Assessing capacity to deploy direct air capture technology at the country level – an expert and information entropy comparative analysis
Bibliographic record
Abstract
Abstract An ever-dwindling carbon budget, resulting in temperature rise of 1.5 °C above pre-industrial levels projected between 2030–2035, has generated a necessity to explore climate mitigation technologies such as direct air capture (DAC). DAC typically involves the use of materials and energy to capture CO2 directly from the atmosphere. However, DAC technologies remain a long way from the necessary level of development and scale needed to move the needle on carbon removal and mitigating against climate change. This study conducts a country-level analysis using an expert elicitation and an information entropy method, with a weighted group of variables identified from existing literature as necessary to develop and deploy low-temperature, electrochemical and high-temperature DAC technologies. Here we show that: (1) adopting the expert survey variable weighting, USA, Canada, China and Australia are best positioned to deploy the various DAC technologies; (2) the information entropy approach offers a broadly similar result with traditionally developed nations being best positioned, in addition to land rich countries, to deploy DAC technologies; (3) a comparatively developed policy and financing environment, as well as low carbon energy supply would raise a country’s DAC capacity; (4) developing countries such as China have significant potential to deploy DAC, owing to a well-rounded position across variables. These results produce wide-ranging policy implications for efforts to deploy climate mitigation technologies through the development of a multilateral, coordinated mitigation and carbon dioxide removal deployment strategy.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".