Resilient Innovation and Generative AI for Sustainable Forest Management in the Anthropocene

With increasing impacts of climate change, technological shifts, and socio-economic disruptions, forests, critical components of the Earth system for livelihoods and providers of diverse ecosystem services, are becoming more vulnerable. These disturbances rarely occur in isolation; instead, they interact and cascade across forest landscapes and decision-making systems.
This project addresses the lack of tools to understand such dynamics by examining how hydroclimatic extremes and governance bottlenecks propagate through forests. Using generative AI, network modeling, and social-ecological analysis, it identifies critical risks and actors, offering strategies to enhance forest resilience and support sustainable development.
Objectives and vision
This project aims to investigate how forest disturbances interact and cascade across ecological and governance systems. It focuses on how hydroclimatic extremes, such as droughts, heatwaves, and fires, propagate through forest networks and management hierarchies.
By developing a network-based model grounded in geospatial and historical data, and enhanced with AI, the project will simulate disturbance dynamics and identify critical vulnerabilities within forests. It also examines decision-making bottlenecks to uncover systemic weaknesses and coordination gaps. Through this, the project seeks to generate actionable insights to inform more resilient and adaptive forest management under increasing environmental and societal pressures.
Consortium and collaborative expertise
This project brings together leading experts from KTH Royal Institute of Technology and Shanghai Jiao Tong University with strong backgrounds in environmental science, engineering, global governance, AI, and sustainability. The team offers a unique blend of methodological and applied expertise across disciplines, ensuring a robust approach to studying cascading risks in forest systems. Prof. Zahra Kalantari (KTH) and Prof. Hongzhi Pan (Shanghai Jiao Tong University) lead the project, bringing complementary strengths in socio-environmental systems, risk modeling, and policy analysis. The collaboration will result in the development of innovative approaches to enhance forest resilience under increasing environmental and societal pressures.
Methodology and innovative approaches
A network-based model, enhanced by AI, will be developed to simulate the propagation of hydroclimatic extremes (e.g., droughts, heatwaves, fires) through forest systems. Nodes will represent forest patches, and links will capture ecological or spatial disturbance pathways. The model will integrate geospatial data, historical disturbance records, and decision-tree logic. Graph theory metrics such as betweenness and eccentricity will be applied to identify vulnerable nodes and critical connectors. In parallel, decision-making networks will be mapped to detect structural bottlenecks and high-centrality actors within forest governance, informing strategies for more adaptive and resilient forest management.
Impact and significance
This interdisciplinary project integrates AI and digital technologies with engineering, environmental, and social sciences to advance sustainable forest management and resilience thinking. By addressing cascading ecological and governance risks, the project directly contributes to Climate Action (SDG 13) and promotes the sustainable use and long-term functionality of terrestrial ecosystems, supporting Life on Land (SDG 15). Its focus on minimizing environmental impacts through improved forest governance aligns with Responsible Consumption and Production (SDG 12).
Project period
2025–2028
Funding
KTH
Contact persons
Haozhi Pan (Shanghai Jiao Tong University)
panhaozhi@sjtu.edu.cn