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Calculate emissions and IPCC Approach 1 uncertainty from arithmetic means

Source: R/fct_arithmetic_mean3.R
fct_arithmetic_mean3.Rd

Deterministic counterpart of the Monte Carlo chain (fct_combine_mcs_E(), fct_combine_mcs_P(), fct_combine_mcs_ER()), following the same steps with means instead of simulations, and propagating uncertainty with the IPCC Approach 1 rules (IPCC 2006, Vol. 1, Ch. 3, Eq. 3.1 and 3.2):

  • product: relative standard errors combine in quadrature, \(U_{a b} = \sqrt{U_a^2 + U_b^2}\);

  • sum / difference: absolute standard errors combine in quadrature, \(se_{a + b} = \sqrt{se_a^2 + se_b^2}\).

Calculation chain:

  1. Carbon stock of each land use from fct_make_formula2(). Composite factors are resolved first: (1 + RS) and (AGB + BGB). Then product rule within each term, sum rule across terms.

  2. Degraded land uses: DG_ratio * C_intact (product rule), plus pools excluded from degradation when dg_pool is not "ALL".

  3. Emission factors: EF = (C_i - C_f) * 44/12 (sum rule). For intact to degraded transitions EF = (1 - DG_ratio) * C_intact * 44/12 (product rule), so the intact stock is not counted twice.

  4. Emissions: E = AD * EF (product rule), annualised as in fct_combine_mcs_E().

  5. Emissions per time period and REDD+ activity (sum rule).

  6. Reference and monitoring levels: length-weighted mean of the annual emissions of their time periods (sum rule).

  7. Emission reductions: ER = FREL - E_MON (sum rule).

Approach 1 assumes all combined terms are independent. Correlations from shared inputs (the carbon fraction, a carbon stock used by several transitions, the same emission factors in the reference and monitoring periods) are ignored; the Monte Carlo simulations (Approach 2) account for them.

Usage

fct_arithmetic_mean3(.checked_data)

Arguments

.checked_data

The list returned by fct_checkinput().

Value

A list with: ER (reference / monitoring levels and emission reductions with mean, se, U\ gg_emissions (figure), emissions_table (tidy table for the app), c_stock (carbon stock per land use) and trans (EF and E per transition). U\ setup$conf_level.

Examples

path <- system.file("extdata/mocaredd-templatev2-simple.xlsx", package = "mocaredd")

checked <- fct_checkinput(.path = path)
#> Loading data... - progress: 0%.
#> ✓ Tables loaded successfully from template v2
#> Checking column names... - progress: 14%.
#> ✓ Column names: all required columns present
#> Checking table dimensions... - progress: 29%.
#> ✓ Table sizes: all tables have sufficient rows
#> Checking column data types... - progress: 43%.
#> ✓ Data types: all columns have correct data types
#> Checking category values... - progress: 57%.
#> ✓ Category values: all categories are valid
#> Checking unique IDs... - progress: 71%.
#> ✓ Unique IDs: no duplicates or missing IDs found
#> Checking cross-table and intra_table consistency... - progress: 86%.
#> ✓ Cross-table consistency: all references match
#> -- All checks passed.

res <- fct_arithmetic_mean3(.checked_data = checked)
res$c_stock
#> # A tibble: 5 × 6
#>   c_lu_id            c_form              c_form_U                  C  C_se   C_U
#>   <chr>              <chr>               <chr>                 <dbl> <dbl> <dbl>
#> 1 evergreen          AGB * (1 + RS) * CF AGB * (1 + RS) * CF … 172.   45.4  43.3
#> 2 mountain           AGB * (1 + RS) * CF AGB * (1 + RS) * CF … 129.   32.1  40.8
#> 3 cropland           ALL                 ALL_se                  0     0    NA  
#> 4 evergreen_degraded DG_ratio * C_intact DG_ratio * C_intact … 124.   43.0  57.0
#> 5 mountain_degraded  DG_ratio * C_intact DG_ratio * C_intact …  93.1  35.4  62.4
res$ER
#> # A tibble: 3 × 6
#>   period_type         E     E_se   E_U  E_lower   E_upper
#>   <chr>           <dbl>    <dbl> <dbl>    <dbl>     <dbl>
#> 1 REF         21874156. 7680506.  57.8 9240847. 34507464.
#> 2 E-MON1       7925065. 2690695.  55.8 3499266. 12350864.
#> 3 ER-MON1     13949091. 8138183.  96.0  562972. 27335210.