Promises and risks of nonstate action in climate and sustainability governance
Bibliographic record
Abstract
Sustainable Development Goals and the Paris Agreement stand as milestone diplomatic achievements. However, immense discrepancies between political commitments and governmental action remain. Combined national climate commitments fall far short of the Paris Agreement's 1.5/2°C targets. Similar political ambition gaps persist across various areas of sustainable development. Many therefore argue that actions by nonstate actors, such as businesses and investors, cities and regions, and nongovernmental organizations (NGOs), are crucial. These voices have resonated across the United Nations (UN) system, leading to growing recognition, promotion, and mobilization of such actions in ever greater numbers. This article investigates optimistic arguments about nonstate engagement, namely: (a) “the more the better”; (b) “everybody wins”; (c) “everyone does their part”; and (d) “more brings more.” However, these optimistic arguments may not be matched in practice due to governance risks. The current emphasis on quantifiable impacts may lead to the under‐appreciation of variegated social, economic, and environmental impacts. Claims that everybody stands to benefit may easily be contradicted by outcomes that are not in line with priorities and needs in developing countries. Despite the seeming depoliticization of the role of nonstate actors in implementation, actions may still lead to politically contentious outcomes. Finally, nonstate climate and sustainability actions may not be self‐reinforcing but may heavily depend on supporting mechanisms. The article concludes with governance risk‐reduction strategies that can be combined to maximize nonstate potential in sustainable and climate‐resilient transformations. This article is categorized under: Policy and Governance > Multilevel and Transnational Climate Change Governance
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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 | 0.057 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".