Corporate environmental footprint and product market competition
Bibliographic record
Abstract
• How does product market competition affect corporate environmental footprint? • We examine restructuring of U.S. electric utilities, the number one emissions-intensive sector. • Cost-cutting actions are the key driver of changes in operations and emissions of electric plants. • Cost-cutting actions lower environmental footprint when plant technology allows greener production. • Without such technology, competition worsens environmental outcomes. Banks face pressure to integrate a wider range of risks into lending decisions, including both traditional product-market risks and the increasingly important environmental risk. Yet how these two types of risk interact remains unclear. We show that production technology is pivotal in shaping the impact of product-market competition on environmental risk. Focusing on the restructuring of the US electric utility industry, which introduced product-market competition into a highly polluting sector, we find that technological capacity is key. When technology enables cost-saving production decisions that also improve environmental performance, competition reduces environmental footprint. Otherwise, it exacerbates it. These findings suggest that lenders must assess not only individual risk factors of borrowers but also their potential interactions, with firms’ technological capacity playing a crucial role.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".