From e6cc85ffcb1755c3b24f98492586d25be4db678d Mon Sep 17 00:00:00 2001 From: Phyllis Muniu Date: Tue, 8 Oct 2024 14:20:10 +0300 Subject: [PATCH 1/8] Adding vignettes on personal use --- vignettes/Personal_carbon_emissions.Rmd | 245 ++++++++++++++++++++++++ vignettes/Personal_use.Rmd | 19 ++ 2 files changed, 264 insertions(+) create mode 100644 vignettes/Personal_carbon_emissions.Rmd create mode 100644 vignettes/Personal_use.Rmd diff --git a/vignettes/Personal_carbon_emissions.Rmd b/vignettes/Personal_carbon_emissions.Rmd new file mode 100644 index 0000000..9c2fa48 --- /dev/null +++ b/vignettes/Personal_carbon_emissions.Rmd @@ -0,0 +1,245 @@ +--- +title: "Introduction to carbonr" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Personal Carbon Emissions} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +## Personal Carbon Emissions with carbonr + +### Introduction +Personal carbon emissions refer to the total amount of greenhouse gases, primarily carbon dioxide (CO₂), that an individual is responsible for emitting directly and indirectly through their daily activities.The carbonr package in R provides a convenient way to calculate these emissions. +The emissions values used in these calculations primarily derive from the [UK Government report (2023)](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2023), where available. If calculations are based on sources other than the UK Government report, it will be specified accordingly. + +### Motivation +The aim of calculating Personal Carbon Emissions is to provide a reliable and reproducible method for assessing individual carbon footprints. This ensures that results can be easily saved, edited, and redistributed, fostering transparency and accuracy in carbon emission reporting. + +### Installation +To install the development version of carbonr, you can use the following commands to download it from [GitHub](https://github.com/): + +```{r install_package, eval = FALSE} +# Uncomment the following line if you haven't installed devtools yet +# install.packages("devtools") +devtools::install_github("IDEMSInternational/carbonr") +``` + +Once installed, load the carbonr package with the following command: +```{r, eval=FALSE} +library(carbonr) +library(knitr) +library(rmarkdown) +``` + +### Usage +The carbonr package allows users to calculate personal emissions from various sources, including travel-related activities, building energy use, and household emissions. For personal emissions, we consider the following categories: +``` +*Travel-Related Emissions*: + +airplane_emissions() Calculates emissions from air travel. +ferry_emissions() Estimates emissions from ferry travel +land_emissions() Computes emissions from land-based travel. +rail_emissions() Determines emissions from rail travel. +vehicle_emissions() Assesses emissions from personal vehicle use. + +*Household Emissions*: +household_emissions() Estimates emissions from household activities. + +*Building Emissions*: +building_emissions() Calculates emissions associated with building energy use. +``` + +These all return carbon-equivalent emissions in tonnes. + +A shiny app is also available by shiny_emissions() to calculate carbon-equivalent emissions with a GUI. + +We give some small examples in using the functions in `carbonr()`. +We started by Travel related Emissions: +1.`airplane_emissions()` +To calculate emissions for a flight between Nairobi and Kisumu, we first want to find the name of the airports. We do this using the `airport_finder()` function: + +```{r load_tidyverse, message = FALSE, warning = FALSE} +library(tidyverse) +``` + + +```{r find_airport_nairobi, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +airport_finder(name = "Nairobi") + %>% + knitr::kable() +``` + + + +```{r get_airport_kisumu, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +airport_finder(name = "Kisumu") + %>% + knitr::kable() + +``` + +Now we can find the overall emission value using the appropriate IATA code. These distances are calculated using the Haversine formula: + +```{r airplane_emissions_chunk, message = FALSE, warning = FALSE} +result <- carbonr::airplane_emissions("NBO", "KIS") +knitr::kable(result) +``` +A similar approach can be performed for ferry emissions. For example, to calculate emissions for a round trip ferry from Melbourne to New York, we first find the appropriate seaport code with the `seaport_finder()` function: +```{r mombasa_findsea, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +seaport_finder(city = "Shimoni") +%>% + knitr::kable() +``` + +```{r mogadishu_findsea, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +seaport_finder(city = "Giohar") +%>% + knitr::kable() +``` + +# Ensure the seaports data is loaded +```{r load_seaports, message = FALSE, warning = FALSE} +data("seaports", package = "carbonr") +``` + +Now we can find the overall emission value using the appropriate seaport code: +```{r ferry_example, message = FALSE, warning = FALSE} +result <-carbonr::ferry_emissions("SMN", "GHR", round_trip = TRUE) +knitr::kable(result) +``` + +For the UK we can calculate emissions for a train journey. Like with `airplane_emissions()` and `ferry_emissions()`, the distances are calculated using the Haversine formula - this is calculated as the crow flies. As before, we first find the stations. As always, for a more accurate estimation we can include via points: + +To calculate emissions for a train journey from Bristol Temple Meads to Edinburgh Waverley, via Birmingham New Street. We can use a data frame and `purrr::map()` to read through the data easier: +```{r load_stations, message = FALSE, warning = FALSE} +data("stations", package = "carbonr") +``` + +```{r multiple_example, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +multiple_ind <- tibble::tribble(~ID, ~station, + "From", "Bristol", + "To", "Edinburgh", + "Via", "Birmingham") +purrr::map(.x = multiple_ind$station, .f = ~rail_finder(.x)) %>% + dplyr::bind_rows() +``` +```{r multiple_eg, message = FALSE, warning = FALSE, echo=FALSE} +multiple_ind <- tibble::tribble(~ID, ~station, + "From", "Bristol", + "To", "Edinburgh", + "Via", "Birmingham") +purrr::map(.x = multiple_ind$station, .f = ~carbonr::rail_finder(.x)) %>% + dplyr::bind_rows() %>% + knitr::kable() +``` + +Then we can estimate the overall tCO2e emissions for the journey: +```{r rail_em, message = FALSE, warning = FALSE} +result <- carbonr::rail_emissions(from = "Bristol Temple Meads", to = "Edinburgh", via = "Birmingham New Street") +knitr::kable(result) +``` + +We can use a data frame to read through the data easier in general. For example, if we had data for multiple individuals, or journeys: +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +multiple_ind <- tibble::tribble(~ID, ~rail_from, ~rail_to, ~air_from, ~air_to, ~air_via, + "Clint", "Bristol Temple Meads", "Paddington", "LHR", "KIS", "NBO", + "Zara", "Bristol Temple Meads", "Paddington", "LHR", "LAX", "ORL") +multiple_ind %>% + dplyr::rowwise() %>% + dplyr::mutate(plane_emissions = carbonr::airplane_emissions(air_from, + air_to, + air_via)) %>% + dplyr::mutate(train_emissions = carbonr::rail_emissions(rail_from, + rail_to)) %>% + dplyr::mutate(total_emissions = plane_emissions + train_emissions) +``` +```{r, message = FALSE, warning = FALSE, echo = FALSE} +multiple_ind <- tibble::tribble(~ID, ~rail_from, ~rail_to, ~air_from, ~air_to, ~air_via, + "Clint", "Bristol Temple Meads", "Paddington", "LHR", "KIS", "NBO", + "Zara", "Bristol Temple Meads", "Paddington", "LHR", "LAX", "ORL") +multiple_ind %>% + dplyr::rowwise() %>% + dplyr::mutate(plane_emissions = carbonr::airplane_emissions(air_from, + air_to, + air_via)) %>% + dplyr::mutate(train_emissions = carbonr::rail_emissions(rail_from, + rail_to)) %>% + dplyr::mutate(total_emissions = plane_emissions + train_emissions) %>% + knitr::kable() +``` + +### Land Emissions +For land-based travel, we use the `land_emissions()` function. This allows you to calculate emissions based on vehicle type and fuel type. + +```{r land_emissions_example, message = FALSE, warning = FALSE} +result<-carbonr::land_emissions(distance = 100, vehicle = "Bus", fuel = "Petrol") +knitr::kable(result) +``` +### Vehicle Emissions + +The `vehicle_emissions()` function estimates emissions from personal vehicle use based on distance, fuel type, and fuel consumption. + +```{r vehicle_emissions_example, message = FALSE, warning = FALSE} +result <- carbonr::vehicle_emissions(distance = 150, fuel = "Petrol", car_type = "Dual purpose 4X4" ) +knitr::kable(result) +``` + +Additional emissions can be calculated as well. For example, household and building emissions +### Household Emissions +The `household_emissions()` is used to estimate carbon-equivalent emissions based on household energy use. It allows users to input various types of energy sources, such as electricity or gas, and calculates the emissions based on the consumption levels provided. + +# Input Parameters +energy_type: This parameter defines the type of energy source used (e.g., "electricity", "gas", "oil"). +consumption: The amount of energy consumed, typically measured in kilowatt-hours (kWh) for electricity or cubic meters for gas. +emission_factor: A factor that converts the energy usage into carbon-equivalent emissions. This factor is based on standard values but can be customized in some cases. + +```{r household_emissions_example, message = FALSE, warning = FALSE} +result <- carbonr::household_emissions(compost_waste_disposal = "Combustion", fridges = 113 , units = "kg") +knitr::kable(result) +``` + +### Building Emissions +The `building_emissions()`calculates carbon-equivalent emissions based on energy consumption in a building. The function considers various energy sources, such as electricity, natural gas, and oil, and returns emissions in tonnes of CO₂-equivalent. +# Input Parameters +electricity: The amount of electricity consumed in kilowatt-hours (kWh). +natural_gas: The amount of natural gas consumed in cubic meters (m³). +oil: The amount of oil consumed in liters (L). +other_fuel: The amount of any other type of fuel consumed (optional). + + +```{r building_emissions_example, message = FALSE, warning = FALSE} +result <- carbonr::building_emissions(electricity_kWh = 500, water_supply = 1000, water_unit = "cubic metres") +knitr::kable(result) +``` +## Shiny App + +An interactive calculator using Shiny can be accessed by the `shiny_emissions()` function. This calculator uses some of the functions in the `carbonr` package: +```{r, eval = FALSE} +shiny_emissions() +``` + +## References + +#### Other Online Calculators: +- [Carbonfund.org](https://carbonfund.org/calculation-methods/) +- [Carbon Footprint Calculator](https://www.carbonfootprint.com/calculatorfaqs.html) + +#### Sources +1. UK Government Report: Department for Energy Security and Net Zero. (2023). [Greenhouse Gas Reporting: Conversion Factors 2023](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2023). + +2. Radiative Forcing Factor: DEFRA, 2016. Government GHG conversion factors for company reporting. + +3. Clinical Anaesthetic Emissions: Various sources including - + +Varughese, S. and Ahmed, R., 2021. Environmental and occupational considerations of anesthesia: a narrative review and update. Anesthesia & Analgesia, 133(4), pp.826-835; + +McGain, F., Muret, J., Lawson, C. and Sherman, J.D., 2020. Environmental sustainability in anaesthesia and critical care. British Journal of Anaesthesia, 125(5), pp.680-692; + +Wyssusek, K., Chan, K.L., Eames, G. and Whately, Y., 2022. Greenhouse gas reduction in anaesthesia practice: a departmental environmental strategy. BMJ Open Quality, 11(3), p.e001867; + +Sherman, J., Le, C., Lamers, V. and Eckelman, M., 2012. Life cycle greenhouse gas emissions of anesthetic drugs. Anesthesia & Analgesia, 114(5), pp.1086-1090. + +4. Clinical Wet Waste Emissions: Department of Climate Change, Energy, the Environment and Water. (2022). [National Greenhouse Accounts Factors: 2022](https://www.dcceew.gov.au/climate-change/publications/national-greenhouse-accounts-factors-2022). + + diff --git a/vignettes/Personal_use.Rmd b/vignettes/Personal_use.Rmd new file mode 100644 index 0000000..30365ec --- /dev/null +++ b/vignettes/Personal_use.Rmd @@ -0,0 +1,19 @@ +--- +title: "Personal_use" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Personal_use} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` + +```{r setup} +library(carbonr) +``` From e7756e966be2ae89d76d21641ac760dee088acd3 Mon Sep 17 00:00:00 2001 From: Phyllis Muniu Date: Thu, 31 Oct 2024 17:41:06 +0300 Subject: [PATCH 2/8] Clinical and Company Emissions Vignettes --- vignettes/Clinical_emissions.Rmd | 95 +++++++++++++++++ vignettes/Company_emissions.Rmd | 134 ++++++++++++++++++++++++ vignettes/Personal_carbon_emissions.Rmd | 2 + 3 files changed, 231 insertions(+) create mode 100644 vignettes/Clinical_emissions.Rmd create mode 100644 vignettes/Company_emissions.Rmd diff --git a/vignettes/Clinical_emissions.Rmd b/vignettes/Clinical_emissions.Rmd new file mode 100644 index 0000000..305223e --- /dev/null +++ b/vignettes/Clinical_emissions.Rmd @@ -0,0 +1,95 @@ +--- +title: "Clinical Emissions Vignette" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Clinical Emissions Vignette} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +## Calculating Clinical Theatre Emissions + +This vignette provides an overview of the `clinical_theatre_emissions` function, guiding users through calculating CO₂-equivalent emissions for various clinical resources in an operating theatre, such as clinical waste, utilities, and materials. This function is designed to offer flexibility for different units and waste types, making it adaptable to various clinical settings. + +### Overview of Parameters + +The `clinical_theatre_emissions` function includes a wide range of parameters to capture emissions across clinical activities. Here’s a breakdown of the main categories: + +- **Clinical Waste**: Accounts for wet clinical waste typically incinerated in a clinical setting. The input can be in kilograms or tonnes to ensure flexibility for different data sources. +- **Anaesthetic Gases**: Emissions from various gases commonly used in surgical procedures, such as desflurane, sevoflurane, and nitrous oxide (N₂O). +- **Utilities**: Includes water usage (in cubic meters or million liters), electricity (kWh), and heating (kWh). Each utility parameter offers options to adjust for transmission and distribution losses (TD) and well-to-tank (WTT) factors to capture indirect emissions. +- **Materials**: Considers emissions from material waste, including glass, paper, and plastic. This category allows for waste disposal types, such as recycling or landfill, to assess the environmental impact of different disposal methods. + +## Examples + +The following examples demonstrate how to use the `clinical_theatre_emissions` function in different scenarios. + +### Load the Package + +To use this function, first load the required package: + +```r +# Load necessary package +devtools::load_all() +``` + + +### Example 1: Basic Calculation with Default Values + +In this example, we calculate emissions for clinical waste, water supply, electricity, and heat with default values. This example is useful for a straightforward scenario without specific anesthetic gases or waste types. + +In this calculation, wet_clinical_waste is provided in kilograms (150 kg). We use cubic metres as the unit for water_supply, and the electricity and heating inputs are provided in kWh. This setup calculates the emissions from basic clinical waste and utility usage. +```{r example_data, eval = FALSE} +example_data <- data.frame( + wet_clinical_waste = 150, # 150 kg of wet clinical waste + water_supply = 100, # 100 cubic meters of water + electricity_kWh = 500, # 500 kWh of electricity + heat_kWh = 200 # 200 kWh of heating +) + +emissions <- clinical_theatre_emissions( + wet_clinical_waste = example_data$wet_clinical_waste, + wet_clinical_waste_unit = "kg", + electricity_kWh = example_data$electricity_kWh, + heat_kWh = example_data$heat_kWh, + water_supply = example_data$water_supply, + water_unit = "cubic metres" +) +print(emissions) +``` + +### Example 2: Including Anaesthetic Gases + +This example includes emissions from anesthetic gases along with clinical waste, electricity, heating, and water. Adding anesthetic gases can give a more comprehensive view of the emissions, particularly in settings with significant use of these gases. + +This example demonstrates emissions including anesthetic gases—specifically, desflurane, sevoflurane, and N₂O. These gases have high greenhouse gas potential, so their inclusion can significantly affect total emissions in a clinical setting where they are frequently used. + +```{r emissions_with_gases, eval=FALSE} +emissions_with_gases <- clinical_theatre_emissions( + wet_clinical_waste = 150, # Wet clinical waste in kg + wet_clinical_waste_unit = "kg", + desflurane = 10, # 10 liters of desflurane + sevoflurane = 5, # 5 liters of sevoflurane + N2O = 100, # 100 liters of N₂O + electricity_kWh = 500, + heat_kWh = 200, + water_supply = 100 +) +print(emissions_with_gases) +``` +### Example 3: Reducing Waste and Emissions + +This example shows how reduced resource usage affects emissions. Reductions in clinical waste, water, electricity, and heat are used to model a more sustainable scenario. + +In this, wet_clinical_waste is significantly reduced to 0.1 tonnes, demonstrating the effect of minimized clinical waste on emissions. This example also shows the impact of reduced utility usage, providing insights into how efficient resource management can decrease the environmental footprint of clinical operations. + +```{r reduced_emissions, eval=FALSE} +reduced_emissions <- clinical_theatre_emissions( + wet_clinical_waste = 0.1, # Clinical waste in tonnes + wet_clinical_waste_unit = "tonnes", + electricity_kWh = 300, # Reduced electricity + heat_kWh = 100, # Reduced heating + water_supply = 50 # Reduced water +) +print(reduced_emissions) +``` diff --git a/vignettes/Company_emissions.Rmd b/vignettes/Company_emissions.Rmd new file mode 100644 index 0000000..25e6693 --- /dev/null +++ b/vignettes/Company_emissions.Rmd @@ -0,0 +1,134 @@ +--- +title: "Introduction to carbonr" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Company Emissions} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +## Company Emissions with carbonr + This vignette demonstrates a step-by-step process for calculating and combining different types of company emissions, including office emissions, hotel emissions, and travel emissions, using various functions to track and calculate the carbon footprint associated with different company activities. + + +### Introduction +Understanding the overall carbon footprint of a company is crucial for meeting sustainability goals. This vignette details the process for calculating emissions from company office operations, employee travel (air, ferry, rail), and hotel accommodations, ultimately allowing the organization to identify high-emission areas and take action to reduce them. + +To begin, install and load the necessary packages. +```{r install_and_loading_packages, eval = FALSE} +install.packages("emissionsPkg") +library(emissionsPkg) +install.packages("carbonr") +library(carbonr) +library(tidyverse) +``` +## Office Emissions: +Start by calculating emissions from office-related activities such as energy consumption, waste production, and equipment usage. + +### Example Office Emission Calculation + For calculating office emissions, use `office_emissions()` function from the `carbonr` package. This function requires input values for energy use (e.g., electricity and heating). + +```{r office_emissions_example, message=FALSE, warning=FALSE} + office_emissions <- carbonr::office_emissions(specify = TRUE, + electricity_kWh = 200, + water_supply = 100, + water_unit = "cubic metres",water_trt = FALSE + ) + knitr::kable(office_emissions) +``` + +### Hotel Emissions: + Next, we calculate emissions related to hotel stays for business trips using `hotel_emissions()`. This function calculates the emissions based on the number of nights, room type, and occupancy rate. + +### Example Hotel Emission Calculation +```{r hotel_emissions_example, message=FALSE, warning=FALSE} +hotel_emissions <- carbonr::hotel_emissions(location = "Fiji", nights = 2, rooms=1) + knitr::kable(hotel_emissions) +``` +### Travel-related Emissions: + +## 1. Airplane Emissions +Using the `carbonr` package, we can calculate emissions for a flight between Nairobi and Kisumu by first finding the airport codes. +### Finding Airport Codes +```{r find_airport_nairobi, eval=FALSE, message=FALSE, warning=FALSE} + airport_finder(name = "Nairobi") %>% knitr::kable() +``` +```{r find_airport_kisumu, eval=FALSE, message=FALSE, warning=FALSE} + airport_finder(name = "Kisumu") %>% knitr::kable() +``` + +### Calculating Flight Emissions +With the IATA codes (`"NBO"` for Nairobi and `"KIS"` for Kisumu), we can calculate emissions: +```{r airplane_emissions_chunk, message=FALSE, warning=FALSE} +result <- carbonr::airplane_emissions("NBO", "KIS") +knitr::kable(result) +``` + +## 2. Ferry Emissions +To estimate emissions for ferry travel, for instance, from Shimoni to Giohar, we start by locating seaport codes. + +### Finding Seaport Codes +```{r shimoni_seaport, eval=FALSE, message=FALSE, warning=FALSE} +seaport_finder(city = "Shimoni") %>% knitr::kable() +``` + +```{r giohar_seaport, eval=FALSE, message=FALSE, warning=FALSE} +seaport_finder(city = "Giohar") %>% knitr::kable() +``` + +## Ensure the seaports data is loaded +```{r load_seaports, message = FALSE, warning = FALSE} +data("seaports", package = "carbonr") +``` + +### Calculating Ferry Emissions +With codes `"SMN"` for Shimoni and `"GHR"` for Giohar: +```{r ferry_example, message=FALSE, warning=FALSE} +result <- carbonr::ferry_emissions("SMN", "GHR", round_trip = TRUE) +knitr::kable(result) +``` +## 3. Train Emissions +Train journey emissions are calculated similarly. To estimate emissions from Bristol Temple Meads to Edinburgh Waverley via Birmingham New Street: + +### Finding Station Names +```{r load_stations, message=FALSE, warning=FALSE} +data("stations", package = "carbonr") +``` + +```{r find_station_bristol, eval=FALSE, message=FALSE} +rail_finder("Bristol") %>% knitr::kable() +``` + +```{r find_station_edinburgh, eval=FALSE, message=FALSE} + rail_finder("Edinburgh") %>% knitr::kable() +``` + +### Calculating Train Emissions +```{r rail_em, message=FALSE, warning=FALSE} +result <- carbonr::rail_emissions(from = "Bristol Temple Meads", to = "Edinburgh", via = "Birmingham New Street") +knitr::kable(result) +``` + +## 4. Aggregating Emissions for Multiple Individuals +Using data frames and mapping functions, we can calculate combined emissions for multiple travel types. + +### Example +```{r, eval=FALSE} +multiple_ind <- tibble::tribble( + ~ID, ~rail_from, ~rail_to, ~air_from, ~air_to, ~air_via, +"Clint", "Bristol Temple Meads", "Paddington", "LHR", "KIS", "NBO", +"Zara", "Bristol Temple Meads", "Paddington", "LHR", "LAX", "ORL" + ) + +multiple_ind %>% +rowwise() %>% +mutate( +plane_emissions = carbonr::airplane_emissions(air_from, air_to, air_via), +train_emissions = carbonr::rail_emissions(rail_from, rail_to) +) %>% +mutate(total_emissions = plane_emissions + train_emissions) %>% +knitr::kable() +``` + +## References: +Emission factors are based on the UK Government’s 2022 greenhouse gas reporting conversion factors: [Greenhouse Gas Conversion Factors](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2022). diff --git a/vignettes/Personal_carbon_emissions.Rmd b/vignettes/Personal_carbon_emissions.Rmd index 9c2fa48..bc78082 100644 --- a/vignettes/Personal_carbon_emissions.Rmd +++ b/vignettes/Personal_carbon_emissions.Rmd @@ -189,6 +189,8 @@ Additional emissions can be calculated as well. For example, household and build ### Household Emissions The `household_emissions()` is used to estimate carbon-equivalent emissions based on household energy use. It allows users to input various types of energy sources, such as electricity or gas, and calculates the emissions based on the consumption levels provided. + + # Input Parameters energy_type: This parameter defines the type of energy source used (e.g., "electricity", "gas", "oil"). consumption: The amount of energy consumed, typically measured in kilowatt-hours (kWh) for electricity or cubic meters for gas. From 026fe02f1d5d30b4527680c26bb40a6053d5d310 Mon Sep 17 00:00:00 2001 From: PhyllisMuniu <166037875+PhyllisMuniu@users.noreply.github.com> Date: Fri, 1 Nov 2024 14:42:26 +0300 Subject: [PATCH 3/8] Update Company_emissions.Rmd --- vignettes/Company_emissions.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/Company_emissions.Rmd b/vignettes/Company_emissions.Rmd index 25e6693..3ed1b90 100644 --- a/vignettes/Company_emissions.Rmd +++ b/vignettes/Company_emissions.Rmd @@ -1,5 +1,5 @@ --- -title: "Introduction to carbonr" +title: "Company Emissions" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Company Emissions} From 857f454bbe01dc2472bff5a5efdaf2b50ea9b129 Mon Sep 17 00:00:00 2001 From: lilyclements Date: Tue, 3 Dec 2024 16:12:37 +0000 Subject: [PATCH 4/8] Update Clinical_emissions.Rmd --- vignettes/Clinical_emissions.Rmd | 66 +++++++++++++++++++++++--------- 1 file changed, 48 insertions(+), 18 deletions(-) diff --git a/vignettes/Clinical_emissions.Rmd b/vignettes/Clinical_emissions.Rmd index 305223e..4be970b 100644 --- a/vignettes/Clinical_emissions.Rmd +++ b/vignettes/Clinical_emissions.Rmd @@ -18,7 +18,7 @@ The `clinical_theatre_emissions` function includes a wide range of parameters to - **Clinical Waste**: Accounts for wet clinical waste typically incinerated in a clinical setting. The input can be in kilograms or tonnes to ensure flexibility for different data sources. - **Anaesthetic Gases**: Emissions from various gases commonly used in surgical procedures, such as desflurane, sevoflurane, and nitrous oxide (N₂O). - **Utilities**: Includes water usage (in cubic meters or million liters), electricity (kWh), and heating (kWh). Each utility parameter offers options to adjust for transmission and distribution losses (TD) and well-to-tank (WTT) factors to capture indirect emissions. -- **Materials**: Considers emissions from material waste, including glass, paper, and plastic. This category allows for waste disposal types, such as recycling or landfill, to assess the environmental impact of different disposal methods. +- **Materials**: Includes emissions from material waste, including glass, paper, and plastic. This category allows for waste disposal types, such as recycling or landfill, to assess the environmental impact of different disposal methods. ## Examples @@ -29,42 +29,71 @@ The following examples demonstrate how to use the `clinical_theatre_emissions` f To use this function, first load the required package: ```r -# Load necessary package -devtools::load_all() +# Load necessary packages for this vignette +library(carbonr) +library(dplyr) ``` - ### Example 1: Basic Calculation with Default Values -In this example, we calculate emissions for clinical waste, water supply, electricity, and heat with default values. This example is useful for a straightforward scenario without specific anesthetic gases or waste types. +In this example, we calculate emissions for clinical waste, water supply, electricity, and heat with default values. This example is useful for a straightforward scenario without specific anaesthetic gases or waste types. + +In this calculation, `wet_clinical_waste` is provided in kilograms (150 kg). We use cubic metres as the unit for `water_supply`, and the electricity and heating inputs are provided in kWh. This setup calculates the emissions from basic clinical waste and utility usage. -In this calculation, wet_clinical_waste is provided in kilograms (150 kg). We use cubic metres as the unit for water_supply, and the electricity and heating inputs are provided in kWh. This setup calculates the emissions from basic clinical waste and utility usage. -```{r example_data, eval = FALSE} +```{r example_data, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +# Create an example data frame, called example_data. example_data <- data.frame( wet_clinical_waste = 150, # 150 kg of wet clinical waste water_supply = 100, # 100 cubic meters of water electricity_kWh = 500, # 500 kWh of electricity heat_kWh = 200 # 200 kWh of heating ) +example_data +``` -emissions <- clinical_theatre_emissions( - wet_clinical_waste = example_data$wet_clinical_waste, - wet_clinical_waste_unit = "kg", - electricity_kWh = example_data$electricity_kWh, - heat_kWh = example_data$heat_kWh, - water_supply = example_data$water_supply, - water_unit = "cubic metres" +```{r, message = FALSE, warning = FALSE, echo = FALSE} +example_data <- data.frame( + wet_clinical_waste = 150, # 150 kg of wet clinical waste + water_supply = 100, # 100 cubic meters of water + electricity_kWh = 500, # 500 kWh of electricity + heat_kWh = 200 # 200 kWh of heating +) +example_data %>% knitr::kable() +``` + +```{r emissions, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +# We can call our example_data frame to get the values +example_data %>% + dplyr::mutate(emissions = clinical_theatre_emissions( + wet_clinical_waste = wet_clinical_waste, + wet_clinical_waste_unit = "kg", + electricity_kWh = electricity_kWh, + heat_kWh = heat_kWh, + water_supply = water_supply, + water_unit = "cubic metres")) ) -print(emissions) +``` + +```{r, message = FALSE, warning = FALSE, echo = FALSE} +example_data %>% + dplyr::mutate(emissions = clinical_theatre_emissions( + wet_clinical_waste = wet_clinical_waste, + wet_clinical_waste_unit = "kg", + electricity_kWh = electricity_kWh, + heat_kWh = heat_kWh, + water_supply = water_supply, + water_unit = "cubic metres")) +) %>% knitr::kable() ``` ### Example 2: Including Anaesthetic Gases -This example includes emissions from anesthetic gases along with clinical waste, electricity, heating, and water. Adding anesthetic gases can give a more comprehensive view of the emissions, particularly in settings with significant use of these gases. +This example includes emissions from anaesthetic gases along with clinical waste, electricity, heating, and water. Adding anaesthetic gases can give a more comprehensive view of the emissions, particularly in settings with significant use of these gases. -This example demonstrates emissions including anesthetic gases—specifically, desflurane, sevoflurane, and N₂O. These gases have high greenhouse gas potential, so their inclusion can significantly affect total emissions in a clinical setting where they are frequently used. +This example demonstrates emissions including anaesthetic gases - specifically, desflurane, sevoflurane, and N₂O. These gases have high greenhouse gas potential, so their inclusion can significantly affect total emissions in a clinical setting where they are frequently used. ```{r emissions_with_gases, eval=FALSE} +# We do not have to call from a data frame. For example, here we just input the values emissions_with_gases <- clinical_theatre_emissions( wet_clinical_waste = 150, # Wet clinical waste in kg wet_clinical_waste_unit = "kg", @@ -77,11 +106,12 @@ emissions_with_gases <- clinical_theatre_emissions( ) print(emissions_with_gases) ``` + ### Example 3: Reducing Waste and Emissions This example shows how reduced resource usage affects emissions. Reductions in clinical waste, water, electricity, and heat are used to model a more sustainable scenario. -In this, wet_clinical_waste is significantly reduced to 0.1 tonnes, demonstrating the effect of minimized clinical waste on emissions. This example also shows the impact of reduced utility usage, providing insights into how efficient resource management can decrease the environmental footprint of clinical operations. +In this, `wet_clinical_waste` is significantly reduced to 0.1 tonnes, demonstrating the effect of minimised clinical waste on emissions. This example also shows the impact of reduced utility usage, providing insights into how efficient resource management can decrease the environmental footprint of clinical operations. ```{r reduced_emissions, eval=FALSE} reduced_emissions <- clinical_theatre_emissions( From 2f578bc3def58fd87ee32666e6b08509c5e41901 Mon Sep 17 00:00:00 2001 From: lilyclements Date: Tue, 3 Dec 2024 16:18:37 +0000 Subject: [PATCH 5/8] Update Clinical_emissions.Rmd Fixing typos --- vignettes/Clinical_emissions.Rmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/vignettes/Clinical_emissions.Rmd b/vignettes/Clinical_emissions.Rmd index 4be970b..efe8aa3 100644 --- a/vignettes/Clinical_emissions.Rmd +++ b/vignettes/Clinical_emissions.Rmd @@ -28,7 +28,7 @@ The following examples demonstrate how to use the `clinical_theatre_emissions` f To use this function, first load the required package: -```r +```{r} # Load necessary packages for this vignette library(carbonr) library(dplyr) @@ -70,7 +70,7 @@ example_data %>% electricity_kWh = electricity_kWh, heat_kWh = heat_kWh, water_supply = water_supply, - water_unit = "cubic metres")) + water_unit = "cubic metres") ) ``` @@ -82,8 +82,8 @@ example_data %>% electricity_kWh = electricity_kWh, heat_kWh = heat_kWh, water_supply = water_supply, - water_unit = "cubic metres")) -) %>% knitr::kable() + water_unit = "cubic metres")) %>% + knitr::kable() ``` ### Example 2: Including Anaesthetic Gases From a91a24ee1212270e406ee882e782785283f25542 Mon Sep 17 00:00:00 2001 From: lilyclements Date: Tue, 3 Dec 2024 18:22:46 +0000 Subject: [PATCH 6/8] Update Company_emissions.Rmd --- vignettes/Company_emissions.Rmd | 162 +++++++++++++++++++------------- 1 file changed, 98 insertions(+), 64 deletions(-) diff --git a/vignettes/Company_emissions.Rmd b/vignettes/Company_emissions.Rmd index 3ed1b90..4db0be9 100644 --- a/vignettes/Company_emissions.Rmd +++ b/vignettes/Company_emissions.Rmd @@ -12,108 +12,142 @@ vignette: > ### Introduction -Understanding the overall carbon footprint of a company is crucial for meeting sustainability goals. This vignette details the process for calculating emissions from company office operations, employee travel (air, ferry, rail), and hotel accommodations, ultimately allowing the organization to identify high-emission areas and take action to reduce them. +Understanding the overall carbon footprint of a company is important to meet company sustainability goals. This vignette details the process for calculating emissions from company office operations, employee travel (e.g., car, air, ferry, rail), and hotel accommodations, ultimately allowing the organisation to identify high-emission areas and take action to reduce them. To begin, install and load the necessary packages. -```{r install_and_loading_packages, eval = FALSE} -install.packages("emissionsPkg") -library(emissionsPkg) -install.packages("carbonr") +```{r} library(carbonr) -library(tidyverse) +library(dplyr) +library(purrr) ``` + ## Office Emissions: Start by calculating emissions from office-related activities such as energy consumption, waste production, and equipment usage. ### Example Office Emission Calculation For calculating office emissions, use `office_emissions()` function from the `carbonr` package. This function requires input values for energy use (e.g., electricity and heating). -```{r office_emissions_example, message=FALSE, warning=FALSE} - office_emissions <- carbonr::office_emissions(specify = TRUE, +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} + carbonr::office_emissions(specify = TRUE, electricity_kWh = 200, water_supply = 100, - water_unit = "cubic metres",water_trt = FALSE + water_unit = "cubic metres", + water_trt = FALSE ) - knitr::kable(office_emissions) ``` ### Hotel Emissions: - Next, we calculate emissions related to hotel stays for business trips using `hotel_emissions()`. This function calculates the emissions based on the number of nights, room type, and occupancy rate. +We calculate emissions related to hotel stays for business trips using `hotel_emissions()`. This function calculates the emissions based on the number of nights, room type, and occupancy rate. ### Example Hotel Emission Calculation -```{r hotel_emissions_example, message=FALSE, warning=FALSE} -hotel_emissions <- carbonr::hotel_emissions(location = "Fiji", nights = 2, rooms=1) - knitr::kable(hotel_emissions) +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +carbonr::hotel_emissions(location = "Fiji", nights = 2, rooms = 1) ``` -### Travel-related Emissions: -## 1. Airplane Emissions +## Travel-related Emissions + +### Airplane Emissions Using the `carbonr` package, we can calculate emissions for a flight between Nairobi and Kisumu by first finding the airport codes. -### Finding Airport Codes -```{r find_airport_nairobi, eval=FALSE, message=FALSE, warning=FALSE} - airport_finder(name = "Nairobi") %>% knitr::kable() + +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} + airport_finder(name = "Nairobi") ``` + +```{r, message = FALSE, warning = FALSE, echo = FALSE} +airport_finder(name = "Nairobi") %>% + knitr::kable() +``` + ```{r find_airport_kisumu, eval=FALSE, message=FALSE, warning=FALSE} - airport_finder(name = "Kisumu") %>% knitr::kable() + airport_finder(name = "Kisumu") ``` -### Calculating Flight Emissions -With the IATA codes (`"NBO"` for Nairobi and `"KIS"` for Kisumu), we can calculate emissions: -```{r airplane_emissions_chunk, message=FALSE, warning=FALSE} -result <- carbonr::airplane_emissions("NBO", "KIS") -knitr::kable(result) +```{r, message = FALSE, warning = FALSE, echo = FALSE} +airport_finder(name = "Kisumu") %>% + knitr::kable() ``` -## 2. Ferry Emissions -To estimate emissions for ferry travel, for instance, from Shimoni to Giohar, we start by locating seaport codes. +With the IATA codes (`"NBO"` for Nairobi and `"KIS"` for Kisumu), we can calculate emissions -### Finding Seaport Codes -```{r shimoni_seaport, eval=FALSE, message=FALSE, warning=FALSE} -seaport_finder(city = "Shimoni") %>% knitr::kable() +```{r airplane_emissions_chunk, message=FALSE, warning=FALSE} +airplane_emissions("NBO", "KIS") ``` -```{r giohar_seaport, eval=FALSE, message=FALSE, warning=FALSE} -seaport_finder(city = "Giohar") %>% knitr::kable() +### Ferry Emissions +To estimate emissions for ferry travel, for instance, from Shimoni to Giohar, we start by locating seaport codes: + +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} + seaport_finder(city = "Shimoni") ``` -## Ensure the seaports data is loaded -```{r load_seaports, message = FALSE, warning = FALSE} -data("seaports", package = "carbonr") +```{r, message = FALSE, warning = FALSE, echo = FALSE} +seaport_finder(city = "Shimoni") %>% + knitr::kable() ``` -### Calculating Ferry Emissions -With codes `"SMN"` for Shimoni and `"GHR"` for Giohar: -```{r ferry_example, message=FALSE, warning=FALSE} -result <- carbonr::ferry_emissions("SMN", "GHR", round_trip = TRUE) -knitr::kable(result) +```{r, eval=FALSE, message=FALSE, warning=FALSE} + seaport_finder(city = "Giohar") ``` -## 3. Train Emissions -Train journey emissions are calculated similarly. To estimate emissions from Bristol Temple Meads to Edinburgh Waverley via Birmingham New Street: -### Finding Station Names -```{r load_stations, message=FALSE, warning=FALSE} -data("stations", package = "carbonr") +```{r, message = FALSE, warning = FALSE, echo = FALSE} +seaport_finder(city = "Giohar") %>% + knitr::kable() ``` -```{r find_station_bristol, eval=FALSE, message=FALSE} -rail_finder("Bristol") %>% knitr::kable() +With codes `"SMN"` for Shimoni and `"GHR"` for Giohar we can now calculate the ferry emissions: + +```{r ferry_example, message=FALSE, warning=FALSE} +ferry_emissions("SMN", "GHR", round_trip = TRUE) ``` -```{r find_station_edinburgh, eval=FALSE, message=FALSE} - rail_finder("Edinburgh") %>% knitr::kable() +### Train Emissions +For the UK we can calculate emissions for a train journey. Like with `airplane_emissions()` and `ferry_emissions()`, the distances are calculated using the Haversine formula - this is calculated as the crow flies. As before, we first find the stations. As always, for a more accurate estimation we can include via points: + +To calculate emissions for a train journey from Bristol Temple Meads to Edinburgh Waverley, via Birmingham New Street. We can use a data frame and `purrr::map()` to read through the data easier. + +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +multiple_ind <- tibble::tribble(~ID, ~station, + "From", "Bristol", + "To", "Edinburgh", + "Via", "Birmingham") +purrr::map(.x = multiple_ind$station, .f = ~rail_finder(.x)) %>% + dplyr::bind_rows() +``` +```{r, message = FALSE, warning = FALSE, echo=FALSE} +multiple_ind <- tibble::tribble(~ID, ~station, + "From", "Bristol", + "To", "Edinburgh", + "Via", "Birmingham") +purrr::map(.x = multiple_ind$station, .f = ~rail_finder(.x)) %>% + dplyr::bind_rows() %>% + knitr::kable() ``` -### Calculating Train Emissions -```{r rail_em, message=FALSE, warning=FALSE} -result <- carbonr::rail_emissions(from = "Bristol Temple Meads", to = "Edinburgh", via = "Birmingham New Street") -knitr::kable(result) +Then we can estimate the overall tCO2e emissions for the journey: +```{r, message = FALSE, warning = FALSE} +rail_emissions(from = "Bristol Temple Meads", to = "Edinburgh", via = "Birmingham New Street") ``` -## 4. Aggregating Emissions for Multiple Individuals +## Aggregating Emissions for Multiple Individuals Using data frames and mapping functions, we can calculate combined emissions for multiple travel types. -### Example -```{r, eval=FALSE} +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +multiple_ind <- tibble::tribble( + ~ID, ~rail_from, ~rail_to, ~air_from, ~air_to, ~air_via, +"Clint", "Bristol Temple Meads", "Paddington", "LHR", "KIS", "NBO", +"Zara", "Bristol Temple Meads", "Paddington", "LHR", "LAX", "ORL" + ) + +multiple_ind %>% + rowwise() %>% + mutate( + plane_emissions = carbonr::airplane_emissions(air_from, air_to, air_via), + train_emissions = carbonr::rail_emissions(rail_from, rail_to) + ) %>% + mutate(total_emissions = plane_emissions + train_emissions) +``` + +```{r, message = FALSE, warning = FALSE, echo=FALSE} multiple_ind <- tibble::tribble( ~ID, ~rail_from, ~rail_to, ~air_from, ~air_to, ~air_via, "Clint", "Bristol Temple Meads", "Paddington", "LHR", "KIS", "NBO", @@ -121,14 +155,14 @@ multiple_ind <- tibble::tribble( ) multiple_ind %>% -rowwise() %>% -mutate( -plane_emissions = carbonr::airplane_emissions(air_from, air_to, air_via), -train_emissions = carbonr::rail_emissions(rail_from, rail_to) -) %>% -mutate(total_emissions = plane_emissions + train_emissions) %>% -knitr::kable() + rowwise() %>% + mutate( + plane_emissions = carbonr::airplane_emissions(air_from, air_to, air_via), + train_emissions = carbonr::rail_emissions(rail_from, rail_to) + ) %>% + mutate(total_emissions = plane_emissions + train_emissions) %>% + knitr::kable() ``` ## References: -Emission factors are based on the UK Government’s 2022 greenhouse gas reporting conversion factors: [Greenhouse Gas Conversion Factors](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2022). +Emission factors are based on the UK Government’s 2024 greenhouse gas reporting conversion factors: [Greenhouse Gas Conversion Factors](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024). From fb9169300eb58e5be8f5bde9e34f58ff2ed9dc34 Mon Sep 17 00:00:00 2001 From: lilyclements Date: Tue, 3 Dec 2024 21:02:46 +0000 Subject: [PATCH 7/8] Update Personal_carbon_emissions.Rmd --- vignettes/Personal_carbon_emissions.Rmd | 252 +++++++++++++++++------- 1 file changed, 176 insertions(+), 76 deletions(-) diff --git a/vignettes/Personal_carbon_emissions.Rmd b/vignettes/Personal_carbon_emissions.Rmd index bc78082..916bbff 100644 --- a/vignettes/Personal_carbon_emissions.Rmd +++ b/vignettes/Personal_carbon_emissions.Rmd @@ -10,14 +10,14 @@ vignette: > ## Personal Carbon Emissions with carbonr ### Introduction -Personal carbon emissions refer to the total amount of greenhouse gases, primarily carbon dioxide (CO₂), that an individual is responsible for emitting directly and indirectly through their daily activities.The carbonr package in R provides a convenient way to calculate these emissions. -The emissions values used in these calculations primarily derive from the [UK Government report (2023)](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2023), where available. If calculations are based on sources other than the UK Government report, it will be specified accordingly. +Personal carbon emissions refer to the total amount of greenhouse gases, primarily carbon dioxide (CO₂), that an individual is responsible for emitting directly and indirectly through their daily activities. The `carbonr` package in R provides a convenient way to calculate these emissions. + +The emissions values used in these calculations primarily derive from the [UK Government report (2024)](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024), where available. If calculations are based on sources other than the UK Government report, it will be specified accordingly. -### Motivation The aim of calculating Personal Carbon Emissions is to provide a reliable and reproducible method for assessing individual carbon footprints. This ensures that results can be easily saved, edited, and redistributed, fostering transparency and accuracy in carbon emission reporting. ### Installation -To install the development version of carbonr, you can use the following commands to download it from [GitHub](https://github.com/): +To install the development version of `carbonr`, you can use the following commands to download it from [GitHub](https://github.com/): ```{r install_package, eval = FALSE} # Uncomment the following line if you haven't installed devtools yet @@ -25,16 +25,15 @@ To install the development version of carbonr, you can use the following command devtools::install_github("IDEMSInternational/carbonr") ``` -Once installed, load the carbonr package with the following command: -```{r, eval=FALSE} +Once installed, load the `carbonr` package with the following command: +```{r} library(carbonr) -library(knitr) -library(rmarkdown) +library(dplyr) ``` ### Usage -The carbonr package allows users to calculate personal emissions from various sources, including travel-related activities, building energy use, and household emissions. For personal emissions, we consider the following categories: -``` +The `carbonr` package allows users to calculate personal emissions from various sources, including travel-related activities, building energy use, and household emissions. For personal emissions, we consider the following categories: + *Travel-Related Emissions*: airplane_emissions() Calculates emissions from air travel. @@ -48,65 +47,66 @@ household_emissions() Estimates emissions from household activities. *Building Emissions*: building_emissions() Calculates emissions associated with building energy use. -``` These all return carbon-equivalent emissions in tonnes. -A shiny app is also available by shiny_emissions() to calculate carbon-equivalent emissions with a GUI. +A shiny app is also available by `shiny_emissions()` to calculate carbon-equivalent emissions with a GUI. + +We give some small examples in using the functions in `carbonr()`. We start by Travel related Emissions. -We give some small examples in using the functions in `carbonr()`. -We started by Travel related Emissions: -1.`airplane_emissions()` +#### Travel-Related Emissions To calculate emissions for a flight between Nairobi and Kisumu, we first want to find the name of the airports. We do this using the `airport_finder()` function: -```{r load_tidyverse, message = FALSE, warning = FALSE} -library(tidyverse) -``` +Using the `carbonr` package, we can calculate emissions for a flight between Nairobi and Kisumu by first finding the airport codes. +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} + airport_finder(name = "Nairobi") +``` -```{r find_airport_nairobi, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} -airport_finder(name = "Nairobi") - %>% +```{r, message = FALSE, warning = FALSE, echo = FALSE} +airport_finder(name = "Nairobi") %>% knitr::kable() ``` +```{r find_airport_kisumu, eval=FALSE, message=FALSE, warning=FALSE} + airport_finder(name = "Kisumu") +``` - -```{r get_airport_kisumu, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} -airport_finder(name = "Kisumu") - %>% +```{r, message = FALSE, warning = FALSE, echo = FALSE} +airport_finder(name = "Kisumu") %>% knitr::kable() - ``` Now we can find the overall emission value using the appropriate IATA code. These distances are calculated using the Haversine formula: -```{r airplane_emissions_chunk, message = FALSE, warning = FALSE} -result <- carbonr::airplane_emissions("NBO", "KIS") -knitr::kable(result) +```{r, message = FALSE, warning = FALSE, echo = FALSE} +carbonr::airplane_emissions("NBO", "KIS") ``` -A similar approach can be performed for ferry emissions. For example, to calculate emissions for a round trip ferry from Melbourne to New York, we first find the appropriate seaport code with the `seaport_finder()` function: -```{r mombasa_findsea, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} -seaport_finder(city = "Shimoni") -%>% - knitr::kable() + +A similar approach can be performed for ferry emissions. For example, to calculate emissions for a round trip ferry from Shimoni to Giohar, we first find the appropriate seaport code with the `seaport_finder()` function: + +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} + seaport_finder(city = "Shimoni") ``` -```{r mogadishu_findsea, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} -seaport_finder(city = "Giohar") -%>% - knitr::kable() +```{r, message = FALSE, warning = FALSE, echo = FALSE} +seaport_finder(city = "Shimoni") %>% + knitr::kable() ``` -# Ensure the seaports data is loaded -```{r load_seaports, message = FALSE, warning = FALSE} -data("seaports", package = "carbonr") +```{r, eval=FALSE, message=FALSE, warning=FALSE} + seaport_finder(city = "Giohar") +``` + +```{r, message = FALSE, warning = FALSE, echo = FALSE} +seaport_finder(city = "Giohar") %>% + knitr::kable() ``` Now we can find the overall emission value using the appropriate seaport code: + ```{r ferry_example, message = FALSE, warning = FALSE} -result <-carbonr::ferry_emissions("SMN", "GHR", round_trip = TRUE) -knitr::kable(result) +carbonr::ferry_emissions("SMN", "GHR", round_trip = TRUE) ``` For the UK we can calculate emissions for a train journey. Like with `airplane_emissions()` and `ferry_emissions()`, the distances are calculated using the Haversine formula - this is calculated as the crow flies. As before, we first find the stations. As always, for a more accurate estimation we can include via points: @@ -169,14 +169,12 @@ multiple_ind %>% knitr::kable() ``` -### Land Emissions For land-based travel, we use the `land_emissions()` function. This allows you to calculate emissions based on vehicle type and fuel type. ```{r land_emissions_example, message = FALSE, warning = FALSE} result<-carbonr::land_emissions(distance = 100, vehicle = "Bus", fuel = "Petrol") knitr::kable(result) ``` -### Vehicle Emissions The `vehicle_emissions()` function estimates emissions from personal vehicle use based on distance, fuel type, and fuel consumption. @@ -185,35 +183,137 @@ result <- carbonr::vehicle_emissions(distance = 150, fuel = "Petrol", car_type = knitr::kable(result) ``` -Additional emissions can be calculated as well. For example, household and building emissions -### Household Emissions -The `household_emissions()` is used to estimate carbon-equivalent emissions based on household energy use. It allows users to input various types of energy sources, such as electricity or gas, and calculates the emissions based on the consumption levels provided. +#### Household Emissions +Additional emissions can be calculated as well. For example, household and building emissions. The `household_emissions()` is used to estimate carbon-equivalent emissions based on household energy use. It allows users to input various types of energy sources, such as electricity or gas, and calculates the emissions based on the consumption levels provided. +We can find the [total household waste per 1000 households](https://lginform.local.gov.uk/reports/lgastandard?mod-metric=19664&mod-area=E92000001&mod-group=AllRegions_England&mod-type=namedComparisonGroup&mod-period=1) in the UK is 947 tonnes. This is 0.947 tonnes per household. Using the `household_emissions()` function, we can find the average CO2-equivalent emissions of total household waste in a year. -# Input Parameters -energy_type: This parameter defines the type of energy source used (e.g., "electricity", "gas", "oil"). -consumption: The amount of energy consumed, typically measured in kilowatt-hours (kWh) for electricity or cubic meters for gas. -emission_factor: A factor that converts the energy usage into carbon-equivalent emissions. This factor is based on standard values but can be customized in some cases. +```{r, message = FALSE, warning = FALSE, eval=TRUE, include=TRUE} +household_emissions(household_residual_waste = 0.947, + hh_waste_disposal = "Landfill", + units = "tonnes") +``` -```{r household_emissions_example, message = FALSE, warning = FALSE} -result <- carbonr::household_emissions(compost_waste_disposal = "Combustion", fridges = 113 , units = "kg") -knitr::kable(result) +We can get a more accurate picture by giving more precise measurements. + +```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} +# Create a data frame of the estimates with sources +# Create a data frame of the estimates +average_disposal <- data.frame( + Category = c( + "Glass", "Clothing", "Food", "Paper", + "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", + "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", + "Small Electrical Items", "Batteries Alkaline", "Batteries LiIon", + "Residual waste" + ), + Estimated_Average_Disposal_kg_per_year = c( + 125, 12.6, 341, 450, 5, 2, 10, 20, 15, 30, 20, 10, 1, 0.5, 200 + ), + Notes = c( + "Bottles, jars, and glass containers.", + "Textiles and old garments.", + "Includes food scraps and leftovers.", + "Newspapers, magazines, and office paper.", + "Soda cans and other aluminium beverage containers.", + "Used foil sheets and wraps.", + "Food cans and metal containers.", + "Soda, water, and other PET containers.", + "Milk bottles, detergent containers.", + "Non-recyclable or mixed plastic types.", + "Appliances like washing machines, dishwashers (infrequent).", + "Gadgets, small appliances, and electronics.", + "Household AA/AAA and other alkaline batteries.", + "Rechargeable batteries from devices.", + "Residual waste" + ), + Source = c( + "The average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year.", "In the UK, approximately 350,000 tonnes of clothing are discarded into landfills annually (3.2). With an estimated 27.8 million households in the UK, this equates to an average of about 12.6kg of clothing waste per household per year", "WRAP, 3.3, (food and drink can be given together in the values)", "In the UK, approximately 12.5 million tonnes of paper are used annually. Despite a commendable recycling rate of 71% for household paper waste, around 5 million tonnes still end up in landfills each year, accounting for 40% of all landfill waste (3.4). Given that there are approximately 27.8 million households in the UK, this translates to an average of about 450 kilograms of paper usage per household per year.", "Defra", "Defra", "Defra", "Statista", "Statista", "Statista", + "Defra", "Defra", "Defra", "Defra", "Defra" + ) +) +``` + + +```{r, message = FALSE, warning = FALSE, echo = FALSE} +# Create a data frame of the estimates +average_disposal <- data.frame( + Category = c( + "Glass", "Clothing", "Food", "Paper", + "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", + "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", + "Small Electrical Items", "Batteries Alkaline", "Batteries LiIon", + "Residual waste" + ), + Estimated_Average_Disposal_kg_per_year = c( + 125, 12.6, 341, 450, 5, 2, 10, 20, 15, 30, 20, 10, 1, 0.5, 200 + ), + Notes = c( + "Bottles, jars, and glass containers.", + "Textiles and old garments.", + "Includes food scraps and leftovers.", + "Newspapers, magazines, and office paper.", + "Soda cans and other aluminium beverage containers.", + "Used foil sheets and wraps.", + "Food cans and metal containers.", + "Soda, water, and other PET containers.", + "Milk bottles, detergent containers.", + "Non-recyclable or mixed plastic types.", + "Appliances like washing machines, dishwashers (infrequent).", + "Gadgets, small appliances, and electronics.", + "Household AA/AAA and other alkaline batteries.", + "Rechargeable batteries from devices.", + "Residual waste" + ), + Source = c( + "The average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year.", "In the UK, approximately 350,000 tonnes of clothing are discarded into landfills annually (3.2). With an estimated 27.8 million households in the UK, this equates to an average of about 12.6kg of clothing waste per household per year", "WRAP, 3.3, (food and drink can be given together in the values)", "In the UK, approximately 12.5 million tonnes of paper are used annually. Despite a commendable recycling rate of 71% for household paper waste, around 5 million tonnes still end up in landfills each year, accounting for 40% of all landfill waste (3.4). Given that there are approximately 27.8 million households in the UK, this translates to an average of about 450 kilograms of paper usage per household per year.", "Defra", "Defra", "Defra", "Statista", "Statista", "Statista", + "Defra", "Defra", "Defra", "Defra", "Defra" + ) +) + +# Render the table in the vignette +knitr::kable( + average_disposal, + caption = "Average Annual Household Disposal Estimates for the UK with Sources" +) +``` + +```{r, message = FALSE, warning = FALSE, echo = FALSE} +average_disposal %>% + dplyr::select(-c(Notes, Source)) %>% + tidyr::pivot_wider(names_from = Category, values_from = Estimated_Average_Disposal_kg_per_year) %>% + mutate(emissions = household_emissions(glass_WD = Glass, + clothing_WD = Clothing, + gcb_waste_disposal = "Closed-loop", + food_WD = Food, + paper_WD = Paper, + aluminuim_cans_WD = `Aluminium Cans`, + aluminuim_foil_WD = `Aluminium Foil`, + steel_cans_WD = `Steel Cans`, + PET_WD = `PET (Plastic Bottles)`, + average_WD = `Other Plastics (Mixed)`, + HDPE_WD = `HDPE (Plastic Containers)`, + plastic_waste_disposal = "Landfill", + large_electrical_WD = `Large Electrical Items`, + small_electrical_WD = `Small Electrical Items`, + electric_waste_disposal = "Landfill", + alkaline_batteries_WD = `Batteries Alkaline`, + LiIon_batteries_WD = `Batteries LiIon`, + household_residual_waste = `Residual waste`, + hh_waste_disposal = "Landfill", + units = "kg")) %>% + dplyr::pull(emissions) ``` ### Building Emissions The `building_emissions()`calculates carbon-equivalent emissions based on energy consumption in a building. The function considers various energy sources, such as electricity, natural gas, and oil, and returns emissions in tonnes of CO₂-equivalent. -# Input Parameters -electricity: The amount of electricity consumed in kilowatt-hours (kWh). -natural_gas: The amount of natural gas consumed in cubic meters (m³). -oil: The amount of oil consumed in liters (L). -other_fuel: The amount of any other type of fuel consumed (optional). - ```{r building_emissions_example, message = FALSE, warning = FALSE} result <- carbonr::building_emissions(electricity_kWh = 500, water_supply = 1000, water_unit = "cubic metres") knitr::kable(result) ``` + ## Shiny App An interactive calculator using Shiny can be accessed by the `shiny_emissions()` function. This calculator uses some of the functions in the `carbonr` package: @@ -223,25 +323,25 @@ shiny_emissions() ## References -#### Other Online Calculators: -- [Carbonfund.org](https://carbonfund.org/calculation-methods/) -- [Carbon Footprint Calculator](https://www.carbonfootprint.com/calculatorfaqs.html) - -#### Sources -1. UK Government Report: Department for Energy Security and Net Zero. (2023). [Greenhouse Gas Reporting: Conversion Factors 2023](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2023). +1. UK Government Report: Department for Energy Security and Net Zero. (2024). [Greenhouse Gas Reporting: Conversion Factors 2024](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024). 2. Radiative Forcing Factor: DEFRA, 2016. Government GHG conversion factors for company reporting. -3. Clinical Anaesthetic Emissions: Various sources including - - -Varughese, S. and Ahmed, R., 2021. Environmental and occupational considerations of anesthesia: a narrative review and update. Anesthesia & Analgesia, 133(4), pp.826-835; - -McGain, F., Muret, J., Lawson, C. and Sherman, J.D., 2020. Environmental sustainability in anaesthesia and critical care. British Journal of Anaesthesia, 125(5), pp.680-692; - -Wyssusek, K., Chan, K.L., Eames, G. and Whately, Y., 2022. Greenhouse gas reduction in anaesthesia practice: a departmental environmental strategy. BMJ Open Quality, 11(3), p.e001867; +3. On the Household waste measurements -Sherman, J., Le, C., Lamers, V. and Eckelman, M., 2012. Life cycle greenhouse gas emissions of anesthetic drugs. Anesthesia & Analgesia, 114(5), pp.1086-1090. +3.1 [Business Waste](https://www.businesswaste.co.uk/your-waste/glass-recycling/glass-waste-facts-and-statistics/) +3.2 WRAP, ‘Valuing our clothes: the evidence base’ (2012), p.2 +3.3 [WRAP, 2022](https://www.wrap.ngo/resources/report/household-food-and-drink-waste-united-kingdom-2021-22) +3.4 [](https://www.cladcodecking.co.uk/blog/post/uk-recycling-facts-and-statistics) -4. Clinical Wet Waste Emissions: Department of Climate Change, Energy, the Environment and Water. (2022). [National Greenhouse Accounts Factors: 2022](https://www.dcceew.gov.au/climate-change/publications/national-greenhouse-accounts-factors-2022). + "Glass", "Clothing", "Food", "Drink", "Compost from Garden", + "Compost from Food and Garden", "Paper", "Mixed Paper", + "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", + "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", + "Small Electrical Items", "Batteries (Alkaline)", "Batteries (Li-Ion/NiMH)", + "Residual waste" + + +(Defra: Residual Waste)[https://lginform.local.gov.uk/reports/lgastandard?mod-area=E92000001&mod-group=AllRegions_England&mod-metric=45&mod-type=namedComparisonGroup] From 3ddd4c52ea3a5f2e87e8265c52e12559722babf3 Mon Sep 17 00:00:00 2001 From: lilyclements Date: Tue, 3 Dec 2024 21:50:18 +0000 Subject: [PATCH 8/8] Update Personal_carbon_emissions.Rmd --- vignettes/Personal_carbon_emissions.Rmd | 123 +++++++++++------------- 1 file changed, 58 insertions(+), 65 deletions(-) diff --git a/vignettes/Personal_carbon_emissions.Rmd b/vignettes/Personal_carbon_emissions.Rmd index 916bbff..2fcd428 100644 --- a/vignettes/Personal_carbon_emissions.Rmd +++ b/vignettes/Personal_carbon_emissions.Rmd @@ -199,38 +199,43 @@ We can get a more accurate picture by giving more precise measurements. ```{r, message = FALSE, warning = FALSE, eval=FALSE, include=TRUE} # Create a data frame of the estimates with sources -# Create a data frame of the estimates average_disposal <- data.frame( Category = c( - "Glass", "Clothing", "Food", "Paper", - "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", - "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", - "Small Electrical Items", "Batteries Alkaline", "Batteries LiIon", - "Residual waste" + "Glass", "Clothing", "Disposed Clothing", "Food", "Paper", "Plastic", + "Small Electrical Items", "Batteries Alkaline (bought)", "Batteries Alkaline (disposed)", + "Aluminium Cans (bought)", "Aluminium Cans (disposed)", "Residual waste" ), Estimated_Average_Disposal_kg_per_year = c( - 125, 12.6, 341, 450, 5, 2, 10, 20, 15, 30, 20, 10, 1, 0.5, 200 + 125, 26.7, 10.8, 237.4, 450, 520, 5.57, + 1.932, 0.720, 5.1, 5.1, 516.53 ), Notes = c( "Bottles, jars, and glass containers.", - "Textiles and old garments.", + "New clothing purchased.", + "Textiles and old garments disposed.", "Includes food scraps and leftovers.", "Newspapers, magazines, and office paper.", - "Soda cans and other aluminium beverage containers.", - "Used foil sheets and wraps.", - "Food cans and metal containers.", - "Soda, water, and other PET containers.", - "Milk bottles, detergent containers.", "Non-recyclable or mixed plastic types.", - "Appliances like washing machines, dishwashers (infrequent).", "Gadgets, small appliances, and electronics.", "Household AA/AAA and other alkaline batteries.", - "Rechargeable batteries from devices.", - "Residual waste" + "Household AA/AAA and other alkaline batteries.", + "Soda cans and other aluminium beverage containers.", + "Soda cans and other aluminium beverage containers.", + "Any collected household waste that is not sent for reuse, recycling, or composting." ), Source = c( - "The average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year.", "In the UK, approximately 350,000 tonnes of clothing are discarded into landfills annually (3.2). With an estimated 27.8 million households in the UK, this equates to an average of about 12.6kg of clothing waste per household per year", "WRAP, 3.3, (food and drink can be given together in the values)", "In the UK, approximately 12.5 million tonnes of paper are used annually. Despite a commendable recycling rate of 71% for household paper waste, around 5 million tonnes still end up in landfills each year, accounting for 40% of all landfill waste (3.4). Given that there are approximately 27.8 million households in the UK, this translates to an average of about 450 kilograms of paper usage per household per year.", "Defra", "Defra", "Defra", "Statista", "Statista", "Statista", - "Defra", "Defra", "Defra", "Defra", "Defra" + "Average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year (Busy Bins, 2020).", + "Average of 26.7kg of new clothes each year (Recycle More)", + "300,000 tonnes disposed yearly approximately in the UK, or 10.8kg per household (Recycle More)", + "Estimated 6.6 million tonnes of food waste yearly, which is 237.4kg per household (Recycle More)", + "In the UK, approximately 12.5 million tonnes of paper are used annually. This is an average of about 450kg per household per year (Recycle More)", + "Over 1447000 tonnes of plastic packaging consumed by households. Over 27.8 million households, this is approx. 520g bought and disposed (Recycle More)", + "Approximately 155,000 tonnes of domestic electrical waste are disposed each year. This is approximately 5.57kg of electrical waste per household each year (Recycle More)", + "Every person in the UK uses about 21 batteries a year. A household of four uses 84. At 23g a battery, this is 1.932kg per household (Recycle More)", + "20,000 tonnes of batteries are sent to landfill site in the UK each year, which is approximately 0.720kg per household. (Recycle More)", + "An estimated 340 aluminium drink cans are consumed per household each year. At 15g a can, it is 5.1 kg consumed, and 5.1kg disposed (Recycle More)", + "An estimated 340 aluminium drink cans are consumed per household each year. At 15g a can, it is 5.1 kg consumed, and 5.1kg disposed (Recycle More)", + "From DEFRA via LG Inform (DEFRA, 2024)" ) ) ``` @@ -240,35 +245,41 @@ average_disposal <- data.frame( # Create a data frame of the estimates average_disposal <- data.frame( Category = c( - "Glass", "Clothing", "Food", "Paper", - "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", - "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", - "Small Electrical Items", "Batteries Alkaline", "Batteries LiIon", - "Residual waste" + "Glass", "Clothing", "Disposed Clothing", "Food", "Paper", "Plastic", + "Small Electrical Items", "Batteries Alkaline (bought)", "Batteries Alkaline (disposed)", + "Aluminium Cans (bought)", "Aluminium Cans (disposed)", "Residual waste" ), Estimated_Average_Disposal_kg_per_year = c( - 125, 12.6, 341, 450, 5, 2, 10, 20, 15, 30, 20, 10, 1, 0.5, 200 + 125, 26.7, 10.8, 237.4, 450, 520, 5.57, + 1.932, 0.720, 5.1, 5.1, 516.53 ), Notes = c( "Bottles, jars, and glass containers.", - "Textiles and old garments.", + "New clothing purchased.", + "Textiles and old garments disposed.", "Includes food scraps and leftovers.", "Newspapers, magazines, and office paper.", - "Soda cans and other aluminium beverage containers.", - "Used foil sheets and wraps.", - "Food cans and metal containers.", - "Soda, water, and other PET containers.", - "Milk bottles, detergent containers.", "Non-recyclable or mixed plastic types.", - "Appliances like washing machines, dishwashers (infrequent).", "Gadgets, small appliances, and electronics.", "Household AA/AAA and other alkaline batteries.", - "Rechargeable batteries from devices.", - "Residual waste" + "Household AA/AAA and other alkaline batteries.", + "Soda cans and other aluminium beverage containers.", + "Soda cans and other aluminium beverage containers.", + "Any collected household waste that is not sent for reuse, recycling, or composting." ), Source = c( - "The average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year.", "In the UK, approximately 350,000 tonnes of clothing are discarded into landfills annually (3.2). With an estimated 27.8 million households in the UK, this equates to an average of about 12.6kg of clothing waste per household per year", "WRAP, 3.3, (food and drink can be given together in the values)", "In the UK, approximately 12.5 million tonnes of paper are used annually. Despite a commendable recycling rate of 71% for household paper waste, around 5 million tonnes still end up in landfills each year, accounting for 40% of all landfill waste (3.4). Given that there are approximately 27.8 million households in the UK, this translates to an average of about 450 kilograms of paper usage per household per year.", "Defra", "Defra", "Defra", "Statista", "Statista", "Statista", - "Defra", "Defra", "Defra", "Defra", "Defra" + "Average UK household uses approximately 500 glass bottles and jars annually (3.1). Given that one tonne of glass equates to about 4,000 bottles and jars, this translates to an estimated 125 kilograms of glass waste per household per year (Busy Bins, 2020).", + "Average of 26.7kg of new clothes each year (Recycle More)", + "300,000 tonnes disposed yearly approximately in the UK, or 10.8kg per household (Recycle More)", + "Estimated 6.6 million tonnes of food waste yearly, which is 237.4kg per household (Recycle More)", + "In the UK, approximately 12.5 million tonnes of paper are used annually. This is an average of about 450kg per household per year (Recycle More)", + "Over 1447000 tonnes of plastic packaging consumed by households. Over 27.8 million households, this is approx. 520g bought and disposed (Recycle More)", + "Approximately 155,000 tonnes of domestic electrical waste are disposed each year. This is approximately 5.57kg of electrical waste per household each year (Recycle More)", + "Every person in the UK uses about 21 batteries a year. A household of four uses 84. At 23g a battery, this is 1.932kg per household (Recycle More)", + "20,000 tonnes of batteries are sent to landfill site in the UK each year, which is approximately 0.720kg per household. (Recycle More)", + "An estimated 340 aluminium drink cans are consumed per household each year. At 15g a can, it is 5.1 kg consumed, and 5.1kg disposed (Recycle More)", + "An estimated 340 aluminium drink cans are consumed per household each year. At 15g a can, it is 5.1 kg consumed, and 5.1kg disposed (Recycle More)", + "From DEFRA via LG Inform (DEFRA, 2024)" ) ) @@ -284,22 +295,17 @@ average_disposal %>% dplyr::select(-c(Notes, Source)) %>% tidyr::pivot_wider(names_from = Category, values_from = Estimated_Average_Disposal_kg_per_year) %>% mutate(emissions = household_emissions(glass_WD = Glass, - clothing_WD = Clothing, + clothing = Clothing, + clothing_WD = `Disposed Clothing`, gcb_waste_disposal = "Closed-loop", food_WD = Food, paper_WD = Paper, - aluminuim_cans_WD = `Aluminium Cans`, - aluminuim_foil_WD = `Aluminium Foil`, - steel_cans_WD = `Steel Cans`, - PET_WD = `PET (Plastic Bottles)`, - average_WD = `Other Plastics (Mixed)`, - HDPE_WD = `HDPE (Plastic Containers)`, - plastic_waste_disposal = "Landfill", - large_electrical_WD = `Large Electrical Items`, + average_WD = Plastic, small_electrical_WD = `Small Electrical Items`, - electric_waste_disposal = "Landfill", - alkaline_batteries_WD = `Batteries Alkaline`, - LiIon_batteries_WD = `Batteries LiIon`, + alkaline_batteries = `Batteries Alkaline (bought)`, + alkaline_batteries_WD = `Batteries Alkaline (disposed)`, + aluminuim_cans = `Aluminium Cans (bought)`, + aluminuim_cans_WD = `Aluminium Cans (disposed)`, household_residual_waste = `Residual waste`, hh_waste_disposal = "Landfill", units = "kg")) %>% @@ -327,21 +333,8 @@ shiny_emissions() 2. Radiative Forcing Factor: DEFRA, 2016. Government GHG conversion factors for company reporting. -3. On the Household waste measurements - -3.1 [Business Waste](https://www.businesswaste.co.uk/your-waste/glass-recycling/glass-waste-facts-and-statistics/) -3.2 WRAP, ‘Valuing our clothes: the evidence base’ (2012), p.2 -3.3 [WRAP, 2022](https://www.wrap.ngo/resources/report/household-food-and-drink-waste-united-kingdom-2021-22) -3.4 [](https://www.cladcodecking.co.uk/blog/post/uk-recycling-facts-and-statistics) - - - - "Glass", "Clothing", "Food", "Drink", "Compost from Garden", - "Compost from Food and Garden", "Paper", "Mixed Paper", - "Aluminium Cans", "Aluminium Foil", "Steel Cans", "PET (Plastic Bottles)", - "HDPE (Plastic Containers)", "Other Plastics (Mixed)", "Large Electrical Items", - "Small Electrical Items", "Batteries (Alkaline)", "Batteries (Li-Ion/NiMH)", - "Residual waste" - - -(Defra: Residual Waste)[https://lginform.local.gov.uk/reports/lgastandard?mod-area=E92000001&mod-group=AllRegions_England&mod-metric=45&mod-type=namedComparisonGroup] +3. On the Household waste measurements: +[Busy Bins](https://busybins.co.uk/blog/glass-recycling-guide.) +WRAP, ‘Valuing our clothes: the evidence base’ (2012), p.2 +[Recycle More](https://www.recycle-more.co.uk/what-can-i-recycle/recycling-facts) +[DEFRA, 2024](https://lginform.local.gov.uk/reports/lgastandard?mod-area=E92000001&mod-group=AllRegions_England&mod-metric=45&mod-type=namedComparisonGroup)