Package 'kayadata'

Description

kayadata is a package for working with Kaya identity data for many countries and regions.

The Kaya identity, named for the economist Yoichi Kaya, who introduced it (Kaya, 1998); It decomposes the energy-related carbon dioxide emissions from a nation, region, or the world into the product of four components:

$F = P \times g \times e \times f,$

where F is the total emissions, P is the population, g is the per-capita GDP, e is the energy intensity of the economy, and f is the emissions-intensity of the energy supply. (Nakicenovic and Swart, 2000, Ch. 3, p. 105; Raupach et al, 2007)

The data in this packages covers 1960-2019 for population and GDP, and 1965-2019 for energy and fossil-fuel CO2 emissions.

The package uses data on population and GDP from the World Bank, using market exchange rates (MER) for GDP because those data go back to 1960. From 1990 onward, Purchasing-Power-Parity (PPP) GDP figures are available as G_ppp but using these would require re-calculating G, g, e, and ef in the kaya_data data frame.

The package uses data on energy consumption and fossil-fuel CO2 emissions from the Energy Institute's 2024 Statistical Review of World Energy

License

The kayadata package is open source licensed under the MIT License.

Bug reports

• kayadata issue tracker (https://github.com/jonathan-g/kayadata/issues)

Author(s)

Maintainer: Jonathan Gilligan [email protected] (ORCID)

References

Kaya, Yoichi and Keiichi Yokobori, Environment, Energy, and Economy: Strategies for Sustainability (United Nations University Press, 1998).

Nakicenovic, Nebojsa, and Rob Swart (Eds.), Special Report on Emissions Scenarios (Cambridge University Press, 2000). https://www.ipcc.ch/report/emissions-scenarios/

Raupach, Michael R., et al., "Global and regional drivers of accelerating CO2 emissions," PNAS 104, 10288–10293 (2007) doi:10.1073/pnas.0700609104.

See Also

Useful links:

• https://jonathan-g.github.io/kayadata/

• https://github.com/jonathan-g/kayadata

• doi:10.5281/zenodo.12734769

• Report bugs at https://github.com/jonathan-g/kayadata/issues

Description

Get emission factors for different energy sources

Usage

emissions_factors(collapse_renewables = TRUE)

Arguments

Value

a tibble of values for emissions factors, in million metric tons of carbon dioxide per quad of energy.

See Also

regions

Examples

e_fac <- emissions_factors()
e_fac

Description

A dataset containing the fuel mix of how many quads and what fraction of total primary energy supply comes from coal, gas, oil, nuclear, and renewable sources.

Usage

fuel_mix

Format

A tibble containing 948 rows and 7 variables

region

Country or region name

region_code

Three-letter country or region code

geography

Geographic category: "nation", "region", or "world"

year

The year

fuel

The fuel: "Coal", "Natural Gas", "Oil", "Nuclear", "Hydro", and "Renewables"

quads

The number of quads of that fuel consumed in the given country or region and year

frac

The fraction of that country or region's total primary energy consumption from the fuel

Note

The data for 2022, from the 2023 release of the Energy Institute's Statistical Review, has inconsistencies in the fuel mix for Hong Kong and Sri Lanka: The percentages add up to 98.7% and 102.9%, respectively. The sums of energy in quads are off by -0.095 and +0.095 quads, respectively, from the total energy figure.

Source

https://www.energyinst.org/statistical-review/resources-and-data-downloads

See Also

regions, get_fuel_mix()

Description

Nameplate capacity and capacity factors for different electrical generation technologies. The average power supplied over a year is the nameplate capacity times the capacity factor.

Usage

generation_capacity()

Details

Data for fossil fuels comes from EIA

Value

a tibble of values for generation sources

fuel

Energy source: Coal, Nuclear, Gas, Solar Thermal, Solar Photovoltaic, Onshore Wind, or Offshore Wind

description

Text description of the power source

nameplate_capacity

Maximum sustained power output, in megawatts

capacity_factor

Capacity factor: the fraction of the nameplate capacity that the plant can provide, averaged over a typical year

References

Environmental Protection Agency (2018) "Electric Power Monthly," (October, 2018) https://www.eia.gov/electricity/monthly/archive/october2018.pdf, Table 6.7.A.

Pielke, Jr., Roger A., The Climate Fix (Basic Books, 2010).

Examples

gc <- generation_capacity()
gc

Description

Get fuel mix for one or more countries or regions

Usage

get_fuel_mix(
  region_name,
  collapse_renewables = TRUE,
  quiet = FALSE,
  region_code = NULL
)

Arguments

Value

A tibble of fuel mix for the countries or regions specified. That is, the number of quads of each fuel and the fraction of total primary energy coming from that fuel for each country or region:

region

The name of the country or region

year

The year reported

fuel

The name of the fuel

quads

The number of quads per year the country or region consumes

frac

The fraction of the country's energy that comes from that fuel

Note

In the latest data from the Energy Institute, there are small discrepancies between the sums of energy for each fuel and the totals, in both quads and frac, for Hong Kong and Sri Lanka, as described in the documentation for fuel_mix.

See Also

regions, fuel_mix

Examples

get_fuel_mix("United States")
get_fuel_mix("World", collapse_renewables = FALSE)
get_fuel_mix(region_code = "LCN")

Description

Get Kaya data for one or more countries or regions

Usage

get_kaya_data(
  region_name,
  gdp = c("MER", "PPP"),
  quiet = FALSE,
  region_code = NULL
)

Arguments

Details

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.

Value

a tibble of Kaya identity data for the countries or regions specified:

region

The name of the country or region

year

The year

P

Population, in billions

G

Gross domestic product, in trillions of constant 2015 U.S. dollars.

E

Total primary energy consumption, in quads

F

CO2 emissions from fossil fuel consumption, in millions of metric tons

g

Per-capita GDP, in thousands of dollars per person.

e

Energy intensity of the economy, in quads per trillion dollars.

f

Emissions intensity of the energy supply, in million metric tons per quad.

ef

Emissions intensity of the economy, in metric tons per million dollars of GDP.

See Also

regions

Examples

get_kaya_data("Brazil")
get_kaya_data("United Kingdom", "PPP")
get_kaya_data(region_name = "United States")
get_kaya_data(region_code = "MYS")

Description

Get top-down trends for Kaya variables for one or more countries or regions, using projections from U.S. Energy Information Administration's International Energy Outlook report.

Usage

get_top_down_trends(region_name, quiet = FALSE, region_code = NULL)

Arguments

Value

a tibble of trends for P, G, E, F, g, e, f, and ef for each country or region in percent per year.

See Also

regions

Examples

get_top_down_trends("Spain")
get_top_down_trends(region_code = "RUS")

Description

Get top-down projections of Kaya variables for one or more countries or regions

Usage

get_top_down_values(region_name, quiet = FALSE, region_code = NULL)

Arguments

Value

a tibble of values for P, G, E, F, g, e, f, and ef for each country or region:

region

The name of the country or region

P

Population, in billions

G

Gross domestic product, in trillions of constant 2015 U.S. dollars.

E

Total primary energy consumption, in quads

F

CO2 emissions from fossil fuel consumption, in millions of metric tons

g

Per-capita GDP, in thousands of constant 2015 U.S. dollars per person.

e

Energy intensity of the economy, in quads per trillion dollars.

f

Emissions intensity of the energy supply, in million metric tons per quad.

ef

Emissions intensity of the economy, in metric tons per million dollars of GDP.

See Also

regions

Examples

get_top_down_values("New Zealand")
get_top_down_values("OECD")
get_top_down_values(region_code = "PAK")

Description

A dataset containing Kaya identity parameters P, G, E, F, g, e, f, and ef for many countries

Usage

kaya_data

Format

A tibble containing 5292 rows and 14 variables:

region

Country or region name

region_code

Three-letter country or region code

geography

Geographic category: "nation", "region", or "world"

year

The year

P

Population, in billions

G

Gross domestic product, in trillions of constant 2015 U.S. dollars.

E

Total primary energy consumption, in quads

F

CO2 emissions from fossil fuel consumption, in millions of tons

g

Per-capita GDP, in thousands of constant 2015 U.S. dollars per person.

e

Energy intensity of the economy, in quads per trillion dollars.

f

Emissions intensity of the energy supply, in million metric tons per quad.

ef

Emissions intensity of the economy, in metric tons per million dollars of GDP.

G_ppp

Gross domestic product adjusted for purchasing power parity, in trillions of constant 2017 international dollars

G_mer

Gross domestic product at market-exchange-rate, in trillions of constant 2015 U.S. dollars

Source

https://data.worldbank.org/indicator/SP.POP.TOTL, https://data.worldbank.org/indicator/NY.GDP.MKTP.KD, and https://www.energyinst.org/statistical-review/resources-and-data-downloads

See Also

regions, get_kaya_data()

Description

Get a list of countries in the Kaya data

Usage

kaya_region_list()

Value

a vector of country and region names

See Also

regions

Description

The number of megawatts of average power output over a year to produce one quad of energy

Usage

megawatts_per_quad()

Value

The number of megawatts equivalent to one quad per year.

Examples

mwe <- megawatts_per_quad()
mwe

Description

Plot fuel mix

Usage

plot_fuel_mix(
  fuel_mix,
  collapse_renewables = TRUE,
  title = NULL,
  colors = NULL,
  font_size = 20
)

Arguments

Value

A plot object.

Examples

usa_fuel <- get_fuel_mix("United States", collapse_renewables = FALSE)
plot_fuel_mix(usa_fuel)
plot_fuel_mix(usa_fuel, collapse_renewables = FALSE)
plot_fuel_mix(usa_fuel, collapse_renewables = FALSE,
              colors = c(Coal = "black", "Natural Gas" = "gray60",
                         Oil = "gray30", Nuclear = "forestgreen",
                         Hydro = "royalblue", Renewables="palegreen"))
plot_fuel_mix("United States")
plot_fuel_mix("USA")

Description

Plot Kaya-identity variable

Usage

plot_kaya(
  data,
  variable,
  start_year = NA,
  stop_year = NA,
  y_lab = NULL,
  log_scale = FALSE,
  trend_line = FALSE,
  points = TRUE,
  font_size = 20,
  colors = NULL,
  pre_color = NULL,
  post_color = NULL,
  in_range_color = NULL,
  trend_color = NULL,
  line_sizes = NULL,
  pre_line_size = NULL,
  post_line_size = NULL,
  in_range_line_size = NULL,
  trend_line_size = NULL,
  point_sizes = NULL,
  pre_point_size = NULL,
  post_point_size = NULL,
  in_range_point_size = NULL
)

Arguments

Value

A plot object.

Examples

china <- get_kaya_data("China")
plot_kaya(china, "F", 2001, 2011)
## Not run: 
uk <- get_kaya_data("United Kingdom")
plot_kaya(uk, "e", log_scale = TRUE, trend_line = TRUE)
plot_kaya(uk, "e", log_scale = TRUE, trend_line = TRUE,
          start_year = 1970, stop_year = 2000,
          colors = c(PRE="limegreen", POST="darkgreen",
                     "IN-RANGE" = "cadetblue", TREND="orange"),
          line_sizes = c(PRE=0.5, POST=0.5, "IN-RANGE"=1, TREND=1.5),
          point_sizes = c(PRE=2, POST=2, "IN-RANGE"=3))
plot_kaya(uk, "e", log_scale = TRUE, trend_line = TRUE,
          start_year = 1970, stop_year = 2000,
          pre_color = "limegreen", post_color = "limegreen",
          trend_color = "magenta",
          pre_line_size = 0.5, post_line_size = 0.5,
          trend_line_size = 1.5,
          pre_point_size = 2, post_point_size = 2, in_range_point_size = 3)
plot_kaya("United Kingdom")
plot_kaya("GBR")

## End(Not run)

world <- get_kaya_data("World")
plot_kaya(world, "g", 1982, log_scale = TRUE, trend_line = TRUE)

Description

Get top-down projections of Kaya variables for one or more countries or regions for a given year

Usage

project_top_down(region_name, year, quiet = FALSE, region_code = NULL)

Arguments

Value

a tibble of values for P, G, E, F, g, e, f, and ef for each country or region:

region

The name of the country or region

year

The year

P

Population, in billions

G

Gross domestic product, in trillions of constant 2015 U.S. dollars.

E

Total primary energy consumption, in quads

F

CO2 emissions from fossil fuel consumption, in millions of metric tons

g

Per-capita GDP, in thousands of constant 2015 U.S. dollars per person.

e

Energy intensity of the economy, in quads per trillion dollars.

f

Emissions intensity of the energy supply, in million metric tons per quad.

ef

Emissions intensity of the economy, in metric tons per million dollars of GDP.

See Also

regions

Examples

project_top_down("China", 2037)
project_top_down(region_code = "CHE", year = 2043)

Description

Problems with aggregate regional data

Details

The World Bank is missing GDP data for a number of nations, such as Syria and Taiwan. Because of this and the incommensurability between the regions used for aggregate statistics in the World Bank and Energy Institute data, aggregate regional data (e.g., for the Middle East and Africa) should be treated with caution. This problem does not hold for individual nations, where missing data appears as NA values.

Description

A dataset containing top-down projections of trends in P, G, and E, from the EIA's International Energy Outlook 2017.

Usage

td_trends

Format

A tibble containing 226 rows and 11 variables

region

Country or region name

region_code

Three-letter country or region code

geography

Geographic category: "nation", "region", or "world"

P

Trend in population, in fraction per year

G

Trend in gross domestic product, in fraction per year

E

Trend in total primary energy consumption, in fraction per year

F

Trend in CO2 emissions, in fraction per year

g

Trend in per-capita GDP, in fraction per year

e

Trend in energy intensity of the economy, in fraction per year

f

Trend in emissions intensity of the energy supply, in fraction per year

ef

Trend in emissions intensity of the economy, in fraction per year

Source

https://www.eia.gov/outlooks/archive/ieo17/

See Also

regions, get_top_down_trends()

Description

A dataset containing top-down projections of P, G, and E, from the EIA's International Energy Outlook 2017.

Usage

td_values

Format

A tibble containing 640 rows and 12 variables

region

Country or region name

region_code

Three-letter country or region code

geography

Geographic category: "nation", "region", or "world"

year

The year

P

Population, in billions

G

Gross domestic product, in trillions of constant 2015 U.S. dollars

E

Total primary energy consumption, in quads

F

Total CO2 emissions, in millions of metric tons

g

Per-capita GDP, in thousands of constant 2015 U.S. dollars per person.

e

Energy intensity of the economy, in quads per trillion dollars.

f

Emissions intensity of the energy supply, in million metric tons per quad.

ef

Emissions intensity of the economy, in metric tons per million dollars of GDP.

Source

https://www.eia.gov/outlooks/archive/ieo17/

See Also

regions, get_top_down_values()