Calculate emissions and IPCC Tier 1 uncertainty from arithmetic means

Analytical counterpart of fct_arithmetic_mean(). Instead of Monte Carlo simulations, activity data and emission factors are aggregated deterministically and their uncertainty is propagated with the IPCC Tier 1 (first-order error propagation) rules: relative uncertainties combine in quadrature for products and absolute standard errors combine in quadrature for sums / differences. The propagation is done numerically over the independent input variables (carbon pool values, carbon fraction, degradation ratios and activity data), so correlations introduced by shared inputs (e.g. a degraded land use that reuses the intact carbon stock, or a global carbon fraction) are handled consistently.

The result reports, for each reference / monitoring emission level and for each emission reduction, the mean, its standard error, and the half-width uncertainty $U\% = z \cdot se / |mean| \cdot 100$, where $z$ is the two-sided normal quantile for setup$conf_level.

Usage

fct_arithmetic_mean2(.checked_data)

Arguments

.checked_data: The list returned by fct_checkinput(). Its template_version element drives version-specific behaviour and its data element supplies the setup, time, area and carbon tables.

Value

A list with elements ER (reference / monitoring levels and emission reductions with mean, se, U\ (per time period, with uncertainty), gg_emissions (figure with emissions as lines with confidence interval areas, and emission reductions as points with error bars) and emissions_table (a tidy tibble ready to be turned into a gt table by the app).

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_mean2(.checked_data = checked)
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. 7658975.  57.6 9276262. 34472049.
#> 2 E-MON1       7925065. 2654361.  55.1 3559029. 12291100.
#> 3 ER-MON1     13949091. 5271751.  62.2 5277832. 22620350.