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Running the Numbers: How Clean Electricity Tax Credits Could Save Americans $5 Billion by 2024

The clean energy transition is already well under way, and a new RMI analysis shows that tax credits for clean electricity would accelerate this transition and save American households over $5 billion annually by 2024. While savings vary by state, every state analyzed (all continental US states and D.C.) saw near-term savings. In this article we’ll share details of our analysis, as well as additional findings on the potential benefits of the clean energy tax credits.

In addition to saving ratepayers money, these tax credits could lead to the installation of nearly 83 GW of clean electricity like solar, wind, and batteries, while reducing up to 92 million metric tons (MMT) of carbon dioxide emissions by the end of 2024. This would lead to enormous local and community benefits, providing jobs and economic development in rural communities that serve as prime locations for renewable energy. It would also reduce air pollution in communities near fossil fuel-powered generating stations, which are disproportionately located near low-income communities of color.

In a time of geopolitical conflict that has yet again sparked soaring fossil fuel prices and placed the crushing burden of those prices on the people least able to bear them, our analysis shows that now is the perfect time to accelerate the transition to clean energy. This will not only insulate American households from volatile fossil fuel costs but will also serve as an important signal to the world that our country is serious about our decarbonization commitments. These tax credits, while not a silver bullet, provide a critical foundation that will clean up the electric grid while ensuring reliability.

Methodology Updates: Including Macroeconomic Factors and Increased Scope and Precision

Six months ago, RMI released an initial analysis of the potential savings from replacing the energy from fossil power plants across the country with solar or wind energy, enabled by key provisions in the Build Back Better Act, including potential refinancing deals and debt relief for cooperatives. The intervening months have seen material changes in energy market conditions that merited revisiting that analysis — and have allowed RMI to improve the robustness of our modeling results.

Our initial analysis compared the most recently reported annual fuel and operating costs of just over half of existing fossil capacity with the regional levelized cost of utility-scale wind and solar generation to determine whether each plant’s generation could be cost-effectively replaced with clean energy. The analysis used publicly available financial data to account for varying costs of debt and equity and addressed reliability concerns by adding progressively more battery storage as renewable penetration increased. Savings were allocated to states based on utility plant ownership and service territories.

In the six months since this initial analysis was performed, soaring inflation due in part to supply chain constraints and geopolitical considerations have radically altered fossil fuel price expectations as well as expected near-future costs for clean energy deployment. Further, the initial analysis had limited coverage outside regulated utilities that report to the Federal Energy Regulatory Commission (FERC) and did not model constraints and costs associated with moving the generated clean energy to when and where it is needed to replace fossil energy, beyond the intra-day shifting enabled by battery storage.

To address these issues, we have updated our analysis to incorporate both shifting macroeconomic conditions as well as constraints associated with daily variable renewable production into our cost analysis methodology, all while extending coverage to 98 percent of US fossil fuel-powered plants.

In more detail, our projections for renewable and fossil costs have been updated to:

  • Account for inflation: We have now included current market inflation expectations and forward interest rates, based on current treasury yield curves and treasury inflation-protected securities (TIPS) spreads, in the calculation of all capital and operating costs for clean and fossil energy.
  • Account for volatile fossil fuel prices: We now use granular hourly fossil plant operational data reported to the US Environmental Protection Agency via continuous emission monitoring systems (CEMS) over each of the past 13 years as independent fossil fuel scenarios to mimic potential future levels of plant operation under varying market price conditions. To accurately reflect the high fossil fuel costs caused by Russia’s invasion of Ukraine, we use fuel costs from 2008, which saw remarkably similar coal and natural gas prices to current and future prices in the near and medium term.
  • Cover 98 percent of US fossil plants: Previously, publicly available data was limited mainly to regulated entities, municipal utilities, and rural electric cooperatives. However, this left out broad swaths of independent power producers that serve competitive electricity markets across the country, largely across the Northeast, Midwest, and California. We now fill in that gap by estimating fossil plant capital, fuel, and operating costs under varying levels of utilization, based on regression analysis of historical fuel cost data from the US Energy Information Administration (EIA) and historical plant- and technology-level capital, fuel, and operating cost data reported to FERC. We also control for plant fuel characteristics (fuel type, mine source and distance transported, contractual status, and plant state) and plant characteristics (prime mover, boiler or burner technology characteristics, pollution control equipment installed, plant age, and state-specific wage factors) reported to EIA.
  • Account for solar and wind variability: We improved our renewable replacement cost analysis by using estimated local daily renewable generation based on historical reanalysis data from Renewables.ninja to optimize the mix of wind and solar to offset historic daily fossil generation, thereby only calculating avoided costs based on reduced operation rather than full replacement of any individual asset, with any excess renewable generation assumed to avoid average balancing area fossil fuel generation unit costs.
  • Account for transmission costs: As shown by multiple deep decarbonization studies such as the Berkeley 2035 Report and the Princeton REPEAT Project, adequate transmission capacity, or the ability to send this electricity over long distances, will be critical to meet electricity demand while ensuring a reliable grid. Thus, we also account for needed transmission costs based on utility-specific and aggregate estimates of transmission costs per unit capacity and mile from FERC data and the average distance between nearby renewable resources and each fossil plant.
  • Account precisely for which state will see savings from which plants: We also accounted for the sale and purchase of electricity from utilities and other electricity suppliers and delivery services to accurately reflect the savings attributable to each state using EIA sales and operational data.

Our analysis is not a capacity expansion model — we do not intend to replace each utility’s integrated resource planning process, nor do we perfectly optimize resource mixes. Rather, our analysis is intended to conservatively estimate the possibility and opportunities clean energy can unlock, especially with the right policies. By including storage and transmission costs that scale with increasing penetration and scale of clean energy deployment, we roughly account for increased system costs that will come with a high-renewable grid.

Notably, our analysis is still quite limited in scope, resulting in a lower-bound floor of cost savings from climate legislation. We do not consider other tax credits, whether on the electricity side or in other sectors (electric vehicles, clean hydrogen, etc.) that would further accelerate the clean energy transition. Moreover, while it is unclear which other provisions may pass in a climate bill, previous proposed legislation had numerous other ideas that would have further reduced emissions and saved Americans money, including provisions to address tax normalization constraints for storage and transmission, new lending programs, and specific programs targeting the challenges faced by rural cooperatives in deploying clean energy.

Rural cooperatives in particular present a unique challenge due to their wide geographic service areas and financing constraints. Preliminary results from a forthcoming RMI analysis show that cooperatives currently face financial constraints that limit their ability to cost-effectively decarbonize more than a fraction of their fleets – but that targeted government lending or grants could alleviate these constraints. Furthermore, cooperatives are often located in prime locations for clean energy. Allowing cooperatives to sell clean energy to other customers and pass those benefits on to their members (currently prohibited by federal legislation regarding nonprofits) would make cooperatives leaders in the clean energy transition.

Finally, externality costs avoided, both from climate change as well as pollution-related health costs, would add enormous benefits that are not currently quantified in our analysis. One recent study found that meeting US climate goals would save over 50,000 lives and $600 billion in health benefits each year from eliminating air pollution alone. While these electricity tax credits will not decarbonize the entire US economy, they represent a fundamental step in the right direction and will help unlock additional savings and health benefits as other sectors of the economy electrify.

Data Sources

RMI’s analysis uses publicly available data from the following sources: RMI’s Utility Transition Hub, FERC, EIA, the National Renewable Energy Laboratory, EPA, the Treasury Department, Renewables.ninja, and utility financial statements.