The net greenhouse gas impact of the Jubilee line extension in London, UK
Bibliographic record
Abstract
As cities work to reduce their total greenhouse gas (GHG) emissions, the transportation sector is lagging, accounting for a growing percentage of total emissions in many cities. The provision of public transit, and specifically urban rail transit, is widely seen as a useful tool for reducing urban transportation GHG emissions. More research, however, is needed to understand the net impact of new metro rail infrastructure on urban emissions and guide efforts to maximise the GHG savings through rail investments. This paper examines the net GHG emissions of the Jubilee line extension (JLE) in London, UK. The GHG emissions associated with construction, operation, ridership and changes in urban density associated with the provision of the new metro rail infrastructure are assessed. These components are then combined and compared to calculate the net GHG impact across the study period, which extends from opening in 1999 through 2011. The capital GHG emissions from construction of the JLE are calculated as 530 kilotonnes carbon dioxide equivalent (CO2e). The initial mode shift from other rail lines and long-term mode change from automobiles result in yearly GHG savings from riders on the JLE. A quasi-experimental analysis of land-use change near the JLE finds no calculable GHG saving from increased residential density. The GHG payback period is calculated between 13 and 19 years.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".