cdmAmsIIf implements the Clean Development Mechanism (CDM) small-scale methodology AMS-II.F Energy efficiency and fuel switching measures for agricultural facilities.
The package follows tidyverse design principles and exposes equation-level helpers, applicability checks, and meta-calculation wrappers to reproduce emission reduction estimates for agricultural energy efficiency interventions.
# install.packages("devtools")
devtools::install_github("independent-impact/GHG_methodologies/cdmAmsIIf")
library(cdmAmsIIf)
monitoring <- simulate_ams_iif_dataset(n_facilities = 2, n_periods = 6)
annual_monitoring <- aggregate_monitoring_periods_iif(
monitoring,
period_col = monitoring_label,
group_cols = "facility_id"
)
applicability <- assess_ams_iif_applicability(
baseline_data = annual_monitoring,
project_data = annual_monitoring,
monitoring_data = annual_monitoring
)
if (all(applicability$is_met)) {
baseline <- calculate_baseline_agricultural_emissions(annual_monitoring, group_cols = "facility_id")
project <- calculate_project_agricultural_emissions(annual_monitoring, group_cols = "facility_id")
leakage <- calculate_leakage_emissions_iif(annual_monitoring,
group_cols = "facility_id",
leakage_col = "leakage_emissions_tco2e"
)
emission_reductions <- calculate_emission_reductions_iif(
baseline,
project,
leakage,
group_cols = "facility_id"
)
emission_reductions_meta <- estimate_emission_reductions_ams_iif(
baseline_data = annual_monitoring,
project_data = annual_monitoring,
leakage_data = annual_monitoring,
group_cols = "facility_id",
leakage_args = list(leakage_col = "leakage_emissions_tco2e")
)
}For a full walk-through see the vignette in vignettes/cdmAmsIIf-methodology.Rmd.
Projects must satisfy core AMS-II.F requirements before emission reductions can be claimed. Use the package helpers to document each criterion:
check_applicability_energy_intensity_iif() – verifies the project delivers a meaningful reduction in specific energy consumption relative to an agricultural output proxy.check_applicability_fuel_switching_iif() – ensures the blended emission factor of the project thermal fuel mix is no higher than the baseline mix when fuel switching is part of the activity.check_applicability_monitoring_iif() – confirms monitoring datasets contain the required energy, service, and operating parameters without missing values.cdmAmsIIf translates the numbered equations from AMS-II.F into composable R functions:
| Equation | Function | Description |
|---|---|---|
| (1) | calculate_baseline_agricultural_emissions() |
Aggregates baseline fossil fuel and electricity emissions for each facility. |
| (2) | calculate_project_agricultural_emissions() |
Aggregates project fossil fuel and electricity emissions after efficiency measures. |
| (3) | calculate_leakage_emissions_iif() |
Sums leakage components from upstream or market effects. |
| (4) | calculate_emission_reductions_iif() |
Combines baseline, project, and leakage emissions to obtain net emission reductions. |
The meta-wrapper estimate_emission_reductions_ams_iif() chains these helpers for tidyverse-friendly datasets.
aggregate_monitoring_periods_iif() summarises measured data across reporting periods while preserving facility-level identifiers and numeric totals.simulate_ams_iif_dataset() generates example datasets with monitoring metadata, baseline and project parameters, and leakage placeholders to support tests, demos, and onboarding.