The net zero wave: identifying patterns in the uptake and robustness of national and corporate net zero targets 2015–2023
Bibliographic record
Abstract
Since the 2015 Paris Agreement, a growing number of states and firms have adopted targets to reach net zero emissions. These pledges vary significantly both in the timing of adoption and in robustness – measured by whether they adopt procedural best practices. We introduce a novel time-series dataset measuring the uptake and robustness of net zero targets of states and the world’s largest listed firms between 2015 and 2023. The new data allow us to identify patterns that speak to a key debate in the literature: what explains the rapid uptake of net zero targets by firms and countries? Descriptive inference yields several insights. First, the timing of net zero adoption by both states and firms strongly tracks international mobilization efforts, highlighting the importance of the United Nations (UN) process for target setting. Second, on average, firms set targets before countries. Third, there is an increase in some best practices for companies, such as setting interim targets and including Scope three emissions in targets, alongside a lack of progress in others, such as safeguards on the use of offsetting. Importantly, we do not find significant variation in timing or robustness of net zero pledges across firms in different sectors. For countries, early adopters tend to have more robust targets from the beginning than late adopters, suggesting the latter may be adopting more symbolic targets. In sum, our results show the rapid growth of the net zero wave, but also its limits in driving robust targets.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".