Fossil energy price and outdoor air pollution: predictions from a QUAIDS model
Bibliographic record
Abstract
Cheap fossil energy leads to overconsumption of energy and hazardous levels of air pollution. In this study, we provide a framework to connect fossil energy price policy to private consumption of energy and outdoor air pollution. We used a consumer demand system and reassessed it for the recent status of the Iranian economy. We extracted household consumption information from Iran’s 2011 and 2014−2016 annual household surveys (n=154683), prices from the Central Bank of Iran’s detailed monthly price indices from 2008 to 2016, and air pollution information from Iran’s Energy Balance Sheets. We estimated that an average Iranian household would reduce its energy consumption by 2%, 16%, 29%, 38%, and 45% if energy prices were hiked by 10%, 50%, 100%, 150%, and 200%, respectively. The corresponding reductions in total outdoor air pollution in the post-hike period would be 2.6, 26.3, 47.6, 62.9, and 74.5 million tons, respectively. Besides highlighting the importance of fossil energy price policy as a short-term strategy to reduce air pollution, this study calls attention to shifting the existing subsidies on fossil fuels to sustainable sources of energy such as waste-oriented biofuels as a -long-term solution.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".