A Bargaining Game Analysis of International Climate Negotiations

Climate negotiations under the United Nations Framework Convention on Climate Change have so far failed to achieve a robust international agreement to reduce greenhouse gas emissions. Game theory has been used to investigate possible climate negotiation solutions and strategies for accomplishing them. Negotiations have been primarily modelled as public goods games such as the Prisoner’s Dilemma, though coordination games or games of conflict have also been used. Many of these models have solutions, in the form of equilibria, corresponding to possible positive outcomes—that is, agreements with the requisite emissions reduction commitments. Other work on large-scale social dilemmas suggests that it should be possible to resolve the climate problem. It therefore seems that equilibrium selection may be a barrier to successful negotiations. Here we use an N-player bargaining game in an agent-based model with learning dynamics to examine the past failures of and future prospects for a robust international climate agreement. The model suggests reasons why the desirable solutions identified in previous game-theoretic models have not yet been accomplished in practice and what mechanisms might be used to achieve these solutions

Investment In Energy Efficiency: Do The Characteristics Of Firms Matter?

The literature on energy efficiency provides numerous examples of apparently profitable technologies that are not universally adopted. Yet according to the standard neoclassical theory of investment, profit-maximizing firms should undertake all investments with a positive net present value. The standard theory also holds that the discount rate for computing the present value of a project should be the return available on other projects in the same risk class, and therefore should not depend on characteristics of the firm. This model as applied to energy-saving investments is tested by examining whether firms'characteristics influence their decision to join the Environmental Protection Agency's voluntary Green Lights program. A discrete choice regression is estimated over a large sample of participating and nonparticipating firms. Missing values in the data matrix are replaced with multiple imputations from a distribution estimated using the expectation-maximization algorithm. The results show that (1) substantial improvements in the power of hypothesis tests can be achieved through maximum-likelihood imputation of missing data, and (2) contrary to the conventional theory, the characteristics of firms do affect their decision to join Green Lights and commit to a program of investments in lighting efficiency. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology