Climate Change and Companion Animals: Identifying Links and Opportunities for Mitigation and Adaptation Strategies
Bibliographic record
Abstract
Recent natural disasters and weather extremes are a stark reminder that we live in a climate crisis. Climate scientists and policymakers have asked each discipline to anticipate and create mitigation and adaptation plans in preparation for a worsening future. Companion animals both impact and are impacted by the changing climate through their intrinsically linked relationships to human society. In this theoretical paper, we argue that companion animal scientists are well-suited to address climate change issues. We identify several anticipated climate change outcomes, such as an increase in extreme weather events, human migration, disasters, and an increase in human inequity, and connect these outcomes to identified or hypothesized impacts on companion animals and the human-animal bond. We suggest opportunities to reduce climate change impacts on companion animals that include alterations to owner caretaking behaviors and breeding practices, and education of owners and governments on zoonosis and disaster preparedness. Furthermore, building climate resilience through decreasing inequity in companion animal fields is paramount; and we propose that a starting place can be in animal sheltering and other support services. We also summarize how companion animals and owners' caretaking behaviors are impacting climate change through the use of finite natural resources as well as pollution and carbon emissions. We propose that replacement, reduction, and refinement, that guide laboratory animal research, can also be useful to mitigate the effects of companion animals on the environment. We suggest criteria for successful mitigation and adaptation plans to include equitability, sustainability, respect for animals, and measurability. Finally, we end on a call to all companion animal professionals to actively consider their role in mitigating the impact of companion animals on the climate and preparing for the fallout of climate change in their communities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".