Five Tips for Better Emission Factor Selection

Emission factors (EFs) are the backbone of carbon accounting. Get them wrong, and your entire GHG inventory crumbles. Get them right, and you've got a clear overview of your impact.

But here's the thing: finding the right EFs can feel like a scavenger hunt through databases, proprietary factors, and supplier spreadsheets.

Here's what you need to know about good emission factor selection.

Primary vs Secondary Data

Primary data comes straight from the source:

• Life cycle assessments (LCAs) from your actual suppliers
• On-site measurements from your facilities
• Supplier-specific emission factors (PCFs)

It's precise, detailed, and shows exactly what's happening in your operations and supply chain (when receiving suppliers’ PCFs).

Secondary data relies on external sources:

• Industry averages
• National statistics
• Third-party databases

It’s cost-effective and accessible. However, it might miss the specificities of your operations.

The million-pound question: Should you always use primary data? No. Sometimes it's simply not available. Sometimes it's not worth spending weeks hunting down hyper-accurate data for something that's a tiny fraction of your footprint. That said, you can work on getting more and more primary data over time.

How to Select the Right Emission Factors

1. Source from Trusted Databases

You need emission factors from reputable sources that have been reviewed and validated. Look for:

• Government databases: DEFRA, EPA, eGRID for operational emissions
• LCA databases: Ecoinvent, WFLDB, Higg MSI for product-level factors

(Or use Altruistiq and access them all in one searchable system. Just saying.)

2. Match Your Reporting Year

Emission factors change over time. Always match your emission factors to your reporting year.

⚡️ For example… The electricity grid constantly evolves, hopefully getting more renewable electricity in the mix. Using outdated factors means you're working with yesterday's energy mix – likely with less renewable electricity than today's reality.

3. Match Your Geography

Different regions have vastly different production methods. Farming practices, energy sources, and regulations all vary by location. Using emission factors from the wrong geography can completely skew your calculations. If you can't find exact matches, at least pick similar geographies.

🐄 For example… Beef farming practices in North America and France produce different emission factors due to their differing farming methods.

4. Check Your Boundaries

Emission factors can cover different lifecycle stages. Some factors include the full journey from raw materials to finished product. Others cover just one step. Make sure you have the right boundary for what you're looking to calculate.

🥫 For example… Two tomato paste emission factors – one covers growing tomatoes all the way to making the paste, the other just the processing stage. Pick the wrong boundary and you're either double-counting or underestimating emissions.

5. Stick to One Methodology

Different emission factors are essentially estimations with different methodologies behind them. Switch from one source to another, even if both are reputable, and you'll almost certainly see a change in your emissions. Not because anything real changed, but because the calculations differ. You might then need to go back and recalculate previous emissions using that same database. Otherwise, your emissions will go up and down unrelated to anything you're actually doing.

💡 Watch out… The SBTi recommends recalculating your baseline if methodology changes alter emissions by more than 5%.

Bonus Tip

Primary data doesn't just improve accuracy. It enables action.

Imagine you are working with a supplier to switch to renewable energy. Their new PCF shows the impact of this action immediately. However, if you rely on industry averages, you'll never see the change. Your supplier could eliminate emissions entirely and your footprint wouldn't budge. The bottom line is:

• Start with quality secondary data to establish your baseline.
• Layer in primary data for material categories.
• Drive change using primary data insights.