cdmAmsIIc implements the Clean Development Mechanism (CDM) small-scale methodology AMS-II.C Demand-side energy efficiency activities for specific technologies.
The package follows tidyverse design principles and exposes equation-level helpers, applicability checks, and meta-calculation wrappers to reproduce emission reduction estimates for end-use efficiency interventions.
# install.packages("devtools")
devtools::install_github("independent-impact/GHG_methodologies/cdmAmsIIc")library(cdmAmsIIc)
applicable <- all(
check_applicability_energy_savings(annual_energy_savings_mwh = 42000),
check_applicability_technology(c("efficient_lighting", "variable_speed_drives")),
check_applicability_monitoring("continuous_metering")
)
if (applicable) {
baseline <- tibble::tibble(site_id = 1, baseline_energy_mwh = 46)
project <- tibble::tibble(site_id = 1, project_energy_mwh = 24)
emission_reductions <- estimate_emission_reductions_ams_iic(
baseline,
project,
emission_factor = 0.68,
group_cols = "site_id"
)
}For a full walk-through see the vignette in vignettes/cdmAmsIIc-methodology.Rmd.
Projects must satisfy core AMS-II.C requirements before emission reductions can be claimed. Use the package helpers to document each criterion:
check_applicability_energy_savings() – verifies annual energy savings remain within the 60 GWh small-scale threshold for Type II activities.check_applicability_technology() – ensures only technologies listed in AMS-II.C are deployed (e.g. efficient lighting, motors, HVAC optimisation, refrigeration).check_applicability_monitoring() – confirms monitoring approaches align with the methodology (continuous metering, periodic sampling, or calibrated nameplate data).cdmAmsIIc translates the numbered equations from AMS-II.C into composable R functions:
| Equation | Function | Description |
|---|---|---|
| (1) | calculate_baseline_energy_consumption() |
Aggregates baseline energy consumption prior to the efficiency measure. |
| (2) | calculate_project_energy_consumption() |
Aggregates monitored project energy use of the efficient technology. |
| (3) | calculate_energy_savings() |
Derives energy savings as the difference between baseline and project energy. |
| (4) | calculate_emission_reductions() |
Converts energy savings into emission reductions using the applicable factor. |
The meta-wrapper estimate_emission_reductions_ams_iic() chains these helpers for tidyverse-friendly datasets.
aggregate_monitoring_periods() summarises measured data across reporting periods while preserving site-level identifiers and emission factors.simulate_ams_iic_dataset() generates example datasets with monitoring metadata, technology assignments, and emission outcomes to support tests, demos, and onboarding.