Social Benefits and Private Costs of Driving Restriction Policies: The Impact of Madrid Central on Congestion, Pollution, and Consumer Spending
Bibliographic record
Abstract
Abstract Low Emission Zones are defined areas within a city where driving restrictions are introduced with the aim to reduce pollution, but they may also unintentionally distort consumer spending decisions. By increasing transportation costs to ban-affected areas, driving restrictions could discourage spending in stores of those areas. This paper empirically evaluates the effects of a driving restriction regulation in Madrid, Spain, known as Madrid Central. First, using a difference-in-differences identification strategy, we find an immediate decrease of 19% in pollution and of 16% in congestion with pollution dropping further once fines were levied. Second, we rely on credit card transaction data to show consumers affected by the regulation reduced their brick-and-mortar spending in the regulated area by 21%. Finally, because affected consumers partially substitute their consumption spending from brick-and-mortar to online shopping, we find suggestive evidence that e-commerce may smooth the impact of changes in transportation costs due to environmental regulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".