| Title: | Predict Carbon Emissions for UK SMEs |
| Version: | 1.0.0 |
| Description: | Predict Scope 1, 2 and 3 carbon emissions for UK Small and Medium-sized Enterprises (SMEs), using Standard Industrial Classification (SIC) codes and annual turnover data. The 'carbonpredict' package 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 methodology used to produce the estimates in this package is fully detailed in the following peer-reviewed publication in the Journal of Industrial Ecology: Phillpotts, A., Owen. A., Norman, J., Trendl, A., Gathergood, J., Jobst, Norbert., Leake, D. (2025) <doi:10.1111/jiec.70106> "Bridging the SME Reporting Gap: A New Model for Predicting Scope 1 and 2 Emissions". |
| License: | MIT + file LICENSE |
| URL: | https://github.com/david-leake/carbonpredict |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.5.0) |
| RoxygenNote: | 7.3.2 |
| Imports: | dplyr, ggplot2, progress, lmerTest, networkD3, htmltools, htmlwidgets |
| Suggests: | testthat (≥ 3.0.0), mockery, png, grid |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2025-10-08 16:51:03 UTC; Hamza |
| Author: | Hamza Suleman [aut, cre, cph], Alec Phillpotts [ctb, aut], David Leake [ctb, aut] |
| Maintainer: | Hamza Suleman <Hamza.Suleman@lloydsbanking.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-14 18:00:12 UTC |
Batch Predict Emissions
Description
Prediction entry point for batch SME and agriculture emissions
Usage
batch_predict_emissions(data, output_path = NULL, company_type = "sme")
Arguments
data |
A single entry (list or named vector), a data frame, or a path to a CSV file. The data should contain company_name, 2-digit UK sic_code, and annual turnover columns. |
output_path |
Optional file path to save the results as a CSV. If NULL, results are not saved to a file. |
company_type |
A single parameter "sme" or "farm" to determine which emission prediction functions to call (defaults to "sme"). |
Value
A data frame with input columns and predicted emissions for each scope (in tCo2e). Optionally saved to a CSV file.
Examples
sample_data <- read.csv(system.file("extdata", "sme_examples.csv", package = "carbonpredict"))
sample_data <- head(sample_data, 3)
batch_predict_emissions(data = sample_data, output_path = NULL, company_type = "sme")
Batch SME Plots
Description
Batch plot SME Scope 1 & 2 emissions
Usage
batch_sme_plots(data, output_path = NULL)
Arguments
data |
A data frame or path to a CSV file with columns "sic_code", "turnover", and optionally "company_name". |
output_path |
Optional directory to save plots. If NULL, plots are not saved. |
Value
Donut chart plots showing scope 1 and 2 predicted emissions (in tCo2e) for each row in the data. Optionally saved to a directory as PNG files.
Examples
sample_data <- read.csv(system.file("extdata", "sme_examples.csv", package = "carbonpredict"))
sample_data <- head(sample_data, 3)
batch_sme_emissions <- batch_predict_emissions(
data = sample_data,
company_type = "sme",
output_path = NULL)
batch_sme_plots(data = batch_sme_emissions, output_path = NULL)
Plot Scope 3 Emissions Breakdown
Description
Plots a Sankey diagram showing the breakdown of Scope 3 emissions by category.
Usage
plot_scope3_emissions(scope3_df, company_name = NULL)
Arguments
scope3_df |
Data frame output from sme_scope3 (must contain 'Category', 'Description', and 'Predicted Emissions (tCO2e)'). |
company_name |
Optional company name to include in the chart title (character string). |
Value
A Sankey plot showing a breakdown for predicted emissions of each Scope 3 category.
Examples
scope3_df <- sme_scope3(85, 12000000)
plot_scope3_emissions(scope3_df, company_name = "Carbon Predict LTD")
Plot SME Emissions
Description
Plot a donut chart of Scope 1,2 and 3 emissions
Usage
plot_sme_emissions(
scope1_emissions,
scope2_emissions,
scope3_emissions,
company_name = NULL
)
Arguments
scope1_emissions |
Value for total Scope 1 emissions (numeric). |
scope2_emissions |
Value for total Scope 2 emissions (numeric). |
scope3_emissions |
Value for total Scope 3 emissions (numeric). |
company_name |
Optional company name to include in the chart title (character string). |
Value
A ggplot2 donut chart showing predicted emissions for each scope.
Examples
scope_1 = sme_scope1(85, 12000000)
scope_2 = sme_scope2(85, 12000000)
scope_3 = sme_scope3(85, 12000000)
plot_sme_emissions(
scope1_emissions = scope_1$`Predicted Emissions (tCO2e)`,
scope2_emissions = scope_2$`Predicted Emissions (tCO2e)`,
scope3_emissions = scope_3[scope_3$Category == "Total", "Predicted Emissions (tCO2e)"][[1]],
company_name = "Carbon Predict LTD")
SME Emissions Profile
Description
Calls the Scope 1, 2 and 3 emissions prediction functions and returns their results as a list and plots a donut chart
Usage
sme_emissions_profile(sic_code, turnover, company_name = NULL)
Arguments
sic_code |
A 2-digit UK SIC code (numeric). |
turnover |
Annual turnover value (numeric). |
company_name |
Optional company name for labeling plots (character string). |
Value
A list with four elements: scope1, scope2 scope3, scope3_hotspots, each containing the predicted carbon emissions data frame (in tCo2e), the top 5 scope 3 emissions hotspots, as well as a donut chart and Sankey diagram showing the emissions breakdowns.
Examples
sme_emissions_profile(sic_code = 85, turnover = 12000000, company_name = "Carbon Predict LTD")
Predict SME Scope 1 Emissions
Description
This function loads a pre-trained emission model to predict scope 1 carbon emissions for a given SIC code and turnover.
Usage
sme_scope1(sic_code, turnover)
Arguments
sic_code |
A 2-digit UK SIC code (numeric). |
turnover |
Annual turnover value (numeric). |
Value
A data frame with predicted emissions (in tCo2e).
Examples
sme_scope1(sic_code = 85, turnover = 12000000)
Predict SME Scope 2 Emissions
Description
This function loads a pre-trained emission model to predict scope 2 carbon emissions for a given SIC code and turnover.
Usage
sme_scope2(sic_code, turnover)
Arguments
sic_code |
A 2-digit UK SIC code (numeric). |
turnover |
Annual turnover value (numeric). |
Value
A data frame with predicted emissions (in tCo2e).
Examples
sme_scope2(sic_code = 85, turnover = 12000000)
Predict SME Scope 3 Emissions
Description
This function loads pre-trained emissions models to predict scope 3 carbon emissions for a given SIC code and turnover.
Usage
sme_scope3(sic_code, turnover)
Arguments
sic_code |
A 2-digit UK SIC code (numeric). |
turnover |
Annual turnover value (numeric). |
Value
A data frame with predicted emissions (in tCo2e) for each scope 3 category.
Examples
sme_scope3(sic_code = 85, turnover = 12000000)
Predict Top 5 SME Scope 3 Emissions Hotspots
Description
This function uses pre-computed results to predict the top 5 scope 3 carbon emissions hotspots for a given SIC code.
Usage
sme_scope3_hotspots(sic_code)
Arguments
sic_code |
A 2-digit UK SIC code (numeric). |
Value
A data frame with the top 5 emissions hotspots for scope 3.
Examples
sme_scope3_hotspots(sic_code = 85)