Can Science-Based Targets Make the Private Sector Paris-Aligned? A Review of the Emerging Evidence
Bibliographic record
Abstract
Purpose of Review: Companies increasingly set science-based targets (SBTs) for reducing greenhouse gas emissions. We review literature on SBTs to understand their potential for aligning corporate emissions with the temperature goal of the Paris Agreement. Recent Findings: SBT adoption by larger, more visible companies in high-income countries has accelerated. These companies tend to have a good prior reputation for managing climate impacts and most appear on track for meeting their scope 1 and 2 SBTs. More research is needed to distinguish between substantive and symbolic target-setting and understand how companies plan to achieve established SBTs. There is no consensus on whether current target-setting methods appropriately allocate emissions to individual companies or how much freedom companies should have in setting SBTs. Current emission accounting practices, target-setting methods, SBT governance, and insufficient transparency may allow companies to report some emission reductions that are not real and may result in insufficient collective emission reductions. Lower rates of SBT diffusion in low- and middle-income countries, in certain emission-intensive sectors, and by small- and medium-sized enterprises pose potential barriers for mainstreaming SBTs. While voluntary SBTs cannot substitute for more ambitious climate policy, it is unclear whether they delay or encourage policy needed for Paris alignment. Summary: We find evidence that SBT adoption corresponds to increased climate action. However, there is a need for further research from a diversity of approaches to better understand how SBTs may facilitate or hinder a just transition to low-carbon societies.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".