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Data-driven multilayer complex networks of sustainable development goals.

Viktor Sebestyén1, Miklós Bulla2, Ákos Rédey1

  • 1Institute of Environmental Engineering University of Pannonia, Veszprém, Hungary.

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Summary

This study models Sustainable Development Goals (SDGs) using multilayer networks and World Bank data. This approach enables country-specific sustainability assessments and improves strategic environmental evaluations.

Keywords:
Data driven modellingEvidence-based decision makingNetwork analysisSpatial analysisSustainabilitySustainable development goals

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Area of Science:

  • Environmental Science
  • Network Science
  • Data Science

Background:

  • The Sustainable Development Goals (SDGs) require robust models to understand interconnections.
  • Existing analyses may lack detailed, spatially resolved assessments.
  • Long-term datasets are crucial for evaluating progress and interdependencies.

Purpose of the Study:

  • To present a multilayer network model for SDGs, targets, and indicators.
  • To link indicators with The World Bank's long-term datasets.
  • To enable country-specific sustainability assessments using spatial data distributions.

Main Methods:

  • Formulation of multilayer networks to represent SDG interconnections.
  • Correlation-based linking of sustainable development indicators.
  • Utilizing The World Bank's 2018 long-term datasets.
  • Analysis of spatial distribution of time series data.

Main Results:

  • A network model detailing the relationships among SDGs, targets, and indicators.
  • Integration of indicator data with available long-term datasets.
  • Capability for country-specific sustainability assessments.
  • Foundation for improved strategic environmental assessments.

Conclusions:

  • Multilayer network models offer a powerful framework for analyzing SDG interdependencies.
  • The developed datasets facilitate data-driven sustainability assessments.
  • This approach enhances the quality and specificity of strategic environmental evaluations.