Enhancing Climate Change Initiatives with AI Support for National Designated Authorities in Evaluating Funding Proposals

Climate change initiatives depend heavily on effective funding decisions made by national designated authorities (NDAs). These authorities play a critical role in reviewing funding proposals and issuing no objection letters (NOLs), which are essential for advancing projects that address climate challenges. Yet, the volume and complexity of proposals can overwhelm human capacity, leading to delays or missed opportunities. Artificial intelligence (AI) offers promising tools to support NDAs in this process, and could significantly improve accuracy, efficiency, and ultimately the success of climate change initiatives.

In past consulting work I have done to assist clients in their institutional strengthening efforts, particularly around the role of their NDAs on climate change projects; I recall noting the intense work involved in NDAs evaluation of project funding proposals. I have also had to develop comprehensive tools to support the project evaluation work carried out by such NDAs. In performing these technical support work it dawned on me then that this is definitely an area in which AI could seriously assist in increasing the efficiency and accuracy of the project funding proposal evaluation process. Against this background, I wrote this article to share some reflective thoughts on how AI may assist NDAs in evaluating funding proposals and generating no objection letters, and to also highlight practical applications, benefits, and challenges of AI when deployed in this context.

The Role of National Designated Authorities in Climate Funding

I recall repeating it on numerous occasions in meetings with my client that NDAs act as “gatekeepers” for climate finance, ensuring that projects align with both national priorities, and international climate goals. Their responsibilities include:

• Reviewing funding proposals submitted by project developers or implementing agencies.

• Assessing technical, financial, and environmental aspects of proposals.

• Coordinating with stakeholders to verify project feasibility.

• Issuing no objection letters to approve projects for funding.

  
  

This process requires careful scrutiny to avoid funding ineffective or risky projects. However, NDAs often face challenges such as limited staff, tight deadlines, and complex documentation.

How AI Can Support Proposal Evaluation

From my research I discovered that AI technologies can help NDAs manage the workload and improve decision quality by automating and enhancing several key tasks:

1. Automated Document Analysis

Anyone who has interacted with a GCF or other donor agencies’ funding proposal documents will attest to the fact that these funding proposals, especially when completed by executing entities, typically include lengthy reports, technical data, and financial plans. With respect to these, AI-powered natural language processing (NLP) tools can:

• Extract key information such as project objectives, budgets, timelines, and expected outcomes.

• Identify inconsistencies or missing data.

• Summarize large documents to highlight critical points for human reviewers.

  
  

This reduces the time spent on manual reading and helps focus attention on important details.

2. Risk Assessment and Validation

Machine learning models can analyze historical data from past projects to predict risks related to budget overruns, delays, or environmental impact. AI can:

• Flag proposals with high-risk indicators.

• Cross-check project data against national climate strategies and international standards.

• Detect potential fraud or misrepresentation by comparing proposal details with external databases.

  
  

This supports NDAs in making informed decisions and avoiding funding projects with low chances of success.

3. Prioritization and Scoring

AI algorithms can score proposals based on predefined criteria such as alignment with climate goals, cost-effectiveness, and social benefits. This enables NDAs to:

• Rank proposals objectively.

• Allocate resources to the most promising projects.

• Ensure transparency and consistency in evaluation.

  
  

4. Drafting No Objection Letters

Generating no objection letters involves compiling evaluation results and formal approval statements. AI can assist by:

• Creating draft letters based on evaluation data.

• Ensuring compliance with regulatory language and formats.

• Allowing NDAs to review and finalize letters quickly.

  
  

This streamlines administrative tasks and speeds up the approval process.

AI analyzing climate project documents to support national authorities

Real-World Examples of AI in Climate Finance

Several initiatives demonstrate AI’s potential to support NDAs and climate funding bodies:

• The Green Climate Fund (GCF) has experimented with AI tools to analyze project proposals, improving review speed and consistency.

• In Kenya, AI-based platforms help government agencies assess renewable energy projects by automatically extracting technical data and comparing it with national targets.

• AI-driven risk models developed by research institutions assist in predicting project success rates, helping funders prioritize investments.

  
  

These examples show that AI can complement human expertise rather than replace it, enabling NDAs to focus on strategic decisions.

Benefits of Using AI for NDAs

Adopting AI tools offers several advantages:

• Efficiency gains: Automating routine tasks reduces workload and shortens evaluation timelines.

• Improved accuracy: AI can detect errors and inconsistencies that humans might miss.

• Consistency: Standardized scoring and analysis reduce subjective bias.

• Better resource allocation: Prioritizing high-impact projects increases the effectiveness of climate finance.

• Transparency: Clear AI-generated reports support accountability and stakeholder trust.

  
  
  
  

Challenges and Considerations

While AI offers clear benefits, NDAs must address challenges to ensure successful implementation:

• Data quality and availability: AI models require reliable data, which may be limited or fragmented in some countries.

• Technical capacity: NDAs need training and infrastructure to use AI tools effectively.

• Ethical concerns: Transparency in AI decision-making is essential to avoid unfair biases.

• Integration with existing processes: AI should complement, not disrupt, established workflows.

• Cost: Developing or acquiring AI solutions involves upfront investment.

  
  

Careful planning and collaboration with technology providers can help overcome these hurdles.

Steps for NDAs to Adopt AI Solutions

NDAs interested in using AI can follow these practical steps:

• Assess needs: Identify bottlenecks and tasks that could benefit from AI support.

• Pilot projects: Start with small-scale AI tools for document analysis or scoring.

• Build partnerships: Collaborate with AI developers, research institutions, and international organizations.

• Train staff: Provide training on AI tools and data management.

• Monitor and evaluate: Continuously assess AI performance and make improvements.

• Ensure transparency: Maintain clear documentation of AI processes and decisions.

  
  

Looking Ahead: AI’s Role in Strengthening Climate Action

AI has the potential to transform how NDAs evaluate funding proposals and issue no objection letters, making climate finance more effective and responsive. By embracing AI, national designated authorities can better manage growing workloads, reduce errors, and focus on supporting projects that deliver real environmental and social benefits.

As climate challenges intensify, leveraging AI tools will be key to accelerating progress and ensuring that funding reaches the initiatives that matter most.

Please feel free to share your thoughts and comments with me at orane.bailey@oranton.ca