Introduction
Within the M2030 PCF builder, PCFs can be built at an individual SKU level, or a category level. Category level PCF requests can be responded to in numerous ways, depending on the data that your organization has available.
This article will help you to:
- Understand the different approaches to Category PCFs that can be used within the M2030 builder
- Decide which approach to building a Category level PCF in the M2030 platform is most suitable, based on the data your organization has available
Individual vs. Category PCFs
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💡 Understanding the type of PCF your customer is requesting is important to make your build as efficient as possible whilst also delighting your customers and improving your relationship. |
Individual PCFA PCF value that represents the greenhouse gas (GHG) emissions associated with the lifecycle of a specific individual product, from raw material extraction to the point at which the product leaves the factory gate. Your customer may request a named product or a SKU code.
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Category PCFA PCF value that represents the greenhouse gas (GHG) emissions associated with a group of sold products. Your customer may request a PCF for their own definition of a group of products so ensure you clarify which products they are referring to, so you cover the correct scope. |
Category PCF Scenarios
Given the many independent factors that can affect the type of category PCF you create, the M2030 solution offers three distinct options to choose from.
Explore each scenario, then use the decision tree below to find the best option for your data availability
Scenario 1 | Averaged Weightings
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This scenario is applicable when there are known/ expected significant differences in emissions intensities among products within a category, or if weighted activity data is available across the products within the product category. -
➕ Benefits
- Improved accuracy in emissions representation - account for variations in emissions intensities across products by weighting material inputs and activity data according to sold volumes, offering a more realistic category-level footprint.
- Alignment with market activity - reflect actual market conditions of the category by emphasizing the unique contribution of each product, higher-volume products, which dominate the category’s overall environmental impact.
- Streamlined reporting - simplify emissions reporting by providing one consolidated PCF per category, making it easier to communicate results.
➖ Limitations
- Data dependency - requires accurate and comprehensive activity data (e.g., sold volumes) across all products in the category, which can be challenging to obtain.
- Reduced granularity - producing a single PCF for the category will obscure SKU-specific insights, which would be needed for SKU-targeted improvements or external benchmarking.
- Improved accuracy in emissions representation - account for variations in emissions intensities across products by weighting material inputs and activity data according to sold volumes, offering a more realistic category-level footprint.
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How to build: Add all material inputs from the products within the category into the M2030 PCF builder. Weight material inputs and activity data by sold volumes.
Output: One PCF per product category, which will represent a weighted average across all the individual products in the category.
Guidance: Ensure to reflect the scenario chosen in the product description, by listing the relevant products that are being accounted for within the category.
Scenario 2 | Representative Product
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This approach is suitable if all products in the category are expected to have similar emissions intensities, or weighted activity data is not available across products within a category. -
➕ Benefits
- Efficiency - save time and resources by representing product categories instead of individual products.
- Scalability across product portfolios - enable rapid and broad application for large product ranges or complex supply chains.
- Strategic insights for category level decarbonization - identify high-impact categories to prioritize emission reductions
➖ Limitations
- Reduced accuracy - as proxies rely on generalized data, which may not capture product-specific variations or unique supply chain details.
- Potential misrepresentation - may oversimplify emissions, leading to over- or under-estimations that could misinform decisions or reporting.
- Hinders granular insights - lacks the detail needed for precise product-level improvements or differentiation in sustainability efforts.
- Efficiency - save time and resources by representing product categories instead of individual products.
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How to build: Select a representative product to create a PCF which will represent the category. This representative product acts as a "proxy" product for the category.
Output: One category level PCF representing the product category.
Guidance: Ensure to select the PCF is for a "group of similar products" in the product set up, and reflect the scenario chosen in the product description, by writing that it is a representative product for [name of the category].
Scenario 3 | Individual SKUs for a Category
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This scenario should be used when activity data is readily available for all products on an individual SKU basis. -
➕ Benefits
- Maximum granularity - provides precise carbon footprints for each product, enabling detailed insights and targeted decarbonization actions for specific SKUs.
- Emissions transparency - offers transparency and credibility by showing detailed, SKU-specific calculations, appealing to buyers, regulators, and sustainability-focused customers.
- Supports differentiation - enables product-level benchmarking and comparison, helping identify and promote low-emission products within a category.
➖ Limitations
- Resource intensive - requires creating as many PCFs as there are products in the category, demanding significant time, effort, and data.
- Complex descriptions - when adding products to your product library, you will need to include for each product-level PCF, it's category context in the description, increasing effort
- Redundancy risk - as many products in the category may share similar emissions profiles, making the effort to create individual PCFs redundant without adding significant new insights
- Maximum granularity - provides precise carbon footprints for each product, enabling detailed insights and targeted decarbonization actions for specific SKUs.
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How to build: Create individual PCFs for each product in the category using the M2030 Platform.
Output: Multiple PCFs, one for each product within the product category.
Guidance: Ensure to reflect the scenario chosen in the product description, by writing what category it sits in.
Choosing a scenario
Use the decision tree below to find the best scenario* for your needs:
Input data - key data points needed to complete the builder
Emission intensities - the amount of GHG produced in production
*If you can subgroup your products based on similar or expected emissions intensities, consider using a representative product approach by creating a PCF for each of these subgroups.
If you have questions about the different Category PCF Scenarios or are not sure which option to select, contact the M2030 team.