Buch, Englisch, 284 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 576 g
No Poverty
Buch, Englisch, 284 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 576 g
ISBN: 978-1-032-84845-7
Verlag: CRC Press
Sustainable Development Goals or SDGs refer to the UN stipulated road-map for development in 17 defined areas, by 2030. It was built on the previously established Millennium Development Goals (MDG). This first volume (SDG-1) deals with eradicating poverty with the help of modern ICT technologies needed to end poverty and create a better society. This much needed book, which is the first of its type to offer a specific focus on the relationship between technology and the SDG-1 goal, will be valuable for all working in the subject of global sustainable development. Global organisations and representatives of governments targeting no poverty share knowledge on these ICT practices to eradicate poverty in all means.
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Fachgebiete
- Geowissenschaften Umweltwissenschaften Nachhaltigkeit
- Geowissenschaften Umweltwissenschaften Lebensmittelsicherheit und -versorgung
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Lebensmitteltechnologie und Getränketechnologie
- Geisteswissenschaften Architektur Ökologische Aspekte in der Architektur
- Geowissenschaften Umweltwissenschaften Umweltwissenschaften
- Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft
- Wirtschaftswissenschaften Volkswirtschaftslehre Internationale Wirtschaft Entwicklungsökonomie & Emerging Markets
Weitere Infos & Material
Preface. Introduction to Technological Assistance for Poverty Reduction. Food Security and Safety to End Poverty. ICT Alleviates Poverty Focusing on Healthcare, Agriculture and Entrepreneurship. Role of ICT in Harnessing Poverty Reduction: An Empirical Study using an ARDL Approach. Developing Social Protection Systems to End Poverty in Indonesia. Smart Media Technology in No Poverty Reduction. Sustainable Livestock Production and Poverty Alleviation. Modern Farming Techniques to Sustainable Livelihoods. Artificial Intelligence for Hunger Monitoring and Mitigation. Machine Learning Methods to Predict and Classify Poverty. Machine Learning Technologies for Agricultural Prediction to Enhance Economic Growth. Empowering Poverty Prediction with Sustainable AI Techniques. Precision Poverty Evaluation: Leveraging Generalized Linear Models and XG Boost Algorithms. Transforming Data into Action: Leveraging Big Data Analytics to End Poverty. Eradication of Poverty using Smart Governance: Case Study of European Union Cities. Index.