The role of individuals and institutions in climate change mitigation
Bibliographic record
Abstract
People seeking to lower their carbon footprints can consult a broad, quantitative literature to help them identify the most impactful lifestyle decisions. Absent from this literature is research into which political actions might be more effective in reducing greenhouse gas emissions. Here I report my findings on obstacles and opportunities for motivated individuals to contribute to climate change mitigation both by changing their lifestyles and by taking political actions. I begin by examining how individuals can model low-carbon mobility in the workplace. Using data collected at the University of British Columbia, I found preliminary evidence that academics could lead by example in reducing air travel without limiting their academic productivity. Next, I surveyed 965 members of the North America public and found that individuals underestimated the emissions associated with air travel and meat consumption, while overestimating the emissions of symbolic actions like eating organic food. Furthermore, participants rarely considered political actions to be the most effective way to reduce emissions. To follow up on the question of how effective political actions are, I used the 2019 Canadian federal election as a case study. In that election, where climate change was a central concern of voters, I found the emissions responsibility associated with voting was higher than the emissions typically associated with lifestyle choices. While I was unable to quantify the emissions associated with other political actions, I attempted to further our understanding of which political actions are more effective through a field experiment. In partnership with a non-profit organization, members of the public sent generic emails to their elected officials, requesting that the officials post a pro-climate message to their social media accounts. I analyzed the elected officials’ social media accounts, and combined with interviews of their staffers, the data suggest that generic campaign emails are only marginally persuasive. I conclude that motivated members of the public may be missing opportunities, in multiple domains, to maximize their impact on the climate.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".