Local Government Environmental Policy Innovation: Emerging Regulation of GHG Emissions in the Transportation-for-Hire Industry
Bibliographic record
Abstract
Given that most environmental regulation is performed on the international and national scale and that municipal powers are often limited, what are the necessary conditions for local government to take on leadership and policy innovation in this space? The transportation-for-hire industry provides an interesting case study as municipalities around the world recently updated their local regulatory frameworks because of the disruption caused by the advent of ridehailing. Over time, policy learning resulted in policy convergence of major components, including license requirements, vehicle standards, and rate standardization, across the industry. However, overlooked from these initial regulations, but now gaining traction is the regulation of greenhouse gas (GHG) emissions of ridehailing vehicles and the transportation-for-hire industry more generally. Because of how many transportation-for-hire vehicles are in use, activists argue that environmental fleet standards ought to be tougher than that for personal-use vehicles. This paper investigates the emerging regulatory frameworks for GHG emissions by examining four case studies of the first-mover cities (London, Amsterdam, Mexico City, and New York City) in this emerging policy space, untangling the complex web of multi-level regulatory governance to tease our policy learning outcomes. Through the innovative lens of examining multi-level governance for policy learning, we conclude that GHG emissions in the transportation industry at the local level only occurred after international and national frameworks were set, giving “permission” to local leaders to demonstrate innovation and leadership on how to achieve targets set by higher-order governments. Cities, assisted with policy-learning transfer through international not-for-profit organizations, acted via local government powers to fill in the gaps of international and national frameworks with policies implemented at higher orders of government in other places. These local environmental transportation-for-hire bylaws took a three-pronged form: (a) vehicle permit standards; (b) regulations of public space and infrastructure; and (c) public–private partnerships to assist in the transition to electric vehicles.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".