In May 2026 seven Caribbean governments launched the Regional Platform for Catalysing Resilience and Climate Action: Barbados, Belize, Grenada, Guyana, Saint Kitts and Nevis, Saint Vincent and the Grenadines, and Trinidad and Tobago. Its purpose is to get more climate finance into the region, faster. This piece sets out where AI can help it do that, starting with the least glamorous use, preparing funding proposals, and ending with who should act first.
What the platform is and what it will produce
Barbados Prime Minister Mia Mottley called the platform a "bold collective response" to the climate threat facing the region. It has three stated goals: accelerate climate finance, strengthen collective resilience, and attract investment to member states. More countries are expected to join.
Two outputs are planned. The Caribbean Development Bank (CDB) is holding consultations to shape country-specific investment plans, and a CARICOM Climate Finance Action Plan for 2026 to 2030 will set out how pledged money is meant to reach governments and communities.
The logic is sound. A single small state struggles to staff the specialist teams that international climate funds expect; seven states pooling that work can.
The exposure in numbers
Hurricane Maria in 2017 caused damage equal to 226 percent of Dominica's GDP. Caribbean small island states produce less than 0.1 percent of global greenhouse gas emissions and carry some of the highest climate losses relative to the size of their economies. The region imports more than 80 percent of its food, and most islands generate power from imported fuel oil, so a storm can cut both supply chains at once.
Money for adaptation exists at the Green Climate Fund (GCF), the Adaptation Fund, the CDB and bilateral donors. The binding constraint for small states is capacity: people who can design a fundable project, write the proposal, manage the grant and report results.
Proposal writing is the fastest win
AI's quickest return for the platform is in climate finance paperwork. GCF funding proposals run to hundreds of pages across feasibility studies, gender and environmental assessments and economic analysis, and a finance ministry in a country of 50,000 people may have one or two staff who know the process.
Language models can help that team in four concrete ways:
- Check a draft against the fund's published investment criteria and flag missing sections before submission.
- Summarise approved proposals from comparable countries, which the GCF publishes on its website.
- Draft first versions of standard sections, such as the theory of change and the monitoring framework, for staff to correct.
- Build the economic comparison between options, for example a seawall against mangrove restoration, with the assumptions stated.
The platform can go further by combining member states' exposure data into one regional risk portfolio. Funders are more used to reading expected losses and returns on resilience spending for a region than seven separate national arguments.
Forecasting and risk maps
The Caribbean Institute for Meteorology and Hydrology (CIMH) in Barbados and the national meteorological services carry the region's forecasting load on small budgets. AI models can extend what they do.
AI weather models such as Google DeepMind's GraphCast and NVIDIA's FourCastNet produce global forecasts in minutes with skill comparable to leading physics-based models. In 2025 the US National Hurricane Center began consulting an experimental Google DeepMind cyclone model alongside its usual guidance. Intensity forecasting, especially rapid intensification, remains the hardest problem for every kind of model.
The larger gap is community-level risk maps. Few Caribbean countries have maps showing where sea level rise, storm surge, flooding, drought and erosion will bite over five, ten and twenty years at the scale of a district. Machine learning can downscale global climate projections and combine them with elevation and land use data to produce them, provided the underlying elevation data exists.
Early warning, evacuation and damage mapping
Regional coordination through the Caribbean Disaster Emergency Management Agency (CDEMA) has already reduced deaths relative to storm strength. Three AI applications can add to it:
- Flood forecasting. Models that combine rainfall, river gauges, satellite soil moisture and terrain can predict flooding hours to days ahead. Google's Flood Hub has run this kind of forecasting in Bangladesh and India.
- Evacuation sequencing. Route and timing models can decide which communities move first, based on road capacity, shelter space and when the storm is due.
- Damage maps. Satellite and drone images analysed by machine learning can map damage within hours of a storm passing, so relief goes to the worst-hit areas first.
Farm advice and grid management
Changing rainfall is already shifting planting seasons. Satellite crop monitoring can flag drought stress and pest outbreaks before farmers see them, and phone apps can diagnose crop diseases from a photograph. A regional advisory service run through the Caribbean Agricultural Research and Development Institute (CARDI) could serve farmers from Belize to Trinidad on shared technology adapted to local crops.
On energy, island grids have no neighbours to import from when a cloud bank crosses a solar farm. Forecasts of solar and wind output hours to days ahead, paired with battery scheduling, let grid operators run a higher share of renewables without outages. Batteries are the expensive part, and that is where concessional finance from the platform would matter most.
Where the platform is likely to struggle
Regional bodies in the Caribbean have a mixed record on shared technical services, largely because member contributions arrive late and in uneven amounts. A regional climate AI service that depends on annual CARICOM contributions will be fragile. My view is that it should be funded from the start as a line in the climate finance projects themselves, so that each funded project pays for the forecasting and data services it uses. I have not seen the platform's governance documents, and that funding question is the one I would check first.
AI prompt templates for Caribbean climate teams
I am a [government official/development practitioner/researcher] in [Caribbean country] working on climate resilience. Our country is part of the Regional Platform for Catalysing Resilience and Climate Action. List four AI tools we could deploy in the next 12 months given that we face [hurricane/drought/flooding] risk and have [describe team size]. For each, give likely cost, time to set up, and which international funder might pay for it.
Review this draft Green Climate Fund concept note for [project] in [country] against the GCF's six investment criteria. List each criterion, say where the draft addresses it, and flag gaps and unsupported claims. Do not invent data; mark anything that needs a source.
Create a three-year climate resilience roadmap for a Caribbean island state with under 200,000 people and GDP under US$2 billion. Rank interventions by cost and likely benefit, name an institutional home for each, and list the data each one needs before it can start.
What platform members should do in the next 12 months
- Finance ministries of member states: set up a shared proposal desk. Assign one officer per country and give the group AI drafting tools plus a library of approved GCF proposals from small island states.
- CDB: publish the platform's pipeline of projects. A public list of candidate projects by country lets national teams see where to start and stops duplicate proposals.
- CIMH and national met services: run AI weather models alongside current forecasts for one hurricane season. Record their track and intensity errors against official forecasts, and publish the results.
- National lands and survey departments: identify gaps in elevation data. List which coastal areas lack high-resolution LiDAR, since community risk maps cannot be built without it.
- CARDI with national extension services: pilot a phone-based crop advisory in two member states. Start with one crop per country and measure how many farmers use it.
- Platform secretariat: write data and forecasting costs into every funded project. Make shared AI services a budget line in grants, not a separate appeal to member contributions.
Frequently Asked Questions
How does a Caribbean government get Green Climate Fund money?
A country applies through its National Designated Authority, usually in the ministry of finance or environment, which must endorse every proposal. Projects are submitted by accredited entities: regional bodies such as the Caribbean Community Climate Change Centre in Belize and the Caribbean Development Bank are accredited, as are multilateral banks and UN agencies. Most countries start with a concept note, and the GCF's Readiness Programme funds early preparation work.
What is the difference between the Green Climate Fund and the Adaptation Fund?
Both were created under the UN climate process. The Green Climate Fund is the larger of the two and finances both mitigation and adaptation, often in multi-million-dollar projects. The Adaptation Fund finances adaptation only, usually in smaller grants, and was an early pioneer of direct access, letting accredited national institutions receive money directly. Small island states often use the Adaptation Fund for first projects and the GCF to scale.
How much climate finance have developed countries pledged?
Developed countries pledged in 2009 to mobilise US$100 billion a year for developing countries by 2020, a goal the OECD reports was first met in 2022. At COP29 in Baku in 2024, countries agreed a new goal of at least US$300 billion a year by 2035. Pledges are global totals, not a set share for any region, so Caribbean states still compete for each project.
What is the Bridgetown Initiative, and how does it relate to the platform?
The Bridgetown Initiative, led by Prime Minister Mia Mottley, is a proposal to reform international finance so that climate-vulnerable countries can borrow more cheaply and pause debt payments after disasters. One practical result has been the spread of climate-resilient debt clauses, which let a country defer debt payments after a qualifying disaster. The regional platform works on the delivery side: turning available finance into approved projects.
Can AI tools write a funding proposal on their own?
No. AI can draft standard sections, check a draft against criteria and summarise past proposals, but funders assess the data, the local consultation and the institution's ability to deliver, none of which a model can supply. Any figure a model produces must be traced to a source before submission. Proposals with invented or unsourced figures are a common reason for rejection or long delays.
Key Takeaway: The platform's first measurable test is how much climate finance it brings in. AI tools can shorten the proposal work behind that, improve forecasts and risk maps, and speed damage assessment, but each depends on data and staff that member states still need to fund.