All the numbers on this site are estimates. We can't know exactly how many jobs will be created or deaths will be avoided thirty years in the future. But we do have good data on how many people it takes to install a solar panel, and how pollution affects human health. Using these data, it's possible to make projections about how moving towards net zero will create benefits for Americans.
The estimates we use are all based on publicly available scientific projections. They're not always available at a local level, however, so we use various techniques to sort out where in America the benefits are likely to end up. The description below explains how we do that. We're continuing to improve our methods, so if you have ideas on how we could do a better job, be in touch!
Stanford University's 100% Wind, Water, and Solar (WWS) Project (Jacobson et al. 2022) studies how the U.S. can make the transition to fully renewable power by 2050. As part of their study, they project how many long-term jobs will be created by building and operating new renewable energy infrastructure in every state (a "long-term" job is one that creates 40 years of employment). They also project the total amount of compensation these new jobs will earn in each state. The Jacobson et al. data include separate estimates for different kinds of renewable infrastructure (residential rooftop solar and offshore wind, for example, are likely to generate jobs in different places).
To estimate where these jobs will show up, we consider where renewable energy sources are most likely to be built within each state. The Global Development Potential Indices project (Oakleaf et al. 2020) assesses the suitability of every square kilometer of land for different kinds of infrastructure, including solar photovoltaic power, concentrated solar power, hydroelectric power, and onshore wind power. Each square kilometer is rated on a seven-point scale from 0 (no potential) to 6 (very high potential). For every congressional district, we add together the ratings of all the land in the district, and then calculate each district's share of the whole state's potential. We then use these shares to allocate the state's estimated job gains for each infrastructure type.
There are two groups of renewables in the Jacobson et al. data that are not represented in the Oakleaf et al. estimates: geothermal power and offshore power (wave devices, tidal turbines, and offshore wind turbines). For geothermal power, we use a specialized estimate of geothermal potential from Coro and Trumpy (2020), and apply the same procedure described above. For offshore power sources, we calculate each district's share of the state's total coastal population using a list of coastal zip codes from NOAA's Office of Coastal Management.
The Jacobson et al. data also include jobs created by the need for more electricity transmission and distribution infrastructure. We assign the state-level transmission jobs based on district land area, and distribution jobs based on district population.
All of these figures are estimates. In reality, not every power plant will be sited in the ideal location. Some people will be live in one district but commute to another for work. Some jobs tied to a given power source might be located far from that source. But in general these calculations are intended to represent reasonable, informed projections of which kinds of employment benefits are likely to accrue to each place.
The American Council for an Energy-Efficient Economy (ACEEE) calculates how many jobs will be created by energy efficiency investments by 2050 (Laitner et al. 2012). We use the estimates from their "Phoenix Scenario" (p. viii), in which existing buildings and machines are both retrofitted and in some cases replaced with more efficient technology.
These calculations are only available at the national level. To estimate where these jobs will be created within America, we turn to the National Renewable Energy Laboratory (NREL). NREL studies how American energy use will change over the next few decades. We start with their projections of the total efficiency potential in each state (NREL 2022a). We then assign that potential to individual counties based on the total electricity and gas energy consumed by the residential, industrial, and commercial sectors in each county (NREL 2022b). We then aggregate the county data to the congressional district based on population shares.
Rewiring America calculates how many households in each county could save money by undertaking at least one electrification project, and then calculates the average electricity savings for each of those households. We reproject these estimates to the Congressional District level, and calculate an average savings per household that takes into account the fact that not every household will be electrifying.
The U.S. Environmental Protection Agency's 2021 Social Vulnerability Report projects future commuting delays based on different temperature scenarios. The EPA projects these impacts at 1, 2, 3, 4, and 5 °C of warming. For our estimates, we calculate the difference between 1.5°C (averaging between impacts at 1° and 2°) and 3°C. We mapped census tracts to Congressional districts using data from the Geocorr project at the Missouri Census Data Center.
The U.S. Environmental Protection Agency's 2021 Social Vulnerability Report also projects the average number of hours and wages lost by workers to extreme heat conditions. The EPA projects these impacts at 1, 2, 3, 4, and 5 °C of warming. For our estimates, we calculate the difference between 1.5°C (averaging between impacts at 1° and 2°) and 3°C. We mapped census tracts to Congressional districts using data from the Geocorr project at the Missouri Census Data Center.
"High-risk" workers are those who either work outside or in hot conditions with poor cooling. To arrive at estimates of the total number of workers and wages at risk, we obtain county-level estimates of high-risk workers by combining census-tract-level employment data from the Bureau of Labor Statistics and the definitions for high-risk sectors from Graff Zivin and Neidell (2014), excluding workers in fossil fuel sectors (which will presumably contract).
G. Coro & E. Trumpy (2020). "Predicting geographical suitability of geothermal power plants." Journal of Cleaner Production, 267, 121874. https://doi.org/10.1016/j.jclepro.2020.121874
EPA. 2021. "Climate Change and Social Vulnerability in the United States: A Focus on Six Impacts." U.S. Environmental Protection Agency, EPA 430-R-21-003. https://www.epa.gov/cira/social-vulnerability-report.
J. Graff Zivin & M. Neidell (2014). "Temperature and the allocation of time: Implications for climate change." Journal of Labor Economics, 32(1), 1–26. https://doi.org/10.1086/671766
M.Z. Jacobson, A.-K. von Krauland, S.J. Coughlin, F.C. Palmer, M.M. Smith (2022). "Zero air pollution and zero carbon from all energy at low cost and without blackouts in variable weather throughout the U.S. with 100% wind-water-solar and storage." Renewable Energy, 184, 430-442. http://web.stanford.edu/group/efmh/jacobson/Articles/I/21-USStates-PDFs/21-USStatesPaper.pdf
National Renewable Energy Laboratory (2022a). "Economic Potential," State and Local Planning for Energy. https://maps.nrel.gov/slope
National Renewable Energy Laboratory (2022b). "Net Electricity and Natural Gas Consumption," State and Local Planning for Energy. https://maps.nrel.gov/slope
Oakleaf, J. R., Kennedy, C. M., Baruch-Mordo, S., Gerber, J. S., West, P. C., Johnson, J. A., & Kiesecker, J. (2019). Mapping Global Development Potential for Renewable Energy, Fossil Fuels, Mining and Agriculture Sectors. Scientific Data, 6(101), 1–17. https://doi.org/10.1038/s41597-019-0084-8
Oakleaf, J. R., Kennedy, C. M., Baruch-Mordo, S., Gerber, J. S., West, P. C., Johnson, J. A., & Kiesecker, J. (2020). Global Development Potential Indices. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/k9t6-gh59
