A gridded inventory for global CFC-11 emissions from 2008 to 2019
Bibliographic record
Abstract
CFC-11 is a potent ozone depleting gas and is regulated under the Montreal Protocol. The rate of decline in global CFC-11 concentrations has slowed since 2013 largely due to the renewed, increasing emissions from eastern China (Montzka et al, Nature, 2018; Rigby et al, Nature, 2019). However, regional inversions suggest that this increase only accounts for 40-60% of the global rise. Therefore, there is an urgent need for emission estimates in other regions or countries. A global 3D inversion of atmospheric measurements is essential to improve our understanding of CFC-11 emission trends and sources, but it requires a reliable emission inventory as a prior estimate. In this study, we develop a gridded bottom-up inventory of global CFC-11 emissions from 2008 to 2019. Our inventory is driven by various, gridded proxy datasets including population, energy consumption, GDP per capita, and industrial clusters. A machine learning model is built between the proxy data and the previous emission estimates for eastern China, Korea, and Japan derived from inversions of AGAGE and NOAA surface measurements (Rigby et al, Nature, 2019). Our model is cross-validated in the East Asia and then applied to the other regions and countries to construct the gridded inventory with error characterization. Use of our inventory as prior information in future inverse analyses can help better quantify spatial distributions and sources of CFC-11 emissions as well as better guide the regulation of CFC-11.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".