GLOBAL SUSTAINABLE FUTURES CIO

Building Inclusive Pathways to Global Sustainability

Organisation’s Approach to Directing Topics of Learning to Relevant Disciplines

1. Needs Assessment

  • Conduct participatory consultations with local communities, members, and stakeholders.
  • Identify pressing challenges in climate change, biodiversity loss, sustainable agriculture, and livelihood resilience.
  • Prioritise learning needs in green skills, innovation, and strategic climate adaptation.

2. Discipline Alignment

  • Map identified needs to relevant disciplines (e.g., renewable energy skills → green technology; sustainable farming practices → agroecology; disaster preparedness → resilience planning).
  • Ensure each learning topic is anchored to SDGs, national climate priorities, and the organisation’s mission.

3. Delivery Mechanisms

  • Online Training Modules – accessible, structured courses on core topics.
  • One-to-One Mentoring – personalised guidance to deepen practical skills.
  • Hybrid Sessions – combining local workshops with global expertise.
  • Peer Learning Circles – sharing lived experiences and innovative solutions.

4. Stakeholder Engagement

  • Involve local delivery partners to co-design curriculum for cultural and contextual relevance.
  • Engage enthusiastic stakeholders as co-facilitators, mentors, and guest experts.
  • Build partnerships with universities, NGOs, and innovation hubs for knowledge exchange.

5. Monitoring & Continuous Improvement

  • Collect feedback after each training session.
  • Evaluate learning outcomes against set capacity-building goals.
  • Adapt and refine training materials to evolving local and global needs.

Planned Topics of Learning (Examples)

Climate Change Strategies: adaptation planning, resilience building, carbon credit literacy, community-based mitigation.

Green Skills: renewable energy applications, sustainable agriculture, waste-to-resource innovations.

Innovation: digital tools for biodiversity monitoring, smart water management, and circular economy models.