Data and Analytics for Sustainability: Leveraging Insights for a Resilient Future

Erscheinungsjahr 2026
ISBN: 978-3-032-36056-4
Verlag: Springer Nature Switzerland AG

Buch, Englisch, 236 Seiten, Format (B × H): 155 mm x 235 mm

This book provides a holistic perspective of various data sources, methodologies, and technologies that can be harnessed to measure, monitor, and enhance sustainability efforts across diverse sectors. Sustainability is a social goal and a broad paradigm on environmental, societal, and economic considerations for people to co-exist on Earth over a long time. The recent rapid advances in artificial intelligence (AI) and data analytics research are claimed to enable machines to exhibit human-like cognition and have been used to tackle different aspects of sustainability related issues. Cognizant of the holistic link of historical achievements to future developments, data analytics is, therefore, claimed to be the driving force to achieve sustainable development. From water management and transportation to energy systems and agriculture, each chapter delves into the unique opportunities and challenges associated with leveraging data and analytics for sustainable development.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Fang

Zhou, Jianlong

Li, Zhidong

Ou, Yuming

Liang, Bin

Tian, Hongda

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Geowissenschaften Umweltwissenschaften Nachhaltigkeit

Inhaltsverzeichnis

AI, Data and Sustainability.- Data Collection Techniques for Sustainability.- Drinking Water Quality Prediction for Sustainable Future.- Leveraging Data Analytics in Digital Twins to Improve Railway Situation Awareness.- LLM-Based Carbon-Aware Forecasting for the National Electricity Market..- Applications of Artificial Intelligence in Smart Building Energy Management Systems for Sustainability Enhancement.- Predictive Agriculture for Sustainability with Multimodal Data and Accountability AI.- Improving Agricultural Carbon Emission Estimation with Process-Based Models and Residual Learning.- Data-Driven Innovations in Carbon Estimation.- Ethics and Sustainability.