The net zero wave: identifying patterns in the uptake and robustness of national and corporate net zero targets 2015–2023
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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 it