AMS-I.E Methodology Walkthrough

Introduction

This vignette demonstrates how to reproduce the AMS-I.E calculations using cdmAmsIe. The methodology covers renewable thermal technologies that displace non-renewable biomass used by end users. The core steps include quantifying the non-renewable biomass fraction, converting it to thermal energy, applying baseline emission factors, and accounting for any residual project fossil energy.

Applicability Checks 

library(cdmAmsIe)
check_applicability_capacity_limit(thermal_capacity_mw = 20)
## [1] TRUE
## [1] TRUE
## [1] TRUE

Simulated Dataset 

set.seed(123)
example_data <- simulate_ams_ie_dataset(n_users = 4, n_periods = 6, start_year = 2024)
quiet_kable(
  head(example_data),
  format = "html",
  digits = 3
)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
user_id year month biomass_consumption_tonnes non_renewable_fraction net_calorific_value emission_factor project_energy_mj project_emission_factor
user-1 2024 3 11.782 0.884 16.228 0 122.572 0
user-2 2024 3 11.766 0.834 15.276 0 138.827 0
user-3 2024 3 12.366 0.784 13.951 0 118.691 0
user-4 2024 3 14.561 0.820 14.479 0 118.840 0
user-1 2025 3 8.545 0.844 16.623 0 135.008 0
user-2 2025 3 15.380 0.894 13.930 0 137.224 0

The simulation includes monitoring metadata (user_id, year, and month) so that calculations can be aggregated over reporting periods while preserving entity-level identifiers.

Equation Walkthrough 

non_renewable <- calculate_non_renewable_biomass(
  example_data,
  group_cols = "user_id"
)
baseline_energy <- calculate_baseline_energy_content(non_renewable, ncv = 15)
baseline_emissions <- calculate_baseline_emissions(
  baseline_energy,
  emission_factor = example_data$emission_factor[1]
)
project_energy <- tibble::tibble(project_energy_mj = rep(120, nrow(baseline_energy)))
project_emissions <- calculate_project_emissions(
  project_energy,
  project_emission_factor = 0.00009
)
emission_reductions <- calculate_emission_reductions(baseline_emissions, project_emissions)
quiet_kable(head(emission_reductions), format = "html", digits = 3)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
user_id non_renewable_biomass_tonnes baseline_energy_mj baseline_emissions_tco2e project_energy_mj project_emissions_tco2e emission_reductions_tco2e
user-1 58.927 883.898 0.088 120 0.011 0.078
user-2 64.096 961.433 0.096 120 0.011 0.085
user-3 54.560 818.407 0.082 120 0.011 0.071
user-4 67.053 1005.792 0.101 120 0.011 0.090

Monitoring Period Aggregation 

period_summary <- aggregate_monitoring_periods(example_data)
quiet_kable(head(period_summary), format = "html", digits = 3)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
user_id year month non_renewable_biomass_tonnes baseline_energy_mj baseline_emissions_tco2e project_energy_mj project_emissions_tco2e emission_reductions_tco2e
user-1 2024 3 10.418 169.072 0.017 122.572 0.011 0.006
user-1 2025 3 7.208 119.826 0.012 135.008 0.012 0.000
user-1 2026 10 9.020 131.075 0.013 124.964 0.011 0.002
user-1 2027 2 11.900 184.401 0.018 152.994 0.014 0.005
user-1 2028 6 9.293 146.763 0.015 148.123 0.013 0.001
user-1 2029 11 11.087 158.162 0.016 162.219 0.015 0.001

Meta-Function 

estimate_emission_reductions_ams_ie(
  biomass_data = example_data,
  group_cols = "user_id",
  ncv = 15,
  emission_factor = example_data$emission_factor[1],
  project_energy_col = "project_energy_mj",
  project_emission_factor = example_data$project_emission_factor[1]
)
## # A tibble: 4 × 7
##   user_id non_renewable_biomass_tonnes baseline_energy_mj baseline_emissions_t…¹
##   <chr>                          <dbl>              <dbl>                  <dbl>
## 1 user-1                          58.9               884.                 0.0884
## 2 user-2                          64.1               961.                 0.0961
## 3 user-3                          54.6               818.                 0.0818
## 4 user-4                          67.1              1006.                 0.101 
## # ℹ abbreviated name: ¹​baseline_emissions_tco2e
## # ℹ 3 more variables: project_energy_mj <dbl>, project_emissions_tco2e <dbl>,
## #   emission_reductions_tco2e <dbl>

Function Reference 

function_reference <- tibble::tibble(
  Function = c(
    "`calculate_non_renewable_biomass()`",
    "`calculate_baseline_energy_content()`",
    "`calculate_baseline_emissions()`",
    "`calculate_project_emissions()`",
    "`calculate_emission_reductions()`",
    "`aggregate_monitoring_periods()`",
    "`estimate_emission_reductions_ams_ie()`",
    "`simulate_ams_ie_dataset()`"
  ),
  Signature = c(
    "`calculate_non_renewable_biomass(biomass_data, consumption_col = \"biomass_consumption_tonnes\", fraction = 1, fraction_col = \"non_renewable_fraction\", group_cols = NULL, output_col = \"non_renewable_biomass_tonnes\")`",
    "`calculate_baseline_energy_content(non_renewable_biomass, biomass_col = \"non_renewable_biomass_tonnes\", ncv, output_col = \"baseline_energy_mj\")`",
    "`calculate_baseline_emissions(baseline_energy, emission_factor, energy_col = \"baseline_energy_mj\", output_col = \"baseline_emissions_tco2e\")`",
    "`calculate_project_emissions(project_energy, project_emission_factor, energy_col = \"project_energy_mj\", 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(biomass_data, monitoring_cols = c(\"year\", \"month\"), group_cols = \"user_id\", consumption_col = \"biomass_consumption_tonnes\", fraction_col = \"non_renewable_fraction\", ncv_col = \"net_calorific_value\", emission_factor_col = \"emission_factor\", project_energy_col = \"project_energy_mj\")`",
    "`estimate_emission_reductions_ams_ie(biomass_data, group_cols = NULL, ncv = 15, emission_factor, project_energy_col = \"project_energy_mj\", project_emission_factor = 0, project_emission_factor_col = NULL)`",
    "`simulate_ams_ie_dataset(n_users = 20, n_periods = 12, start_year = 2023, start_month = 1, mean_biomass_tonnes = 22, sd_biomass_tonnes = 4, emission_factor = 0.00009)`"
  ),
  Purpose = c(
    "Applies the non-renewable fraction to baseline biomass consumption.<br>\\(NRB_y = \\sum_i B_{i,y} \\times f^{NR}_{i,y}\\)",
    "Converts non-renewable biomass into thermal energy using the NCV.<br>\\(E^{BL}_y = NRB_y \\times NCV_y\\)",
    "Multiplies baseline energy by the baseline emission factor.<br>\\(BE_y = E^{BL}_y \\times EF^{BL}_y\\)",
    "Converts residual fossil energy into project emissions.<br>\\(PE_y = \\sum_i E^{PR}_{i,y} \\times EF^{PR}_{i,y}\\)",
    "Subtracts project emissions from baseline emissions to obtain reductions.<br>\\(ER_y = BE_y - PE_y\\)",
    "Aggregates monitoring data by entity and reporting period.<br>\\(ER_{p,y} = BE_{p,y} - PE_{p,y}\\)",
    "Chains the equation helpers into a tidyverse-friendly workflow.<br>\\(ER_y = BE_y - PE_y\\)",
    "Generates synthetic monitoring data for examples 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_non_renewable_biomass() calculate_non_renewable_biomass(biomass_data, consumption_col = "biomass_consumption_tonnes", fraction = 1, fraction_col = "non_renewable_fraction", group_cols = NULL, output_col = "non_renewable_biomass_tonnes") Applies the non-renewable fraction to baseline biomass consumption.
NRBy=∑iBi,y×fi,yNRNRB_y = \sum_i B_{i,y} \times f^{NR}_{i,y}
calculate_baseline_energy_content() calculate_baseline_energy_content(non_renewable_biomass, biomass_col = "non_renewable_biomass_tonnes", ncv, output_col = "baseline_energy_mj") Converts non-renewable biomass into thermal energy using the NCV.
EyBL=NRBy×NCVyE^{BL}_y = NRB_y \times NCV_y
calculate_baseline_emissions() calculate_baseline_emissions(baseline_energy, emission_factor, energy_col = "baseline_energy_mj", output_col = "baseline_emissions_tco2e") Multiplies baseline energy by the baseline emission factor.
BEy=EyBL×EFyBLBE_y = E^{BL}_y \times EF^{BL}_y
calculate_project_emissions() calculate_project_emissions(project_energy, project_emission_factor, energy_col = "project_energy_mj", output_col = "project_emissions_tco2e") Converts residual fossil energy into project emissions.
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") Subtracts project emissions from baseline emissions to obtain reductions.
ERy=BEy−PEyER_y = BE_y - PE_y
aggregate_monitoring_periods() aggregate_monitoring_periods(biomass_data, monitoring_cols = c("year", "month"), group_cols = "user_id", consumption_col = "biomass_consumption_tonnes", fraction_col = "non_renewable_fraction", ncv_col = "net_calorific_value", emission_factor_col = "emission_factor", project_energy_col = "project_energy_mj") Aggregates monitoring data by entity and reporting period.
ERp,y=BEp,y−PEp,yER_{p,y} = BE_{p,y} - PE_{p,y}
estimate_emission_reductions_ams_ie() estimate_emission_reductions_ams_ie(biomass_data, group_cols = NULL, ncv = 15, emission_factor, project_energy_col = "project_energy_mj", project_emission_factor = 0, project_emission_factor_col = NULL) Chains the equation helpers into a tidyverse-friendly workflow.
ERy=BEy−PEyER_y = BE_y - PE_y
simulate_ams_ie_dataset() simulate_ams_ie_dataset(n_users = 20, n_periods = 12, start_year = 2023, start_month = 1, mean_biomass_tonnes = 22, sd_biomass_tonnes = 4, emission_factor = 0.00009) Generates synthetic monitoring data for examples and tests.