What drives companies’ progress on their emission reduction targets?
Bibliographic record
Abstract
As the importance of non-state mitigation actions in the transition to a low-carbon economy becomes firmly established, a rapidly growing number of companies are setting corporate climate mitigation targets. Shareholders increasingly value these commitments, conveying the impression of good future carbon performance. However, a critical question emerges: why do some companies progress better than others toward their climate mitigation targets? There is currently a lack of empirical literature assessing companies’ progress against their mitigation targets. Using a new indicator to evaluate the progress against individual corporate climate mitigation targets in a comparable manner, this study presents an explanatory analysis of 120 determinants applied to 4341 climate mitigation targets (scope 1 and 2 emissions) of 2975 companies reporting to the 2020 CDP questionnaire. The target progress assessment shows that 30% of targets have increased emissions since their base year, 15% have reduced their emissions but not at a sufficient pace, while 55% were on track to achieving or had already achieved their targets. In addition, 18% of targets were already achieved the year the target was set, which may be due to choosing a base year with unusually high emissions. The findings reveal 19 key determinants significantly associated with the progress against corporate targets and highlight future research orientation. Our results indicate better progression by companies having absolute targets with longer timeframes and disclosing additional, as well as remuneration links to climate-related issues. Companies with more ambitious targets progress less than others, except when the ambitious targets are approved by the Science-Based Targets initiative. The latter implies ambitious targets from some firms may only be symbolic, and that investors should consider both target ambition and progress. Clear guidance and regulations should be implemented by policymakers to prevent misleading target information. Future research should address limitations related to reliance on self-reported data and exclusion of scope 3 emissions targets, along with the research directions suggested by the findings.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| 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".