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Build the livestock greenhouse-gas emissions extension.

Source: R/livestock_ghg_extension.R
build_livestock_ghg_extension.Rd

Aggregate per-animal IPCC livestock emissions into a footprint extension keyed by (year, area_code, item_cbs_code), expressed in kilograms of carbon-dioxide equivalent (CO2e). This bridges the cohort-level emissions pipeline (calculate_livestock_emissions()) to the input-output grain used by build_io_model() and compute_footprint(), exactly like build_grassland_land_extension() does for land.

Live-animal head counts come from get_primary_production(), are bridged to IPCC species with prepare_livestock_emissions(), and the resulting enteric and manure emissions are converted to CO2e and summed back to the live-animal commodity sector (item_cbs_code, e.g. 961 for non-dairy cattle), which is itself a sector in build_io_model().

Two IPCC tiers are available, selected with tier:

  • 1 (default): Tier 1 regional emission factors (IPCC 2019). It needs only species, country and head counts, so it is complete for every country in get_primary_production(). It covers enteric and manure methane and manure N2O (direct and indirect, from default per-head nitrogen excretion rates).

  • 2: Tier 2 cohort energy balance (IPCC 2019). It derives enteric CH4 and manure N2O from a per-animal energy and nitrogen balance, for finer resolution, but requires cohort weight and diet inputs. Animals whose emissions cannot be resolved (missing diet or energy data) are dropped with a warning rather than entering the footprint as NA. Its per-head enteric and manure emissions now sit in the same range as the Tier 1 regional factors. Tier 1 remains the default because it is complete for every country in get_primary_production(), whereas Tier 2 needs cohort and diet inputs.

The CO2e conversion uses 100-year global warming potentials selected with gwp:

  • "ar6" (default): IPCC AR6 (2021) Table 7.15, biogenic CH4 = 27, N2O = 273.

  • "ar5": IPCC AR5 (2013), CH4 = 28, N2O = 265 (no climate-carbon feedback).

  • "ar4": IPCC AR4 (2007), CH4 = 25, N2O = 298.

Usage

build_livestock_ghg_extension(
  tier = 1,
  gwp = c("ar6", "ar5", "ar4"),
  data = list(),
  example = FALSE
)

Arguments

tier

IPCC tier, 1 (default) or 2.

gwp

100-year global warming potential standard, "ar6" (default), "ar5" or "ar4".

data

Optional named list of pre-loaded inputs to avoid remote reads: primary_prod (the get_primary_production() output). It falls back to its reader when absent.

example

If TRUE, return a small fixture instead of reading remote data. Defaults to FALSE.

Value

A tibble with columns year, area_code, item_cbs_code, impact_u (livestock emissions in kilograms CO2e) and method_ghg (the chosen tier and GWP standard, e.g. "IPCC_2019_Tier1_AR6"), plus the polity columns below.

Polity columns

Every area-keyed output carries the polity its area_code resolves to in that row's year:

  • polity_area_code: The numeric key rows are AGGREGATED on, for the matrix workflows. It is a bucket, not an identity: use reporting_polity_code to say which territory a row belongs to.

  • reporting_polity_code: The polity itself, e.g. ESP-1846-1914. It is year-aware, so the same area_code resolves to different polities in different years, which is the point of the crosswalk.

  • reporting_polity_name: Its name. It can differ from the area's own name where the area folds into an aggregate.

  • reporting_polity_has_geometry: Whether the polity has a polygon in the WHEP polity database, for callers that need to map or intersect it. FALSE is a documented gap upstream, not an error.

Rows whose area_code resolves to no polity keep the columns with NA rather than being dropped, so a gap is visible instead of silent.

Rows before the back-cast anchor year resolve to the polity live in that anchor year rather than to the polity live in the row's own year, because WHEP's pre-anchor series are back-cast onto the anchor-year territory. See add_polity_code() for the reasoning. Where that polity is not live in the row's own year – 41.5% of the pre-1961 (area, year) cells – add_polity_code() says so as mapping_status == "backcast_anchor", and polity_coverage_gaps() reports it as gap_kind == "backcast_anchor". These columns do not say so either way.

A row whose year no mapped period covers is resolved to the NEAREST period of the same area instead, so reporting_polity_code can name a polity that did not exist in that row's year – FAOSTAT bucket 206 "Sudan (former)" keeps reporting after SUD-1956-2011 ends, and its post-2011 rows carry that code. These columns do not say so: add_polity_code() reports such a row as mapping_status == "out_of_span", and that column is dropped here so that adding it does not change the schema of every area-keyed output at once. polity_coverage_gaps() reports the stand-in rows of a built table, and options(whep.polity_mapping_status = "flag") (or "status") carries the signal on the outputs themselves. Both are opt-in; the default is no extra column.

Examples

build_livestock_ghg_extension(example = TRUE)
#> # A tibble: 6 × 9
#>    year area_code polity_area_code reporting_polity_code reporting_polity_name
#>   <int>     <int>            <int> <chr>                 <chr>                
#> 1  1986        10               10 AUS-1901-2025         Australia            
#> 2  1986        10               10 AUS-1901-2025         Australia            
#> 3  1986        10               10 AUS-1901-2025         Australia            
#> 4  1986       100              100 IND-1949-2025         India                
#> 5  1987        10               10 AUS-1901-2025         Australia            
#> 6  1987       100              100 IND-1949-2025         India                
#> # ℹ 4 more variables: reporting_polity_has_geometry <lgl>, item_cbs_code <int>,
#> #   impact_u <dbl>, method_ghg <chr>