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~ Hamza SulemanCarbon Predict is an R package for predicting Scope 1 and Scope 2 carbon emissions for UK SMEs using SIC codes and turnover data. It provides 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 and 2 emissions for UK SMEs.
The methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publication:
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.
You can install the package from CRAN (coming soon!):
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")
library(carbonpredict)
sme_scope1(85, 12000000)
sme_scope2(85, 12000000)
# Note: all predicted emissions values are in tonnes of Co2e (tCo2e).
scp1 <- sme_scope1(85, 12000000)
scp2 <- sme_scope2(85, 12000000)
plot_sme_emissions(scp1$predicted_emissions, scp2$predicted_emissions, "Carbon Predict LTD")
sme_emissions_profile(85, 12000000, "Carbon Predict LTD")
# 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")
sample_data <- system.file("extdata", "sme_examples.csv", package = "carbonpredict")
batch_sme_plots(data = sample_data, output_path = "temp/plots")Full documentation is available on our GitHub Pages site, in the package manual (PDF), and via R help pages (e.g., ?sme_scope1).