Costless CO <sub>2</sub> emissions abatement through improved government effectiveness
Bibliographic record
Abstract
The withdrawal of Canada, Japan, New Zealand, and Russia from the Kyoto Protocol, the non-ratification of the United States at the onset, and the exemption given to China and India indicate that five out of the ten largest economies in the world, accounting for approximately 50% of the world GDP (as of 2021), have turned away from the Kyoto Protocol. This apprehension can plausibly be explained by the potential loss of productivity resulting from abatement of CO2 emissions. Therefore, this study examines the marginal effect of CO2 emission abatement on technical efficiency and how it can be moderated by government effectiveness. The following are the findings of this study: (1) inclusion of the pollution effect in the modelling of technical efficiency is necessary as it significantly changes the technical efficiency score ranking; (2) the opportunity cost of CO2 emissions abatement exists in the form of productivity loss but is significantly moderated by government effectiveness. These findings are important as they aid policymakers in mapping out a strategy for the desired costless abatement of CO2 emissions.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".