http://www.travelimpactmodel.org

In this document we describe the modeling assumptions and input specifications behind the Travel Impact Model (TIM), a state of the art emission estimation model that Google's Travel Sustainability team has compiled from several external data sources. The TIM predicts greenhouse gas (GHG) emissions for future flights to help travelers plan their travel.

For each flight, the TIM considers several factors, such as the Great Circle distance between the origin and destination airports and the aircraft type being used for the route. Actual GHG emissions at flight time may vary depending on factors not known at modeling time, such as speed and altitude of the aircraft, the actual flight route, and weather conditions at the time of flight.

The Travel Impact Model estimates fuel burn based on the Tier 3 methodology for emission estimates from the Annex 1.A.3.a Aviation 2019 published by the European Environment Agency (EEA).

There are several resources about the EEA model available:

• the main documentation
• the data set
• further documentation on pre-work for the EEA model

Additionally, the Travel Impact Model updates the fuel burn to emissions conversion factor to align with the ISO 14083 Fuel Heat Combustion factor and CORSIA Life Cycle Assessment, and breaks down emissions estimates into Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions.

Tank-to-Wake emissions account for emissions produced by burning jet fuel during flying, take-off and landing. Well-to-Tank emissions account for emissions generated during the production, processing, handling and delivery of jet fuel. Well-to-Wake (WTW) emissions is the sum of Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions.

The EEA model takes the efficiency of the aircraft into account. As shown in Figure 1, a typical flight is modeled in two stages: take off and landing (LTO, yellow) and cruise, climb, and descend (CCD, blue).

(Fig 1)

For each stage, there are aircraft-specific and distance-specific fuel burn estimates. Table 1 shows an example fuel burn forecast for a Boeing 787-9 (B789) aircraft:

(Table 1)

By using these numbers together with linear interpolation or extrapolation, it is possible to deduce the emission estimate for flights of any length on supported aircraft:

• Interpolation is used for flights that are in between two distance data points. As a theoretical example, a 5250 nautical miles flight on a Boeing 787-9 will burn approximately 54902.5 kg of fuel during the CCD phase (where 54902.5 equals 52375 + (57430 - 52375)/2, with figures for 5000nm and 5500nm taken from Table 1).
• Extrapolation is used for flights that are either shorter than the smallest supported distance, or longer than the longest supported distance for that aircraft type.
• The Lower Heating Value from ISO 14083 (43.1 MJ/kg averaged over US and EU numbers from source Table K1 and Table K3) and CORSIA Carbon Intensity value (74 gCO₂e/MJ from source Table 5) are used to calculate the jet fuel combustion to CO₂e conversion factor of 3.1894. The CORSIA Life Cycle Assessment methodology is used to calculate a WTT CO₂e emissions factor of 0.6465 (WTT 15g CO₂e/MJ added to the TTW 74 gCO₂e/MJ Carbon Intensity to total up to the WTW lifecycle Carbon Intensity of 89 gCO₂e/MJ from source page 22 and Table 7). The factors used are as follows:

CO₂e is short for CO₂ equivalent and includes Kyoto Gases (GHG) as described here. Warming effects produced by short-lived climate pollutants (such as contrail-induced cirrus clouds) are not yet included in CO₂e as calculated by the Travel Impact Model.

There is information for most commonly-used aircraft types in the EEA data, but some are missing. For missing aircraft types, one of the following alternatives is applied in ranked order:

• Supported by correction factor: If an aircraft type is supported in another data set and a comparable type is supported both in the other and the EEA data set, a correction factor is derived by comparing the output for both types across a range of missions. The correction factor will be applied to the LTO and CCD numbers of the comparable type in the EEA database. Two data sources are used for correction factors:
  • For aircraft supported in EEA 2023 but not EEA 2019, derive it from EEA 2023.
  • For all aircrafts with a winglet or sharklet variant for which no native data exists (see Appendix A), use a 3% discount factor on top of EEA 2019 estimates. We are basing the 3% factor on a literature review (Airbus, FlightGlobal, Boeing, SimpleFlying, NASA, Cirium, AviationBenefits, FlightGlobal).
• Supported by fallback to previous generation aircraft type: If there are estimates in the EEA data set for a previous generation aircraft type in the same family, from the same manufacturer, the previous generation aircraft is used for the estimate.
• Supported by fallback to least efficient aircraft in the family: For umbrella codes that refer to a group of aircraft, the least efficient aircraft in the family will be assumed.
• Supported by fallback to similar aircraft type: If there are estimates in the EEA data set for a similar aircraft, it is used for the estimate.
• Not supported: For aircraft types for which none of the cases above apply, there are no emissions estimates available.

See Appendix A for a table with detailed information about aircraft type support status.

Used for flight level emissions:

• EEA Report No 13/2019 1.A.3.a Aviation 1 Master emissions calculator 2019 (link)
• EMEP/EEA air pollutant emission inventory guidebook 2023 Annex 1 (link)
• CORSIA Eligible Fuels Life Cycle Assessment Methodology (link)
• ISO 14083 (link)

In addition to predicting a flight's emissions, it is possible to estimate the emissions for an individual passenger on that flight. To perform this estimate, it's necessary to perform an individual breakdown based on three relevant factors:

1. Number of total seats on the plane in each seating class (first, business, premium economy, economy)
2. Number of occupied seats on the plane
3. Amount of cargo being carried

The emission estimates are higher for premium economy, business and first seating classes because the seats in these sections take up more space. As a result, those seats account for a larger share of the flight's total emissions. Different space allocations on narrow and wide-body aircraft are considered using separate weighing factors.

Used to determine which aircraft type was used for a given flight:

• Aircraft type from published flight schedules

Used to determine seating configuration and calculate emissions per available seat:

• Aircraft Configuration/Version (ACV) from published flight schedules
• Fleet-level aircraft configuration information from the "Seats (Equipment Configuration) File" provided by OAG

If there are no individual seat configuration numbers for a flight available from the published flight schedules, we query the fleet-level seating data for a unique match by carrier and aircraft. This is only possible in cases where a carrier uses the same seating configuration for all their aircraft of a certain aircraft model.

If there are no individual seat configuration numbers for a flight available from the published flight schedules, nor from the fleet-level data, or if they are incorrectly formatted or implausible, the TIM uses aircraft-specific medians derived from the overall dataset instead. Basic correctness checks based on reference seat configurations for the aircraft are performed, specifically:

• The calculated total seat area for a flight is the total available seating area. This is calculated based on seating data and seating class factors. For example, the total seat area for a wide-body aircraft would be:

  1.0 * num_economy_class_seats +
  1.5 * num_premium_economy_class_seats +
  4.0 * num_business_class_seats +
  5.0 * num_first_class_seats
  

• The reference total seat area for an aircraft is roughly the median total seat area.

• During a comparison step: If the calculated total seat area for a given flight is within certain boundaries of the reference for that aircraft, the filed seating data from published flight schedules is used. Otherwise the reference total seat area is used.

Seating class factors

Seating parameters follow IATA RP 1726. An analysis of seat pitch and width in each seating class in typical plane configurations confirmed the accuracy of these factors.

Load factors

Passenger load factors are predicted based on historical passenger statistics. The TIM uses a tiered approach to determine passenger load factors. High resolution, specific data (i.e. by route) is preferred where available, and in the absence of more granular data, the model falls back to a generic value (i.e. global default).

Tier 1: Highly specific passenger load factors

1. For flights within, to, and from the United States and its territories, we consider the T-100 historical dataset from the US Department of Transportation Bureau of Transportation Statistics (see below for more details).

   • When the data is available for a given carrier, route, and month of travel, we calculate the aggregate passenger load factors, looking back up to six years.
   • When the data is available for a given carrier and month of travel, but not the specific route, we use the average passenger load factor across all the routes, up to six years back.
   • If fewer than three years of data are available, we consider ch-aviation load factors described below.

2. For all other flights, we consider the historical load factor data provided by ch-aviation:

   • When the data is available for a given carrier and month of travel, we calculate the aggregate passenger load factors, looking back up to six years.
   • If fewer than three years of data are available, we use the global average fallback value instead as described below ("Global default passenger load factor").

Tier 2: Global default passenger load factor

• For all other flights for which an equivalent public-domain dataset with similar granularity is not currently available, the TIM falls back to use a load factor value of 84.5%. This value is derived from historical data for the U.S. from 2019.
• An analysis of load factors sourced from publicly available airline investor reports indicates that this value is a good approximation for the passenger load factor globally.

Cargo load factors are not included.

Load factor data source specifics

T-100 from U.S. Department of Transportation Bureau of Transportation Statistics and ch-aviation

• Only data from the last six years is used.
• Data is updated on a monthly basis (TIM version number will not increase).
• Any month of data for which the overall load factor (aggregated over all airlines and routes) differs more than 10% from the average load factor since 2017 is removed as an outlier month. March 2020–February 2022 (inclusive) are removed from the data as a result.
• To account for patterns of seasonality that do not correspond with the exact month of travel (e.g. public holidays), the previous and next month are taken into account for the average load factor of any given month of travel. E.g. For future flights in March, we aggregate over all flights in February, March, and April.

For this example, we'll use a flight from Zurich (ZRH) to San Francisco (SFO) on a Boeing 787-9 aircraft with the following seating configuration.

To get the total emissions for the flight, let's follow the process below:

1. Calculate great circle distance between ZRH and SFO: 9369 km (= 5058.9 nautical miles)

2. Look up the static LTO numbers and the distance-based CCD number from aircraft performance data (see Table 1), and interpolate fuel burn for a 9369 km long flight:

   • LTO 1727 kg of fuel burn

   • CCD 52970 kg of fuel burn calculated

     52375 + (5058.9 - 5000) * (57430 - 52375) / (5500 - 5000) = 52970
     

3. Sum LTO and CCD number for total flight-level result:

   1727 kg + 52970 kg = 54697 kg of fuel burn
   

4. Convert from fuel burn to CO₂e emissions for total flight-level result:

   • Well-to-Tank (WTT) emissions in kg of CO₂e: 54697 * 0.6465 = 35362
   • Tank-to-Wake (TTW) emissions in kg of CO₂e: 54697 * 3.1894 = 174451
   • Well-to-Wake (WTW) emissions in kg of CO₂e: (54697 * 0.6465) + (54697 * 3.1894) = 209812

Once the total flight emissions are computed, let's compute the per passenger break down:

1. Determine which seating class factors to use for the given flight. In the ZRH-SFO example, we will use the wide-body factors (Boeing 787-9).

2. Calculate the equivalent capacity of the aircraft according to the following

   C = first_class_seats * first_class_multiplier +
       business_class_seats * business_class_multiplier +
       …
   

   In this specific example, the estimated area is:

   0 * 5 + 48 * 4 + 1.5 * 21 + 188 * 1 = 411.5
   

3. Divide the total CO₂e emissions by the equivalent capacity calculated above to get the CO₂e emissions per economy passenger.

   • Well-to-Tank (WTT) emissions in kg of CO₂e: 35362 / 411.5 = 85.934
   • Tank-to-Wake (TTW) emissions in kg of CO₂e: 174451 / 411.5 = 423.939
   • Well-to-Wake (WTW) emissions in kg of CO₂e: 85.934 + 423.939 = 509.873

4. Emissions per passenger for other cabins can be derived by multiplying by the corresponding cabin factor.

   • First:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 85.934 * 5 = 9.67
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 423.939 * 5 = 9.695
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 509.873 * 5 = 9.365
   • Business:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 85.934 * 4 = 3.736
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 423.939 * 4 = 5.756
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 509.873 * 4 = 9.492
   • Premium Economy:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 85.934 * 1.5 = 128.901
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 423.939 * 1.5 = 635.909
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 509.873 * 1.5 = 764.81
   • Economy:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 85.934
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 423.939
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 509.873

5. Scale to estimated load factor 0.845 by apportioning emissions to occupied seats:

   • First:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 429.67 / 0.845 = 508.485
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 2119.695 / 0.845 = 2508.515
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 2549.365 / 0.845 = 3017
   • Business:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 343.736 / 0.845 = 406.788
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 1695.756 / 0.845 = 2006.812
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 2039.492 / 0.845 = 2413.6
   • Premium Economy:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 128.901 / 0.845 = 152.546
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 635.909 / 0.845 = 752.555
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 764.81 / 0.845 = 905.101
   • Economy:
     • Well-to-Tank (WTT) emissions in kg of CO₂e: 85.934 / 0.845 = 101.697
     • Tank-to-Wake (TTW) emissions in kg of CO₂e: 423.939 / 0.845 = 501.703
     • Well-to-Wake (WTW) emissions in kg of CO₂e: 509.873 / 0.845 = 603.4

Note that the model generates emission estimates for all cabin classes, including cabin classes where the seat count is zero, as cabin classifications are not always consistent across data providers. Therefore, providing estimates for all cabin classes simplifies integration of the TIM's data with other datasets.

The GHG emission estimate data are available via API under the Creative Commons Attribution-ShareAlike CC BY-SA 4.0 open source license (legal code).

Developer documentation is available on the Google Developers site for the Travel Impact Model API.

The model will be developed further over time, e.g. with improved load factors methodology or more fine grained seat area ratios calculation. New versions will be published.

A full model version will have four components: MAJOR.MINOR.PATCH.DATE, e.g. 1.3.1.20230101. The four tiers of change tracking are handled differently:

• Major versions: Changes to the model that would break existing client implementations if not addressed (e.g. changes in data types or schema) or major methodology changes (e.g. adding new data sources to the model that lead to major output changes). We expect these to be infrequent but they need to be managed with special care.
• Minor versions: Changes to the model that, while being consistent across schema versions, change the model parameters or implementation.
• Patch versions: Implementation changes meant to address bugs or inaccuracies in the model implementation.
• Dated versions: Model datasets are recreated with refreshed input data but no change to the algorithms regularly.

Migrating data sources for aircraft performance for some aircraft models.

Expanding T-100 coverage to include US territories. See section on load factors for information on the T-100 dataset.

Adding carrier-level passenger load factors from ch-aviation for flights that are not already covered by the T-100 dataset from the US Department of Transportation Bureau of Transportation Statistics. Also adjusting the load factors outlier exclusion criteria from 20% to 10% deviation from average load factor since 2017, resulting in removing March 2020–February 2022 (inclusive) (previously March 2020–February 2021). See the section on load factors for more details.

Adding Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions break-downs to all flight emissions. Updating the jet fuel combustion to CO₂ conversion factor from the minimum value of 3.1672 to the value of 3.1894 (using Lower Heating Value from ISO 14083 and CORSIA Carbon Intensity value), and using the CORSIA Life Cycle Assessment methodology to implement a WTT CO₂e emissions factor 0.6465. Reference: ISO, CORSIA.

Updating the jet fuel combustion to CO₂ conversion factor from 3.15 based on the EEA methodology to 3.1672 to align with the CORSIA methodology's recommended factor.

Adding carrier and route specific passenger load factors for flights from, to, and within the U.S., taking seasonality patterns into account. We are using data from the U.S. Department of Transportation Bureau of Transportation Statistics. For more details, see the section on load factors.

Adding a fleet-level source for seating configuration data. For airlines that don't file seating configuration information in flight schedules but use the same seating configuration for all their aircraft of a certain model, a fall back to the "Seats (Equipment Configuration) File" provided by OAG is performed.

Following recent discussions with academic and industry partners, we are adjusting the TIM to focus on CO₂ emissions. While we strongly believe in including non-CO₂ effects in the model long-term, the details of how and when to include these factors requires more input from our stakeholders as part of a governance model that's in development. With this change, we are provisionally removing contrails effects from our CO₂e estimates but will keep the labeling as “CO₂e” in the model to ensure future compatibility.

We believe CO₂e factors are critical to include in the model, given the emphasis on them in the IPCC's AR6 report. We want to make sure that when we do incorporate them into the model, we have a strong plan to account for time of day and regional variations in contrails' warming impact. We are committed to providing consumers the most accurate information as they make informed choices about their travel options.

We continue to invest into research and collaborate with leading scientists, NGOs, and partners to better incorporate contrails and other non-GHG impact into our model, and we look forward to sharing updates at a later date.

Initial public version of the Travel Impact Model.

The model described in this document produces estimates of GHG emissions. Emission estimates aim to be representative of what the typical emissions for a flight matching the model inputs would be. Estimates might differ from actual emissions based on a number of factors.

Actual flight distances: When modeling the distance between a given origin and destination, the Great Circle Distance between the origin and destination airport is used, as opposed to the actual distance flown.

This simplifying assumption enables the model to be used even when precise flight path information is not available, such as when computing emission estimates for future flights.

Aircraft types: The emissions model accounts for the equipment type as published in the flight schedules. The majority of aircraft types in use are covered. See Appendix A for a list of supported aircraft types.

Some aircraft types are supported by falling back to a related model thought to have comparable emissions. See Flight level emission estimates for more details.

If no reasonable approximation is available for a given aircraft, the model will not produce estimates for it.

Cargo load factors: Cargo load is not yet supported in the model.

Engine information: Beyond the aircraft type, there are other aircraft characteristics that can have an effect on the flight emissions (e.g. engine type, engine age, etc.) that are not currently included when computing emission estimates.

Fuel type: The emissions model assumes that all flights operate on 100% conventional fuel. Alternative fuel types (e.g. Sustainable Aviation Fuel) are not supported.

Seat configurations: If there are no seat configurations individual numbers for a flight available from published flight schedules, or if they are incorrectly formatted or implausible, aircraft specific medians derived from the overall dataset are employed.

Contrail-induced cirrus clouds: Warming effects produced by short-lived climate pollutants such as contrail-induced cirrus clouds are not yet included in emissions as calculated by the Travel Impact Model.

The CO₂ estimates were validated by comparing against a limited amount of real-world fuel burn data. The finding was that the TIM is underestimating by 7% on average.

The EEA guidebook (chapter 4) cites sources from ICAO that estimate the uncertainty of the LTO factors between 5 and 10%. The CCD factor uncertainty is estimated between 15 and 40%.

You are welcome to use the Travel Impact Model (TIM) in your publications. When referencing the TIM, please cite it as in the following example:

Google. (2022, April). Travel Impact Model (TIM) (Version A.B.C.YYYYMMDD) [Computer software]. Retrieved September 28, 2024 via API, https://github.com/google/travel-impact-model

The TIM is a dynamic model that is regularly updated with new data and methodologies. To ensure that others can access the same data and calculations you used, it is essential to include the version number and retrieval date in your citation.

BibTeX example:

@misc{google_tim_2022,
  institution = {Google},
  title = {Travel Impact Model (TIM)},
  year = {2022},
  month = {April},
  note = {Version A.B.C.YYYYMMDD. Retrieved September 28, 2024},
  url = {https://github.com/google/travel-impact-model}
}

If you access the TIM programmatically through the API, please mention this in your citation as well.

We welcome feedback and enquiries. Please get in touch using this form.

CCD: The flight phases Climb, Cruise, and Descend occur above a flight altitude of 3,000 feet.

CO₂: Carbon dioxide is the most significant long-lived greenhouse gas in Earth's atmosphere. Since the Industrial Revolution anthropogenic emissions – primarily from use of fossil fuels and deforestation – have rapidly increased its concentration in the atmosphere, leading to global warming.

CO₂e: CO₂e is short for CO₂ equivalent, and is a metric measure used to compare the emissions from various greenhouse gases on the basis of their global-warming potential (GWP), by converting amounts of other gases to the equivalent amount of carbon dioxide with the same global warming potential (source).

Contrail-induced cirrus clouds: Cirrus clouds are atmospheric clouds that look like thin strands. There are natural cirrus clouds, and also contrail induced cirrus clouds that under certain conditions occur as the result of a contrail formation from aircraft engine exhaust.

CORSIA: Carbon Offsetting and Reduction Scheme for International Aviation, a carbon offset and reduction scheme to curb the aviation impact on climate change developed by the International Civil Aviation Organization.

Effective Radiative Forcing (ERF): Radiative forcing effects can create rapid responses in the troposphere, which can either enhance or reduce the flux over time, and makes RF a difficult proxy for calculating long-term climate effects. ERF attempts to capture long-term climate forcing, and represents the change in net radiative flux after allowing for short-term responses in atmospheric temperatures, water vapor and clouds.

European Environment Agency (EEA): An agency of the European Union whose task is to provide sound, independent information on the environment.

Google's Travel Sustainability team: A team at Google focusing on travel sustainability, based in Zurich (Switzerland) and Cambridge (U.S.), with the goal to enable users to make more sustainable travel choices.

Great circle distance: Defined as the shortest distance between two points on the surface of a sphere when measured along the surface of the sphere.

ICAO: The International Civil Aviation Organization, a specialized agency of the United Nations.

ISO 14083: The international standard that establishes a common methodology for the quantification and reporting of greenhouse gas (GHG) emissions arising from the operation of transport chains of passengers and freight (source), published by the International Organization for Standardization (ISO).

LTO: The flight phases Take Off and Landing occur below a flight altitude of 3000 feet at the beginning and the end of a flight. They include the following phases: taxi-out, taxi-in (idle), take-off, climb-out, approach and landing.

Radiative Forcing (RF): Radiative Forcing is the instantaneous difference in radiative energy flux stemming from a climate perturbation, measured at the top of the atmosphere.

Short Lived Climate Pollutants (SLCPs): Pollutants that stay in the atmosphere for a short time (e.g. weeks) in comparison to Long Lived Climate Pollutants such as CO₂ that stay in the atmosphere for hundreds of years.

Tank-to-Wake (TTW): Emissions produced by burning jet fuel during takeoff, flight, and landing of an aircraft.

TIM: The Travel Impact Model described in this document.

Well-to-Tank (WTT): Emissions generated during the production, processing, handling, and delivery of jet fuel.

Well-to-Wake (WTW): The sum of Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions.