A Simple Model of International Environmental Agreements with Bayesian Learning

Abstract

In this paper I study the economics of self-enforcing international environmental agreements where agents never know what exactly the state of the world is. Explicitly, I consider countries using Bayesian learning to update their beliefs on the state of the world. Using a very simple framework of allowing pollution as a common bad, I study how Bayesian learning conveys message to countries and whether a full disclosure of information can necessarily improve the aggregate welfare. Interestingly, I find that the value of information is always negative which suggests that strategic interactions between countries significantly make the countries worse off. I also consider a dynamic setting where countries emissions can affect the learning process and surprisingly I find that the equilibrium breaks down to a coalition that cannot have more than two countries.