This article shows how to calculate the descriptive analysis of
products for emission and sector profile.
Distinct europages products
|
companies_id
|
company_name
|
country
|
company_city
|
postcode
|
address
|
main_activity
|
clustered
|
min_headcount
|
max_headcount
|
|
warriorlike_graysquirrel
|
warriorlike_graysquirrel
|
germany
|
grünwald
|
82031
|
bavariafilmplatz 7 | 82031 grünwald
|
wholesaler
|
exhibitions, stand fittings
|
1
|
10
|
|
leathery_acornwoodpecker
|
leathery_acornwoodpecker
|
germany
|
düsseldorf
|
40468
|
ulmenstrasse 275 | 40468 düsseldorf
|
wholesaler
|
exhibitions, stand fittings
|
1
|
10
|
|
automotive_alaskanmalamute
|
automotive_alaskanmalamute
|
germany
|
trier
|
54292
|
rheinstrasse 60 | 54292 trier
|
wholesaler
|
exhibitions, stand fittings
|
1
|
10
|
|
weatherproof_roadrunner
|
weatherproof_roadrunner
|
germany
|
münchen
|
80337
|
tumblingerstrasse 32 | 80337 münchen
|
wholesaler
|
exhibitions, stand fittings
|
1
|
10
|
|
angular_oregonsilverspotbutterfly
|
angular_oregonsilverspotbutterfly
|
germany
|
veitsbronn
|
90587
|
stockäckerstrasse 9 | 90587 veitsbronn
|
distributor
|
exhibitions, stand fittings
|
1
|
10
|
|
bereft_anchovy
|
bereft_anchovy
|
germany
|
langenselbold
|
63505
|
am felsenkeller 7 | 63505 langenselbold
|
distributor
|
exhibitions, stand fittings
|
1
|
10
|
|
asteria_megalotomusquinquespinosus
|
asteria_megalotomusquinquespinosus
|
austria
|
wilhelmsburg
|
3150
|
fleschplatz 2, top 5 | 3150 wilhelmsburg
|
wholesaler
|
tent
|
1
|
10
|
|
reversible_affenpinscher
|
reversible_affenpinscher
|
germany
|
quickborn
|
25451
|
friedrichsgaber strasse 21 | 25451 quickborn
|
wholesaler
|
tent
|
1
|
10
|
|
exportable_widgeon
|
exportable_widgeon
|
germany
|
vlotho
|
32602
|
lange strasse 100 | 32602 vlotho
|
wholesaler
|
tent
|
1
|
10
|
|
comely_manta
|
comely_manta
|
germany
|
henstedt-ulzburg
|
24558
|
hohenbergen 64 | 24558 henstedt-ulzburg
|
wholesaler
|
tent
|
1
|
10
|
Output
cat("Distinct europages products:", n_distinct(example_europages_companies$clustered))
Distinct europages products: 2
Distinct europages products that matched to ecoinvent
| comp_1 |
a |
all |
high |
| comp_1 |
b |
all |
high |
| comp_1 |
c |
all |
high |
| comp_1 |
d |
NA |
NA |
| comp_1 |
e |
NA |
NA |
| comp_2 |
a |
all |
high |
| comp_2 |
b |
all |
high |
| comp_2 |
c |
all |
high |
| comp_3 |
d |
NA |
NA |
| comp_3 |
e |
NA |
NA |
Output
cat("Distinct europages products:", n_distinct(matched_ecoinvent$product))
Distinct europages products: 3
Distinct europages products that have
sector_target
| comp_1 |
a |
0.12 |
high |
| comp_1 |
b |
0.96 |
high |
| comp_1 |
c |
0.64 |
high |
| comp_1 |
d |
NA |
NA |
| comp_1 |
e |
NA |
NA |
| comp_2 |
a |
0.12 |
high |
| comp_2 |
b |
0.96 |
high |
| comp_2 |
c |
0.64 |
high |
| comp_3 |
d |
NA |
NA |
| comp_3 |
e |
NA |
NA |
Output
cat("Distinct europages products:", n_distinct(products_sector_target$product))
Distinct europages products: 3
Average amount of distinct ep products per company for emission
profile if the company have atleast one matched ecoinvent product
avg_distinct_products_per_company_atleast_one_matched_ecoinvent <- function(data, col) {
companies_with_atleast_one_matched_product <- data |>
select(all_of(c("companies_id", "product", col))) |>
filter(!is.na(.data[[col]])) |>
distinct()
result <- data |>
select(all_of(c("companies_id", "product"))) |>
filter(companies_id %in% companies_with_atleast_one_matched_product$companies_id) |>
mutate(distinct_products_per_company = n_distinct(product, na.rm = TRUE), .by = "companies_id") |>
select(-all_of(c("product"))) |>
distinct()
sum(result$distinct_products_per_company) / n_distinct(result$companies_id)
}
cat("Average amount of distinct ep products per company for emission profile if the company have atleast one matched ecoinvent product:", avg_distinct_products_per_company_atleast_one_matched_ecoinvent(example_emission_profile, "emission_category"))
Average amount of distinct ep products per company for emission
profile if the company have atleast one matched ecoinvent product: 4
Average amount of ep distinct products per company for sector
profile if the company have atleast one product with sector target
cat("Average amount of distinct ep products per company for sector profile if the company have atleast one product with sector target:", avg_distinct_products_per_company_atleast_one_matched_ecoinvent(example_sector_profile, "sector_category"))
Average amount of distinct ep products per company for sector profile
if the company have atleast one product with sector target: 4
Average amount of distinct ep products per company for transition
risk profile if the company have atleast one product with transition
risk category
| comp_1 |
germany |
a |
1.5C RPS_2030_all |
high |
| comp_1 |
germany |
b |
1.5C RPS_2030_all |
high |
| comp_1 |
germany |
c |
1.5C RPS_2030_all |
high |
| comp_1 |
germany |
d |
NA |
NA |
| comp_1 |
germany |
e |
NA |
NA |
| comp_2 |
germany |
a |
1.5C RPS_2030_all |
high |
| comp_2 |
germany |
b |
1.5C RPS_2030_all |
high |
| comp_2 |
germany |
c |
1.5C RPS_2030_all |
high |
| comp_3 |
germany |
d |
NA |
NA |
| comp_3 |
germany |
e |
NA |
NA |
cat("Average amount of distinct ep products per company for sector profile if the company have atleast one product with transition risk category:", avg_distinct_products_per_company_atleast_one_matched_ecoinvent(example_transition_risk_profile, "transition_risk_category"))
Average amount of distinct ep products per company for sector profile
if the company have atleast one product with transition risk category:
4
Average amount of distinct ep products per company
cat("Average amount of distinct ep products per company: ", avg_distinct_products_per_company(example_transition_risk_profile))
Average amount of distinct ep products per company: 3.333333
Distinct ep products without a risk category for emission, sector,
and transition risk profile
cat("Distinct ep products without a risk category for emission profile:", distinct_products_without_risk_category(example_emission_profile, "emission_category"))
Distinct ep products without a risk category for emission profile:
2
cat("Distinct ep products without a risk category for sector profile:", distinct_products_without_risk_category(example_sector_profile, "sector_category"))
Distinct ep products without a risk category for sector profile:
2
cat("Distinct ep products without a risk category for transition risk profile:", distinct_products_without_risk_category(example_transition_risk_profile, "transition_risk_category"))
Distinct ep products without a risk category for transition risk
profile: 2
Average profile ranking of all ep products per
grouping_emission
| tent |
unit |
1 |
| tent |
unit_isic_4digit |
1 |
| tent |
unit_tilt_subsector |
1 |
| sheds, construction site |
unit |
1 |
| sheds, construction site |
unit_isic_4digit |
1 |
| sheds, construction site |
unit_tilt_subsector |
1 |
| open space amenities |
unit |
1 |
| open space amenities |
unit_isic_4digit |
1 |
| open space amenities |
unit_tilt_subsector |
1 |
| garden fittings |
unit |
1 |
Output
avg_profile_ranking_df <- profile_rank_df |>
mutate(sum_profile_ranking = sum(.data$emission_rank, na.rm = TRUE), .by = "grouping_emission") |>
mutate(distinct_products_per_benchmark = n_distinct(.data$product, na.rm = TRUE), .by = "grouping_emission") |>
mutate("emission rank average" = sum_profile_ranking/distinct_products_per_benchmark) |>
select(-all_of(c("sum_profile_ranking", "distinct_products_per_benchmark", "emission_rank", "product"))) |>
distinct()
Average profile ranking of all ep products per grouping_emission
|
grouping_emission
|
emission rank average
|
|
unit
|
0.9047619
|
|
unit_isic_4digit
|
1.0000000
|
|
unit_tilt_subsector
|
1.0000000
|
Average reduction targets of all ep products per
grouping_sector
| tent |
0.18 |
1.5C RPS_2030 |
| tent |
0.98 |
1.5C RPS_2050 |
| tent |
0.40 |
NZ 2050_2030 |
| tent |
0.97 |
NZ 2050_2050 |
| sheds, construction site |
0.18 |
1.5C RPS_2030 |
| sheds, construction site |
0.98 |
1.5C RPS_2050 |
| sheds, construction site |
0.40 |
NZ 2050_2030 |
| sheds, construction site |
0.97 |
NZ 2050_2050 |
| open space amenities |
0.18 |
1.5C RPS_2030 |
| open space amenities |
0.98 |
1.5C RPS_2050 |
Output
Average reduction targets of all ep products per grouping_sector
|
grouping_sector
|
sector target average
|
|
1.5C RPS_2030
|
0.1685714
|
|
1.5C RPS_2050
|
0.9714286
|
|
NZ 2050_2030
|
0.3414286
|
|
NZ 2050_2050
|
0.9638095
|
Average transition risk scores of all ep products per tilt_subsector
benchmarks
| tent |
1.5C RPS_2030_unit |
0.590 |
| tent |
1.5C RPS_2050_unit |
0.990 |
| tent |
NZ 2050_2030_unit |
0.700 |
| tent |
NZ 2050_2050_unit |
0.985 |
| tent |
1.5C RPS_2030_unit_isic_4digit |
0.590 |
| tent |
1.5C RPS_2050_unit_isic_4digit |
0.990 |
| tent |
NZ 2050_2030_unit_isic_4digit |
0.700 |
| tent |
NZ 2050_2050_unit_isic_4digit |
0.985 |
| tent |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| tent |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| tent |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| tent |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
| sheds, construction site |
1.5C RPS_2030_unit |
0.590 |
| sheds, construction site |
1.5C RPS_2050_unit |
0.990 |
| sheds, construction site |
NZ 2050_2030_unit |
0.700 |
| sheds, construction site |
NZ 2050_2050_unit |
0.985 |
| sheds, construction site |
1.5C RPS_2030_unit_isic_4digit |
0.590 |
| sheds, construction site |
1.5C RPS_2050_unit_isic_4digit |
0.990 |
| sheds, construction site |
NZ 2050_2030_unit_isic_4digit |
0.700 |
| sheds, construction site |
NZ 2050_2050_unit_isic_4digit |
0.985 |
Output
Average transition risk scores of all ep products per transition risk
groups
|
group
|
scenario
|
transition risk average
|
|
unit
|
1.5C RPS_2030
|
0.5366667
|
|
1.5C RPS_2050
|
0.9380952
|
|
NZ 2050_2030
|
0.6230952
|
|
NZ 2050_2050
|
0.9342857
|
|
unit_isic_4digit
|
1.5C RPS_2030
|
0.5842857
|
|
1.5C RPS_2050
|
0.9857143
|
|
NZ 2050_2030
|
0.6707143
|
|
NZ 2050_2050
|
0.9819048
|
|
unit_tilt_subsector
|
1.5C RPS_2030
|
0.5842857
|
|
1.5C RPS_2050
|
0.9857143
|
|
NZ 2050_2030
|
0.6707143
|
|
NZ 2050_2050
|
0.9819048
|
Average transition risk scores of all ep products per tilt_subsector
and country
| tent |
austria |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| tent |
austria |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| tent |
austria |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| tent |
austria |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
| sheds, construction site |
austria |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| sheds, construction site |
austria |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| sheds, construction site |
austria |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| sheds, construction site |
austria |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
| open space amenities |
france |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| open space amenities |
france |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| open space amenities |
france |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| open space amenities |
france |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
| garden fittings |
france |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| garden fittings |
france |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| garden fittings |
france |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| garden fittings |
france |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
| tent |
germany |
1.5C RPS_2030_unit_tilt_subsector |
0.590 |
| tent |
germany |
1.5C RPS_2050_unit_tilt_subsector |
0.990 |
| tent |
germany |
NZ 2050_2030_unit_tilt_subsector |
0.700 |
| tent |
germany |
NZ 2050_2050_unit_tilt_subsector |
0.985 |
Output
Average transition risk scores of all ep products per transition risk
group per country
|
group
|
country
|
scenario
|
transition risk average
|
|
unit_tilt_subsector
|
austria
|
1.5C RPS_2030
|
0.5900000
|
|
1.5C RPS_2050
|
0.9900000
|
|
NZ 2050_2030
|
0.7000000
|
|
NZ 2050_2050
|
0.9850000
|
|
france
|
1.5C RPS_2030
|
0.5900000
|
|
1.5C RPS_2050
|
0.9900000
|
|
NZ 2050_2030
|
0.7000000
|
|
NZ 2050_2050
|
0.9850000
|
|
germany
|
1.5C RPS_2030
|
0.5807692
|
|
1.5C RPS_2050
|
0.9830769
|
|
NZ 2050_2030
|
0.6526923
|
|
NZ 2050_2050
|
0.9800000
|
|
netherlands
|
1.5C RPS_2030
|
0.5900000
|
|
1.5C RPS_2050
|
0.9900000
|
|
NZ 2050_2030
|
0.7000000
|
|
NZ 2050_2050
|
0.9850000
|
|
spain
|
1.5C RPS_2030
|
0.5900000
|
|
1.5C RPS_2050
|
0.9900000
|
|
NZ 2050_2030
|
0.7000000
|
|
NZ 2050_2050
|
0.9850000
|
Average NA share per company
Companies with atleast one matched ecoinvent product for emission
profile
| asteria_megalotomusquinquespinosus |
tent |
high |
| skarn_gallinule |
sheds, construction site |
high |
| relegable_southernhairnosedwombat |
tent |
high |
| psychodelic_airedale |
tent |
high |
| ergophilic_fieldspaniel |
tent |
high |
| preexistent_africanmolesnake |
tent |
high |
| wealthy_ocelot |
tent |
high |
| fulltime_mollusk |
tent |
high |
| gentlemanlike_asiaticlesserfreshwaterclam |
tent |
high |
| frowsy_metalmarkbutterfly |
tent |
high |
Average NA share per company which have atleast one matched
ecoinvent product for emission profile
| asteria_megalotomusquinquespinosus |
0 |
| skarn_gallinule |
0 |
| relegable_southernhairnosedwombat |
0 |
| psychodelic_airedale |
0 |
| ergophilic_fieldspaniel |
0 |
| preexistent_africanmolesnake |
0 |
| wealthy_ocelot |
0 |
| fulltime_mollusk |
0 |
| gentlemanlike_asiaticlesserfreshwaterclam |
0 |
| frowsy_metalmarkbutterfly |
0 |
cat("Average NA share per company with atleast one matched ecoinvent product for emission profile:", average_na_emission_profile)
Average NA share per company with atleast one matched ecoinvent
product for emission profile: 0
Companies with atleast one product with sector category/sector
target
| asteria_megalotomusquinquespinosus |
tent |
high |
| skarn_gallinule |
sheds, construction site |
high |
| relegable_southernhairnosedwombat |
tent |
high |
| psychodelic_airedale |
tent |
high |
| ergophilic_fieldspaniel |
tent |
high |
| preexistent_africanmolesnake |
tent |
high |
| wealthy_ocelot |
tent |
high |
| fulltime_mollusk |
tent |
high |
| gentlemanlike_asiaticlesserfreshwaterclam |
tent |
high |
| frowsy_metalmarkbutterfly |
tent |
high |
Average NA share per company which have atleast one product with
sector category
| asteria_megalotomusquinquespinosus |
0 |
| skarn_gallinule |
0 |
| relegable_southernhairnosedwombat |
0 |
| psychodelic_airedale |
0 |
| ergophilic_fieldspaniel |
0 |
| preexistent_africanmolesnake |
0 |
| wealthy_ocelot |
0 |
| fulltime_mollusk |
0 |
| gentlemanlike_asiaticlesserfreshwaterclam |
0 |
| frowsy_metalmarkbutterfly |
0 |
cat("Average NA share per company with atleast one product with sector category:", average_na_sector_profile)
Average NA share per company with atleast one product with sector
category: 0
Companies with atleast one product with transition risk
category
| asteria_megalotomusquinquespinosus |
tent |
high |
| asteria_megalotomusquinquespinosus |
tent |
medium |
| skarn_gallinule |
sheds, construction site |
high |
| skarn_gallinule |
sheds, construction site |
medium |
| relegable_southernhairnosedwombat |
tent |
high |
| relegable_southernhairnosedwombat |
tent |
medium |
| psychodelic_airedale |
tent |
high |
| psychodelic_airedale |
tent |
medium |
| ergophilic_fieldspaniel |
tent |
high |
| ergophilic_fieldspaniel |
tent |
medium |
Average NA share per company which have atleast one product with
transition risk category
| asteria_megalotomusquinquespinosus |
0 |
| skarn_gallinule |
0 |
| relegable_southernhairnosedwombat |
0 |
| psychodelic_airedale |
0 |
| ergophilic_fieldspaniel |
0 |
| preexistent_africanmolesnake |
0 |
| wealthy_ocelot |
0 |
| fulltime_mollusk |
0 |
| gentlemanlike_asiaticlesserfreshwaterclam |
0 |
| frowsy_metalmarkbutterfly |
0 |
cat("Average NA share per company with atleast one product with transition risk category:", average_na_transition_risk_profile)
Average NA share per company with atleast one product with transition
risk category: 0