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close this bookThe Costs of Climate Protection: A Guide for the Perplexed (WRI, 1997, 60 pages)
View the document(introduction...)
View the documentACKNOWLEDGMENTS
View the documentFOREWORD
View the documentINTRODUCTION
Open this folder and view contents3. MORE DETAIL ON THE KEY ASSUMPTIONS
Open this folder and view contents4. TRADE AND EQUITY ISSUES
View the documentREFERENCES
View the documentANNEX
View the documentABOUT THE AUTHORS
View the documentBOARD OF DIRECTORS


To clarify how key modeling assumptions affect the predicted economic impacts of a carbon tax, we have assembled 162 different predictions from 16 of the most reputable and widely used economic models. Each of the models differs in its basic features and for each model, different simulation "runs" reflect either different policy assumptions (e.g., about the disposition of tax revenues) or different abatement targets and carbon tax rates. Each simulation "run" generates a predicted economic impact - measured here as the percentage change in GDP in some future year - and a corresponding percentage change in carbon dioxide emissions in the same year. Both variables are measured with reference to a baseline scenario, particular to each model, predicting what would happen in that year if no carbon tax or equivalent policy were adopted.

This measure of economic impact - future year GDP - is not ideal but was adopted because it is predicted in all models. If the predicted economic adjustment involves an initial slump from which the economy then recovers, a better measure would be the (discounted) loss of income and consumption over the entire period, but such a measure is not available in all models. More fundamentally, GDP is a measure of economic activity, not economic wellbeing: for one thing, it does not measure the nonmarket value of environmental quality.

The collection of model "runs" - plotted in Figure 1 - shows how variable the predicted economic impacts are.

For example, a carbon tax that induces a 35 percent reduction in CO2 emissions could be expected to raise GDP over its projected baseline level by more than 1.5 percent or to reduce GDP by about 3 percent, depending on the economic models and modeling assumptions used. The majority of predictions suggest that abating CO2 emissions will reduce economic activity and that eliminating a greater percentage of emissions will lower GDP more than proportionately. However, this apparent consensus does not imply that this prediction is likely to be accurate, but only that most modeling exercises have employed similar assumptions.

For each of these 162 modeling predictions, we have listed the main assumptions underlying the predictions. Some of these revolve around the basic features of the model; others refer to the policy options assumed for the specific simulation. Our list includes most of the key assumptions discussed in the previous section. Of the structural features of the models, the salient distinctions are:

1. Is the model of the CGE type, which assumes that the economy adjusts efficiently in the long-run, or is it a macro-model that assumes the economy suffers persistent transitional inefficiencies?

2. How much scope for inter-fuel and product substitution does the model assume, as indicated by the number of different energy sources and industrial sectors in the model?1

3. Does the model assume that one or more backstop non-fossil energy sources are available at some constant cost?

4. How many years does the model assume to be available to achieve the specified CO2 reduction target, expressed as a percentage reduction from projected baseline emissions in the final year?

5. Does the model assume that reducing CO2 emissions would avoid some economic costs from climate change, or that no such costs exist?

6. Does the model assume that reducing fossil fuel combustion would avoid some damages from air pollution, or not?

1 This is just one indicator of potential substitution the other, not so easily measured is the ease with which one product can be replaced by another in response to changes in relative costs.

FIG. 1 - The predicted impact of carbon abatement on the U.S. economy (162 estimates from 16 models)

The salient policy assumptions that differentiate the model predictions are:

7. Does the model assume that carbon tax revenues are returned to the economy through the reduction of a distorting tax rate, or through lump-sum rebates?

8. Does the model assume that joint implementation options are available, or not?

FIG. 2 - Predicted impacts on GDP for various CO2 abatement targets in 2020: the effects of changing underlying assumptions one-by-one

To show how these assumptions affect the predicted economic impacts, we expressed these assumptions either as binary variables (yes = 1; no = 0) or numerical variables and used statistical techniques to relate the assumptions to the data points portrayed in Figure 1. Doing this shows how the assumptions affect the predicted impacts, not in any one model but across all 16 models in 162 different simulations. As suggested by these data points, we assumed that the economic impact of each additional one percent reduction in carbon emissions would be greater, the greater the percentage reduction.2 We also assumed (consistent with the definition of the baseline projection) that imposing no carbon tax would not affect the economy's baseline trajectory, so that any statistical function would include the zero point in Figure 1.

2 Specifically, we assumed that the percentage change in GDP was quadratically related to the percentage change in carbon emissions, both measured relative to the baseline projection.

Surprisingly, these eight assumptions (along with the size of the CO2 emissions reduction) account for fully 80 percent of the variation in predicted economic impacts.3 This is remarkable because it implies that all the other modeling assumptions - hundreds of assumed parameter values and relationships - are comparatively unimportant. Together, they account for only 20 percent of the differences among predicted impacts. Only a handful of basic assumptions really matters.

3 This was established by estimating a linear multiple regression model that incorporates variables representing the eight assumptions, taking the percentage change in GDP as the variable to be "explained" (The estimated regression equation can be found in the Annex.) An alternative regression analysis that included only the reduction in carbon emissions as an explanatory variable and excluded all the variables representing model assumptions explained only about half as much of the variation in predictions.

This is good news. People don't have to be Ph.D economists to understand the debate over the economic impacts of climate policy. Rather, people can use their own judgment and common sense to decide which of these basic assumptions are more realistic. Having decided that, they can then determine for themselves which predictions are more credible and what the economic impacts of a carbon tax or a climate stabilization policy are likely to be.

To illustrate, we have used the statistical relationship between predictions and assumptions to plot several cost curves in Figure 2. Each cost curve represents a different set of modeling assumptions selected from those listed above, starting from a set of "worst case" assumptions and then successively replacing them, one-by-one, with more favorable assumptions until a set of "best-case" assumptions is arrived at. In the statistical analysis underlying these curves, the slope of the curve connecting GDP change to emissions reduction was allowed to shift with each of the eight assumptions, but the year for achieving the abatement target was held constant.4

4 The variable representing the presence or absence of a backstop energy source was specified to affect the curvature of the cost curve, since backstop sources become relevant only at higher energy prices and then limit the rate of cost increase.

The worst case assumptions are that:

1. there is no non-carbon backstop energy source;
2. the economy does not respond efficiently to policy changes, even in the long-run;
3. the scope for inter-fuel and product substitution is minimal;
4. there is no possibility of joint implementation;
5. revenues are returned through lump-sum rebates;
6. there are no averted damages from air pollution; and
7. there are no averted damages from climate change.

Under these assumptions, many of which are obviously unrealistic, the adverse economic impacts of a carbon tax or equivalent policy would be severe, reaching 6 percent of end-year GDP for a 50 percent reduction in projected baseline emissions by 2020. (See the bottommost curve in Figure 2.)

Surprisingly, these eight assumptions (along with the size of the CO2 emissions reduction) account for fully 80 percent of the variation in predicted economic impacts.

Under all the best-case assumptions, a reduction in CO2 emissions by 2020 would result in a substantial improvement in GDP relative to its business-as-usual path.

Scanning Figure 2 from the bottom up reveals the effects on predicted economic impact of changing these worst-case assumptions one-by-one. For example, assuming that backstop energy sources exist improves the predicted economic impact substantially - by about one percent of GDP for a 50 percent emissions reduction. In Figure 2, what is notable about the predicted impacts on the U.S. economy is that changing only five worst-case assumptions - by assuming backstop energy sources, efficient long-run adjustment in the economy, greater substitution possibilities, joint implementation, and recycling of carbon tax revenues by reducing other burdensome tax rates - dramatically alters the predicted economic impacts. Instead of a six percent loss of GDP by 2020, there would be modest positive impact on GDP relative to the business-as-usual scenario.

Judging from all these simulations using a wide variety of economic models, the doomsday prediction of heavy economic losses if carbon emissions are reduced is implausible. It is more reasonable to predict that with sensible economic policies and international cooperation, carbon dioxide emissions can be reduced with minimal impacts on the economy.

Going further, Figure 2 indicates that if reducing fossil fuel combustion avoids economic damages from climate change or air pollution, then the overall economic impacts could be favorable.5 The top-most curve in Figure 2 indicates that under all the best-case assumptions, a reduction in CO2 emissions by 2020 would result in a substantial improvement in GDP relative to its business-as-usual path.6 Of course, there is some degree of emissions reduction beyond which the incremental abatement costs exceed the value of the environmental damages that more pollution would create. This turning point is not adequately reflected in Figure 2, which should not be interpreted to suggest that if some carbon abatement is good, more is necessarily better. Figure 2 does imply, however, that models that take the environmental benefits of carbon taxes into account predict substantially more favorable economic impacts than models that ignore such benefits.

5 This prediction is consistent with the interpretation of a carbon tax as a collective tax that reduces a market failure - namely, the unintended effect of carbon emissions on the global climate Economists agree that, if set at the proper rate a tax to collect a market failure should improve an economy's productivity.

6 When an pollution damages are assumed, an expanded measure of GDP in which environmental damages are recorded is the relevant indicator of economic impact.

One target that has been analyzed extensively by the Interagency Analytical Team in preparation for the COP-3 meeting in Kyoto in December 1997 is a freeze on carbon emissions at 1990 levels by 2010 and stabilization of emissions thereafter. For the United States, it has been estimated that this target implies about a 26 percent reduction below projected baseline emissions in 2020, if the baseline is calculated on the basis of policies now in place (U.S. EIA, 1996). Figure 3 uses the same statistical analysis to show in detail the range of predicted long-run economic impacts if this target is attained.7 Under unfavorable assumptions, GDP would be 2.4 percent lower in 2020 than under baseline conditions; under favorable assumptions, GDP would be 2.4 percent higher. Figure 3 also quantifies the relative importance of several modeling assumptions in creating this range of predictions.

7 This analysis cannot encompass short-run transitional impacts predicted by some macroeconomic forecasting models.

Four assumptions stand out in terms of magnitude:

· whether the economy will adapt efficiently;
· whether international joint implementation will be achieved;
· whether carbon tax or permit auction revenues will be recycled by reducing other taxes; and
· whether there will be economic benefits from abating pollution.

FIG. 3 - The predicted impacts on GDP in 2020 of stabilizing CO2 emissions at 1990 levels: the effect of changing underlying assumptions.

Most economists believe that the U.S. market economy, with high mobility of capital and labor, can adapt efficiently to moderate the impacts of policy changes. There is general agreement that a carbon tax that discouraged coal use in electricity generation would have the effect of reducing air pollution, even with current air pollution regulations in place. Whether to use revenue-raising policy instruments to limit emissions and how to dispose of resulting revenues are decisions that the U.S. government must make. Finally, international cooperation in joint implementation of carbon reduction targets is a possibility subject to negotiation. Under reasonable assumptions, the predicted economic impact of stabilizing emissions at 1990 levels would be neutral or even favorable.