This paper is concerned with the determinants of international steam-coal trade. Most work in projecting coal trade has been either qualitative (a consensus of experts) or, if quantitative, has been based on competitive spatial-equilibrium models. Unfortunately, the competitive model of trade...

This paper considers the case of Bayesian learning about the relationship between the greenhouse-gas level and temperature rise. Learning takes time because of a stochastic shock to the realized global mean temperature. The paper illustrates the difficulty of quickly learning about the...

A standard assumption in computational models of climate change (integrated assessment models) is that population and technology are growing, but at a decreasing rate. We explore the significance of the assumption of population and technology growth for greenhouse gas control levels.

This paper illustrates the major issues influencing interaction between environmental regulation and international trade by reviewing the empirical studies and theoretical results of the last 20 years. The second section of the paper discusses how environmental regulation distorts comparative...

The paper begins with the problem of a firm subject to random productivity shocks drawn from a particular distribution. We are concerned with the case whereby the distribution of the shocks changes without the knowledge of the firm. Over time the firm learns about the nature and extent of the...

This paper concerns computational models in environmental economics and policy, particularly so-called integrated assessment models. For the most part, such models are simply extensions of standard neoclassical growth models, entended by including the environment and pollution generation.

This paper reviews the impact of the literature in depletable resources and energy economics over the period 1973-98, particularly the period of publication of the Journal of Environmental and Management, 1974-98. A discussion of prominent policy issues in this arena provided, along with an...