costs, over the discounted expected electricity production. We find that the LCOE computed against _future_ production, investment and prices should be equal to the average _future_ electricity price. But in practice, fossil fuel, carbon and electricity prices and the resale value of power plants of any given technology may be difficult to anticipate. They depend on future investment and production from all technologies, which depend in turn on future prices and regulations. We thus investigate whether LCOEs computed against _current_ signals can provide an operational rule of thumb to guide investment decisions.<sup>5</sup> In a numerical calibration using data from the European electricity sector, we find that such _operational LCOE_ should be several times higher for new renewable power plants than for new gas power plants. Indeed, the operational LCOE does not account for future increases in fuel and carbon prices, and does not consider that gas plants will be under-used in the medium term once replaced by renewable power. As a result, if an agent was to assess investment in new capacity for the European electricity market by computing LCOEs from current prices, she would recommend to invest too much in gas capacity, and not enough in renewable capacity.

The bad performance of such imperfectly computed LCOEs is neither surprising nor a theoretical challenge under the rational expectation hypothesis. It stresses that low-carbon capital cannot be valued from current price signals only, such valuation requires to anticipate entirely a future decarbonization strategy.<sup>6</sup> The novelty here is in determining the direction and magnitude of the error induced by myopic decision rules in the transition to clean electricity.

The remainder of the paper is structured as follows. Section 1 details the model. Section 2 derives the first-order conditions, optimal _production_ decisions at each point in time, and the electricity price. It then studies the different phases through which the power sector may go during its transition to renewable power. Section 3 tackles optimal _investment_ decisions and the levelized cost of electricity. Section 4 provides numerical simulations calibrated with data from the European electricity sector. Section 5 concludes.

# **1. Model**

A social planner controls the supply of electricity, using and investing in three different technologies: an existing high-carbon technology ( _h_ , coal power), a fossil-fueled low-carbon technology ( _l_ , gas), and an inexhaustible zero-carbon technology ( _z_ , renewable power).

> 5 Such usage of the LCOE would be consistent with the view, popular in the early literature on investment, that firms may take optimal investment decisions based on current prices and current returns only (e.g., Arrow, 1964; Jorgenson, 1967, p. 145). It has already been signaled that the efficiency of such myopic investment decisions critically depends on investment prices being exogenous: under endogenous investment costs, optimal investment decisions require to perfectly anticipate all future prices (Gould, 1968; Mussa, 1977).

> 6 In practice, such pathways are usually provided by numerical models of the electricity sector (e.g., Seebregts et al., 2002; Loulou, 2008) or wider energy-environment-economy models (e.g., Weyant, 2004; Waisman et al., 2012; Kriegler et al., 2014). These models do not always model adjustment costs explicitly, but they take into account the limited ability to switch quickly from high- to low-carbon capital by building on maximum investment speeds (Wilson et al., 2013). Several authors have stressed that in this case optimal investment pathways are in part driven by such exogenous bounds, therefore calling for more explicit discussion of their calibration (Wilson et al., 2013; Iyer et al., 2014; Vogt-Schilb et al., 2014a).

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