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Translation of Earth observation data into sustainable development indicators: An analytical framework

Author

Listed:
  • Ana Andries
  • Stephen Morse
  • Richard Murphy
  • Jim Lynch
  • Emma Woolliams
  • John Fonweban

Abstract

In 2015, member countries of the United Nations adopted the 17 Sustainable Development Goals at the Sustainable Development Summit in New York. These global goals have 169 targets and 232 indicators that are based on the three pillars of sustainable development: economic, social, and environmental. Substantial challenges remain in obtaining data of the required quality, especially in developing countries, given the often limited resources available. One promising and innovative way of addressing this issue of data availability is to use Earth observation (EO). This paper presents the results of research to develop a novel analytical framework for assessing the potential of EO approaches to populate the SDG indicators. We present a Maturity Matrix Framework and apply it to all of the 232 SDG indicators. The results demonstrate that although the applicability of EO‐derived data do vary between the Sustainable Development Goal indicators, overall, EO has an important contribution to make towards populating a wide diversity of the Sustainable Development Goals indicators.

Suggested Citation

  • Ana Andries & Stephen Morse & Richard Murphy & Jim Lynch & Emma Woolliams & John Fonweban, 2019. "Translation of Earth observation data into sustainable development indicators: An analytical framework," Sustainable Development, John Wiley & Sons, Ltd., vol. 27(3), pages 366-376, May.
    • Handle: RePEc:wly:sustdv:v:27:y:2019:i:3:p:366-376
    DOI: 10.1002/sd.1908
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    Cited by:

    1. Nannan Wang & Minxun Ma, 2021. "Public–private partnership as a tool for sustainable development – What literatures say?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 243-258, January.
    2. Xuantong Wang & James Hopeward & Ilcheong Yi & Mark W. McElroy & Paul C. Sutton, 2022. "Supporting the Sustainable Development Goals: A context sensitive indicator for sustainable use of water at the facility level," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 1184-1199, October.
    3. Juan A. García-Esparza & Javier Pardo & Pablo Altaba & Mario Alberich, 2023. "Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(2), pages 323-337, April.
    4. Walter Leal Filho & Peter Yang & João Henrique Paulino Pires Eustachio & Anabela Marisa Azul & Joshua C. Gellers & Agata Gielczyk & Maria Alzira Pimenta Dinis & Valerija Kozlova, 2023. "Deploying digitalisation and artificial intelligence in sustainable development research," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 4957-4988, June.
    5. Keith R. Skene, 2021. "No goal is an island: the implications of systems theory for the Sustainable Development Goals," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 9993-10012, July.
    6. Shivam Gupta & Mahsa Motlagh & Jakob Rhyner, 2020. "The Digitalization Sustainability Matrix: A Participatory Research Tool for Investigating Digitainability," Sustainability, MDPI, vol. 12(21), pages 1-27, November.

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