Hybrid Social Forestation
Climate Adaptation & Resilience
- 50+ organizations
- 2024-2027
- 12 Publications
Lead: Dr. Sarah Johnson
Summary
Hybrid Social Forestation, active since April 2023, integrates disaster preparedness and youth engagement across Bangladesh by planting location-specific tree species and promoting women-led community ownership for effective management and governance. Over 6,000 trees have been planted with community involvement, supported by educational institutions. This initiative not only safeguards the environment but also ensures long-term financial inclusion, fostering inter-community cooperation and collective resilience. As a signature project of Daahuk, it restores degraded forests in the Barind Tract and Haor regions by combining scientific expertise with community action, protecting biodiversity and traditional livelihoods. Rooted in philosophies like Development for Profit and Integrated Social Capital, it empowers locals through sustainable models, aligning with broader goals of climate adaptation and inclusive growth in vulnerable rural areas.
Key Objectives
- Climate Intelligence: Develop AI systems that forecast and mitigate climate risks in vulnerable regions.
- Social Impact Analytics: Use data to identify inequities and design targeted social interventions.
- Sustainable Development: Support environmental restoration and sustainable resource use through predictive modeling.
- Collaborative Innovation: Build partnerships across sectors to ensure ethical and inclusive AI implementation.
Key Objectives
50+ Organization
25+Countries of operation
100k+Beneficiaries reached
Key Challenges We Aim to Overcome
The project faces several challenges, including limited access to reliable and diverse data, ethical and privacy concerns in AI usage, and difficulties in ensuring transparency and fairness. Collaboration across global organizations can lead to coordination complexities, while technological and infrastructure gaps in some regions may hinder implementation
Data Accessibility and Quality
Obtaining reliable, diverse, and up-to-date datasets from different regions can be difficult
Ethical and Privacy Concerns
Ensuring transparency and fairness in AI decisions is crucial to prevent bias and misuse
Limited Technological Infrastructure
Many developing regions lack the resources, connectivity needed to effectively use AI solutions.
Project Highlights
Explore our journey through impact-driven innovations showcased in our Project Gallery
