Stanford Energy Modeling Forum project confirms carbon pricing can be effective way to curb GHG emissions
The Stanford Energy Modeling Forum (EMF) was established at Stanford more than 40 years ago to bring together leading experts and decision makers from government, industry, universities, and other research organizations to study important energy and environmental issues. For each study, the Forum organizes a working group to develop the study design, analyze and compare each model’s results and discuss key conclusions.
The EMF 32 project, which launched in 2014, is an ongoing modeling exercise intended to assess emissions, energy and economic outcomes from a plausible range of US policies to reduce greenhouse gases (GHGs). In addition to the standard emphasis on the effects of such policies on emissions, energy prices and macroeconomic performance, the EMF 32 researchers are particularly interested in how fiscal decisions on revenue distribution might also affect these outcomes.
This study gathered together 11 modeling teams to run a common set of carbon tax scenarios, in order to explore how environmental and economic outcomes depend on the initial carbon tax rates, the rate at which the tax escalates, and how the government uses the revenue collected by the carbon tax. The study is also motivated by a desire to inform critical policy design decisions with information such as: the policies’ expected revenue and tax rate outcomes; options for balancing distributional and efficiency goals; the likely significance of international emissions leakage and competitiveness effects; and the outcomes of an emissions tax approach relative to regulation under the Clean Air Act. A key objective of this model inter-comparison project is to understand which insights are robust across models and scenarios and which are more sensitive.
This study provides timely analysis for public policy debates. Despite the skepticism from critics, recent polls suggest as many as two-thirds of those who voted for President Trump support regulating or taxing greenhouse gas (GHG) emissions. A number of groups and organizations, including some that are Republican, Libertarian, or conservative, have promoted the idea of a revenue-neutral carbon tax, with a variety of perspectives on how revenue neutrality should work in practice. Of course, a primary reason for implementing a carbon or GHG tax is to reduce emissions, but the revenue can serve other goals. The revenue could reduce the federal deficit, help finance tax reform, support new spending on infrastructure or other priorities, or provide rebates to households. For example, a group of senior Republican leaders have proposed a $40 per ton CO2 tax rising over time, with revenues rebated to US families through a monthly dividend.
As policymakers contemplate a carbon tax, potentially in the broader context of other potential fiscal reforms, this EMF study seeks to inform questions such as: How much revenue would different carbon tax trajectories generate? How will revenues change over time? How do economic outcomes depend on how the revenues are used? What might be the effect of a carbon tax on revenue from other taxes?—McFarland et al.
The EMF 32 modeling project consists of one reference scenario and several policy scenarios. The baseline scenario projects a future for emissions and economic activity without new climate policy or GHG regulations on stationary sources by the US Environmental Protection Agency (EPA). The policy scenarios impose different designs of a carbon tax in the United States. All of the scenarios are coordinated across models to the extent feasible.
That means that the teams harmonized their baseline economic projections and policy representations so that the differences in their results primarily derive from the difference in models rather than differences in economic forecasts and policy implementation.
Now, the results of this work have been published in a special issue of the journal Climate Change Economics. The special issue contains 15 papers: 10 written by modeling teams and 5 overview/synthesis papers.
The results of this part of the EMF 32 study are consistent with much of the existing modeling literature on carbon pricing in the United States, the researchers said.
Across all models, they found that the core carbon price scenarios lead to significant reductions in CO2 emissions, with the vast majority of the reductions occurring in the electricity sector and disproportionately through reductions in coal.
Emissions reductions are largely independent of the uses of the revenues modeled.
Expected economic costs (not accounting for any of the benefits of greenhouse gas and conventional pollutant mitigation), in terms of either GDP or welfare, are modest, but they vary across models and policies.
Using revenues to reduce preexisting capital or, to a lesser extent labor taxes, reduces welfare losses in most models relative to providing household rebates, but the magnitudes of the cost savings vary. The use of revenue can also have important distributional implications, revenue recycling in the form of capital tax reductions is the most efficient but the most regressive, while lump sum rebates to households is the most progressive but the least efficient.
The EMF 32 researchers found that it is possible to protect low-income households with a modest share of revenues, while using the remainder of revenues on capital tax reductions allows the policy-maker to attain efficiency close to that of a pure capital tax reduction.
The models of the present study do not agree on the regional implications of the various revenue recycling schemes. Beyond 2030, the researchers concluded that model uncertainties are too large to make quantitative results useful for near term policy design.
“Special Issue on EMF 32 Study on U.S. Carbon Tax Scenarios”; Guest Editors: A. A. Fawcett, J. McFarland, A. C. Morris and J. P. Weyant (2018) Climate Change Economics
James R. McFarland et al. (2018) “Overview Of The EMF 32 Study On U.S. Carbon Tax Scenarios” Clim. Change Econ. 09, 1840002 doi: 10.1142/S201000781840002X