the fact that the surcharge can be interpreted as an implicit heterogeneous emissions tax on fossil fuels. The derivation of policy recommendations based on these analytical results has to be done with care. A first decisive question is whether the above presented policy design would actually be implementable. In this respect, it is important to consider that changes of environmental policies cannot be realized ad hoc. They rather have to be approved in a tedious political process. Thus, policy modifications bring about positive transaction costs of decision-making. Against this background, the efficient implementation of a revenue-neutral fixed tariff – which requires continuous adaptation of the tariff and the emissions tax – may be a challenging, if not impossible, task for decision-makers. The necessary adaptations in the tariff may only be mimicked to a very limited extent by the legally established policy revisions every couple of years which are common in many countries. These are only suitable to consider long-term trends in electricity prices and changes in learning curves (Söderholm and Klaassen, 2007). Additional challenges are associated with the fact that the European Union has implemented an Emissions Trading Scheme (EU ETS) instead of an emission tax. On the one hand, it is fair to assume that the EU ETS allowance price is below the Pigovian price level as the emissions cap has not been set stringent enough for political reasons (Lehmann and Gawel, 2013). This implies that the reduction of the emissions price suggested in this article to correct the distortion of the add-on to the electricity price is already in place. On the other hand, it is important to point out that the price discrimination across fuels which is also discussed above cannot be implemented under an ETS. These considerations illustrate that meeting the theoretical requirements for efficient policy design may be tedious under a revenue-neutral fixed tariff. In this case, the subsequent question is whether an alternative policy design – i.e. a government-funded premium tariff – would be more suitable from an implementation perspective. Administrating

this approach efficiently seems to be relatively easier as no continuous adaptation of the tariff and the tax is needed. There may be important caveats, however. First, switching to a premium tariff increases investment uncertainty and transaction costs for operators of renewable energy plants (Finon and Perez, 2007). That is, premium tariffs may be less suitable to address barriers to energy sector transitions beyond learning spillovers, such as capital market constraints and more broadly path dependencies and lock-in effects (Neuhoff, 2005). Second, several concerns may be associated with funding tariffs from public budgets. In this case, general equilibrium effects need to be examined carefully. These are costs and benefits which arise on other markets than the electricity market due to taxes which are raised by the government to fund the tariff. In fact, such taxes may produce substantial deadweight losses (see, e.g., Browning, 1976, 1987). Moreover, government funding makes tariffs contingent on rather erratic political concerns and considerations and may produce additional, politically induced investment uncertainties. Finally, financing feed-in tariffs from public budgets may qualify them as national subsidies in legal terms (in contrast to a revenue-neutral approach under which electricity customers pay) and violate EU competition law. Overall, making optimal policy choices may therefore be a challenging task once a more comprehensive framework is applied. So it is eventually an empirical question which feed-in tariff should be preferred, which can only be answered on a case-by-case basis. These considerations may also reveal avenues for further research providing a yet more comprehensive analysis of support schemes for renewable energy technologies and their role in a policy mix. Moreover, the basic structure of the partial equilibrium models used in this and previous articles to analyze policy choices in the presence of pollution externalities and knowledge spillovers can be further developed. This may help to improve the understanding of interactions between emissions and technology policies. Firstly, the electricity price should be made endogenous to the model. This is due to two policy effects. On the one hand, emissions policies are likely to increase the cost of electricity generation and, thus, the wholesale electricity price. On the other hand, the promotion of renewable energies may drive down the wholesale electricity price due to the merit order effect (Rathmann, 2007; Sijm et al., 2006; Traber and Kemfert, 2009). Obviously, both effects may influence the actual performance of a climate policy mix. Furthermore, it may also be necessary to consider the emissions price as endogenous to the model. This modification is relevant when an emissions trading scheme with variable permit prices is analyzed instead of a fixed emissions tax. In this case, the promotion of renewable energy sources may drive down the permit price. A corresponding development of policy mix models would allow integrating two literature strands presented in the introduction: studies highlighting dynamic efficiency gains from combing emissions and technology policies and analyses showing static welfare losses with such a policy mix. Such integration could provide for a more careful and comprehensive evaluation of benefits and costs of a policy mix in the long term.

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