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New approaches to addressing environmental and health challenges

Published: November 2025

Technological innovations are revolutionizing the way environmental data can be collected and analyzed, making an important contribution to better understanding complex environmental systems, finding effective solutions to numerous environmental and health challenges, and strengthening crisis resilience. For example, AI systems enable real-time monitoring of environmental changes such as sea level rise, ice melt, and temperature increase. Machine learning helps to identify patterns in complex environmental data. Deep learning is used to process large volumes of data from environmental sensors. IoT sensors enable real-time monitoring of environmental parameters. Satellite technologies such as the Copernicus Earth Monitoring provide large-scale data. Drones are used for local environmental observations, e.g., for monitoring forests and biodiversity.

Guiding questions

  • What strategies can Thuringia develop to integrate sustainable approaches in urban planning and supply chain management?
  • How can AI-supported models for predicting and preventing environmental disasters (e.g., floods, wildfires) be further developed for Thuringia and integrated into regional planning?
  • To what extent can innovative environmental technologies contribute to achieving Thuringia's climate goals, and what political measures support these developments?
  • How can data-driven sustainability strategies in the supply chain help reduce emissions and save resources?
Bildbeschreibung / description: A futuristic globe with a satellite in the center.

Challenges

One of the biggest challenges lies in data protection, as environmental and health data often contain personal information that must be protected from unauthorized access. Furthermore, transparency and accountability of AI decisions in the environmental sector are critical points. There is a risk that AI systems may produce biased or discriminatory results, making ethical review of the algorithms essential. AI systems for environmental monitoring have high energy and resource consumption. This contradicts sustainability goals. Solution approaches include the development of energy-efficient algorithms and the use of renewable energies for data centers.

Concrete examples

  • AI-supported real-time monitoring of environmental factors such as air quality, flood events, or ocean currents
  • AI-powered analysis tool for illegal sales advertisements of protected animal species on online platforms
  • Automatic detection of wind energy and ground-mounted PV systems on satellite images
  • „Global Forest Watch“ for real-time detection of illegal deforestation
  • EU project "Destination Earth“ creates a precise Earth model that depicts the interactions between natural events and human activities
Bildbeschreibung / description: Foresight – Thuringia, Blue Eye in Focus

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Reference

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Reference

Michel Reichardt

Michel Reichardt Project Manager Strategic Foresight

Christoph Grollman

Christoph Grollman Project Manager Strategic Foresight

Dr. Sophia Gänßle

Dr. Sophia Gänßle Project Manager Data Science Strategic Foresight

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