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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda.

David Requejo-Castro1, Ricard Giné-Garriga2, Agustí Pérez-Foguet1

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The Science of the Total Environment
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Summary

This study introduces a Bayesian network (BN) approach to map Sustainable Development Goals (SDGs) interlinkages. The data-driven method effectively identifies complex relationships, particularly for SDG 6 (water and sanitation), enhancing global development monitoring.

Keywords:
Bayesian networksData-drivenInterlinkagesSDG 6Sustainable Development Goals

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Area of Science:

  • Environmental Science
  • Data Science
  • Sustainable Development Studies

Background:

  • The Sustainable Development Goals (SDGs) are recognized as interconnected and indivisible.
  • Conventional indicator-based SDG monitoring frameworks often overlook the complex links and interdependencies between goals and targets.
  • A need exists for advanced analytical approaches to capture and interpret these interlinkages effectively.

Purpose of the Study:

  • To propose and validate a data-driven Bayesian network (BN) approach for identifying and interpreting interlinkages among the SDGs.
  • To specifically analyze the interlinkages of SDG 6 (water and sanitation) within the broader 2030 Agenda.
  • To demonstrate the utility of the BN approach in uncovering and understanding complex SDG relationships.

Main Methods:

  • Utilized a data-driven Bayesian network (BN) model.
  • Employed global SDG data for 179 countries, covering 16 goals, 28 targets, and 44 indicators.
  • Validated the BN approach by assessing the robustness of indicator relationships across different country sample sizes and comparing results with UN-Water studies.

Main Results:

  • The Bayesian network approach robustly identifies significant interlinkages between SDG indicators.
  • The analysis revealed previously undocumented interlinkages, expanding upon existing literature.
  • The findings demonstrate the coherency and added value of the BN method in mapping SDG connections, with a specific focus on SDG 6.

Conclusions:

  • The proposed data-driven Bayesian network approach is a valuable tool for analyzing and interpreting the complex interdependencies within the Sustainable Development Goals.
  • This method enhances the understanding of SDG linkages, offering insights beyond traditional indicator-based frameworks.
  • The approach provides a robust foundation for more integrated SDG monitoring and policy development, particularly concerning water and sanitation (SDG 6).