The Market Valuation of Environmental Capital Expenditures by Pulp and Paper Companies
Bibliographic record
Abstract
The objective of this study is to examine the market valuation of environmental capital expenditure investment related to pollution abatement in the pulp and paper industry. The total environmental capital expenditure of $8.7 billion by our sample firms during 1989–2000 supports the focus on this industry. In order to be capitalized, an asset should be associated with future economic benefits. The existing environmental literature suggests that investors condition their evaluation of the future economic benefits arising from environmental capital expenditure on an assessment of the firms' environmental performance. This literature predicts the emergence of two environmental stereotypes: low-polluting firms that overcomply with existing environmental regulations, and high-polluting firms that just meet minimal environmental requirements. Our valuation evidence indicates that there are incremental economic benefits associated with environmental capital expenditure investment by low-polluting firms but not high-polluting firms. We also find that investors use environmental performance information to assess unbooked environmental liabilities, which we interpret to represent the future abatement spending obligations of high-polluting firms in the pulp and paper industry. We estimate average unbooked liabilities of $560 million for high-polluting firms, or 16.6 percent of market capitalization.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".