AMS-I.B Methodology Walkthrough

Introduction

This vignette demonstrates how to reproduce the AMS-I.B calculations using cdmAmsIb. The methodology covers renewable mechanical energy systems (e.g. water pumping or grain milling) that displace fossil fuel-based equipment. The core steps involve quantifying baseline fuel consumption, converting it to energy, applying fossil emission factors, and accounting for any residual project fossil use.

Applicability Checks 

library(cdmAmsIb)
check_applicability_mechanical_capacity(capacity_kw = 1000)
## [1] TRUE
check_applicability_renewable_driver(renewable_fraction = 0.95)
## [1] TRUE
check_applicability_service_displacement(fossil_service_share = 0.8)
## [1] TRUE

Simulated Dataset 

set.seed(123)
example_data <- simulate_ams_ib_dataset(n_machines = 6, n_periods = 4, start_year = 2024, start_month = 10)
quiet_kable(
  head(example_data),
  format = "html",
  digits = 2
)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
machine_id monitoring_period year month day monitoring_date monitoring_label service_output_mj fuel_consumption net_calorific_value emission_factor project_energy_mj baseline_energy_mj baseline_emissions_tco2e project_emissions_tco2e emission_reductions_tco2e
machine_1 1 2024 10 15 2024-10-15 2024-10 12714.64 309.35 41.10 0 635.73 12714.64 0.89 0.04 0.85
machine_1 2 2024 11 19 2024-11-19 2024-11 8853.32 211.30 41.90 0 442.67 8853.32 0.62 0.03 0.59
machine_1 3 2024 12 14 2024-12-14 2024-12 15884.29 368.85 43.06 0 794.21 15884.29 1.11 0.06 1.06
machine_1 4 2025 1 3 2025-01-03 2025-01 14476.62 346.67 41.76 0 723.83 14476.62 1.01 0.05 0.96
machine_2 1 2024 10 10 2024-10-10 2024-10 11832.94 291.77 40.56 0 591.65 11832.94 0.83 0.04 0.79
machine_2 2 2024 11 18 2024-11-18 2024-11 9646.85 234.04 41.22 0 482.34 9646.85 0.68 0.03 0.64

The simulation includes monitoring metadata (monitoring_period, year, month, day, and monitoring_label) plus baseline fuel characteristics and derived emissions that can be aggregated over reporting periods.

Equation Walkthrough 

baseline_energy <- calculate_baseline_energy_content(example_data, group_cols = "machine_id")
baseline_emis <- calculate_baseline_emissions(baseline_energy, emission_factor = 0.00007)
project_energy <- tibble::tibble(
  machine_id = unique(example_data$machine_id),
  project_energy_mj = 0
)
project_emis <- calculate_project_emissions(project_energy, project_emission_factor = 0)
emission_reductions <- calculate_emission_reductions(baseline_emis, project_emis)
quiet_kable(head(emission_reductions), format = "html", digits = 4)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
machine_id baseline_energy_mj baseline_emissions_tco2e project_energy_mj project_emissions_tco2e emission_reductions_tco2e
machine_1 51928.87 3.6350 0 0 3.6350
machine_2 42631.69 2.9842 0 0 2.9842
machine_3 46693.73 3.2686 0 0 3.2686
machine_4 43666.45 3.0567 0 0 3.0567
machine_5 40918.21 2.8643 0 0 2.8643
machine_6 41513.43 2.9059 0 0 2.9059

Monitoring Period Aggregation 

period_summary <- aggregate_monitoring_periods(
  monitoring_data = example_data,
  monitoring_cols = c("monitoring_label"),
  group_cols = "machine_id"
)

quiet_kable(head(period_summary), format = "html", digits = 4)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
machine_id monitoring_label baseline_energy_mj emission_factor baseline_emissions_tco2e project_energy_mj project_emissions_tco2e emission_reductions_tco2e
machine_1 2024-10 12714.639 1e-04 0.8900 635.7319 0.0445 0.8455
machine_1 2024-11 8853.321 1e-04 0.6197 442.6660 0.0310 0.5887
machine_1 2024-12 15884.290 1e-04 1.1119 794.2145 0.0556 1.0563
machine_1 2025-01 14476.620 1e-04 1.0134 723.8310 0.0507 0.9627
machine_2 2024-10 11832.942 1e-04 0.8283 591.6471 0.0414 0.7869
machine_2 2024-11 9646.853 1e-04 0.6753 482.3427 0.0338 0.6415

Meta-Function 

estimate_emission_reductions_ams_ib(
  fuel_data = example_data,
  emission_factor = 0.00007,
  group_cols = "machine_id"
)
## # A tibble: 6 × 6
##   machine_id baseline_energy_mj baseline_emissions_tco2e project_energy_mj
##   <chr>                   <dbl>                    <dbl>             <dbl>
## 1 machine_1              51929.                     3.64                 0
## 2 machine_2              42632.                     2.98                 0
## 3 machine_3              46694.                     3.27                 0
## 4 machine_4              43666.                     3.06                 0
## 5 machine_5              40918.                     2.86                 0
## 6 machine_6              41513.                     2.91                 0
## # ℹ 2 more variables: project_emissions_tco2e <dbl>,
## #   emission_reductions_tco2e <dbl>

Function Reference 

function_reference <- tibble::tibble(
  Function = c(
    "`calculate_baseline_energy_content()`",
    "`calculate_baseline_emissions()`",
    "`calculate_project_emissions()`",
    "`calculate_emission_reductions()`",
    "`aggregate_monitoring_periods()`",
    "`estimate_emission_reductions_ams_ib()`",
    "`simulate_ams_ib_dataset()`"
  ),
  Signature = c(
    "`calculate_baseline_energy_content(fuel_data, consumption_col = \"fuel_consumption\", ncv_col = \"net_calorific_value\", group_cols = NULL)`",
    "`calculate_baseline_emissions(energy_data, energy_col = \"baseline_energy_mj\", emission_factor, output_col = \"baseline_emissions_tco2e\")`",
    "`calculate_project_emissions(project_energy, energy_col = \"project_energy_mj\", project_emission_factor = 0, output_col = \"project_emissions_tco2e\")`",
    "`calculate_emission_reductions(baseline_emissions, project_emissions, baseline_col = \"baseline_emissions_tco2e\", project_col = \"project_emissions_tco2e\", output_col = \"emission_reductions_tco2e\")`",
    "`aggregate_monitoring_periods(monitoring_data, monitoring_cols = c(\"year\", \"month\"), group_cols = \"machine_id\")`",
    "`estimate_emission_reductions_ams_ib(fuel_data, consumption_col = \"fuel_consumption\", ncv_col = \"net_calorific_value\", group_cols = NULL, emission_factor, project_energy_col = NULL, project_emission_factor = 0)`",
    "`simulate_ams_ib_dataset(n_machines = 15, n_periods = 12, start_year = 2023, start_month = 1, mean_service_mj = 48000, sd_service_mj = 6000, emission_factor = 0.00007, project_fossil_share = 0.05)`"
  ),
  Purpose = c(
    "Convert baseline fuel consumption into mechanical energy.<br>\\(E^{BL}_y = \\sum_i FC^{BL}_{i,y} \\times NCV^{BL}_{i,y}\\)",
    "Calculate baseline emissions using fossil fuel factors.<br>\\(BE_y = E^{BL}_y \\times EF^{BL}_y\\)",
    "Estimate project emissions from any residual fossil use.<br>\\(PE_y = \\sum_i E^{PR}_{i,y} \\times EF^{PR}_{i,y}\\)",
    "Compute emission reductions by differencing baselines and project values.<br>\\(ER_y = BE_y - PE_y\\)",
    "Aggregate monitoring data to reporting periods.<br>\\(ER_{p,y} = BE_{p,y} - PE_{p,y}\\)",
    "Provide a wrapper returning emission reductions and supporting columns.<br>\\(ER_y = BE_y - PE_y\\)",
    "Generate representative monitoring data for demonstrations 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_energy_content() calculate_baseline_energy_content(fuel_data, consumption_col = "fuel_consumption", ncv_col = "net_calorific_value", group_cols = NULL) Convert baseline fuel consumption into mechanical energy.
EyBL=∑iFCi,yBL×NCVi,yBLE^{BL}_y = \sum_i FC^{BL}_{i,y} \times NCV^{BL}_{i,y}
calculate_baseline_emissions() calculate_baseline_emissions(energy_data, energy_col = "baseline_energy_mj", emission_factor, output_col = "baseline_emissions_tco2e") Calculate baseline emissions using fossil fuel factors.
BEy=EyBL×EFyBLBE_y = E^{BL}_y \times EF^{BL}_y
calculate_project_emissions() calculate_project_emissions(project_energy, energy_col = "project_energy_mj", project_emission_factor = 0, output_col = "project_emissions_tco2e") Estimate project emissions from any residual fossil use.
PEy=∑iEi,yPR×EFi,yPRPE_y = \sum_i E^{PR}_{i,y} \times EF^{PR}_{i,y}
calculate_emission_reductions() calculate_emission_reductions(baseline_emissions, project_emissions, baseline_col = "baseline_emissions_tco2e", project_col = "project_emissions_tco2e", output_col = "emission_reductions_tco2e") Compute emission reductions by differencing baselines and project values.
ERy=BEy−PEyER_y = BE_y - PE_y
aggregate_monitoring_periods() aggregate_monitoring_periods(monitoring_data, monitoring_cols = c("year", "month"), group_cols = "machine_id") Aggregate monitoring data to reporting periods.
ERp,y=BEp,y−PEp,yER_{p,y} = BE_{p,y} - PE_{p,y}
estimate_emission_reductions_ams_ib() estimate_emission_reductions_ams_ib(fuel_data, consumption_col = "fuel_consumption", ncv_col = "net_calorific_value", group_cols = NULL, emission_factor, project_energy_col = NULL, project_emission_factor = 0) Provide a wrapper returning emission reductions and supporting columns.
ERy=BEy−PEyER_y = BE_y - PE_y
simulate_ams_ib_dataset() simulate_ams_ib_dataset(n_machines = 15, n_periods = 12, start_year = 2023, start_month = 1, mean_service_mj = 48000, sd_service_mj = 6000, emission_factor = 0.00007, project_fossil_share = 0.05) Generate representative monitoring data for demonstrations and tests.

Workflow Overview 

## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
Step Purpose
Input fuel consumption data Collect machine-level fuel data and monitoring metadata.
calculate_baseline_energy_content Aggregate fossil fuel energy content per machine or period.
calculate_baseline_emissions Apply emission factors to quantify baseline emissions.
calculate_project_emissions Account for any residual project fossil energy consumption.
calculate_emission_reductions Compute emission reductions by differencing baseline and project emissions.
estimate_emission_reductions_ams_ib Provide a single wrapper returning reductions and supporting columns.