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Double materiality methodology
This page describes how the Upright data engine assesses double materiality (i.e. impact materiality and financial materiality)
The Upright data engine considers impact materiality on both product-level and company-level, with the former being the backbone. On both levels, there are different types of factors that can trigger materiality for a given topic. These factors are called materiality triggers. If any of them triggers, the relevant topic is considered material for a product (for product-level triggers) or company (for company-level triggers).
On company-level, materiality can be triggered by:
- Share of material products: The company is creating a substantial share of revenue from products that are considered material for the topic (example: getting more than 5% of revenue from products that are considered material for topic "pollution of air")
- The company is operating in a specific country (example: operating in Congo leads to materiality for topic "child labour")
- Data points related to specific topics (example: reported corruption incidents trigger materiality for topic "corruption & bribery")
On product-level materiality can be triggered by:
- Impact scores (e.g. the negative impact of this product within the impact category "GHG emissions" is more than 10 cents per dollar of revenue, hence the topic "climate change mitigation is material for this product")
- EU taxonomy metrics (e.g. this product is linked to EU taxonomy objective "climate change adaptation", hence the topic "climate change adaptation" is material for this product)
- Categorical associations (e.g. the primary purpose of this product is to increase the energy efficiency of another product; hence the topic "energy" is material for this product)
- UN SDG metrics (e.g. this product is strongly linked to SDG 3, hence the topic "health and safety" is material for this product)
No netting of impact scores
Using net scores or other similar metrics would be inappropriate for determining materiality; it is clear, for example, that the topic "climate change mitigation" would be material for a company creating a large amount of GHG emissions while removing the exact same amount.
When Upright uses impact scores for triggering materiality, this is based on either the positive score or the negative score for a given category, but never their net.
Upright has defined thousands of materiality triggers, each having its own criteria for triggering materiality (e.g. strong alignment with SDG target 13.1 under SDG 13 "Climate action" triggers materiality for the topic "Climate change adaptation"). The criteria have been constructed taking into account the likelihood, scale, scope, and irremediable character of the (potential) impact, noting that the first is only relevant for potential impacts, and the last is only relevant for negative impacts.
Example of defining materiality triggers for the topic "Waste"
As the ESRS topic "Waste" aligns closely with the Upright impact category "Waste", the net impact scores can be directly utilized:
- If the Waste score is larger than 5 cents per dollar or revenue, the scale of the impact is considered to be "high", which triggers materiality.
- If the Waste score is between 2.5-5 cents per dollar of revenue, the scale of the impact is considered to be "medium", which triggers materiality.
- If the Waste score is lower than 2.5 cents per dollar of revenue, the scale of the impact is considered to be "low", which does not trigger materiality.
The scope of the impact is evaluated at the company level, by examining the revenue shares of products. If the revenue share of products with a high or medium scale in "Waste" exceeds 5%, then the topic "Waste" is material for the company.
"Waste" is not classified as an irremediable topic. In cases of irremediable topics, the thresholds for both scale and scope are lowered.
The thresholds and qualitative aspects of the criteria are calibrated by taking advantage of Upright's database of 20,000+ companies and their materiality results, such that a sensible amount of topics end up being material for each company (on average). This calibration is updated annually to match the prevailing interpretation of how "sensitively" materiality ought to be triggered for each topic.
The input data used for product-level triggers incorporates value chain information from all parts of a product's value chain. Hence, all parts of the value chain are automatically considered.
The way in which value chain information is represented differs based on the type of the materiality trigger. For example, in materiality triggers based on impact scores, the impact scores used as input data include an explicit subdivision of the share of the impact that is produced internally, upstream and downstream. In materiality triggers based on categorical association, the relevant value chain part is a property of the category.
Example of value chain information within categorical associations
An example of a materiality trigger based on a categorical association is that the topic "energy" is always considered material for products that belong to a category of products whose primary purpose is to improve the energy efficiency of other products. In this case, the relevant value chain part is "downstream".
Regardless of the underlying factors, the underlying value chain information from all product-level triggers (and their input data) is incorporated into a final result that states the relevant value chain parts on topic-level. This works additively, i.e. if one product-level trigger triggers materiality based on a downstream impact, and another triggers materiality based on an internal impact, the value value chain parts listed for the topic will be "internal and downstream".
Similar to value chains, the input data used for product-level materiality triggers incorporates impacts from all relevant time horizons including the short-term, medium-term and long-term time horizons defined in the ESRS.
Similar to value chains, regardless of the underlying factors, the underlying temporal information from all product-level triggers (and their input data) is incorporated into a final result that states the relevant time horizons on topic-level. This works additively, i.e. if one product-level trigger triggers materiality based on a short-term impact, and another triggers materiality based on an medium-term impact, the value value chain parts listed for the topic will be "short-to-medium-term".
Financial materiality (FM) is assessed in terms of risks and opportunities. Financial materiality for a topic is triggered when risks or opportunities related to a given topic are deemed sufficiently significant.
There are two types of risks and opportunities:
- 1.Impact-driven risks and opportunities: These are caused by a (material) impact that the company has (e.g. company creates negative impacts on biodiversity and there is a risk that demand for their products will reduce when consumers gain more awareness on the importance of biodiversity, hence the topic "biodiversity" is material for the company)
- 2.Dependency-driven risks and opportunities: These relate to dependencies on natural, human and social resources (e.g. company has HQ in area that is at risk of flooding due to climate change, "hence the topic climate change adaptation" is material for the company)
In Upright's data engine, the former are identified by patterns that translate impacts into risks and opportunities, whereas the latter are caught by product-level or company-level triggers that directly trigger risks and opportunities.
Impacts are translated into risks and opportunities using risk patterns. Each risk pattern has:
- Criteria on when it is relevant: Criteria on when the risk pattern is relevant at all. Typical criteria are that the risk pattern is relevant only when a company has a positive (or negative) impact related to a given topic. Relevance does not mean that a risk is significant, as a relevant risk might still have a very low probability and scale.
- Intrinsic risk probability for pattern: This defines how likely the risk is for a company meeting only the relevance criteria, but without taking yet into account any specifics
- Factors modulating risk probability: Factors that increase or lower the risk probability. For example, risks related to negative impacts harming demand are lessened if a company is (mainly) B2B.
A list of impact-driven risks is produced by evaluating the impact materiality results against the risk patterns. This yields a list of risks, along with their probabilities and scales. The scale of impact-driven risks is driven by the share of revenue from products with impacts driving a given risk.
Example of a risk pattern that translates negative impacts related to biodiversity to a financial risk
- Description: Risk of decrease of demand to the company's product and services due to consumers becoming increasingly averse to negative impacts related to "biodiversity"
- Relevant when: Company has negative impacts related to the topic "biodiversity"
- Intrinsic risk probability: Medium (consumers' awareness of biodiversity-related impacts is high and growing)
- Factors modulating risk probability:
- The risk is reduced, if the company's products are B2B
- The risk is increased, if the company's products are B2C
- The risk is reduced, if the products have an overall net positive impact
- The risk is increased, if the relevant products have an overall net negative impact
- The risk is reduced, if the products are necessities (such as medicines)
- The risk is increased, if the products are non-necessities
This example above was related to consumers and negative impacts. Similar risk patterns exist related to investors, employees, and regulators (governments), and both positive and negative impacts.
Opportunity patterns work similar to risk patterns. Each opportunity pattern has:
- Criteria on when it is relevant: Criteria on when the opportunity pattern is relevant at all. Typical criteria are that the opportunity pattern is relevant only when a company has a positive impact related to a given topic. Relevance does not mean that an opportunity is significant, as a relevant opportunity might still have a very low opportunity level.
- Intrinsic opportunity level: This defines the (average) opportunity level for a company meeting only the relevance criteria, but without taking yet into account any specifics about the company
- Factors modulating opportunity level: Factors that increase or lower the opportunity level, based on specifics about the company. For example, the opportunity level related to positive impacts helping to recruit highly qualified workers increases if the company is in a business that relies on a highly educated workforce.
A list of impact-driven opportunities is produced by evaluating the impact materiality results against the opportunity patterns. This yields a list of opportunities, along with their opportunity levels.
Dependency-driven risks and opportunities relate to dependencies on natural, human and social resources.
As detailed in the European Sustainability Reporting Standards (ESRS), dependencies may trigger effects in two possible ways:
- They may influence a company's ability to continue to use or obtain the resources needed in its business processes, as well as the quality and pricing of those resources
- They may affect a company's ability to rely on relationships needed in its business processes on acceptable terms.
- A company has HQ in an area that is at risk of flooding due to climate change, hence the topic "climate change adaptation" is material for the company.
- A company manufactures weapons. Climate change increases the amount of armed conflicts in poor countries, increasing the demand for the company's products. Hence, there may be (grotesk) opportunities for the company related to "climate change".
- A company grows vegetables and is dependent on ecosystem services such as pollination and nutrition cycling. Ecosystem services face threats from climate change and pollution, restricting the company's ability to leverage these services. Hence, "Impacts and dependencies on ecosystem services" is material for the company.
Dependency-driven risks are captured by product-level and company-level materiality triggers that seek to capture key dynamics around how companies are affected by changes in environmental and social conditions. Of the examples, the first one would be captured by a company-level trigger, while the second one would be captured by a product-level trigger.
Risk probabilities, magnitudes, and opportunity levels are assessed on a 4-point scale:
Materiality for a given topic is triggered if there are any risks or opportunities relating to the topic that exceed the thresholds.
Thresholds for triggering financial materiality based on risks are defined by a combination of risk probability and risk magnitude as outlined in the triggering matrix below:
Figure 1: Triggering matrix for risks. For example, a medium-magnitude low-probability risk will trigger materiality, while a low-magnitude low-probability risk will not.
Financial materiality is triggered when there are any opportunities with an opportunity level higher or equal to medium.