Bibliographic record
Abstract
Following in the footsteps of the European Union’s Emissions Trading Scheme (EU ETS), which was set up in 2005, some states in the United States, some provinces in Canada,1 the United Kingdom, New Zealand, and (until 2014) Australia have been among the first common law countries to construct carbon markets in line with the prediction of Bill Gates that ‘clean energy could one day be a multi-trillion-dollar market.’2 This article builds on the strong support for carbon pricing expressed by world leaders at the twenty-first Conference of the Parties (COP-21) of the United Nations Framework Convention on Climate Change (UNFCCC) in Paris in December 2015, which called on companies and countries to put a price on carbon to drive investment towards a cleaner, greener future.3 There has been a call for carbon pricing by the World Bank, the International Monetary Fund, and the six heads of state and government (Canada, Chile, Ethiopia, France, Germany, and Mexico).4 The World Bank reported that almost forty countries and more than twenty cities, states, and provinces already use carbon-pricing mechanisms or are planning to implement them. Combined, these jurisdictions are responsible for more than 22 percent of global emissions. If the jurisdictions that are developing or considering carbon-pricing mechanisms in the future are added to this number, the total number of carbon-pricing mechanisms will encompass almost half of the global carbon.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".