Disagreement is one the few things on which all of the modelers that participated in EMF 12 can agree. This disagreement, which includes all aspects of energy demand, economic activity, and policy instruments, is summarized by the models’ forecast of the carbon tax that needed to achieve a given level of emission reductions. For example, the forecast for carbon taxes in Scenario III, which calls for stable emissions by the U.S. at 1990 levels in 2000, runs the gamut from $25 per ton of carbon to $360 per ton. The taxes forecast by the TGAS model to achieve a given level of emission reduction tend to be at the upper end of the range. For example, TGAS forecasts the $360 tax that is required to stabilize emissions in the U.S.
Consistent with the herding instinct in modellers, I investigate why the taxes forecast by the TGAS model are consistently higher than those forecast by other models. A comparison of model structures and results indicates that the level of aggregation used to simulate energy demand and priori assumptions about consumer behavior and the malleability of capital are responsible for a large portion of the variation among models in the forecast for the carbon tax that is required to achieve a given level of emission reduction. Because there is no “correct” level of aggregation or a priori assumption about consumer behavior or capital malleability, no model is “better” than another. Nevertheless, choices regarding these aspects of the model make some models’ forecast more reliable than other regarding particular aspects of emission reductions.
The effects of aggregation and a priori assumptions on a model’s forecast for the carbon tax needed to achieve a given level of emission reduction are of more than academic interest. The wide range of taxes that they forecast are of little use to policy makers. Policy makers need to understand the differences among models that generate this range and how model structures and assumptions qualify some models to examine aspects of emission reductions better than others. To help policymakers use the information generated by EMF 12, I attempt to identify the structures and assumptions in TGAS that cause its forecast for carbon taxes to fall near the upper end of the range forecast by the models that participated in EMF 12. Based on this comparison, I also attempt to identify issues associated with emission reductions for which the level of taxes forecast by TGAS may be a more reliable indicator of effort needed to achieve a given level of emissions reduction.