GA No.308481

Report on the optimal policy mix

in a global general equilibrium setting

Oliver Schenker (Centre of European Economic Research, ZEW) and

Jan Witajewski (Fondazione Eni Enrico Mattei, FEEM)

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Executive Summary

Additional market failures beyond the climate externality, in particular regarding the innovation process, need to be addressed with specific instruments within a cost-efficient climate and energy policy. This is key to reduce long-term decarbonisation costs. This report examines instruments such as R&D and learning-by-doing subsidies to induce innovation and reduce the costs of renewable or efficient energy technologies.

In the ENTRACTE project, the two project partners FEEM and ZEW studied such policy mixes from to different perspectives. FEEM analysed what policies are required to achieve particular energy efficiency targets. They did so by extending the integrated assessment model WITCH with a new module that includes R&D policies addressing energy efficiency measures, ZEW, studied the interaction of output and R&D subsidies for immature renewable electricity generation technologies as well as subsidies for improving energy efficiency in the presence of carbon pricing. They did so by using a new calibrated model of the European electricity sector which incorporates the externalities described above.

The analysis with the new WITCH module shows that after introducing high subsidy rates for R&D in energy efficiency measures in the first years, its level could be reduced in subsequent years. In fact, a high subsidy rate would be removed completely after 30 years.

Using a new multiple-market failure model of the EU power sector, the second part of the report focuses not only on optimal policy mixes but puts special emphasis on second-best situations in which one of the optimal policy instruments is unavailable. Key in this case is to discuss how to re-adjust the remaining instruments in such circumstances.

The results of these exercises provide valuable insights for energy and climate policy design with incomplete and interacting sets of policy instruments under the existence of multiple market failures.

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Table of Contents

Executive Summary .............................................................................................................. 2

1. Introduction .................................................................................................................... 4

2. Summary of Work Performed ......................................................................................... 5

2.1. Improve the Representation of Energy Efficiency Measures in WITCH ....................... 5

2.2. Better Understanding of Second Best Policy Mixes .................................................... 6

3. Results and Conclusion .................................................................................................. 7

3.1. The Role of R&D investments for energy efficiency improvements ............................. 7

3.2. How to Adjust Policy Instruments in a Second-Best World .........................................11

References ...........................................................................................................................15

List of Abbreviations .............................................................................................................16

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1. Introduction

Additional instruments beyond a carbon price are necessary to reduce the long-term costs of decarbonisation. While the most important policy instrument to deal with GHG emissions is carbon pricing to foster economy-wide mitigation actions, other instruments are necessary to address additional market failures.

As almost all possible decarbonisation pathways in the most recent IPCC report show, new energy technologies are a crucial ingredient of any cost-efficient mitigation strategy. It is therefore necessary to use technology support policies to reduce their costs. Three market failures hamper the market economy from realizing the full benefits from innovations:

First, a learning-by-doing externality needs to be addressed, since learning-by-doing is not a purely private good but has a public good component. Other technology users profit from the knowledge generated through cumulative experiences.

Second, there is a learning-by-searching externality, since the generation of knowledge through R&D is partly a public good.

Third, when analysing the demand side carefully, one can also identify market failures that hamper the uptake of energy efficient technologies

Addressing all these market failure needs specific instruments. Hence, there is in indeed a positive rationale for combining different policy instruments? (Bennear and Stavins 2007, Fischer and Preonas 2010). Within the ENTRACTE project, FEEM and ZEW studied such policy mixes from to different perspectives. FEEM analysed what policies are required to achieve particular energy efficiency targets. To this end, they extended the integrated assessment model WITCH with a new module that includes R&D policies that address energy efficiency measures, ZEW studied the interaction of output and R&D subsidies for immature renewable electricity generation as well as subsidies for improving energy efficiency in the presence of carbon pricing. They did so by using a new calibrated model of the European electricity sector that incorporates the externalities described above.

The analysis with the new WITCH module shows that after introducing high subsidy rates for R&D in energy efficiency measures in the first years, its level could be reduced in subsequent years. In fact, a high subsidy rate would be removed completely after 30 years

Using the a new multiple-market failure model of the EU power sector, ZEW focused not only on optimal policy mixes but put special emphasis on second-best situations in which one of the optimal policy instruments is unavailable and discuss how to re-adjust instruments in such circumstances.

The results of these exercises provide valuable insights for energy and climate policy design with incomplete and interacting sets of policy instruments under the existence of multiple market failures. It shows in particular that correcting the market failure in R&D generation –both on the demand and supply side of energy market – is a necessity if the policy costs of a long-term decarbonisation shall be minimised. So far, Europe focused on the externality related to learning-by-doing and diffusion of technologies. Public spending on diffusion has been two orders of magnitude larger than on R&D support. In 2010, the five largest EU countries spend about 48 billion Euros on deployment, but only 315 million Euros on R&D support (Zachmann et al., 2014). Even in the light of second-best considerations, taking into account that first-best R&D policies are hard to implement and to calibrate, this relation seems rather unbalanced.

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2. Summary of Work Performed

2.1. Improve the Representation of Energy Efficiency Measures in WITCH A new module within the WITCH model has been developed, which predicts the growth of energy efficiency and evaulates to what extent energy efficiency could be enhanced through the use of R&D policies. In a first step, an analytical model which is compatatible with the structure of the WITCH model has been developed. This analytical model helps to (1) understand how different types of innovation can reduce energy demand, (2) highlight the drivers of this type of innovative activity, and (3) evaluate the role of R&D subsidies and carbon prices in shaping the pattern of energy efficiency dynamics. In a second step, an econometric model has been developed to estimate the key parameters of the analytical model. The calibrated model was then included in WITCH and used to predict what policies are required to achieve particular energy efficiency targets.

The theoretical model is based on the the Directed Technological Change (DTC) approach that arose from the contributions of Acemoglu (1998, 2010). The DTC approach combines the intuition of earlier work on price-induced innovations (Hicks 1932) with the microfoundations of the endogenous growth theory (Romer 1990, Grossman and Helpman 1991, and Aghion and Howitt 1992). The key prediction of DTC model is that innovative effort to advance a given technology is goverened by the value of a target market for this technology: the bigger the market, the higher the inventive effort. FEEM applied the DTC framework to highlight the forces driving the improvements in energy efficiency and extend it to accomodate spillovers across countries. The importance of this type of spillovers has been highlighted by several endogenous growth models, among others Rivera-Batiz and Romer (1991). This model allows for the presence of subsidies for R&D investments in energy intensive sectors.

The model has been set up such that it allows for a transparent and intuitive two stages estimation. The first stage examines the effect of energy expenditures and spillovers on energy saving patents; the second stage uses the predicted values from the first stage to study the impact of induced innovation on the energy demand. Our effort is thus similar in spirit to the seminal contribution of Caballero and Jaffe (1992) but with a focus on directed technological change rather than on estimating an endogenous growth model. To our knowledge our work is the first study that estimates DTC hypothesis using a two stage estimation procedure.

Then, the calibrated model in has been incorporated in the structure of the WITCH (World Induced Technical Change Hybrid) Integrated Assessment Model. First, the original WITCH model is used to predict the future path of energy expenditure. The calibrated DTC model uses this information to predict future innovativeness and growth of energy efficiency. The predicted energy efficiency path is then fed back into the WITCH model and new predictions of the energy expenditure path are generated. This process is iterated until the two paths converge.

With this new model improvements at hand, FEEM performed a policy exercise and analysed what level of research subsidy is required to achieve a target rate of autonomous energy efficiency improvement (AEEI). The target AEEI rate was set to be half of a percentage point above the rate that was previously assumed in WITCH’s business as usual scenario. This more optimistic rate has been being assumed in other policy exercises with the WITCH model as the ‘optimistic scenario’. We will refer to this raised rate as the ‘SSP1 rate’ and we will label the rate that has been previously assumed to be the most realistic BAU (Business as Usual) scenario as the ‘SSP2 rate’. SSP refers to Shared Socioeconomic Pathways, a set of socioeconomic scenarios that the climate change research community had adopted and that has been widely used in the latest fifth assessment report of the IPCC.

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Obviously, a higher rate of AEEI implies higher growth of output per unit of energy (or faster reduction in energy intensity). Figure 1 translates the SSP1 and SSP2 AEEI paths into the dynamic paths of the GDP to energy consumption ratio.

2.2. Better Understanding of Second Best Policy Mixes

ZEW focused its work on improving the understanding of how to adjust first-best policy instruments to improve welfare in second-best situations, in which one of the optimal policy instruments is unavailable. Economists often tend to give advice under the implicit assumption of the full availability of first-best instruments and institutions. This is of limited use for policy makers since institutions are often imperfect and policy instruments are unavailable due to political constraints, incomplete information or prohibitive transaction and compliance costs (Rodrik, 2008).

As we know from second-best theory, if there are political constraints that prevent the attainment of at least one of the conditions of Pareto optimality, then the attainment of the other Pareto optimal conditions is no longer necessarily welfare improving (Lipsey and Lancaster, 1956). Thus, if there are multiple market failures and distortions that prevent the attainment of multiple Pareto optimal conditions, the elimination of only one of the market failures does not necessarily lead to a welfare improvement. Eliminating one market failure might either reduce the welfare losses from the other, exacerbate welfare losses from the other, or not affect the welfare losses from the other market failure.

ZEW’s work focuses on the interaction of the policy instruments in the power sector. In particular, it studied how the remaining policy instruments can be re-adjusted to improve welfare, when at least one of the first-best optimal instruments is unavailable. Feed-in tariffs to promote electricity generating renewable energy sources (RES-E) are a cornerstone in many climate and energy policy portfolios. Therefore, ZEW examined whether different implementations of feed-in tariffs can substitute policy instruments that address knowledge spillovers and imperfect perception of benefits in a second-best world. These aspects are largely unexplored in the literature.

The model used for this exercise is based on Fischer and Newell (2008) and Fischer et al. (2013), which extended the analysis by taking into account the imperfect perception of consumers' benefits of energy efficiency improvements. ZEW build our analysis on this theoretical model by asking how the second-best choice of the remaining policy instruments deviates from the first-best choice when leaving out at least one of the first-best instruments.

In a first step, the model has been set up and was solved analytically. It describes the electricity market including its multiple interacting market failures and captures simple dynamics by differentiating between two periods. Electricity can be generated with renewable and fossil fuel-based technologies. Both are subject to convex increasing production costs. The analysis focuses on immature renewable energy technologies that are subject to cost reductions from R&D efforts and learning by doing. Today's R&D creates knowledge that helps to reduce future production costs. Today's accumulation of experience, represented by the volume of electricity generation, reduces future production costs as well. The utilization of a technology is governed by a price-taking representative producer that maximizes profits, i.e. revenues from electricity generation minus production costs, R&D expenditures, and the costs of regulation.

Electricity is demanded by a representative consumer who draws utility from its consumption. Besides spending her income on electricity, she can invest in three types of energy efficiency measures that take effect either in the short-term (within the first period), in the long-term (within the second period), or both (across both periods). Examples are the replacement of

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devices by newer, more efficient ones such as modern refrigerators or the improvement of building insulation in combination with efficient electrical heating.

The energy efficiency measures reduce the required electricity expenditures for a given level of utility from consumption. The representative consumer maximizes utility, i.e. the value of consumed electricity services minus the cost of electricity input required to generate these services minus expenditures on energy efficiency improvements.

With this model at hand, first and second best policy portfolios have been derived analytically. In particular, it has been focused on how to adjust the remaining instruments when not all instruments were available.

Thus, in a second step, the model has been calibrated. A numerical implementation of the model is needed in order to (i) quantify the opposing effects, (ii) check which effect dominates and in which direction the total effect finally points. The slopes of supply curves are calibrated comparing electricity prices and respective quantities of the baseline and reference scenario used in Capros et al. (2009). Capros et al. (2009) provide long-term projections under different scenarios of the EU's energy system until 2030, based on extensive simulation exercises with the energy system model PRIMES. Both scenarios under comparison already include policies to support renewable energies and reduce carbon emissions. Thus, in a first step, policy-free quantities had to be constructed in order to derive technology-based supply curves. The first period runs from 2016 to 2020 and the second period runs from 2021 to 2040. Cost reductions via learning and R&D are modelled as a two-factor Cobb-Douglas learning curve. The reduction of electricity demand through investments in energy efficiency is modelled as an exponential function with endogenous investments. With this calibrated model at hand, the adjustment of policy instruments and the effects on the policy compliance costs vis-à-vis the first best policy portfolio has been analysed quantitatively.

3. Results and Conclusion

After having explained the methodology and calibration of the two models used in this exercise, the focus turns now to the results. First, using the newly calibrated and extended WITCH model, the role of R&D investments for energy efficiency improvements is analysed by FEEM. Second, with a newly calibrated model of the different market failures in the European electricity sector, ZEW discusses how policy portfolios need to be adjusted if one of the optimal policy instruments is not available.

3.1. The Role of R&D investments for energy efficiency improvements

Before discussing the policy exercise, we briefly present the conclusions that could be derived from the analytical model, its estimation and inclusion in the WITCH model. First, using the analytical model FEEM shows that information about energy expenditures, R&D subsidies, knowledge spillovers and the parameters governing the R&D process are sufficient to predict the R&D effort in efficiency-improving technologies. Given energy expenditure, the equilibrium R&D effort does not depend on any parameter of the demand for energy or utilization of other inputs. Second, the estimation of the model provides the evidence that all three factors -- value of the energy market, international and intertemporal spillover -- play a significant role in determining the level of innovative activity. It also shows

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that rise of innovation induced by energy expenditure growth has a significant negative effect on energy demand.

Figure 1 shows that when the exogenous SSP2 rate, which was previously assumed in the WITCH model, is replaced with the rate determined endogenously by our calibrated DTC model, FEEM finds that the expected future AEEI is closer to the SSP1 optimistic scenario than previously assumed. The SSP1 target is only 0.13% higher than the recomputed annual AEEI rate. One of the key reasons for this finding is the growth of energy expenditure, which is predicted by the WITCH model, is going to stimulate energy saving innovation and therefore pushes AEEI up. This effect has not been taken into account in the previous forecasts of the AEEI.

Figure 1. Ratio of GDP to Total Primary Energy Supply [T$/PWh] under different Autonomous Energy Efficiency Improvement scenarios. Note that the scale is logarithmic.

Figure 2. Level of R&D subsidy required to achieve the (smoothed) tfpn assumed in the ssp1 scenario. To double the number of patents we need approximately a subsidy

rate of 250%.

The second finding is that even though the distance to the target is small the policy to reach the target is expensive. The estimates imply that an increase of the AEEI rate by 0.2% involves doubling energy saving innovation in the nearest future. Due to decreasing returns to the research effort, the R&D investment would need to more than double. Figure 2 shows the respective subsidy rates. The R&D subsidy required to incentivise such efforts would have to reach the level of around 300%, that is every dollar spent on research with private funds would have to be accompanied with three dollars of government subsidy.

The results suggest that after introducing the high subsidy rate in the first years its level could be reduced in subsequent years. In fact, a high subsidy rate would be necessary only for the first four model periods (20 years) and could be removed completely after 30 years. A simple reason for this pattern is the presence of intertemporal spillover: a massive investment in research in the first years stimulated by the large subsidy would help scientists and engineers to gain experience and increase their future productivity. Thus, in future high level of innovativeness could be sustained even without any subsidies. Since a similar pattern can be observed in nearly all variants of the model we will limit our focus to the first 12 periods (corresponding to the period between 2020 and 2080).

The role of intertemporal spillover is also illustrated in Figure 3. If we do not allow for the presence of the intertemporal spillover effects in our model, a very high subsidy would need to be sustained for all future periods. The sensitivity of the model to the parameter governing the size of spillovers is pictured in Figure 4. The low spillovers scenario assumes that the

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spillover parameter takes the value which is two standard deviations below the point estimate of the intertemporal spillover coefficient taken from the regression. The high spillovers scenario assumes that the spillover parameter is two standard deviations above the point estimate.

Since the parameter is estimated explicitly in the empirical regression we can recover its 95% confidence interval and use it to construct the confidence interval for the required subsidy. If other parameters of the model are estimated accurately, then the required level of R&D is unlikely to be outside the interval bounded by the green and blue lines.

Figure 3. Effect of intertemporal spillovers on the level of R&D subsidies required for the ssp1

energy efficiency improvements.

Figure 4. Level of required R&D subsidy for different assumptions on intertemporal spillover effects.

In Figure 5, we present our findings on the role of international spillovers. If we disregard this type of spillovers the model predicts that required level of subsidy must be higher and the subsidy must be in place for much longer period. The low spillovers scenario assumes that the spillover parameter takes the value which is two standard deviations below the point estimate of the international spillover coefficient from the regression. The high spillovers scenario assumes that the spillover parameter is two standard deviations above the point estimate.

As in the case of intertemporal spillovers, the confidence interval reported in can be used in the regression to examine how precise is our estimate and examine whether the imprecision could affect the results about the subsidy. The narrowness of the confidence interval suggests that the imprecision of the estimate of the elasticity parameter is small and cannot seriously alter the level of required subsidy predicted by the model.

The third parameter which is crucial for the result is the elasticity of research output (number of innovations) with respect to research investment. Figure 6 presents a sensitivity analysis of the respective elasticity. Obviously, a higher elasticity is associated with higher productivity of researchers and thus lower level of required subsidy. The low elasticity scenario corresponds to the assumption that the parameter is two standard deviations below its point estimate in the regression. The wide confidence interval is suggesting that the imprecision of the econometric estimate could substantially alter the results of the model.

In Figure 7 shows how the results alter for different levels of parameter determining the impact of innovations on AEEI. The results suggest that the results are sensitive to the value of this parameter. Furthermore the regression results suggest that the coefficient cannot be precisely determined. One standard deviation of the estimate could raise or lower the implied subsidy rate by 100%.

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FEEM evaluated how the results could be affected by different assumptions on the transfer of technologies between countries. In the baseline model the AEEI in one region can be affected by the innovations generated in other regions. However, the effect of foreign innovations is not estimated explicitly in the regression. Instead the coefficients using statistics on patents citations between countries have been computed. If one tries to estimate the coefficients directly from the regression and then use them in the model, the results change substantially.

Figure 5. Level of required R&D subsidy for

different assumptions on international spillover

effect.

Figure 6. Level of required R&D subsidy for different assumptions productivity of R&D (the elasticity of patents generation with respect to R&D effort.

In this case the model predicts 3000% subsidy in the first period. However the regression results suggest that the precision of these estimates is very low. For this reason we prefer the coefficients estimated in our baseline model, which is based on patents citations data.

In the last exercise we analyze the interplay between research subsidies and carbon price. In addition to our AEEI target a climate target has been set. A global market for carbon emissions permits has been assumed and the equilibrium price of a permit that is necessary to achieve a 2.6 radiative forcing target has been computed. This price has two opposite effects on the energy saving innovation: on the one hand it increases the price of energy incentivising more research, on the other hand it reduces energy consumption lowering the energy intensity in the economy and thus disincentivising energy saving research. The results, presented in Figure 8, suggests that, although the former effect symbolically dominates, the two effects almost balance each other out: setting an ambitious radiative forcing target (involving high carbon price) has no significant effect on private investment in the energy saving innovations. As a result, governments must maintain high level of subsidy even after the introduction of stringent carbon policies.

This result stands in opposition to the finding of price-induced innovation models (Magat 1979, Popp 2002, Lafont and Tirole 1996, Jakeman et al. 2004). These models concentrate on the price of energy as the sole driver of energy saving innovation. As a result, they predict that an increase in price of permits, which drives up energy prices, must increase energy saving technological progress. Since they ignore the role of the volume of energy consumption, they do not take into account that the positive effect of price increase on innovation could be offset by the negative effect of reduction in energy consumption.

Although an increase in carbon price does not affect the level of subsidy required to maintain high rate of AEEI, the presence of the subsidy does have a substantial impact on the costs of carbon policy. Figure 9 plots the total spending on permits for CO2 emissions in European industries. The blue line represents the spending in the absence of any R&D subsidy, while the red line represents the spending when the subsidy is in place. Clearly, the high rate of

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AEEI which is generated by the R&D subsidy reduces the consumption of energy and thus brings down the costs of stringent carbon policy

This logic is confirmed in Figure 10 which presents the decomposition of the effect on policy costs into the effect on price of permits and the effect on the volume of emissions. Since the price of permits is set globally, it is only marginally affected by the R&D subsidy in Europe. In contrast, the subsidy has a clear impact on European demand for emission permits.

Figure 7. Level of required R&D subsidy for different assumption on impact of patents. Low impact corresponds to the assumption that the impact parameter is one standard deviation below the impact parameter estimated in the regression. ‘Estimated (model 2)’ values correspond to model based on estimates produced by the model where the impact of domestic patents and the impact of foreign patents is estimated separately.

Figure 8. Level of required R&D subsidy under different policy scenarios. Rf26 corresponds to a scenario with global carbon emission market and global carbon budget set to achieve a 3.7 radiative forcing target.

Figure 9. Total spending on emission permits in European Industry.

Figure 10. Price of CO2 emission permits and the volume of CO2 emissions in European industry.

3.2. How to Adjust Policy Instruments in a Second-Best World In the previous exercise it has been assumed that policy instruments to correct market failures can be implemented in their first-best configuration. But policy makers face often a different situation. How to adjust policy instruments in a second-best world has been analysed by ZEW and its results are described in the following.

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Using the previously developed analytical model, more could be learned about how in a second-best world, where one of the optimal policy instruments is not available, policy makers would need to adjust the remaining instruments. Note that the analytical model allows to adjust only one instrument at a time. When moving to the numerical model, we are able to adjust several instruments simultaneously.

We are able to derive the following lessons from the analytical model:

If the R&D subsidy is below its optimal rate, the optimal output subsidy for renewable energy technologies needs, ceteris paribus, to be adjusted downwards in the short-term and a upwards in the long-term in compared to the first-best in order to raise welfare.

Below-optimal energy efficiency subsidies require ceteris paribus a higher (lower) output subsidy for immature renewable energy technologies in the short- and long- term in the second-best compared to the first-best in order to raise welfare if the price elasticity of electricity demand is above (below) unity.

A below-optimal output (learning) subsidy requires ceteris paribus a higher (lower) R&D subsidy in the second-best compared to the first-best in order to raise welfare if the output increase via R&D is larger (smaller) than the replacement of learning by R&D.

Below-optimal energy efficiency subsidies require ceteris paribus a higher (lower) R&D subsidy in the second-best compared to the first-best in order to raise welfare if the price elasticity of electricity demand is above (below) unity.

A below-optimal R&D subsidy suggests ceteris paribus a higher subsidy for energy efficiency investment affecting the short-term and lower energy efficiency subsidies affecting the long-term in the second-best compared to the first-best in order to raise welfare.

A below-optimal output (learning) subsidy for immature renewable energy technologies requires ceteris paribus lower energy efficiency subsidies affecting the short- and long-term in the second-best compared to the first-best in order to raise welfare.

In the following, we move to the numerical model and describe and interpret the results from its analysis. In the theoretical considerations the impact of one below-optimal or non-available policy instrument on each other instrument separately has been studied. With the numerical model at hand, the impact of one non-available policy instrument on all remaining instruments could be studied simultaneously. This is supposed to reveal possible interactions

between the adjustments of second-best instruments.

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Figure 11: Electricity mix in the scenario Base for the EU in 2020 (the first model period)

The electricity mix changes endogenously across scenarios but is not substantially different compared to the mix in the Base scenario as depicted in Figure 11. Electricity prices, measured in Euros, endogenously determine the electricity market equilibrium. Wind and solar R&D as well as energy efficiency improvements emerge endogenously, too. Investments in energy efficiency are normalized such that Base investments are set to zero. Thus, investments smaller than in Base show up as negative numbers. The investments in energy efficiency denoted by 'Effic 1/2' are attributed to both periods. The relative magnitudes of policy instruments are expressed in percent of 1st-Best implementations. (Since the 1st-Best scenario assumes zero second-period output subsidies, second-period output subsidies are expressed relative to their first-period values.) The values in brackets report the absolute magnitudes of policy instruments in EUR/MWh.

No Policy Base First Best No-R&D-

No-Output- No-Effic- Feed-in

Sub Sub Sub

Policy costs wrt Base [%] 0.00 100.00 67.56 68.49 67.59 99.37 100.26

Share Renewables 1 [%] 25.42 26.45 26.23 26.23 26.15 26.45 26.60

Share Renewables 2 [%] 29.34 32.44 32.63 32.48 32.63 32.60 32.57

Wind output sub 1 [% (EUR/MWh)] 0 0 100 (0.66) 97 (0.63) 0 0 123 (0.82)

Solar output sub 1 [% (EUR/MWh)] 0 0 100 (5.11) 97 (4.93) 0 0 138 (7.07)

Wind output sub 2 [% (EUR/MWh)] 0 0 0 18 (0.12) 0 0 123 (0.82)

Solar output sub 2 [% (EUR/MWh)] 0 0 0 18 (0.91) 0 0 138 (7.07)

Wind R&D sub 1 [% (EUR/MWh)] 0 0 100 (0.41) 0 100 (0.41) 80 (0.37) 0.30

Solar R&D sub 1 [% (EUR/MWh)] 0 0 100 (0.81) 0 100 (0.82) 80 (0.77) 0.61

Effic sub short 1 [% (EUR/MWh)] 0 0 100 (0.12) 99.7 (0.16) 99.2 (0.12) 0 0

Effic sub short 2 [% (EUR/MWh)] 0 0 100 (0.45) 99.9 (0.46) 99.8 (0.45) 0 0

Effic sub long 1/2 [% (EUR/MWh)] 0 0 100 (0.64) 99.9 (0.64) 99.8 (0.64) 0 0

Elec tax 1 [EUR/MWh] 0 0 0 0 0 0 0.46

Elec tax 2 [EUR/MWh] 0 0 0 0 0 0 0.76

Wind R&D

0 0 0.06 0.01 0.06 0.07 0.02

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Effic 1

-1.68 0 3.46 3.48 3.47 -0.03 -0.02

Effic 2

-4.69 0 5.46 5.56 5.46 -0.11 -0.02

Elec price 1 [EUR/MWh] 97.69 101.00 94.93 94.91 95.15 101.04 100.90

Elec price 2 [EUR/MWh] 85.28 98.00 86.97 87.24 86.99 97.67 97.95

CO2 price 1 [EUR/t] 0 22.00 20.86 20.86 20.91 22.01 21.89

CO2 price 2 [EUR/t] 0 39.00 29.46 29.70 29.48 38.72 38.30

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Table 1: Simulation results for the ten EU climate and energy policy scenarios in periods 1 (short-term) and 2 (long-term). Policy costs are expressed in percent of the policy costs of the Base scenario with a carbon price only. The relative magnitudes of the policy instruments are expressed in percent of 1st-Best. (*Since 1st-Best assumes zero second-period output subsidies, second-period output subsidies are expressed relative to their first-period values.) The values in brackets denote the absolute magnitudes of policy instruments in EUR/MWh.

Scenario Base with carbon pricing only creates a loss, i.e. a policy cost, compared to No-Policy without any carbon or energy policy. This cost reflects about 7.5 billion Euros. It serves as our reference and is set to 100%. The policy costs of the other scenarios are measured in percent of the Base cost. 1st-Best entails policy costs which are about three quarters of the Base cost. The remaining columns contain the second-best scenarios that generate smaller cost savings than 1st-Best. The cost reduction due to leaving out energy efficiency subsidies is much more pronounced than the cost saving due to leaving out R&D or output subsidies for immature renewable energies. This result supports the important role of energy efficiency improvements and related policies in achieving emissions reductions effectively. Our last scenario assumes that apart from a carbon price, a feed-in tariff is the only available instrument. This reflects to large part the current situation in Europe. This implementation excludes R&D and energy efficiency subsidies. The output subsidy and tax rates exceed those of the 1st-Best scenario. The Feed-in scenario creates even higher policy costs than Base. This means, the distortion created by the tax instrument overcompensates the benefit of using the output subsidy, which is not present in scenario Base. These results suggest, not making use of feed-in tariffs is welfare superior to making use of them. They also suggest that feed-in tariffs are inappropriate instruments for addressing the market failures in the domain of R&D and in the domain of energy efficiency. The carbon price declines in scenario 1st-Best, especially in the second period, because the policy instruments used in this scenario reduce electricity demand and foster wind and solar power. As a consequence, carbon emitting electricity generation is dampened. The second-best scenarios in the subsequent columns are in most cases less effective in reducing carbon prices. The Feed-in scenario introduces a tax on fossil fuel generation to fund the output subsidy which induces an additional shift of electricity generation from fossil to renewable energies. This is reflected by lower carbon prices. Note that total emissions generated by the power sector are constant. As predicted by Table1, the second-period renewable energy share (electricity generation from wind and solar power divided by total electricity generation) never exceeds that of 1st-Best. The first-period renewable energy share, on the contrary, exceeds it in scenarios without energy efficiency subsidies. In these scenarios, the higher electricity demand goes along with a higher renewable energy share. This implies that the additional energy production is biased towards renewable energies. In the second-best scenarios the remaining policy instruments are re-adjusted so that welfare is maximized under the restriction of the non-availability of a specific policy instrument. In accordance with the theoretical findings, the first-period output subsidies for immature renewable energy technologies are in scenario No-R&D-Sub lower than in 1st-Best. The second-period output subsidies for immature renewable energies are positive and hence higher than in 1st-Best. Both energy efficiency subsidies affecting the long-term, 'Effic sub short 2' and 'Effic sub long 1/2' are slightly reduced in No-R&D-Sub compared to 1st-Best.

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Due to the interaction of policy instruments, the adjustment of the short-term subsidy for energy efficiency is more complicated. When comparing the scenario without output subsidy, No-Output-Sub, with the 1st-Best, the welfare maximizing second-best choice of the R&D subsidies for wind and solar turns out to be slightly higher than the first-best choice. This ambiguous outcome is also confirmed by the theoretical considerations. A clearer difference appears when calculating R&D subsidy payments per (first-period) physical output volume of the corresponding technology (not shown in the table). Then the R&D subsidy rate per output clearly rises in No-Output-Sub compared to 1st-Best. We conclude that in our model specification and calibration, the output increase resulting from higher R&D subsidies is larger than the replacement of learning by R&D. All types of energy efficiency subsidies slightly decrease in the second-best scenario Output-Sub compared to 1st-Best. Assuming that energy efficiency subsides are not available as policy instruments as examined in scenario No-Effic-Sub, the first-period output subsidies drop to zero. In this case the model generates a strong effect by finding a corner solution for the output subsidies. A possible reason is that the output subsidies' potential for reducing energy supply and demand is by far smaller than the potential of the energy efficiency subsidies which they replace and the R&D subsidies are reduced to 80% of the 1st-Best values. This implies that the price elasticity of electricity demand is below unity in our model specification and calibration. As a consequence, the reduction of electricity from renewable energies translates into lower electricity demand. The reduction of electricity from renewable energies leads to a more than proportional increase in the electricity price, which in turn incentivizes higher investments in energy efficiency. Nonetheless, the non-availability of energy efficiency subsidies results in a strong reduction in investments in energy efficiency as indicated by the negative figures.

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List of Abbreviations

AEEI Autonomous Energy Efficiency Improvement

BAU Business as Usual

DTC Directed Technical Change

GDP Gross Domestic Product

R&D Research & Development

RES-E Electricity Generating Renewable Energy Sources

SSP Shared Socioeconomic Pathway

WITCH World Induced Technical Change Hybrid model