What falls inside each category
Category 6 covers emissions from employee travel for business purposes in vehicles not owned or operated by the reporting company: commercial flights, rail, rental cars, taxis and rideshare, and hotel stays associated with that travel. If a company owns or leases the vehicle itself, those emissions belong in Scope 1 or Scope 2, not here. Category 6 is specifically about travel purchased from third parties.
Category 7 covers emissions from employees getting to and from their normal workplace, across whatever mode they use: driving alone, carpooling, public transit, cycling, or walking. The GHG Protocol also allows companies to include teleworking or remote-work emissions here as an optional addition, covering the energy used to heat, cool, and power a home office on days an employee doesn't commute at all.
Both categories are upstream and both are, in GHG Protocol terms, largely outside the reporting company's operational control. The company isn't running the airline or the transit system. That's part of why measurement approaches for these two categories look different from the supplier-facing methods used in categories like Purchased Goods & Services.
Three ways to measure business travel
For Category 6, three methods are in common use, and they map onto different rungs of the Scope 3 data quality hierarchy (spend-based, average-data, hybrid, supplier-specific):
- Spend-based: multiply the amount spent on flights, hotels, and ground transport by an emission factor expressed per unit of currency. This is fast because the data already lives in the expense system, but it's the weakest method — it can't distinguish a short domestic flight from a long-haul one if their costs happen to be similar.
- Distance-based: multiply distance traveled by a mode-specific emission factor (per passenger-kilometre for air, rail, or car travel). This requires knowing origin, destination, and mode, which most corporate travel booking platforms already capture, making it more accessible than it first appears.
- Fuel-based: use actual fuel consumption data where it's available, most often for chartered or company-arranged transport. This is the most accurate of the three but rarely feasible for commercial travel booked through third-party providers, since airlines and rail operators don't typically share fuel-burn data per booking.
Most companies end up running a hybrid: distance-based calculations for air and rail (sourced from travel management systems), spend-based as a fallback for categories of travel where booking data is incomplete, such as taxis or informal ground transport.
Measuring commuting is a different problem
Category 7 has no invoice trail, because the company isn't paying for the commute. That rules out spend-based methods almost entirely and pushes measurement toward two approaches:
- Survey-based: ask employees directly about commute distance, mode, and frequency (including remote-work days). This is the closest thing to primary data available for this category and is the method the GHG Protocol treats as most robust, but response rates and survey fatigue are real constraints, especially at large or distributed organisations.
- Distance-based with average data: where a full survey isn't practical, estimate an average commute distance and mode split using a representative sample, government transport statistics, or site-level averages, then apply mode-specific emission factors. This trades some accuracy for scalability and is often used to fill gaps between periodic surveys.
A practical pattern is to run a full survey every year or two and use its results as the average-data baseline in between, adjusting for known changes such as office relocations or shifts in remote-work policy.
Why these categories punch above their weight
For most organisations, Categories 6 and 7 are a small share of total Scope 3 emissions — usually far behind Category 1 (Purchased Goods & Services) or Category 11 (Use of Sold Products) for companies that manufacture energy-consuming products. Yet they attract disproportionate attention internally and externally for a few reasons.
First, they're the categories employees can see and personally influence. A change in travel policy or a shift to hybrid work shows up in a way that a supplier's emissions factor never will to most staff. Second, they're relatively tractable from a data standpoint compared to sprawling categories like purchased goods, so they're often where companies build their first real measurement muscle, including their first attempt at moving up the data quality hierarchy from spend-based estimates toward genuine primary data. Third, travel and commuting sit close to policies boards and investors already ask about — flexible work, travel budgets, fleet electrification — so the numbers get scrutinised even when they're a small slice of the total footprint.
That visibility cuts both ways. It's an opportunity to demonstrate credible measurement practice early, but it also means sloppy spend-based estimates in these categories get noticed and questioned faster than similar shortcuts buried in a larger, less intuitive category. Getting the method right here is often as much about building internal trust in the inventory as it is about the tonnes themselves.
For companies preparing disclosures under frameworks that require Scope 3 reporting, such as CSRD or the phased-in requirements under California's SB 253, having a defensible, documented method for Categories 6 and 7 — even a modest one — is a low-cost way to strengthen the credibility of the inventory as a whole.