Seit dem Atomausstieg am 15. April 2023 sind die Großhandelsstrompreise in Deutschland deutlich gesunken. Innerhalb des deutschen Merit-Order-Systems galten Atomkraftwerke als die kostengünstigste Form der Stromerzeugung. Hätten die Atomkraftwerke weiterbetrieben werden können, wären die Großhandelsstrompreise für den Zeitraum vom 16. April 2023 bis zum 31. Dezember 2023 voraussichtlich um 1% bis 8% niedriger gewesen. Insbesondere im Oktober hätte der Weiterbetrieb der Atomkraftwerke die Großhandelsstrompreise gesenkt, vor allem in Zeiten hoher Stromnachfrage und geringer Verfügbarkeit erneuerbarer Energien.

In this paper, we discuss how environmental damage and emission reduction policies affect the conduct of monetary policy in a two-sector (clean and dirty) dynamic stochastic general equilibrium model. In particular, we examine the optimal response of the interest rate to changes in sectoral inflation due to standard supply shocks, conditional on a given environmental policy. We then compare the performance of a nonstandard monetary rule with sectoral inflation targets to that of a standard Taylor rule. Our main results are as follows: first, the optimal monetary policy is affected by the existence of environmental policy (carbon taxation), as this introduces a distortion in the relative price level between the clean and dirty sectors. Second, compared with a standard Taylor rule targeting aggregate inflation, a monetary policy rule with asymmetric responses to sector-specific inflation allows for reduced volatility in the inflation gap, output gap, and emissions. Third, a nonstandard monetary policy rule allows for a higher level of welfare, so the two goals of welfare maximization and emission minimization can be aligned.

This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.