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Accounting and Statistical Analyses for Sustainable Development: Multiple Perspectives and Information-Theoretic Complexity Reduction (Sustainable Management, Wertschöpfung und Effizienz) - Softcover

 
9783658332457: Accounting and Statistical Analyses for Sustainable Development: Multiple Perspectives and Information-Theoretic Complexity Reduction (Sustainable Management, Wertschöpfung und Effizienz)

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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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About the Author

Dr. Claudia Lemke holds a doctorate degree in Sustainability Accounting and Management Control from the Technische Universität Berlin. She worked several years as a Senior Research Associate in the field of sustainability science and corporate sustainability. She is now employed as a Supply Chain Sustainability Manager and is responsible for driving sustainability in the FMCG industry.

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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.

About the author

Dr. Claudia Lemke holds a doctorate degree in Sustainability Accounting and Management Control from the Technische Universität Berlin. She worked several years as a Senior Research Associate in the field of sustainability science and corporate sustainability. She is now employed as a Supply Chain Sustainability Manager and is responsible for driving sustainability in the FMCG industry.


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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783658332457

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 296 pp. Englisch. Seller Inventory # 9783658332457

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