The Determinants of Mergers & Acquisitions in a Resource-Based Industry: What Role for Environmental Sustainability?
Bibliographic record
Abstract
This paper examines the relationship between environmental stringency and mergers and acquisitions (M&A) activity in a highly polluting, resource-based industry. Specifically, it seeks to determine whether buyers are targeting countries with the same or different levels of environmental stringency than in their own country, i.e. whether pollution havens exist in the global mining industry. Rather than aggregate investment, which has been used by most previous studies, we analyze a dataset of individual investment choices. We model the choice of country and the amount invested jointly as the two variables are likely to be correlated. The choice of country is modeled using a random parameters multinomial Logit model. We use a hitherto unanalyzed data set of the value paid for all completed M&A in the mining industry worldwide between 1994 and 2006. We find no evidence of pollution havens in this industry. If anything, buyers from countries with high levels of environmental stringency are more likely to invest in countries with a similar level of environmental stringency and make larger investments in them than in less environmentally stringent countries.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".