Integrating diverse decision models into agent-based models (ABMs) enhances the realism of adaptation decision-making in climate change scenarios, showing that behavioral heterogeneity can introduce as much or more uncertainty than economic or environmental factors while improving predictive accuracy.
Nicholas Magliocca; Ruchie Pathak; Ashleigh Price; Hashir Tanveer; Mukesh Kumar; Hamid Moradkhani. 2024-01-01. Increasing Behavioral Richness and Managing Structural Uncertainty in Social-Ecological System Agent-Based Models. https://sesmo.org/article/view/18749/18243 (AI & Environment Resource Hub; record paper-047; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
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Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
The climate crisis often leaves us feeling powerless, creating a cycle of inaction that worsens the situation. While the knowledge and solutions to transition exist, gaps in policy and action hold us back. Isabell Steidel shares an example of planting trees to inspire climate action, only for systemic resistance to lead to failure, showing how the environment pays the price for inaction. Isabell Steidel is a Sustainability Advisor and Founder driving socio-ecological transformation in Heilbronn.
Source metadata available
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.
Coursera
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
In her TEDx talk, Tamma shares how her journey from South Africa to the UK shaped her deep understanding of sustainability and the climate crisis, emphasizing the urgent need to reduce consumption to protect Earth's interconnected ecosystems.
youtube.com
Shared topics: Cross-cutting sustainability; Climate and greenhouse gases; Biodiversity and ecosystems; Weather, hazards, and adaptation
This article examines how advanced digitalization and AI can enhance spatial energy planning and environmental assessments in Austria, offering solutions to data deficiencies that currently hinder biodiversity-friendly renewable energy transitions.
