Carbon Content of Electricity Futures in Phase II of the EU ETS.

AuthorFell, Harrison
  1. INTRODUCTION

    Thermal electricity from fossil sources generates C[O.sub.2] emissions as a by-product, and carbon policies aim to internalize the social cost of emissions by placing a price on them. If emissions are costly, they should be treated like any other input for electricity generation such as labor, capital and fuel. The costs of emitting C[O.sub.2] are thus passed through to the ultimate "polluters," i.e., the consumers who demand energy-intensive goods. The degree to which carbon costs are passed forward to electricity prices depends on market conditions (e.g. the degree of competition and consumers' demand response), and is important to determine the full distributional costs of climate policy, as well as its effect.

    One important recent example where incidence effects gave rise to a heated debate is the "cost pass-through" discussion on EU ETS and electricity prices. (1) The debate started with a report by Sijm et al. (2006), which focuses on the electricity sector and reports peak and base load pass-through estimates for Germany and the Netherlands using data for the first half year of 2005. (2) Sijm et al. (2008) extend the analysis to seven other EU ETS countries and a longer period. Both studies find positive pass-through rates for most countries, which is consistent with the interpretation of carbon as an opportunity cost.

    The approach taken in these studies consists in applying a relatively simple econometric OLS framework to electricity spreads, which implies a series of restrictive assumptions. First, the price-setting generation technology is imposed a priori by using either the dark or the spark spread, although the true marginal generator may change every hour. This introduces a measurement error to the extent that the true marginal generator differs from the one used to compute the spread. Second, this type of analysis imposes a complete pass-through of fuel costs while estimating the degree of carbon cost pass-through, thus creating an artificial distinction among inputs of production. Third, it does not allow for interactions between prices for electricity, input fuels and carbon; and fourth, it assumes that carbon costs are passed through either immediately or within a short time period.

    It is likely that electricity and input prices are determined jointly. For instance, an increase in carbon prices may (over time) lead to a shift in generation from coal to natural gas. This decreases the demand for coal and increases that for gas, thereby increasing the gas/coal price ratio. At the same time, the increase in electricity prices will lead to a decrease in demand in the long run, which in turn can impact the demand for C[O.sub.2] permits and for input fuels. This interdependency may lead to complex and possibly prolonged adjustments in the system of prices to a shock in a particular variable. We address this by applying a vector error correction model (VECM).

    Several papers have addressed the issue of cost pass-through by means of a cointegration framework. Fezzi and Bunn (2010) use a structural VECM that jointly models UK electricity, natural gas prices, and EU-ETS allowance (EUA) prices over Phase I of the EU-ETS. Their results imply that electricity and input prices are in fact cointegrated and find that a 1% increase in EUA prices led to a 0.32% long-run increase in U.K. electricity prices. Similarly, Fell (2010) carries out a VECM analysis of the Nordic electricity market (Nord Pool) for the years 2005-2008 using a dependent variable vector that includes prices for hourly spot electricity, natural gas, coal, and EU-ETS allowances (EUAs). He reports theoretically-consistent cost pass-through rates in the shortterm, but also pronounced differences between short-term and long-term price adjustments.

    Zachmann and von Hirschhausen (2008) also use a cointegration framework, though in a single-equation form rather than a VECM, using futures data for 2005-2006. They argue that carbon costs are passed through asymmetrically in Germany: The response to an increase in carbon prices had an immediate positive effect on electricity prices, but carbon price decreases did not elicit an electricity price response of the same magnitude. Extending the analysis to France, Belgium and the Netherlands for the period 2007-2010, Lo Prete and Norman (2013) again find evidence of cost pass-through, but not of asymmetry.

    While these papers focus on estimations for separate electricity markets, Bosco et al. (2010) provide evidence that electricity prices are cointegrated across national markets. This suggests that an assessment of carbon cost pass-through in a multi-country framework may be warranted. To allow for such cross-market relationships, we use a VECM that includes one-year futures for electricity (baseload and peakload) as well as input prices and a set of control variables. We focus on electricity markets in Germany, France, the Netherlands, Nord Pool and Spain (abbreviated as DE, FR, NL, NP, and ES, respectively). A second contribution lies in our focus on market data for the delivery period November 2009-2012 (more precisely, on one-year futures between November 2008 and December 2011), making our paper one of the first studies that measures the post financial crises impact of Phase 2 of the EU ETS on electricity prices exclusively. (3) This examination of more recent data also allows us to see how the fast growth in renewable energy, particularly in Spain and Germany, may affect cost pass-through.

    The major drawback of a multi-country, multi-commodity cointegration framework is its complexity. The impact of a shock in one variable on all other variables in the system is determined by the interaction of a series of parameters and has to be estimated using impulse-response functions (IRFs), but little economic interpretation (and therefore intuitive verification) can be attached to a single parameter estimate (see Lutkepohl, 2005). At the same time, VECMs tend to be sensitive to the choice of lags of the underlying vector autoregressive process and other specifications. The combination of high complexity and sensitivity to parameter choices implies caution in the interpretation of the results. For this reason, we also estimate cost pass-through using somewhat simpler autoregressive conditional heteroskedasticity (ARCH) approaches that treat fuel and C[O.sub.2] prices as exogenous to the electricity price. We believe that by combining the results from all models, we obtain a better understanding of the underlying processes than by relying on one estimation method alone.

    We find that carbon costs are passed through to electricity futures, that electricity and input prices are cointegrated, and that there appear to be further cointegrating relationships between electricity prices of adjacent markets. The results also show how sensitive cost pass-through estimates are for model specification. In the specifications that do not allow for cross-market relationships, we find that the C[O.sub.2] price affects electricity prices even more during peakload than during baseload in some markets, although the difference is not always statistically significant. This is surprising, because the lower carbon intensive gas plants have traditionally been the marginal generators during peak demand periods and, thus, we would expect a lower response to carbon price movements during peakload. These findings change considerably in the multi-country cointegration framework where the results are more in line with expectations in that base load pass-through is greater than that of peakload. However, pass-through rates for both baseload and peakload for many markets in the multi-country framework are somewhat higher than expected.

    In the next sections, we describe the theoretical relationship between carbon and electricity prices and present our methodology and data. Section 5 contains our results, and Section 6 concludes.

  2. THEORETICAL FRAMEWORK

    We start by showing the theoretical relationship between input and electricity prices. In a competitive wholesale electricity market, the electricity price is equal to the marginal cost of generation for the marginal generator, which is usually fossil-based. Let R refer to the residual demand for fossil-based electricity, which is total demand net of generation by technologies other than coal, oil and natural gas. Residual demand is a function of exogenous factors X (such as economic activity, temperature and the availability of renewable energy), and it will also depend on prices for electricity, at least in the long run, and possibly also on fuel and allowance prices:

    R = r(P,F,A;X) (1)

    Here, P, F and A refer to the price of electricity, fuel and allowances, respectively, and r(*) is the ordinary demand function for fossil-based generation. The supply of fossil-based electricity has to equal its demand. This establishes a relationship between electricity prices, input prices and demand:

    P = p(F, A;X)= K(R)+ F *[eta](R)+ A * [psi](R) (2)

    with R defined by (1). K refers to the per-unit cost of labor, capital and other non-fuel costs, g is the heat rate (MWh of fuel per MWh of electricity) and w is the emission intensity (C[O.sub.2] per MWh of electricity), all of which depend on the identity of the marginal generator and thus on residual demand. The interpretation of p(*) is that of the marginal cost function or inverse supply function.

    We define cost pass-through as the total effect of an exogenous shock in the allowance price on the electricity price. Totally differentiating (2), setting dF= dX=0, and rearranging (see Appendix) leads to:

    [mathematical expression not reproducible] (3)

    where [p.sub.R] and [P.sub.R] represent the slope of the inverse supply and demand functions for electricity, respectively. Equation (3) describes the equilibrium effect of an exogenous change in the allowance price on the electricity price, allowing for indirect effects via demand...

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