Get Kaya data for one or more countries or regions
get_kaya_data(
region_name,
gdp = c("MER", "PPP"),
quiet = FALSE,
region_code = NULL
)The name of one or more countries or regions to look up
Use market exchange rates (MER) or purchasing power
parity (PPP). Default is MER.
Suppress warnings if there is no such country or region.
Optional three-letter country or region codes to look up
instead of the region_name
a tibble of Kaya identity data for the countries or regions specified:
The name of the country or region
The year
Population, in billions
Gross domestic product, in trillions of constant 2015 U.S. dollars.
Total primary energy consumption, in quads
CO2 emissions from fossil fuel consumption, in millions of metric tons
Per-capita GDP, in thousands of dollars per person.
Energy intensity of the economy, in quads per trillion dollars.
Emissions intensity of the energy supply, in million metric tons per quad.
Emissions intensity of the economy, in metric tons per million dollars of GDP.
Units for G, g, e, and ef depend on whether the data is requested in MER or PPP dollars: For MER, dollars are constant 2015 U.S. dollars. For PPP, dollars are constant 2017 international dollars.
_P_ and MER values for GDP and related quantities are available
from 1960 onward.
PPP values for GDP and related quantities are only available from
1990 onward.
Energy-related values (_E_, _F_, and derived quantities) are
available from 1965 onward.
Note that emissions (_F_, _f_, and _ef_) are reported as millions
of metric tons of carbon dioxide, not carbon.get_kaya_data("Brazil")
#> # A tibble: 63 × 10
#> region year P G E F g e f ef
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Brazil 2022 0.215 1.90 12.7 443. 8.83 6.68 34.9 233.
#> 2 Brazil 2021 0.214 1.85 12.2 462. 8.62 6.59 37.9 250.
#> 3 Brazil 2020 0.213 1.76 11.6 408. 8.26 6.58 35.3 232.
#> 4 Brazil 2019 0.212 1.82 12.1 436. 8.59 6.63 36.1 239.
#> 5 Brazil 2018 0.210 1.80 11.9 442. 8.55 6.60 37.3 246.
#> 6 Brazil 2017 0.209 1.77 11.8 462. 8.47 6.69 39.1 261.
#> 7 Brazil 2016 0.207 1.74 11.7 456. 8.43 6.72 38.9 262.
#> 8 Brazil 2015 0.205 1.80 12.0 495. 8.78 6.66 41.2 275.
#> 9 Brazil 2014 0.203 1.87 12.2 512. 9.18 6.51 42.1 274.
#> 10 Brazil 2013 0.202 1.86 11.9 487 9.22 6.38 41.1 262.
#> # ℹ 53 more rows
get_kaya_data("United Kingdom", "PPP")
#> # A tibble: 63 × 10
#> region year P G E F g e f ef
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 United Kingdom 2022 0.0670 3.14 6.93 345. 46.8 2.21 49.7 110.
#> 2 United Kingdom 2021 0.0670 3.01 6.82 341. 44.9 2.27 50.0 113.
#> 3 United Kingdom 2020 0.0671 2.80 6.73 321. 41.7 2.40 47.7 115.
#> 4 United Kingdom 2019 0.0668 3.15 7.50 381. 47.1 2.38 50.9 121.
#> 5 United Kingdom 2018 0.0665 3.10 7.66 396. 46.6 2.47 51.8 128.
#> 6 United Kingdom 2017 0.0661 3.05 7.69 404. 46.1 2.52 52.6 133.
#> 7 United Kingdom 2016 0.0656 2.97 7.69 415. 45.3 2.59 54.0 140.
#> 8 United Kingdom 2015 0.0651 2.91 7.76 439. 44.7 2.67 56.6 151.
#> 9 United Kingdom 2014 0.0646 2.84 7.64 456. 44.0 2.69 59.7 161.
#> 10 United Kingdom 2013 0.0641 2.75 8.09 498. 42.9 2.94 61.5 181.
#> # ℹ 53 more rows
get_kaya_data(region_name = "United States")
#> # A tibble: 63 × 10
#> region year P G E F g e f ef
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 United States 2022 0.333 21.0 90.9 4826. 62.9 4.34 53.1 230.
#> 2 United States 2021 0.332 20.5 88.5 4768. 61.8 4.31 53.9 232.
#> 3 United States 2020 0.332 19.4 83.9 4463. 58.5 4.33 53.2 230.
#> 4 United States 2019 0.328 19.9 90.7 4982. 60.7 4.55 54.9 250.
#> 5 United States 2018 0.327 19.5 91.3 5133. 59.6 4.69 56.2 263.
#> 6 United States 2017 0.325 18.9 88.1 4979. 58.2 4.66 56.5 263.
#> 7 United States 2016 0.323 18.5 87.8 5038 57.3 4.74 57.4 272.
#> 8 United States 2015 0.321 18.2 87.9 5138. 56.8 4.83 58.5 282.
#> 9 United States 2014 0.318 17.7 88.7 5252. 55.7 5.00 59.2 296.
#> 10 United States 2013 0.316 17.3 87.8 5247. 54.8 5.07 59.8 303.
#> # ℹ 53 more rows
get_kaya_data(region_code = "MYS")
#> # A tibble: 63 × 10
#> region year P G E F g e f ef
#> <ord> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Malaysia 2022 0.0339 0.386 4.59 273. 11.4 11.9 59.5 707.
#> 2 Malaysia 2021 0.0336 0.355 4.34 256. 10.6 12.2 58.9 720.
#> 3 Malaysia 2020 0.0332 0.344 4.08 247 10.4 11.8 60.6 717.
#> 4 Malaysia 2019 0.0328 0.365 4.24 256. 11.1 11.6 60.4 702.
#> 5 Malaysia 2018 0.0324 0.349 4.12 251. 10.8 11.8 60.9 719.
#> 6 Malaysia 2017 0.0320 0.333 4.06 241. 10.4 12.2 59.5 724.
#> 7 Malaysia 2016 0.0315 0.315 4.00 253. 9.98 12.7 63.2 803.
#> 8 Malaysia 2015 0.0311 0.301 3.80 247. 9.70 12.6 65.0 820.
#> 9 Malaysia 2014 0.0306 0.287 3.74 243. 9.37 13.1 65.0 848.
#> 10 Malaysia 2013 0.0301 0.271 3.71 234. 8.98 13.7 63.2 865.
#> # ℹ 53 more rows