Differentiation of greenhouse gases in corporate science-based targets improves alignment with Paris temperature goal
Bibliographic record
Abstract
Abstract Companies are increasingly setting greenhouse gas (GHG) emission reduction targets to align with the 1.5 °C goal of the Paris Agreement. Currently, companies set these science-based targets (SBTs) for aggregate GHGs expressed in CO 2 -equivalent emissions. This approach does not specify which gases will be reduced and risk misalignment with ambitious mitigation scenarios in which individual gas emissions are mitigated at different rates. We propose that companies instead set reduction targets for separate baskets of GHGs, defined according to the atmospheric lifetimes and global mitigation potentials of GHGs. We use a sector-level analysis to approximate the average impact of this proposal on company SBTs. We apply a multiregional environmentally extended input output model and a range of 1.5 °C emissions scenarios to compare 1-, 2- and 3-basket approaches for calculating sector-level SBTs for direct (scope 1) and indirect (scope 2 and upstream scope 3) emissions for all major global sectors. The multi-basket approaches lead to higher reduction requirements for scope 1 and 2 emissions than the current single-basket approach for most sectors, because these emission sources are usually dominated by CO 2 , which is typically mitigated faster than other gases in 1.5 °C scenarios. Exceptions are scope 1 emissions for fossil and biological raw material production and waste management, which are dominated by other GHGs (mainly CH 4 and N 2 O). On the other hand, upstream scope 3 reduction targets at the sector level often become less ambitious with a multi-basket approach, owing mainly to substantial shares of CH 4 and, in some cases, non-CO 2 long-lived emissions. Our results indicate that a shift to a multi-basket approach would improve the alignment of SBTs with the Paris temperature goal and would require most of the current set of companies with approved SBTs to increase the ambition of their scope 1 and scope 2 targets. More research on the implications of a multi-basket approach on company-level SBTs for all scope 3 activities (downstream, as well as upstream) is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".