Framework for Climate Change Mitigation and Adaptation Policies in Mountain Environments
Bibliographic record
Abstract
Climate change has profound impacts on mountain ecosystems, making it imperative for local authorities to implement effective mitigation and adaptation strategies in order to improve the resilience of these important environments. In the Aosta Valley (Western Italian Alps) region, composed by mountainous terrain for 100% of its territory, regional and local stakeholders are actively committed to address climate change challenges. However, the complexity of the mountainous landscape, combined with the socio-economic needs of local communities, creates unique difficulties in defining and implementing policies that effectively address both environmental and societal resilience.In this work we present the coordinated framework developed by the Aosta Valley Region to integrate mitigation, adaptation, and sustainability measures. Key policy initiatives include a status quo of climate change in Aosta Valley (Rapport Climat), a road map for mitigation at 2040 (Fossil Fuel Free), adaptation (Regional Strategy for Climate Change Adaptation (SRACC)) and sustainability policies (Regional Strategy for Sustainable Development (SRSVS)), and lately the Regional Plan for Climate Change Adaptation (PRACC). This framework provides pathways to find innovative solutions including the active participation of scientists, stakeholders and citizens. Notably, the SRACC and PRACC policies are based on an interdisciplinary approach, focusing on specific actions that needed to be implemented in a short- or long-term vision for several socio-economic sectors. These documents also address cross-cutting challenges to define the priority efforts.In this context, the European Project Agile Arvier, especially through the Green Lab, aims to strengthen science-based polices communication to raise awareness and actively involve the population to foster the capacity to implement effective solutions in the mountains. The communication strategy will be oriented in positive terms, transmitting adaptation tools, focusing on the potential of the territory, thus enabling mountain communities to adapt and mitigate the impacts of climate change while achieving long-term sustainability.These coordinated efforts underscore the importance of integrating scientific knowledge, policy frameworks, and societal engagement to address the complex challenges of climate change in mountain environments.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".