An identification of models to help in the design of national strategies and policies to reduce greenhouse gas emissions.
Bibliographic record
Abstract
ABSTRACT: In response to the rapid increase in global greenhouse gas (GHG) emissions, 196 countries have made a legal commitment to implement a strategy to decarbonize their economies, under the Paris Agreement, particularly with respect to the transportation sector. As part of their long-term climate actions, these countries are defining various Avoid/Reduce, Shift and Improve (A-S-I) strategies, aimed at reducing or avoiding unnecessary travel, promoting and shifting to public transport and active modes, and improving energy efficiency and vehicle technology. In this paper, we take a closer look at some of the regional and national strategies and policies to reduce GHG emissions in Europe, as well as the models and methods used to assist in policy development. Keeping in mind the limited guidance on concrete avoidance/reduction and transfer strategies mentioned in the climate actions, we list some of the land use and transport interaction (LUTI) models that can be used to improve climate change control at the urban scale. The aim of this paper is to gain a better understanding of how methods and tools can assist in decision making and policy development with respect to GHG emission reduction goals in the transportation sector.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".