Input and Supply Chain Analysis Model • tiltIndicatorAfter

This article is a brief documentation of the methodology and tools that tilt provides to analyse the inputs of a company and its supply chain risk.

The tilt input and supply chain risk analysis is based on the two main indicators provided by the tilt methodology and can be either applied to the inputs used for producing a product (“Input Analysis”) or to the suppliers from which products are purchased (“Supplier Analysis”).

The two main indicators of the tilt methodology are the relative emission and the sector decarbonisation indicator. The relative emission indicator is a proxy for the emission intensity of a company’s products in comparison to other products and thereby gives an indication of the impact the product has on the environment. This indicator uses Life-Cycle-Assessment (LCA) data to match a product to their emission footprint and then ranks it in comparison to groups of other products. The sector decarbonisation indicator proxies the transition pressure a company is facing based on the sector decarbonisation targets for its products. This indicator uses sector level climate scenario data mapped to the products and thereby provides information on how much a company must transition to meet the reduction targets of the sector in which it is active. For more details on the methodology of these two indicators, please refer to our detailed documentation provided on our online platform which you can access via this link.

1 Input Analysis

These two indicators cannot only be applied to the products of a company, but also to its inputs, to better understand input-related climate impact and risks. For applying it to inputs, we follow the following two steps:

We source the input data needed to produce a product in an automated way from Ecoinvent, our LCA data provider.
As a next step, we then apply the indicators to the inputs to receive results for the inputs’ relative emission and sector decarbonisation indicator.
We show results on product-level and aggregate them on company-level.

Below, we show the code used to apply these indicators and a more detailed description of each input-level indicator:

1.1 Input Sector Decarbonisation Indicator

The Input Sector Decarbonisation Indicator assesses the transition pressure of the input products based on the sector’s decarbonisation targets the input product belongs to. This indicator can be aggregated on company level and as such inform about the supply chain risk of an SME, based on its inputs’ transition pressure. The sector emission reduction targets vary across scenarios (e.g., WEO NZ 2050 or IPR 1.5 RPS) and the time horizon (e.g., reduction needed until 2030 or 2050 to achieve the targets). The input products are sourced from the LCA data provider Ecoinvent and consist, for example, of resources, packaging materials, energy and enabling services (such as tractor use on farm) to produce the product.

After assigning each input product to a tilt subsector, we can map it to a scenario sector and thereby derive the sector decarbonisation target. Depending on the magnitude of the reduction target, the product is categorised as having a low, medium or high reduction target. The categorisation method based on thresholds is explained in the Thresholds section or in the detailed documentation on our Online Platform (see link in intro).

After assessing the input products for each product, they are aggregated at company-level to derive what percentage of the input products required by the company to produce its products have high, medium and low sector decarbonisation targets.

NOTE: While the scenario data are freely available, input data from ecoinvent are licensed and can therefore not be shown below. Therefore, the calculations are based on fake data. If a user has an ecoinvent license to access the real data, this code can be used to calculate real results.

Below, the code first loads the input data, and then passes the input data to the profile_sector_upstream function (from tiltIndicatorAfter). At last, this function calculates the indicator’s product and company-level results.

# Load input data
sector_companies <- read_csv(toy_sector_profile_upstream_companies())
sector_inputs <- read_csv(toy_sector_profile_upstream_products())
scenarios <- read_csv(toy_sector_profile_any_scenarios())
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())
ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())

# `profile_sector_upstream()` creates product and company level outputs for
# Input Sector Decarbonisation Indicator
input_sector_decarbonisation_indicator <- profile_sector_upstream(
  sector_companies,
  scenarios,
  sector_inputs,
  europages_companies = europages_companies,
  ecoinvent_activities = ecoinvent_activities,
  ecoinvent_inputs = ecoinvent_inputs,
  ecoinvent_europages = ecoinvent_europages,
  isic = isic_name
)

1.1.1 Input Sector Decarbonisation Indicator at product level

input_sector_decarbonisation_indicator_at_product_level <- input_sector_decarbonisation_indicator |>
  unnest_product()

kable(input_sector_decarbonisation_indicator_at_product_level |> head(10), format = "html", escape = FALSE) |>
  column_spec(1:32, width = "auto", extra_css = "white-space: nowrap;") |>
  row_spec(0, extra_css = "white-space: nowrap;")

companies_id	company_name	country	sector_profile_upstream	reduction_targets	scenario	year	ep_product	matched_activity_name	matched_reference_product	unit	tilt_sector	multi_match	matching_certainty	matching_certainty_company_average	input_name	input_unit	input_tilt_sector	input_tilt_subsector	company_city	postcode	address	main_activity	activity_uuid_product_uuid	input_isic_4digit	sector_scenario	subsector_scenario	ei_input_geography	min_headcount	max_headcount	input_isic_4digit_name	ei_geography
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	medium	0.18	1.5C RPS	2030	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	0.98	1.5C RPS	2050	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	0.40	NZ 2050	2030	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	total	residential	tilt_land	1	10	Construction of buildings	tilt_world
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	0.97	NZ 2050	2050	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	total	residential	tilt_land	1	10	Construction of buildings	tilt_world
skarn_gallinule	skarn_gallinule	austria	medium	0.18	1.5C RPS	2030	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world
skarn_gallinule	skarn_gallinule	austria	high	0.98	1.5C RPS	2050	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world
skarn_gallinule	skarn_gallinule	austria	high	0.40	NZ 2050	2030	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	total	residential	tilt_land	1	10	Construction of buildings	tilt_world
skarn_gallinule	skarn_gallinule	austria	high	0.97	NZ 2050	2050	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	total	residential	tilt_land	1	10	Construction of buildings	tilt_world
psychodelic_airedale	psychodelic_airedale	austria	medium	0.18	1.5C RPS	2030	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	neustift im stubaital	6167	neugasteig 76 \| 6167 neustift im stubaital	distributor	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world
psychodelic_airedale	psychodelic_airedale	austria	high	0.98	1.5C RPS	2050	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	construction	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	neustift im stubaital	6167	neugasteig 76 \| 6167 neustift im stubaital	distributor	76269c17-78d6-420b-991a-aa38c51b45b7	‘4100’	buildings	NA	tilt_land	1	10	Construction of buildings	tilt_world

1.1.2 Input Sector Decarbonisation Indicator at company level

input_sector_decarbonisation_indicator_at_company_level <- input_sector_decarbonisation_indicator |>
  unnest_company()

kable(input_sector_decarbonisation_indicator_at_company_level |> head(10), format = "html", escape = FALSE) |>
  column_spec(1:13, width = "auto", extra_css = "white-space: nowrap;") |>
  row_spec(0, extra_css = "white-space: nowrap;")

companies_id	company_name	country	sector_profile_upstream_share	sector_profile_upstream	scenario	year	matching_certainty_company_average	company_city	postcode	address	main_activity	reduction_targets_avg
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	high	1.5C RPS	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.18
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	1	medium	1.5C RPS	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.18
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	low	1.5C RPS	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.18
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	NA	1.5C RPS	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.18
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	1	high	1.5C RPS	2050	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.98
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	medium	1.5C RPS	2050	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.98
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	low	1.5C RPS	2050	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.98
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	NA	1.5C RPS	2050	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.98
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	1	high	NZ 2050	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.40
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	0	medium	NZ 2050	2030	low	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.40

1.2 Input Relative Emission Indicator

The Input Relative Emission Indicator assesses the relative emission intensity of the input products based on their relative GHG emission footprint in comparison to groups of other products (for more information on the different groups at disposal, please refer to our detailed documentation on our online platform). Input products with a higher emission intensity also face a higher risk from climate policy interventions or low-carbon market changes. The input products are sourced from the LCA data provider Ecoinvent and consist, for example, of resources, packaging materials, energy and enabling services (such as tractor use on farm) to produce the product.

After assessing the input products’ climate impact based on their emission intensity, they are aggregated at the company-level. We derive what percentage of the input products have a high, medium and low emission intensity.

NOTE: GHG emission and input data from ecoinvent are licensed and can therefore not be shown below. Therefore, the calculations are based on fake data. If a user has an ecoinvent license to access the real data, this code can be used to calculate real results.

Below, the code first loads the input data, and then passes the input data to the profile_emissions_upstream function (from tiltIndicatorAfter). At last, this function calculates the indicator’s product and company-level results.

# Load input data
emissions_companies <- read_csv(toy_emissions_profile_any_companies())
emission_inputs <- read_csv(toy_emissions_profile_upstream_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())
ecoinvent_inputs <- read_csv(toy_ecoinvent_inputs())

# `profile_emissions_upstream()` creates product and company level outputs for
# Input Relative Emission Indicator
input_relative_emission_indicator <- profile_emissions_upstream(
  emissions_companies,
  emission_inputs,
  europages_companies = europages_companies,
  ecoinvent_activities = ecoinvent_activities,
  ecoinvent_inputs = ecoinvent_inputs,
  ecoinvent_europages = ecoinvent_europages,
  isic = isic_name
)

1.2.1 Input Relative Emission Indicator at product level

input_relative_emission_indicator_at_product_level <- input_relative_emission_indicator |>
  unnest_product()

kable(input_relative_emission_indicator_at_product_level |> head(10), format = "html", escape = FALSE) |>
  column_spec(1:31, width = "auto", extra_css = "white-space: nowrap;") |>
  row_spec(0, extra_css = "white-space: nowrap;")

companies_id	company_name	country	emission_upstream_profile	benchmark	ep_product	matched_activity_name	matched_reference_product	unit	multi_match	matching_certainty	matching_certainty_company_average	input_name	input_unit	input_tilt_sector	input_tilt_subsector	input_isic_4digit	input_isic_4digit_name	company_city	postcode	address	main_activity	activity_uuid_product_uuid	profile_ranking	ei_input_geography	min_headcount	max_headcount	ei_geography	input_activity_uuid_product_uuid	co2e_lower	co2e_upper
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	all	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	0.9375	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	-0.0161997	8.766183e+08
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	input_isic_4digit	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	295.1030659	5.780160e+02
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	input_tilt_subsector	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	-82.9665824	5.177974e+02
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	input_unit	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	183.1011401	4.507805e+02
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	input_unit_input_isic_4digit	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	-127.5366357	5.577096e+02
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	austria	high	input_unit_input_tilt_subsector	tent	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wilhelmsburg	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	49.0916694	7.352646e+02
skarn_gallinule	skarn_gallinule	austria	high	all	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	0.9375	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	-0.0161997	8.766183e+08
skarn_gallinule	skarn_gallinule	austria	high	input_isic_4digit	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	295.1030659	5.780160e+02
skarn_gallinule	skarn_gallinule	austria	high	input_tilt_subsector	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	-82.9665824	5.177974e+02
skarn_gallinule	skarn_gallinule	austria	high	input_unit	sheds, construction site	market for shed, large, wood, non-insulated, fire-unprotected	shed, large, wood, non-insulated, fire-unprotected	m2	FALSE	low	low	shed, large, wood, non-insulated, fire-unprotected	m2	construction	construction residential	‘4100’	Construction of buildings	wiener neudorf	2355	iz nö-süd, strasse 14, objekt 15 \| 2355 wiener neudorf	wholesaler	76269c17-78d6-420b-991a-aa38c51b45b7	1.0000	tilt_land	1	10	tilt_world	44e5e288-4f81-40d0-88b4-e79eaea6574c	183.1011401	4.507805e+02

1.2.2 Input Relative Emission Indicator at company level

input_relative_emission_indicator_at_company_level <- input_relative_emission_indicator |>
  unnest_company()

kable(input_relative_emission_indicator_at_company_level |> head(10), format = "html", escape = FALSE) |>
  column_spec(1:13, width = "auto", extra_css = "white-space: nowrap;") |>
  row_spec(0, extra_css = "white-space: nowrap;")

companies_id	company_name	company_city	country	emission_upstream_profile_share	emission_upstream_profile	benchmark	matching_certainty_company_average	postcode	address	main_activity	profile_ranking_avg	co2_avg
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	1	high	all	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.9375	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	medium	all	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.9375	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	low	all	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.9375	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	NA	all	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	0.9375	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	1	high	input_isic_4digit	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	medium	input_isic_4digit	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	low	input_isic_4digit	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	NA	input_isic_4digit	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	1	high	input_tilt_subsector	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75
asteria_megalotomusquinquespinosus	asteria_megalotomusquinquespinosus	wilhelmsburg	austria	0	medium	input_tilt_subsector	low	3150	fleschplatz 2, top 5 \| 3150 wilhelmsburg	wholesaler	1.0000	297.75

2 Supply Chain Analysis

In addition to the input-level analysis, the tilt methodology can also be applied to the supply chain of a company, to better understand the supply chain-related climate impact and risks. For applying the indicators to suppliers, the following approach is used in our Supply Chain Risk Model:

We collect information on the supplier of a company from the firm we are analysing. Per supplier, we need information on their address, business type and products.
As a next step, we then apply the indicators to each supplier based on the collected information.
Then, we show the results in a heatmap to show insights on the suppliers’ climate impact and risks at one glance.

Below, we show the heatmap after applying the input relative emission indicator to an example set of suppliers. Please check out the full documentation for creation of the heatmap here in this repository.