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:

1. Understand the different approaches to Category PCFs that can be used within the M2030 builder
2. 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

Individual PCF A 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.	Category PCF A 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 guidance offers two distinct options for building a category level PCF.

Scenario 1 | Averaged Weightings

• 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.

• 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

• 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.

• 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].

Choosing a scenario

Which category-level PCF scenario should I choose?

1. Scenario 1: Averaged Weightings
   Choose this and follow the in-platform guidance above, if you have input data weighted by the volumes of each product in the category.
2. Scenario 2: Representative Product
   Choose this and follow the in-platform guidance above if you do not have weighted input data. Select a single product that best represents the category, or the product in the category which has the highest supplied volume.

• Input data - key data points needed to complete the builder
• Emission intensities - the amount of GHG produced in production