International environmental agreements with a stock pollutant, uncertainty and learning
|Speaker:||Alistair Ulph, University of Southampton|
|Date:||Friday 11 October 2002|
|Location:||Room 106 Streatam Court|
In this paper I address the question of how uncertainty about damage costs and the possibility of resolving that uncertainty in the future affects the incentives for countries to join an international environmental agreement. I use a model which goes beyond earlier models that tried to address such issues by allowing for an arbitrarily large number of countries, by having a dynamic model with a stock pollutant, by using a solution concept which does not restrict the size of an IEA, and by using the dynamic structure to model different membership rules for an IEA: fixed (countries commit) or variable (countries decide each period whether to join). I show that, with fixed membership, no learning will lead to more members and higher global welfare than learning for high expected damage costs and high uncertainty; but otherwise learning results in at least as high membership and global welfare as no learning. With variable membership, learning leads to higher membership (in the second period) but lower global welfare than no learning. Fixed membership leads to higher global welfare than variable membership if it results in at least as many signatories who abate pollution in each period and state of the world as variable membership; otherwise variable membership results in higher global welfare than fixed membership. The parameter values for which fixed membership is better than variable occur in 20% of cases with no learning and 2% of cases with learning.