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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Full text (MCPHS users only) |
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| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken, NJ :
John Wiley & Sons, Inc.,
2018
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| Series: | Operations research and management science
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
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| 100 | 1 | |a Sueyoshi, T. |q (Toshiyuki), |d 1954- |e author. | |
| 245 | 1 | 0 | |a Environmental assessment on energy and sustainability by data envelopment analysis / |c Toshiyuki Sueyoshi, Mika Goto. |
| 264 | 1 | |a Hoboken, NJ : |b John Wiley & Sons, Inc., |c 2018. | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource | ||
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| 490 | 0 | |a Operations research and management science | |
| 504 | |a Includes bibliographical references and index. | ||
| 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 | 8 | |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 | 8 | |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. | |
| 505 | 8 | |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 | 8 | |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. | |
| 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. 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. 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, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on March 15, 2018). | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Data envelopment analysis. | |
| 650 | 0 | |a Environmental impact analysis. | |
| 650 | 0 | |a Factory and trade waste |x Management. | |
| 650 | 7 | |a environmental impact statements. |2 aat | |
| 700 | 1 | |a Goto, Mika, |e author. | |
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