Neptune Fountain and park at Schönbrunn Palace in Vienna

CONFERENCE

IN FOCUS: ARTIFICIAL INTELLIGENCE (AI) IN GREEN SCIENCES

SGEM Vienna GREEN, Schloß Schönbrunn
Photo: Simon Matzinger · CC BY-SA 3.0

Robotic hands above grass, illustrating artificial intelligence and green technology

ARTIFICIAL INTELLIGENCE (AI) IN GREEN SCIENCES

This section delves into the intersection of AI and environmental sciences, examining how artificial intelligence can enhance green technologies, improve sustainability efforts, and foster innovative solutions to environmental challenges. AI is rapidly becoming a key tool for optimizing resource efficiency, monitoring ecosystems, predicting climate change, and advancing clean technologies. This section addresses the ethical implications, opportunities, and challenges of deploying AI in green sciences, from biodiversity conservation to energy transition and circular economy solutions.

In this direction, we will consider definitions and outlines covering the following SDGs goals:

Precision agriculture, air-pollution monitoring, renewable-energy optimisation, circular resources, climate modelling and biodiversity monitoring.

Thematic alignment with the UN targets. SDG 17 connects the programme through international scientific cooperation.

• AI for Climate Change Modeling and Prediction
Investigates how AI is used to enhance climate models, predict environmental changes, and support data-driven climate adaptation strategies.
• AI-Driven Renewable Energy Optimization
Focuses on the role of AI in improving the efficiency of renewable energy systems, such as solar, wind, and hydropower, through predictive maintenance and energy management.
• Sustainable Agriculture and AI for Precision Farming
Examines how AI technologies support sustainable agriculture, including precision farming, soil health monitoring, and resource-efficient crop management.
• AI in Biodiversity Conservation and Ecosystem Monitoring
Explores how AI is used to monitor ecosystems, track species populations, and protect biodiversity by analyzing environmental data and identifying threats to habitats.
• Green AI and Sustainable Development
Looks at the concept of Green AI, focusing on reducing the environmental footprint of AI technologies themselves while promoting their application in sustainability projects.
• AI and the Circular Economy
Investigates how AI supports the circular economy by optimizing recycling processes, reducing waste, and enabling resource-efficient production cycles.
• AI for Pollution Control and Environmental Remediation
Discusses AI’s role in detecting pollutants, managing waste, and improving environmental remediation processes to reduce the impact of industrial activities.

This section would provide a cutting-edge platform to discuss how AI is transforming green technologies, contributing to sustainability goals, and addressing some of the most pressing environmental challenges of our time.


Publication Dissemination & Discovery

Scopus

Scopus Scientific Database

Clarivate

Scientific Databases

EBSCO

EBSCO
Science Services Database

ProQuest

Scientific Databases
Part of Clarivate

Crossref

We are a voting CrossRef member - the official DOI provider

Petroleum Abstracts

Scientific Database
Part of Clarivate

Google Scholar

Google Scholar
Scholarly Search

RSCI

Russian Science Citation Index
Part of Web of Science

Mendeley

Scientific Databases
Part of Elsevier

GeoRef

GeoRef — AGI
Geoscience Bibliographic Database