UNEP

United Nations Environment Programme

20
Activities on AI
35
Countries

About UNEP

The United Nations Environment Programme (UNEP) is the leading global environmental authority that sets the global environmental agenda, promotes the coherent implementation of the environmental dimension of sustainable development within the United Nations system, and serves as an authoritative advocate for the global environment.

Activities by UNEP

UNEP
EnvironmentGPT
Global Kenya

1. What is EnvironmentGPT?

EnvironmentGPT is UNEP’s first public beta chatbot designed to make trusted environmental science easier to access. Built on a Retrieval-Augmented Generation (RAG) architecture, it provides accurate, cited answers drawn exclusively from UNEP-approved publications. Unlike general-purpose AI models trained on open internet data, EnvironmentGPT ensures transparency, scientific integrity, and reduced hallucinations. Future versions will be integrated into UNEP’s World Environment Situation Room (WESR).

2. Why It Was Built

UNEP’s mandate includes strengthening the science–policy interface. Policymakers, practitioners, and the public need rapid access to reliable environmental knowledge, yet information remains fragmented and complex to navigate. EnvironmentGPT helps bridge this gap by enabling users to:
- Summarize large volumes of information quickly
- Receive explanations tailored to different expertise levels
- Integrate knowledge across climate, biodiversity, pollution, water, land, health, and economic domains
-Access referenced, authoritative content in accessible formats

General-purpose LLMs cannot be assumed reliable for technical environmental topics; EnvironmentGPT offers a domain-specific, retrieval-based alternative aligned with UNEP’s priorities on climate stability, healthy ecosystems, and pollution reduction.

3. What Documents It Uses

The knowledge base is built using a three-tier selection framework prioritizing high-consensus global assessments, authoritative UN-led publications, and high-quality regional or peer-reviewed literature. Over 220 validated reports form the initial corpus, including:

- GEO-7
- Emissions & Adaptation Gap Reports
- Frontiers Reports
- IPCC & IPBES summaries
- Relevant FAO, WHO, and other UN publications
- Selected open-access scientific meta-analyses and systematic reviews

All documents were reviewed by UNEP’s Office of Science to ensure quality and thematic coherence.

4. Key Features

- Latest UNEP flagship assessments integrated from launch
- Filtering by publication or publication series
- Audience modes: Public, Policymaker, Scientist
- Multiple LLM options with sustainability profiles
- Confidence score based on breadth of supporting evidence
- Environmental impact metrics (energy, water, emissions, minerals per query), using EcoLogits
- Conversation memory for follow-up questions
- Full reference transparency

Improved AI environmental impact assessments

Activity Type AI Tools/Solutions
UNEPUN Secretariat - OICT
Global Partnership on Marine Litter (GPML) Recommender
Global

The Global Partnership on Marine Litter (GPML) Digital Platform is a multi-stakeholder platform that compiles different resources, connects stakeholders, and integrates data. This project is developing a matchmaking system to automatically match stakeholders based on their interests, skills, and other relevant information using a content-based recommender engine, natural language processing, and collaborative filtering.

Activity Type AI Tools/SolutionsResearch/Reports/Assessments
UNEP
Freshwater Ecosystems Explorer
Global

This project uses machine learning to identify areas of surface water from satellite imagery at a global level. It produces an annual dataset of surface water extent as a formal reporting indicator for SDG 6.6.1, which is the only globally derived satellite-based indicator in the SDG framework that member states can use in their national reporting.

Activity Type AI Tools/SolutionsResearch/Reports/Assessments
UNEPUN Trade & Development
Using Machine Learning to Make Government Spending Greener
Africa ZambiaHaitiDemocratic Republic of the CongoSolomon IslandsLiberiaMadagaskar

This exploratory research venture between UNEP and UNCTAD shows how machine learning models can help policymakers and researchers design data-driven policies that efficiently and effectively allocate government resources to maximize inclusive and sustainable prosperity and development.

Activity Type Research/Reports/AssessmentsAI Tools/Solutions