Purpose of AI initiative
Workflow optimisation / internal support: Automating or improving internal processes (e.g. task routing, document handling, code assistance); Modelling / forecasting / nowcasting: Estimating relationships or predicting current or future values (e.g. trends, indicators).; Data exploration / analysis: Supporting exploratory analysis, pattern detection, or analytical insights from data

Business need
Highly climate-exposed countries often lack integrated, decision-ready systems that translate climate and disaster risk data into timely action. In Vanuatu, data on cyclones, droughts, floods, and slow-onset climate impacts existed across multiple agencies, but there was no unified system to analyse this information and trigger preparedness, response, or financing decisions. This resulted in delayed assessments, fragmented coordination, and reactive responses that increased losses for vulnerable populations. The initiative addresses this gap by creating a national, AI-enabled loss and damage decision system that links climate risk indicators to causal impact models, operational thresholds, and trigger-based decision processes for anticipatory action, response, and recovery.

Description
AI and machine learning are used to analyse satellite imagery and environmental data to generate real-time indicators of climate change and disaster impacts, such as land cover, flooding, soil health, coastal erosion, and temperature trends. These indicators are integrated with census and administrative data through automated analytical pipelines to estimate exposure, damage, resource needs, and financial losses. The outputs feed into a national management information system that supports trigger-based decisions for anticipatory action, emergency response, and recovery financing.

AI type(s)
Machine learning; Deep learning; Computer vision AI

Statistical domain(s)
Agriculture & environment; Geospatial / Earth observation

GSBPM phase(s)
Specify needs - Identify user needs and define the purpose of the statistics; Design - Design outputs, methods, data collection, and processing workflows; Build - Develop and test tools, systems, and workflows for production; Collect - Gather data from all sources using the designed collection systems; Process - Clean, integrate, classify, validate, and transform data; Analyse - Produce, interpret, validate, and finalize statistical outputs; Disseminate - Release and promote statistical products and support users; Evaluate - Review the process and outputs to identify improvements

Maturity
Production (operational): The AI application is integrated into routine statistical operations and is used on a regular basis to support official statistics

Benefits
- Rapid estimation of disaster impacts and resource needs through automated analytical pipelines.
- Integration of machine learning outputs, census data, and administrative data into a single national decision platform.
- Improved timeliness and consistency of impact assessments across sectors.
- Enhanced coordination across ministries and disaster response clusters through a centralised system.
- Strengthened local capacity in geospatial analysis, machine learning, and reproducible analytical pipelines.
- Land classification model achieved approximately 82% accuracy, enabling reliable environmental monitoring.

Challenges / risks
- Fragmented legacy data systems and inconsistent data quality across sectors.
- Limited technical capacity for machine learning, remote sensing, and open-source analytical tools.
- Need for extensive data cleaning, harmonisation, and interoperability between systems.
- Institutional coordination challenges across ministries and stakeholders.
- Requirement for human-in-the-loop validation to ensure ethical use and accurate interpretation of AI-derived indicators.
- Sustainability risks without long-term funding, governance structures, and technical maintenance capacity.

Personal / sensitive data
Yes – sensitive data

Governance / ethical measures
Data protection / confidentiality procedures; Human-in-the-loop oversight; Documentation of model and limitations

Ethics / bias / accountability note
The system incorporates human-in-the-loop validation, transparent indicator formulas, and documented assumptions to ensure accountable and ethical use of AI-derived outputs. Special attention is given to the representation of vulnerable groups through the integration of census and administrative data, including gender and protection indicators. Data governance protocols, role-based access controls, and ethical data-sharing agreements are applied to protect sensitive information. The system is designed to support decision-making rather than replace it, with final actions determined through established government coordination and accountability mechanisms.

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