Financial innovation for climate justice: central banks and transformative ‘creative disruption’
Bibliographic record
Abstract
Global financial architectures, including central banks and their monetary policies, are critical to leveraging transformative change for climate justice.Yet, currently central banks are exacerbating rather than mitigating the climate crisis and climate injustices.By following a neoliberal policy paradigm and narrowly interpreted mandates for price stability and financial stability, central banks are focusing on stabilizing a system that is inherently unstable.This accelerates climate chaos around the world and is worsening future financial instability.Recognizing both the potential of central banks to advance climate justice and the inattention of the role of central banks in the climate crisis, this paper contributes to the emerging field of financial innovation for climate justice.First, we review what central banks are currently doing to advance and hinder climate justice.Then we explore monetary policy tools that central banks could deploy for transformative climate justice.We then make the case for 'creative disruption' in monetary policy which requires expanding the narrow mandate of central banks and new kinds of global coordination.This call for intentional creative disruption changes policy assumptions regarding financial stability and climate politics and reconceptualizes how to achieve transformative systemic change to move toward a more equitable, just, healthy, sustainable future.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".