WE have to change! The carbon footprint of ECPR general conferences and ways to reduce it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it