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Profile emissions and upstream emissions

Source: R/profile.R
profile_emissions.Rd

These functions wrap the output of the corresponding function in tiltIndicator.

Usage

profile_emissions(
  companies,
  co2,
  europages_companies,
  ecoinvent_activities,
  ecoinvent_europages,
  isic,
  isic_tilt = lifecycle::deprecated(),
  low_threshold = 1/3,
  high_threshold = 2/3
)

Arguments

companies, co2

A dataframe like the dataset with a matching name in tiltToyData (see Reference).

europages_companies

Dataframe. Companies from europages.

ecoinvent_activities

Dataframe. Activities from ecoinvent.

ecoinvent_europages

Dataframe. Mapper between europages and ecoinvent.

isic

Dataframe. ISIC data.

isic_tilt

[Deprecated]

low_threshold

A numeric value to segment low and medium emission profile products.

high_threshold

A numeric value to segment medium and high emission profile products.

Value

A data frame with the column companies_id, and the list columns product and company holding the outputs at product and company level.

The columns co2e_lower and co2e_upper show the lowest and highest value of co2_footprint within the group to which the product was compared, plus some randomness. Therefore, every benchmark can have different co2e_lower and co2e_upper, because every benchmark can contain a different set of products.

See also

Other top-level functions: profile_sector(), score_transition_risk()

Other profile functions: profile_emissions_impl(), profile_sector()

Examples

library(tiltToyData)
library(withr)
library(readr, warn.conflicts = FALSE)

local_seed(1)
restore <- options(readr.show_col_types = FALSE)

companies <- read_csv(toy_emissions_profile_any_companies())
products <- read_csv(toy_emissions_profile_products_ecoinvent())
europages_companies <- read_csv(toy_europages_companies())
ecoinvent_activities <- read_csv(toy_ecoinvent_activities())
ecoinvent_europages <- read_csv(toy_ecoinvent_europages())
isic_name <- read_csv(toy_isic_name())

result <- profile_emissions(
  companies,
  products,
  europages_companies = europages_companies,
  ecoinvent_activities = ecoinvent_activities,
  ecoinvent_europages = ecoinvent_europages,
  isic = isic_name
)
#> Warning: Splitting `companies` into 4 chunks.

result |> unnest_product()
#> # A tibble: 456 × 32
#>    companies_id       company_name country emission_profile benchmark ep_product
#>    <chr>              <chr>        <chr>   <chr>            <chr>     <chr>     
#>  1 asteria_megalotom… asteria_meg… austria high             all       tent      
#>  2 asteria_megalotom… asteria_meg… austria high             isic_4di… tent      
#>  3 asteria_megalotom… asteria_meg… austria high             tilt_sub… tent      
#>  4 asteria_megalotom… asteria_meg… austria high             unit      tent      
#>  5 asteria_megalotom… asteria_meg… austria high             unit_isi… tent      
#>  6 asteria_megalotom… asteria_meg… austria high             unit_til… tent      
#>  7 skarn_gallinule    skarn_galli… austria high             all       sheds, co…
#>  8 skarn_gallinule    skarn_galli… austria high             isic_4di… sheds, co…
#>  9 skarn_gallinule    skarn_galli… austria high             tilt_sub… sheds, co…
#> 10 skarn_gallinule    skarn_galli… austria high             unit      sheds, co…
#> # ℹ 446 more rows
#> # ℹ 26 more variables: matched_activity_name <chr>,
#> #   matched_reference_product <chr>, unit <chr>, multi_match <lgl>,
#> #   matching_certainty <chr>, matching_certainty_company_average <chr>,
#> #   tilt_sector <chr>, tilt_subsector <chr>, isic_4digit <chr>,
#> #   isic_4digit_name <chr>, company_city <chr>, postcode <chr>, address <chr>,
#> #   main_activity <chr>, activity_uuid_product_uuid <chr>, …

result |> unnest_company()
#> # A tibble: 1,728 × 15
#>    companies_id     company_name country emission_profile_share emission_profile
#>    <chr>            <chr>        <chr>                    <dbl> <chr>           
#>  1 asteria_megalot… asteria_meg… austria                      1 high            
#>  2 asteria_megalot… asteria_meg… austria                      0 medium          
#>  3 asteria_megalot… asteria_meg… austria                      0 low             
#>  4 asteria_megalot… asteria_meg… austria                      0 NA              
#>  5 asteria_megalot… asteria_meg… austria                      1 high            
#>  6 asteria_megalot… asteria_meg… austria                      0 medium          
#>  7 asteria_megalot… asteria_meg… austria                      0 low             
#>  8 asteria_megalot… asteria_meg… austria                      0 NA              
#>  9 asteria_megalot… asteria_meg… austria                      1 high            
#> 10 asteria_megalot… asteria_meg… austria                      0 medium          
#> # ℹ 1,718 more rows
#> # ℹ 10 more variables: benchmark <chr>,
#> #   matching_certainty_company_average <chr>, company_city <chr>,
#> #   postcode <chr>, address <chr>, main_activity <chr>,
#> #   profile_ranking_avg <dbl>, min_headcount <dbl>, max_headcount <dbl>,
#> #   co2_avg <dbl>



inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent())
ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())

result <- profile_emissions_upstream(
  companies,
  inputs,
  europages_companies = europages_companies,
  ecoinvent_activities = ecoinvent_activities,
  ecoinvent_inputs = ecoinvent_inputs,
  ecoinvent_europages = ecoinvent_europages,
  isic = isic_name
)
#> Warning: Splitting `companies` into 4 chunks.

result |> unnest_product()
#> # A tibble: 3,966 × 31
#>    companies_id company_name country emission_upstream_pr…¹ benchmark ep_product
#>    <chr>        <chr>        <chr>   <chr>                  <chr>     <chr>     
#>  1 asteria_meg… asteria_meg… austria high                   all       tent      
#>  2 asteria_meg… asteria_meg… austria high                   input_is… tent      
#>  3 asteria_meg… asteria_meg… austria high                   input_ti… tent      
#>  4 asteria_meg… asteria_meg… austria high                   input_un… tent      
#>  5 asteria_meg… asteria_meg… austria high                   input_un… tent      
#>  6 asteria_meg… asteria_meg… austria high                   input_un… tent      
#>  7 skarn_galli… skarn_galli… austria high                   all       sheds, co…
#>  8 skarn_galli… skarn_galli… austria high                   input_is… sheds, co…
#>  9 skarn_galli… skarn_galli… austria high                   input_ti… sheds, co…
#> 10 skarn_galli… skarn_galli… austria high                   input_un… sheds, co…
#> # ℹ 3,956 more rows
#> # ℹ abbreviated name: ¹​emission_upstream_profile
#> # ℹ 25 more variables: matched_activity_name <chr>,
#> #   matched_reference_product <chr>, unit <chr>, multi_match <lgl>,
#> #   matching_certainty <chr>, matching_certainty_company_average <chr>,
#> #   input_name <chr>, input_unit <chr>, input_tilt_sector <chr>,
#> #   input_tilt_subsector <chr>, input_isic_4digit <chr>, …

result |> unnest_company()
#> # A tibble: 1,728 × 13
#>    companies_id         company_name company_city country emission_upstream_pr…¹
#>    <chr>                <chr>        <chr>        <chr>                    <dbl>
#>  1 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      1
#>  2 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  3 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  4 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  5 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      1
#>  6 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  7 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  8 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#>  9 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      1
#> 10 asteria_megalotomus… asteria_meg… wilhelmsburg austria                      0
#> # ℹ 1,718 more rows
#> # ℹ abbreviated name: ¹​emission_upstream_profile_share
#> # ℹ 8 more variables: emission_upstream_profile <chr>, benchmark <chr>,
#> #   matching_certainty_company_average <chr>, postcode <chr>, address <chr>,
#> #   main_activity <chr>, profile_ranking_avg <dbl>, co2_avg <dbl>

# Cleanup
options(restore)