The dynamics of advancing climate policy in federal political systems
Bibliographic record
Abstract
Abstract To avoid irreversible climate damages, countries with different political systems must commit to reducing greenhouse gas emissions across the world. The challenge posed to federal countries participating in international climate agreements through the United Nations Framework Convention on Climate Change is that successful implementation requires buy‐in from their subnational governments. However, subnational climate action may not be aligned with the political priorities of the national government and could either undermine or support commitments made through the agreement. To explore these dynamics, we review the incoherence between international commitments, national and subnational policies of Canada and the United States, relying on case studies of investments in low‐carbon energy and emissions outcomes. Political and policy incoherence has flowed in both directions. Both countries have seen periods of federal government action, which have been undermined by subnational inaction or opposition. Similarly, both countries have seen periods of federal government inaction, which has placed the onus on subnational governments. Our analysis enables a greater understanding about how the dynamics of federalist political systems influence policy, thus the energy investment and emissions outcomes of national commitments to international agreements. The implications of our findings for other federalist political systems are discussed. We suggest that during national leadership voids, subnational governments can counteract the associated negative policy outcomes by implementing climate policies that support low‐carbon technologies and reducing emissions. This type of system supports the development of a coordinated strategy among subnational governments in achieving international goals through cooperative federalism via subnational policy compacts.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".