Title:	Calculate Carbon-Equivalent Emissions
Version:	0.2.7
Description:	Provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024, it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for 'shiny' applications and community-driven extensions.
License:	LGPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	knitr, purrr, rmarkdown, sp, testthat (≥ 2.0.0), tibble
Config/testthat/edition:	2
Imports:	airportr, checkmate, cowplot, dplyr, emojifont, ggplot2, ggpp, htmltools, lubridate, magrittr, readxl, rlang, shiny, shinydashboard, stringr, tidyr, tidyselect
Depends:	R (≥ 4.1.0)
LazyData:	true
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-08-27 19:24:24 UTC; lclem
Author:	Lily Clements [aut, cre]
Maintainer:	Lily Clements <lily@idems.international>
Repository:	CRAN
Date/Publication:	2025-08-27 20:00:02 UTC

carbonr: Calculate Carbon-Equivalent Emissions

Description

The carbonr package provides a flexible tool for calculating carbon-equivalent emissions.

Author(s)

Maintainer: Lily Clements lily@idems.international (ORCID)

See Also

See the README on GitHub

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Create multiple textInput functions in Shiny

Description

For use in the shiny_emissions() function. Adding an unknown quantity of textInputs.

Usage

add_inputs(numeric_input, label, value)

Arguments

Value

Returns textInput for use in the shiny_emissions() function.

Examples

if(interactive()) {
ui <- shinydashboard::dashboardPage(header = shinydashboard::dashboardHeader(),
                                    sidebar = shinydashboard::dashboardSidebar(),
                                    shinydashboard::dashboardBody(
                                    shiny::fluidRow(
                                    shiny::column(12, align = "left",
                                    shiny::splitLayout(shinydashboard::box(width = NULL,
                                    shiny::numericInput("newbox_add",
                                                        "Number of new boxes:",
                                                        value = 0, min = 0),
                                    shiny::uiOutput("newbox_input")))))))
server <- function(input, output) {
  K_plane <- shiny::reactive({ input$newbox_add })
  output$newbox_input <- shiny::renderUI({ add_inputs(numeric_input = K_plane(),
                                                      label = "New Box:",
                                                      value = "textbox") })
}
shiny::shinyApp(ui, server)
}

Calculate CO2e emissions from an airplane journey

Description

This function calculates the CO2e emissions between airports based on the provided parameters. The distances are calculated using the "airport_distance" function from the "airportr" package.

Usage

airplane_emissions(
  from,
  to,
  via = NULL,
  num_people = 1,
  radiative_force = TRUE,
  include_WTT = TRUE,
  round_trip = FALSE,
  class = c("Average passenger", "Economy class", "Business class",
    "Premium economy class", "First class")
)

Arguments

Details

The distances are calculated using the "airport_distance" function from the "airportr" package. This means that the distances between locations uses the Haversine formula. This is calculated as the crow flies.

Value

Returns CO2e emissions in tonnes.

Examples

# Calculate emissions for a flight between Vancouver (YVR) and Toronto (YYZ)
airplane_emissions("YVR", "YYZ")
# Calculate emissions for a flight between London Heathrow (LHR)
# and Kisumu Airport (KIS), with layovers in Amsterdam (AMS) and Nairobi
# (NBO), flying in Economy class.
airplane_emissions("LHR", "KIS", via = c("AMS", "NBO"),
                   class = "Economy class")

Find the airport code for an airport

Description

Find the name, city, country, and IATA code of an airport. For use in the airplane_emissions function.

Usage

airport_finder(
  name,
  city,
  country,
  IATA_code,
  distance = 0.1,
  ignore.case = FALSE
)

Arguments

Value

Data frame containing the name, city, country, and IATA code of an airport.

Examples

# Can get the IATA code from the name of an airport. Gets similar matches.
airport_finder(name = "Bristo")

# Can get the IATA code from the name and city of an airport
airport_finder(name = "Bristo", country = "United Kingdom")

# Can find the name and city of an airport given the IATA code
airport_finder(IATA_code = "BRS")

Table of airport detail data

Description

This dataset is adapted from the airportr package. Full credit and acknowledgment go to the original authors of the airportr package for their contribution. A dataset containing names, codes, locations, altitude, and timezones for airports.

Usage

airports

Format

A data frame with 7698 rows and 14 variables:

OpenFlights ID

OpenFlights database ID

Name

Airport name, sometimes contains name of the city

City

Name of the city served by the airport

IATA

3-letter IATA code

ICAO

4-letter ICAO code

Country

Country name as in OpenFlights database. Note that country names may not be ISO 3166-1 standard.

Country Code

ISO 3166-1 numeric country code

Country Code (Alpha-2)

Two-letter ISO country code

Country Code (Alpha-3)

Three-letter ISO country code

Latitude

Latitude in decimal degrees

Longitude

Longitude in decimal degrees

Altitude

Altitude in feet

UTC

Hours offset from UTC

DST

Daylight Savings Time. One of E (Europe), A (US/Canada), S (South America), O (Australia), Z (New Zealand), N (None) or U (Unknown)

Timezone

Timezone in Olson format

Type

Type of airport (e.g., large airport, medium airport, small airport)

Source

Source of data, generally sourced from OurAirports

Source

https://cran.r-project.org/package=airportr

Anaesthetic Emissions

Description

Estimates the CO2e emissions associated with different anaesthetic agents.

Usage

anaesthetic_emissions(
  desflurane = 0,
  sevoflurane = 0,
  isoflurane = 0,
  N2O = 0,
  methoxyflurane = 0,
  propofol = 0
)

Arguments

Details

These estimates are based on available literature and may vary depending on factors such as specific anaesthetic agents, usage conditions, and waste gas management practices.

Value

The total CO2e emissions in tonnes.

References

• McGain F, Muret J, Lawson C, Sherman JD. Environmental sustainability in anaesthesia and critical care. Br J Anaesth. 2020 Nov;125(5):680-692. DOI: 10.1016/j.bja.2020.06.055. Epub 2020 Aug 12. PMID: 32798068; PMCID: PMC7421303.

• ACS Sustainable Chem. Eng. 2019, 7, 7, 6580–6591. Publication Date: January 20, 2019. Link

• Sherman, Jodi MD*; Le, Cathy; Lamers, Vanessa; Eckelman, Matthew PhD. Life Cycle Greenhouse Gas Emissions of Anesthetic Drugs. Anesthesia & Analgesia 114(5):p 1086-1090, May 2012. DOI: 10.1213/ANE.0b013e31824f6940. Link

Examples

anaesthetic_emissions(desflurane = 200, sevoflurane = 30, N2O = 5)

Building emissions (UK govt schema; table-driven)

Description

Building emissions (UK govt schema; table-driven)

Usage

building_emissions(
  water_supply = 0,
  water_trt = TRUE,
  water_unit = c("cubic metres", "million litres"),
  electricity_kWh = 0,
  electricity_TD = TRUE,
  electricity_WTT = TRUE,
  heat_kWh = 0,
  heat_TD = TRUE,
  heat_WTT = TRUE,
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Value

numeric total CO2e in requested units.

Examples

# specify emissions in an office
# Basic office: include water treatment, electricity TD+WTT, and heat TD+WTT
building_emissions(
  water_supply = 10, water_unit = "cubic metres", water_trt = TRUE,
  electricity_kWh = 100, electricity_TD = TRUE, electricity_WTT = TRUE,
  heat_kWh = 50, heat_TD = TRUE, heat_WTT = TRUE,
  units = "kg"
)

# Water only, in million litres, reported in tonnes, using 2024 factors
building_emissions(
  water_supply = 0.002, water_unit = "million litres", water_trt = TRUE,
  electricity_kWh = 0, heat_kWh = 0,
  value_col = "value_2024", units = "tonnes"
)

# Electricity without TD (but with WTT generation)
building_emissions(
  electricity_kWh = 10, electricity_TD = FALSE, electricity_WTT = TRUE,
  heat_kWh = 0, water_supply = 0
)

# Heat only, include WTT but exclude distribution
building_emissions(
  heat_kWh = 20, heat_TD = FALSE, heat_WTT = TRUE,
  water_supply = 0, electricity_kWh = 0
)

Calculate carbon price credit

Description

This function calculates the carbon price credit for a given jurisdiction, year, period, and CO2e value. It uses CPI (Carbon Price Index) data to determine the carbon price for the specified jurisdiction and time period. The carbon price credit is calculated by multiplying the CO2e value by the corresponding carbon price.

Usage

carbon_price_credit(
  jurisdiction = NULL,
  year = NULL,
  period = 0,
  manual_price = NULL,
  co2e_val
)

Arguments

Value

The calculated carbon price credit in USD ($).

Examples

# Calculate carbon price credit for the United Kingdom in the year 2000,
# period 2, and CO2e value of 100
carbon_price_credit("United Kingdom", 2022, 2, co2e_val = 100)

# Or manually enter a value
carbon_price_credit(manual_price = 66.9, co2e_val = 100)

Check which jurisdictions are in the Carbon Credits data

Description

Find jurisdictions available in the Carbon Credits data. If a jurisdiction is specified, find the years associated with that jurisdiction.

Usage

check_CPI(jurisdiction = NULL, period = FALSE)

Arguments

Value

A vector or data frame containing the information.

Examples

which_jur <- check_CPI()
which_years <- check_CPI(jurisdiction = "Switzerland")
which_years_and_period <- check_CPI(jurisdiction = "Switzerland", period = TRUE)

Clinical Emissions: Data Frame and Plot

Description

Calculate clinical theatre emissions row-by-row from a data frame. Each row is expanded into a call to clinical_theatre_emissions() using the columns you specify. Optionally, results can be combined with carbon price credit information and plotted.

Usage

clinical_theatre_data(
  data,
  time,
  date_format = "%d/%m/%Y",
  name,
  wet_clinical_waste,
  wet_clinical_waste_unit = c("tonnes", "kg"),
  desflurane = 0,
  sevoflurane = 0,
  isoflurane = 0,
  methoxyflurane = 0,
  N2O = 0,
  propofol = 0,
  water_supply = NULL,
  water_trt = TRUE,
  water_unit = c("cubic metres", "million litres"),
  electricity_kWh = NULL,
  electricity_TD = TRUE,
  electricity_WTT = TRUE,
  heat_kWh = NULL,
  heat_TD = TRUE,
  heat_WTT = TRUE,
  paper_vars = NULL,
  plastic_vars = NULL,
  metal_vars = NULL,
  electrical_vars = NULL,
  construction_vars = NULL,
  paper_waste = TRUE,
  plastic_waste = TRUE,
  metal_waste = TRUE,
  electrical_waste = TRUE,
  construction_waste = TRUE,
  paper_material_production = "Primary material production",
  metal_material_production = "Primary material production",
  construction_material_production = "Primary material production",
  paper_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill"),
  plastic_waste_disposal = c("Landfill", "Open-loop", "Closed-loop", "Combustion"),
  metal_waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  electrical_waste_disposal = c("Landfill", "Open-loop"),
  construction_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill",
    "Open-loop"),
  units = "kg",
  value_col = c("value", "value_2024"),
  strict = TRUE,
  include_cpi = FALSE,
  jurisdiction = NULL,
  year = NULL,
  period = 0,
  manual_price = NULL,
  gti_by = c("default", "month", "year"),
  overall_by = c("default", "month", "year"),
  single_sheet = FALSE
)

Arguments

Value

If single_sheet = NULL: a tibble with emissions (and carbon_price_credit if include_cpi = TRUE).

If single_sheet = TRUE/FALSE: a list with the emissions table and a plot object generated by output_display().

Examples

df <- data.frame(
  time = c("10/04/2000","11/04/2000"),
  theatre = c("A","A"),
  clinical_waste = c(80,90),
  electricity_kwh = c(100,110),
  general_waste = c(65,55)
)

clinical_theatre_data(
  df,
  time = time,
  name = theatre,
  wet_clinical_waste = clinical_waste,
  wet_clinical_waste_unit = "kg",
  electricity_kWh = electricity_kwh,
  plastic_vars = c(average="general_waste"),
  units = "kg"
)

Clinical Emissions

Description

Calculate CO2e from an operating theatre session by summing: (1) wet clinical waste; (2) building use (water/electricity/heat); (3) material purchases + end-of-life (paper, plastics, metal, electrical, construction); (4) anaesthetic agents.

Usage

clinical_theatre_emissions(
  wet_clinical_waste,
  wet_clinical_waste_unit = c("tonnes", "kg"),
  desflurane = 0,
  sevoflurane = 0,
  isoflurane = 0,
  methoxyflurane = 0,
  N2O = 0,
  propofol = 0,
  water_supply = 0,
  water_trt = TRUE,
  water_unit = c("cubic metres", "million litres"),
  electricity_kWh = 0,
  electricity_TD = TRUE,
  electricity_WTT = TRUE,
  heat_kWh = 0,
  heat_TD = TRUE,
  heat_WTT = TRUE,
  paper_use = stats::setNames(numeric(), character()),
  plastic_use = stats::setNames(numeric(), character()),
  metal_use = stats::setNames(numeric(), character()),
  electrical_use = stats::setNames(numeric(), character()),
  construction_use = stats::setNames(numeric(), character()),
  paper_waste = TRUE,
  plastic_waste = TRUE,
  metal_waste = TRUE,
  electrical_waste = TRUE,
  construction_waste = TRUE,
  paper_material_production = "Primary material production",
  metal_material_production = "Primary material production",
  construction_material_production = "Primary material production",
  paper_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill"),
  plastic_waste_disposal = c("Landfill", "Open-loop", "Closed-loop", "Combustion"),
  metal_waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  electrical_waste_disposal = c("Landfill", "Open-loop"),
  construction_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill",
    "Open-loop"),
  value_col = c("value", "value_2024"),
  units = "kg",
  strict = TRUE
)

Arguments

Details

Wet clinical waste factor defaults to 0.879 tCO2e per tonne (NGA 2022). Building emissions are computed via building_emissions() with units = "kg". Materials are computed via material_emissions() with units = "kg". Anaesthetic emissions are computed via anaesthetic_emissions() (tonnes) and converted to kg to sum. The function sums all components in kg and returns in the units requested.

Value

Total CO2e in the units specified by units (kg or tonnes).

Inputs – Material vectors

For each material family, pass a named numeric vector of tonnages (in tonnes). Names must be canonical keys (case/space/punctuation is normalised internally, but using the canonical forms below is recommended):

• Paper paper_use: board, mixed, paper

• Plastics plastic_use: average, average_film, average_rigid, hdpe, ldpe, lldpe, pet, pp, ps, pvc

• Metal metal_use: use your package’s canonical metal keys (e.g. aluminium_cans, aluminium_foil, mixed_cans, scrap, steel_cans)

• Electrical electrical_use: fridges, freezers, large_electrical, it, small_electrical, alkaline_batteries, liion_batteries, nimh_batteries

• Construction construction_use: e.g. aggregates, average, asbestos, asphalt, bricks, concrete, insulation, metals, soils, mineral_oil, plasterboard, tyres, wood

For each family you can also set a single waste toggle (e.g. plastic_waste = TRUE) to send the same tonnage to a single disposal route (e.g. plastic_waste_disposal = "Landfill").

Examples

# Minimal example using vector-first materials
clinical_theatre_emissions(
  wet_clinical_waste = 100, wet_clinical_waste_unit = "kg",

  # Building (kWh)
  electricity_kWh = 250, heat_kWh = 120,

  # Materials: paper/plastic/metal/electrical/construction
  paper_use = c(paper = 20),
  paper_waste = TRUE, paper_waste_disposal = "Closed-loop",

  plastic_use = c(pet = 10),
  plastic_waste = TRUE, plastic_waste_disposal = "Landfill",

  metal_use = c(steel_cans = 0.2),
  metal_waste = TRUE, metal_waste_disposal = "Open-loop",

  electrical_use = c(alkaline_batteries = 0.05),
  electrical_waste = TRUE, electrical_waste_disposal = "Open-loop",

  construction_use = c(concrete = 1),
  construction_waste = FALSE,

  value_col = "value",
  units = "kg"
)

Calculate construction emissions (UK govt schema)

Description

Computes embodied GHG emissions for construction materials using the UK government conversion factors table (uk_gov_data), specifically rows with Level 2 = "Construction". Factors are taken from the selected column (value or value_2024) and are assumed to be kg CO2e per tonne.

Usage

construction_emissions(
  use = stats::setNames(numeric(), character()),
  waste = TRUE,
  material_production = c("Primary material production", "Re-used", "Closed-loop source"),
  waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill", "Open-loop"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Details

Material-production options (Column Text under Material use):

• Aggregates, Asphalt: "Primary material production", "Closed-loop source", "Re-used".

• Asbestos, Average Construction, Bricks: "Primary material production" only.

• Concrete, Insulation, Metals, Mineral Oil, Plasterboard: "Primary material production" or "Closed-loop source".

• Soils: "Closed-loop source" only.

• Tyres, Wood: "Primary material production" or "Re-used".

Waste-disposal options (Column Text under Waste disposal):

• "Closed-loop" is valid for aggregates, average, asphalt, concrete, insulation, metal, soils, mineral oil, plasterboard, tyres, wood.

• "Combustion" is valid for average, mineral oil, wood.

• "Composting" is valid for wood only.

• "Landfill" is valid for everything except average, mineral oil, tyres.

• "Open-loop" is valid for aggregates, average, asphalt, bricks, concrete (and any other materials where the table provides a factor).

These rules are enforced by the presence/absence of rows in uk_gov_data. If a requested material-route pair has no factor in the table, the lookup yields NA: with strict = TRUE a descriptive error is thrown; with strict = FALSE it contributes zero to the total.

Units: Factors are kg CO2e / tonne; if units = "tonnes", the result is divided by 1000.

Value

Numeric total emissions in the requested units.

Examples

# 1) Basic: primary production for all materials, landfill waste = use
construction_emissions(
  use = c(Aggregates = 1000, Concrete = 500, Wood = 2000),
  material_production = "Primary material production",
  waste_disposal = "Landfill",
  waste = TRUE,
  strict = FALSE,
  units = "kg"
)

# 2) Per-material production + synonyms ("closed loop" ->
# "Closed-loop source", "reused" -> "Re-used")
construction_emissions(
  use = c(aggregates = 100, concrete = 50, wood = 10),
  material_production = c(aggregates = "closed loop",
                          concrete = "Closed-loop source",
                          wood = "reused"),
  waste_disposal = "Landfill",
  waste = TRUE,
  units = "tonnes",
  value_col = "value_2024"
)

# 3)  Tolerant mode treats missing factors as zero:
construction_emissions(
  use = c(bricks = 10),
  material_production = "Re-used",
  strict = FALSE
)

Convert degrees to radians

Description

Convert degrees to radians.

Usage

degree_conversion(deg)

Arguments

Value

Value in radians.

Examples

# Convert 90 degrees into radians
degree_conversion(90)

Distance calculator

Description

Calculate distances between locations in miles using the Haversine formula. This is calculated as the crow flies.

Usage

distance_calc(lat1, lat2, long1, long2)

Arguments

Value

Distance between locations in miles

Examples

# Distance between the London Eye and the Eiffel Tower in miles
distance_calc(51.5033, 48.8584, 0.1196, 2.2945)

Electrical emissions

Description

Computes embodied GHG emissions for electrical items using uk_gov_data rows with Level 2 = "Electrical items". Material-use factors come from ⁠Level 1 = "Material use", Column Text = material_production⁠ (your table currently provides Primary material production only). Waste factors come from ⁠Level 1 = "Waste disposal", Column Text = waste_disposal⁠. Factors are kg CO2e per tonne.

Usage

electrical_emissions(
  use = stats::setNames(numeric(), character()),
  waste = TRUE,
  material_production = "Primary material production",
  waste_disposal = c("Landfill", "Open-loop"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Value

Numeric total emissions in requested units.

Examples

# Primary production + landfill; waste = use
electrical_emissions(
  use = c(fridges = 1, freezers = 0.5, large_electrical = 0.2),
  waste_disposal = "Landfill",
  waste = TRUE,
  units = "kg"
)

# 2024 factors, no waste, report in tonnes
electrical_emissions(
  use = c(it = 0.01, liion_batteries = 0.002),
  value_col = "value_2024",
  waste = FALSE,
  units = "tonnes"
)

Clinical Theatre Data

Description

This dataset contains activities within a clinical theatre setting related to healthcare services or medical supplies. It is structured to facilitate analysis of healthcare operations, costs, and estimate CO2e emissions.

Usage

example_clinical_theatre

Format

A data frame with 4,386 rows and 6 columns, including:

time

The date of transaction or activity in DDMMYYYY format.

prices

The cost associated with each transaction or activity in AUD, related to services provided or medical supplies used.

quantities

The amount or volume of a product or service provided, indicating the number of items used or procedures performed.

prodID

A unique identifier for each type of product or service, such as medical supplies, medications, or surgical procedures.

retID

A code identifying the department, supplier, or healthcare provider involved in the transaction, or the entity receiving the product or service.

description

Categorises the clinical theatre activities into medical specialties or types of procedures, with 'ent' (Ear, Nose, and Throat), 'colorectal', 'hbt' (hematology or blood treatment), 'ortho' (orthopedics), 'paedsurg' (pediatric surgery), and 'gensurg' (general surgery).

Details

The dataset provides an overview of clinical theatre operations.

Calculate CO2e emissions from ferry journeys

Description

A function that calculates CO2e emissions between ferry ports.

Usage

ferry_emissions(
  from,
  to,
  via = NULL,
  type = c("Foot", "Car", "Average"),
  num_people = 1,
  times_journey = 1,
  include_WTT = TRUE,
  round_trip = FALSE
)

Arguments

Details

The distances are calculated using the Haversine formula. This is calculated as the crow flies.

Value

Returns CO2e emissions in tonnes for the ferry journey.

Examples

# Emissions for a ferry journey between Belfast and New York City
seaport_finder(city = "Belfast")
seaport_finder(city = "New York")
ferry_emissions(from = "BEL", to = "BOY")

Create a Value Box for Reports

Description

This function creates a value box for use in reports.

Usage

gg_value_box(values, information, icons)

Arguments

Details

This function creates a value box with customizable values, information, and icons. The function takes inputs for the values, information, icons, and color of the value box. The values and information are provided as vectors, while the icons are specified using Font Awesome unicode symbols. The color of the value box can be customized using a factor variable. The resulting value box is a ggplot2 object that can be further customized or combined with other plots or elements in a report.

Value

A ggplot2 object with a value box for report use.

References

Modified from Stack Overflow post: https://stackoverflow.com/questions/47105282/valuebox-like-function-for-static-reports

Examples

# Create a value box with custom values and icons
gg_value_box(
  values = c(100, 500, 1000),
  information = c("Sales", "Revenue", "Customers"),
  icons = c("\U0000f155", "\U0000f155", "\U0000f0f7")
)

Calculate CO2e emissions from a hotel stay

Description

Indirect emissions from a stay at a hotel. Values to calculate emissions are from UK government 2024 report.

Usage

hotel_emissions(location = "UK", nights = 1, rooms = 1)

Arguments

Value

Tonnes of CO2e emissions for a stay in a hotel.

Examples

# Emissions for a two night stay in Australia.
hotel_emissions(location = "Australia", nights = 2)

Calculate household material emissions (vector-first)

Description

Sums emissions from paper, plastics, metals, electrical, construction (delegated to their calculators) plus household-specific streams: Glass/Clothing/Books (GCB), Organic (food/drink/compost), and Household residual waste.

Usage

household_emissions(
  paper_use = stats::setNames(numeric(), character()),
  plastic_use = stats::setNames(numeric(), character()),
  metal_use = stats::setNames(numeric(), character()),
  electrical_use = stats::setNames(numeric(), character()),
  construction_use = stats::setNames(numeric(), character()),
  paper_waste = TRUE,
  plastic_waste = TRUE,
  metal_waste = TRUE,
  electrical_waste = TRUE,
  construction_waste = TRUE,
  paper_material_production = "Primary material production",
  metal_material_production = "Primary material production",
  construction_material_production = "Primary material production",
  paper_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill"),
  plastic_waste_disposal = c("Landfill", "Open-loop", "Closed-loop", "Combustion"),
  metal_waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  electrical_waste_disposal = c("Landfill", "Open-loop"),
  construction_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill",
    "Open-loop"),
  gcb_use = stats::setNames(numeric(), character()),
  gcb_waste = TRUE,
  gcb_waste_disposal = c("Closed-loop", "Combustion", "Landfill"),
  organic_use = stats::setNames(numeric(), character()),
  organic_waste = TRUE,
  compost_waste_disposal = c("Anaerobic digestion", "Combustion", "Composting",
    "Landfill"),
  household_residual_waste = 0,
  hh_waste_disposal = c("Combustion", "Landfill"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Value

Numeric total emissions in requested units.

Inputs

Provide named ⁠*_use⁠ vectors in tonnes and ⁠*_waste = TRUE/FALSE⁠ flags. Unknown names in gcb_use / organic_use are ignored with a warning.

Examples

household_emissions(
  gcb_use = c(glass = 3, books = 0.5),
  organic_use = c(food = 1, drink = 0.5),
  household_residual_waste = 0.8,
  gcb_waste = TRUE, organic_waste = TRUE,
  gcb_waste_disposal = "Closed-loop",
  compost_waste_disposal = "Anaerobic digestion",
  hh_waste_disposal = "Combustion",
  units = "kg"
)

Import CPI data from an Excel file

Description

This function uses the downloaded data from the World Bank Carbon Pricing Dashboard (https://carbonpricingdashboard.worldbank.org/). It imports data from an Excel file containing CPI (Carbon Price Index) data. It filters the data for ETS (Emissions Trading Scheme) instruments, performs necessary transformations, and returns a processed data frame.

Usage

import_CPI(path, sheet = "Data_Price", skip = 2)

Arguments

Value

A processed data frame containing CPI data.

Calculate CO2e emissions from land-travel journeys

Description

A function that calculates CO2e emissions on a journey on land.

Usage

land_emissions(
  distance,
  units = c("miles", "km"),
  num = 1,
  vehicle = c("Cars", "Motorbike", "Taxis", "Bus", "National rail", "International rail",
    "Light rail and tram", "London Underground", "Coach"),
  fuel = c("Petrol", "Diesel", "Unknown", "Battery Electric Vehicle",
    "Plug-in Hybrid Electric Vehicle"),
  car_type = c("Average car", "Small car", "Medium car", "Large car", "Mini",
    "Supermini", "Lower medium", "Upper medium", "Executive", "Luxury", "Sports",
    "Dual purpose 4X4", "MPV"),
  bike_type = c("Average", "Small", "Medium", "Large"),
  bus_type = c("Local bus (not London)", "Local London bus", "Average local bus"),
  taxi_type = c("Regular taxi", "Black cab"),
  TD = TRUE,
  include_WTT = TRUE,
  include_electricity = TRUE,
  owned_by_org = TRUE
)

Arguments

Value

Tonnes of CO2e emissions per mile travelled.

Examples

# Emissions for a 100 mile car journey
land_emissions(distance = 100)

# Emissions for a 100 mile motorbike journey where the motorbike is 500+cc
land_emissions(distance = 100, vehicle = "Motorbike", bike_type = "Large")

Material (and waste) emissions — vector-first wrapper

Description

Convenience wrapper that sums emissions from paper, plastics, metals, electrical items, construction materials, glass, and industrial waste. It forwards to the dedicated calculators: paper_emissions(), plastic_emissions(), metal_emissions(), electrical_emissions(), and construction_emissions().

Usage

material_emissions(
  paper_use = stats::setNames(numeric(), character()),
  plastic_use = stats::setNames(numeric(), character()),
  metal_use = stats::setNames(numeric(), character()),
  electrical_use = stats::setNames(numeric(), character()),
  construction_use = stats::setNames(numeric(), character()),
  paper_waste = TRUE,
  plastic_waste = TRUE,
  metal_waste = TRUE,
  electrical_waste = TRUE,
  construction_waste = TRUE,
  paper_material_production = "Primary material production",
  metal_material_production = "Primary material production",
  construction_material_production = "Primary material production",
  paper_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill"),
  plastic_waste_disposal = c("Landfill", "Open-loop", "Closed-loop", "Combustion"),
  metal_waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  electrical_waste_disposal = c("Landfill", "Open-loop"),
  construction_waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill",
    "Open-loop"),
  glass = 0,
  glass_waste = TRUE,
  glass_waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  industrial_waste = 0,
  industrial_waste_disposal = c("Combustion", "Landfill"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Details

For each family you provide a named use vector in tonnes plus a waste = TRUE/FALSE flag (waste tonnage equals use when TRUE). Unknown names are ignored by the underlying calculators (with warnings).

Value

Total emissions in the requested units.

Canonical names (examples)

• Paper: board, mixed, paper

• Plastics: average, average_film, average_rigid, hdpe, ldpe, lldpe, pet, pp, ps, pvc

• Metals: aluminium (cans/foil), mixed_cans, scrap, steel_cans

• Electrical: fridges, freezers, large_electrical, it, small_electrical, alkaline_batteries, liion_batteries, nimh_batteries

• Construction: use the material names supported by construction_emissions()

Backwards compatibility

Legacy scalar arguments (e.g. board, HDPE, fridges, aluminuim_cans, …) are still accepted and are added into the corresponding ⁠*_use⁠ vectors. Legacy ⁠*_WD⁠ arguments (separate waste tonnages) are deprecated and ignored; supply ⁠*_waste = TRUE⁠ instead.

Examples

# Paper + Metals + Glass, with waste to the same tonnages
material_emissions(
  paper_use = c(board = 10, paper = 5),
  metal_use = c(aluminium = 0.4, steel_cans = 0.2),
  glass = 3, glass_waste = TRUE,
  paper_waste_disposal = "Closed-loop",
  metal_waste_disposal = "Landfill",
  glass_waste_disposal = "Closed-loop",
  units = "kg"
)

Metal emissions (UK govt schema; table-driven, with material_production)

Description

Computes embodied GHG emissions for metals using uk_gov_data rows with Level 2 = "Metal". Material-use factors come from ⁠Level 1 = "Material use", Column Text = material_production⁠. Waste factors come from ⁠Level 1 = "Waste disposal", Column Text = waste_disposal⁠. Factors are kg CO2e per tonne.

Usage

metal_emissions(
  use = stats::setNames(numeric(), character()),
  waste = TRUE,
  material_production = "Primary material production",
  waste_disposal = c("Closed-loop", "Combustion", "Landfill", "Open-loop"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Value

Numeric total emissions in requested units.

Examples

# Primary for all; landfill; waste = use
metal_emissions(
  use = c(aluminium = 1.2, steel_cans = 0.4),
  material_production = "Primary material production",
  waste_disposal = "Landfill",
  waste = TRUE,
  units = "kg"
)

# Per-metal: aluminium closed-loop source, others primary; no waste; 2024 factors
metal_emissions(
  use = c(`Metal: aluminium cans and foil (excl. forming)` = 0.5, mixed_cans = 0.2),
  material_production = c(aluminium = "closed loop"),
  waste = FALSE,
  value_col = "value_2024",
  units = "tonnes"
)

Office emissions (uses building_emissions + homeworking factors)

Description

Computes office emissions either from a per-person average (when specify = FALSE) or from specified utilities via building_emissions() (when specify = TRUE), plus optional homeworking emissions (Level 1 = "Homeworking").

Usage

office_emissions(
  specify = FALSE,
  office_num = 1,
  WFH_num = 0,
  WFH_hours = 0,
  WFH_type = c("Office Equipment", "Heating"),
  water_supply = 0,
  water_trt = TRUE,
  water_unit = c("cubic metres", "million litres"),
  electricity_kWh = 0,
  electricity_TD = TRUE,
  electricity_WTT = TRUE,
  heat_kWh = 0,
  heat_TD = TRUE,
  heat_WTT = TRUE,
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Details

Factors are assumed kg CO2e per unit (e.g., per person-hour for homeworking), and the result is returned in requested units.

Value

Numeric total emissions in requested units.

Examples

# 1) Use specified utilities (building_emissions) + homeworking
office_emissions(
  specify = TRUE,
  electricity_kWh = 200, heat_kWh = 100,
  water_supply = 10, water_unit = "cubic metres",
  WFH_num = 5, WFH_hours = 8, WFH_type = c("Office Equipment","Heating")
)

# 2) Use per-person average for 12 staff, with WFH equipment only
office_emissions(
  specify = FALSE, office_num = 12,
  WFH_num = 6, WFH_hours = 4, WFH_type = "Office Equipment",
  units = "tonnes"
)

Display a grid of plots and tables

Description

This function generates a grid of plots and tables, including a value box, data table, relative GPI plot, and total output plot.

Usage

output_display(
  data = x$data,
  time = time,
  date_format = c("%d/%m/%Y"),
  name = theatre,
  relative_gpi_val = emissions,
  gti_by = c("default", "month", "year"),
  plot_val = carbon_price_credit,
  plot_by = "default",
  pdf = TRUE
)

Arguments

Details

The function utilises other auxiliary functions such as relative_gti() and total_output().

Value

A grid of plots and tables showing the value box, data table, relative GPI plot, and total output plot.

Paper emissions

Description

Computes embodied GHG emissions for paper using uk_gov_data rows with Level 2 = "Paper". Material-use factors come from ⁠Level 1 = "Material use", Column Text = material_production⁠. Waste factors come from ⁠Level 1 = "Waste disposal", Column Text = waste_disposal⁠. Factors are kg CO2e per tonne.

Usage

paper_emissions(
  use = stats::setNames(numeric(), character()),
  waste = TRUE,
  material_production = "Primary material production",
  waste_disposal = c("Closed-loop", "Combustion", "Composting", "Landfill"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Value

Numeric total emissions in requested units.

Examples

# Closed-loop source for all paper types; landfill; waste = use
paper_emissions(
  use = c(board = 10, paper = 100),
  material_production = "Closed-loop source",
  waste_disposal = "Landfill",
  waste = TRUE
)

# Per-material: board closed-loop, mixed primary (default), no waste
paper_emissions(
  use = c(board = 5, mixed = 2),
  material_production = c(board = "closed loop"),
  waste = FALSE,
  value_col = "value_2024",
  units = "tonnes"
)

Calculate plastic emissions

Description

Computes embodied GHG emissions for plastics using uk_gov_data rows with Level 2 = "Plastic". Material-use factors come from ⁠Level 1 = "Material use", Column Text = "Primary material production"⁠. Waste factors come from ⁠Level 1 = "Waste disposal", Column Text = waste_disposal⁠. Factors are assumed kg CO2e per tonne (UOM = tonne, ⁠GHG/Unit = kg CO2e⁠).

Usage

plastic_emissions(
  use = stats::setNames(numeric(), character()),
  waste = TRUE,
  waste_disposal = c("Landfill", "Open-loop", "Closed-loop", "Combustion"),
  units = c("kg", "tonnes"),
  value_col = c("value", "value_2024"),
  strict = TRUE
)

Arguments

Details

Material use: Plastics generally use "Primary material production" as the ⁠Column Text⁠. This function always uses that for material-use factors.

Waste disposal: Factors are taken from the specified waste_disposal route. Availability varies by plastic type and year; this is enforced by the presence/absence of rows in uk_gov_data. Missing pairs error under strict = TRUE or contribute zero under strict = FALSE.

Units: Factors are kg CO2e per tonne; if units = "tonnes", the total is divided by 1000.

Value

Numeric total emissions in requested units.

Examples

# 1) Basic: primary material production + landfill; waste tonnage = use
plastic_emissions(
  use = c(average_plastics = 100, hdpe = 50, pet = 25),  # tonnes
  waste_disposal = "Landfill",
  waste = TRUE,
  units = "kg"
)

# 2) Choose 2024 factors and report in tonnes; no waste applied
plastic_emissions(
  use = c(average_plastic_film = 10, average_plastic_rigid = 5, pp = 2),
  waste_disposal = "Closed-loop",
  waste = FALSE,
  value_col = "value_2024",
  units = "tonnes"
)

# 3) Strict behaviour: error if a required waste route is unavailable
## Not run: 
plastic_emissions(
  use = c(ps = 1),
  waste_disposal = "Combustion",
  waste = TRUE,
  strict = TRUE
)
## End(Not run)
# Tolerant: treat missing waste factors as 0
plastic_emissions(
  use = c(ps = 1),
  waste_disposal = "Combustion",
  waste = TRUE,
  strict = FALSE
)

Calculate CO2e emissions from a train journey

Description

A function that calculates CO2e emissions between train stations in the UK.

Usage

rail_emissions(
  from = NULL,
  to = NULL,
  via = NULL,
  distance = 0,
  num_people = 1,
  times_journey = 1,
  include_WTT = TRUE,
  round_trip = FALSE
)

Arguments

Details

The distances are calculated using the Haversine formula. This is calculated as the crow flies. As a result, inputting the "via" journeys will make for a more reliable function.

Value

Returns CO2e emissions in tonnes for the train journey.

Examples

# Emissions for a train journey between Southampton Central and
# Manchester Piccadilly Station
rail_emissions("Southampton Central", "Manchester Piccadilly")

# Emissions for a train journey between Bristol Temple Meads and
# London Paddington via Bath, Swindon, and Reading
# Use the \code{rail_finder} function to find the name of London Paddington
rail_finder(region = "London")
# Then calculate emissions
rail_emissions("Bristol Temple Meads", "Paddington", via = c("Bath Spa",
"Swindon", "Reading"))

# Alternatively state the distance of a journey
rail_emissions(distance = 100)

Find the station code for a train station

Description

Find the name, area, and code of a train station in the UK. For use in the rail_emissions function.

Usage

rail_finder(
  station,
  region,
  county,
  district,
  station_code,
  distance = 0.1,
  ignore.case = FALSE
)

Arguments

Value

Data frame containing the station code, station name, region, county, district, latitude, and longitude of a train station in the UK.

Examples

# Can get the station code from the station. Gets similar matches.
rail_finder(station = "Bristo")

# Can get the code from the station and city.
rail_finder(station = "Bristo", county = "Bristol")

# Can find the name and district of a train station given the IATA code
rail_finder(station_code = "BRI")

Raw Fuels Emissions

Description

Raw Fuels Emissions

Usage

raw_fuels(
  num_people = 1,
  butane = 0,
  CNG = 0,
  LPG = 0,
  LNG = 0,
  natural_gas = 0,
  natural_gas_mineral = 0,
  other_petroleum_gas = 0,
  propane = 0,
  aviation = 0,
  aviation_fuel = 0,
  burning_oil = 0,
  diesel = 0,
  diesel_mineral = 0,
  fuel_oil = 0,
  gas_oil = 0,
  lubricants = 0,
  naptha = 0,
  petrol_biofuel = 0,
  petrol_mineral = 0,
  residual_oil = 0,
  distillate = 0,
  refinery_miscellaneous = 0,
  waste_oils = 0,
  marine_gas = 0,
  marine_fuel = 0,
  coal_industrial = 0,
  coal_electricity_gen = 0,
  coal_domestic = 0,
  coking_coal = 0,
  petroleum_coke = 0,
  coal_home_produced_gen = 0,
  bioethanol = 0,
  biodiesel = 0,
  biomethane = 0,
  biodiesel_cooking_oil = 0,
  biodiesel_tallow = 0,
  biodiesel_HVO = 0,
  biopropane = 0,
  bio_petrol = 0,
  renewable_petrol = 0,
  wood_log = 0,
  wood_chips = 0,
  wood_pellets = 0,
  grass = 0,
  biogas = 0,
  landfill_gas = 0,
  butane_units = c("kwh", "litres", "tonnes"),
  CNG_units = c("kwh", "litres", "tonnes"),
  LPG_units = c("kwh", "litres", "tonnes"),
  LNG_units = c("kwh", "litres", "tonnes"),
  natural_gas_units = c("kwh", "cubic metres", "tonnes"),
  natural_gas_mineral_units = c("kwh", "cubic metres", "tonnes"),
  other_petroleum_gas_units = c("kwh", "litres", "tonnes"),
  propane_units = c("kwh", "litres", "tonnes"),
  aviation_units = c("kwh", "litres", "tonnes"),
  aviation_fuel_units = c("kwh", "litres", "tonnes"),
  burning_oil_units = c("kwh", "litres", "tonnes"),
  diesel_units = c("kwh", "litres", "tonnes"),
  diesel_mineral_units = c("kwh", "litres", "tonnes"),
  fuel_oil_units = c("kwh", "litres", "tonnes"),
  gas_oil_units = c("kwh", "litres", "tonnes"),
  lubricants_units = c("kwh", "litres", "tonnes"),
  naptha_units = c("kwh", "litres", "tonnes"),
  petrol_biofuel_units = c("kwh", "litres", "tonnes"),
  petrol_mineral_units = c("kwh", "litres", "tonnes"),
  residual_oil_units = c("kwh", "litres", "tonnes"),
  distillate_units = c("kwh", "litres", "tonnes"),
  refinery_miscellaneous_units = c("kwh", "litres", "tonnes"),
  waste_oils_units = c("kwh", "tonnes"),
  marine_gas_units = c("kwh", "tonnes"),
  marine_fuel_units = c("kwh", "tonnes"),
  coal_industrial_units = c("kwh", "tonnes"),
  coal_electricity_gen_units = c("kwh", "tonnes"),
  coal_domestic_units = c("kwh", "tonnes"),
  coking_coal_units = c("kwh", "tonnes"),
  petroleum_coke_units = c("kwh", "tonnes"),
  coal_home_produced_gen_units = c("kwh", "tonnes"),
  bioethanol_units = c("litres", "GJ", "kg"),
  biodiesel_units = c("litres", "GJ", "kg"),
  biomethane_units = c("litres", "GJ", "kg"),
  biodiesel_cooking_oil_units = c("litres", "GJ", "kg"),
  biodiesel_tallow_units = c("litres", "GJ", "kg"),
  biodiesel_HVO_units = c("litres", "GJ", "kg"),
  biopropane_units = c("litres", "GJ", "kg"),
  bio_petrol_units = c("litres", "GJ", "kg"),
  renewable_petrol_units = c("litres", "GJ", "kg"),
  wood_log_units = c("kwh", "tonnes"),
  wood_chips_units = c("kwh", "tonnes"),
  wood_pellets_units = c("kwh", "tonnes"),
  grass_units = c("kwh", "tonnes"),
  biogas_units = c("kwh", "tonnes"),
  landfill_gas_units = c("kwh", "tonnes")
)

Arguments

Details

This function calculates CO2e emissions from a wide variety of fuels, considering different unit conversions for each type of fuel. It supports the calculation of emissions from commonly used fuels such as diesel, petrol, natural gas, and biodiesel, as well as more specific fuels like aviation fuel, marine fuel, and landfill gas.

Unit conversions are done internally based on the specified units for each type of fuel (e.g., kWh, litres, tonnes). The function is useful for assessing the carbon footprint associated with different fuel sources over a specified time period.

Value

A data frame with calculated emissions in tonnes of CO2e for each type of fuel input.

References

• DEFRA Conversion Factors for Greenhouse Gas (GHG) Reporting: https://www.gov.uk/government/collections/government-conversion-factors-for-company-reporting

• Descriptions from 2021 UK Government Report: https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2021

Examples

# Calculate emissions for 100 litres of diesel and 500 kWh of natural gas:
raw_fuels(
  diesel = 100, diesel_units = "litres",
  natural_gas = 500, natural_gas_units = "kwh",
)

# Calculate emissions for 10 tonnes of aviation fuel:
raw_fuels(
  aviation_fuel = 10, aviation_fuel_units = "tonnes",
)

Relative GTI Plot

Description

Calculate and plot the relative growth to index (GTI) over time

Usage

relative_gti(
  data = data,
  time = time,
  date_format = c("%d/%m/%Y"),
  name = theatre,
  val = emissions,
  gti_by = c("default", "month", "year")
)

Arguments

Value

A ggplot2 object showing the relative GTI (Growth to Index) over time.

Examples

# Example dataset
emission_data <- data.frame(
  theatre = c("Theatre A", "Theatre A", "Theatre B", "Theatre B", "Theatre A", "Theatre B"),
  emissions = c(200, 250, 150, 180, 300, 220),
  date = c("01/01/2023", "01/02/2023", "01/01/2023", "01/02/2023", "01/03/2023", "01/03/2023")
  )
  
# Using the relative_gti function
relative_gti_plot <- relative_gti(
  data = emission_data,
  time = date,
  date_format = "%d/%m/%Y",  # Date format used in the dataset
  name = theatre,
  val = emissions,
  gti_by = "default"  # Calculating based on default time period
)

# Plot the relative GTI
print(relative_gti_plot)

Check the code or name of a seaport

Description

Find the name and/or code of a seaport. For use in the ferry_emissions function.

Usage

seaport_finder(city, country, port_code, distance = 0.1, ignore.case = FALSE)

Arguments

Value

Data frame containing the country, city, country code, port code, latitude, and longitude of a seaport.

Examples

# Look up the city of Aberdeen to find the port_code for it
seaport_finder(city = "Aberdeen")

# Search for a country and city and it finds matches
seaport_finder(country = "United", city = "borunemouth", ignore.case = TRUE)

Dataset of different seaports

Description

A dataset containing the country, country code, city, city code, and coordinates of seaports

Usage

data(seaports)

Format

A data frame with 8736 rows and 7 variables:

country

Name of the country with the port

city

Name of the city with the port

country_code

Code of the country name

port_code

Code of the city port

latitude

Latitude of the port

longitude

Longitude of the port

Source

https://www.kaggle.com/therohk/world-seaport-airport-dataset-and-codes/script

Run 'shiny' App to Calculate Carbon Emissions

Description

Runs a GUI to the functions in the 'carbonr' package to calculate carbon-equivalent emissions.

Usage

shiny_emissions()

Value

'shiny' app to calculate carbon-equivalent emissions

Examples

if(interactive()){shiny_emissions()}

Dataset of UK train stations

Description

A dataset containing the city, station code, and coordinates of seaports

Usage

data(stations)

Format

A data frame with 2608 rows and 4 variables:

station

Name of the station

station_code

Code of the station

region

Region of the station. One of "London", "Scotland", "Wales - Cymru", "North West", "West Midlands", "North East", "East", "South East", "East Midlands", "Yorkshire And The Humber", "South West", NA

county

County of the station

district

District of the station

latitude

Latitude of the station

longitude

Longitude of the station

Source

https://www.data.gov.uk/dataset/ff93ffc1-6656-47d8-9155-85ea0b8f2251/naptan

https://www.theguardian.com/news/datablog/2011/may/19/train-stations-listed-rail#data

Calculate Total Output and Generate Plot

Description

This function calculates the total output and generates a plot based on the specified parameters.

Usage

total_output(
  data = x$data,
  time = time,
  date_format = c("%d/%m/%Y"),
  name = theatre,
  val = carbon_price_credit,
  plot_by = c("default", "month", "year")
)

Arguments

Details

This function calculates the total output by grouping the data based on the specified parameters (grouping variable and time dimension). It then summarises the specified variable (CPI or emissions) using the sum function. The resulting data is used to create a line plot showing the total output over time, with each group represented by a different color. The plot can be grouped by the default grouping, month, or year, based on the plot_by parameter.

Value

A ggplot object showing the total output plot.

Calculate CO2e emissions from land-travel journeys

Description

A function that calculates CO2e emissions on a journey on land.

Usage

vehicle_emissions(
  distance,
  units = c("miles", "km"),
  num = 1,
  vehicle = c("Cars", "Motorbike"),
  fuel = c("Petrol", "Diesel", "Unknown", "Battery Electric Vehicle",
    "Plug-in Hybrid Electric Vehicle", "Hybrid", "CNG", "LPG"),
  car_type = c("Average car", "Small car", "Medium car", "Large car", "Mini",
    "Supermini", "Lower medium", "Upper medium", "Executive", "Luxury", "Sports",
    "Dual purpose 4X4", "MPV"),
  bike_type = c("Average", "Small", "Medium", "Large"),
  TD = TRUE,
  include_WTT = TRUE,
  include_electricity = TRUE,
  owned_by_org = TRUE
)

Arguments

Value

Tonnes of CO2e emissions per mile travelled.

Examples

# Emissions for a 100 mile car journey
 vehicle_emissions(distance = 100)

# Emissions for a 100 mile motorbike journey where the motorbike is 500+cc
 vehicle_emissions(distance = 100, vehicle = "Motorbike", bike_type = "Large")