Testing of a Monitoring, Reporting & Verification (MRV) Scheme for the integration of non-CO2 aviation effects into EU ETS
Bibliographic record
Abstract
In addition to carbon dioxide, aviation affects the climate through other emissions and atmospheric processes, such as contrail formation and the impact of NOx emissions on ozone and methane. No non-CO2 policy instruments have yet been established in aviation, since non-CO2 effects are not yet fully understood and still linked with medium to high uncertainties. But with non-CO2 effects accounting for about 2/3 of the total climate impact of aviation, and with uncertainties not going to disappear in the near future, we have to learn to cope with them. Risk assessment is required to better understand the impact of uncertainties on the calculation of non-CO2 effects and thereby on the potential of setting wrong incentives. To address the lack of incentivizing airlines to internalize their climate costs, this study focuses on the development and testing of a monitoring, reporting and verification (MRV) system, as a first step for the full integration of non-CO2 effects into the EU ETS. For this purpose, non-CO2 effects are integrated according to the principle of equivalent CO2 emissions (CO2e). Since several CO2e calculation methods are in principle available, the selection process involves a trade-off between the level of atmospheric uncertainties, the level of climate mitigation incentives, and the resulting effort of MRV activities. Simple CO2e factors (constant, distance- or latitudedependent) are heavily criticized and proven to be inappropriate since they further increase the focus on CO2 reduction, might create false incentives (incentive to fly higher rather than lower) and “penalize" climate-cost-efficient routings (due to the increased fuel burn). To incentivize mitigation of non-CO2 impacts, more comprehensive CO2e factors (altitude-, location- or weather dependent) are therefore needed, which all require monitoring of flight data. Within this study, we test the operational feasibility of a location-dependent CO2e approach, which seems to be technically possible today. For this purpose, we use flight monitoring data from 400 intra-European flights provided by European Air Transportation Leipzig, a German cargo airline owned by Deutsche Post. To keep the MRV effort as low as possible, most monitoring and reporting (airline perspective) as well as verification steps (authority perspective) are automated via software tools that might be provided for the users (e.g. by the EC). We show some possible steps forward and formulate recommendations for integrating non-CO2 effects into the EU ETS. As next steps, we recommend policymakers to select promising CO2e approaches, to analyze the economic impact and to conduct further pilot projects.
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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 | 0.048 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".