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|a 9783658332464
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|a (MiAaPQ)EBC6531639
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|a HC79.E5
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|a Lemke, Claudia.
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|a Accounting and Statistical Analyses for Sustainable Development :
|b Multiple Perspectives and Information-Theoretic Complexity Reduction.
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|a 1st ed.
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|a Wiesbaden :
|b Springer Fachmedien Wiesbaden GmbH,
|c 2021.
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|c ©2021.
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|a 1 online resource (288 pages)
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|a text
|b txt
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|2 rdamedia
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|a online resource
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|2 rdacarrier
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|a Sustainable Management, Wertschöpfung und Effizienz Series
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|a Intro -- Preface -- Foreword -- Acknowledgement -- Table of contents -- List of abbreviations -- List of figures -- List of tables -- List of equations -- List of symbols -- Chapter 1 Introduction -- 1.1 Background and motivation -- 1.2 Research question and aim of the dissertation -- 1.3 Procedure -- Chapter 2 Conceptual framework of sustainable development -- 2.1 Definition of sustainable development and sustainability -- 2.2 The three contentual domains of sustainable development -- 2.2.1 Environmental protection -- 2.2.2 Social development -- 2.2.3 Economic prosperity -- 2.2.4 Integration of the three contentual domains -- 2.3 Stakeholders and change agents of sustainable development -- 2.3.1 The multilevel perspective -- 2.3.2 Corporate sustainability -- 2.3.3 Political goal setting: The United Nations's (UN) Sustainable Development Goals (SDGs) -- 2.3.4 Sustainability science -- 2.4 Summary -- Chapter 3 Measuring and assessing contributions to sustainable development -- 3.1 Principles of sustainable development measurement and assessment methods -- 3.2 Overview of quantitative sustainable development assessment methods -- 3.3 Sustainable development indicators -- 3.3.1 Corporate indicator frameworks -- 3.3.2 Meso-level indices -- 3.3.3 Macro-level indices -- 3.4 Summary -- Chapter 4 Methodology -- 4.1 Overview of sustainable development indices' calculation steps and methodological requirements -- 4.2 Methodological evaluation of sustainable development indices -- 4.3 Methodology of the Multilevel Sustainable Development Index (MLSDI) -- 4.3.1 Collection of sustainable development key figures -- 4.3.2 Preparation of sustainable development key figures -- 4.3.2.1 Meso-level transformation to macro-economic categories -- 4.3.2.2 Macro-level transformation of statistical classifications -- 4.3.3 Imputation of missing values.
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|a 4.3.3.1 Characterisation of missing values -- 4.3.3.2 Single time series imputation: Various methods depending on the missing data pattern -- 4.3.3.3 Multiple panel data imputation: Amelia II algorithm -- 4.3.3.4 Statistical tests of model assumptions -- 4.3.4 Standardisation to sustainable development key indicators -- 4.3.5 Outlier detection and treatment -- 4.3.5.1 Characterisation of outliers -- 4.3.5.2 Univariate Interquartile Range (IQR) method -- 4.3.6 Scaling -- 4.3.6.1 Characterisation of scales -- 4.3.6.2 Rescaling between ten and 100 -- 4.3.7 Weighting -- 4.3.7.1 Overview of weighting methods -- 4.3.7.2 Multivariate statistical analysis: Principal Component Analysis (PCA) -- 4.3.7.3 Multivariate statistical analysis: Partial Triadic Analysis (PTA) -- 4.3.7.4 Information theory: Maximum Relevance Minimum Redundancy Backward (MRMRB) algorithm -- 4.3.7.5 Statistical tests of model assumptions -- 4.3.8 Aggregation -- 4.3.9 Sensitivity analyses -- 4.4 Summary and interim conclusion -- Chapter 5 Empirical findings -- 5.1 Data base, objects of investigation, and time periods -- 5.2 Sustainable development key figures -- 5.2.1 Collection and preparation of sustainable development key figures -- 5.2.2 Imputation of missing values -- 5.3 Sustainable development key indicators -- 5.3.1 Alignment of the Global Reporting Initiative (GRI) and the Sustainable Development Goal (SDG) disclosures -- 5.3.1.1 Environmental sustainable development key indicators -- 5.3.1.2 Social sustainable development key indicators -- 5.3.1.3 Economic sustainable development key indicators -- 5.3.2 Summary statistics of the sustainable development growth indicators -- 5.3.3 Outlier detection and treatment -- 5.3.4 Empirical findings of the cleaned and rescaled sustainable development key indicators -- 5.3.4.1 Summary statistics.
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|a 5.3.4.2 Comparative analysis of the selected branches -- 5.4 Weighting -- 5.4.1 The Principal Component (PC) family's eigenvalues and explained cumulative variances -- 5.4.2 The Maximum Relevance Minimum Redundancy Backward (MRMRB) algorithm's discretisation and backward elimination -- 5.4.3 Comparative analysis of weights -- 5.4.4 Statistical tests of the Principal Component (PC) family -- 5.5 Empirical findings of the four composite sustainable development measures -- 5.5.1 Summary statistics -- 5.5.2 Comparative analysis of the selected branches -- 5.6 Sensitivity analyses -- 5.7 Summary -- Chapter 6 Discussion and conclusion -- 6.1 Implications for research -- 6.2 Implications for practice -- 6.3 Limitations and future outlook -- 6.4 Summary and conclusion -- Appendix -- A.1 Statistical classification scheme of economic activities in the European Union (EU) -- A.2 German health economy's statistical delimitation -- A.3 Statistical tests of sustainable development key figures -- A.4 Summary statistics of the sustainable development key indicators -- A.5 Outlier thresholds of the sustainable development key indicators -- A.6 Normality tests of z-score scaled sustainable development key indicators -- A.7 Sensitivities by the four composite sustainable development measures -- References.
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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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|i Print version:
|a Lemke, Claudia
|t Accounting and Statistical Analyses for Sustainable Development
|d Wiesbaden : Springer Fachmedien Wiesbaden GmbH,c2021
|z 9783658332457
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|a ProQuest (Firm)
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|a Sustainable Management, Wertschöpfung und Effizienz Series
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6531639
|z Click to View
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