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Scope 3, or value chain emissions, typically accounts for 70-95% of a company's total carbon footprint. It is also the hardest to measure and manage because it sits outside your direct operational control.
If you would like to outsource this work, then please contact us (big yellow button top of page) and we will glad to start no obligation dialog, prior to setting out costs.
The measurement of scope 3 emissions is very labor intensive, as such it is important to have a strategy that prioritises the "low hanging fruit" focusing on the easy to collect data first that captures the highest percentage of your Scope 3 Emissions.
Analogy For Super Market: Your strategy may aim to footprint goods sold based on carbon intensity. Choose things like avocados and cheese first. And leave the carrots until later. Why. Because you could footprint 10 varieties of root vegetable to account for 2% of your scope 3 emissions, or you could footprint just 1 type of cheese (cheddar) and capture the same 2%. Significantly less work for the same % progress towards Net Zero.
Note: In the AI generated images on this page all of the stick figures look stressed out. . . . TBH this work is not that bad. So don't let those guys put you off.
Even for fairly simple products (such as a Yoga Mat) data collection can be tricky.
How accurate does it have to be?
What to include?
Where do I find data?
Let break down these questions:
We tend to find a balance between effort & accuracy. It is a case of diminishing returns. One account for as much carbon as possible, but not waste hours and hours of time accounting for 0.000000001kg here and there.
Example: Imagine a machine trimming you Yoga Mat in to shape. It is part of the manufacturing process. We know the Yoga Mat has been cut to shape, it is obvious from it appearance, but we don't know how it was done. So lets make an assumption. We will assume the matt is "die-cut" (like a giant cookie cutter) so we look up an appropriately sized machine, and look up it cycle speed, and power rating. We can now work out the energy use per may. We will assume one mat is cut at t a time (worst case). So the machine rated at 10Kw takes 20 second to cut out one yoga matt. So 0.06kWH of electricity. In china this would translate to 0.029 kgCO2e.
So continuing from the same example. The cutting machine could cut 2 Billion yoga mats (another assumption) before it needs replacing. Although the machine might have quite a high carbon footprint (to make it) of say 30,000 kgCO2e, the footprint per yoga mat could be 0.000015 kgCO2e. So it is worth working that out? Not really. If you did the calculations for every machine involved the the process then it would be a very long job.
Quite often you have to make your own data. AI has helped massively with this. Just 1 or 2 years ago we would have been watching YouTube videos trying to figure out how many Yoga Mats a die-cutting machine could process in a set time. But now a single well worded AI query can provide all the data you need to make very firm assumptions.
For the most part however we do rely on databases such as Eco Invent which may be used in connection with Life Cycle software or bespoke calculations involving very large spreadsheets.
That is an excellent summary of the strategic challenges in corporate carbon accounting! The transition from quick estimates (like the yoga mat example) to formal, auditable reporting requires a structured approach based on the internationally recognized GHG Protocol Corporate Value Chain (Scope 3) Standard.
The formal process for quantifying and reporting Scope 3 emissions is a multi-step project designed to ensure completeness, transparency, and accuracy, as required by the GHG Protocol. The steps are generally sequential, moving from a broad assessment to granular data collection and reporting.
The first step is a strategic one, aligning with your prioritization approach. Companies must first establish the organizational and operational boundaries and then systematically screen all 15 Scope 3 categories (covering both upstream and downstream activities) to determine which are relevant and material to the business.
Standard Reference: This process is mandated by the GHG Protocol Scope 3 Standard.
Relevance Assessment: Categories are typically deemed relevant if they contribute significantly to the total emissions, are seen as a high risk for the company, or are a key focus for stakeholders. This is where your strategy of targeting high-intensity categories like purchased goods (Category 1) first comes into play, confirming that they are your "hotspots" before proceeding.
For each relevant category, a calculation method must be chosen, balancing the desire for accuracy with the availability of data. The standard requires movement toward Activity-Based (primary) data wherever possible, as it is far more accurate than financial estimates.
Calculation Methods: The three main calculation methods are:
Spend-Based: Uses financial data multiplied by industry-average emission factors (e.g., total spend on packaging x packaging factor). This is the fastest, but least accurate, method.
Activity-Based: Uses physical data multiplied by specific emission factors (e.g., liters of diesel used in transport x diesel factor). This provides a higher level of accuracy.
Supplier-Specific (Primary): The gold standard, involving collecting actual footprint data directly from suppliers.
Once data is collected, emissions are calculated category-by-category, aggregated, and then subjected to rigorous quality checks. The final output is an inventory report that is fully documented and transparent.
Data Verification: Assumptions (like the die-cutting machine estimate) and any data gaps must be clearly documented. For public reporting, the inventory is often audited or verified by an independent third party to ensure compliance with the GHG Protocol's principles of relevance, completeness, consistency, transparency, and accuracy.
SWEL as the Enabler: This is where a specialist partner becomes invaluable. SWEL, with its deep expertise in both the GHG Protocol framework and data acquisition, excels at streamlining this difficult process. They are adept at prioritizing the right categories (the "avocados and cheese" approach) and designing efficient data collection pipelines, allowing companies to quickly move beyond rough estimates to auditable, high-accuracy reporting, accelerating their progress toward verifiable Net Zero targets.