How much carbon dioxide does travelling to the annual EGU General Assembly emit and how it can be reduced to less than 5% of current emissions in short time.
Milan Klöwer
Atmospheric, Oceanic and Planetary Physics, University of Oxford
milan.kloewer@physics.ox.ac.uk
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16,273 scientists from 113 countries participated in the European Geoscience Union's (EGU) General Assembly 2019 in Vienna, Austria. We estimate that these scientists travelled in total 94 million km to Vienna and back, which emitted 22,300 tC02e, an average of ca 1.4 tCO2e per scientist. 86% of these carbon emissions result from long-haul flights (>1500km), 13% from short-haul (between 700 and 1500km) and <1% from rail journeys (<700km). Scientists from China and the United States are responsible for 40% of emissions. If all short-haul flights were replaced by rail journeys the total travel carbon footprint would be reduced by 11.5% down to 19,750 tCO2e. If the equivalent of the 9% highest emitting participants would participate virtually, then the carbon emissions would be reduced by 34%. Virtual participation for 26% of the highest emitting participants would reduce the carbon footprint by 80%. An EGU General Assembly with most European scientists arriving by train and 26% virtual participation reduces the current travel carbon emissions by 91%. Combining this scenario with a completely virtual format every other year, travel emissions to the EGU General Assembly would be reduced by 96% to less than 1000 tCO2e.
International aviation is projected to contribute 22% to global greenhouse gas emissions in 2050 [1], as doubling of passengers is expected by 2036 [2]. In Paris 2015, world governments have agreed to keep the global temperature increase well below 2°C [3], which requires drastic reductions in greenhouse gas emissions. Universities and other research institutions are high emitters [4], with individual carbon emissions an order of magnitude larger [5] than the suggested personal carbon allowance [6, 7, 8]. Research-associated carbon emissions are dominated by air travel to conferences, meetings and for fieldwork [4]. Recent studies suggest that academic air travel has limited direct link to professional success [5, 9].
Most conferences do not provide live-streaming, nor allow for remote-speaking, although alternatives exist. Very few conferences are fully virtual (e.g. virtual island summit) and therefore often almost carbon neutral [10]. Continuous virtual seminar series allow for frequent academic exchange (e.g. Virtual Blue COP 25) sometimes with a focus on field-specific subjects (e.g. EBUS Webinars). Live-streaming is provided by more conferences (e.g. JuliaCon, with an automatic archive on YouTube), mainly to make them more accesible for participants with constraints on time, money, or freedom of travel.
The carbon footprint of most conferences and meetings is dominated by a small amount of participants with disproportionate travel emissions due to long distance flights [11,12]. For the EGU General Assembly 2012 it was estimated that 20% of the highest emitting participants are responsible for 70% of the travel emissions [4]. Here, we calcuate the travel emissions for the same conference in 2019 and present reduction scenarios based on an increased number of rail journeys and virtual participation.
Figure 1: The journeys of all 16,273 scientists are illustrated on an equi-distant map, which preserves the distances with respect to Vienna. Line thicknesses are weighted by the amount of participants per country. Capital cities or largest cities are assumed as the departure location, with a few exceptions (see section 4.1). The total distance travelled is ca 94 million km. A version of this Figure covering only Europe can be found here.
Figure 2: a) Splitting the total carbon footprint of 22,300 tCO2e into the modes of transport shows that long-haul flights are the major contributor with 86%. Contribution of rail journeys are less than 1%. b) A scenario, in which all short-haul flights are replaced with rail journeys, decreases the carbon footprint by 11.5% to 19,750 tCO2e. This scenario makes long-haul flights the dominating contribution to the overall carbon footprint with 97% and the emissions from rail journeys are negligible (<3%).
Figure 3: a) China (1194 scientists) and USA (1068) are the biggest contributors, due to large number of participants and large distances to Vienna. Although many scientists come from Germany (2587), UK (1355), Italy (1191), France (1151), Austria (754), and Switzerland (723), their contribution is minor due to short distances to Vienna, despite short-haul flights. On the other hand, 51 scientists (0.3% of participants) from New Zealand contribute 2% of the overall carbon emissions. b) A scenario, in which short-haul flights are replaced with rail journeys, decreases the carbon emissions from the United Kingdom, Germany, France by a factor of 7, which results from the assumptions on carbon emission per mode of transport (30gCO2e / km / person for rail versus 200gCO2e / km / person for short-haul (see section 4.5). Same holds for other countries in regions like Benelux, Scandinavia and Eastern Europe (incl. South-East and North-East, like Greece, Turkey, Romania, Estonia, Bulgaria, Latvia and Ukraine, to name a few), although travel times from these countries may be considerable given current rail and bus infrastructure.
Figure 4: Carbon emissions sorted by highest per capita emissions. Each grey rectangle represents one country, some of the largest in terms of emissions and participants are named. The 26% furthest-travelling EGU participants (green lines) are responsible for 80% of the conference's total travel carbon footprint, with the top 9% (blue lines) responsible for 34% of the total. The Gini-coefficient is 63% and therefore similar to the global inequality of income.
Data is based on number of participants per country, published by EGU. The processed data, including coordinates of departure location, distance to Vienna and sum of emissions per country, can be found in data/data_processed.csv
All scripts can be found in src/ and the assumptions are discussed in section 4.6.
The departure location per country is chosen as the capital or largest city (see data), with a few exceptions that are explained in the following:
a) Germany: Participants from Germany are split into 4 groups (Berlin 20%, Hamburg 20%, Munich 20%, Cologne 40%) to better represent the participant distribution and their distance to Vienna across Germany.
b) United Kingdom: For similar reasons scientists from the UK are split into 2 groups (London 70%, Manchester 30%).
c) United States: Washington DC 70%, Los Angeles 30%.
d) Austria: Vienna 50%, Graz 50%. Graz has a relatively high number to account for journey distances of participants from Innsbruck, Salzburg, etc.
e) Canada: Toronto 80%, Vancouver 20%.
The named location city, country is converted to geographical coordinates with Nominatim from the OpenStreetMap database (see src/get_locations.py).
Every participant is assumed to travel back to their departure location with the same mode of transport. Due to the lack of data, we have to assume that every scientists only came to Vienna for the purpose of the EGU General Assembly. Some scientists likely connected their journey to Vienna with other conferences, meetings or holidays, which has to be taken into account in case the carbon footprint of individuals or a whole research field is calculated.
Rail is assumed for all journeys with distances of less than 700km. Airplanes are assumed for longer distances. Short-haul is defined as distances of less than 1500km, longer distances are long-haul.
We assume all journeys to be direct, that means, we calculate the distance as the great circle distance. This is more accurate for long-haul than for short-haul, and may have some considerable errors for railways, but less than a factor of 2. More in section 4.6.
Rail journeys are assumed to emit 30gCO2e / km / person. [13, 14, 15, 16]
Short-haul flights are assumed to emit 200gCO2e / km / person, long-haul flights are assumed to emit 250gCO2e / km / person. These values take into account factors that typically decrease the per km emissions for long-haul flights such as [17, 18,19]
- increased fuel consumption for take-off
- decreased detour factors for longer flights
- average aircraft types and their fuel consumption
- average passenger load factors for average airlines.
Additionally, we take into account factors that typically increase the per km emissions for longer flights, which on average tend to outweigh the factors from above [17]
- increased fuel weight for longer flights
- increased flight altitudes depending on distance covered
- indirect CO2 effects on ozone and cloud formation depending on flight altitude [20].
Some emission calculators do not include all of the factors above (e.g. 18 and 19). To our knowledge, the atmosfair calculator [17] is the most sophisticated. It includes the indirect CO2 effects not just as a factor 2, as an approximation recommended by Jungbluth and Meili, 2019 ([20]) but makes this factor flight altitude dependent (as recommended as a next order accuracy therein). Additionally, atmosfair's calculator uses a database which analysed the aircraft types, their fuel consumption and passenger loads typically flown on specific routes. We therefore obtained our assumed emissions values by searching for typical flight routes to Vienna and simplified the results.
We assume economy class for every participant.
Carbon emissions of live-streaming are assumed to be negligible.
Sensitivity to the assumptions is fairly low. Main contributions to the uncertainty of the carbon footprint are
a) The carbon dioxide equivalent emissions of long-haul flights: These are assumed to be 250gCO2e / km / person, which is a representative average with probably less than 10% error [17]. The emissions of individual flights have much higher uncertainty and depend on number of passengers, airline / flight class, type of aircraft, potential detours, flight altitude, and weather conditions. The carbon dioxide equivalent emissions of super long-haul flights (>10,000km) are usually higher due to additional fuel weight and flight altitude, although increased fuel consumption from start and detour contribute less for such long distances.
b) The exact departure location of scientists from USA: A flight from Los Angeles to Vienna emits 1.8 times more tCO2e than a flight from New York City to Vienna [17]. We assume that a ratio of 70% of scientists depart from Washington DC and 30% from Los Angeles is representative to account for longer journeys (but therefore probably also fewer scientists) from Midwestern, Southern USA or the West Coast. Assuming 50% of scientists from the USA depart from Washington and 50% from Los Angeles, would increase the emission of those by 17%. As the USA contribution to the overall carbon dioxide emissions of EGU travel is 20%, this uncertainty accounts for less than 4% in total.
c) The exact departure location of scientists from China: We assume that all scientists from China fly in from Beijing. A flight from Shanghai emits less than 20% more tCO2e than a flight from Beijing. Assuming half of the scientists from China flew in from Shanghai, this would increase China's emission by 10%. Taking into account that China contributes 20% to the overall emissions of EGU travel, this uncertainty is less than 2% in total.
d) Similar arguments hold for the exact departure locations of scientists from Canada, Brazil, Australia, and India. Smaller countries like New Zealand, Taiwan, South Korea, contribute even less to the uncertainty.
e) The carbon dioxide equivalent emissions of rail journeys. These are assumed to be 30gCO2e / km / person [13, 14, 15, 16], which can be considered as an European average. Emissions from individual trains can, however, be lower by an order of magnitude depending on the type of train (electric, diesel, highspeed or regional), the local energy mix (for electric trains), number of passengers, etc. The highspeed train in France is estimated to emit only 3gCO2e / km / person [21], due to a very low carbon electric grid, but average trains in the UK emit 40gCO2e / km / person [16] as many services are not electrified and diesel trains are used instead. As the contribution of rail journeys to the overall carbon footprint of EGU-related travel is negligible (<1%), the uncertainty here is negligible too.
f) Indirect rail journeys. We assume great circle distances of rail journeys such that we likely underestimate the actually travelled distance. However, this error is within a factor of 2. Since the contribution of rail travel to the overall carbon emissions is very small, the resultant uncertainty in the overall budget is negligible.
[2] International Air Transport Association, 2017. 20 Year Passenger Forecast
[3] Paris Agreement of the United Nations Framework Convention on Climate Change, 2016.
[10] Ken Hiltner, 2016. A Nearly Carbon Neutral Conference Model
[13] Knoerr, W and R Huettermann, 2016. EcoPassenger: Environmental Methodology and Data, Update 2016
[15] European Environment Agency EEA, 2017. Energy efficiency and specific CO2 emissions
[17] Atmosfair, 2016. atmosfair Flight Emissions Calculator: Documentation of the Method and Data.
[19] Foundation myclimate, 2019. The myclimate Flight Emission Calculator.
[21] OUI SNCF, 2018. Calculation of CO2 emissions on your train journey.