Examining Climate Anxiety and Sustainability Engagement in the Undergraduate Engineering Student Population
Bibliographic record
Abstract
Abstract It has been previously documented that severe weather events cause a wide range of direct mental health concerns, including depression, PTSD and anxiety in individuals living in the affected community. However, as the urgency around broader climate change has increased, a new phenomenon known as "Climate Change Anxiety" has emerged. Climate anxiety is a form of anxiety induced by the existence of climate change and concerns about this change, rather than discrete weather events. Simply being aware of climate change and its negative impacts on our natural and social systems can cause a severe anxiety response. The recently developed Climate Change Anxiety Scale (CAS) includes questions related to both cognitive and emotional impairment as a result of anxiety about climate change, and is designed to enhance our understanding of the anxiety response to this critical global challenge. Although climate anxiety is studied in the general population, very few studies have been conducted with university students, and no studies have been found that focus on the undergraduate engineering student population. Engineers play an important role in the race to meet the critical 2050 goal of net zero emissions to limit global warming, and in supporting societies in adapting to the impacts of climate change. This presents opportunity for the profession, but also demands a sense of resilience from those working in engineering, who must devise complex sociotechnical solutions and combat rampant politicization in the space. This paper describes the process of surveying 200 undergraduate engineering students at a large, public research institution in Canada. The CAS was adapted for this survey, with additional qualitative questions added to understand the student experience with sustainability curriculum and their ideas about pursuing careers in sustainability. The survey demonstrated a positive relationship between behavioural engagement in sustainability-related activities and climate anxiety, which is in contrast to the studies conducted with a general population. These results have important implications for considering how to best support students engaged in sustainability and climate-change related activism, and those considering careers in this space. Despite agreement that engineers have a role to play in climate change, many of the students surveyed view sustainability and climate change studies as a discipline distinct from their own engineering work, which may be impacted by relatively low coverage in their curriculum. This is also significant, as students indicated a barrier for pursuing a career in sustainability is lack of knowledge/training.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".