Call for special session contributions: IAMs & Industrial Ecology

Sustainable Circular Economy
This spring, there will be a very interesting special session at the EGU conference in Vienna.
The deadline for abstract submission is Jan-15, 2025. https://www.egu25.eu/about/deadlines_and_milestones.html 
 

Session Information

The urgent need for sustainable development strategies has amplified the importance of innovative tools that can evaluate the impact of industrial activities on ecosystems and human health. Integrated Assessment Models (IAMs) and Industrial Ecology (IE) tools such as Material Flow Analysis (MFA), Life Cycle Assessment (LCA), and Input-Output (IO) analysis are crucial for evaluating and mitigating environmental impacts. Despite their importance, the synergistic integration of these tools to provide a comprehensive perspective, in response to emerging research needs, is still relatively unexplored. This special session seeks to address this gap by examining the potential synergies between IAMs and IE tools, thus providing nuanced sustainability insights. Participants will engage in discussions about methodologies, case studies, and future trajectories for merging these analytical frameworks.

This session aims to share new tools and case studies to answer the following questions.
• What recent advancements have been made in the integration of IAMs and IE tools?
• What new insights can these integrated tools provide?
• What are the methodological inconsistencies that affect the accuracy of these tools?
Goal
• To explore the theoretical and practical aspects of integrating IAMs with IE tools
• To showcase successful case studies where integration has led to actionable sustainability insights
• To identify challenges and solutions in the integration process
• To foster a network of practitioners and researchers focused on this interdisciplinary approach
• To discuss policy implications and support mechanisms that enhance the integration of these tools for better decision-making
Scope
MFA, LCA, IO, IAM, prospective modeling