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Measuring global interlinkages between SDGs applying linear dimensionality reduction tools

In: Interlinkages between the Sustainable Development Goals

Author

Listed:
  • Jean-Pierre Cling
  • Clément Delecourt

Abstract

We measure interlinkages between SDGs, applying linear dimensionality reduction techniques to a dataset derived from the UN Global SDG Database. This study is the first to use this methodology for a world-level analysis. Multiple factor analysis finds that SDGs whose indicators mostly measure human (that is, economic and social) development explain the performances of most countries. Two other components of sustainable development contribute to the variance of the dataset: the environment and governance. At the global level, we observe strong synergies overall and no trade-offs between SDGs. Hierarchical cluster analysis designs three homogeneous country clusters that are empirically differentiated from each other according to their level of GNI per capita. Nonetheless, relations between development components are found to be similar across groupings. This validates the opportunity for global analysis of interlinkages between SDGs and the objective of universality at the heart of their conception.

Suggested Citation

  • Jean-Pierre Cling & Clément Delecourt, 2023. "Measuring global interlinkages between SDGs applying linear dimensionality reduction tools," Chapters, in: Ranjula Bali Swain & Yongyi Min (ed.), Interlinkages between the Sustainable Development Goals, chapter 8, pages 163-188, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21646_8
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    File URL: https://www.elgaronline.com/doi/10.4337/9781803924946.00013
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