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Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry.
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This content will become publicly available on January 1, 2026
Persistent and emerging threats to Arctic biodiversity and ways to overcome them: a horizon scan
Arctic biodiversity is under threat from both climate-induced environmental change and anthropogenic activity. However, the rapid rate of change and the challenging conditions for studying Arctic environments mean that many research questions must be answered before we can strategically allocate resources for management. Addressing threats to biodiversity in the Arctic is further complicated by the region's complex geopolitics, as eight countries claim jurisdiction over the area, with multiple local considerations such as Indigenous sovereignty and resource rights. Here, we identify research priorities to serve as a starting point for addressing the most pressing threats to Arctic biodiversity. We began by collecting pressing research questions about Arctic biodiversity, thematizing them as either threats or actions, and then categorizing them further into 18 groups. Then, drawing on cross-disciplinary and global expertise of professionals in Arctic science, management, and policy, we considered the barriers to answering these questions and proposed potential solutions that could be implemented if barriers were overcome. Overall, our horizon scan provides an expert assessment of threats (e.g., species’ responses to climate change) and actions (e.g., a lack of fundamental information regarding Arctic biodiversity) needing attention and is intended to guide future conservation action within the Arctic.
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- Award ID(s):
- 1836839
- PAR ID:
- 10594461
- Publisher / Repository:
- Arctic Science
- Date Published:
- Journal Name:
- Arctic Science
- Volume:
- 11
- ISSN:
- 2368-7460
- Page Range / eLocation ID:
- 1 to 29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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