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Loss Aversion In International Environmental Agreements

Finally, in section 4.3, we will test the robustness of our three-point simulation results. Footnote 15 First, we check if the results are available in the sects. 4.1 and 4.2, derived from B-1.1, consider the higher values of B. Second, we will conduct simulations using the restriction conditions adopted in Lemmas 2 and 3, Section 3.4, to verify the prioritization of learning scenarios with respect to expected IEA membership and expected benefits. Third, in sects. 4.1 and 4.2, we assume that each country has a constant relative risk aversion (CRRA) Utility Function, and we will test whether our key digital results are still in place, even if we use a constant absolute risk version (CARA) Utility Function. In the partial learning scenario, countries must decide whether or not to join the IEA without knowing the true cost of emissions damage, but they can make future emissions decisions based on this knowledge. As a result, countries` emissions decisions do not depend on risk aversion and are therefore the same as in Kolstad and Ulph (2008). Since the benefit of an emission unit exceeds the cost of damage in each of the world`s countries, a peripheral country will optimally determine the cost of x_ (x_, s, 1). For an IEA member, optimal emissions depend on the size of the IEA; The same is true for «n_ age»; «Rightarrow x_» (n) » (n) – 0 , , , , < n_ n_ x_,c,l, n) 1 , and "x_ "c,h" (n) – 0 0." (n x_ < n_) In other words, peripheral countries are still polluting; If there are at least IEA members, EEA members will always relax; when IEA members are less numerous, IEA members still pollute; Otherwise, IEA members pollute in a low-cost state of damage and relax in the state of high damage costs. In this section, we outlined the balance of the IEA model for each of the three risk averse learning models and generalized the kolstad and Ulph (2008) results that supported risk neutrality. The evidence is contained in Appendix A1. In addition to declaring IEA membership and expected benefits for each of our three learning models, m, we also report the expected damage costs as a percentage of GDP, footnote 14, which we define as Dm.

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