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Intensity and Accounting","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"## Travel Impact Model 3.0.0\n\nhttp://www.travelimpactmodel.org\n\n## Background\n\nIn this document we describe the modeling assumptions and input specifications\nbehind the Travel Impact Model (TIM), a state of the art emission estimation\nmodel that Google's Travel Sustainability team has compiled from several\nexternal data sources. The TIM predicts greenhouse gas (GHG) emissions for\nfuture flights to help travelers plan their travel.\n\nISO 14083 defines a user's travel journey from when they leave their origin\n(point A) to when they arrive at their destination (point B). Figure 1[^1] below\nillustrates an example of a user's travel journey. To calculate the total\nemissions of this user's journey, ISO 14083 recommends summing up the emissions\nproduced by each individual piece of the journey. In this example, it includes\nthe emissions created driving to the airport, the emissions to run the origin\nairport, the flight's emissions, the emissions to run the destination airport,\nand the train's emissions to the user's destination. The Travel Impact Model\nonly estimates the flight's emissions, highlighted in green.\n\n![ISO 14083 defined user journey (car to airport, in airport, flight (green), in airport, train from airport)](images/image4.png)\n\n(Figure 1[^2])\n\nAs shown in Figure 2[^3], the TIM supports two types of flights:\n\n* multi-class flights with passengers\n* multi-class flights with passengers and cargo\n\n![Image of flight splitting into 2 types: flight with passengers only and flight with passengers and cargo](images/image5.png)\n\n(Figure 2[^4])\n\n## Model overview\n\nThe TIM is a model[^5] designed to estimate GHG emissions generated from an\naircraft transporting passengers with or without cargo from an origin to\ndestination. For each flight, the TIM considers several factors, such as an\nestimate of the distance flown between the origin and destination airports, and\nthe aircraft type being used for the route. Actual GHG emissions at flight time\nmay vary depending on factors not known at modeling time, such as speed and\naltitude of the aircraft, the actual flight route, and weather conditions at the\ntime of flight.\n\n### Flight level emission estimates\n\n#### Flight level CO₂e estimates\n\nThe Travel Impact Model estimates fuel burn based on the Tier 3 methodology for\nemission estimates from the\n[Annex 1.A.3.a Aviation 2023](https://www.eea.europa.eu/publications/emep-eea-guidebook-2023/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-a-aviation.3/view)\npublished by the European Environment Agency (EEA).\n\nThere are several resources about the EEA model available:\n\n* the main\n [documentation](https://www.eea.europa.eu/publications/emep-eea-guidebook-2023/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-a-aviation-2023/view)\n* the\n [data set](https://www.eea.europa.eu/publications/emep-eea-guidebook-2023/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-a-aviation.3/view)\n* further\n [documentation](https://www.eurocontrol.int/sites/default/files/content/documents/201807-european-aviation-fuel-burn-emissions-system-eea-v2.pdf)\n on pre-work for the EEA model\n\nAdditionally, the Travel Impact Model uses the fuel burn to emissions\nconversion factor to align with the\n[ISO 14083](https://www.iso.org/standard/78864.html) Fuel Heat Combustion factor\nand\n[CORSIA Life Cycle Assessment](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf)[^6],\nand breaks down emissions estimates into Well-to-Tank (WTT) and Tank-to-Wake\n(TTW) emissions.\n\nTank-to-Wake emissions account for emissions produced by burning jet fuel during\nflying, take-off and landing. Well-to-Tank emissions account for emissions\ngenerated during the production, processing, handling and delivery of jet fuel.\nWell-to-Wake (WTW) emissions is the sum of Well-to-Tank (WTT) and Tank-to-Wake\n(TTW) emissions.\n\nThe EEA model takes the efficiency of the aircraft into account. As shown in\nFigure 3, a typical flight is modeled in two stages: *take off and landing*\n(LTO, yellow) and *cruise, climb, and descend* (CCD, blue).\n\n![Fixed fuel burn allocated during LTO, variable during CCD](images/image3.png)\n\n(Figure 3)\n\nFor each stage, there are aircraft-specific and distance-specific fuel burn\nestimates. Table 1 shows an example fuel burn forecast for a Boeing 787-9 (B789)\naircraft:\n\nAircraft | Distance (NM) | LTO fuel forecast (kg) | CCD fuel forecast (kg)\n-------- | ------------: | ---------------------: | ---------------------:\nB789 | 500 | 1638 | 5852\nB789 | 1000 | 1638 | 10874\nB789 | ... | ... | ...\nB789 | 5000 | 1638 | 52962\nB789 | 5500 | 1638 | 58072\n\n(Table 1)\n\nBy using these numbers together with linear interpolation or extrapolation, it\nis possible to deduce the emission estimate for flights of any length on\nsupported aircraft:\n\n* Interpolation is used for flights that are in between two distance data\n points. As a theoretical example, a 5250 nautical miles (NM) flight on a\n Boeing 787-9 will burn approximately 55517 kg of fuel during the CCD phase\n (where 55517 equals 52962 + (58072 - 52962)/2, with figures for 5000 NM\n and 5500 NM taken from Table 1).\n* Extrapolation is used for flights that are either shorter than the smallest\n supported distance, or longer than the longest supported distance for that\n aircraft type.\n* The Lower Heating Value from ISO 14083 (43.1 MJ/kg for jet kerosene averaged over EU and US\n numbers from [source](https://www.iso.org/standard/78864.html) Table K.1 and\n Table K.3 respectively) and CORSIA Carbon Intensity value (74 gCO₂e/MJ from\n [source](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf)\n Table 5) are used to calculate the jet fuel combustion to CO₂e\n conversion factor of 3.1894. The CORSIA Life Cycle Assessment methodology is\n used to calculate a WTT CO₂e emissions factor of 0.6465 (WTT 15g\n CO₂e/MJ added to the TTW 74 gCO₂e/MJ Carbon Intensity\n to total up to the WTW lifecycle Carbon Intensity of 89 gCO₂e/MJ\n from\n [source](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf)\n page 24 and Table 7). The factors used are as follows:\n\nLife Cycle Stage | Carbon Intensity Value from CORSIA \u003cbr\u003e (g CO₂e/MJ) | Lower Heating Value from ISO 14083 \u003cbr\u003e (MJ/kg) | Factor \u003cbr\u003e (kg CO₂e/kg)\n--------------------|-----------------|------|-------------------------------\nTank-To-Wake (TTW) | 74 | 43.1 | 3.1894 (= 74 * 43.1 / 1000)\nWell-To-Tank (WTT) | 15 (= 89 - 74) | 43.1 | 0.6465 (= 15 * 43.1 / 1000)\nWell-To-Wake (WTW) | 89 | 43.1 | 3.8359 (= 89 * 43.1 / 1000)\n\nCO₂e is short for CO₂ equivalent and includes Kyoto Gases\n(GHG) as described\n[here](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Kyoto_basket#:~:text=The%20Kyoto%20basket%20encompasses%20the,sulphur%20hexafluoride%20\\(SF6\\)).\nWarming effects produced by short-lived climate pollutants and\ncontrail-induced cirrus clouds are not yet included in CO₂e as\ncalculated by the Travel Impact Model. We are working with our stakeholders in\nthe [governance body](https://travelimpactmodel.org/governance) to add non-CO2\neffects to the model.\n\nThere is information for most commonly-used aircraft types in the EEA data, but\nsome are missing. For missing aircraft types, one of the following alternatives\nis applied in ranked order:\n\n* *Supported by winglet/sharklet correction factor:* For all aircraft (with a\n corresponding IATA code) with a winglet or sharklet variant for which no native\n data exists (see [Appendix A](#appendix-a-aircraft-type-support)), a 3% discount factor will\n be applied on top of EEA estimates. The correction factor will be applied to\n the LTO and CCD numbers of the comparable type in the EEA database. We are\n basing the 3% factor on a literature review as a conservative estimate\n ([Airbus](https://aircraft.airbus.com/en/services/enhance/systems-and-airframe-upgrades/fuel-efficiency-solutions#:~:text=Sharklets%20can%20deliver%20fuel%20savings,as%20range%20and%2For%20payload.),\n [AviationBenefits](https://aviationbenefits.org/case-studies/wingtip-devices/),\n [Boeing](http://www.boeing.ch/commercial/aeromagazine/articles/qtr_03_09/pdfs/AERO_Q309_article03.pdf),\n [Cirium](https://www.cirium.com/thoughtcloud/impact-winglets-on-fuel-consumption-and-aircraft-emissions/),\n [NASA](https://spinoff.nasa.gov/Spinoff2010/t_5.html),\n [SimpleFlying](https://simpleflying.com/wing-tip-fuel-efficiency/)).\n* *Supported by fallback to previous generation aircraft type:* If there are\n estimates in the EEA data set for a previous generation aircraft type in the\n same family, from the same manufacturer, the previous generation aircraft is\n used for the estimate.\n* *Supported by fallback to least efficient aircraft in the family:* For\n umbrella codes that refer to a group of aircraft, the least efficient\n aircraft in the family will be assumed.\n* *Supported by fallback to similar aircraft type:* If there are estimates in\n the EEA data set for a similar aircraft, it is used for the estimate.\n* *Not supported:* For aircraft types for which none of the cases above apply,\n there are no emissions estimates available.\n\nSee [Appendix A](#appendix-a-aircraft-type-support) for a table with detailed\ninformation about aircraft type support status.\n\n#### Distance adjustment\n\nActual flight paths are usually longer than the great-circle distance (GCD)\nbetween origin and destination airport due to several factors, like the flown\nroute, airport congestion, airspace restrictions, and bad weather avoidance.\n\nThe TIM includes distance adjustment factors based on historical flight tracking\ndata from ADS-B. These adjustment factors were developed at Imperial College London by\n[Teoh et al.](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-724/egusphere-2023-724.pdf)\nwho found that on average, the actual distance flown is roughly 5% higher\nthan the great-circle distance, and that this percentage varies across regions\nand routes. The data cleaning approach is described\n[here](https://zenodo.org/records/8369564/files/README.txt).\n\nThe distance adjustment is performed as follows:\n\n1. If available, apply the\n [route-based adjustment factor data](https://zenodo.org/records/8369564/files/origin_destination_airport_gaia_vs_eea.csv)\n for the given origin airport and destination airport. This factor represents\n the ratio between the average flown distance on the route and its\n great-circle distance.\n2. Otherwise, if available, apply the\n [country-based adjustment factor data](https://zenodo.org/records/8369564/files/origin_destination_country_gaia_vs_eea.csv)\n for the given origin airport country and destination airport country. This\n factor represents the ratio between the average flown distance for all\n flights between the origin airport country and destination airport country\n and their corresponding great-circle distances.\n3. Otherwise, in the rare case where no adjustment factor is available, apply a factor of\n 1.052 which represents the mean lateral inefficiency increase (+5.2%) for\n 2019 data from [Teoh et al.](https://egusphere.copernicus.org/preprints/2023/egusphere-2023-724/egusphere-2023-724.pdf)\n (see page 18), which is used for the distance adjustment factor.\n\n#### Data sources\n\nUsed for flight level emissions:\n\n* EMEP/EEA air pollutant emission inventory guidebook 2023 Annex 1 version\n v1.5_18_09_2024\n ([link](https://www.eea.europa.eu/publications/emep-eea-guidebook-2023/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-a-aviation.3/view))\n* Teoh et al., The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 - 2021: Origin-destination statistics ([link](https://zenodo.org/records/8369564))\n* CORSIA Eligible Fuels Life Cycle Assessment Methodology\n ([link](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf)\n* ISO 14083 ([link](https://www.iso.org/standard/78864.html))\n\n### Breakdown from flight level to individual level\n\nIn addition to predicting a flight's emissions, it is possible to estimate the\nemissions for an individual passenger on that flight. To perform this estimate,\nit's necessary to perform an individual breakdown based on three relevant\nfactors:\n\n1. Number of total seats on the plane in each seating class (first, business,\n premium economy, economy)\n2. Number of occupied seats on the plane\n3. Amount of cargo being carried\n\nThe emission estimates are higher for premium economy, business and first\nseating classes because the seats in these sections take up more space. As a\nresult, those seats account for a larger share of the flight's total emissions.\nDifferent space allocations on narrow and wide-body aircraft are considered\nusing separate weighing factors.\n\n#### Data sources\n\nUsed to determine which aircraft type was used for a given flight:\n\n* Aircraft type from published flight schedules\n\nUsed to determine seating configuration and calculate emissions per available\nseat:\n\n* Aircraft Configuration/Version (ACV) from published flight schedules\n* Fleet-level aircraft configuration information from the \"Seats (Equipment\n Configuration) File\" provided by [OAG](https://oag.com)\n\n#### Primary fallback for missing seat configuration\n\nIf there are no individual seat configuration numbers for a flight available\nfrom the published flight schedules, we query the fleet-level seating data for a\nunique match by carrier and aircraft. This is only possible in cases where a\ncarrier uses the same seating configuration for all their aircraft of a certain\naircraft model.\n\n#### Outlier detection and basic correctness checking\n\nIf there are no individual seat configuration numbers for a flight available\nfrom the published flight schedules, nor from the fleet-level data, or if they\nare incorrectly formatted or implausible, the TIM uses aircraft-specific medians\nderived from the overall dataset instead. Basic correctness checks based on\nreference seat configurations for the aircraft are performed, specifically:\n\n* The *calculated total seat area* for a flight is the total available seating\n area. This is calculated based on seating data and seating class factors.\n For example, the total seat area for a wide-body aircraft would be:\n\n ```\n 1.0 * num_economy_class_seats +\n 1.5 * num_premium_economy_class_seats +\n 4.0 * num_business_class_seats +\n 5.0 * num_first_class_seats\n ```\n\n* The *reference total seat area* for an aircraft is roughly the median total\n seat area.\n\n* During a *comparison* step: If the *calculated total seat area* for a given\n flight is within certain boundaries of the reference for that aircraft, the\n filed seating data from published flight schedules is used. Otherwise the\n *reference total seat area* is used.\n\n#### Factors details\n\n**Seating class factors**\n\nSeating parameters follow\n[IATA RP 1726](https://www.iata.org/en/programs/environment/passenger-emissions-methodology/).\nAn analysis of seat pitch and width in each seating class in typical plane\nconfigurations confirmed the accuracy of these factors.\n\nCabin Class | Narrow-body aircraft | Wide-body aircraft\n--------------- | -------------------- | ------------------\nEconomy | 1 | 1\nPremium Economy | 1 | 1.5\nBusiness | 1.5 | 4\nFirst | 1.5 | 5\n\n**Cargo mass fraction**\n\nBelly cargo carried on passenger flights is a contributor to total emissions. We\napportion emissions by mass. The cargo mass fraction (CMF) is defined as the\ncargo mass divided by total payload, which is defined as the sum of cargo mass\nand passenger mass. Passenger mass (including passenger's baggage) is approximated\nby multiplying the number of passengers by 100kg, as defined in ISO 14083, Section A.4.2.\n\nAs the cargo mass fraction determines the amount of emissions apportioned to\nbelly cargo, the remainder is apportioned to passengers. The TIM uses a tiered\napproach to determine cargo mass fraction. High resolution, specific data (i.e.\nby carrier, route, and aircraft class) is preferred where available, and in the\nabsence of more granular data the model falls back to coarser aggregations when\nno suitable high resolution options are available.\n\nFor consistency with passenger load factors, we also exclude March 2020 to\nFebruary 2022, due to the effects of the COVID-19 pandemic.\n\nTier 1: Highly specific cargo mass fraction\n\n* Where data is available for a given carrier, route, and aircraft class\n (distinguishing narrowbody and widebody aircraft), use the average cargo\n mass fraction over the last 6 years.\n* Where data is available for the given route and aircraft class, but not the\n specific carrier, use the average cargo mass fraction across all carriers\n over the last 6 years.\n* If fewer than 2000 flights are available for averaging, we do not calculate\n an average and instead fallback to the \"Coarse cargo mass fraction tier\"\n described below.\n\nTier 2: Coarse cargo mass fraction\n\n* Where specific data is not available, use average cargo mass fraction data,\n matching distance band and aircraft class over the last 6 years.\n\n* Distance bands are defined in 1000 km intervals, i.e. distances 1 km to 1000 km,\n 1001 km to 2000km, etc., are grouped together. The distance is determined between\n origin and destination using the great-circle distance.\n\nThe TIM uses historical data provided by the U.S. Department of Transportation\nBureau of Transportation Statistics to determine cargo mass fraction values. The\ncoarse aggregations by distance band and aircraft class are also used to forecast\ncargo carried for flights outside the United States.\n\n**Load factors**\n\nPassenger load factors are predicted based on historical passenger statistics.\nThe TIM uses a tiered approach to determine passenger load factors. High\nresolution, specific data (i.e. by route) is preferred where available, and in\nthe absence of more granular data, the model falls back to a generic value (i.e.\nglobal default).\n\nTier 1: Highly specific passenger load factors\n\n1. For flights within, to, and from the United States and its territories, we\n consider the T-100 historical dataset from the\n [US Department of Transportation Bureau of Transportation Statistics](https://www.bts.gov/airline-data-downloads)\n (see below for more details).\n\n * When the data is available for a given carrier, route, and month of\n travel, we calculate the aggregate passenger load factors, looking back\n up to six years.\n * When the data is available for a given carrier and month of travel, but\n not the specific route, we use the average passenger load factor across\n all the routes, up to six years back.\n * If fewer than three years of data are available, we consider ch-aviation\n load factors described below.\n\n2. For all other flights, we consider the historical load factor data provided\n by [ch-aviation](https://www.ch-aviation.com/):\n\n * When the data is available for a given carrier and month of travel, we\n calculate the aggregate passenger load factors, looking back up to six\n years.\n * If fewer than three years of data are available, we use the global\n average fallback value instead as described below (\\\"*Global default\n passenger load factor*\\\").\n\nTier 2: Global default passenger load factor\n\n* For all other flights for which an equivalent public-domain dataset with\n similar granularity is not currently available, the TIM falls back to use a\n load factor value of **84.5%**. This value is derived from\n [historical data for the U.S.](https://fred.stlouisfed.org/series/LOADFACTOR)\n from 2019.\n* An analysis of load factors sourced from publicly available airline investor\n reports indicates that this value is a good approximation for the passenger\n load factor globally.\n\n**Load factor data source specifics**\n\nT-100 from\n[U.S. Department of Transportation Bureau of Transportation Statistics](https://www.bts.gov/airline-data-downloads)\nand [ch-aviation](https://www.ch-aviation.com/)\n\n* Only data from the last six years is used.\n* Data is updated on a monthly basis (TIM version number will not increase).\n* Any month of data for which the overall load factor (aggregated over all\n airlines and routes) differs more than 10% from the average load factor\n since 2017 is removed as an outlier month. March 2020–February 2022\n (inclusive) are removed from the data as a result.\n* To account for patterns of seasonality that do not correspond with the exact\n month of travel (e.g. public holidays), the previous and next month are\n taken into account for the average load factor of any given month of travel.\n E.g. For future flights in March, we aggregate over all flights in February,\n March, and April.\n\n## Example emission estimation\n\nFor this example, we'll use a flight from Zurich (`ZRH`) to San Francisco\n(`SFO`) on a `Boeing 787-9` aircraft with the following seating configuration.\n\nCabin Class | Seats\n--------------- | ----\nEconomy | 188\nPremium Economy | 21\nBusiness | 48\nFirst | 0\n\nTo get the total emissions for the flight, let's follow the process below:\n\n1. Calculate great-circle distance between ZRH and SFO: `9369 km` (= `5058.9\n nautical miles (NM)`)\n2. Look up the static LTO numbers and the distance-based CCD number from\n aircraft performance data (see Table 1), and interpolate fuel burn for a\n 9369 km long flight:\n * LTO `1638 kg` of fuel burn\n * CCD `54802 kg` of fuel burn calculated like this and rounded:\n * Apply distance adjustment factor as described\n [here](#distance-adjustment) to determine adjusted distance:\n `5058.9 * 1.0273 = 5197.00797 NM`\n * The EEA model assumes that the aircraft travels 17 NM of the\n complete distance of the flight during the LTO cycle. Subtract 17 NM\n from the adjusted distance to account for the distance travelled in\n the LTO phase: `5197.00797 - 17 = 5180.00797 NM = 5180 NM (rounded)`\n * Calculate the fuel burn for 5180 NM by interpolating between the\n known fuel burn values at 5000 NM (52962 kg) and 5500 NM (58072 kg):\n `52962 kg + (5180 NM - 5000 NM) * (58072 kg - 52962 kg) / (5500 NM - 5000 NM) = 54801.6 kg`\n3. Sum LTO and CCD number for total flight-level result (rounded):\n `1638 kg + 54802 kg = 56440 kg of fuel burn`\n\n4. Convert from fuel burn to CO₂e emissions for total flight-level\n result:\n\n * Well-to-Tank (WTT) emissions in kg of CO₂e (rounded): `56440 * 0.6465 = 36488`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e (rounded): `56440 * 3.1894 = 180010`\n * Well-to-Wake (WTW) emissions in kg of CO₂e (rounded): `(56440 *\n 0.6465) + (56440 * 3.1894) = 216498`\n\nOnce the total flight emissions are computed, we apportion emissions between\nbelly cargo and passengers:\n\n1. Use the cargo mass fraction of 8% to apportion 8% of the emissions to belly\n cargo, and correspondingly 92% of emissions to passengers. All values rounded to kg.\n * Well-to-Tank (WTT) cargo emissions in kg of CO₂e: `36488 * 0.08 = 2919`\n * Tank-to-Wake (TTW) cargo emissions in kg of CO₂e: `180010 * 0.08 = 14401`\n * Well-to-Wake (WTW) cargo emissions in kg of CO₂e: `216498 * 0.08 = 17320`\n * Well-to-Tank (WTT) passenger emissions in kg of CO₂e: `36488 * 0.92 = 33569`\n * Tank-to-Wake (TTW) passenger emissions in kg of CO₂e: `180010 * 0.92 = 165609`\n * Well-to-Wake (WTW) passenger emissions in kg of CO₂e: `216498 * 0.92 = 199178`\n\nOnce the total flight emissions are computed, let's compute the per passenger\nbreak down:\n\n1. Determine which seating class factors to use for the given flight. In the\n `ZRH-SFO` example, we will use the wide-body factors (`Boeing 787-9`).\n2. Calculate the equivalent capacity of the aircraft according to the following\n\n C = first_class_seats * first_class_multiplier +\n business_class_seats * business_class_multiplier + …\n\n In this specific example, the estimated area is:\n ```\n 0 * 5 + 48 * 4 + 1.5 * 21 + 188 * 1 = 411.5\n ```\n\n3. Divide the total CO₂e emissions by the equivalent capacity\n calculated above to get the CO₂e emissions per economy seat.\n\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `33569 / 411.5 = 81.577`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `165609 / 411.5 = 402.452`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `81.577 + 402.452 = 484.029`\n\n4. Emissions per seat for other cabins can be derived by multiplying by\n the corresponding cabin factor.\n\n * First:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `81.577 * 5 = 407.885`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `402.452 * 5 = 2012.26`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `484.029 * 5 = 2420.145`\n * Business:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `81.577 * 4 = 326.308`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `402.452 * 4 = 1609.808`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `484.029 * 4 = 1936.116`\n * Premium Economy:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `81.577 * 1.5 = 122.366`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `402.452 * 1.5 = 603.678`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `484.029 * 1.5 = 726.044`\n * Economy:\n * Well-to-Tank (WTT) emissions in kg of CO₂e: `81.577`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e: `402.452`\n * Well-to-Wake (WTW) emissions in kg of CO₂e: `484.029`\n\n5. Scale to estimated load factor 0.845 by apportioning emissions to occupied\n seats. This results in per-passenger emissions:\n\n * First:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `407.885 kg / 0.845 = 482.704 kg`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `2012.26 kg / 0.845 = 2381.373 kg`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `2420.145 kg / 0.845 = 2864.077 kg`\n * Business:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `326.308 kg / 0.845 = 386.163 kg`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `1609.808 kg / 0.845 = 1905.098 kg`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `1936.116 kg / 0.845 = 2291.262 kg`\n * Premium Economy:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `122.366 kg / 0.845 = 144.812 kg`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `603.678 kg / 0.845 = 714.412 kg`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `726.044 kg / 0.845 = 859.224 kg`\n * Economy:\n * Well-to-Tank (WTT) emissions in kg of CO₂e:\n `81.577 kg / 0.845 = 96.541 kg`\n * Tank-to-Wake (TTW) emissions in kg of CO₂e:\n `402.452 kg / 0.845 = 476.275 kg`\n * Well-to-Wake (WTW) emissions in kg of CO₂e:\n `484.029 kg / 0.845 = 572.815 kg`\n\nNote that the model generates emission estimates for all cabin classes,\nincluding cabin classes where the seat count is zero, as cabin classifications\nare not always consistent across data providers. Therefore, providing estimates\nfor all cabin classes simplifies integration of the TIM's data with other\ndatasets.\n\nIn cases where flight emissions data is sourced from the EASA [flight emissions\nlabel](#flight-emissions-label), estimates will not be generated for any\ncabin class not explicitly included in the label.\n\n## Contrails impact prediction\n\nThe Travel Impact Model estimates the contrail warming impact potential per\nflight. This impact is communicated through classifications, or \"buckets,\" which\nrepresent the warming impact relative to the fuel burn emissions for that\nspecific flight.\n\n### Caveats and limitations\n\nContrail formation and their resulting warming effects are highly dependent on\nspecific weather conditions on the day of a flight. Specifically, contrail\nformation is heavily influenced by atmospheric humidity, temperature, and wind\nconditions at high altitudes. This makes precise predictions impossible at the\ntime of booking. Therefore, the contrail impact information we provide can only\nrepresent the potential risk of contrail warming for a given flight.\n\nTo overcome this predictive challenge, we analyze extensive historical\nmeteorological data alongside past aircraft flight paths, identifying recurring\ngeospatial and temporal patterns related to contrail formation. These patterns\nare then applied to future flight schedules, generating an estimated range of\npotential warming. Our model achieves this by first calculating the warming\nimpact if contrails were to persist, and then multiplying this by the probability\nthat persistent contrails will actually form for a given flight. While this\nestimate lacks the precision to predict the exact outcome of a specific flight,\nit provides a reliable directional trend when the impact of that flight is\nconsidered over a broader timeframe, such as a year or season.\n\nThat’s how the Travel Impact Model (TIM) provides customers with emissions\ninformation at the time of booking a flight that is happening in the future.\n\nContrail warming impact is categorized into relative impact levels (“buckets”)\ncompared to the fuel burn emissions for the flight. This means that the\ncontrail impact is assessed in comparison to the warming effect of the carbon\ndioxide released from burning fuel.\n\n### Recommended best practices\n\n1. **Acknowledge Uncertainty:** When presenting contrail impact levels to users,\nemphasize that these levels represent the risk of contrail warming. For example,\nuse phrases like:\n * \"Risk of high contrail impact\"\n * \"High likelihood of contrail warming impact.\"\n * \"Moderate potential for contrail warming\"\n\n2. **Keep Contrail Impact Separate from Fuel Burn Emissions:** Avoid combining\n these metrics into a single value. Due to the inherent uncertainty in\n contrail warming impact, it's not yet possible to create a combined metric\n that is both accurate and easily understandable for users.\n\n3. **Consider Both Contrail Impact and Fuel Burn:** Do not rely solely on\n contrail impact levels when making decisions. Both fuel burn and contrail\n impact contribute significantly to climate impact and should be taken into\n account. Because fuel burn impact is currently more predictable, it should\n remain the main impact factor communicated.\n\n4. **Avoid CO2e Conversion:** Do not convert contrail impact levels into a CO2e\n value. This can create a misleading impression of precision. It's best to\n represent contrail warming impact using the established impact level\n categories.\n\n5. **Aggregate for Multi-Leg Journeys:** For trips with multiple flights, you can\n combine contrail impact levels relative to each flight's fuel burn. Since the\n contrail impact is relative to fuel burn emissions, the values can be\n aggregated for multi-leg flights. It is recommended to use the mean relative\n impact of each bucket range for computing this aggregate. Since the highest\n impact category has no upper limit, a value of 1.2 is used as a\n representative mean for calculations to ensure a consistent and conservative\n approach.\n\n ```\n Example 1\n\n Bucket ranges: LOW [0.0, 0.2), MODERATE [0.2, 1.0), HIGH [1.0, ∞)\n\n Flight A: fuel burn = 100 kg CO2e, contrail impact = LOW\n Flight B: fuel burn = 50 kg CO2e, contrail impact = HIGH\n\n Aggregate = (100kg * (0.1 for LOW) + 50kg * (1.2 for HIGH)) / (total fuel burn)\n = 70kg / 150kg\n = 0.4667\n\n Aggregate classification: MODERATE contrail impact bucket\n ```\n\n ```\n Example 2\n\n Bucket ranges: LOW [0.0, 0.2), MODERATE [0.2, 1.0), HIGH [1.0, ∞)\n\n Flight A: fuel burn = 50 kg CO2e, contrail impact = MODERATE\n Flight B: fuel burn = 500 kg CO2e, contrail impact = HIGH\n\n Aggregate = (50kg * (0.6 for MODERATE) + 500kg * (1.2 for HIGH)) / (total fuel burn)\n = 630kg / 550kg\n = 1.15\n\n Aggregate classification: HIGH contrail impact bucket\n ```\n\n## Airport emissions\n\nThe TIM estimates flight emissions only, but ISO 14083 recommends including the\nairport emissions, highlighted in blue below, when calculating the emissions for\na user's journey. The airport emissions are emissions generated by a passenger\nwhile at the airport (i.e. electric walkways to move passengers, vehicles to move\nluggage, air conditioning, etc).\n\n![ISO 14083 defined user journey (car to airport, in airport (blue), flight (green), in airport (blue), train from airport)](images/image6.png)\n\nFollowing Airport Carbon Accreditation (ACA)'s\n[2024 annual report](https://www.airportcarbonaccreditation.org/wp-content/uploads/2025/04/Airport-Carbon-Accreditation-Annual-Report-2023-2024.pdf)\n(page 21), we suggest adding the global average value of 1.71 kg per passenger\nfor every airport visited to the total flight emissions calculated.[^7]\n\n## Flight emissions label\n\nThe [Flight Emissions Label (FEL)](https://www.flightemissions.eu/) empowers\npassengers to make informed decisions by providing clear and trusted information\nabout their carbon emissions, in accordance with Article 14 of the\n[ReFuelEU Aviation Regulation](https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32023R2405).\nIt relies on real data from past flight performance to label flights in the\nfuture. The Flight Emissions Label (FEL) is calculated using aircraft operators\ndata such as fuel purchases and consumption, aircraft seating configurations and\ncargo. This data is verified, and [EASA](https://www.easa.europa.eu/en/light)\nprocesses it to estimate the emissions in accordance with the EN ISO 14083:2023\nstandard.\n\nTIM users can easily access FEL via the TIM distribution network due to the\ninteroperability of the two methodologies. For flights with FEL issued, these\nlabels replace TIM estimates and are clearly marked as \"EASA\" to indicate the\nsource.\n\nPlease refer to [www.flightemissions.eu](https://www.flightemissions.eu) for\nmore information about The Flight Emissions Label (FEL) and display guidelines.\n\n## Legal base for model data sharing\n\nThe GHG emission estimate data are available via API under the\n[Creative Commons Attribution-ShareAlike CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)\nopen source license\n([legal code](https://creativecommons.org/licenses/by-sa/4.0/legalcode)).\n\n## API access\n\nDeveloper documentation is available on the Google Developers site for the\n[Travel Impact Model API](https://developers.google.com/travel/impact-model).\n\nFor non-developers, access to the Travel Impact Model API is also available\nvia the [Google Sheets Add-on](https://workspace.google.com/marketplace/app/flight_emissions_for_sheets/655425728274).\n\n## Versioning\n\nThe model will be developed further over time, e.g. with improved load factors\nmethodology or more fine grained seat area ratios calculation. New versions will\nbe published.\n\nA full model version will have four components: **MAJOR.MINOR.PATCH.DATE**, e.g.\n1.3.1.20230101. The four tiers of change tracking are handled differently:\n\n* **Major versions**: Changes to the model that would break existing client\n implementations if not addressed (e.g. changes in data types or schema) or\n major methodology changes (e.g. adding new data sources to the model that\n lead to major output changes). We expect these to be infrequent but they\n need to be managed with special care.\n* **Minor versions**: Changes to the model that, while being consistent across\n schema versions, change the model parameters or implementation.\n* **Patch versions**: Implementation changes meant to address bugs or\n inaccuracies in the model implementation.\n* **Dated versions**: Model datasets are recreated with refreshed input data\n but no change to the algorithms regularly.\n\n## Changelog\n\n### 3.0.0\n\nAdded support for EASA label attribution, contrails impact, and ISO 14083\nrelated documentation updates.\n\n### 2.0.0\n\nUpdating base model data to EEA 2023, adding support for cargo mass fraction,\nand introducing distance adjustment.\n\n### 1.10.0\n\nMigrating data sources for aircraft performance for some aircraft models.\n\n### 1.9.1\n\nExpanding T-100 coverage to include US territories. See\n[section on load factors](#factors-details) for information on the T-100\ndataset.\n\n### 1.9.0\n\nAdding carrier-level passenger load factors from\n[ch-aviation](https://www.ch-aviation.com/) for flights that are not already\ncovered by the T-100 dataset from the\n[US Department of Transportation Bureau of Transportation Statistics](https://www.bts.gov/airline-data-downloads).\nAlso adjusting the load factors outlier exclusion criteria from 20% to 10%\ndeviation from average load factor since 2017, resulting in removing March\n2020–February 2022 (inclusive) (previously March 2020–February 2021). See the\n[section on load factors](#factors-details) for more details.\n\n### 1.8.0\n\nAdding Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions break-downs to all\nflight emissions. Updating the jet fuel combustion to CO₂ conversion\nfactor from the minimum value of 3.1672 to the value of 3.1894 (using Lower\nHeating Value from ISO 14083 and CORSIA Carbon Intensity value), and using the\nCORSIA Life Cycle Assessment methodology to implement a WTT CO₂e\nemissions factor 0.6465. Reference:\n[ISO](https://www.iso.org/standard/78864.html),\n[CORSIA](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf).\n\n### 1.7.0\n\nUpdating the jet fuel combustion to CO₂ conversion factor from 3.15\nbased on the EEA methodology to 3.1672 to align with the\n[CORSIA methodology's](https://www.icao.int/sites/default/files/environmental-protection/CORSIA/Documents/SCS-Evaluation/CORSIA_Supporting_Document_CORSIA-Eligible-Fuels_LCA_Methodology_V6.pdf)\nrecommended factor.\n\n### 1.6.0\n\nAdding carrier and route specific passenger load factors for flights from, to,\nand within the U.S., taking seasonality patterns into account. We are using data\nfrom the\n[U.S. Department of Transportation Bureau of Transportation Statistics](https://www.bts.gov/).\nFor more details, see the [section on load factors](#factors-details).\n\n### 1.5.1\n\nAdding a fleet-level source for seating configuration data. For airlines that\ndon't file seating configuration information in flight schedules but use the\nsame seating configuration for all their aircraft of a certain model, a fall\nback to the \"Seats (Equipment Configuration) File\" provided by OAG is performed.\n\n### 1.5.0\n\nFollowing recent discussions with academic and industry partners, we are\nadjusting the TIM to focus on CO₂ emissions. While we strongly\nbelieve in including non-CO₂ effects in the model long-term, the\ndetails of how and when to include these factors requires more input from our\nstakeholders as part of a governance model that's in development. With this\nchange, we are provisionally removing contrails effects from our CO₂e\nestimates but will keep the labeling as “CO₂e” in the model to ensure\nfuture compatibility.\n\nWe believe CO₂e factors are critical to include in the model, given\nthe emphasis on them in the IPCC's AR6 report. We want to make sure that when we\ndo incorporate them into the model, we have a strong plan to account for time of\nday and regional variations in contrails' warming impact. We are committed to\nproviding consumers the most accurate information as they make informed choices\nabout their travel options.\n\nWe continue to invest into research and collaborate with leading scientists,\nNGOs, and partners to better incorporate contrails and other non-GHG impact into\nour model, and we look forward to sharing updates at a later date.\n\n### 1.4.0\n\nInitial public version of the Travel Impact Model.\n\n## Limitations\n\nThe model described in this document produces estimates of GHG emissions.\nEmission estimates aim to be representative of what the typical emissions for a\nflight matching the model inputs would be. Estimates might differ from actual\nemissions based on a number of factors. All calculation results use the TIM model and no default GHG emissions intensities are used as fallbacks. The TIM does not use country-specific GHG emissions factors and therefore, it is not recommended for official reporting purposes in those locations where country specific factors are mandated.\n\n**Aircraft types:** The emissions model accounts for the equipment type as\npublished in the flight schedules. The majority of aircraft types in use are\ncovered. See [Appendix A](#appendix-a-aircraft-type-support) for a list of\nsupported aircraft types.\n\nSome aircraft types are supported by falling back to a related model thought to\nhave comparable emissions. See\n[Flight level emission estimates](#flight-level-emission-estimates) for more\ndetails.\n\nIf no reasonable approximation is available for a given aircraft, the model will\nnot produce estimates for it.\n\n**Engine information:** Beyond the aircraft type, there are other aircraft\ncharacteristics that can have an effect on the flight emissions (e.g. engine\ntype, engine age, etc.) that are not currently included when computing emission\nestimates.\n\n**Fuel type:** The emissions model assumes that all flights operate on 100%\nconventional fuel. Alternative fuel types (e.g. Sustainable Aviation Fuel) are\nnot supported.\n\n**Seat configurations:** If there are no seat configurations individual numbers\nfor a flight available from published flight schedules, or if they are\nincorrectly formatted or implausible, aircraft specific medians derived from the\noverall dataset are employed.\n\n**Contrail-induced cirrus clouds:** In regions of high humidity, water vapor in\nthe air condenses around particles of soot from an aircraft’s exhaust and\nfreezes. This forms cloud-like trails of condensation, or contrails for short.\nMost contrails dissipate quickly, but for a small fraction of flights,\natmospheric conditions align to produce contrails that persist and spread out,\ntrapping heat in the atmosphere.\n\n**Empty flights:** ISO 14083 defines empty flights as any additional flights with no passengers and no cargo that are required to happen in order to operate a passenger flight (i.e. repositioning flights, maintenance flights, etc). It recommends that the GHG emissions for a flight includes any empty flights required for that flight to operate. At present, we are unable to support empty flights due to lack of data available.\n\n## Data quality\n\nSee [technical brief](https://travelimpactmodel.org/static/media/tim_model_selection.pdf) on TIM base model selection.\n\n## How to cite TIM in publications\n\nYou are welcome to use the Travel Impact Model (TIM) in your publications. When\nreferencing the TIM, please cite it as in the following example:\n\n\u003e Google. (2022, April). *Travel Impact Model (TIM)* (Version A.B.C.YYYYMMDD)\n\u003e [Computer software]. Retrieved September 28, 2024 via API,\n\u003e https://github.com/google/travel-impact-model\n\nThe TIM is a dynamic model that is regularly updated with new data and\nmethodologies. To ensure that others can access the same data and calculations\nyou used, it is essential to include the version number and retrieval date in\nyour citation.\n\n**BibTeX example:**\n\n```bibtex\n@misc{google_tim_2022,\n institution = {Google},\n title = {Travel Impact Model (TIM)},\n year = {2022},\n month = {April},\n note = {Version A.B.C.YYYYMMDD. Retrieved September 28, 2024},\n url = {https://github.com/google/travel-impact-model}\n}\n```\n\nIf you access the TIM programmatically through the [API](#api-access), please\nmention this in your citation as well.\n\n## Contact\n\nWe welcome feedback and enquiries. Please get in touch using this\n[form](https://support.google.com/travel/contact/tim?pcff=category:travel_impact_model_\\(TIM\\)_specifications).\n\n## Glossary\n\n**CCD:** The flight phases *Climb*, *Cruise*, *and* *Descend* occur above a\nflight altitude of 3,000 feet.\n\n**CO₂**: Carbon dioxide is the most significant long-lived greenhouse\ngas in Earth's atmosphere. Since the Industrial Revolution anthropogenic\nemissions – primarily from use of fossil fuels and deforestation – have rapidly\nincreased its concentration in the atmosphere, leading to global warming.\n\n**CO₂e**: CO₂e is short for CO₂ equivalent, and\nis a metric measure used to compare the emissions from various greenhouse gases\non the basis of their global-warming potential (GWP), by converting amounts of\nother gases to the equivalent amount of carbon dioxide with the same global\nwarming potential\n([source](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Carbon_dioxide_equivalent)).\n\n**Contrail-induced cirrus clouds**: Cirrus clouds are atmospheric clouds that\nlook like thin strands. There are natural cirrus clouds, and also contrail\ninduced cirrus clouds that under certain conditions occur as the result of a\ncontrail formation from aircraft engine exhaust.\n\n**CORSIA**: Carbon Offsetting and Reduction Scheme for International Aviation, a\ncarbon offset and reduction scheme to curb the aviation impact on climate change\ndeveloped by the International Civil Aviation Organization.\n\n**Effective Radiative Forcing (ERF):** Radiative forcing effects can create\nrapid responses in the troposphere, which can either enhance or reduce the flux\nover time, and makes RF a difficult proxy for calculating long-term climate\neffects. ERF attempts to capture long-term climate forcing, and represents the\nchange in net radiative flux after allowing for short-term responses in\natmospheric temperatures, water vapor and clouds.\n\n**European Environment Agency (EEA):** An agency of the European Union whose\ntask is to provide sound, independent information on the environment.\n\n**Google's Travel Sustainability team**: A team at Google focusing on travel\nsustainability, based in Zurich (Switzerland) and Cambridge (U.S.), with the\ngoal to enable users to make more sustainable travel choices.\n\n**Great-circle Distance:** Defined as the shortest distance between two points\non the surface of a sphere when measured along the surface of the sphere.\n\n**ICAO:** The International Civil Aviation Organization, a specialized agency of\nthe United Nations.\n\n**ISO 14083**: The international standard that establishes a common methodology\nfor the quantification and reporting of greenhouse gas (GHG) emissions arising\nfrom the operation of transport chains of passengers and freight\n([source](https://www.iso.org/standard/78864.html)), published by the\nInternational Organization for Standardization (ISO).\n\n**LTO:** The flight phases *Take Off and Landing* occur below a flight altitude\nof 3000 feet at the beginning and the end of a flight. They include the\nfollowing phases: taxi-out, taxi-in (idle), take-off, climb-out, approach and\nlanding.\n\n**Radiative Forcing (RF):** Radiative Forcing is the instantaneous difference in\nradiative energy flux stemming from a climate perturbation, measured at the top\nof the atmosphere.\n\n**Short Lived Climate Pollutants (SLCPs):** Pollutants that stay in the\natmosphere for a short time (e.g. weeks) in comparison to Long Lived Climate\nPollutants such as CO₂ that stay in the atmosphere for hundreds of\nyears.\n\n**Tank-to-Wake\n(TTW):** Emissions produced by burning jet fuel during takeoff, flight, and\nlanding of an aircraft.\n\n**TIM:** The Travel Impact Model described in this document.\n\n**Well-to-Tank\n(WTT):** Emissions generated during the production, processing, handling, and\ndelivery of jet fuel.\n\n**Well-to-Wake\n(WTW):** The sum of Well-to-Tank (WTT) and Tank-to-Wake (TTW) emissions.\n\n## Appendix\n\n### Appendix A: Aircraft type support\n\nAircraft full name | IATA aircraft code | Mapping (ICAO aircraft code) | Support status\n-------------------------------------------- | ------------------ | ---------------------------- | --------------\nAirbus A220 | 220 | BCS3 | Mapped to least efficient in family\nAirbus A220-100 | 221 | BCS1 | Direct match in EEA\nAirbus A220-300 | 223 | BCS3 | Direct match in EEA\nAirbus A300-600 | AB6 | A306 | Direct match in EEA\nAirbus A300B2/B4 | AB4 | A30B | Direct match in EEA\nAirbus A310 | 310 | A310 | Direct match in EEA\nAirbus A310-300 | 313 | A310 | Direct match in EEA\nAirbus A318 | 318 | A318 | Direct match in EEA\nAirbus A318 (Sharklets) | 31A | A318 | Supported via winglet/sharklet correction factor\nAirbus A318/319/320/321 | 32S | A321 | Mapped to least efficient in family\nAirbus A319 | 319 | A319 | Direct match in EEA\nAirbus A319neo | 31N | A321 | Mapped to least efficient in family\nAirbus A319 (Sharklets) | 31B | A319 | Supported via winglet/sharklet correction factor\nAirbus A320 | 320 | A320 | Direct match in EEA\nAirbus A320neo | 32N | A20N | Direct match in EEA\nAirbus A320 (Sharklets) | 32A | A320 | Supported via winglet/sharklet correction factor\nAirbus A321 | 321 | A321 | Direct match in EEA\nAirbus A321neo | 32Q | A21N | Direct match in EEA\nAirbus A321 (Sharklets) | 32B | A321 | Supported via winglet/sharklet correction factor\nAirbus A330 | 330 | A332 | Mapped to least efficient in family\nAirbus A330-200 | 332 | A332 | Direct match in EEA\nAirbus A330-300 | 333 | A333 | Direct match in EEA\nAirbus A330-800neo | 338 | A332 | Mapped onto older model\nAirbus A330-900neo | 339 | A339 | Direct match in EEA\nAirbus A340 | 340 | A345 | Mapped to least efficient in family\nAirbus A340-200 | 342 | A345 | Mapped to least efficient in family\nAirbus A340-300 | 343 | A343 | Direct match in EEA\nAirbus A340-500 | 345 | A345 | Direct match in EEA\nAirbus A340-600 | 346 | A346 | Direct match in EEA\nAirbus A350-1000 | 351 | A35K | Direct match in EEA\nAirbus A350 | 350 | A35K | Mapped to least efficient in family\nAirbus A350-900 | 359 | A359 | Direct match in EEA\nAirbus A380 | 380 | A388 | Mapped to least efficient in family\nAirbus A380-800 | 388 | A388 | Direct match in EEA\nAntonov An-140 | A40 | A140 | Direct match in EEA\nAntonov AN148-100 | A81 | A148 | Direct match in EEA\nAntonov An-24 | AN4 | AN24 | Direct match in EEA\nAntonov An-26/30/32 | AN6 | AN26 | Mapped to least efficient in family\nAntonov An-26 | A26 | AN26 | Direct match in EEA\nAntonov An-32 | A32 | AN32 | Direct match in EEA\nATR42/ATR72 | ATR | AT72 | Mapped to least efficient in family\nATR 42-300 | AT4 | AT43 | Direct match in EEA\nATR 42-500 | AT5 | AT45 | Direct match in EEA\nATR 72 | AT7 | AT72 | Direct match in EEA\nAvro RJ100 | AR1 | RJ1H | Direct match in EEA\nAvro RJ85 | AR8 | RJ85 | Direct match in EEA\nBeechcraft 1900 Airliner | BE1 | B190 | Mapped to least efficient in family\nBeechcraft 1900D Airliner | BEH | B190 | Direct match in EEA\nBoeing 717-200 | 717 | B712 | Direct match in EEA\nBoeing 727-100 | 721 | B721 | Direct match in EEA\nBoeing 737 | 737 | B734 | Mapped to least efficient in family\nBoeing 737 | 73M | B732 | Direct match in EEA\nBoeing 737-200 | 732 | B732 | Direct match in EEA\nBoeing 737-200 | 73L | B732 | Direct match in EEA\nBoeing 737-300 | 733 | B733 | Direct match in EEA\nBoeing 737-300 | 73N | B733 | Direct match in EEA\nBoeing 737-300 (Winglets) | 73C | B733 | Supported via winglet/sharklet correction factor\nBoeing 737-400 | 734 | B734 | Direct match in EEA\nBoeing 737-400 | 73Q | B734 | Direct match in EEA\nBoeing 737-500 | 735 | B735 | Direct match in EEA\nBoeing 737-500 (Winglets) | 73E | B735 | Supported via winglet/sharklet correction factor\nBoeing 737-600 | 736 | B736 | Direct match in EEA\nBoeing 737-700 | 73G | B737 | Direct match in EEA\nBoeing 737-700 | 73R | B732 | Direct match in EEA\nBoeing 737-700 (Scimitar Winglets) | 7S7 | B737 | Supported via winglet/sharklet correction factor\nBoeing 737-700 (Winglets) | 73W | B737 | Supported via winglet/sharklet correction factor\nBoeing 737-800 | 738 | B738 | Direct match in EEA\nBoeing 737-800 (Scimitar Winglets) | 7S8 | B738 | Supported via winglet/sharklet correction factor\nBoeing 737-800 (Winglets) | 73H | B738 | Supported via winglet/sharklet correction factor\nBoeing 737-900 | 739 | B739 | Direct match in EEA\nBoeing 737-900 (Winglets) | 73J | B739 | Supported via winglet/sharklet correction factor\nBoeing 737MAX 7 | 7M7 | B734 | Mapped onto older model\nBoeing 737MAX 8 | 7M8 | B38M | Direct match in EEA\nBoeing 737MAX 9 | 7M9 | B39M | Direct match in EEA\nBoeing 737MAX 10 | 7M1 | B734 | Mapped onto older model\nBoeing 747-300/747-100/200 SUD | 743 | B744 | Mapped to least efficient in family\nBoeing 747 | 747 | B744 | Mapped to least efficient in family\nBoeing 747-400 | 744 | B744 | Direct match in EEA\nBoeing 747-400 | 74E | B744 | Direct match in EEA\nBoeing 747-8 | 74H | B744 | Mapped onto older model\nBoeing 757 | 757 | B753 | Mapped to least efficient in family\nBoeing 757-200 | 752 | B752 | Direct match in EEA\nBoeing 757-200 (Winglets) | 75W | B752 | Supported via winglet/sharklet correction factor\nBoeing 757-300 | 753 | B753 | Direct match in EEA\nBoeing 757-300 (Winglets) | 75T | B753 | Supported via winglet/sharklet correction factor\nBoeing 767 | 767 | B764 | Mapped to least efficient in family\nBoeing 767-200 | 762 | B762 | Direct match in EEA\nBoeing 767-300 | 763 | B763 | Direct match in EEA\nBoeing 767-300 (Winglets) | 76W | B763 | Supported via winglet/sharklet correction factor\nBoeing 767-400 | 764 | B764 | Direct match in EEA\nBoeing 777 | 777 | B773 | Mapped to least efficient in family\nBoeing 777-200/200ER | 772 | B772 | Direct match in EEA\nBoeing 777-200LR | 77L | B772 | Mapped onto newer model\nBoeing 777-300 | 773 | B773 | Direct match in EEA\nBoeing 777-300ER | 77W | B77W | Direct match in EEA\nBoeing 787 | 787 | B789 | Mapped to least efficient in family\nBoeing 787-8 | 788 | B788 | Direct match in EEA\nBoeing 787-9 | 789 | B789 | Direct match in EEA\nBoeing 787-10 | 781 | B78X | Direct match in EEA\nBoeing (Douglas) MD-82 | M82 | MD82 | Direct match in EEA\nBoeing (Douglas) MD-83 | M83 | MD83 | Direct match in EEA\nBoeing (Douglas) MD-90 | M90 | MD90 | Direct match in EEA\nBombardier Challenger 300 | CL3 | CL30 | Direct match in EEA\nBombardier Regional Jet 550 | CR5 | CRJ7 | Mapped to least efficient in family\nBritish Aerospace 146 | 146 | B463 | Mapped to least efficient in family\nBritish Aerospace 146-100 | 141 | B461 | Direct match in EEA\nBritish Aerospace 146-200 | 142 | B462 | Direct match in EEA\nBritish Aerospace 146-300 | 143 | B463 | Direct match in EEA\nBritish Aerospace Jetstream | JST | JS41 | Mapped to least efficient in family\nBritish Aerospace Jetstream 31 | J31 | JS31 | Direct match in EEA\nBritish Aerospace Jetstream 32 | J32 | JS32 | Direct match in EEA\nBritish Aerospace Jetstream 41 | J41 | JS41 | Direct match in EEA\nBritten-Norman BN-2A/BN-2B Islander | BNI | BN2P | Direct match in EEA\nCanadair Regional Jet | CRJ | CRJ9 | Mapped to least efficient in family\nCanadair Regional Jet 100 | CR1 | CRJ1 | Direct match in EEA\nCanadair Regional Jet 200 | CR2 | CRJ2 | Direct match in EEA\nCanadair Regional Jet 700 | CR7 | CRJ7 | Direct match in EEA\nCanadair Regional Jet 900 | CR9 | CRJ9 | Direct match in EEA\nCanadair Regional Jet 1000 | CRK | CRJ9 | Mapped to least efficient in family\nCessna 208B Caravan | CNF | C208 | Direct match in EEA\nCessna Citation | CNJ | C500 | Direct match in EEA\nCessna Light Aircraft. | CNA | C208 | Direct match in EEA\nCessna Light Aircraft (Single piston engine) | CN1 | C208 | Direct match in EEA\nCessna Light Aircraft (Twin piston engines) | CN2 | C208 | Direct match in EEA\nCessna Light Aircraft (Single Turboprop) | CNC | C208 | Direct match in EEA\nCessna Light Aircraft (Twin Turboprop) | CNT | C208 | Direct match in EEA\nDe Havilland-Bombardier DHC2 Beaver | DHP | DHC2 | Direct match in EEA\nDe Havilland-Bombardier DHC6 Twin Otter | DHT | DHC6 | Direct match in EEA\nDe Havilland-Bombardier DHC7 Dash 7 | DH7 | DHC7 | Direct match in EEA\nDe Havilland-Bombardier DHC8 Dash 8 | DH8 | DH8D | Mapped to least efficient in family\nDe Havilland-Bombardier DHC8-100 Dash 8/8Q | DH1 | DH8A | Direct match in EEA\nDe Havilland-Bombardier DHC8-200 Dash 8/8Q | DH2 | DH8B | Direct match in EEA\nDe Havilland-Bombardier DHC8-300 Dash 8/8Q | DH3 | DH8C | Direct match in EEA\nDe Havilland-Bombardier DHC8-400 Dash 8/8Q | DH4 | DH8D | Direct match in EEA\nEmbraer 110 Bandeirante | EMB | E110 | Direct match in EEA\nEmbraer 120 Brasilia | EM2 | E120 | Direct match in EEA\nEmbraer 170 | E70 | E170 | Direct match in EEA\nEmbraer 170/195 | EMJ | E190 | Mapped to least efficient in family\nEmbraer 175 | E75 | E75S | Direct match in EEA\nEmbraer 175 (Enhanced Winglets) | E7W | E75L | Direct match in EEA\nEmbraer 190 | E90 | E190 | Direct match in EEA\nEmbraer 190 E2 | 290 | E290 | Direct match in EEA\nEmbraer 195 | E95 | E195 | Direct match in EEA\nEmbraer 195 E2 | 295 | E295 | Direct match in EEA\nEmbraer RJ 135/140/145 | ERJ | E145 | Mapped to least efficient in family\nEmbraer RJ135 | ER3 | E135 | Direct match in EEA\nEmbraer RJ140 | ERD | E145 | Direct match in EEA\nEmbraer RJ145 | ER4 | E145 | Direct match in EEA\nFairchild Dornier 228 | D28 | D228 | Direct match in EEA\nFairchild Dornier 328JET | FRJ | J328 | Direct match in EEA\nFairchild SA26/SA226/SA227 Merlin/Metro | SWM | SW4 | Mapped to least efficient in family\nFokker 50 | F50 | F50 | Direct match in EEA\nFokker 70 | F70 | F70 | Direct match in EEA\nFokker 100 | 100 | F100 | Direct match in EEA\nIlyushin Il-76 | IL7 | IL76 | Direct match in EEA\nIlyushin Il-96 | IL9 | IL96 | Direct match in EEA\nLet 410 | L4T | L410 | Direct match in EEA\nPilatus PC-12 | PL2 | PC12 | Direct match in EEA\nSaab 2000 | S20 | SB20 | Direct match in EEA\nSaab 340B | SFB | SF34 | Direct match in EEA\nSaab 340 | SF3 | SF34 | Mapped to least efficient in family\nSukhoi Superjet 100-95 | SU9 | SU95 | Direct match in EEA\nTecnam P2012 Traveller | T12 | P212 | Direct match in EEA\n\n### Appendix B: Term mapping table\n\nTIM terminology | ISO terminology\n---------------------- | -------------------------------------------------------------------------------------------------------------------------------\nActual emissions | Primary data as defined in 7.2.3 in ISO 14083 documentation\nAirport | Hub operation categories (HOC), specifically a location where passengers are transferred from one mode of transport to another\nAirport emissions | HOC's GHG emissions\nBelly cargo | Freight transportation\nCargo | Freight\nEmpty flight | Empty trip\nFlight | Transportation chain element (TCE), specifically a single aircraft transporting a group of passengers and potentially freight\nFlight emissions | TCE's GHG emissions\nTank-to-Wake (TTW) | G\u003csub\u003evo, TCE \u003c/sub\u003e\nTravel journey | Transportation chain\nType of flight | Transport operation category (TOC)\nWell-to-Tank (WTT) | G\u003csub\u003evep,TCE\u003c/sub\u003e\nWell-to-Wake (WTW) | G\u003csub\u003eTCE\u003c/sub\u003e\n\n### Appendix C: Reporting details for passenger transport\n\nThis table shows the reporting details in the TIM as defined in ISO 14083 Table\n2.\n\nReporting elements | Description\n-------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\nIdentification of the services covered | All transport chain elements related to passenger transport by airplane for the next 11 months and the average airport emissions per passenger\nOverall results on GHG emissions | Total emissions of an air transport chain per passenger is sum of all airport hub GHG emissions plus all flight GHG emissions for each passenger in that transport chain \u003cbr\u003e\u003cbr\u003e(e.g. If the transport chain is a non-direct flight from ZRH-BOS with a stop in LHR, the transport chain's GHG emissions will include the sum of airports' GHG emissions in ZRH, LHR, BOS and the flights GHG emissions for ZRH-LHR and LHR-BOS.)\nTransport activity | Sum of flight GHG emissions\u003cbr\u003e\u003cbr\u003eThe GHG emissions are Well-to-Wake (WTW) emissions that are the sum of the Well-to-Tank (WTT), the emissions of the production, processing and delivery of the fuel used, and Tank-to-Wake (TTW), the emissions created by the flight itself.\u003cbr\u003e\u003cbr\u003eThe CORSIA methodology includes emissions for CO2, CH4 and N2O in its carbon intensity factor. Contrails and other Kyoto gases not mentioned are not included in the TIM GHG emissions value.\nTransport activity distance | For flight GHG emissions, uses the great-circle distance plus the distance adjustment factor\nHub activity | Sum of all airport emissions\u003cbr\u003e\u003cbr\u003eThe airport emissions can be calculated as 1.71 kgs * number of airport hubs visited.\nAdditional information | Additional information can be found in https://github.com/google/travel-impact-model and https://travelimpactmodel.org/governance\n\n### Appendix D: Model parameters\n\nThis table shows the TIM's model type as defined in ISO 14083 Table\n3.\n\nModel Type | Yes/No\n-------------- | ------\nEnergy based | No\nActivity based | Yes\n\nThis table shows the input parameters into the TIM as defined in ISO 14083 Table\n3.\n\nParameter | Included? | Additional information\n------------------------------------ | --------- | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n**Aircraft Characteristics** | |\nAircraft type | Yes | **Primary:** Uses the specific aircraft model operating this flight from flight schedules data\u003cbr\u003e\u003cbr\u003e**Modelled:** Uses fallback as described in Appendix A\nEngine age | No |\nEngine type | Yes | **Default value:** Uses the base EEA model for most common engine types for a given aircraft operated in Europe\nFuel type | No |\nSeating configuration | Yes | **Primary:** Uses the actual configuration for the aircraft model\u003cbr\u003e\u003cbr\u003e**Modelled:** Uses OAG fleet data, matching a unique config for the carrier and aircraft\u003cbr\u003e\u003cbr\u003e**Default Value:** Uses the typical configuration across all carriers\n**Journey Characteristics** | |\nAirport locations | Yes | **Primary:** Uses exact longitude/latitude of the airport location\nAircraft energy consumption profile | Yes | **Modelled:** Uses aircraft's EEA estimated fuel burn with the CORSIA lifecycle carbon intensity factor\nCross or tail wind | No |\nRoute taken | Yes | **Modelled:** Uses the calculated GCD with either route-based or country-based distance adjustment factor\u003cbr\u003e\u003cbr\u003e**Default value:** Uses the calculated GCD with a distance adjustment factor\n**Operational** | |\nCargo carried | Yes | **Modelled:** Uses modelled historical trends of a route's cargo mass fraction and aircraft type, or aircraft type and distance, which is divided by total payload, to apportion emissions between cargo and passenger\nEmpty trips | No |\nLoad factor | Yes | **Modelled:** Uses actual load factor data aggregated over 6 years for apportioning cargo and passenger data\u003cbr\u003e\u003cbr\u003e**Default value:** Uses load factor data average for all U.S. flights in 2019 for apportioning cargo and passenger data\n\n[^1]: This figure is based on Figure 2 on page ix in ISO 14083 (2023).\n[^2]: This figure uses icons from the following libraries, [Google Material Design Icons](https://github.com/google/material-design-icons) and [Material Design Icons](https://github.com/Templarian/MaterialDesign). All icons are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n[^3]: This figure is based on Figure 6 on page 23 in ISO 14083 (2023).\n[^4]: This figure uses icons from the following libraries, [Google Material Design Icons](https://github.com/google/material-design-icons) and [Vaadin Icons](https://github.com/vaadin/vaadin-icons). All icons are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n[^5]: The Travel Impact Model is specifically an activity-based model as defined in ISO 14083 Annex M.\n[^6]: The Travel Impact Model uses the global standard CORSIA emissions conversion factor of 3.8359 which is more recent (2022) compared to ISO 14083's factor of 3.645.\n[^7]: The ACA's 2024 annual report includes regional airport emissions data as well. There is some variation between airports, across years and some regional data values are higher than 1.71 kg per passenger. We chose to recommend the 1.71kg average value to remain conservative with our estimate.","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["traveling"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/87885","html_url":"https://ost.ecosyste.ms/projects/87885"}