National climate change mitigation efforts for aviation: Lessons from post-Covid state action plans
Bibliographic record
Abstract
The aviation sector contributes to climate change through its significant and growing production of greenhouse gas emissions. Many have suggested mitigation efforts that should be undertaken, but in general these approaches have not resulted in significant change or have ultimately been too expensive. Further, approaches have tended to be fragmented, a challenge for a global industry. There are some exceptions to this, including the International Civil Aviation Organization (ICAO)’s carbon offsetting mechanism, but when it comes to understanding actions at a national level, much remains unclear. While ICAO encourages its member states to develop State Action Plans (SAPs) to address the industry’s impact on climate change, limited research has sought to analyze their proposed actions. In the aftermath of the Covid-19 pandemic’s impact, many called for a renewed effort to mitigate aviation’s contribution to climate change which resulted in a significant number of new SAPs being submitted. As such, this study analyzed 61 SAPs produced in 2021 and 2022 to identify measures being pursued by states to reduce emissions from aviation. We were also interested in partnerships across countries, given the emphasis on collaboration within ICAO’s environment programs. Findings show that most countries have focused on mitigation measures projected to have smaller emission reduction potential in the long term and what evidence we found of more ambitious “deep” decarbonization efforts was largely in the early stages of development. Additionally, we find limited evidence of ICAO’s Buddy Partnership programs in the SAPs which signals a need to re-imagine these opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".