BOTTOM-UP VERSUS TOP-DOWN HANDS-ON OPTIONS FOR MEASURING GHG AND POLLUTANTS IN SMART CITIES
Bibliographic record
Abstract
Urbanized areas account for more than 70% of the carbon dioxide equivalent emissions.Their current greenhouse gas (GHG) emission is based on a bottom-up approach that adds the different sources of emissions (activity data multiplied by emission factors).Those current estimated inventories, based on statistics, are expensive and take months for data collection.Moreover, they raised scepticism for municipal decision-makers who are not certain how to understand and use them in urban policies planning support.Indeed, cities are lacking reliable, accessible information of a high standard on which to base GHG emission reduction decisions.To help smart cities measure and lower their emissions, another approach is currently under investigation: the top-down approach, based on real GHG measurements.In this paper, we present the current and potential hands-on options for measuring GHG: network of sensors, network of sensors coupled with atmospheric inversion modelling, and the laser beam system.We conclude by making recommendations for municipal decision makers to help them take ownership of in order to tackle climate change issues.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".