Accounting and Statistical Analyses for Sustainable Development : Multiple Perspectives and Information-Theoretic Complexity Reduction

• Autore: Lemke Claudia
• Pubblicazione: Springer Nature, 2021; Wiesbaden : , : Springer Fachmedien Wiesbaden GmbH, , 2021; ©2021
• Descrizione fisica: 1 online resource (288 pages)
• Soggetto topico: Environmental economics
• Soggetto non controllato: Environmental Economics; Sustainability; Sustainable Development Goals (SDGs); Composite indicators; Multilevel perspective; Principal component analysis; Information theory; Open Access
• Nota di contenuto: 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. ; 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. ; 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Accounting and Statistical Analyses for Sustainable Development
• ISBN: 3-658-33246-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910473452403321
• Lo trovi qui: Univ. Federico II
• Serie: Sustainable Management, Wertschöpfung und Effizienz