Environmental assessment on energy and sustainability by data envelopment analysis

Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a po...

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Bibliographic Details
Main Authors: Sueyoshi, T., Goto, Mika (Author)
Format: eBook
Language:English
Published: Hoboken, NJ John Wiley & Sons, Inc. 2018
Series:Operations research and management science
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
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245 0 0 |a Environmental assessment on energy and sustainability by data envelopment analysis  |c Toshiyuki Sueyoshi, Mika Goto 
260 |a Hoboken, NJ  |b John Wiley & Sons, Inc.  |c 2018 
300 |a 1 online resource 
505 0 |a Includes bibliographical references and index 
505 0 |a 6.6.1 Aggregation6.6.2 Frontier Shift Measurability; 6.6.3 Invariance to Alternate Optima; 6.6.4 Formal Definitions on Other Desirable Properties; 6.6.5 Efficiency Requirement; 6.6.6 Homogeneity; 6.6.7 Strict Monotonicity; 6.6.8 Unique Projection for Efficiency Comparison; 6.6.9 Unit Invariance; 6.6.10 Translation Invariance; 6.7 SUMMARY; APPENDIX; Proof of Proposition 6.1; Proof of Proposition 6.6; Proof of Proposition 6.7; Proof of Proposition 6.8; Proof of Proposition 6.10; Proof of Proposition 6.11; CHAPTER 7 STRONG COMPLEMENTARY SLACKNESS CONDITIONS; 7.1 INTRODUCTION. 
505 0 |a 3.1 INTRODUCTION3.2 ORIGIN OFÂ L1 REGRESSION; 3.3 ORIGIN OFÂ GOAL PROGRAMMING; 3.4 ANALYTICAL PROPERTIES OFÂ L1 REGRESSION; 3.5 FROM L1 REGRESSION TOÂ L2 REGRESSION ANDÂ FRONTIERÂ ANALYSIS; 3.5.1 L2 Regression; 3.5.2 L1-Based Frontier Analyses; 3.6 ORIGIN OFÂ DEA; 3.7 RELATIONSHIPS BETWEEN GP AND DEA; 3.8 HISTORICAL PROGRESS FROMÂ L1 REGRESSION TOÂ DEA; 3.9 SUMMARY; CHAPTER 4 RADIAL MEASUREMENT; 4.1 INTRODUCTION; 4.2 RADIAL MODELS: INPUT-ORIENTED; 4.2.1 Input-Oriented RM(v) under Variable RTS; 4.2.2 Underlying Concept; 4.2.3 Input-Oriented RM(c) under Constant RTS. 
505 0 |a Intro; TITLE PAGE; COPYRIGHT PAGE; CONTENTS; PREFACE; SECTION I DATA ENVELOPMENT ANALYSIS (DEA); CHAPTER 1 GENERAL DESCRIPTION; 1.1 INTRODUCTION; 1.2 STRUCTURE; 1.3 CONTRIBUTIONS IN SECTIONS I AND II; 1.4 ABBREVIATIONS AND NOMENCLATURE; 1.4.1 Abbreviations Used in This Book; 1.4.2 Nomenclature Used in This Book; 1.4.3 Mathematical Concerns; 1.5 SUMMARY; CHAPTER 2 OVERVIEW; 2.1 INTRODUCTION; 2.2 WHAT IS DEA?; 2.3 REMARKS; 2.4 REFORMULATION FROM FRACTIONAL PROGRAMMING TO LINEAR PROGRAMMING; 2.5 REFERENCE SET; 2.6 EXAMPLE FOR COMPUTATIONAL DESCRIPTION; 2.7 SUMMARY; CHAPTER 3 HISTORY. 
505 0 |a 4.6.3 Illustrative Example4.7 SUMMARY; CHAPTER 5 NON-RADIAL MEASUREMENT; 5.1 INTRODUCTION; 5.2 CHARACTERIZATION ANDÂ CLASSIFICATION ONÂ DMUs; 5.3 RUSSELL MEASURE; 5.4 ADDITIVE MODEL; 5.5 RANGE-ADJUSTED MEASURE; 5.6 SLACK-ADJUSTED RADIAL MEASURE; 5.7 SLACK-BASED MEASURE; 5.8 METHODOLOGICAL COMPARISON: ANÂ ILLUSTRATIVE EXAMPLE; 5.9 SUMMARY; CHAPTER 6 DESIRABLE PROPERTIES; 6.1 INTRODUCTION; 6.2 CRITERIA FORÂ OE; 6.3 SUPPLEMENTARY DISCUSSION; 6.4 PREVIOUS STUDIES ONÂ DESIRABLE PROPERTIES; 6.5 STANDARD FORMULATION FOR RADIAL AND NON-RADIAL MODELS; 6.6 DESIRABLE PROPERTIES FORÂ DEA MODELS. 
505 0 |a 4.3 RADIAL MODELS: DESIRABLE OUTPUT-ORIENTED4.3.1 Desirable Output-oriented RM(v) under Variable RTS; 4.3.2 Desirable Output-oriented RM(c) under Constant RTS; 4.4 COMPARISON BETWEEN RADIAL MODELS; 4.4.1 Comparison between Input-Oriented and Desirable Output-Oriented Radial Models; 4.4.2 Hybrid Radial Model: Modification; 4.5 MULTIPLIER RESTRICTION AND CROSS-REFERENCE APPROACHES; 4.5.1 Multiplier Restriction Methods; 4.5.2 Cone Ratio Method; 4.5.3 Cross-reference Method; 4.6 COST ANALYSIS; 4.6.1 Cost Efficiency Measures; 4.6.2 Type of Efficiency Measures in Production and Cost Analyses 
653 |a Déchets industriels / Gestion 
653 |a Environnement / Études d'impact 
653 |a environmental impact statements / aat 
653 |a Data envelopment analysis / http://id.loc.gov/authorities/subjects/sh94003984 
653 |a BUSINESS & ECONOMICS / Real Estate / General / bisacsh 
653 |a Factory and trade waste / Management 
653 |a Data envelopment analysis / fast 
653 |a Environmental impact analysis / http://id.loc.gov/authorities/subjects/sh85044178 
653 |a Data envelopment analysis 
653 |a Factory and trade waste / Management / fast 
653 |a Environmental impact analysis / fast 
700 1 |a Goto, Mika  |e author 
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490 0 |a Operations research and management science 
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776 |z 9781118979341 
776 |z 1118979338 
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776 |z 9781118979259 
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520 |a Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries-the world's largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development.  
520 |a The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions.  
520 |a In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors.-Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth -Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA -Explores new statistical modeling strategies and explores their economic and business implications -Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more -Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is must-reading for researchers, academics, graduate students,  
520 |a and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution