Valuing the Contributions of Nonstate and Subnational Actors to Climate Governance
Bibliographic record
Abstract
Nonstate and subnational climate governance activities are proliferating. Alongside them are databases and registries that attempt to calculate their contributions to global decarbonization. We label these registries “orchestration platforms” because they both aggregate disparate initiatives and attempt to steer them toward overarching objectives such as improved transparency, accountability, and effectiveness. While well-intentioned, many orchestration platforms adopt a narrow conception of “value” as either quantifiable greenhouse gas (GHG) reductions or relevant outputs. We offer a more comprehensive approach to valuing nonstate and subnational climate governance that is rooted in recognizing the potential for initiatives to become far-reaching (i.e., achieve scale) and durable (i.e., become entrenched). We illustrate the comparative advantage of our approach with reference to a particular case of nonstate governance: The Carbon Trust’s attempt to create product carbon footprints. By tracing the direct and indirect impacts of product carbon footprinting, we show that initial failures to generate quantifiable GHG reductions or produce relevant outputs do not reflect the intervention’s broader impacts through scaling to other jurisdictions and entrenching business practices that contribute to decarbonization. Taking this broader view of “value” can help policy-makers better understand and gauge the contribution of nonstate and subnational climate governance to global decarbonization.
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.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".