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Accounting and Statistical Analyses for Sustainable Development

By: Material type: TextTextSeries: Sustainable Management, Wertschöpfung und EffizienzSpringer Nature 2021Description: 1 online resource (31 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
Other title:
  • Multiple Perspectives and Information-Theoretic Complexity Reduction
Subject(s): Online resources: Summary: In this Open Access publication Claudia Lemke develops a comprehensive Multi-Level Sustainable Development Index (MLSDI) that is applicable to micro, meso, and macro objects by conducting methodological and empirical research. Multi-level comparability is crucial because the Sustainable Development Goals (SDGs) at macro level can only be achieved if micro and meso objects contribute. The author shows that a novel information-theoretic algorithm outperforms established multivariate statistical weighting methods such as the principal component analysis (PCA). Overcoming further methodological shortcomings of previous sustainable development indices, the MLSDI avoids misled managerial and political decision making.
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In this Open Access publication Claudia Lemke develops a comprehensive Multi-Level Sustainable Development Index (MLSDI) that is applicable to micro, meso, and macro objects by conducting methodological and empirical research. Multi-level comparability is crucial because the Sustainable Development Goals (SDGs) at macro level can only be achieved if micro and meso objects contribute. The author shows that a novel information-theoretic algorithm outperforms established multivariate statistical weighting methods such as the principal component analysis (PCA). Overcoming further methodological shortcomings of previous sustainable development indices, the MLSDI avoids misled managerial and political decision making.

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