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 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.

Auteur

TOSHIYUKI SUEYOSHI, PhD, is a full professor at New Mexico Institute of Mining and Technology, Soccorro, New Mexico, USA. Dr. Sueyoshi has published more than 300 articles in well-known international (SCI/SSCI listed) journals. MIKA GOTO, PhD, is a full professor at Tokyo Institute of Technology, Tokyo, Japan. Dr. Goto has published more than 100 articles in well-known international (SCI/SSCI listed) journals.

Contenu
PREFACE xv

SECTION I DATA ENVELOPMENT ANALYSIS (DEA) 1

1 General Description 3

1.1 Introduction 3

1.2 Structure 4

1.3 Contributions in Sections I and II 10

1.4 Abbreviations and Nomenclature 13

1.4.1 Abbreviations Used in This Book 13

1.4.2 Nomenclature Used in This Book 18

1.4.3 Mathematical Concerns 23

1.5 Summary 24

2 Overview 25

2.1 Introduction 25

2.2 What is DEA? 26

2.3 Remarks 33

2.4 Reformulation from Fractional Programming to Linear Programming 35

2.5 Reference Set 38

2.6 Example for Computational Description 39

2.7 Summary 44

3 History 45

3.1 Introduction 45

3.2 O rigin of L1 Regression 46

3.3 O rigin of Goal Programming 50

3.4 Analytical Properties of L1 Regression 53

3.5 From L1 Regression to L2 Regression and Frontier Analysis 55

3.5.1 L2 Regression 55

3.5.2 L1?-based Frontier Analyses 55

3.6 O rigin of DEA 59

3.7 Relationships between GP and DEA 61

3.8 Historical Progress From L1 Regression to DEA 64

3.9 Summary 64

4 Radial Measurement 67

4.1 Introduction 67

4.2 Radial Models: Input?-Oriented 70

4.2.1 Input?-Oriented RM(v) under Variable RTS 70

4.2.2 Underlying Concept 72

4.2.3 Input?-Oriented RM(c) under Constant RTS 74

4.3 Radial Models: Desirable Output?-Oriented 75

4.3.1 Desirable Output?-oriented RM(v) under Variable RTS 75

4.3.2 Desirable Output?-oriented RM(c) under Constant RTS 77

4.4 Comparison Between Radial Models 79

4.4.1 Comparison Between Input?-Oriented and Desirable OutputOriented Radial Models 79

4.4.2 Hybrid Radial Model: Modification 81

4.5 Multiplier Restriction and Cross?-Reference Approaches 82

4.5.1 Multiplier Restriction Methods 82

4.5.2 Cone Ratio Method 84

4.5.3 Cross?-reference Method 86

4.6 Cost Analysis 88

4.6.1 Cost Efficiency Measures 88

4.6.2 Type of Efficiency Measures in Production and Cost Analyses 89

4.6.3 Illustrative Example 91

4.7 Summary 94

5 Non?-Radial Measurement 95

5.1 Introduction 95

5.2 Characterization and Classification on DMUs 97

5.3 Russell Measure 99

5.4 Additive Model 103

5.5 Range?-Adjusted Measure 105

5.6 Slack?-Adjusted Radial Measure 106

5.7 Slack?-Based Measure 108

5.8 Methodological Comparison: An Illustrative Example 111

5.9 Summary 113

6 Desirable Properties 115

6.1 Introduction 115

6.2 Criteria For OE 117

6.3 Supplementary Discussion 119

6.4 Previous Studies on Desirable Properties 120

6.5 Standard Formulation for Radial and Non?-Radial Models 122

6.6 Desirable Properties for DEA Models 126

6.6.1 Aggregation 126

6.6.2 Frontier Shift Measurability 128

6.6.3 Invariance to Alternate Optima 131

6.6.4 Formal Definitions on Other Desirable Properties 132

6.6.5 Efficiency Requirement 133

6.6.6 Homogeneity 134

6.6.7 Strict Monotonicity 136

6.6.8 Unique Projection for Efficiency Comparison 137

6.6.9 Unit Invariance 138

6.6.10 Translation Invariance 139

6.7 Summary 140

6.A Appendix 142

6.A.1 Proof of Proposition 6.1 142

6.A.2 Proof of Proposition 6.6 143

6.A.3 Proof of Proposition 6.7 145

6.A.4 Proof of Proposition 6.8 146

6.A.5 Proof of Proposition 6.10 147

6.A.6 Proof of Proposition 6.11 147

7 Strong Complementary Slackness Conditions 149

7.1 Introduction 149

7.2 Combination Between Primal and Dual Models for SCSCs 150

7.3 Three Illustrative Examples 154

7.3.1 First Example 155 7.3.2 Second Examp...