# Publications by Year: Working Paper

*Economics Letters*). [Internet]. Working Paper. Publisher's VersionAbstract

Equilibrium climate sensitivity (ECS), the link between concentrations of greenhouse gases in the atmosphere and eventual global average tempera- tures, has been persistently and perhaps deeply uncertain. Its ‘likely’ range has been approximately between 1.5 and 4.5 degrees Centigrade for almost 40 years (Wagner and Weitzman, 2015). Moreover, Roe and Baker (2007), Weitzman (2009), and others have argued that its right-hand tail may be long, ‘fat’ even. Enter Cox et al. (2018), who use an ‘emergent constraint’ approach to characterize the probability distribution of ECS as having a cen- tral or best estimate of 2.8◦C with a 66% confidence interval of 2.2-3.4 ◦C. This implies, by their calculations, that the probability of ECS exceeding 4.5◦C is less than 1%. They characterize such kind of result as “renewing hope that we may yet be able to avoid global warming exceeding 2[◦C]”. We share the desire for less uncertainty around ECS (Weitzman, 2011; Wagner and Weitzman, 2015). However, we are afraid that the upper-tail emergent constraint on ECS islargely a function of the assumed normal error terms in the regression analysis. We do not attemptto evaluate Cox et al. (2018)’s physical modeling (aside from the normality assumption), leaving that task to physical scientists. We take Cox et al. (2018)’s 66% confidence interval as given and explore the implications of applying alternative probability distri- butions. We find, for example, that moving from a normal to a log-normal distribution, while giving identical probabilities for being in the 2.2-3.4◦C range, increases the probability of exceeding 4.5◦C by over five times. Using instead a fat-tailed Pareto distribution, an admittedly extreme case, increases the probability by over forty times.

Keywords: climate change, climate sensitivity, fat tails

We extend the standard ‘Prices vs. Quantities’ framework to cover two independent and identical jurisdictions, A and B. Both jurisdictions set a price or quantity to maximize their own expected welfare conditional on the instrument type and amount chosen by the other jurisdiction. With iid uncertainty, a dominant strategy of both jurisdictions is to choose a price instrument when the slope of marginal benefit is less than the slope of marginal cost and a quantity instrument when the condition is reversed. With n countries, if the slope of marginal benefit is equal to the slope of marginal cost, the welfare cost at the equilibrium in which countries coordinate on prices is higher, by a factor of n, than the welfare cost at the equilibrium in which countries coordinate on quantity. By extending the standard ‘Prices vs. Quantities’ criterion from the basic choice framework to a strategic setting, we allow the choice of policy type and amount to take into account the free-riding by other jurisdictions and discover the welfare benefit of coordination on quantities.

JEL Codes: C7, D8, F5, H21, Q28, Q58

Keywords: prices versus quantities, regulatory instruments, pollution, climate change

The possibility of intertemporal banking and borrowing of tradeable permits is often viewed as tilting the various policy debates about optimal pollution control instruments toward favoring such time-flexible quantities. The present paper shows that this view is incorrect for a natural dynamic extension of the original 'prices vs. quantities' information structure that allows the firms to know and act upon the realization of uncertain future costs two full periods ahead of the regulators. For any given circumstance, this paper shows that either a fixed price or a fixed quantity is superior in expected welfare to time-flexible banking and borrowing. Furthermore, the standard original formula for the comparative advantage of prices over quantities contains sufficient information to completely characterize the regulatory role of intertemporal banking and borrowing. The logic and implications of these results are analyzed and discussed.

JEL Codes: Q50, Q51, Q52, Q54, Q58

Keywords: prices, quantities, prices versus quantities, regulatory instruments, pollution, climate change