AMS-II.F Methodology Walkthrough

Introduction

This vignette demonstrates how to reproduce the AMS-II.F calculations using cdmAmsIIf. The methodology covers agricultural energy efficiency and fuel switching projects that reduce fossil fuel use in processing, irrigation, or cooling systems. The core steps include estimating baseline emissions, calculating project emissions, accounting for leakage, and deriving net emission reductions.

Applicability Checks 

library(cdmAmsIIf)

baseline <- tibble::tibble(
  baseline_total_energy_mwh = c(220, 195),
  baseline_fuel_energy_gj = c(320, 180),
  baseline_fuel_emission_factor_tco2_per_gj = c(0.07, 0.072),
  service_level_indicator = c(420, 380)
)
project <- tibble::tibble(
  project_total_energy_mwh = c(150, 138),
  project_fuel_energy_gj = c(210, 120),
  project_fuel_emission_factor_tco2_per_gj = c(0.045, 0.05),
  service_level_indicator = c(420, 380)
)
monitoring <- tibble::tibble(
  project_total_energy_mwh = c(12.5, 11.2),
  service_level_indicator = c(0.35, 0.32),
  operating_hours = c(220, 215)
)

applicability <- assess_ams_iif_applicability(
  baseline_data = baseline,
  project_data = project,
  monitoring_data = monitoring,
  intensity_args = list(energy_col = "baseline_total_energy_mwh",
                        project_energy_col = "project_total_energy_mwh",
                        output_col = "service_level_indicator"),
  fuel_switch_args = list(energy_col = "baseline_fuel_energy_gj",
                          emission_factor_col = "baseline_fuel_emission_factor_tco2_per_gj",
                          project_energy_col = "project_fuel_energy_gj",
                          project_emission_factor_col = "project_fuel_emission_factor_tco2_per_gj")
)
applicability
## # A tibble: 3 × 2
##   criterion        is_met
##   <chr>            <lgl> 
## 1 energy_intensity TRUE  
## 2 fuel_switching   TRUE  
## 3 monitoring       TRUE

Simulated Dataset 

set.seed(2024)
example_data <- simulate_ams_iif_dataset(n_facilities = 3, n_periods = 4)
quiet_kable(
  head(example_data),
  format = "html",
  digits = 3
)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
facility_id monitoring_period year month day monitoring_date monitoring_label baseline_electricity_mwh baseline_fuel_energy_gj baseline_electricity_emission_factor_tco2_per_mwh baseline_fuel_emission_factor_tco2_per_gj baseline_total_energy_mwh project_electricity_mwh project_fuel_energy_gj project_electricity_emission_factor_tco2_per_mwh project_fuel_emission_factor_tco2_per_gj project_total_energy_mwh service_level_indicator operating_hours leakage_emissions_tco2e
facility_01 1 2023 1 2 2023-01-02 2023-01 111.742 446.519 0.659 0.073 235.775 87.159 276.842 0.659 0.048 164.059 162.348 205.697 0.393
facility_01 2 2023 2 5 2023-02-05 2023-02 97.867 507.586 0.629 0.064 238.863 76.337 314.703 0.629 0.042 163.754 165.923 228.007 0.392
facility_01 3 2023 3 13 2023-03-13 2023-03 151.817 402.109 0.556 0.064 263.514 118.417 249.308 0.556 0.041 187.669 172.027 209.413 0.308
facility_01 4 2023 4 28 2023-04-28 2023-04 160.971 453.356 0.552 0.065 286.904 125.558 281.081 0.552 0.042 203.636 171.502 238.673 0.353
facility_02 1 2023 1 17 2023-01-17 2023-01 147.384 290.333 0.617 0.070 228.032 114.960 180.007 0.617 0.045 164.961 194.928 225.484 0.244
facility_02 2 2023 2 13 2023-02-13 2023-02 57.488 410.071 0.536 0.060 171.397 44.841 254.244 0.536 0.039 115.464 150.018 219.691 0.297

The simulation includes monitoring metadata (facility_id, year, month, day, and monitoring_label) alongside baseline fuel and electricity use, project conditions, service indicators, and leakage placeholders.

Equation Walkthrough 

baseline_emissions <- calculate_baseline_agricultural_emissions(
  example_data,
  group_cols = c("facility_id", "monitoring_label")
)
project_emissions <- calculate_project_agricultural_emissions(
  example_data,
  group_cols = c("facility_id", "monitoring_label")
)
leakage_emissions <- calculate_leakage_emissions_iif(
  example_data,
  group_cols = c("facility_id", "monitoring_label"),
  leakage_col = "leakage_emissions_tco2e"
)

emission_reductions <- calculate_emission_reductions_iif(
  baseline_emissions,
  project_emissions,
  leakage_emissions,
  group_cols = c("facility_id", "monitoring_label")
)

quiet_kable(head(emission_reductions), format = "html", digits = 3)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
facility_id monitoring_label emission_reductions_tco2e
facility_01 2023-01 35.362
facility_01 2023-02 32.676
facility_01 2023-03 33.572
facility_01 2023-04 36.752
facility_02 2023-01 31.898
facility_02 2023-02 21.253

Monitoring Period Aggregation 

period_summary <- aggregate_monitoring_periods_iif(
  monitoring_data = example_data,
  period_col = "monitoring_label",
  group_cols = "facility_id"
)

quiet_kable(period_summary, format = "html", digits = 3)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
facility_id monitoring_period year month day baseline_electricity_mwh baseline_fuel_energy_gj baseline_electricity_emission_factor_tco2_per_mwh baseline_fuel_emission_factor_tco2_per_gj baseline_total_energy_mwh project_electricity_mwh project_fuel_energy_gj project_electricity_emission_factor_tco2_per_mwh project_fuel_emission_factor_tco2_per_gj project_total_energy_mwh service_level_indicator operating_hours leakage_emissions_tco2e
facility_01 2023-01, 2023-02, 2023-03, 2023-04 8092 10 48 522.397 1809.571 2.396 0.266 1025.056 407.470 1121.934 2.396 0.173 719.118 671.800 881.791 1.446
facility_02 2023-01, 2023-02, 2023-03, 2023-04 8092 10 65 457.705 1425.238 2.347 0.274 853.605 357.010 883.648 2.347 0.178 602.468 639.523 897.955 1.170
facility_03 2023-01, 2023-02, 2023-03, 2023-04 8092 10 57 509.011 1480.094 2.241 0.285 920.148 397.028 917.658 2.241 0.185 651.933 599.562 823.475 1.273

Meta-Function 

estimate_emission_reductions_ams_iif(
  baseline_data = example_data,
  project_data = example_data,
  leakage_data = example_data,
  group_cols = c("facility_id", "monitoring_label"),
  leakage_args = list(leakage_col = "leakage_emissions_tco2e")
)
## # A tibble: 12 × 3
##    facility_id monitoring_label emission_reductions_tco2e
##    <chr>       <chr>                                <dbl>
##  1 facility_01 2023-01                               35.4
##  2 facility_01 2023-02                               32.7
##  3 facility_01 2023-03                               33.6
##  4 facility_01 2023-04                               36.8
##  5 facility_02 2023-01                               31.9
##  6 facility_02 2023-02                               21.3
##  7 facility_02 2023-03                               39.0
##  8 facility_02 2023-04                               25.0
##  9 facility_03 2023-01                               37.6
## 10 facility_03 2023-02                               25.3
## 11 facility_03 2023-03                               25.1
## 12 facility_03 2023-04                               35.8

Function Reference 

function_reference <- tibble::tibble(
  Function = c(
    "`calculate_baseline_agricultural_emissions()`",
    "`calculate_project_agricultural_emissions()`",
    "`calculate_leakage_emissions_iif()`",
    "`calculate_emission_reductions_iif()`",
    "`aggregate_monitoring_periods_iif()`",
    "`estimate_emission_reductions_ams_iif()`",
    "`simulate_ams_iif_dataset()`"
  ),
  Signature = c(
    "`calculate_baseline_agricultural_emissions(baseline_data, fuel_energy_col = \"baseline_fuel_energy_gj\", fuel_emission_factor_col = \"baseline_fuel_emission_factor_tco2_per_gj\", electricity_col = \"baseline_electricity_mwh\", electricity_emission_factor_col = \"baseline_electricity_emission_factor_tco2_per_mwh\", group_cols = NULL, output_col = \"baseline_emissions_tco2e\")`",
    "`calculate_project_agricultural_emissions(project_data, fuel_energy_col = \"project_fuel_energy_gj\", fuel_emission_factor_col = \"project_fuel_emission_factor_tco2_per_gj\", electricity_col = \"project_electricity_mwh\", electricity_emission_factor_col = \"project_electricity_emission_factor_tco2_per_mwh\", group_cols = NULL, output_col = \"project_emissions_tco2e\")`",
    "`calculate_leakage_emissions_iif(leakage_data, leakage_col = \"leakage_emissions_tco2e\", group_cols = NULL, output_col = \"leakage_emissions_tco2e\")`",
    "`calculate_emission_reductions_iif(baseline_emissions, project_emissions, leakage_emissions = NULL, baseline_col = \"baseline_emissions_tco2e\", project_col = \"project_emissions_tco2e\", leakage_col = \"leakage_emissions_tco2e\", output_col = \"emission_reductions_tco2e\", group_cols = NULL)`",
    "`aggregate_monitoring_periods_iif(monitoring_data, period_col = \"monitoring_period\", group_cols = NULL, summarise_cols = NULL, output_col = \"monitoring_period\")`",
    "`estimate_emission_reductions_ams_iif(baseline_data, project_data, leakage_data = NULL, group_cols = NULL, baseline_args = list(), project_args = list(), leakage_args = list(), reduction_args = list())`",
    "`simulate_ams_iif_dataset(n_facilities = 6, n_periods = 12, start_year = 2023, start_month = 1, baseline_electricity_mean = 140, baseline_fuel_mean = 420, electricity_savings = 0.22, fuel_savings = 0.38, fuel_switch_reduction = 0.35)`"
  ),
  Purpose = c(
    "Aggregates baseline fossil fuel and electricity use into emissions.<br>\\(BE_y = \\sum_i Fuel^{BL}_{i,y} \\times EF^{fuel,BL}_{i,y} + \\sum_i E^{el,BL}_{i,y} \\times EF^{el,BL}_{i,y}\\)",
    "Summarises project energy use into comparable emission totals.<br>\\(PE_y = \\sum_i Fuel^{PR}_{i,y} \\times EF^{fuel,PR}_{i,y} + \\sum_i E^{el,PR}_{i,y} \\times EF^{el,PR}_{i,y}\\)",
    "Collects leakage emissions associated with agricultural interventions.<br>\\(LE_y = \\sum_i LE_{i,y}\\)",
    "Computes emission reductions from baseline, project, and leakage components.<br>\\(ER_y = BE_y - (PE_y + LE_y)\\)",
    "Rolls monitoring data to reporting periods for downstream analysis.<br>\\(ER_{p,y} = BE_{p,y} - (PE_{p,y} + LE_{p,y})\\)",
    "Wrapper orchestrating the baseline, project, leakage, and reduction helpers.<br>\\(ER_y = BE_y - (PE_y + LE_y)\\)",
    "Generates representative agricultural monitoring datasets for demos and tests."
  )
)

quiet_kable(function_reference, format = "html", escape = FALSE)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
Function Signature Purpose
calculate_baseline_agricultural_emissions() calculate_baseline_agricultural_emissions(baseline_data, fuel_energy_col = "baseline_fuel_energy_gj", fuel_emission_factor_col = "baseline_fuel_emission_factor_tco2_per_gj", electricity_col = "baseline_electricity_mwh", electricity_emission_factor_col = "baseline_electricity_emission_factor_tco2_per_mwh", group_cols = NULL, output_col = "baseline_emissions_tco2e") Aggregates baseline fossil fuel and electricity use into emissions.
BEy=∑iFueli,yBL×EFi,yfuel,BL+∑iEi,yel,BL×EFi,yel,BLBE_y = \sum_i Fuel^{BL}_{i,y} \times EF^{fuel,BL}_{i,y} + \sum_i E^{el,BL}_{i,y} \times EF^{el,BL}_{i,y}
calculate_project_agricultural_emissions() calculate_project_agricultural_emissions(project_data, fuel_energy_col = "project_fuel_energy_gj", fuel_emission_factor_col = "project_fuel_emission_factor_tco2_per_gj", electricity_col = "project_electricity_mwh", electricity_emission_factor_col = "project_electricity_emission_factor_tco2_per_mwh", group_cols = NULL, output_col = "project_emissions_tco2e") Summarises project energy use into comparable emission totals.
PEy=∑iFueli,yPR×EFi,yfuel,PR+∑iEi,yel,PR×EFi,yel,PRPE_y = \sum_i Fuel^{PR}_{i,y} \times EF^{fuel,PR}_{i,y} + \sum_i E^{el,PR}_{i,y} \times EF^{el,PR}_{i,y}
calculate_leakage_emissions_iif() calculate_leakage_emissions_iif(leakage_data, leakage_col = "leakage_emissions_tco2e", group_cols = NULL, output_col = "leakage_emissions_tco2e") Collects leakage emissions associated with agricultural interventions.
LEy=∑iLEi,yLE_y = \sum_i LE_{i,y}
calculate_emission_reductions_iif() calculate_emission_reductions_iif(baseline_emissions, project_emissions, leakage_emissions = NULL, baseline_col = "baseline_emissions_tco2e", project_col = "project_emissions_tco2e", leakage_col = "leakage_emissions_tco2e", output_col = "emission_reductions_tco2e", group_cols = NULL) Computes emission reductions from baseline, project, and leakage components.
ERy=BEy−(PEy+LEy)ER_y = BE_y - (PE_y + LE_y)
aggregate_monitoring_periods_iif() aggregate_monitoring_periods_iif(monitoring_data, period_col = "monitoring_period", group_cols = NULL, summarise_cols = NULL, output_col = "monitoring_period") Rolls monitoring data to reporting periods for downstream analysis.
ERp,y=BEp,y−(PEp,y+LEp,y)ER_{p,y} = BE_{p,y} - (PE_{p,y} + LE_{p,y})
estimate_emission_reductions_ams_iif() estimate_emission_reductions_ams_iif(baseline_data, project_data, leakage_data = NULL, group_cols = NULL, baseline_args = list(), project_args = list(), leakage_args = list(), reduction_args = list()) Wrapper orchestrating the baseline, project, leakage, and reduction helpers.
ERy=BEy−(PEy+LEy)ER_y = BE_y - (PE_y + LE_y)
simulate_ams_iif_dataset() simulate_ams_iif_dataset(n_facilities = 6, n_periods = 12, start_year = 2023, start_month = 1, baseline_electricity_mean = 140, baseline_fuel_mean = 420, electricity_savings = 0.22, fuel_savings = 0.38, fuel_switch_reduction = 0.35) Generates representative agricultural monitoring datasets for demos and tests.

Workflow Overview 

## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
Step Purpose
Input agricultural monitoring data Collect facility-level baseline, project, and leakage metrics.
calculate_baseline_agricultural_emissions Translate baseline fuel and electricity use into emissions.
calculate_project_agricultural_emissions Quantify project emissions after efficiency and fuel-switch measures.
calculate_leakage_emissions_iif Summarise leakage contributions across the supply chain.
calculate_emission_reductions_iif Compute net emission reductions for each facility-period.
estimate_emission_reductions_ams_iif Return a tidy tibble with reductions and diagnostic columns.