Rail emissions: making sense of operator-specific data

The challenge of comparing emissions across rail routes

Rail is one of the most sustainable ways to travel, but its environmental impact can vary widely between routes. Some journeys offer significant carbon savings, while others are less efficient.

Why route-level rail emissions vary so much

Different countries vary in network electrification levels, fuel types, and grid carbon intensity — so using a single "national rail" average, based on DEFRA emission factors, hides key contrasts, like France's fully electrified TGV lines versus diesel-heavy routes in Ireland. 

Without route-, operator-, and train-level data, travellers can't identify the lowest-carbon options, and operators investing in decarbonisation go unrewarded.

Operators Are Publishing Their Own Data

To address this, rail operators — the public and private entities running train services globally — are increasingly publishing their own emission factors. An emission factor is a representative value that translates a unit of activity into an estimate of carbon dioxide equivalent (CO₂e) emissions; for rail, it is typically expressed in grams of CO₂e per passenger-kilometre (gCO₂e/pkm). However, comparing these factors across operators can be challenging, as methodologies, boundaries, and underlying assumptions are not always consistent.

At Thrust Carbon, we adjust for differences between operators to provide accurate, granular comparisons that highlight the most sustainable travel options. Here’s an overview of the factors we consider when comparing rail emissions, and when data can or cannot be meaningfully compared.

What makes rail emissions data comparable?

You can only meaningfully compare emissions factors when their underlying operational components are aligned. In practice, that means the emission factors should be calculated using consistent methodologies , so differences reflect real performance, not quirks in how they were calculated.

Operator data points you can meaningfully compare:

•Traction type (diesel, electric, bi-mode): determines if emissions come from fuel combustion or grid electricity

•Grid emission factors: shows the carbon intensity of the electricity supply

•Country and route specificity: highlights differences between electrified corridors and diesel-heavy lines

•Occupancy: affects per-passenger emissions (higher occupancy = lower emissions per person)

When these operational components differ, you're comparing real-world performance differences. To achieve the lowest carbon rail travel, traction type, type of fuel, energy efficiency, and occupancy need to be optimised.

But what happens when the methodology differs?

Barriers to comparing operator-specific rail emission factors

Not all publicly available operator data is directly comparable. You cannot meaningfully compare emissions factors that differ in methodology — these four areas fundamentally change what’s being measured:

These four areas are critical and where operator data often diverges.

1. Combustion vs. Full Lifecycle Emissions

• Tank-to-wheel (TTW) emissions are from the engine or motor as the train moves: the direct combustion or electricity use during the journey itself.

• Well-to-tank (WTT) emissions are from upstream extraction, refining, electricity generation, and delivery of the fuel or power before it reaches the train.

• Well-to-wheel (WTW) emissions are the sum of both: the full lifecycle emissions from energy production through to use.

Why it matters: Looking only at TTW emissions underestimates the true climate impact by ignoring the emissions embedded in producing the energy.

Real-world example: Deutsche Bahn publishes full lifecycle (WTW) emissions data, accounting for upstream electricity generation and fuel production. In contrast, some operators, for example the US EPA rail emissions dataset, report only direct traction energy (TTW), excluding emissions from producing that electricity or diesel. Comparing a German rail journey based on full lifecycle data with a US operator reporting combustion-only data would make the US route appear to have an artificially lower carbon intensity unless the scope is adjusted.

Complicating this further, many operators fail to clarify whether their rail emission factors are combustion-only or full lifecycle. This lack of transparency makes it difficult to compare routes between operators and identify the lowest carbon options.

2. Location-based vs. Market-based Accounting

For electrified rail, how operators report their Scope 2 emissions can give very different results for the same journey.

• Location-based accounting:
A location-based method reflects the average emissions intensity of grids on which energy consumption occurs (using mostly grid-average emission factor data).

• Market-based accounting:
A market-based method reflects emissions from electricity that companies have purposefully chosen (or their lack of choice).

*Definitions taken from the Greenhouse Gas Protocol. For more information, see page 10 of the GHG Protocol Scope 2 Guidance.

Why it matters: These methods capture different aspects of emissions and can give very different results for the same electrified service. For instance, an operator on a carbon-intensive grid may report near-zero emissions under a market-based approach if they purchase enough renewable energy certificates.

There is no “right” or “wrong” method, but the differences between them add complexity when comparing between operators. Location-based accounting reflects the grid’s actual carbon intensity, while market-based accounting reflects the emissions from a company’s electricity choices, such as renewable energy certificates.

The GHG Protocol provides guidance on reporting energy consumption through its emission factor hierarchy, which prioritises the most specific data first, route- or operator-specific, then industry averages, and finally regional or national grid averages. If market-based data isn’t available, companies should default to location-based factors.

*See page 46 of the GHG Protocol Scope 2 Guidance for more information.

Real-world example: A company measuring its rail travel using DEFRA’s location-based factors would report 35 g CO₂e per passenger-kilometre, reflecting the carbon intensity of the UK’s electricity grid. For travel in the Netherlands, the same company reports 0 g CO₂e per passenger-kilometre using market-based accounting, which credits purchased renewable energy rather than the actual grid mix. This makes the Dutch travel appear carbon-free, but if location-based factors were applied, reflecting the real energy mix of the Dutch grid, the figure would be much higher and potentially comparable to the UK.

This highlights why it is important to know whether emission factors are location- or market-based, as this determines whether comparisons reflect actual grid emissions or contractual renewable energy purchases.

3. Emission Factors Used

Why it matters: Emission factors need to be current, location-specific, and relevant to the technology used. Using outdated grid data, global averages instead of local ones, fuel efficiency from the wrong type of train, or random load factors can give misleading results.

This can overstate emissions for operators who have invested in low-carbon technology and understate them for those who have not. The GHG Protocol recommends using factors that match the location, technology, and operation as closely as possible to ensure accurate and fair reporting. *See table on page 5 of Quantitative Inventory Uncertainty

Real-world example: The UK's electricity grid has rapidly decarbonised over the past decade, with coal largely phased out and renewables comprising over 50% of generation. Using 2015 grid factors in 2025 would overstate emissions from electric trains by around 50%. Similarly, an operator that hasn't updated its diesel fuel factors won't reflect improvements in fuel quality, engine efficiency, or the shift to biodiesel blends.

4. Third-party Verification

Why it matters: Independent assurance confirms methodology, data quality, and calculation accuracy. Unverified data may use inconsistent assumptions, outdated information, or selective boundaries that make performance look better than it is.

Real-world example: Operators adhering to standards like the GHG Protocol or holding certifications such as ISO 14064 have their emissions data independently audited by third parties. Others may self-report figures without external scrutiny allowing for the selective use of data. Verified and unverified data reflect different levels of transparency and accountability. Therefore, to make a fair comparison between operators, data credibility (e.g., audits or certifications) should be taken into account.

How does granular rail data drive sustainable travel decisions?

Air-to-rail shift is one of the most significant decarbonisation levers, but its impact varies dramatically by country, a difference that broad DEFRA averages obscure. With detailed emissions factors, corporate budgets can better prioritise regions for air-to-rail switch and support operators investing in electrification, efficient trains, and low-carbon energy procurement. Travellers also gain more decarbonisation options, choosing the most sustainable train based on time, route, or operator. This channels spending toward genuinely low-carbon networks, creating a feedback loop in which accurate data funds and rewards decarbonisation efforts.

Transparent Data Unlocks the Lowest-Carbon Rail Choices

To make meaningful comparisons across routes, the rail industry needs far greater standardisation and transparency in emissions reporting. Adopting consistent methodologies and harmonised disclosure practices will enable travellers and corporations to make truly informed decisions, ensuring that the most sustainable infrastructure investments are properly recognised and rewarded.

At Thrust Carbon, we are working to bridge this gap, but industry-wide alignment is essential to unlock rail's full potential as a decarbonisation solution.