Climate adaptation planning in the higher education sector
Bibliographic record
Abstract
Purpose There is a growing interest in climate change action in the higher education sector. Higher education institutions (HEIs) play an important role as property owners, employers, education and research hubs as well as leaders of societal transformations. The purpose of this paper was therefore to benchmark how universities globally are addressing climate risks. Design/methodology/approach An international survey was conducted to benchmark the sector’s organisational planning for climate change and to better understand how the higher education sector contributes to local-level climate adaptation planning processes. The international survey focused especially on the assessment of climate change impacts and adaptation plans. Findings Based on the responses of 45 HEIs located in six different countries on three continents, the study found that there are still very few tertiary institutions that plan for climate-related risks in a systematic way. Originality/value The paper sheds light on the barriers HEIs face in engaging in climate adaptation planning and action. Some of the actions to overcome such hindering factors include integrating climate adaptation in existing risk management and sustainability planning processes, using the internal academic expertise and curriculum to assist the mapping of climate change impacts and collaborating with external actors to guarantee the necessary resources. The higher education sector can act as a leader in building institutional resilience at the local scale.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".