Accounting and Statistical Analyses for Sustainable Development: Multiple Perspectives and Information-Theoretic Complexity Reduction
Person/Institution:
Publisher:
Springer Fachmedien Wiesbaden
Imprint: Springer Gabler
Place of publication:
Wiesbaden
Date:
2021.
Extent, illustration, format:
1 Online-Ressource(CCLXIII, 31 p. 52 illus.)
Language:
Englisch
Providing institution:
Additional information
Abstract:
Introduction -- Conceptual framework of sustainable development -- Measuring and assessing contributions to sustainable development -- Methodology -- Empirical fndings -- Discussion and conclusion.
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.
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.
Object text:
by Claudia Lemke
Open Access
Open Access
Access and usage options
Contact
Universität Erfurt
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Administrative details
Created:
2023-04-13
Last changed:
2021-10-18
Added to portal:
2023-04-13
Feedback
Our data sets are in constant development. If you have additional information about this object or discovered an error, please write to us. Information on privacy policy