Understanding supply-side climate policies: towards an interdisciplinary framework
Bibliographic record
Abstract
Abstract Once marginal in climate governance, supply-side policies which seek to restrict the production of climate warming fossil fuels are now gaining greater prominence. From national level bans and phase out policies to divestment campaigns and the creation of ‘climate clubs’ such as the Beyond Oil and Gas Alliance, an increasing number of such policies are being adopted by governments, cities and financial actors around the world. But why would states voluntarily relinquish potentially profitable reserves of fossil fuels? How can we account for the rise of supply-side policies, the form they take and the sites in which they are being adopted? What conditions and contexts are most conducive to the adoption and sustainability of ‘first mover’ bans and phase out policies? This paper seeks to build an interdisciplinary account fusing insights from diverse theoretical traditions from international political economy, political science, sociology and the literature on socio-technical transitions in order to capture the interaction of political, economic and socio-cultural drivers in national and international settings which can provide the basis of a more integrated and multi-dimensional understanding of supply-side policies. Such an account, we suggest, helps to understand the origins and evolution of supply-side policies and, more critically, the conditions which might enable the expansion of supply-side climate policies to new sites.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".