Estimering av indirekta emissioner i fordonssektorn, fossila-bränslen-sektorn och energisektorn
Bibliographic record
Abstract
To combat climate change multiple initiatives have been launched to steer the financial market towards a more sustainable and resilient path. For example the Montreal Pledge that have committed over 120 investors to measure and disclose their carbon footprints of their portfolios. ISS-Ethix Climate Solution provides climate change related services to investors. In order to evaluate companies’ sustainability ISS-Ethix Climate Solution estimates companies’ direct and indirect greenhouse gas emissions. To simplify these estimations, the emissions from corporations are divided into three scopes, where scope 1 and 2 cover the emissions from the combustion of fuels used in the company and electricity generation. Scope 3 corresponds to all other emissions generated upstream and downstream the companies’ supply chain. The aim of this study was to help ISS-Ethix Climate Solution to develop a model that estimates the indirect scope-3-emission intensity for companies in the automobile sector, fossil fuel sector and utility sector. The first objective was to examine if the variations within the sectors could be explained and categorized. To carry this out each sector was defined and their emission sources identified. The emissions could be explained and categorized for the automobile sector and fossil fuel sector. However, the emissions for the utility sector could only partly be explained and categorized. The second objective was to examine which parameters and subcategories were relevant for estimating the emissions. Two methods were investigated to carry out the second objective; correlation analysis and the average-data method. No correlations could be found between any of the sectors and the selected parameters. The estimated emissions using the average-data method were verified to the companies reported emissions. For the automobile and the fossil fuel companies the estimated emissions followed the same trend as the reported data. However, no trend could be found for the utility companies. Estimating emissions using the average-data method requires a certain corporation structure. The method can be used for corporations with a specific output, but does not suit corporations with a more complex structure. The largest limitation with the models was the information shortages from the corporations. Therefore increased transparency from the companies is a necessity in order to develop the models.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".