A blockchain-based user-centric emission monitoring and trading system\n for multi-modal mobility
Bibliographic record
Abstract
Since the transport sector accounts for one of the highest shares of\ngreenhouse gases (GHG) emissions, several existing proposals state the idea to\ncontrol the by the transportation sector caused GHG emissions through an\nEmission Trading Systems (ETS). However, most existing approaches integrate GHG\nemissions through the fuel consumption and car registration, limiting the\ntracing of emissions in more complex modes e.g. shared vehicles, shared rides\nand even public transportation. This paper presents a new design of a\nuser-centric ETS and its implementation as a carbon Blockchain framework for\nSmart Mobility Data-market (cBSMD). The cBSMD allows for the seamless\ntransactions of token-equivalent GHG emissions when realizing a trip, or an\nemission trading action as well as the transaction of individual, service or\nsystem-wide emission performance data. We demonstrate an instance of the cBSMD\nimplementation for the transactions of an ETS where all travellers receive a\ncertain amount of emission credits in the form of tokens, linked to the GHG\nprice and a total emission cap. Travellers use their tokens each time they emit\nGHG when travelling in a multi-modal network, purchase tokens for a given trip\nwhen they have an insufficient token amount or sell when having a surplus of\ntokens due to a lower amount of emitted GHG. This instance of cBSMD is then\napplied to a case-study of 24hours of mobility of 3,186 travellers from\nOakville, Ontario, Canada, where we showcase different cBSMD transactions and\nanalyze token usage and emission performance.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".