Bibliographic record
Abstract
Environmental economics may sound like an oxymoron to those who believe that saving the environment must be based on a moral, rather than financial, imperative. This chapter argues otherwise. Here, Fullerton suggests that by identifying the market failures responsible for the production of pollution, and by helping to design policy proposals that maximize cost-effectiveness, economics can be a powerful tool for environmental protection. Before the first Earth Day in 1970, ‘environmental economics’ did not yet exist, per se, although individual economists had certainly considered pollution issues. Fullerton explores the ideas and legacies of the early pioneers (Pigou, Coase, Hardin, Dales, Baumol, Oates, Weitzman), and discusses the relationship between these ideas and environmental policies enacted (such as the 1990 US Clean Air Act Amendments, that initiated sulfur dioxide permit trading and thus largely eliminated the problem of acid rain). In this chapter, Fullerton not only explores the history of environmental economics, but discusses which policies (e.g. taxation and permitting) are more suited for different kinds and degrees of pollution. The discipline of environmental economics – which, in recent years, has incorporated new theoretical ideas, and has become more empirically driven by advances in ‘big data’ – has a role to play in tackling many of the environmental issues we face today, from contaminated water, to endangered species.
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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.115 | 0.036 |
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".