WE have to change! The carbon footprint of ECPR general conferences and ways to reduce it
Bibliographic record
Abstract
Abstract The political consequences of climate change have been topics at numerous political science conferences. Contrary to the plurality of discussions at these meetings, it is striking that there is no systematic account of the carbon footprint of political science conferences themselves. Applying a GIS-based approach I estimate the travel induced greenhouse gas emissions of the last six ECPR General Conferences (2013–18). The results show that for the five conferences that took part in Europe the average emissions per attendee were between 0.5–1.3 tons CO2-equivalents. At the 2015 conference in Montreal it were even 1.9–3.4 tons. Compared to estimations based on the latest IPCC reports which call for a reduction of per capita emissions to 2.5 tons by 2030 and even 0.7 tons by 2050 in order to keep on track with the 1.5-degree goal, the travel induced GHG-emissions of ECPR conferences are very high. Yet, further estimations demonstrate that significant emission reductions are possible: by choosing more central conference venues, promoting low-emission landbound means of transportation and introducing online participation for researchers from far away, the carbon footprint could be reduced by 75–90 per cent. The article also gives concrete recommendations how the carbon footprint of conferences could be reduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".