Primary energy and exergy aggregates — primary

Calculates aggregate primary energy from a data frame of Supply-Use matrices.

Usage

primary_aggregates(
  .sutdata = NULL,
  p_industries,
  add_net_gross_cols = FALSE,
  piece = "all",
  notation = RCLabels::notations_list,
  pattern_type = c("exact", "leading", "trailing", "anywhere", "literal"),
  prepositions = RCLabels::prepositions_list,
  R = Recca::psut_cols$R,
  V = Recca::psut_cols$V,
  Y = Recca::psut_cols$Y,
  by = c("Total", "Product", "Industry", "Flow"),
  aggregate_primary = Recca::aggregate_cols$aggregate_primary,
  net_aggregate_primary = Recca::aggregate_cols$net_aggregate_primary,
  gross_aggregate_primary = Recca::aggregate_cols$gross_aggregate_primary
)

Arguments

.sutdata

A data frame with columns of matrices from a supply-use analysis.

p_industries

A vector of names of industries to be aggregated as "primary." If .sutdata is a data frame, p_industries should be the name of a column in the data frame. If .sutdata is NULL, p_industries can be a single vector of industry names. These industries in p_industries will appear in rows of the resource (R) and make (V) matrices and columns of the final demand matrix (Y). Entries in Y_p will be subtracted from entries in R_p + V_p to obtain the total primary energy aggregate, where *_p is the primary part of those matrices. The function find_p_industry_names() might be helpful to find primary industry names if they can be identified by prefixes.

add_net_gross_cols

A boolean that tells whether to add net and gross columns (TRUE) or not (FALSE). Default is FALSE.

piece, notation, pattern_type, prepositions

Arguments that control the way row and column matching is accomplished when selecting parts of the R, V, and Y matrices for primary aggregation. These arguments are passed to matsbyname::select_rowcol_piece_byname() and eventually RCLabels::match_by_pattern() and RCLabels::make_or_pattern(). Default values are piece = "all", notation = RCLabels::notations_list, pattern_type = "exact", and prepositions = RCLabels::prepositions_list.

R, V, Y

See Recca::psut_cols.

by

One of "Total", "Product", "Industry", or "Flow" to indicate the desired aggregation:

"Total": aggregation over both Product and Flow (the default),
"Product": aggregation by energy carrier (Crude oil, Primary solid biofuels, etc.), or
"Industry" or "Flow": aggregation by Industry (Production, Imports, Exports, etc.).

aggregate_primary, net_aggregate_primary, gross_aggregate_primary

The names for aggregates of primary energy on output.

Value

A list or data frame containing aggregate primary energy.

Details

By default, this function adds a single column of primary energy aggregates with the name aggregate_primary. If add_net_gross_cols is TRUE (default is FALSE), two columns are created: net_aggregate_primary and gross_aggregate_primary. With net and gross output (add_net_gross_cols = TRUE), the columns contain identical values. Use add_net_gross_cols = TRUE if you later wish to combine with results from finaldemand_aggregates(), which provides both net and gross outputs.

Examples

library(matsbyname)
p_industries <- c("Resources - Crude", "Resources - NG")
# Calculate primary total aggregates
res <- UKEnergy2000mats %>%
  tidyr::pivot_wider(names_from = "matrix.name", values_from = "matrix") %>%
  Recca::primary_aggregates(p_industries = p_industries, by = "Total")
tibble::as_tibble(res)
#> # A tibble: 4 × 13
#>   Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>   <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#> 1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR      2000 X           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 5 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   EX.p <dbl>
res[[Recca::aggregate_cols$aggregate_primary]]
#> [1] 0 0 0 0
# Above, only 1 aggregate column is created, because there is no
# difference between net and gross aggregation for primary energy.
# Below, both net and gross aggregation columns are created,
# for compatibility with the [finaldemand_aggregates()] function.
# Net and gross primary aggregates are identical.
res2 <- UKEnergy2000mats %>%
  tidyr::pivot_wider(names_from = "matrix.name", values_from = "matrix") %>%
  Recca::primary_aggregates(p_industries = p_industries,
                            add_net_gross_cols = TRUE,
                            by = "Total")
tibble::as_tibble(res2)
#> # A tibble: 4 × 14
#>   Country  Year Energy.type Last.stage R             U        U_EIOU   U_feed  
#>   <chr>   <dbl> <chr>       <chr>      <list>        <list>   <list>   <list>  
#> 1 GBR      2000 E           Final      <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 2 GBR      2000 E           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 3 GBR      2000 E           Useful     <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> 4 GBR      2000 X           Services   <dbl [2 × 2]> <dbl[…]> <dbl[…]> <dbl[…]>
#> # ℹ 6 more variables: V <list>, Y <list>, r_EIOU <list>, S_units <list>,
#> #   EX.p_net <dbl>, EX.p_gross <dbl>
res2[[Recca::aggregate_cols$net_aggregate_primary]]
#> [1] 0 0 0 0
res2[[Recca::aggregate_cols$gross_aggregate_primary]]
#> [1] 0 0 0 0