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The Use of Internet of Things, Big Data Analytics and Artificial Intelligence for Attaining UN's SDGs

In: Handbook of Big Data and Analytics in Accounting and Auditing

Author

Listed:
  • David Teh

    (RMIT University)

  • Tarek Rana

    (RMIT University)

Abstract

With world’s population projected to grow to 9.7 billion in 2050, the demand for food and water will increase drastically. When population increases it also raises consumption and waste, managing waste can be more challenging. If urgent actions not taken, global waste is expected to increase by 70%; to an estimated 4 billion tons by 2050, projected by the World Bank. Further, the link between humanity’s impacts on ecosystems and biodiversity, and the rise of emerging and certain diseases, such as the novel coronavirus (COVID-19) shows the severity. This chapter seeks to further understand and explore how the use of emerging technologies such as the Internet of Things, Big Data Analytics and Artificial Intelligence can accelerate the progress on the 17 UN Sustainable Development Goals (SDGs). Brief case studies based one documentary evidence are presented to capture how technologies can create solutions in the areas of smart waste management, water management, and agriculture and farming. Since IoT has offered the opportunity to digitize many operations that can bring many benefits, it can help combat climate change and protect the environment. For instance, IoT can be used to develop smarter and more effective ways of managing and reducing waste. IoT could also impact the sustainability of the planet in different areas, such as water use, water efficiency and harvest productivity. The technologies discussed provide the opportunity to drive success and accelerate the progress of attaining many of the SDGs such as SDG 2, 3, 6, 9, 11, 12, 13, 14, 15.

Suggested Citation

  • David Teh & Tarek Rana, 2023. "The Use of Internet of Things, Big Data Analytics and Artificial Intelligence for Attaining UN's SDGs," Springer Books, in: Tarek Rana & Jan Svanberg & Peter Öhman & Alan Lowe (ed.), Handbook of Big Data and Analytics in Accounting and Auditing, chapter 0, pages 235-253, Springer.
  • Handle: RePEc:spr:sprchp:978-981-19-4460-4_11
    DOI: 10.1007/978-981-19-4460-4_11
    as

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