Impacts of greenhouse and local gases mitigation options on air pollution in the Buenos Aires Metropolitan Area: Valuation of human health effects
Bibliographic record
Abstract
The objective of this work is to assess through the avoided health cost method what would be the economic benefits of undertaking greenhouse (and local) gases mitigation policies in the Buenos Aires Metropolitan Area. To do so, we have developed six steps: Mitigation Scenarios (which policies to undertake), Emissions Inventory according to those, an Ambient Air Pollution Model to calculate the physical impacts, Health Effects Estimation to assess the health consequences of reducing air pollution, and Economic Valuation of those health impacts. The mitigation measures valued have to do with the transportation sector (greater penetration of compressed natural gas, consumption improvements, and some mode substitution) and the energy sector (the introduction of new dams and the rational use of energy by reducing energy consumption in residential, commercial and public buildings). There are three scenarios: a Baseline or Business-as-Usual scenario, a scenario that considers GHG mitigation options for Argentina with impacts in terms of local pollution, and an Integrated scenario which in addition to GHG mitigation includes policies related to local air quality and rational use of energy programs. All scenarios were built up to the year 2012. Particulate matter is the pollutant whose impact is valued.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".