Toxic release inventories and green consumerism: empirical evidence from Canada
Bibliographic record
Abstract
Abstract. Do firms abate pollution in response to actual or anticipated green consumerism? Lacking direct observational data on the extent of green consumerism, we construct an indirect method to elicit its effect on pollution abatement. If environmentally motivated consumers target companies rather than particular facilities of a multi‐product firm, green consumerism can be identified through intra‐firm inter‐plant spillover effects in pollution abatement. We test the prediction that ‘environmentally‐leveraged’ firms with consumer market exposure experience larger emission reductions. We use 1993–99 panel data from Canada's National Pollutant Release Inventory (NPRI), with pollutants adjusted for toxicity. Our empirical results find statistically significant evidence of green consumerism, but its economic magnitude is very small. JEL Classification: Q2, R3 Inventaires des émanations toxiques et consumérisme vert : résultats pour le Canada. Est‐ce que les entreprises réduisent la pollution en réponse au consumérisme vert – présent ou anticipé? Comme on n’a pas de façon de le savoir directement, on élabore une méthode indirecte de mesurer son impact sur l réduction de la pollution. Si des consommateurs intéressés à l’environnement ciblent certaines sociétés plutôt que des installations particulières d’une entreprise multi‐produit, le consumérisme vert a un impact détectable via des effets de retombées des politiques de réduction de pollution intra‐firme entre les installations. On met au test l’hypothèse que les entreprises qui sont exposées à des pressions sur le marché effectuent des réductions de pollution plus importantes. Utilisant des données de l’inventaire national des rejets de polluants du Canada pour 1993–99, on trouve que l’effet du consumérisme vert est statistiquement significatif mais économiquement très faible.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".