From the Paris Agreement to corporate climate commitments: evaluation of seven methods for setting ‘science-based’ emission targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract While large companies routinely announce greenhouse gas emissions targets, few have derived targets based on global climate goals. This changed in 2015 with the creation of the science based targets (SBTs) initiative, which provides guidelines for setting emission targets in line with the temperature goal of the Paris Agreement. SBTs have now been set by more than 500 companies. Methods for setting such targets are not presented in a comparable way in target-setting guidelines and concerns that certain methods may lead to overshoot of the temperature goal have not been investigated. Here, we systematically characterize and compare all seven broadly applicable target-setting methods and quantify the balance between collective corporate SBTs and global allowable emissions for individual methods and different method mixes. We use a simplified global production scenario composed of eight archetypical companies to evaluate target-setting methods across a range of company characteristics and global emission scenarios. The methods vary greatly with respect to emission allocation principles, required company variables and embedded global emission scenarios. Some methods treat companies largely the same, while others differentiate between company types based on geography, economic sector, projected growth rate or baseline emission intensity. The application of individual target-setting methods as well as different mixes of methods tend to result in an imbalance between time-integrated aggregated SBTs and global allowable emissions. The sign and size of this imbalance is in some cases sensitive to the shape of the global emission pathway and the distribution of variables between the company archetypes. We recommend that the SBT initiative (a) use our SBT method characterisation to present methods in a systematic way, (b) consider our emission imbalance analysis in its method recommendations, (c) disclose underlying reasons for its method recommendations, and (d) require transparency from companies on the calculation of established SBTs.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it