A blockchain-based emissions trading system for the road transport sector: policy design and evaluation
Bibliographic record
Abstract
Emissions trading is a cost-effective climate policy for reducing greenhouse gas emissions. It could also be useful for addressing road transport emissions, especially given that this sector is the largest CO2 emitter in the transportation sector and its emissions continue to increase. However, emissions trading for road transport (ETS-RT) has rarely been implemented due to its complexity. This paper designs a novel and practical policy framework for an ETS-RT based on advanced blockchain technology, including all related entities upstream, midstream and downstream of the road transport sector. First, the government determines the cap and allocates the initial permits. Then, fuel producers, vehicle manufacturers, and vehicle users are involved as regulated entities with tradable emission permits. They are responsible for the three determinants of CO2 emissions in the road transport sector: fuel emission factors, vehicle fuel economy, and vehicle miles travelled, respectively. With all the regulated entities collaborating on compliance, the three determinants can be synergistically optimized so that the efficiency of the emissions abatement can be maximized. In addition, all trading, monitoring, reporting, and verification of the emission permits are automatically executed and recorded via a smart contract deployed on a decentralized blockchain. This approach can dramatically reduce administrative costs, improve transparency and traceability, and eliminate double counting and fraud. Finally, the proposed policy was evaluated using a multicriteria analysis method compared with other possible ETS-RT approaches.Key policy insights Fuel producers, vehicle manufacturers, and vehicle users – who are respectively responsible for fuel emission factors, vehicle fuel economy and vehicle miles travelled – should be synergistically regulated in an ETS-RT to maximize the efficiency of emissions abatement.This can be enabled by advanced blockchain technology, which can eliminate the need for a central authority, while enhancing transparency, traceability and cost-effectiveness.Blockchain technology could also be useful for monitoring, reporting and verification under the Paris Agreement.A blockchain-based ETS-RT is found to outperform other forms of ETS on criteria of acceptability, feasibility and environmental performance.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".