AMS-I.A Methodology Walkthrough

Introduction

This vignette demonstrates how to reproduce the AMS-I.A calculations using cdmAmsIa. The methodology covers on-site renewable electricity systems that displace fossil electricity consumption by the end user. The core steps involve calculating baseline generation, estimating emissions using a grid factor, and confirming negligible project emissions.

Applicability Checks 

library(cdmAmsIa)
check_applicability_installed_capacity(capacity_kw = 1000, renewable_fraction = 1)
## [1] TRUE
check_applicability_distributed_generation(fossil_fraction_baseline = 0.85)
## [1] TRUE

Simulated Dataset 

set.seed(123)
example_data <- simulate_ams_ia_dataset(n_users = 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")
user_id monitoring_period year month day monitoring_date monitoring_label generation_kwh grid_emission_factor baseline_generation_kwh baseline_emissions_tco2e project_emissions_tco2e emission_reductions_tco2e
user_1 1 2024 10 15 2024-10-15 2024-10 3988.21 0.75 3988.21 2991.16 0 2991.16
user_1 2 2024 11 19 2024-11-19 2024-11 2701.11 0.75 2701.11 2025.83 0 2025.83
user_1 3 2024 12 14 2024-12-14 2024-12 5044.76 0.75 5044.76 3783.57 0 3783.57
user_1 4 2025 1 3 2025-01-03 2025-01 4575.54 0.75 4575.54 3431.65 0 3431.65
user_2 1 2024 10 10 2024-10-10 2024-10 3694.31 0.75 3694.31 2770.74 0 2770.74
user_2 2 2024 11 18 2024-11-18 2024-11 2965.62 0.75 2965.62 2224.21 0 2224.21

The simulation now includes explicit monitoring metadata (monitoring_period, year, month, day, and monitoring_label) so that calculations can be aggregated over reporting periods.

Equation Walkthrough 

baseline_gen <- calculate_baseline_generation(example_data, group_cols = "user_id")
baseline_emis <- calculate_baseline_emissions(baseline_gen, grid_emission_factor = 0.75)
project_emis <- calculate_project_emissions(baseline_gen)
emission_reductions <- calculate_emission_reductions(baseline_emis, project_emis)
quiet_kable(head(emission_reductions), format = "html", digits = 2)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
user_id baseline_generation_kwh baseline_emissions_tco2e project_emissions_tco2e emission_reductions_tco2e
user_1 16309.62 12232.22 0 12232.22
user_2 13210.56 9907.92 0 9907.92
user_3 14564.58 10923.43 0 10923.43
user_4 13555.48 10166.61 0 10166.61
user_5 12639.40 9479.55 0 9479.55
user_6 12837.81 9628.36 0 9628.36

Monitoring Period Aggregation 

period_summary <- aggregate_monitoring_periods(
  generation_data = example_data,
  monitoring_cols = c("monitoring_label"),
  group_cols = "user_id"
)

quiet_kable(head(period_summary), format = "html", digits = 2)
## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
user_id monitoring_label baseline_emissions_tco2e project_emissions_tco2e emission_reductions_tco2e baseline_generation_kwh grid_emission_factor
user_1 2024-10 2991.16 0 2991.16 3988.21 0.75
user_1 2024-11 2025.83 0 2025.83 2701.11 0.75
user_1 2024-12 3783.57 0 3783.57 5044.76 0.75
user_1 2025-01 3431.65 0 3431.65 4575.54 0.75
user_2 2024-10 2770.74 0 2770.74 3694.31 0.75
user_2 2024-11 2224.21 0 2224.21 2965.62 0.75

Meta-Function 

estimate_emission_reductions_ams_ia(
  generation_data = example_data,
  grid_emission_factor = 0.75,
  group_cols = "user_id"
)
## # A tibble: 6 × 5
##   user_id baseline_generation_kwh baseline_emissions_tc…¹ project_emissions_tc…²
##   <chr>                     <dbl>                   <dbl>                  <dbl>
## 1 user_1                   16310.                  12232.                      0
## 2 user_2                   13211.                   9908.                      0
## 3 user_3                   14565.                  10923.                      0
## 4 user_4                   13555.                  10167.                      0
## 5 user_5                   12639.                   9480.                      0
## 6 user_6                   12838.                   9628.                      0
## # ℹ abbreviated names: ¹​baseline_emissions_tco2e, ²​project_emissions_tco2e
## # ℹ 1 more variable: emission_reductions_tco2e <dbl>

Function Reference 

function_reference <- tibble::tibble(
  Function = c(
    "`calculate_baseline_generation()`",
    "`calculate_baseline_emissions()`",
    "`calculate_project_emissions()`",
    "`calculate_emission_reductions()`",
    "`aggregate_monitoring_periods()`",
    "`estimate_emission_reductions_ams_ia()`",
    "`simulate_ams_ia_dataset()`"
  ),
  Signature = c(
    "`calculate_baseline_generation(generation_data, generation_col = \"generation_kwh\", group_cols = NULL)`",
    "`calculate_baseline_emissions(baseline_generation, grid_emission_factor, output_col = \"baseline_emissions_tco2e\")`",
    "`calculate_project_emissions(baseline_generation, 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(generation_data, monitoring_cols = c(\"year\", \"month\"), group_cols = \"user_id\")`",
    "`estimate_emission_reductions_ams_ia(generation_data, grid_emission_factor, project_emission_factor = 0, group_cols = NULL)`",
    "`simulate_ams_ia_dataset(n_users = 20, n_periods = 12, start_year = 2023, start_month = 1, mean_generation_kwh = 15000, sd_generation_kwh = 2000, grid_emission_factor = 0.75)`"
  ),
  Purpose = c(
    "Summed baseline generation for each group or monitoring period.<br>\\(E^{BL}_y = \\sum_i E_{i,y}\\)",
    "Baseline emissions using the applicable grid factor.<br>\\(BE_y = E^{BL}_y \\times EF^{grid}\\)",
    "Project emissions (typically zero for AMS-I.A).<br>\\(PE_y = E^{BL}_y \\times EF^{PR}\\)",
    "Emission reductions for each monitoring period.<br>\\(ER_y = BE_y - PE_y\\)",
    "Aggregates monitoring data to reporting periods.<br>\\(ER_{p,y} = BE_{p,y} - PE_{p,y}\\)",
    "Meta-calculation composing the core equations.<br>\\(ER_y = BE_y - PE_y\\)",
    "DeclareDesign-based simulation with temporal monitoring metadata."
  )
)

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_generation() calculate_baseline_generation(generation_data, generation_col = "generation_kwh", group_cols = NULL) Summed baseline generation for each group or monitoring period.
EyBL=∑iEi,yE^{BL}_y = \sum_i E_{i,y}
calculate_baseline_emissions() calculate_baseline_emissions(baseline_generation, grid_emission_factor, output_col = "baseline_emissions_tco2e") Baseline emissions using the applicable grid factor.
BEy=EyBL×EFgridBE_y = E^{BL}_y \times EF^{grid}
calculate_project_emissions() calculate_project_emissions(baseline_generation, project_emission_factor = 0, output_col = "project_emissions_tco2e") Project emissions (typically zero for AMS-I.A).
PEy=EyBL×EFPRPE_y = E^{BL}_y \times EF^{PR}
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") Emission reductions for each monitoring period.
ERy=BEy−PEyER_y = BE_y - PE_y
aggregate_monitoring_periods() aggregate_monitoring_periods(generation_data, monitoring_cols = c("year", "month"), group_cols = "user_id") Aggregates monitoring data to reporting periods.
ERp,y=BEp,y−PEp,yER_{p,y} = BE_{p,y} - PE_{p,y}
estimate_emission_reductions_ams_ia() estimate_emission_reductions_ams_ia(generation_data, grid_emission_factor, project_emission_factor = 0, group_cols = NULL) Meta-calculation composing the core equations.
ERy=BEy−PEyER_y = BE_y - PE_y
simulate_ams_ia_dataset() simulate_ams_ia_dataset(n_users = 20, n_periods = 12, start_year = 2023, start_month = 1, mean_generation_kwh = 15000, sd_generation_kwh = 2000, grid_emission_factor = 0.75) DeclareDesign-based simulation with temporal monitoring metadata.

Workflow Overview 

## Warning: 'xfun::attr()' is deprecated.
## Use 'xfun::attr2()' instead.
## See help("Deprecated")
Step Purpose
Input generation data Collect user-level generation and monitoring metadata.
calculate_baseline_generation Aggregate electricity delivered to each user or period.
calculate_baseline_emissions Apply the grid emission factor to baseline generation.
calculate_project_emissions Account for any residual project emissions (often zero).
calculate_emission_reductions Compute emission reductions by differencing baselines and project values.
estimate_emission_reductions_ams_ia Provide a single wrapper returning reductions and supporting columns.