The Climate Establishment and the Structural Constraints of the Paris Partnerships
Bibliographic record
Abstract
Abstract The Paris Agreement created an institutionalized role for non-state actors through voluntary cooperation. Many international NGOs (INGOs) are particularly active in these “Paris partnerships,” often working with multinational corporations to reduce emissions. Though there is ample work on both the effectiveness of the Paris partnerships and on the role of INGOs in the global climate regime, much of this work focuses “outward” – on how INGOs contribute to climate mitigation and adaptation, or influence norms, discourse and policy. Yet, there is considerably less work that focuses “inward” – asking why climate INGOs act the way they do. To better understand the incentives and opportunities that drive INGO behavior, I examine their relationships with large corporations. Specifically, this paper uses a network analysis to map the relationships between Fortune100 firms and a key subset of well-resourced INGOs, whom I refer to as “the climate establishment,” through the Paris partnerships. The climate establishment can be understood as insider INGOs who work within the multilateral process and with large corporations to influence rulemaking, soft law and firm behavior. I present new social network data to provide insights into the structural incentives and constraints that these partnerships create for INGOs. These findings suggest that in addition to assessing partnership effectiveness, scholars should also consider how relationships with multinational corporations shape the climate establishment’s organizational behavior.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".