conference_footprint

README.md (7733B)

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 # Compute the CO2e footprint of an academic conference
 
 This repository contains scripts to compute the CO2e footprint of trips made for
 an academic conference.
 
 It was used to compute the footprint of the [Highlights'22
 conference](https://highlights-conference.org/2022/).
 
 ## Data collection
 
 We collected information about the travel plans of participants using a [web
 form](https://framaforms.org/highlights-participant-travel-information-1664806487)
 ([archive](https://web.archive.org/web/20221003161159/https://framaforms.org/highlights-participant-travel-information-1664806487)).
 To ensure that everyone filled the form, the link to payment was only given once
 the form was completed.
 
 The transportation modes proposed on the form were: train, plane, bus/coach,
 local transportation (for local participants), and other. Car travel was not
 supported, but could be added for further years.
 
 We manually removed duplicate records and fake data.
 
 For people who did not fill in the details of their travel, we:
 
 - assumed that they were coming to/from the institution of their first
   affiliation
 - when the transportation mechanism was unspecified, we assumed that trips of
   <=400km were done by rail and trips of >400km were done by plane, following:
   https://github.com/ConferenceCarbonTracker/CarbonFootprintAGU#44-mode-of-transport
 
 Afterwards, we discarded the name and institution of participants.
 
 We manually translated the free-form city and country to a
 machine-understandable location by searching by hand for the closest
 three-letter code (airport or metropolitan area). This step could be automated.
 
 The result is a CSV file in the following format:
 
 - Field 1: 3-letter airport or metropolitan area code of origin (first leg, before the conference)
 - Field 2: Transportation means of the first leg: "train", "plane", or "bus/coach".
 - Field 3: 3-letter code of destination (second leg, after the conference)
 - Field 4: Transportation means of the second leg
 - Additional fields, e.g., fields indicating if the participant is extending
     their stay for scientific reasons other than the conference. These files are
     ignored.
 
 ## Running the computation
 
 You need python3, standard shell utilities, and `GeodSolve` from Debian package
 `geographiclib-tools`.
 
 Run `./run.sh FILE CODE LAT LON NOISE` where:
 
 - FILE is the CSV file above
 - CODE is the 3-letter code used for local participants (their trips will be
     ignored, as well as any trips with mode "local")
 - LAT and LON are the geographical coordinates where the conference is taking
     place.
 - NOISE is the percentage of random error added to the distance (e.g., 0.2 for
     20%). Specifically, for x the true value, the anonymization will return some
     value chosen uniformly at random between (1-NOISE) x and (1+NOISE) x
 
 The script will generate:
 
 - map.geojson: a Geojson file displaying the various points of travel with color
     describing whether they are by plane or not. This can be plotted, e.g., with
     [uMap](http://umap.openstreetmap.fr/fr/).
 - `trips_anonymized.csv`, a list of trips with headers and with the following fields:
       - Field 1: mode of trip (as above)
       - Field 2: distance of trip, with random error added
 - `trips_with_footprint`, a comma-separated list of trips with the following
     fields:
       - Field 1: name (note that commas are dropped from names)
       - Field 2: institution (ditto)
       - Field 3: distance of trip in meters
       - Field 4: mode of trip (inferred if missing)
       - Field 5: CO2e footprint of trip in kilograms
 - It will also output some aggregate values on the standard error output, and prepare temporary files `trips`
     and `trips_with_dist`
 
 ## Footprint computation methodology
 
 ### Local participants
 
 We ignore local participants, for which we estimate a CO2 footprint of 0.
 
 ### Geocoding and distance computation
 
  We used the OpenFlights
 database airport-extended.dat on [this page](https://openflights.org/data.html) to convert these
 to geographical coordinates, and used known geographic coordinates for the
 conference venue. We used GeodSolve to compute the distance of each trip.
 
 ### Carbon footprint
 
 We compute the CO2 fotprint following the
 [labos1point5](https://labos1point5.org/ges-1point5) data, which is adapted from
 the French agency [Ademe](https://www.ademe.fr/).
 
 - For train, we count **37 gCO2e/pkm** (international train). This is pessimistic in France, very
   pessimistic for TGV, but similar to the 41 gCO2e/pm for national (UK) rail
   given by [Our World in
   Data](https://ourworldindata.org/travel-carbon-footprint).
 - Plane is counted following
     [labos1point5](https://labos1point5.org/ges-1point5), including the effect
     of contrails:
   - 258 gCO2e/pkm for less than 1000km
   - 187 gCO2e/pkm between 1001km and 3500km
   - 152 gCO2e/pkm above 3500km. This value is consistent to the 150 gCO2e/pkm
       value for long-haul flight given by [Our World in
       Data](https://ourworldindata.org/travel-carbon-footprint) (also including
       contrails)
 - For bus/coach, we count 28 gCO2e/pkm as the coach value given by [Our World in
     Data](https://ourworldindata.org/travel-carbon-footprint) as there is no
     value in labos1point5.
 
 We then sum the total emissions to arrive at the final value.
 
 ## Highlights'22 methodology
 
 ### Data collection
 
 The [Highlights registration
 form](https://framaforms.org/highlights2022-on-site-registration-1652701135) 
 ([archive](https://web.archive.org/web/20220622164245/https://framaforms.org/highlights2022-on-site-registration-1652701135))
 asked particiants:
 
 - "To estimate the carbon footprint of this edition of Highlights, please give
   us some information about your travel"
 - "Arriving from": city and country, free text
 - "Arriving by": other / plane / train / bus or coach / car / local transportation (for locals)
 - ditto for departure
 - Extended stays: we asked whether:
   - They participated to a co-located conference
   - They participated to an extended stay support scheme
   - They were "extending their stay for scientific reasons by another way"
 
 The fields were optional, but almost everyone filled them.
 
 ### Adjusting for other scientific reasons
 
 In the original carbon footprint given at the conference, to account for
 participants whose stay had other scientific justifications (no matter which),
 we counted only the longest of the two trips. The effect is basically to halve
 their emissions by considering that the conference carries half the
 responsibility. The reason why we do this instead of dividing the total by two
 is to make sure that we account for one of the "long trips" required between
 their institution and conference venue: indeed, some participants gave details
 of these long trips, whereas other gave details of one long trip and one trip to
 a neighboring place, e.g., for an extended stay.
 
 Because of differing opinions on the method, the code presented here no longer
 does this computation, i.e., it forgets about extended stay information.
 
 ### Anonymized data
 
 The file `trips_anonymized.csv` in this repository describes the trips of
 Highlights'22 participants, computed by running the script `run.sh` above.
 
 Each line describes one individual trip (not a return trip), with the declared
 mode of transportation, and the distance in meters with a multiplicative noise
 of `1.2` (i.e., each distance `d` was replaced by a value between `0.8 d` and
 `1.2 d` chosen uniformly at random).
 
 The total CO2 footprint of the file `trips_anonymized.csv` accounts for 42 tons
 of CO2e, which (after rounding) is the same value as the [publicly released
 value](https://highlights-conference.org/2022/#discussion) computed on the data
 without noise.