# This YAML file holds some of the content of the website, for convenience. # Learn YAML, it's worth it: http://sweetohm.net/article/introduction-yaml.html # /!. IF YOU BREAK THIS FILE YOU BREAK THE WEBSITE. TREAD CAREFULLY. # Metadata about this website meta: title: Travel Carbon Footprint Calculator description: | A travel carbon footprint calculator for researchers and event organizers. authors: - name: Didier Barret email: dbarret@irap.omp.eu role: Principal Investigator - name: Antoine Goutenoir email: antoine@goutenoir.com role: Software Ninja - name: Jean-Michel Glorian email: Jean-Michel.Glorian@irap.omp.eu role: Benevolent Wizard - name: Élodie Bourrec email: elodie.bourrec@irap.omp.eu role: Administratrice # Global configuration for all models shared_config: # In kg/km. train_emission: 0.023 # Multiplier to approximate conversion of Great Circle Distances to Road gcd_to_road_scale: 1.3 # Aka. Laws models: - name: Atmosfair (mul. factor=3 for altitude > 9 km) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: atmosfair-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#9933ff" # Whether this model is selected by default in the list. selected: false # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [ 0.0, 0.0 ] - [ 299.9, 0.0 ] - [ 300.0, 100.771075677223 ] - [ 3999.9, 866.237395584634 ] - [ 4000.0, 844.203401814933 ] - [ 19999.9, 5606.129335551792 ] - [ 25000.0, 7094.270252762378 ] - name: ADEME (2021, mul. factor=2, from Carbon database) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: ademe-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#33ff99" # Whether this model is selected by default in the list. selected: true # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [500.0, 129.0 ] - [1000.0, 258.0 ] - [1500.0, 280.5 ] - [2000.0, 374.0 ] - [2500.0, 467.5 ] - [3000.0, 561.0 ] - [3500.0, 654.5 ] - [4000.0, 608.0 ] - [5000.0, 760.0 ] - [6000.0, 912.0 ] - [7000.0, 1064.0 ] - [8000.0, 1216.0 ] - [9000.0, 1368.0 ] - [10000.0, 1520.0 ] - [11000.0, 1672.0 ] - [12000.0, 1824.0 ] - [13000.0, 1976.0 ] - [14000.0, 2128.0 ] - [15000.0, 2280.0 ] - [16000.0, 2432.0 ] - [17000.0, 2584.0 ] - [18000.0, 2736.0 ] - [19000.0, 2888.0 ] - [20000.0, 3040.0 ] - [21000.0, 3192.0 ] - [22000.0, 3344.0 ] - [23000.0, 3496.0 ] - [24000.0, 3648.0 ] - [25000.0, 3800.0 ] - name: MYCLIMATE (mul. factor=2) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: my-climate-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#99ff33" # Whether this model is selected by default in the list. selected: true # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 95 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [299.9, 0.0 ] - [300.0, 120.42928309599999 ] - [1499.9, 278.86980907246215 ] - [1500.0, 278.8830135500002 ] - [2499.9, 438.3026953954595 ] - [2500, 438.3186389580005 ] - [19999.9, 3335.611941663665 ] - [25000.0, 4163.431314509144 ] - name: DEFRA (mul. factor=RFI=1.9) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: defra-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#3399ff" # Whether this model is selected by default in the list. selected: true # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [299.9, 0.0 ] - [300.0, 47.496000000000016 ] - [499.9, 79.14416800000002 ] - [500.0, 78.03232675954409 ] - [3699.9, 576.0667775328388 ] - [3700.0, 541.2334271596557 ] - [19999.9, 2878.9047733493503 ] - [25000.0, 3596.0006143105275 ] - name: French Ministry of Ecology (no mul. factor, not recommended) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: fr-ministry-ecology-no-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#333333" # Whether this model is selected by default in the list. selected: false # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [299.9, 0.0 ] - [300.0, 103.02499999999998 ] - [999.9, 158.492075 ] - [1000.0, 158.50000000000003 ] - [1999.9, 250.32414999999997 ] - [2000, 250.33333333333326 ] - [2999.9, 301.9948333333333 ] - [3000, 302.0000000000004 ] - [3999.9, 447.9854000000004 ] - [4000, 448.0000000000002 ] - [4999.9, 587.48605 ] - [5000, 587.4999999999999 ] - [5999.9, 608.9978499999997 ] - [6000, 609.0000000000002 ] - [6999.9, 629.9979000000001 ] - [7000, 629.9999999999999 ] - [7999.9, 719.9909999999996 ] - [8000, 720.0000000000002 ] - [8999.9, 760.4959500000004 ] - [9000, 760.4999999999997 ] - [9999.9, 864.9895499999997 ] - [10000, 865.0000000000005 ] - [10999.9, 1039.482549999999 ] - [11000, 1035.9000000000015 ] - [19999.9, 1879.1906300000016 ] - [25000.0, 2347.7000000000016 ] - name: KLM data best fit (no mul. factor, not recommended) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: klm-data-no-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#ff3399" # Whether this model is selected by default in the list. selected: false # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [299.9, 0.0 ] - [300.0, 41.883578628476144 ] - [2499.9, 214.70184186944394 ] - [2500.0, 261.6807087092951 ] - [14999.9, 962.2587735768744 ] - [25000.0, 1522.7313138757793 ] - name: ICAO data best fit (no mul. factor, not recommended) # Slugged version of the display name above # Only lowercase alphanumeric, starting with a letter, using - as liaison # In other words, MUST match ^[a-z]([a-z0-9-]*[a-z0-9])?$ eg: icao-with-rfi # MUST be unique, two models MUST NOT have the same slug. slug: icao-no-rfi # There MUST exist a python file named like this in `flaskr/laws` # And it MUST contain a class named EmissionModel # Please keep this lowercased, letters-only (or I will breathe fire) file: travel_emission_lerp_fit # Color MUST be in the hex form, without alpha color: "#ff9933" # Whether this model is selected by default in the list. selected: false # The configuration that will be fed to the model. # May be anything, really. Go bonkers! config: plane_emission_linear_fit: # A coefficient applied to the distance connecting_flights_scale: 1.05 # Radiative Forcing Index # Multiplier after scaling # DEPRECATED rfi: 1.0 # Flat scalar to add before scaling with laws offset_before: 0 # Flat scalar to multiply before scaling with laws scale_before: 1 # The travel_emission_lerp_fit uses points instead of intervals # List of (distance in km, footprint in kg) points: - [0.0, 0.0 ] - [299.9, 0.0 ] - [300.0, 67.00585705041823 ] - [2499.9, 211.09825928042045 ] - [2500.0, 206.528816915788 ] - [14999.9, 663.8204584246745 ] - [25000.0, 1029.6603566841018 ] # The grouped barchart displayed on the home page. laws_plot: distances: - 350.0 - 500.0 - 1000.0 - 1500.0 - 2500.0 - 3000.0 - 4500.0 - 5000.0 - 8000.0 - 10000.0 - 12000.0 - 15000.0 - 20000.0 - 25000.0 # The content is Markdown. HTML is also allowed. # If you also want Markdown in the titles, just ask. home: # The hero block (aka. jumbotron) is the top-level, salient block. # It's like a welcoming mat :) hero: title: Estimate your travel carbon footprint # Using a pipe (|) allows you to set multiline content # Careful, indentation matters. description: | Travel footpint calculator provided by Didier Barret
CNRS, [IRAP](http://www.irap.omp.eu), [@DidierBarret](https://twitter.com/DidierBarret), [didier.barret@gmail.com](mailto:didier.barret@gmail.com) # Three blocks per section. # sections: # You can also use columns. columns: - blocks: - title: What does the tool do? content: | The tool computes the carbon footprint associated with round trip flights, according to the methodology of several publicly available calculators. It does so for a set of trips from a given city of origin to a set of destinations. Similarly, the tool allows to compute the carbon footprint of a larger set of trips, corresponding to a conference, a meeting and so on. For this, the city of departure for each participant to the event has first to be provided. If multiple destination cities are provided, the tool ranks those cities according to the associated carbon footprint. While online calculators enable to compute the footprint of a limited number of trips, this tool enables to compute the footprint of a larger number of trips in an automated way. Furthermore, it provides an estimate based on data from seven different methods, whose estimates can differ significantly. If more than one method is selected by the user, the tool returns the mean of the estimates of all selected methods. - title: How does the tool work? content: | A round trip is defined by a city pair. The two cities are geolocated and from their longitude and latitude, the great circle distance is computed. This is the shortest path a plane can follow. Some methods thus consider uplift correction factors to account for deviations from the shortest paths, e.g., when a plane avoids bad weather conditions. In addition, two cities may not be connected by a direct flight. This is accounted for by increasing by 5% the great circle distance. Each method provides the carbon dioxide emission in kg as a function of the flight distance in km. Thus from the increased great circle distance, the carbon dioxide emission of a flight associated with a trip between a city pair is computed and multiplied by two to account for a round trip. - title: Which methods are used? content: | The tool incorporates seven different methods, among the most widely used, and for which the methodology used is documented (see additional resources later). This tool is by no means a critical review of the different methods. It takes the methods as they are described and presented. The methods may differ in their assumptions (e.g. with or without radiative forcing included) or in the perimeter considered (e.g. adding to the flight emissions, the one associated with the production phase and transport of the kerosene). The tool attempts to implement each method to the highest possible level of accuracy. Providing more than one method enables to get a mean value, while illustrating the significant differences in their estimates. In alphabetic order, the data considered are from ADEME: the French Environment & Energy Management Agency, atmosfair: a German carbon offsetting company, DEFRA: the UK Department for Environment, Food & Rural Affairs, the French Ministry for the Ecological and Inclusive Transition, ICAO: International Civil Aviation Organization, from the KLM carbon compensation service data and finally from myclimate, a Carbon offseting company, used in particular by Lufthansa. _This list is obviously not exhaustive but represents a variety of estimates from lower to higher values._ - title: Updating the emission factors content: | The emission factors will be regularly updated. In April 2021, the emission factors from ADEME were updated. The average emission factors for short, medium, long flights are considered. The same data are considered by the Labos1.5 organisation (https://labos1point5.org/ges-1point5). - title: How are the different methods built? content: | What is needed for each method is a function giving the carbon dioxide emission as a function of the flight distance. ADEME and DEFRA provide mean emission factors, as a function of flight distance. Myclimate provides an analytical formula. For ICAO and atmosfair, the on-line calculators have been run for a wide range of flights of varying distances (~100 flights spanning from 300 km to 12000 km) and the estimates have been fitted with linear functions, covering adjacent distance intervals. For its carbon compensation service, KLM provides on its web site a table of emissions for a large range of flights. The KLM data have been also been fitted with linear functions. Differences up to a few tens of percent may be found between the data and the linearly interpolated values. - title: Accounting for non CO2 effects? slug: rfi content: | Aviation contributes to climate change more than just with the emission of Carbon dioxide from burning fuels, by releasing gases and particles directly into the upper troposphere and lower stratosphere where they have an impact on atmospheric composition. To quantify to total climate impact of burning fuels, the direct CO2 emission of aircrafts is then multiplied by a factor, which in the literature refers either to a Radiative Forcing Index (RFI) or a Global Warming Potential (GWP) integrated over some time period (a 100-year time horizon was adopted for the Kyoto Protocol to the United Nations Framework Convention on Climate Change). There is a debate in the science community on how non-CO2 effects should be modelled. For instance, a RFI multiplier of 1.9–2 is used by DEFRA, and a multiplier of 2 is considered by myclimate and ADEME (see discussion in [Jungbluth, N. & Meili, C. Int J Life Cycle Assess (2019) 24: 404](https://doi.org/10.1007/s11367-018-1556-3).). ATMOSFAIR considers a multiplier of 3, for all emissions above 9 km, accounting for the profile of the flight. ICAO, KLM and the French Ministry for the Ecological and Inclusive Transition do not include a multiplier. Therefore, the methods based on ICAO, the French Ministry for the Ecological and Inclusive Transition and KLM data are not recommended, as they do not account for non-CO2 effects, but are still given as methods providing the lowest emissions. [![Models used](static/img/recap_scaling_laws.jpg)](static/img/recap_scaling_laws.jpg) - title: Seating category content: | The tool assumes economy seats in computing the travel footprint. On average it can be considered that the footprint can be multiplied by ∼ 1.5, ∼ 2.0 and ∼ 2.5 for flying in Premium Economy, Business and First class. - title: Accounting for train emission content: | The minimum distance for flying (one leg of the round trip) is an input to be selected by the user (it is set to 500 km by default). Below the minimum distance for flying, it is assumed that train is used. Deviations from the shortest path is accounted by a 1.35 multiplication factor. The tool thus assumes the mean of the emission factors of national and international rails, as provided by DEFRA (i.e. 23 grams per passenger km). To relate the duration of a train journey to a travel distance, an average speed of 100 km/h is assumed. - blocks: - title: Input and output data content: | The inputs are provided in US English for the city and country names, without diacritics. On each line, the city and country names must be separated by a comma. Pasting a CSV file in the form is possible, provided that a comma separates the city and country names. A round trip is defined by a city pair. If the user enters _cityA_ as the origin city, and twice _cityB_ as destinations, the tool returns the cumulative emission and distance from two round trips involving _cityA_ and _cityB_, and indicates that 2 round trips were involved. The same happens if the user enters twice _cityA_ as the city of origin and _cityB_ as the sole destination. Three types of inputs can be considered depending on whether the user wants an “individual” estimate or an estimate for a conference, meeting and so on. In the former case, the “origin” city is unique and the “destination” cities may be multiple. The tool returns the carbon dioxide equivalent emission for each city of destination. The tool can thus be run also for an organization interested to know about its travel footprint, in which case the "origin" city is the city from which employees travel. In the later case, the “origin” cities are multiple (i.e. the cities from which the participants to the conference depart from), and the “destination” city may be a single host city or multiple host cities if the user wants to compare their associated footprint. If there is one destination, the tool returns the carbon dioxide equivalent for each city of origin. If multiple destinations are provided the tool returns the carbon dioxide equivalent emission summed over all cities of origin and for each city of destination. The cumulative round trip distance to each city of destination is also provided. The result page provides a summary plot which can be downloaded, as well as a csv and raw yaml file, which can be used for further processing. The csv file lists the name of the city as in the form, the address to which it was geolocated, the carbon dioxide emission (in kg), the distance travelled, the number of trips possible by train (i.e. when the distance is less than the minimum flying distance, e.g. 500 km) and the number of trips by plane. The plot and the csv file rank the cities against the carbon dioxide emissions. - title: Trouble shooting content: | The estimation can go wrong if a city is not properly geolocated. This may happen because the name of the city is wrongly spelled or the geolocator (OpenStreetMap) is confused. An error should be listed at the end of the result page. Don’t be surprised, if the name recovered by the geolocator is not exactly the one you had expected (e.g. a city is located at the address of an embassy). An error may also occur if the input file submitted does not comply with the requested format, including font encoding. If nothing happens during a simple request, it is most likely caused by the geolocator being unavailable. In this case, try again a few minutes later. In case of very large many-to-many origin-to-destination combinations, the computing time may become very large. The advice is to split the inputs into parts. This may happen when comparing tens of thousands origins with hundreds of destinations. - title: Caveats content: | The numbers provided by the tool do not come with uncertainties, and shall only be considered indicative of the true emission. However, the numbers can be used for relative comparisons, e.g. when comparing two cities for hosting a conference. - title: Confidentiality content: | The data provided in the form will remain confidential, as will be the results. - title: Disclaimer content: | This tool is provided on a best effort basis as a service to members of the science community. The numbers provided are informative and have obviously no legal value. - title: Reference content: | Results from the tool may reference to [Barret (2020), Estimating, monitoring and minimizing the travel footprint associated with the development of the Athena X-ray Integral Field Unit, An on-line travel footprint calculator released to the science community, Experimental Astronomy, 49, 183. doi:10.1007/s10686-020-09659-8](https://ui.adsabs.harvard.edu/abs/2020arXiv200405603B/abstract). - title: Original motivation content: | Global warming poses a threat for life on our planet. Emissions of carbon dioxide by aircrafts keeps increasing, as the world economy keeps growing (it is about 3% of the anthropogenic emissions nowadays). Carrying scientific research is generally associated with traveling across the world, and thus air travel is likely to dominate the carbon footprint of most scientists and is likely to be large for developing international projects. This tool was first developed to compute the travel footprint associated with the development of the X-ray Integral Field Unit to fly on board the Athena space observatory in the early 2030s. The rather large number derived, typically one hundred ton of carbon dioxide per bi-annual consortium meeting, imposed concrete actions to reduce the footprint of the project, by re-considering the number of large meetings, implementing different ways of interacting and working collectively in a world-wide consortium. The tool was further improved to easily compute the travel footprint associated with individual traveling or with the organization of events, involving a large number of travels. Finally, by comparing different, widely used methods, providing so different estimates, the tool is expected to raise awareness within the scientific community (and hopefully the authorities and medias) about the lack of regulations or framework on the critical matter of estimating aircraft emissions. - title: Concluding note content: | As a personal note, I would like to stress that, as a scientist, I find it very worrying that there are no standards for computing the flight emissions. May this tool help to raise awareness on this issue. - title: Additional resources content: | - Offset your flight with [atmosfair](https://www.atmosfair.de/en/offset/flight) - [ADEME](http://www.bilans-ges.ademe.fr/en/accueil) (French Environment & Energy Management Agency) - [DEFRA](https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2019) emission conversion factors 2019 - [ICAO](https://www.icao.int/environmental-protection/carbonoffset/pages/default.aspx) Carbon Emissions Calculator - [KLM data](https://www.klm.com/travel/nl_en/prepare_for_travel/fly_co2_neutral/all_about_sustainable_travel/index.htm) KLM data - [GHG information for transport services, June 2019](https://www.ecologique-solidaire.gouv.fr/sites/default/files/Information_GES%20-%202019.pdf) from the French Ministry for the Ecological and Inclusive Transition - [MyClimate](https://www.myclimate.org) MyClimate - L. Hackel [travel footprint calculator](https://lhackel.shinyapps.io/travel_footprint/) based on DEFRA emission factors - [Wikipedia article on the environmental impact of aviation](https://en.wikipedia.org/wiki/Environmental_impact_of_aviation) Wikipedia article on the environmental impact of aviation estimate: hero: title: Request an estimation description: | The results will be available (almost) immediately.
It may take from a few seconds up to a few minutes, depending on the amount of locations you provided. help: first_name: Fill these to say hello. last_name: We will never share your data with anyone. origin_addresses: | Use en_US city and country names, without diacritics.
The comma matters.
This is either a home city and a country or the cities and countries of the participants to the conference, meeting… origin_addresses_file: | If you provide a file, we'll use it instead of the list on the left.
The spreadsheet's first sheet must have an Address column, or a City and Country columns.
Example CSV ⋅ Example XLS destination_addresses: | This is either the cities and countries to travel to or the host city and country of the conference, meeting…
Please provide multiple cities and countries to compute the location of the minimum emission. destination_addresses_file: | Need inspiration? Look at the airports in [Africa](/static/public/sample/Large_airports_in_Africa.csv), [Asia](/static/public/sample/Large_airports_in_Asia.csv), [Europe](/static/public/sample/Large_airports_in_Europe.csv), [North America](/static/public/sample/Large_airports_in_NorthAmerica.csv), [Oceania](/static/public/sample/Large_airports_in_Oceania.csv), [South America](/static/public/sample/Large_airports_in_SouthAmerica.csv), or [all of them](/static/public/sample/Large_airports_in_all_continents.csv). use_train_below_km: | For (single) trips below this distance, we'll ignore the plane models and use 23 g.km-1. # Labels accept HTML, but not markdown # Descriptions accept neither, since we use the HTML title attribute form: email: label: Email Address description: Make sure you provide a valid address or you won't receive the results! run_name: label: Run Name description: Usually the name of the event. Results will look better if you fill this. placeholder: "Global Peace Summit 2020 – Estimation N°07" first_name: label: First Name description: Also known as given name, eg. `Didier`. placeholder: Adèle last_name: label: Last Name description: Also known as family name, eg. `Barret`. placeholder: Bellego institution: label: Institution / Enterprise description: If any? comment: label: Leave a comment description: Any input is appreciated. Everyone's a critic. use_train_below_km: label: Use train below description: We will use the train carbon dioxide equivalent emission factor below this great circle distance (one leg of the round trip). values: - label: Do not consider train value: 0 - label: 100 km (~ 1h by train) value: 100 - label: 300 km (~ 5h by train) value: 300 - label: 500 km (~ 8h by train) value: 500 - label: 700 km (~ 10h by train) value: 700 - label: 1000 km (~ 13h by train) value: 1000 origin_addresses: label: Origin Cities description: | One address per line, in the form `City, Country`. Make sure your addresses are correctly spelled. # We MUST use the dumb CRLF pair for dumb windows users placeholder: "Paris, France\r\nBerlin, Germany" # placeholder: | # Paris, France # Berlin, Germany destination_addresses: label: Destination Cities description: | One address per line, in the form `City, Country`. Make sure your addresses are correctly spelled. placeholder: | Washington, United States of America origin_addresses_file: label: Origin Cities description: | Accepted files: CSV, XLS, XLSX. We will use the Address column, or the City and Country columns. error: Please only use spreadsheet files. (CSV, XLS, XLSX) destination_addresses_file: label: Destination Cities description: | Accepted files: CSV, XLS, XLSX. We will use the Address column, or the City and Country columns. error: Please only use spreadsheet files. (CSV, XLS, XLSX) # compute_optimal_destination: # label: | # Compute the destination city that will minimize emissions
# (useful when setting up a meeting/conference) # description: | # We will only look through Cities specified in the Destination Cities. captcha: label: Prove you are not a bot description: | We have to protact against malicious spammers. use_atmosfair_rfi: label: | Use the RFI multiplier recommended by atmosfair (i.e. 3 for all emissions above 9km)
For long flights, the multiplier may reach 2.8 or so. Otherwise, by default, 1.9 will be used. description: | We will only look through Cities specified in the Destination Cities. estimate_queue: hero: title: Please wait… description: | Your estimation is being computed. This may take several minutes.
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Bookmark this webpage, it is private and unlisted. failure: hero: title: Your estimation has failed! description: | Sorry about that. Please find the error message below.
Thank you for using our service. lolliplot: one_to_one: | The carbon dioxide equivalent emission is provided for each city of destination. Identical trips (i.e. identical destinations) are summed and the cumulative distance is provided. one_to_many: | The carbon dioxide equivalent emission is provided for each city of destination. Identical trips (i.e. identical destinations) are summed and the cumulative distance is provided. many_to_one: | The carbon dioxide equivalent emission is provided for each city of origin. Identical trips (i.e. identical origins) are summed and the cumulative distance is provided. many_to_many: | The carbon dioxide equivalent emission is summed over all cities of origin and provided for each city of destination. The cumulative distance to each city of destination is provided. Duplicates in the destinations are removed. footer: | ### See also - [EPA's Equivalencies Calculator](https://www.epa.gov/sites/production/files/widgets/ghg-calc/calculator.html#emissions) ### Contact Us - [didier.barret@irap.omp.eu](mailto:didier.barret@irap.omp.eu) - [Report](https://gitlab.irap.omp.eu/carbon/travel-carbon-footprint.irap.omp.eu/issues) an issue - [Hire](mailto:antoine@goutenoir.com) a nerd footer: credits: | Didier Barret (IRAP) © 2019