Climate Change Mitigation Pathways for the Aviation Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even though the contribution of the aviation sector to the global economy is very notable, it also has an adverse impact on climate change. Improvements have been made in different areas (i.e., technology, sustainable aviation fuel, and design) to mitigate these adverse effects. However, the rate of improvement is small compared to the increase in the demand for air transportation. Hence, greenhouse gas emissions in the aviation sector are steadily increasing and this trend is expected to continue unless adequately addressed. In this context, this study examined the following: (i) the factors that affect the growth of aviation, (ii) trends in greenhouse gas emissions in the sector, (iii) trends in energy demand, (iv) mitigation pathways of emissions, (v) mitigation challenges for the International Civil Aviation Organization, (vi) achievements in mitigating emissions, (vii) barriers against mitigating emissions, and (viii) approaches of overcoming barriers against emissions mitigation. This study finds that continued research and development efforts targeting aircraft fuel burn efficiency are crucial in reducing greenhouse gas emissions. Although biofuels are promising for the reduction of aviation emissions, techniques to reduce NOx emissions could enhance large-scale deployment. Pragmatic market-based mechanisms, such as the Emissions Trading Scheme (ETS) and/or carbon tax must be enforced on a global scale to capitalize on a collective stakeholder effort to curb CO2 emissions. The findings of this study will help in understanding the emissions and energy consumption scenarios, which will provide a comprehensive package of mitigation pathways to overcome future emissions reduction challenges in the aviation sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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 it