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Compute a footprint end-to-end from an extension table.

Source: R/build_footprint.R
build_footprint.Rd

Trace a long-format environmental extension table through the supply chain for one or more years and return a tidy footprint. This wraps the three steps that the footprint driver scripts used to repeat inline: build (or reuse) the input-output model with build_io_model(), align the extension to each year's sector labels with align_extension(), and trace it with compute_footprint().

The extension table is the output of any build_*_extension() builder, such as build_grassland_land_extension() or build_livestock_ghg_extension(): rows keyed by year, area_code and item_cbs_code, with the pressure magnitude in value_col.

Usage

build_footprint(
  extension,
  years = NULL,
  io = NULL,
  method = c("mass", "value"),
  value_col = "impact_u",
  ...
)

Arguments

extension

Long-format extension tibble with columns year, area_code, item_cbs_code and the column named by value_col.

years

Years to compute. Defaults to the distinct years present in extension. Ignored when io is supplied.

io

Optional pre-built build_io_model() result (a tibble with one row per year). Supply it to reuse one model across several extensions instead of rebuilding it. When NULL (default), it is built for years.

method

Co-product allocation method passed to build_io_model(), "mass" (default) or "value". Ignored when io is supplied (the model already encodes its allocation).

value_col

Name of the extension magnitude column, "impact_u" by default.

...

Further arguments passed to compute_footprint() (e.g. conserve_extensions, report_conservation).

Value

A tibble of footprint flows as returned by compute_footprint(), with an added year column.

Examples

io <- tibble::tibble(
  year = 2000L,
  Z = list(matrix(c(0, 5, 10, 0), nrow = 2)),
  X = list(c(100, 200)),
  Y = list(matrix(c(85, 195), ncol = 1)),
  labels = list(tibble::tibble(
    index = 1:2,
    area_code = c(1L, 1L),
    item_cbs_code = c(1L, 2L)
  )),
  fd_labels = list(tibble::tibble(area_code = 1L, fd_col = "food"))
)
extension <- tibble::tibble(
  year = 2000L,
  area_code = 1L,
  item_cbs_code = c(1L, 2L),
  impact_u = c(50, 30)
)
build_footprint(extension, io = io)
#> ℹ Computing footprint for 2 sectors.
#>   2 sectors have non-zero extensions.
#>   Final demand: 1 column.
#> Sparse solve path (no dense Leontief inverse).
#> Computing footprints...
#> ✔ Footprint complete: 4 non-zero flows.
#> # A tibble: 4 × 13
#>   origin_area origin_polity_code origin_polity_name origin_polity_has_geometry
#>         <int> <chr>              <chr>              <lgl>                     
#> 1           1 ARM-1991-2025      Armenia            TRUE                      
#> 2           1 ARM-1991-2025      Armenia            TRUE                      
#> 3           1 ARM-1991-2025      Armenia            TRUE                      
#> 4           1 ARM-1991-2025      Armenia            TRUE                      
#> # ℹ 9 more variables: origin_item <int>, target_area <int>,
#> #   target_polity_code <chr>, target_polity_name <chr>,
#> #   target_polity_has_geometry <lgl>, target_item <int>, target_fd <chr>,
#> #   value <dbl>, year <int>