In May 2025, the Climate Technology Centre and Network (CTCN) hosted a webinar showcasing locally led artificial intelligence (AI) and digital solutions developed by its members to address climate change. The session not only highlighted the growing diversity of grassroots innovation but also invited deeper reflection on how the CTCN, together with its Network and National Designated Entities (NDEs), can more intentionally and inclusively harness these technologies.
This article builds on insights from the session, identifying key emerging themes, showcasing project examples, including those presented in the webinar, and offering reflections on how to better support locally led, inclusive AI solutions aligned with the CTCN’s mission.
Grounding AI solutions in strategic priorities and research
Emerging locally led AI solutions hold transformative potential for taking action to address climate change, offering tools for real‑time data monitoring and sharing, resilient infrastructure planning, and optimized climate finance allocation while also supporting Nature‑based Solutions (NbS).
Their strategic importance is increasingly recognized across key UN frameworks and the CTCN’s strategic priorities in its Programme of Work 2023-2027 and the UNFCCC Technology Mechanism’s #AI4ClimateAction Initiatives.
In addition, effective implementation of emerging locally led AI solutions for climate action should be grounded in robust academic frameworks, emphasizing the need for assessing solutions against local user co-created benchmarks, integrated decolonized Indigenous and local knowledge sharing, transparent model design, participatory validation, ethical and socioeconomic impact analysis, capacity building, and continuous monitoring to ensure AI interventions are fairly accessible, user-centered, culturally sensitive, and oriented towards equitable climate outcomes in the most in-need developing countries.
Tackling climate challenges with AI
The key thematic areas of AI solutions and use cases, which serve as examples of successful implementation from across the Network and beyond, include:
1. Decentralized monitoring and early warning systems
Edge AI and decentralized data platforms are empowering communities to collect and analyze environmental data independently of central infrastructure, enhancing early warning systems. For instance, Network member Syecomp Ghana is using Earth observation satellites and multispectral drone sensors across Ghana, Kenya, and Uganda to support climate-smart agriculture.
Similarly, ASM Global’s RecyclX platform enables plastic traceability through decentralized data collection. These use cases illustrate how countries can deploy on‑device inference solutions, co‑design participatory mapping exercises, and integrate community‑generated data into national early warning systems through federated learning approaches that preserve data sovereignty.
2. Inclusive climate finance and insurance
AI‑driven risk models and parametric insurance products are extending coverage to local and informal climate stakeholders and AI solution beneficiaries. For example, Pula Advisors’ locally governed AI-driven parametric insurance for East African smallholder farmers uses community-defined satellite rainfall and vegetation benchmarks.
These approaches enable farmers to independently assess their risk of drought and receive insurance payouts, while also ensuring they retain ownership of their data and benefit financially from its use. Grounded in data justice and community‑driven innovation frameworks, such pilots offer insights into how countries can use multi‑stakeholder data trusts and co‑designed insurance schemes that align with AI ethics and local climate finance regulatory requirements.
3. Human‑centered training and capacity building
Building AI literacy and trust is fundamental. Harnessing AI for climate resilience in vulnerable regions requires both capacity building and model adaptation to overcome persistent challenges in digital access and literacy. For example, Network member Aiming Change for Tomorrow strengthens local capabilities by providing specialized training in water, sanitation, and hygiene (WASH) and rainwater harvesting systems.
The latest research from Imperial College London Centre for Environmental Policy highlights the efficiency-saving potential of Generative AI as well as the challenges of training potential generative AI large language model (LLM) users in developing and climate-vulnerable contexts – pointing to low digital literacy and the need to consider intersectional vulnerabilities in digital access across user groups.
As an alternative, the potential of leveraging low-emission AI models such as TinyML to simulate predicted climate event phenomena such as rainfall using ethical and effective prompt engineering for accountability and accuracy improvement is also being widely discussed among the scientific and practitioner communities.
4. Optimizing the carbon removal process
AI models are being used to advance carbon removal by using digital monitoring, reporting, and verification (MRV) systems and financial structuring. Take Octavia Carbon — a pioneering company in Kenya using geothermal heat to power direct air capture (DAC) that efficiently extracts carbon dioxide (CO2) from the atmosphere. AI-driven infrastructure is used to optimize its energy use and capture rates and produce robust and high-integrity carbon credits.
As these technologies mature, countries can explore scaling such efforts to generate trusted carbon credits through robust carbon removal processes.
5. Addressing climate data collection and quality barriers
Persistent data challenges in developing contexts can be tackled with targeted AI solutions. A compelling example comes from Denominator Collective, a Network member, which is leveraging an AI-powered MRV platform to ensure data integrity and support cross-border trade compliance in energy transition projects. This innovation helps developers in emerging markets gain access to Northern commodity markets through verifiable and sharable environmental attribute certificates.
To bridge climate data gaps, countries can adopt several approaches. One is Participatory Mapping and Verification, which combines offline‑capable mobile tools with community‑led workshops to improve data accuracy. Another is the creation of Regional Labeling Hubs – low‑bandwidth annotation centers staffed by trained local experts for consistent data labeling.
Finally, establishing Data Trusts and Certification Incentives can foster multi‑stakeholder collaboration, rewarding contributions of high‑quality data and encouraging the transparent sharing of compliance documentation. Together these solutions promote both procedural and distributive climate justice.
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Creating synergies through partnerships
To unlock the full potential of AI within the Network, the CTCN can deepen its collaboration with NDEs, grounding AI solutions in strategic priorities and research agendas. Sharing practical use cases that demonstrate how AI addresses climate challenges will help countries strengthen the effectiveness and impact of climate technology implementation.
In addition, the following recommendations, including ways to leverage CTCN platforms and resources, can be considered for future planning:
- Exchanging knowledge by sharing case studies and best practices with the Network to inform local and national strategies and avoid duplication via platforms such as WIPO GREEN;
- Engaging in thematic working groups by joining or establishing Network working groups (e.g., possibly themed around AI and monitoring, finance and insurance, or NbS optimization) to co‑develop guidelines, toolkits, and joint proposals with peers;
- Participating in Network-supported events by joining and being actively involved in CTCN-hosted webinars and regional workshops to showcase local innovations and secure technical support;
- Forming cross-country and regional collaboration clusters by leveraging the Network’s offering to connect with NDEs and innovators in other developing countries facing similar climate vulnerabilities, and considering organizing multi-country pilots for higher scalability;
- Mobilizing collective funding proposals by coordinating through the Network to submit joint funding requests (e.g., to the Global Environment Facility (GEF), the Adaptation Fund) that bundle multiple locally led AI pilots under a unified theme;
- Establishing rapid response teams among the Network, comprising technical, policy, and local community engagement experts, to scale successful local AI solutions quickly across regional groups in response to emerging and predicted climate events;
- Implementing Network-driven monitoring and evaluation by adopting standardized guidance provided by the Network via webinars, the CTCN website Resources section, and via the Network portal to benchmark progress across member projects in line with the CTCN’s Monitoring and Evaluation Framework; and
- Advocating for policies and collaborating with other NDEs to push for regional or cohort-based policy or member State-led initiative recommendations for supporting new locally led AI climate action project development.
These suggestions offer valuable insights for crafting a concrete pathway to unlock the full potential of the CTCN’s platforms, resources, and partnerships. For additional inspiration, practical examples, and success stories from Network members, we invite you to explore the webinar recording and presentation materials here. These resources showcase the diverse and innovative strategies already driving impact within our Network, serving as a catalyst for future collaboration and scalable climate solutions.
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This article was developed by the Climate Technology Centre and Network and Marta Koch, Imperial College London Researcher and UNFCCC/CTCN RINGO Delegate. It was originally published by the International Institute for Sustainable Development (IISD) and is republished here as part of an editorial collaboration with the IISD.
Editor’s Note: The opinions expressed here by Impakter.com columnists are their own, not those of Impakter.com — In the Cover Photo: Cover Photo Credit:
