The Annual Carbon Footprint of In-Person Attendance at Major Neurological Conferences
Bibliographic record
Abstract
Background: Anthropogenic climate change is the greatest threat to health of our century, including increased risk and severity of neurological diseases. Policies to contain the progression of climate change to within the adaptability of the human system demand urgent reduction in carbon emissions across all sectors. Neurologists should not be exempt. Global travel to conferences is the main source of academia’s carbon emissions. Lack of awareness of the extent of the problem and of the urgency of effective intervention may explain the lack of mitigating actions from the neurological community. We aimed to provide a conservative estimate of neurologists’ annual CO2 emissions from in-person attendance at some of their main global conferences. Methods: The number of in-person attendees at some major international neurological conferences in 2022/2023 was assessed by a Google search. The CO2 emissions per capita (tCO2/person) was calculated by obtaining the average of the values published in four studies of non-neurological conferences. The total tCO2 emissions from all attendees at the neurological conferences was then compared to the annual tCO2/person in various countries world-wide. Results: Thirteen conferences were identified with a total of 47,956 attendees. The total tCO2 emissions was then calculated at 93,994 (1.96 tonnes x 47,956 attendees). This equated to annual tCO2/person ranging from >2.0 million people in several African countries to 6.700 in Canada. Conclusion: This simple analysis based on literature values to calculate carbon emissions by attendees, shows staggering figures that only represent a partial annual contribution from the neurological discipline. In the context of a climate crisis that requires urgent actions, neurologists should carefully consider their choices and implement actions to effectively reduce the carbon cost of their conferences.
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How this classification was reachedexpand
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".