Abstract: The Energy Modeling Forum (EMF) 32 study on carbon tax scenarios analyzed a set of illustrative policies in the United States that place an economy-wide tax on fossil-fuel-related carbon dioxide (CO₂) emissions, a carbon tax for short. Eleven modeling teams ran these stylized scenarios, which vary by the initial carbon tax rate, the rate at which the tax escalates over time, and the use of the revenues. Modelers reported their results for the effects of the policies, relative to a reference scenario that does not include a carbon tax, on emissions, economic activity, and outcomes within the U.S. energy system. This paper explains the scenario design, presents an overview of the results, and compares results from the participating models. In particular, we compare various outcomes across the models, such as emissions, revenue, gross domestic product, sectoral impacts, and welfare.

From the Conclusion: In short, the results here are consistent with much of the existing modeling literature on carbon pricing in the United States. Across all models, we find 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 here. Expected economic costs (not accounting for any of the benefits of GHG 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.

MIT Joint Program research assistant Arun Singh, a master’s degree student in the Institute for Data, Systems and Society’s Technology and Policy Program (TPP), has analyzed climate policy options for India by building and applying a model of the Indian economy with detailed representation of the electricity sector.

Developed with his advisors, MIT Sloan School of Management Assistant Professor Valerie Karplus and MIT Joint Program Principal Research Scientist Niven Winchester, the model enables researchers to gauge the cost-effectiveness and efficiency of different technology and policy choices designed to transition India to a low-carbon energy system. Singh used the model to assess the economic, energy, and emissions impacts of implementing India’s Nationally Determined Contribution (NDC) to the Paris Agreement — which aims to reduce carbon dioxide emissions intensity by 33 to 35 percent from 2005 levels and increase non-fossil based electric power to about 40 percent of installed capacity by 2030.

Singh determined that compared to a no-policy scenario in 2030, the average cost per unit of emissions reduced is lowest under a carbon dioxide (CO₂) pricing regime. Adding a renewable portfolio standard (RPS) to simulate electricity capacity targets increases the cost by more than ten times. Projected electricity demand in 2030 decreases by 8% under the CO₂ price, while introducing an RPS further suppresses electricity demand. Importantly, a reduction in the costs of wind and solar power induced by favorable policies may result in cost convergence across instruments, paving the way for more aggressive decarbonization policies in the future.