Call for Proposals 2025: Apply to join Klarna’s AI for Climate Resilience Program (Deadline: 31 August, 2025)

Funding details

• Grant size: Up to $300k per project
• Project start date: 1 January 2026
• Project length: 12-18 Months

Project Focus for Funding

Eligibility

The basic requirements that a project or proposal must meet to be considered for funding, support, or participation:

• Strategic alignment: The proposal addresses a concrete climate-impact challenge in line with the outlined focus for the call.¹

• Implementation-focused: The team will adapt, localize, scale or otherwise deploy a concrete solution; no “research-only”.

• Use of AI: The proposed use of AI has clear potential, is collaborative, and responsible.² There should be a clear, compelling rationale for its use, demonstrating how AI directly contributes to addressing the project’s objectives. We also welcome early stage applications from teams that need support in developing technical details further.

• Geographic focus: The project is supporting underserved, climate-vulnerable communities in low and middle income countries.

• Public-benefit purpose: The project must be committed to a public-benefit purpose, ensuring its outcomes primarily serve the public interest with tangible social and environmental benefits. For-profit entities must demonstrate how their project aligns with public-benefit goals and how they will ensure equitable outcomes that directly support local communities.

¹ Projects that solely focus on reducing GHG emissions are ineligible, but it can be a co-benefit. ² There are multiple definitions of ethical and responsible AI. By “responsible”, we refer to the development and use of AI that is transparent, fair, accountable, and does not lead to social nor environmental harm. We expect funded projects to be user-first focused, and developed in meaningful collaboration with expected users. We also expect funded project teams to identify and explain which safeguards have been put in place to mitigate potential risks, such as algorithmic biases, discrimination, and misuse, while being mindful of the solutions’ environmental impact and energy intensity.

Evaluation Criteria

The evaluation criteria are used to assess the relative strengths and weaknesses of the projects, helping to determine which projects to prioritize for funding. Criteria are applied generically to all proposals; but reviewers use context-specific judgment (e.g., agriculture vs. disaster preparedness) inside each parameter.

• Impact and equity potential: Likely, expected reduction in climate-related vulnerability, with clear benefits for vulnerable and underserved groups (e.g., women, youth, underprivileged, Indigenous peoples). This also includes whether the project is addressing the root causes of vulnerability, i.e. tackling the underlying factors contributing to these disparities.

• Local relevance and ownership: We prioritize initiatives that have been co-designed with local stakeholders, i.e. where needs have been defined by the communities that will benefit from the initiative and includes a realistic plan for local governance and long-term maintenance, as well as equitable IP, and benefit arrangements. The lead and partner organizations must demonstrate a strong, established presence on the ground and ensure that the project’s ownership and benefits are primarily controlled by local stakeholders.

• Technical and adoption readiness: We prioritize projects that demonstrate a readiness to adopt AI. This includes not only the robustness and documented performance of existing tools or similar technologies but also the potential for developing new, AI-driven solutions. We encourage organizations that have not yet adopted AI to explore its potential and consider how it can enhance their approach. Evaluation will focus on the usability, scalability, and alignment of technology with local capacity, as well as the presence of a clear user-support plan.

• Risk Mitigation & Safeguards: Projects should demonstrate a clear strategy for mitigating technological, social, and environmental risks. This includes addressing biases in AI, ensuring transparency, and preventing unintended consequences. Proposals should outline how they will manage social risks, such as exacerbating inequalities, and environmental risks, such as ecological harm.

• Team capacity and quality of partnerships: Track record and team competence, relevant partnerships in place for successful implementation, clarity of roles and responsibilities.

• Catalytic effect: Credible pathway for replication/scalability—via policy uptake, additional funding or open-source dissemination—so benefits extend beyond the initial site.

• Longevity: We prioritize projects that have a pathway for long-term implementation and capacity building beyond the project period.

Project Selection Process

The project selection process is designed to ensure fairness, transparency, and quality. Here’s a breakdown of the process:

Open call for proposals To ensure fairness and transparency, we follow a structured and competitive process for sourcing new initiatives to be part of the Program. Anyone can submit a proposal. We will also source projects directly from our extended network.

Assessment and short listing The submitted proposals are first pre-screened by the Program Manager against our eligibility criteria. All eligible initiatives undergo an in-depth evaluation and full scoring against our evaluation framework. We utilize an extensive scoring matrix, which breaks down criteria into numerous questions to ensure a comprehensive project evaluation. Where needed we conduct interviews and follow-up for additional information with project teams.

Evaluation and input from the Advisory Group The role of the Advisory Group is to provide independent, science-based advice to keep project selection rigorous, transparent and aligned with our theory of change. The group is made up of scientists and practitioners in climate adaptation, resilience and AI. Shortlisted projects are submitted for review by the Advisory Group members and assessments and scoring is discussed in a meeting.

Due diligence The final selection combines the scores with our aim to create a diverse portfolio in terms of geographical coverage and methodologies employed. Milkywire oversees the curation of the Program, and makes a recommendation to Milkywire’s charitable partner WRLD Foundation –  a US based public charity and registered 501(c)(3). Before contracts are signed with selected organisations, WRLD Foundation conducts a standard due diligence screening. This includes for example financial crime related screenings of the non-profit/company and its key staff, an assessment of the internal control environment of the organisation and its legal status.

Contracting and payouts WRLD Foundation enters into contracts with selected grantees and pays out funding.