Calulate Transition Risk Score at product level and company level
See also
Other top-level functions:
profile_emissions()
,
profile_sector()
,
transition_risk_profile()
Examples
library(dplyr)
library(readr, warn.conflicts = FALSE)
library(tiltToyData)
restore <- options(readr.show_col_types = FALSE)
emissions_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())
emissions_profile_at_product_level <- profile_emissions(
companies = emissions_companies,
co2 = products,
europages_companies = europages_companies,
ecoinvent_activities = ecoinvent_activities,
ecoinvent_europages = ecoinvent_europages,
isic = isic_name
) |> unnest_product()
sector_companies <- read_csv(toy_sector_profile_companies())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
sector_profile_at_product_level <- profile_sector(
companies = sector_companies,
scenarios = scenarios,
europages_companies = europages_companies,
ecoinvent_activities = ecoinvent_activities,
ecoinvent_europages = ecoinvent_europages,
isic = isic_name
) |> unnest_product()
result <- score_transition_risk(emissions_profile_at_product_level, sector_profile_at_product_level)
result |> unnest_product()
#> # A tibble: 1,824 × 24
#> companies_id company_name country benchmark_tr_score transition_risk_score
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 asteria_megalo… asteria_meg… austria 1.5C RPS_2030_all 0.59
#> 2 asteria_megalo… asteria_meg… austria 1.5C RPS_2050_all 0.99
#> 3 asteria_megalo… asteria_meg… austria NZ 2050_2030_all 0.7
#> 4 asteria_megalo… asteria_meg… austria NZ 2050_2050_all 0.985
#> 5 asteria_megalo… asteria_meg… austria 1.5C RPS_2030_isi… 0.59
#> 6 asteria_megalo… asteria_meg… austria 1.5C RPS_2050_isi… 0.99
#> 7 asteria_megalo… asteria_meg… austria NZ 2050_2030_isic… 0.7
#> 8 asteria_megalo… asteria_meg… austria NZ 2050_2050_isic… 0.985
#> 9 asteria_megalo… asteria_meg… austria 1.5C RPS_2030_til… 0.59
#> 10 asteria_megalo… asteria_meg… austria 1.5C RPS_2050_til… 0.99
#> # ℹ 1,814 more rows
#> # ℹ 19 more variables: profile_ranking <dbl>, reduction_targets <dbl>,
#> # ep_product <chr>, activity_uuid_product_uuid <chr>,
#> # matched_activity_name <chr>, matched_reference_product <chr>, unit <chr>,
#> # multi_match <lgl>, matching_certainty <chr>,
#> # matching_certainty_company_average <chr>, company_city <chr>,
#> # postcode <chr>, address <chr>, main_activity <chr>, tilt_sector <chr>, …
result |> unnest_company()
#> # A tibble: 1,728 × 9
#> companies_id company_name country benchmark_tr_score_avg
#> <chr> <chr> <chr> <chr>
#> 1 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2030_all
#> 2 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2050_all
#> 3 asteria_megalotomusquinquespinos… asteria_meg… austria NZ 2050_2030_all
#> 4 asteria_megalotomusquinquespinos… asteria_meg… austria NZ 2050_2050_all
#> 5 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2030_isic_4d…
#> 6 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2050_isic_4d…
#> 7 asteria_megalotomusquinquespinos… asteria_meg… austria NZ 2050_2030_isic_4di…
#> 8 asteria_megalotomusquinquespinos… asteria_meg… austria NZ 2050_2050_isic_4di…
#> 9 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2030_tilt_su…
#> 10 asteria_megalotomusquinquespinos… asteria_meg… austria 1.5C RPS_2050_tilt_su…
#> # ℹ 1,718 more rows
#> # ℹ 5 more variables: transition_risk_score_avg <dbl>, company_city <chr>,
#> # postcode <chr>, address <chr>, main_activity <chr>
# Cleanup
options(restore)