Bibliographic record
Abstract
Executive Overview The conventional wisdom concerning environmental protection is that it comes at an additional cost imposed on firms, which may erode their global competitiveness. However, during the last decade, this paradigm has been challenged by a number of analysts (e.g., Porter & van der Linde, 1995), who have argued basically that improving a company' environmental performance can lead to better economic or financial performance, and not necessarily to an increase in cost. The aim of this paper is to review empirical evidence of improvement in both environmental and economic or financial performance. We systematically analyze the mechanism involved in each of the following channels of potential revenue increase or cost reduction owing to better environmental practices: (a) better access to certain markets; (b) differentiating products; (c) selling pollution-control technology; (d) risk management and relations with external stakeholders; (e) cost of material, energy, and services; (f) cost of capital; and (g) cost of labor. In each case, we try to identify the circumstances most likely to lead to a “win-win” situation, i.e., better environmental and financial performance. We also provide a diagnostic of the type of firms most likely to reap such benefits.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".