The Role of Governance in Advancing Climate Change Adaptation Policies: A Comparative Study of U.S. and Global Best Practices
Bibliographic record
Abstract
Climate Change globally represents the most disturbing challenges necessitating urgent action from the state and NGOs, the private sector and civil societies. Exploring the best global practices in climate change adaptation policies, this study employed Multilevel governance (MLG) Theory and systematic literature review to compare efforts of different countries such as the United States, Canada, France, New Zealand and Germany that have mobilized efforts towards achieving zero-carbon emission. Notably, the Paris Agreement and Greening Government Initiative have encouraged collaboration and innovation among countries to achieve zero-carbon emission in the power, manufacturing and transport sectors. In the United States, the Biden-Harris administration's comprehensive climate change agenda mobilized efforts aimed at achieving significant greenhouse gas reductions by 2030. France’s Low Carbon National Strategy, Canada’s Net-Zero Emissions Accountability Act, and Germany's sectoral emissions further demonstrate the efforts of long-term strategies among countries that are vulnerable to extreme weather conditions such as wildfires, hurricanes and heatwaves. Comparing the United States climate change adaptation policies, it was discovered that while the U.S. federal system empowers states to develop climate change policies it also presents challenges in achieving a unified national strategy. Therefore, this paper emphasizes the importance of synchronizing the federal and state climate change adaptation policies within the U.S. federal system. Likewise, there is a need for citizenship and cross-sectoral inclusivity in climate change governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".