A firm level Environmental Kuznets Curve Evaluation: thresholds in a cross-sectional dataset of mid-sized companies
Bibliographic record
Abstract
In a context of insufficient global regulation, a substantial proportion of firms set voluntary greenhouse gas emissions reduction targets. This study draws on a new dataset of about 1000 GHG Protocol-compliant assessments from a thousand Europe and US-based small and medium enterprises and mid-sized firms between 2019 and 2024 to investigate how emissions metrics compare across as companies grow and consolidate their productivity. Moving beyond the conflicting environmental Kuznets curve evidence and the traditional focus on large enterprises, we uncover a new, untheorized link between emissions and firm productivity. Using competing polynomial and threshold regression models, and addressing endogeneity, selection bias and omitted variable bias by relying on Granger causality, Hausman instruments, poststratification, Oster sensitivity tests and split sample testing, we establish that contrary to the classic inverted U shape of the EKC, corporate emissions and emissions intensities do not show a clear turning point but instead stabilize once over a critical threshold in revenue per employee. We find raising productivity can halve per revenue intensities despite simultaneous fourfold and twofold surges in absolute and per-employee emissions. This structural influence of financial productivity challenges the fairness of corporate net-zero targets that assume comparable baseline intensities across firms.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".