Climate and Clean Energy Policy: State Institutions and Economic Implications, by Benjamin H. DeitchmanPolitical Opportunities for Climate Policy: California, New York, and the Federal Government, by Roger Karapin
Bibliographic record
Abstract
Federalism looms increasingly large when designing and implementing policies that attempt to mitigate the risks of global climate change. Canada offers an acid test of the ability of a federal government to work cooperatively with provinces in developing a national carbon pricing system intended to achieve sustained emission reductions in a cost-effective manner. The European Union enters its third decade of trying to sustain a viable greenhouse gas emission reduction strategy involving its members while allowing enormous compliance flexibility to individual nations and their sub-federal units. Even China’s most recent development of national climate mitigation policies draws heavily on prior local and provincial experiments. And then there is the United States. Even prior to the election of Donald Trump and his subsequent withdrawal of the nation from the Paris Climate Accord, the role of states in unilateral and cross-border policy development was highly consequential. Most of the Obama era climate policy initiatives that Trump has challenged target strategies that either give states considerable choice in designing their preferred paths toward compliance (the Clean Power Plan for electricity) or rely on a long-standing statutory waiver process that offers one state considerable latitude in designing its own policies that can then diffuse (California for vehicle emissions).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".