environmental degradation

Environmental degradation remains one of the most pressing global challenges, threatening ecosystems, economies, and human well-being worldwide. To address this crisis, the International Telecommunication Union (ITU), in collaboration with its partners, is proud to announce the 2025 AI for Climate Action Innovation Factory. This initiative aims to harness the power of Artificial Intelligence (AI) to develop innovative solutions that help mitigate environmental impacts and support global adaptation efforts.

The 2025 edition aims to further advance the use of AI in addressing pressing environmental and sustainability challenges, promoting scalable, impactful, and inclusive AI-driven projects that can contribute to meaningful solutions aligned with global priorities and the targets of the Paris Agreement. The finale will take place at COP30 in Brazil, aligning with the conference’s mission to accelerate global environmental action.

Key objectives:

1. Identify and support cutting-edge AI-driven solutions to address environmental and sustainability challenges.
2. Promote the development and deployment of AI-powered tools that drive measurable progress toward environmental resilience and sustainable development.
3. Bring together governments, academia, private sector organizations, startups, and civil society to co-create and scale impactful sustainability-focused solutions.
4. Ensure inclusivity by supporting underrepresented groups, including women, youth, and marginalized communities, in contributing to and benefiting from environmental and sustainability initiatives.
5. Showcase winning solutions during high-profile events, culminating in a grand finale at COP30 in Brazil, where selected projects will gain global recognition and opportunities for adoption.

Stable mobile communication requires understanding radio propagation at specific areas, especially when using high-frequency bands like millimeter waves, which are highly affected by environmental factors such as buildings. Direct measurement of propagation characteristics across areas and frequencies is impractical due to cost and effort. To address this, AI/ML-based methods can estimate area-wide propagation using limited measurement data and environmental information like building layouts. Effective application of this approach involves not only building AI/ML models but also selecting the most relevant data to improve estimation accuracy. This challenge invites participants to explore AI/ML model and data selection methods using provided propagation loss data and 3D maps.

In this webinar, the top three teams from those who participated in the challenge will present their proposed approaches. Various strategies have been suggested to solve problems in the challenge, and we believe that participants will gain new insights into the application of AI/ML for radio wave propagation estimation. Additionally, KDDI Research has allocated a prize of 3,000 CHF for the challenge. Along with an explanation of the evaluation results, the prize amounts will also be announced during the session. Please note that the technical evaluation results for the submitted teams are available under the “Results” tab at the following URL: https://challenge.aiforgood.itu.int/match/matchitem/112.

Institutions

• AI for Good

As the planet faces accelerating environmental degradation and biodiversity loss, emerging technologies have an increasingly vital role to play in enhancing conservation efforts. Artificial intelligence (AI), in particular, is emerging as a powerful tool to address complex environmental challenges, from monitoring ecosystems and predicting habitat changes to combatting illegal wildlife trafficking. 

In this webinar, Professor Tshilidzi Marwala, United Nations Under-Secretary-General and Rector of the United Nations University (UNU), will outline the transformative potential of AI for biodiversity and conservation. He will examine how AI can support the ambitious targets of the Kunming-Montreal Global Biodiversity Framework by enabling scalable, data-driven solutions that enhance our ability to understand and protect our ecosystems. 

Drawing on UNU’s global work at the intersection of AI and sustainability, Prof. Marwala will also reflect on the ethical, environmental, and equity-related challenges associated with AI, such as algorithmic bias, the digital divide, and its carbon footprint. Realising the promise of AI for biodiversity conservation will depend on our ability to govern it wisely, ensuring it serves as a force for equity, sustainability, and environmental stewardship.

Learning Objectives: 

By the end of this webinar, participants will have developed their ability to:

• Identify key facing global biodiversity and conservation efforts.
• Describe the potential applications and limitations of artificial intelligence in addressing these challenges.
• Analyse real-world case studies illustrating how AI is being used to support biodiversity monitoring and conservation.
• Evaluate the role of the United Nations University in advancing research and innovation at the intersection of AI, sustainability, and biodiversity.