Distance-based is the best Scope 3 Category 7 method for multi-site employers; fuel data is rarely complete and averages are a fallback.
For most large employers in Germany, we’d pick the distance-based method. It usually gives the best balance between data effort, result quality, site-level use, and audit support.
Here’s the short answer:
That matters because Scope 3 Category 7 is not just a reporting line. For multi-site employers, the method also affects:
In simple terms: the same workforce can produce different CO₂ results depending on the method you use. So you need one that you can explain, repeat, and use across all sites.
| Method | Main input | Accuracy | Scale across many sites | Audit support | Best fit |
|---|---|---|---|---|---|
| Distance-based | Distance, mode, commuting days, emission factors | High if data is good | High | High | Large multi-site employers |
| Fuel-based | Actual fuel used for commuting | High in theory | Low | Low to medium | Only when fuel use is fully measured |
| Average-data | Headcount and published averages | Low | High | Medium | Baseline reporting only |
A useful rule of thumb: if you can get even basic site-level travel data, distance-based is often the better route. If not, average-data may be enough for a first baseline. And fuel-based only makes sense in the rare case where the records are complete.
The article below compares all three methods by data needs, accuracy, scale, audit trail, and use for site decisions.
These three methods all aim to answer the same thing: how much CO₂ comes from employee commuting. The best choice comes down to the data you can gather and whether you need site-level decisions or only reporting.
| Method | Core input | Best fit at scale |
|---|---|---|
| Distance-based | Employee travel distances by mode | High: scalable, defensible, flexible |
| Fuel-based | Actual fuel consumed during commuting | Low: hard to collect at scale |
| Average-data | National or regional commuting averages | Medium: quick to apply, limited site value |
That’s the short version. The notes below make it clearer why each method works well, or falls short, in day-to-day use.
This method estimates emissions from employee commuting distances by transport mode, then applies mode-specific emission factors. For large employers, it’s usually the most practical default. Why? Because it can work across many sites and reflects how your own workforce travels instead of leaning on a generic average.
That gives it a clear edge when you need a method you can repeat across sites without losing sight of local travel patterns.
This method estimates emissions from fuel used for commuting and then applies the matching fuel factor. On paper, it can be very precise. In practice, though, getting primary fuel data from a large workforce is hard.
That data is often patchy, inconsistent, or just too slow to collect in a way that holds up across multiple sites.
This method uses published national or regional commuting averages when employee-level data isn’t available. It’s the fastest route, which makes it useful when time or data is tight.
The trade-off is pretty plain: you get less site-level detail and weaker planning value. That matters if you want to shape travel policies, not just file a report.
Next, use these differences to choose the method that fits your sites, shifts, and data reality.
Here’s the side-by-side view of the three Scope 3 Category 7 methods. The differences aren’t just technical. They shape how well each method works when you’re rolling reporting out across many sites.
| Method | How it works | Data required | Strengths | Limits |
|---|---|---|---|---|
| Distance-based | Uses distance, mode, commuting days, and emission factors | Postal code data or survey results, mode split, commuting days, emission factors | Balances specificity and scalability | Depends on how well you know your workforce's actual travel patterns |
| Fuel-based | Uses actual commuting fuel and the relevant fuel emission factor | Actual fuel consumed during commuting | Very specific when data exists | Requires direct and traceable commuting fuel data, which is rarely available at scale |
| Average-data | Uses headcount, assumed distance, modal split assumptions, and commuting days | Headcount, assumed distance, modal split assumptions, commuting days | Low data burden | Least specific to actual site conditions |
This method builds an emissions figure from four inputs: how far employees travel, which modes they use, how many days they commute, and the emission factor for each mode.
For a large employer with several sites and different shift patterns, that level of detail matters. Commute patterns can change a lot from one site to another. A city-centre office may have heavy public transport use, while an out-of-town facility may depend far more on cars. That’s exactly where postal code data or survey results help. They let you reflect those site-by-site differences instead of smoothing everything into one average.
The trade-off is pretty simple: better input data leads to better output. If survey response rates are low, or postal code data is patchy, the result will only be as good as what went in.
The fuel-based method converts commuting fuel into emissions using the matching fuel factor.
On paper, that sounds very exact. In practice, it only works when commuting fuel use is direct, complete, and traceable. For most employers, that’s a high bar. Across multiple sites, most workforces do not have fuel records that clearly show how much fuel was used for commuting and by whom.
Unless your organisation has a set-up where commuting fuel use can be measured directly, this usually isn’t a workable option for a large multi-site employer.
The average-data method uses just a few broad inputs:
This makes it the easiest method to get off the ground. If you have no employee-level data at all, it’s the clear fallback.
But there’s a catch. You lose most of the site-level value. A national or regional average commute profile won’t show how one site differs from another, and those differences affect both your emissions figure and the choices you make afterwards. That could mean travel policy, parking, shuttle planning, or support for cycling and public transport.
So yes, average-data can work for baseline reporting. But for day-to-day planning, it doesn’t give you much to work with. That trade-off is often what decides which method makes sense for a large multi-site employer.
This is general information, not legal or tax advice.
For a large multi-site employer, the best method is the one you can repeat across sites, stand behind in reports, and use for site-level decisions.
| Method | Data required | Accuracy | Scalability | Auditability | Best use case |
|---|---|---|---|---|---|
| Distance-based | Postal code data or survey results, modal split, commuting days, emission factors | High when input data is solid | Scales well across sites with consistent data collection | Strong, because inputs are traceable and explainable | Multi-site employers with usable employee or site-level data |
| Fuel-based | Actual commuting fuel consumption per employee | High in theory | Limited, because commuting fuel data is rarely available at scale | Difficult without direct fuel records | Only viable when commuting fuel use is directly and completely measurable |
| Average-data | Headcount, assumed distance, modal split assumptions, commuting days | Low, because it relies on broad assumptions | High, because it needs minimal data | Limited, because it is hard to justify site-specific decisions | Baseline reporting only, when no employee-level data exists |
Once you look at the trade-offs, the choice gets simpler: which method can still hold up across all your sites in day-to-day use?
Use the distance-based method if you have any usable employee or site-level data. It tends to be the strongest fit for multi-site employers because it can scale without turning every location into a guessing game.
Use average-data only as a fallback when you genuinely have nothing else. It can help with baseline reporting, but it's a weak basis for site decisions.
Keep the fuel-based method for the rare case where commuting fuel consumption is directly and completely measurable. If you can't measure it fully, the method starts to fall apart.
One more point matters here: use one method across all sites. That keeps results comparable from year to year and makes internal reporting much easier to defend.
Multiple plants and different shift patterns can change commuting in a big way. One site may have good public transport access. Another may sit in an industrial area where most people have little choice but to drive. Early or late shifts can also rule out some transport options altogether.
That's why site-specific commute patterns, shift timing, and mode access make distance-based inputs more useful than broad averages. Instead of forcing one flat assumption onto every site, a scalable commute model can reflect actual conditions, including:
That extra detail does more than tidy up reporting. It gives you planning input you can actually use.
Next, use that method choice to shape reporting and planning decisions.
Once you’ve picked a method, use that same method for both reporting and planning.
It needs to do two jobs. First, it must support Scope 3 Category 7 reporting. Second, it must help with pre-investment planning before any money is spent. In plain terms, can it model commute measures in advance? And can it deal with site-specific details like multiple plant locations and complex shift patterns?
That matters because the same method should work for audit reporting and mobility planning. If you use one commute model, you can rely on the same inputs for reporting and for pre-investment planning. Then the final check becomes pretty simple: does this method work at every site and in every reporting cycle?
The next step is practical. Use the method that you can collect in a consistent way, defend in an audit, and apply across all sites.
For triply, that means starting with postal codes and shift patterns, combining all sites into one commute model, and simulating measures plus their emissions and cost impact before you commit budget. The same commute model can then produce audit-ready Scope 3 Category 7 reporting for CSRD and ESRS E1.
If you want to see what that looks like for your own sites, book a demo with triply and see a live example for your sites.
