Barriers and drivers of near-term climate change mitigation: a Canadian case study
Bibliographic record
Abstract
Abstract This work investigates, through semi-structured interviews, the prospects for rapid transitions towards low-carbon civil infrastructure systems. Rapid greenhouse gas (GHG) mitigation is critical to facilitate the near-term (i.e. within five years) reductions needed to limit global temperature rise to 2 °C. In addition, ongoing delays in climate action in many countries and sectors mean that rapid interventions will be needed in the 2030s and 2040s as climate change evolves and the need to mitigate becomes more urgent. This work examines, among twenty decisionmakers involved in developing, operating, or using Canadian infrastructure: (1) ongoing and expected near-term GHG mitigation actions (2) barriers constraining faster change, and (3) mitigation goals and expectations of the near future. Interviews were coded to identify common perspectives. Results indicate that organizations prioritize enabling deep change in a more distant future over executing rapid change, that detailed roadmaps for meeting near-term (e.g. 2030) goals were rare, and that participants view government policy certainty as crucial to near-term action. This work identifies deficits in action on the near-term scale and aids policymakers and decisionmakers by describing planned near-term mitigation actions and assessing barriers to going faster.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".