توضیحاتی در مورد کتاب Accounting and Statistical Analyses for Sustainable Development: Multiple Perspectives and Information-Theoretic Complexity Reduction
نام کتاب : Accounting and Statistical Analyses for Sustainable Development: Multiple Perspectives and Information-Theoretic Complexity Reduction
ویرایش : 1
عنوان ترجمه شده به فارسی : حسابداری و تحلیل های آماری برای توسعه پایدار: دیدگاه های چندگانه و کاهش پیچیدگی نظری اطلاعات
سری : Sustainable Management, Wertschöpfung und Effizienz
نویسندگان : Claudia Lemke
ناشر : Springer Gabler
سال نشر : 2021
تعداد صفحات : 288
ISBN (شابک) : 365833245X , 9783658332457
زبان کتاب : English
فرمت کتاب : pdf
حجم کتاب : 7 مگابایت
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توضیحاتی در مورد کتاب :
در این انتشارات دسترسی آزاد، کلودیا لمکه یک شاخص توسعه پایدار چند سطحی جامع (MLSDI) را توسعه میدهد که با انجام تحقیقات روششناختی و تجربی برای اشیاء خرد، مزو و کلان قابل استفاده است. مقایسه چند سطحی بسیار مهم است زیرا اهداف توسعه پایدار (SDGs) در سطح کلان تنها در صورتی قابل دستیابی هستند که اشیاء خرد و مزو مشارکت داشته باشند. نویسنده نشان میدهد که یک الگوریتم جدید اطلاعاتی-نظری از روشهای وزندهی آماری چند متغیره مانند تجزیه و تحلیل مؤلفههای اصلی (PCA) بهتر عمل میکند. با غلبه بر کاستی های روش شناختی بیشتر شاخص های توسعه پایدار قبلی، MLSDI از تصمیم گیری های مدیریتی و سیاسی گمراه شده جلوگیری می کند.
فهرست مطالب :
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
توضیحاتی در مورد کتاب به زبان اصلی :
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.