Harnessing Locally Led AI Solutions for Climate Action: Insights from the CTCN Webinar
In May 2025, the Climate Technology Centre and Network (CTCN) hosted an enlightening webinar that showcased innovative artificial intelligence (AI) and digital solutions developed by local stakeholders to combat climate change. This session not only underscored the exciting diversity of grassroots innovations but also prompted reflection on how the CTCN, along with its Network and National Designated Entities (NDEs), can systematically and inclusively leverage these technologies to enhance their impact.
This article explores key themes that emerged during the webinar, highlights specific project examples presented by stakeholders, and discusses approaches to better support inclusive AI solutions in alignment with the CTCN’s mission.
Grounding AI Solutions in Strategic Priorities and Research
The potential of locally led AI solutions to revolutionize climate action is remarkable. These technologies can provide tools for real-time data monitoring, resilient infrastructure planning, and optimized climate finance allocation while supporting Nature-based Solutions (NbS). Their strategic importance is acknowledged in various UN frameworks, particularly in the CTCN’s Programme of Work for 2023-2027 and the UNFCCC’s #AI4ClimateAction initiatives.
However, effective implementation of these solutions requires a solid academic grounding. This entails assessing AI interventions against local, co-created benchmarks and integrating Indigenous knowledge. Essential components include transparent model design, participatory validation, ethical and socioeconomic impact analysis, and continuous monitoring, ensuring that AI technologies are culturally sensitive and promote equitable climate outcomes in the most vulnerable developing regions.
Tackling Climate Challenges with AI
The thematic areas of AI solutions demonstrated during the webinar include several innovative use cases showing successful implementation across the Network:
1. Decentralized Monitoring and Early Warning Systems
Decentralized AI and data platforms have empowered communities to independently collect and analyze environmental data. For example, Syecomp Ghana employs Earth observation satellites and multispectral drone sensors across Ghana, Kenya, and Uganda to bolster climate-smart agricultural practices. In a similar vein, ASM Global’s RecyclX platform facilitates plastic traceability through decentralized data collection, highlighting how on-device inference solutions and community-generated data can enhance national early warning systems.
2. Inclusive Climate Finance and Insurance
AI-driven risk models and parametric insurance products are bringing climate coverage to local stakeholders and informal sectors. Pula Advisors, for instance, has developed an AI-driven parametric insurance model tailored for East African smallholder farmers, utilizing community-defined satellite rainfall benchmarks. These initiatives not only empower farmers to assess drought risks independently but also protect their data ownership, aligning with principles of data justice and community-driven innovation frameworks.
3. Human-Centered Training and Capacity Building
Building trust and literacy around AI technologies is crucial, particularly in climate-vulnerable regions facing digital access challenges. Aiming Change for Tomorrow, a Network member, enhances local competencies through specialized training in water, sanitation, hygiene (WASH), and rainwater harvesting systems. Research from Imperial College London underscores both the efficiency potentials of Generative AI and the obstacles posed by low digital literacy in developing contexts, urging a nuanced understanding of intersectional vulnerabilities.
Additionally, low-emission AI models like TinyML are being explored for simulating climate events, emphasizing ethical prompt engineering for improved accountability and outcomes.
4. Optimizing the Carbon Removal Process
AI is playing a pivotal role in advancing carbon removal technologies, utilizing digital monitoring, reporting, and verification (MRV) systems. Octavia Carbon, for instance, harnesses geothermal heat for direct air capture (DAC) processes in Kenya, optimizing energy use and enhancing carbon credit integrity via AI-driven infrastructure. The maturation of these technologies presents new avenues for generating trusted carbon credits while scaling up successful practices.
5. Addressing Climate Data Collection and Quality Barriers
Data challenges persist in developing regions, yet targeted AI solutions can help overcome these barriers. Denominator Collective exemplifies this by deploying an AI-enhanced MRV platform that ensures data integrity for energy transition projects, enabling access to Northern commodity markets through sharable environmental certificates. Initiatives such as Participatory Mapping and Verification and Regional Labeling Hubs are critical in improving data accuracy, while establishing Data Trusts promotes transparency in compliance documentation.
Creating Synergies Through Partnerships
To fully leverage AI’s potential, the CTCN must deepen its collaborations with NDEs and align AI solutions with strategic priorities. By sharing practical use cases that illustrate how AI can effectively address climate challenges, stakeholders can strengthen the implementation of climate technology and foster broader regional cooperation.
A variety of recommendations can guide this collaborative approach, including:
- Knowledge Exchange: Sharing case studies and best practices to inform local strategies and avoid duplication via platforms like WIPO GREEN.
- Thematic Working Groups: Engaging with or establishing working groups focused on specific themes to co-develop guidelines and proposals with peers.
- Active Participation: Joining CTCN-hosted webinars and workshops to showcase innovations and secure technical support.
- Collaboration Clusters: Mobilizing cross-country partnerships to link innovators facing similar climate vulnerabilities and exploring multi-country pilots for enhanced scalability.
- Joint Funding Proposals: Coordinating through the Network to submit proposals for funding opportunities that bundle multiple AI pilots under a unified theme.
- Rapid Response Teams: Establishing teams among Network members, equipped with the expertise needed to scale successful local solutions in response to emerging climate events.
- Robust Monitoring and Evaluation: Implementing standardized guidance from the Network to benchmark progress across member projects in line with established frameworks.
- Advocacy: Advocating for regional policies and initiatives to support new, locally led AI-driven climate action projects.
By considering these steps, stakeholders can create a rich pathway to harness the CTCN’s platforms and enhance climate resilience through cutting-edge technologies and community-led innovations. For those eager to explore further, the CTCN’s recent webinar recording and accompanying materials showcase diverse strategies that are already making a meaningful impact in our Network.
This article was developed in collaboration with Marta Koch, a researcher at Imperial College London, originally published by the International Institute for Sustainable Development (IISD).