Bibliographic record
Abstract
Production efficiency of the firm is defined to be the maximum ratio of output to input in this paper. When a society reaches Pareto optima, this society achieves its aggregative efficiency. If the firm does not produce efficiently, transformation rate on production frontier does not make any sense because production frontier can be extended if the firm improves its own production efficiency. Therefore, production efficiency of the firm is the necessary condition of Pareto optima. Since total input is equivalent to total cost in the sense of aggregating different inputs, the concept of production efficiency of the firm can be mathematically converted into minimum cost (i.e., minimum input). When input is minimized, pollution that arises from production is minimized, ether. Thus, pollution is minimized by the efficient firm while the firm grows efficiently. This conclusion implies that government intervention is unnecessary. Consequently, the primary policy to reduce pollution would be promoting anti-pollution technology and subsiding the firm to upgrade its equipment as well as improving production efficiency of the firm rather than charging Pigovian tax and enforcing government regulation except that minimum pollution makes people not endure or environment not sustain. To summarize, this paper integrates efficiency, externality (e.g., pollution), market mechanism (e.g., supply and demand), government intervention (e.g., Pigovian tax), increasing return to scale and economic growth into growth model of the firm by which we are capable to propose pollution policy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".