Descriptive analysis of products

This article shows how to calculate the descriptive analysis of products for emission and sector profile.

Distinct europages products

Input

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

Input

companies_id	product	grouping_emission	emission_category
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

Input

companies_id	product	sector_target	sector_category
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

Input

companies_id	product	grouping_transition_risk	transition_risk_category
comp_1	a	1.5C RPS_2030_all	high
comp_1	b	1.5C RPS_2030_all	high
comp_1	c	1.5C RPS_2030_all	high
comp_1	d	NA	NA
comp_1	e	NA	NA
comp_2	a	1.5C RPS_2030_all	high
comp_2	b	1.5C RPS_2030_all	high
comp_2	c	1.5C RPS_2030_all	high
comp_3	d	NA	NA
comp_3	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

Input

product	grouping_emission	emission_rank
tent	all	1.0000000
tent	tilt_subsector	1.0000000
surface engineering	all	0.3333333
surface engineering	tilt_subsector	1.0000000

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
all	0.6666667
tilt_subsector	1.0000000

Average reduction targets of all ep products per grouping_sector

Input

product	sector_target	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
surface engineering	0.09	1.5C RPS_2030
surface engineering	0.95	1.5C RPS_2050
surface engineering	0.22	NZ 2050_2030
surface engineering	0.96	NZ 2050_2050

Output

Average reduction targets of all ep products per grouping_sector

grouping_sector	sector target average
1.5C RPS_2030	0.135
1.5C RPS_2050	0.965
NZ 2050_2030	0.310
NZ 2050_2050	0.965

Average transition risk scores of all ep products per tilt_subsector benchmarks

Input

product	grouping_transition_risk	transition_risk_score
tent	1.5C RPS_2030_all	0.5900000
tent	1.5C RPS_2050_all	0.9900000
tent	NZ 2050_2030_all	0.7000000
tent	NZ 2050_2050_all	0.9850000
tent	1.5C RPS_2030_tilt_subsector	0.5900000
tent	1.5C RPS_2050_tilt_subsector	0.9900000
tent	NZ 2050_2030_tilt_subsector	0.7000000
tent	NZ 2050_2050_tilt_subsector	0.9850000
surface engineering	1.5C RPS_2030_all	0.2116667
surface engineering	1.5C RPS_2050_all	0.6416667

Output

Average transition risk scores of all ep products per transition risk groups

group	scenario	transition risk average
all	1.5C RPS_2030	0.4008333
	1.5C RPS_2050	0.8158333
	NZ 2050	0.4883333
	NZ 2050	0.8158333
tilt_subsector	1.5C RPS_2030	0.5675000
	1.5C RPS_2050	0.9825000
	NZ 2050	0.6550000
	NZ 2050	0.9825000

Average NA share per company

Companies with atleast one matched ecoinvent product for emission profile

companies_id	product	emission_category
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

companies_id	emission_category_NA
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

companies_id	product	emission_category
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

companies_id	sector_category_NA
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

companies_id	product	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

companies_id	transition_risk_NA_share
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