Environmental Prices, Uncertainty and Learning

Abstract

There is an increasing demand for putting a shadow price on the environment to guide public policy and incentivize private behaviour. In practice, setting that price can be extremely difficult as uncertainties abound. There is often uncertainty not just about individual parameters but about the structure of the problem and how to model it. A further complication is the second-best nature of real environmental policy-making. In this paper, we propose some practical steps for setting prices in the face of these difficulties, drawing on the example of climate change. We consider how to determine the overall target for environmental protection, how to set shadow prices to deliver that target, and how we can learn from the performance of policies to revise targets and prices. Perhaps most significantly, we suggest that estimates of the marginal cost of environmental protection, rather than the marginal benefit, will often provide the more consistent and robust prices for achieving targets.

Keywords: Climate change, cost-benefit analysis, emissions trading, learning, model uncertainty, shadow price

JEL Classification: Q51, Q52, Q54, Q58