Carbon Tax Adjustment Mechanisms (TAMs): How They Work and Lessons from Modeling - Tax adjustment mechanisms can significantly decrease emissions uncertainty under a carbon tax while only modestly increasing the cost of emissions reductions.

Carbon taxes can provide powerful incentives for businesses and households to reduce greenhouse gas emissions. Setting a tax, however, does not on its own guarantee a particular level of future emissions because it is impossible to predict exactly how a complex economy will respond to any given price level. To provide greater assurance about environmental performance, environmental integrity mechanisms (EIMs) can be built into carbon tax legislation. These innovative provisions have already been included in several recent US carbon tax proposals, including the MARKET CHOICE Act and the Energy Innovation and Carbon Dividend Act (both introduced in the 115th Congress and updated and reintroduced in the 116th Congress) and the Stemming Warming and Augmenting Pay (SWAP) Act and the Climate Action Rebate Act (both introduced in the 116th Congress). 

This brief focuses on one type of EIM, a Tax Adjustment Mechanism (TAM), by which the carbon tax price path is automatically adjusted if actual emissions do not meet specified emissions reduction goals. As the TAM concept gains acceptance by the policy community and Congress, research and analysis are needed to evaluate how different TAM designs will affect emissions and economic outcomes. For example, how frequently should a tax adjustment be triggered—on the basis of annual or cumulative emissions, or both? How large should the adjustment be? And how far from a desired trajectory must emissions be before it is triggered? These design choices should be grounded in rigorous analysis with an understanding of their implications for environmental performance and cost.

In response to this critical need, Resources for the Future (RFF), in collaboration with Environmental Defense Fund (EDF), has developed new modeling capacity designed to quantify the range of emissions uncertainty in carbon taxes and to evaluate the effectiveness of different TAM designs. This analysis finds that TAMs can significantly reduce emissions uncertainty and increase the probability of hitting particular emissions targets—often with very modest cost increases—but design details matter considerably in terms of both effectiveness and efficiency.

Coal Plant https://en.wikipedia.org/wiki/Carbon_tax

Results suggest that a TAM can reduce emissions uncertainty in several ways:

by reducing the likelihood of very high emissions outcomes;

by reducing expected emissions and the range of potential expected emissions; and/or

by increasing the probability of meeting a specific emissions target.

This reduced uncertainty comes at a potential cost. By increasing the price if emissions goals are not met, TAMs generally increase expected costs of abatement. 6 These cost increases, however, are often quite modest compared with the reduction in emissions uncertainty.

The performance of a TAM ultimately depends on the design details. For example, the modeling indicates that the TAM included in the 2018 MARKET CHOICE Act (which would increase the carbon tax by $2 every two years if cumulative emissions goals are not met) reduces the upper bound of possible emissions outcomes (as measured by the 97.5th percentile of the distribution) by about 3 percent, reduces expected total cumulative emissions by 1 percent, reduces the standard deviation of the distribution by 17 percent, and increases the probability of achieving the bill’s cumulative emissions target from 54 to 72 percent. The increased certainty over emissions outcomes that the TAM provides results in an additional modest cost of approximately $1 per ton of emissions reduced (Hafstead and Williams 2020b, Table 3).
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Overall emissions uncertainty comes primarily from three underlying sources: uncertainty about future economic growth, uncertainty about the relative costs of fossil fuels and low- or zero-carbon technologies over time, and uncertainty about how responsive the economy is to a carbon price (i.e., the extent to which emissions would fall relative to business-as-usual emissions). This last source of uncertainty—price responsiveness—is particularly important: it is impossible to predict with precision how businesses and households will adjust their behavior in response to a given price on carbon emissions.

Economic models can be used to project the carbon prices necessary to achieve particular levels of emissions reductions, but different models can produce differing projections. For example, the 11 economic models that participated in the Stanford Energy Modeling Forum 32 yielded a wide distribution of emissions: in a scenario with a carbon tax starting in 2020 at $25 per ton (in $2010) in 2020 and rising at 5 percent annually, cumulative reductions (2020–2030) relative to the baseline varied between 13 and 35 percent, with an average of 21 percent (Barron et al. 2018, Table 1).

RFF’s new model incorporates the three types of uncertainties discussed above and finds a large range of potential emissions outcomes under a given carbon tax that is consistent with the range of emissions projections across models. Figure 1 displays historical and projected energy-related carbon dioxide (CO2) emissions from the new modeling tool under an economy-wide carbon price starting in 2020 at $40 (in $2017) and growing at 3 percent above inflation. The red lines represent the 95 percent confidence interval (i.e., the range of possible emissions outcomes with 95 percent probability). By 2035, under this scenario, emissions are expected to be between 2.8 billion and 4.4 billion metric tons.

The uncertainty over emissions outcomes poses concerns for stakeholders focused on addressing climate change and is a major reason that many in the environmental community have tended to prefer policy instruments other than carbon taxes (Brooks and Keohane 2020). In addition to the initial uncertainty of modeled emissions reductions, fundamental factors like energy or economic market dynamics can change over time, affecting the performance of a tax. And because climate damages depend on cumulative greenhouse gas emissions over time, even relatively small deviations from a desired emissions path over several decades could significantly alter the level of future climate damages. Emissions uncertainty is also problematic in the context of the Paris Agreement on climate change, which is structured around quantitative emissions targets. 4 EIMs, including TAMs, can help alleviate environmental stakeholders’ concerns about emissions reduction performance and also align federal climate legislation with the framework and international expectations established by the Paris Agreement.

https://www.rff.org/publications/issue-briefs/tams-how-they-work-and-lessons-modeling/
by Marc Hafstead, Susanne Brooks, Nathaniel Keohane, and Wesley Look

Resources for the Future (RFF) www.RFF.org and the Environmental Defense Fund www.EDF.org
Issue Brief (20-08); August 7, 2020