On the Perils of Stabilizing Prices When Agents are Learning.

We show that price level stabilization is not optimal in an economy where agents have incomplete knowledge about the policy implemented and try to learn it. A systematically more accommodative policy than what agents expect generates short term gains without triggering an abrupt loss of con- fidence, since agents update expectations sluggishly. In the long run agents learn the policy implemented, and the economy converges to a rational expectations equilibrium in which policy does not stabilize prices, economic volatility is high, and agents suffer the corresponding welfare losses. However, these losses are outweighed by short term gains from the learning phase

Evaluating Carbon Capture and Storage in a Climate Model with Directed Technical Change

Carbon capture and storage (CCS) is considered a critical technology needed to curb CO2 emissions and is envisioned by the International Energy Agency (IEA) as an integral part of least-cost greenhouse gas mitigation policy. In this paper, we assess the extent to which CCS and R&D in CCS technology are indeed part of a socially e cient solution to the problem of climate change. For this purpose, we extend the intertemporal model of climate and directed technical change developed by Acemoglu et al. (2012, American Economic Review, 102(1): 131{66) to include a sector responsible for CCS. Surprisingly, even for an optimistic cost estimate available for CCS (60/ton of CO2 avoided), we nd that it is not optimal to deploy CCS or devote resources to R&D in CCS technology either in the near or distant future. Indeed, it is only when the marginal cost of CCS is less than 12/ton that a scenario with an active CCS sector (including R&D) becomes optimal, though not in the near future