Fail-Safe Federalism and Climate Change: The Case of U.S. and Canadian Forest Policy
Bibliographic record
Abstract
Recent research demonstrates the difficulties that federal systems of government may present for international treaty formation, a prime example being legally binding treaties aimed at harnessing global forests to regulate climate change. Some federal constitutions, such as the U.S. and Canadian constitutions, grant subnational governments virtually exclusive direct forest management regulatory authority for non-federally owned forests. With subnational governments controlling sixty-five percent offorests in the United States and eighty-four percent in Canada, the U.S. and Canadian federal governments may be constrained during international negotiations and unable to legally bind subnational governments to any agreement prescribing methods of utilizing these forests to combat climate change. These constraints are especially important since these two countries control fifteen percent of the world's forests. Decentralized forest policy in the U..S. and Canada certainly provides valuable benefits. Yet constitutional decentralization in federal systems should be more effectively balanced with global forest governance if that mechanism for addressing climate change is to be preserved in its most flexible form. Though a binding agreement has yet to materialize, and other increasingly touted mechanisms may be utilized to tackle climate change, it is imperative that world governments maintain every legal and policy tool at their disposal to address the problem. A recent comparative constitutional analysis of five federal systems controlling fiftyfour percent of global forests determined that the United States and Canada lack two of the three key elements offederal constitutional structure that best facilitate a federal nation's ability to enter into and successfully implement an international climate agreement including forests while also preserving the recognized benefits of decentralized forest policy. This Article addresses how these constitutional deficiencies might be remedied to achieve more effective climate and forest governance. In other words, the Article explores mechanisms for establishing "Fail-safe Federalism " for forest management in the United States and Canada, by first highlighting the domestic nuances of both constitutional structure and forest policy in the two countries and next assessing whether top-down, bilateral, horizontal, or transnational approaches are the most effective mechanisms for forging Fail-safe Federalism within each.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.052 | 0.022 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| 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".