______           __                   ____                ___      __ 
  / ____/___ ______/ /_  ____  ____     / __ \________  ____/ (_)____/ /_
 / /   / __ `/ ___/ __ \/ __ \/ __ \   / /_/ / ___/ _ \/ __  / / ___/ __/
/ /___/ /_/ / /  / /_/ / /_/ / / / /  / ____/ /  /  __/ /_/ / / /__/ /_  
\____/\__,_/_/  /_.___/\____/_/ /_/  /_/   /_/   \___/\__,_/_/\___/\__/  
                                                       ~ Hamza Suleman

Carbon Predict

Carbon Predict is an R package for predicting Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), as well as Scope 1 carbon emissions for UK farms using Standard Industrial Classification (SIC) codes and annual turnover data. It provides single and batch prediction, plotting, and workflow tools for carbon accounting and reporting. The package utilises pre-trained models, leveraging rich classified transaction data to accurately predict Scope 1, 2 and 3 carbon emissions for UK SMEs as well as identifying emissions hotspots. The package also predicts Scope 1 carbon emissions for UK farms of types: Cereals ex. rice, Dairy, Mixed farming, Sheep and goats, Cattle & buffaloes, Poultry, Animal production and Support for crop production.

The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publications:

Phillpotts, A., Owen, A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D., 2025. Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions. Journal of Industrial Ecology. http://doi.org/10.1111/jiec.70106.

Phillpotts, A., Owen, A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D., 2025. Completing the SME Carbon Profile: Scalabe Prediction of Scope 3 Emissions. (In Review)

Wells, J., Trendl, A., Owen, A., Barrett, J., Gridley, J., Jobst, N., Leake, D., 2025. A Scalable Tool for Farm-Level Carbon Accounting: Evidence from UK Agriculture. https://doi.org/10.1088/1748-9326/ae20ab.

Installation

You can install the package from CRAN (Available now!):

install.packages("carbonpredict")

Or install the development version from GitHub:

# Clone the repository
# git clone https://github.com/david-leake/carbonpredict.git
# Then install locally
install.packages("devtools")
devtools::install_local("carbonpredict")

Then load it in as normal:

library(carbonpredict)

Usage

Predict total scope 1 emissions for a single farm

farms_scope1(sic_code = 1110, farm_area = 1113, no_beef_cows = 25, no_dairy_cows = 8, no_pigs = 18, no_sheep = 29, annual_revenue = 2986511, annual_fuel_spend = 209055)
# Note: all predicted emissions values are in tonnes of Co2e (tCo2e).

Predict total scope 1 and 2 emissions for a single SME

sme_scope1(85, 12000000)
sme_scope2(85, 12000000)
# Note: all predicted emissions values are in tonnes of Co2e (tCo2e).

Predict SME scope 3 emissions categories and hotspots

sme_scope3(85, 12000000)
sme_scope3_hotspots(85)

Plot emissions for a single SME

scp1 <- sme_scope1(85, 12000000)
scp2 <- sme_scope2(85, 12000000)
scp3 <- sme_scope3(85, 12000000)

# Pie chart showing total emissions for each scope
plot_sme_emissions(
   scp1$`Predicted Emissions (tCO2e)`,
   scp2$`Predicted Emissions (tCO2e)`,
   scp3[scp3$Category == "Total", "Predicted Emissions (tCO2e)"][[1]],
   "Carbon Predict LTD")

# Sankey diagram showing scope 3 emissions broken down for each category
plot_scope3_emissions(scp3, "Carbon Predict LTD")

Get a full emissions profile and plots for a single SME

sme_emissions_profile(85, 12000000, "Carbon Predict LTD")

Batch prediction from CSV for SMEs

# Some sample SME data is included in the package for demonstration purposes.
sample_data <- system.file("extdata", "sme_examples.csv", package = "carbonpredict")
results <- batch_predict_emissions(data = sample_data, company_type = "sme", output_path = "temp/results.csv")

Batch prediction from CSV for farms

# Some sample farm data is included in the package for demonstration purposes.
sample_data <- system.file("extdata", "farm_examples.csv", package = "carbonpredict")
results <- batch_predict_emissions(data = sample_data, company_type = "farm", output_path = "temp/results.csv")

Batch plotting

sample_data <- system.file("extdata", "sme_examples.csv", package = "carbonpredict")
batch_sme_plots(data = sample_data, output_path = "temp/plots")

Documentation

Full documentation is available on our GitHub Pages site, in the package manual (PDF), and via R help pages (e.g., ?sme_scope1).

Contributing

Pull requests and issues are welcome!

License

MIT