Decline by design: Assessing decline policies as a decarbonisation strategy under the Paris Agreement
Bibliographic record
Abstract
Limiting climate change to targets set under the Paris Agreement requires urgent action to reduce the production and use of carbon-intensive technologies, fuels, materials and industrial processes. Accordingly, scholars are increasingly studying 'decline policies', which, by design or effect, induce the reduction or discontinuation of carbon-intensive artefacts and activities. However, understanding of the diversity and decarbonisation potential of such policies is hindered by a lack of large-scale, cross-sectoral and cross-national analyses. Here we present a novel dataset of 233 decline policies formulated by the ten highest-emitting Annex I countries to spur decarbonisation. We examine: (1) decline approaches and policies used across sectors; (2) variations in policy design features expected to influence the magnitude of decline and mitigation outcomes; (3) the relationship between decline approaches, policy design features and mitigation impact. We find that decline policies are widely used across the ten countries, including direct approaches (targeting incumbent carbon-intensive elements), and indirect approaches (promoting substitution with cleaner alternatives). Statistical analysis indicates that policy design is a critical determinant of decarbonisation potential. While evidence that direct decline policies might be more effective compared to indirect policies is limited, the 'intensity' of decline policies - defined by design features such as strictness, reduction speed and geographic coverage - is significantly associated with higher mitigation impact estimates. Finally, by proposing a typology of archetypical decline policies, our study advances an empirically grounded conceptual framework for understanding decline as a critical strategy for accelerating decarbonisation.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".