The Effect of Tightening Standards on Automakers’ Non‐compliance
Bibliographic record
Abstract
This study investigates how tightening standards can result in greater non‐compliance, especially when market and regulatory interests are misaligned. We confirm a causal relationship that explains the highly publicized auto industry non‐compliance phenomenon where on‐road NOx emissions exceeded standards. Based on a 15‐year on‐road vehicle emissions dataset covering 148,837 vehicles from 42 automakers in the EU, we use regression discontinuity to identify the causal impact of standards tightening on non‐compliance by controlling other confounding factors. Our results suggest that in the absence of effective monitoring, tightening standards directly drives up automakers’ non‐compliance. Furthermore, we find that automakers facing more intense substitution pressure from competitors or with less advanced emissions control technology have a higher non‐compliance rate. Our findings speak to both policymakers as well as managers in the private sector. When setting limit‐based performance goals in situations with conflicting interests and imperfect monitoring, they should anticipate non‐compliance from the regulated parties. Our results suggest that tightening standards in such situations should be accompanied by stricter monitoring or other actions that discourage non‐compliance.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".