The goal of footprint is to calculate carbon footprints from air travel based on IATA airport codes or latitude and longitude.

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("acircleda/footprint")

Package footprint uses the the Haversine great-circle distance formula to calculate distance between airports or distance between latitude and longitude pairs. This distance is then used to derive a carbon footprint estimate, which is based on conversion factors from the Department for Environment, Food & Rural Affairs (UK) 2019 Greenhouse Gas Conversion Factors for Business Travel (air): https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019.

Load footprint using

library(footprint)

You can use pairs of three-letter IATA airport codes to calculate distance. This function uses the airportr package, which contains the data and does the work of getting the distance between airports. Note: the airportr package offers a number of useful functions for looking up airports by city or name and getting the IATA airport codes.

The example below calculates a simple footprint estimation for an economy flight from Los Angeles International (LAX) to Heathrow (LHR). The estimate will be in CO₂e (carbon dioxide equivalent, including radiative forcing). The output is always in kilograms.

airport_footprint("LAX", "LHR", "Economy", "co2e")
#> [1] 1312.696

If there is a layover in Chicago, you could calculate each leg of the trip as follows:

airport_footprint("LAX", "ORD", "Economy", "co2e") + 
  airport_footprint("ORD", "LHR", "Economy", "co2e")
#> [1] 1387.167

We can calculate the footprint for multiple itineraries at the same time and add to an existing data frame using mutate. Here is some example data:

library(tibble)
#> Warning: package 'tibble' was built under R version 4.0.3

travel_data <- tibble(
  name = c("Mike", "Will", "Elle"),
  from = c("LAX", "LGA", "TYS"),
  to = c("PUS", "LHR", "TPA")
)

Here is how you can take the from and to data and calculate emissions for each trip. The following function calculates an estimate for CO₂ (carbon dioxide with radiative forcing).

If you have a list of cities, it might be easier to calculate emissions based on longitude and latitude rather than trying to locate the airports used. For example, one could take city and state data and join that with data from maps::us.cities to quickly get latitude and longitude. They can then use the latlong_footprint() function to easily calculate emissions based on either a single itinerary or multiple itineraries:

The following example calculates the footprint of a flight from Los Angeles (34.052235, -118.243683) to Busan, South Korea (35.179554, 129.075638). It assumes an average passenger (no flightClass argument is included) and its output will be in kilograms of CO₂e (the default)

latlong_footprint(34.052235, -118.243683, 35.179554, 129.075638)
#> [1] 1881.589

You can use mutate to calculate emissions based on a dataframe of latitude and longitude pairs.

Here is some example data:

travel_data2 <- tribble(~name, ~departure_lat, ~departure_long, ~arrival_lat, ~arrival_long,
         # Los Angeles -> Busan
        "Mike", 34.052235, -118.243683, 35.179554, 129.075638,
        # New York -> London
        "Will", 40.712776, -74.005974, 51.52, -0.10)

And here is code to apply it to a dataframe:

travel_data2 %>%
  rowwise() %>%
  mutate(emissions = latlong_footprint(departure_lat,
                                       departure_long,
                                       arrival_lat,
                                       arrival_long))