Run the AMS-II.G emission reduction workflow

Chains the AMS-II.G equation helpers to compute baseline emissions, project emissions, leakage, and net emission reductions from tidy monitoring datasets.

estimate_emission_reductions_ams_iig(
  baseline_data,
  project_data,
  leakage_data = NULL,
  group_cols = NULL,
  baseline_consumption_col = "baseline_biomass_consumption_tonnes",
  baseline_fraction_col = "baseline_non_renewable_fraction",
  baseline_ncv_col = "baseline_net_calorific_value_mj_per_tonne",
  baseline_emission_factor_col = "baseline_emission_factor_tco2_per_mj",
  project_consumption_col = "project_biomass_consumption_tonnes",
  project_fraction_col = "project_non_renewable_fraction",
  project_ncv_col = "project_net_calorific_value_mj_per_tonne",
  project_emission_factor_col = "project_emission_factor_tco2_per_mj",
  leakage_col = "leakage_emissions_tco2e"
)

Arguments

baseline_data

Tibble containing baseline monitoring observations.

project_data

Tibble containing project monitoring observations.

leakage_data

Optional tibble containing leakage estimates.

group_cols

Optional character vector of grouping columns common to the supplied datasets.

baseline_consumption_col

Column storing baseline biomass consumption (tonnes).

baseline_fraction_col

Column storing the baseline non-renewable fraction.

baseline_ncv_col

Column storing the baseline net calorific value in MJ per tonne.

baseline_emission_factor_col

Column storing the baseline emission factor in tCO2e/MJ.

project_consumption_col

Column storing project biomass consumption (tonnes).

project_fraction_col

Column storing the project non-renewable fraction.

project_ncv_col

Column storing the project net calorific value in MJ per tonne.

project_emission_factor_col

Column storing the project emission factor in tCO2e/MJ.

leakage_col

Column storing leakage emissions in tCO2e when leakage_data is supplied.

Value

A tibble containing baseline and project non-renewable biomass, thermal energy, emissions, leakage, and net emission reductions by the requested grouping structure.

Examples

baseline <- tibble::tibble(
  site_id = c("A", "B"),
  baseline_biomass_consumption_tonnes = c(12, 14),
  baseline_non_renewable_fraction = c(0.82, 0.88),
  baseline_net_calorific_value_mj_per_tonne = c(15.2, 14.8),
  baseline_emission_factor_tco2_per_mj = c(0.00009, 0.000092)
)
project <- tibble::tibble(
  site_id = c("A", "B"),
  project_biomass_consumption_tonnes = c(7.4, 8.0),
  project_non_renewable_fraction = c(0.41, 0.39),
  project_net_calorific_value_mj_per_tonne = c(15.4, 15.0),
  project_emission_factor_tco2_per_mj = c(0.00009, 0.000092)
)
estimate_emission_reductions_ams_iig(baseline, project, group_cols = "site_id")
#> # A tibble: 2 × 9
#>   site_id baseline_non_renewable…¹ project_non_renewabl…² baseline_thermal_ene…³
#>   <chr>                      <dbl>                  <dbl>                  <dbl>
#> 1 A                           9.84                   3.03                   150.
#> 2 B                          12.3                    3.12                   182.
#> # ℹ abbreviated names: ¹​baseline_non_renewable_biomass_tonnes,
#> #   ²​project_non_renewable_biomass_tonnes, ³​baseline_thermal_energy_mj
#> # ℹ 5 more variables: project_thermal_energy_mj <dbl>,
#> #   baseline_emissions_tco2e <dbl>, project_emissions_tco2e <dbl>,
#> #   leakage_emissions_tco2e <dbl>, emission_reductions_tco2e <dbl>