Promoting CCS in Europe: A Case for Green Strategic Trade Policy?

AuthorAune, Finn Roar
PositionCarbon capture and storage

    According to IEA (2018), there is a huge gap between the first-best social optimal utilization of Carbon Capture and Storage (CCS) technologies to lower global C[O.sub.2] emissions and the current, negligible diffusion of this technology. For example, in the Sustainable Development Scenario developed in IEA (2018), which is a cost-efficient path consistent with the Paris agreement, the global carbon capture capacity in the power sector should be 1500 MtC[O.sub.2] by 2040--the current capacity is only 2.4 MtC[O.sub.2]. Likewise, whereas the global carbon capture capacity in manufacturing and energy transformation sectors should be 1600 MtC[O.sub.2] by 2040, the current capacity is 28 MtC[O.sub.2].

    A number of factors may explain this big mismatch, for example, the price of carbon may be by far too low; costs of renewables may have decreased more rapidly than expected (which tends to push down the carbon price); there might be market imperfections in the CCS value chain of capture, transport and storage that slow down the speed in CCS development; policy makers may be wary of popular concerns about the safety of carbon storage and that CCS is just a way to prolong the fossil-fuel age; and there might be substantial policy uncertainty with respect to whether countries will take sufficient actions to reach the 2 degrees target. Because of impediments, IEA (2013) argues that a key action to kick off innovation and diffusion of CCS is to introduce financial CCS support mechanisms.

    There are two business models to spur CCS. One option is to support purchasers of CCS technologies by covering a part of the additional investment cost of CCS. The alternative model is to focus on the CCS technology suppliers by supporting their research, development and production costs. To our knowledge, the literature has not yet compared the pros and cons of the two routes. Thus, our first research question is to what extent promotion of CCS should be through subsidising development and production of CCS technologies--an upstream subsidy--or by subsidising the purchasers of CCS technologies--a downstream subsidy.

    In the electricity sector, the CCS technology can be applied both to coal power and gas power. These two technologies are likely substitutes in demand. Our second research question is therefore to what extent policy makers should give priority to one of the CCS technologies, that is, whether the subsidy to CCS coal power should exceed the subsidy to CCS gas power.

    We study the two research questions both theoretically within a stylized game theoretic model and numerically by using an economic energy market model that is innovatively integrated with strategic trade policy. In the analyses, we take into account that competition between CCS technology suppliers is imperfect as there is only a few potential suppliers in the world; some of these are located in the EU, others are situated in other regions. While there may be supply of CCS power plants both from EU and non-EU actors, we believe that over time the only significant market for CCS may be in the EU. So far, only the EU has set very ambitious climate targets. In particular, emissions from the electricity sectors are supposed to be reduced by around 95 percent (relative to 1990) by 2050, see European Commission (2011). Therefore, we assume that in the EU there will be both demand for, and supply of, CCS power plants, and we focus on how the EU should design its CCS policy by taking into account the EU suppliers of CCS power plants, the EU purchasers of CCS power plants, as well as how non-EU suppliers will respond to the EU CCS policy.

    Why should public bodies offer subsidies to stimulate deployment of CCS technologies; would not an appropriate carbon tax do the job? As is well known, in an economy where C[O.sub.2] emissions are the only imperfection, efficiency is obtained by imposing a price on emissions equal to the social cost of carbon. In our model, there is, however, an additional source of imperfection, namely market power in the supply of CCS power stations. In order to maximize welfare, additional instruments are therefore required. While this is the case both from a global and an EU perspective, the choice of instruments differs. From a global perspective, instruments should be imposed in order to correct fully for market power in the CCS value chain. In contrast, from an EU perspective also the distribution of profits between the EU and the non-EU producers is of importance. Therefore, for the EU the question is to find instruments that obtain efficiency among the EU producers and also shift profits from the non-EU to the EU producers; the latter is discussed in the literature on strategic trade policy, see, for example, Brander and Spencer (1985), Neary (1994), and Leahy and Neary (1997; 1999).

    The strategic trade literature typically assumes that there is production of a homogenous good in two countries, imperfect competition and no externality. In addition, the homogenous good is consumed in a third country. We depart from this literature by assuming there is consumption in one of the producing countries only; below, this country is referred to as the EU (see the discussion above on the EU climate policy).

    Using a Cournot-type of model with one homogenous good, we show in Section 2.1 that the EU can maximize its welfare by offering an upstream subsidy to its producers. The EU should, however, not offer a downstream subsidy. (1) Both an upstream and a downstream subsidy increases the use of the technology, which is desirable because of imperfect competition in the market. However, by prioritizing an upstream subsidy to the EU producers, production and profits are shifted from the non-EU producers to the EU producers. In contrast, a downstream subsidy stimulates production also from the non-EU producers, and hence does not shift production and profits from non-EU producers to the EU producers. The EU should offer an upstream subsidy that stimulates the EU producers to increase their production by so much that they voluntarily choose their competitive quantities. This requires that the output price is equal to the unit cost of production and therefore there will be no production from the non-EU producers.

    We then solve the model with two goods (Section 2.2). In this case, the EU may offer four subsidies: one upstream subsidy to good (technology) 1, another upstream subsidy to good 2, one downstream subsidy to stimulate purchase of good 1, and another downstream subsidy to promote good 2. Note that we use subsidy rates, that is percentages, not subsidies specified as a value per unit of production. Hence, upstream subsidy rates are percentages of the cost of producing CCS power plants, whereas downstream subsidy rates are percentages of the purchaser price of CCS power plants. Below, we use "subsidy rate" and "subsidy" interchangeably.

    In our model the reason to subsidise is to shift profits from non-EU producers to EU producers, and to increase the supply of goods in general, thereby benefiting also consumers. We find that also with two goods, only upstream subsidies should be offered, and each optimal upstream subsidy should reflect the social value of the good. Hence, the good with the highest social value should receive the greatest upstream subsidy. Finally, the combination of (upstream) subsidies should lower the price by so much that production is not profitable for non-EU producers, thereby shifting profits from non-EU to EU producers. To the best of our knowledge, these results are new to the literature.

    While the theory models in Section 2 give rules of thumb on how to design upstream and downstream subsidies, we develop a numerical framework of the European energy markets to illustrate the magnitude of subsidies that maximizes EU welfare. Although there exist several numerical studies of strategic trade policy, see, for instance, Baldwin and Krugman (1988), Venables (1994) and Greaker and Rosendahl (2008), to our knowledge no study has yet used a comprehensive numerical framework of the energy sector to identify the magnitude of strategic trade policy instruments.

    At the core of our numerical framework is a numerical model of the European energy markets; LIBEMOD. Here, electricity can be produced by a number of technologies, including conventional fossil-fuel based power plants, CCS power plants using either natural gas or coal, and renewables. While costs of investment and operating costs are higher for CCS plants than for conventional fossil-fuel based plants, the CCS technologies remove most of the carbon from the fossil fuels that have been combusted. For a given set of parameters, for example, costs and efficiencies of electricity plants, as well as a set of policy instruments, for example, a uniform carbon tax, LIBE-MOD simulates the equilibrium of a future year. This set up is, however, not suitable to examine the impact of upstream and downstream subsidies: Whereas cost of investment of a CCS power plant is a parameter in LIBEMOD, we want this cost, which is the price of a CCS power plant, to be endogenous. In order to establish a model with endogenous price formation of CCS power plants, we develop a three-stage procedure that integrates Cournot competition with the large-scale numerical LIBEMOD model, which has competitive markets, see Section 3.

    Application of the numerical framework requires a Reference scenario where it is profitable to invest in CCS. To make investors choose CCS power plants instead of conventional fossil-fuel based stations, the carbon price has to be significant. This was not the case in 2020 with a C[O.sub.2] price below 30 euro, see, for example, EMBER (2020), and might neither be the case in 2030, see PRIMES (2019) and Aune and Golombek (2021). In this study we therefore analyze a more distant year than 2030, namely 2050. For this year, the GHG emissions target of the EU is a...

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