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Profile sector and upstream sector

Source: R/profile.R
profile_sector.Rd

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

Usage

profile_sector(
  companies,
  scenarios,
  europages_companies,
  ecoinvent_activities,
  ecoinvent_europages,
  isic,
  isic_tilt = lifecycle::deprecated(),
  low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),
  high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3)
)

Arguments

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.

See also

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

Other profile functions: profile_emissions(), profile_emissions_impl()

Examples

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

restore <- options(readr.show_col_types = FALSE)

companies <- read_csv(toy_sector_profile_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
europages_companies <- read_csv(toy_europages_companies()) |> head(3)
ecoinvent_activities <- read_csv(toy_ecoinvent_activities()) |> head(3)
ecoinvent_europages <- read_csv(toy_ecoinvent_europages()) |> head(3)
isic_name <- read_csv(toy_isic_name()) |> head(3)

result <- profile_sector(
  companies,
  scenarios,
  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: 304 × 28
#>    companies_id   company_name country sector_profile reduction_targets scenario
#>    <chr>          <chr>        <chr>   <chr>                      <dbl> <chr>   
#>  1 antimonarchy_… NA           NA      medium                      0.18 1.5C RPS
#>  2 antimonarchy_… NA           NA      high                        0.98 1.5C RPS
#>  3 antimonarchy_… NA           NA      high                        0.4  NZ 2050 
#>  4 antimonarchy_… NA           NA      high                        0.97 NZ 2050 
#>  5 celestial_lov… NA           NA      medium                      0.18 1.5C RPS
#>  6 celestial_lov… NA           NA      high                        0.98 1.5C RPS
#>  7 celestial_lov… NA           NA      high                        0.4  NZ 2050 
#>  8 celestial_lov… NA           NA      high                        0.97 NZ 2050 
#>  9 nonphilosophi… NA           NA      low                         0.09 1.5C RPS
#> 10 nonphilosophi… NA           NA      high                        0.95 1.5C RPS
#> # ℹ 294 more rows
#> # ℹ 22 more variables: year <dbl>, ep_product <chr>,
#> #   matched_activity_name <chr>, matched_reference_product <chr>, unit <chr>,
#> #   tilt_sector <chr>, tilt_subsector <chr>, multi_match <lgl>,
#> #   matching_certainty <chr>, matching_certainty_company_average <chr>,
#> #   company_city <chr>, postcode <chr>, address <chr>, main_activity <chr>,
#> #   activity_uuid_product_uuid <chr>, isic_4digit <chr>, …

result |> unnest_company()
#> # A tibble: 1,152 × 13
#>    companies_id        company_name country sector_profile_share sector_profile
#>    <chr>               <chr>        <chr>                  <dbl> <chr>         
#>  1 antimonarchy_canine NA           NA                         0 high          
#>  2 antimonarchy_canine NA           NA                         1 medium        
#>  3 antimonarchy_canine NA           NA                         0 low           
#>  4 antimonarchy_canine NA           NA                         0 NA            
#>  5 antimonarchy_canine NA           NA                         1 high          
#>  6 antimonarchy_canine NA           NA                         0 medium        
#>  7 antimonarchy_canine NA           NA                         0 low           
#>  8 antimonarchy_canine NA           NA                         0 NA            
#>  9 antimonarchy_canine NA           NA                         1 high          
#> 10 antimonarchy_canine NA           NA                         0 medium        
#> # ℹ 1,142 more rows
#> # ℹ 8 more variables: scenario <chr>, year <dbl>,
#> #   matching_certainty_company_average <chr>, company_city <chr>,
#> #   postcode <chr>, address <chr>, main_activity <chr>,
#> #   reduction_targets_avg <dbl>



companies <- read_csv(toy_sector_profile_upstream_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
inputs <- read_csv(toy_sector_profile_upstream_products())
europages_companies <- read_csv(toy_europages_companies()) |> head(3)
ecoinvent_activities <- read_csv(toy_ecoinvent_activities()) |> head(3)
ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs()) |> head(3)
ecoinvent_europages <- read_csv(toy_ecoinvent_europages()) |> head(3)
isic_name <- read_csv(toy_isic_name()) |> head(3)

result <- profile_sector_upstream(
  companies,
  scenarios,
  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: 436 × 32
#>    companies_id    company_name country sector_profile_upstr…¹ reduction_targets
#>    <chr>           <chr>        <chr>   <chr>                              <dbl>
#>  1 leathery_acorn… leathery_ac… germany medium                              0.18
#>  2 leathery_acorn… leathery_ac… germany high                                0.98
#>  3 leathery_acorn… leathery_ac… germany high                                0.4 
#>  4 leathery_acorn… leathery_ac… germany high                                0.97
#>  5 warriorlike_gr… warriorlike… germany medium                              0.18
#>  6 warriorlike_gr… warriorlike… germany high                                0.98
#>  7 warriorlike_gr… warriorlike… germany high                                0.4 
#>  8 warriorlike_gr… warriorlike… germany high                                0.97
#>  9 antimonarchy_c… NA           NA      medium                              0.18
#> 10 antimonarchy_c… NA           NA      high                                0.98
#> # ℹ 426 more rows
#> # ℹ abbreviated name: ¹​sector_profile_upstream
#> # ℹ 27 more variables: scenario <chr>, year <dbl>, ep_product <chr>,
#> #   matched_activity_name <chr>, matched_reference_product <chr>, unit <chr>,
#> #   tilt_sector <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>, …

result |> unnest_company()
#> # A tibble: 1,152 × 13
#>    companies_id             company_name          country sector_profile_upstr…¹
#>    <chr>                    <chr>                 <chr>                    <dbl>
#>  1 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  2 leathery_acornwoodpecker leathery_acornwoodpe… germany                      1
#>  3 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  4 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  5 leathery_acornwoodpecker leathery_acornwoodpe… germany                      1
#>  6 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  7 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  8 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#>  9 leathery_acornwoodpecker leathery_acornwoodpe… germany                      1
#> 10 leathery_acornwoodpecker leathery_acornwoodpe… germany                      0
#> # ℹ 1,142 more rows
#> # ℹ abbreviated name: ¹​sector_profile_upstream_share
#> # ℹ 9 more variables: sector_profile_upstream <chr>, scenario <chr>,
#> #   year <dbl>, matching_certainty_company_average <chr>, company_city <chr>,
#> #   postcode <chr>, address <chr>, main_activity <chr>,
#> #   reduction_targets_avg <dbl>

# Cleanup
options(restore)