rpspanel builds a
documented, reproducible state-by-year panel for the 50 US states and
DC. It combines EIA energy, electricity and CO2 data, BEA GDP, income
and population, and standardised Renewable Portfolio Standard (RPS)
stringency measures.
EIA and BEA data need free API keys:
Put them in your .Renviron file (open it with
usethis::edit_r_environ()), then restart R:
EIA_API_KEY=your-eia-key
BEA_API_KEY=your-bea-keyPick variables by group or by name. Derived variables fetch what they need:
small <- build_panel(
states = c("CA", "TX", "NY", "IA"),
years = 2010:2022,
vars = c("co2_pc", "re_share_nonhydro", "gdp_real_pc", "rps_nominal")
)
head(small)If one source cannot be reached, its variables are left out with a warning and the rest of the panel is still returned.
Every variable has a definition, unit, source series and transformation:
head(codebook(), 10)
#> variable group
#> 1 energy_cons_total energy
#> 2 energy_cons_res energy
#> 3 energy_cons_com energy
#> 4 energy_cons_ind energy
#> 5 energy_cons_trans energy
#> 6 energy_prod_total energy
#> 7 renew_cons_total energy
#> 8 energy_price_avg energy
#> 9 energy_expend_total energy
#> 10 gen_total electricity
#> label unit
#> 1 Total energy consumption billion Btu
#> 2 Residential sector energy consumption billion Btu
#> 3 Commercial sector energy consumption billion Btu
#> 4 Industrial sector energy consumption billion Btu
#> 5 Transportation sector energy consumption billion Btu
#> 6 Total primary energy production billion Btu
#> 7 Total renewable energy consumption billion Btu
#> 8 Average price of energy, all sectors (nominal) dollars per million Btu
#> 9 Total energy expenditures (nominal) million dollars
#> 10 Net generation, all fuels, all sectors thousand MWh
#> source series transformation
#> 1 eia_seds TETCB As published
#> 2 eia_seds TERCB As published
#> 3 eia_seds TECCB As published
#> 4 eia_seds TEICB As published
#> 5 eia_seds TEACB As published
#> 6 eia_seds TEPRB As published
#> 7 eia_seds RETCB As published
#> 8 eia_seds TETCD As published
#> 9 eia_seds TETCV As published
#> 10 eia_generation ALL As published
codebook(group = "rps")
#> variable group label
#> 1 rps_adoption rps RPS in force
#> 2 rps_nominal rps Nominal RPS requirement
#> 3 rps_coverage_adj rps Coverage-adjusted RPS requirement
#> 4 rps_incremental rps Incremental RPS requirement (Yin and Powers 2010)
#> 5 rps_baseline_share rps Existing renewable share at RPS enactment
#> 6 rps_oos_rec rps RPS accepts out-of-state renewable credits
#> 7 rps_carveout rps RPS has a resource carve-out
#> 8 rps_acp rps RPS has an alternative compliance payment
#> unit source series
#> 1 0/1 rps adoption
#> 2 share of retail sales (0-1) rps nominal
#> 3 share of retail sales (0-1) rps coverage_adjusted
#> 4 share (can be negative) rps incremental
#> 5 share (0-1) rps incremental
#> 6 0/1 rps flexibility
#> 7 0/1 rps flexibility
#> 8 0/1 rps flexibility
#> transformation
#> 1 rps_stringency(method = "adoption")
#> 2 Legal targets interpolated between target years; 0 before first compliance year; MW targets (TX, IA) converted to a share of covered sales (capacity factor 0.33 by default)
#> 3 rps_nominal * coverage_share
#> 4 rps_coverage_adj - rps_baseline_share while rps_nominal > 0, else 0
#> 5 Renewable share of generation in the enactment year (or the first year with data); includes hydro where the targets count it (NY), otherwise non-hydro
#> 6 Coded design feature times rps_adoption
#> 7 Coded design feature times rps_adoption
#> 8 Coded design feature times rps_adoptioncite_sources() lists the sources used, with the date
each was retrieved:
cite_sources(c("eia_seds", "eia_co2", "bea_regional"))
#> U.S. Energy Information Administration (2026). State Energy Data System (SEDS). https://www.eia.gov/state/seds/.
#>
#> U.S. Energy Information Administration (2026). State Energy-Related Carbon Dioxide Emissions. https://www.eia.gov/environment/emissions/state/.
#>
#> U.S. Bureau of Economic Analysis (2026). Regional Economic Accounts (SAGDP, SAINC tables). https://apps.bea.gov/regional/.
#>
#> Adebayo, M. (2026). rpspanel: US state energy-economy panels with renewable portfolio standards. R package version 0.1.0.Downloads are cached, so a panel can be rebuilt offline with exactly
the same data vintage. Use refresh = TRUE to download fresh
data, rps_cache_list() to see what is cached and
rps_cache_clear() to empty the cache.
RPS stringency measures come from the coded rules bundled with the package, so they work offline:
rps <- build_panel(states = c("CA", "WA", "NC", "TX"), years = 2005:2025,
groups = "rps",
rps_methods = c("adoption", "nominal", "flexibility"))
rps[rps$year %in% c(2010, 2020), ]
#> <rps_panel>: 4 states x 2010-2020 (8 rows), 5 variables
#> Sources: rps_bundled
#> state state_name fips year rps_adoption rps_nominal rps_oos_rec
#> 6 CA California 06 2010 1 0.20 1
#> 16 CA California 06 2020 1 0.33 1
#> 27 NC North Carolina 37 2010 1 0.00 1
#> 37 NC North Carolina 37 2020 1 0.10 1
#> 48 TX Texas 48 2010 1 NA 0
#> 58 TX Texas 48 2020 1 NA 0
#> rps_carveout rps_acp
#> 6 0 0
#> 16 0 0
#> 27 1 0
#> 37 1 0
#> 48 0 1
#> 58 0 1
#> # ... with 2 more rowsSee vignette("rps-stringency") for the methods.
The panel works directly with plm, fixest
and csdm:
library(fixest)
p <- build_panel(years = 2001:2022, vars = c("co2_pc", "rps_nominal", "gdp_real_pc"))
feols(log(co2_pc) ~ rps_nominal + log(gdp_real_pc) | state + year,
data = as_pdata(p, "fixest"), cluster = ~state)Export to CSV, Stata or Parquet, with the codebook alongside: