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Extracts industry level EROIs from tidy Input Output matrices

extract_tidy_industry_erois.Rd

The function extracts industry level EROIs from tidy Input Output matrices, in a tidy format.

Usage

extract_tidy_industry_erois(
  .tidy_io_mats,
  country = IEATools::iea_cols$country,
  method = IEATools::iea_cols$method,
  energy_type = IEATools::iea_cols$energy_type,
  last_stage = IEATools::iea_cols$last_stage,
  year = IEATools::iea_cols$year,
  matnames = IEATools::mat_meta_cols$matnames,
  matvals = IEATools::mat_meta_cols$matvals,
  eroi_g_i = "eroi_g_i",
  eroi_n_i = "eroi_n_i",
  eroi_g_i_feed = "eroi_g_i_feed",
  eroi_n_i_feed = "eroi_n_i_feed",
  type = "Type",
  boundary = "Boundary",
  eroi = "EROI",
  industry = "Industry_name",
  colnames = "colnames",
  rowtypes = "rowtypes",
  coltypes = "coltypes"
)

Arguments

.tidy_io_mats

The .tidy_io_mats data frame from which EROIs should be extracted.

country, method, energy_type, last_stage, year

See IEATools::iea_cols.

matnames

The column name of the column having matrices names. Default is IEATools::mat_meta_cols$matnames.

matvals

The column name of the column reporting matrices values, once expanded. Default is IEATools::mat_meta_cols$matvals.

eroi_g_i

The name of the column containing vectors of industry-level gross EROIs, including both energy use for feedstock and EIOU production. Default is "eroi_g_i".

eroi_n_i

The name of the column containing vectors of industry-level net EROIs, including both energy use for feedstock and EIOU production. Default is "eroi_n_i".

eroi_g_i_feed

The name of the column containing vectors of industry-level gross EROIs, including only energy use for feedstock production. Default is "eroi_g_i_feed".

eroi_n_i_feed

The name of the column containing vectors of industry-level net EROIs, including only energy use for feedstock production. Default is "eroi_g_i_feed".

type

The name of the EROI type column (i.e. gross or net EROI). Default is "Type".

boundary

The name of the boundary column. Default is "Boundary".

eroi

The name of the product EROI column in output. Default is "EROI".

industry

The name of the industry column returned in the output data frame. Default is "Industry_name".

colnames

The name of columns when expanding matrices. Default is "colnames".

rowtypes

The name of row types when expanding matrices. Default is "rowtypes".

coltypes

The name of column types when expanding matrices. Default is "coltypes".

Value

A data frame reporting calculated EROIs in a tidy format.

Details

The function can be called after calculating EROIs through the Recca::calc_erois() function. The type column indicates whether the calculated EROI is gross or net. The boundary column indicates whether the EROI includes:

  • Only energy use for feedstock production ("Feedstock");

  • Both energy use for feedstock production and for EIOU production ("All").

Examples

# Let's first have a look at the raw data obtained when calculating EROIs:
calculated_erois_raw <- ECCTools::tidy_AB_data %>%
 IEATools::prep_psut() %>%
 Recca::calc_io_mats() %>%
 Recca::calc_E_EIOU() %>%
 Recca::calc_erois() %>%
 dplyr::glimpse()
#> Rows: 2
#> Columns: 39
#> $ Country       <chr> "A", "B"
#> $ Method        <chr> "PCM", "PCM"
#> $ Energy.type   <chr> "E", "E"
#> $ Last.stage    <chr> "Final", "Final"
#> $ Year          <dbl> 2018, 2018
#> $ Y             <list> <<matrix[9 x 6]>>, <<matrix[7 x 4]>>
#> $ S_units       <list> <<matrix[12 x 1]>>, <<matrix[9 x 1]>>
#> $ R             <list> <<matrix[4 x 4]>>, <<matrix[3 x 3]>>
#> $ U             <list> <<matrix[11 x 7]>>, <<matrix[8 x 4]>>
#> $ U_feed        <list> <<matrix[9 x 7]>>, <<matrix[5 x 4]>>
#> $ U_EIOU        <list> <<matrix[5 x 7]>>, <<matrix[5 x 4]>>
#> $ r_EIOU        <list> <<matrix[11 x 7]>>, <<matrix[8 x 4]>>
#> $ V             <list> <<matrix[7 x 9]>>, <<matrix[4 x 6]>>
#> $ y             <list> <<matrix[9 x 1]>>, <<matrix[7 x 1]>>
#> $ q             <list> <<matrix[12 x 1]>>, <<matrix[9 x 1]>>
#> $ f             <list> <<matrix[7 x 1]>>, <<matrix[4 x 1]>>
#> $ g             <list> <<matrix[7 x 1]>>, <<matrix[4 x 1]>>
#> $ h             <list> <<matrix[4 x 1]>>, <<matrix[3 x 1]>>
#> $ r             <list> <<matrix[4 x 1]>>, <<matrix[3 x 1]>>
#> $ W             <list> <<matrix[12 x 7]>>, <<matrix[9 x 4]>>
#> $ Z             <list> <<matrix[11 x 7]>>, <<matrix[8 x 4]>>
#> $ K             <list> <<matrix[11 x 7]>>, <<matrix[8 x 4]>>
#> $ C             <list> <<matrix[9 x 7]>>, <<matrix[6 x 4]>>
#> $ D             <list> <<matrix[7 x 12]>>, <<matrix[4 x 9]>>
#> $ A             <list> <<matrix[11 x 12]>>, <<matrix[8 x 9]>>
#> $ O             <list> <<matrix[4 x 4]>>, <<matrix[3 x 3]>>
#> $ L_pxp         <list> <<matrix[12 x 12]>>, <<matrix[9 x 9]>>
#> $ L_ixp         <list> <<matrix[7 x 12]>>, <<matrix[4 x 9]>>
#> $ Z_feed        <list> <<matrix[9 x 7]>>, <<matrix[5 x 4]>>
#> $ K_feed        <list> <<matrix[9 x 7]>>, <<matrix[5 x 4]>>
#> $ A_feed        <list> <<matrix[9 x 12]>>, <<matrix[5 x 9]>>
#> $ L_pxp_feed    <list> <<matrix[12 x 12]>>, <<matrix[9 x 9]>>
#> $ L_ixp_feed    <list> <<matrix[7 x 12]>>, <<matrix[4 x 9]>>
#> $ E_EIOU        <list> <<matrix[5 x 7]>>, <<matrix[5 x 4]>>
#> $ e_EIOU        <list> <<matrix[7 x 1]>>, <<matrix[4 x 1]>>
#> $ eroi_g_p      <list> <<matrix[12 x 1]>>, <<matrix[9 x 1]>>
#> $ eroi_g_i      <list> <<matrix[7 x 1]>>, <<matrix[4 x 1]>>
#> $ eroi_g_p_feed <list> <<matrix[12 x 1]>>, <<matrix[9 x 1]>>
#> $ eroi_g_i_feed <list> <<matrix[7 x 1]>>, <<matrix[4 x 1]>>
# Let's then extract EROIs in a tidy format:
calculated_erois_raw %>%
 extract_tidy_industry_erois() %>%
 print()
#> # A tibble: 22 × 9
#>    Country Method Energy.type Last.stage  Year Type  Boundary  Industry_name    
#>    <chr>   <chr>  <chr>       <chr>      <dbl> <chr> <chr>     <chr>            
#>  1 A       PCM    E           Final       2018 Gross All       Blast furnaces   
#>  2 A       PCM    E           Final       2018 Gross All       Coal mines       
#>  3 A       PCM    E           Final       2018 Gross All       Coke ovens       
#>  4 A       PCM    E           Final       2018 Gross All       Main activity pr…
#>  5 A       PCM    E           Final       2018 Gross All       Natural gas extr…
#>  6 A       PCM    E           Final       2018 Gross All       Oil extraction   
#>  7 A       PCM    E           Final       2018 Gross All       Oil refineries   
#>  8 A       PCM    E           Final       2018 Gross Feedstock Blast furnaces   
#>  9 A       PCM    E           Final       2018 Gross Feedstock Coal mines       
#> 10 A       PCM    E           Final       2018 Gross Feedstock Coke ovens       
#> # ℹ 12 more rows
#> # ℹ 1 more variable: EROI <dbl>