A Sensitivity Analysis of Timing and Costs of Greenhouse Gas Emissions Reductions under Learning Effects and Technology Competition

This paper runs an extensive sensitivity analysis for the optimal timing and macro-economic costs of carbon emission reductions that mitigate the global average atmospheric temperature increase. We use a macro-economic model in which there are two competing energy sources, fossil-fuelled and non-fossil-fuelled that are good but imperfect substitutes. Technological change is represented endogenously through learning curves. Under these conditions, early abatement is found to be an optimal strategy and the costs of this strategy prove to be low under most parameter values. Uncertainties in the learning rate and the elasticity of substitution between the two different energy sources most significantly affect the robustness of our