Researching and applying the potential of Artificial Intelligence to foster climate resilience at the regional and local levels

This is a Mission call, not a standard Cluster call. That changes several things — most importantly, the funding structure and the evaluation logic.

The Structural Feature That Changes Everything

Read this carefully:

"To ensure a balanced portfolio covering both objectives, grants will be awarded to proposals not only in order of ranking but at least also to one proposal that is the highest ranked within each objective, provided that the applications attain all thresholds."

This is a guaranteed-slot structure. The call has two objectives:

• Objective 1 — "AI for more accessible data" — AI to make climate data more accessible and understandable for decision-makers

• Objective 2 — "AI for sectoral adaptation" — AI to help regions and local authorities optimise resource management and adaptation in specific sectors

The call commits to funding at least one proposal in each objective, regardless of overall ranking — as long as it passes the quality threshold.

The practical implication: If you target the objective with fewer applications, your odds improve significantly. You're not competing against all proposals in the call — you're competing against proposals in your objective, for a guaranteed slot.

This is the single most important feature of this call for your consortium.

What the Call Actually Wants

The call is about deploying AI to help regions and local authorities adapt to climate change. Two paths:

Objective 1 — AI for more accessible data:

• AI to process and identify patterns in big climate datasets

• Make climate data more accessible and understandable for decision-makers

• Test with at least 3 regional and local authorities

• Improve data integrity and accessibility

• Could involve Destination Earth (DestinE) data

Objective 2 — AI for sectoral adaptation:

• Use machine learning and AI to help regions and local authorities optimise resource management

• Target specific sectors: smart agriculture, construction, water management, waste management, transport, energy resilience

• Test in at least 5 regional or local authorities

• Engage private sector actors with relevant expertise

Both objectives require:

• Training and dissemination material for target groups

• Attention to bias and data representativeness

• Collaboration with the Mission Implementation Platform

• Links to other projects (UNDERPIN, CLIMAAX)

Where gethelp.network Fits — Both Objectives Are Plausible

This is unusual. Your platform could credibly address either objective, which means you have a choice to make.

If you target Objective 1 — AI for more accessible data:

Your platform's core function is making local data accessible. The pitch: "AI-powered aggregation and synthesis of local climate-relevant data (what exists where, what's available, what's needed) to give regional and local authorities actionable intelligence for climate adaptation planning."

Your platform already:

• Aggregates fragmented local data

• Uses AI to synthesise listings into answers

• Provides a geolocated interface for decision-makers

• Has an API layer for institutional integration

Fit: Strong. Your platform IS a data accessibility tool.

If you target Objective 2 — AI for sectoral adaptation:

You'd need to pick a sector — the call lists smart agriculture, construction, water management, waste management, transport, energy. The most natural for your platform:

• Smart agriculture — the NOBABIO legacy: mapping biomass, optimising agricultural resource use

• Waste management — the CIRCBIO-07 overlap: bio-waste collection, processing, logistics

• Water management — adjacent, less natural

The pitch: "AI-powered platform for regional and local authorities to optimise [sector] resource management and adaptation."

Fit: Strong if sector is agriculture or waste management. Weaker for other sectors.

My recommendation: Objective 1. It's the cleaner fit — your platform's fundamental function is data accessibility. Objective 2 requires picking a sector and going deep, which is possible but adds complexity.

However — there's a strategic argument for Objective 2: If fewer proposals target Objective 2, the guaranteed-slot structure works more in your favour. This is a judgment call based on your assessment of how competitive each objective is likely to be.

The Critical Requirement: 3 or 5 Regional/Local Authorities

This is where the call gets specific:

• Objective 1: "tested with at least 3 regional and local authorities"

• Objective 2: "develop and test the tools in the territory of at least 5 regional or local authorities"

Good news: Your consortium already has access to municipalities through the associations of cities and municipalities of Estonia, Latvia, and Lithuania. These associations connect you to dozens of local authorities.

What you need to demonstrate: Actual engagement with at least 3 (or 5) specific authorities — not just access to them. The proposal must name them and show how they're involved.

Practical approach: Select 3–5 municipalities that are already interested in climate adaptation. Estonia, Latvia, and Lithuania each have municipalities with active climate programs. Your municipal associations can help identify them.

The Mission Context — A Different Funding Logic

This is part of the EU Mission on Adaptation to Climate Change. Missions are different from standard Horizon calls:

1. They have a specific mission framework — you must align with it

2. They require collaboration with the Mission Implementation Platform

3. They participate in the Mission Community of Practice

4. They link to specific prior projects — UNDERPIN and CLIMAAX are named

What this means for the proposal: You need to show awareness of the Mission framework and commit to engaging with it. This is a real requirement, not a formality — the proposal should budget for participation in the Community of Practice and collaboration with the Mission platform.

The positive side: Missions are designed to build a portfolio of projects, not just fund the best ones in isolation. They want diversity of approaches. This can work in your favour if your approach is distinctive.

What You Need to Add to the Consortium

For this call, you need:

1. A regional or local authority partner — at least one, possibly more, that will be the "testing ground"

2. An AI/ML technical partner — the call is explicitly about AI. If your platform's AI is basic, you may need a partner with deeper AI expertise (a university or an AI company)

3. A climate data partner — someone who understands climate datasets (DestinE, Copernicus, etc.)

4. A training/dissemination partner — the call requires training material for target groups

What you already have:

• Municipal associations (access to local authorities)

• Market research firms (data and evaluation)

• University partners (potentially for AI expertise)

• gethelp.network (the platform)

Gap assessment:

• Your AI capabilities are strong but may need a research partner for the RIA-level AI contribution

• Climate data expertise may need to be added — this is a different domain from biomass or local listings

• TalTech or another university could cover the AI/climate research angle

The RIA Challenge — Again

This is a Research and Innovation Action (RIA). That means:

• The proposal needs a research contribution, not just a deployment

• The AI element must be research-grade, not just applied

• The lead partner is typically a university or research institution

Your platform's role: the deployment vehicle and data source for the AI research. Similar to COMMUNITIES-01 — you're a partner, not the lead.

The framing challenge: You need to answer "what is the research contribution?" Your platform is operational. The AI research would be about applying AI to climate resilience data in ways that go beyond the current state of the art — for example, novel methods for synthesising fragmented local data, or new approaches to making climate data actionable at the local level.

This is a real research question — it's not just a deployment. But it needs a research partner to frame it properly.

The Realistic Assessment

Strengths:

• The guaranteed-slot structure gives you a potential advantage if you target the right objective

• 4 projects funded — more than most calls

• €3M per project

• Your platform's data accessibility function fits Objective 1 directly

• Municipal associations give you immediate access to the required authorities

• The Mission context means portfolio diversity matters — distinct approaches are valued

• 8 months to prepare (opening Feb 2027, deadline Sep 2027)

Weaknesses:

• RIA structure means a university lead is expected

• The AI element needs to be research-grade — your platform's AI may need strengthening

• Climate data expertise is a gap

• Budget is smaller than CIRCBIO-07 (€3M vs €7M)

• The Mission framework adds compliance requirements

• You'd be competing with established climate research institutions

Realistic odds: With the right consortium (university lead + your platform as partner), targeting Objective 1, 15–25%. The guaranteed-slot structure could push this higher if fewer proposals target your objective.

The Strategic Question: Which Objective?

Objective 1 (AI for more accessible data):

• Cleaner fit for your platform

• 3 authorities required

• The "data accessibility" theme is literally what you do

Objective 2 (AI for sectoral adaptation):

• Requires picking a sector

• 5 authorities required

• Could overlap with your NOBABIO/CIRCBIO-07 work if sector = agriculture or waste

• Higher bar (5 authorities vs 3)

My recommendation: Objective 1. It's the cleaner strategic fit, and the 3-authority requirement is easier to meet than the 5-authority requirement of Objective 2.

But there's an argument for Objective 2: if your CIRCBIO-07 proposal goes forward with a waste-management focus, you could position CLIMA-02 as its climate-adaptation sibling — targeting the same sector (waste management) with a focus on AI-driven climate resilience. This creates a coherent portfolio across two calls.

This is worth discussing with your consortium before deciding.