ItemBook Chapter

Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress

Title
Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress
Creator
Goswami, Mausumi; Lakshmipriya, Shreenidhi Prasath
Description
Sustainability, a concept that gained prominence with the Brundtland Report in 1987, is defined as a development approach that addresses present needs without jeopardizing the ability of future generations to meet theirs. Over the years, sustainability has evolved beyond its initial environmental focus, now encompassing economic, social, and political dimensionsmaking it an essential pillar of modern development initiatives. To drive global sustainable development forward, the United Nations adopted the 2030 Agenda, featuring 17 Sustainable Development Goals (SDGs). These goals aim to resolve some of the most pressing challenges faced by humanity, including poverty eradication, climate action, gender equality, and economic growth. The SDG Index, which evaluates a countrys progress toward these goals, helps measure and compare performance across nations. The Intersection of Socioeconomic Factors and SDG Progress is significant for the growth of a country. A countrys Gross Domestic Product (GDP) has often been seen as a key economic indicator, reflecting its ability to invest in sustainable initiatives. However, sustainability is not solely dependent on financial resourcessocial factors play a critical role. To assess the connection between well-being and sustainability, researchers often analyze the Happiness Index alongside SDG scores. Countries demonstrating both high happiness levels and strong sustainability scores provide valuable insights into the relationship between social welfare and global progress. Furthermore, machine learning (ML) techniques have emerged as powerful tools in sustainability research. By analyzing vast datasets, AI-driven approaches can predict trends, optimize resources, and enhance policy implementationaccelerating progress toward a sustainable future. The Evolving Landscape of Sustainability and Its Global Impact is realized using statistical and ML approaches in this study. Rethinking Strategies for a Sustainable Tomorrow is very important in 2025 as we are approaching 2030 very fast. Understanding the underlying factors influencing SDG scores allows nations to refine their approaches to sustainability. By tailoring action plans based on socioeconomic conditions, governments can improve their policies, ensuring both environmental stewardship and enhanced quality of life for their citizens. As global challenges evolve, interdisciplinary approachesspanning technology, economics, and social scienceswill continue to shape sustainability efforts, fostering a future where development aligns seamlessly with environmental and societal well-being. 2026 selection and editorial matter, Siddhartha Bhattacharyya, Jan Plato, Soumyadip Dhar, Naba Kumar Mondal, Ivan Zelinka, Jyoti Sekhar Banerjee and Abhijit Das; individual chapters, the contributors.
Source
Data-Driven Environmental Intelligence;pp.304-328
Date
01-01-2026
Publisher
CRC Press
Coverage
Goswami M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Lakshmipriya S.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-104055266-7; 978-103290007-0;
Format
online
Language
English
Type
Book chapter

Citation

Goswami, Mausumi; Lakshmipriya, Shreenidhi Prasath, “Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24389.

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