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研究生：	布里托 Bretholt, Abraham
論文名稱：	Evolving the Latent Variable Model for the Reduction of Undesirable Outputs as an Optimal Environmental Data Envelopment Technology Evolving the Latent Variable Model for the Reduction of Undesirable Outputs as an Optimal Environmental Data Envelopment Technology Envelopment Technology
指導教授：	潘浙楠 Pan, Jack
學位類別：	博士 Doctor
系所名稱：	管理學院 - 國際經營管理研究所 Institute of International Management
論文出版年：	2012
畢業學年度：	101
語文別：	英文
論文頁數：	122
外文關鍵詞：	DEA, Scale efficiency, Multi-criteria optimization, Slacks based model, Malmquist index, Decomposition, Undesirable outputs, Externalities, Resource management, Disposability, Total factor productivity
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This dissertation tests several nonparametric DEA models for their ability to accurately decompose CO2 Emissions change using a Malmquist decomposition framework. The Latent Variable Model exhibited the best results against previous studies from the literature. The new Latent Variable radial input-oriented technology, introduced here as an environmental DEA, simultaneously reduces inputs and undesirable outputs by employing Input Disposability rather than using the Weak Output Disposability assumptions of previous studies. Empirical testing shows that the new Latent Variable Model is closely associated with the Slacks Based Model. Hence, a suitable proof was constructed to show that the Latent Variable radial model is, in fact, equivalent to its additive Slacks Based counterpart in terms of Pareto-Koopmans„ Efficiency. This eliminates the need for a two phase DEA method which is widely used to determine optimal efficiency. That is, the single step Latent Variable radial model independently eliminates slacks and congestion within a production oriented DEA problem and returns an optimal solution.
Further to this discovery, the Latent Variable technology can be extended to simultaneously reduce both inputs or outputs depending on their „desirability‟ within a system space as a whole. Burning fossil fuels, for example, is „undesirable‟ within the context of the environment, but is conventionally considered as a „desirable‟ input.
Under the General LV model, hydrocarbon use can be reduced as an undesirable input while other green inputs can be simultaneously increased as substitutes. Similarly, the Generalized Latent Variable Model (GLVM) can greatly enhance the use of DEA: It can be applied to any causal system of inputs and outputs using appropriate Weak Disposability as its key attribute, thus optimizing efficiency comparisons. The General LVM employs a partitioning scheme of seven mutually exclusive sets based on their interaction within a system space. The purpose of such partitions is to classify inputs and outputs in terms of their impact on a system: either positive, negative, neutral or ambient. Previous analysis has been limited to only a single target efficiency partition such as a set of minimized inputs or maximized outputs, and generally these exclude externalities. In the GLVM, a Latent Variable is placed on each partition to track the efficiency impact of each set upon the system as a whole. Thus the Total Factor Productivity and its interdependencies within the system space are determined by a series of seven Latent Variable efficiency ratings, not just one as in traditional DEA.
Thus the GLVM implies multi-criteria benchmarking while completely characterizing the internal efficiencies of each DMU relative to its peers. Thus, the General Latent Variable Model not only offers a new level of inclusiveness for management and production studies, but it can potentially serve as a basis for quantitative efficiency analysis within any interdependent system of causally related variables in the social or environmental sciences.

TABLE OF CONTENTS
DEDICATION	A
TABLE OF CONTENTS	I
LIST OF TABLES	V
LIST OF FIGURES	VI
ABSTRACT	1
CHAPTER ONE STANDARD INTRODUCTION	3
1 The Motive for a Consistent, Generalized DEA.	3
2 A Data Envelopment Analysis Overview.	4
3 The DEA Framework.	7
4 Main Roadblocks to a Consistent, Generalized DEA.	9
5 Research Purpose and Questions.	11
6 Research Objectives.	12
7 Research Scope and Flow.	14
8 The Structure of this Study.	17
CHAPTER TWO SUPPORTING LITERATURE	18
1 The Need for Energy and Environmental (E&E) DEA.	18
2 Weak Disposability and Undesirable Outputs	19
3 Efficiency Measures, Minimizing U, and Malmquist.	20
4 Research Themes in E&E.	21
5 Methodology and Model Choice.	22
6 Gaps in the Research	22
CHAPTER THREE DEA METHODOLOGY: USED AND DEVELOPED	24
1 Technology Set and Aggregation, Data Conventions, Invariance.	24
2 DEA Compared to the Econometrics of Regression.	26
3 Radial Envelopment Models.	27
3.1 The CCR Model.	27
3.2 The BCC Model.	28
4 Standard Model Specification.	29
4.1 Are Outputs Held Constant or Reductions Proportional?	30
5 Standard Environmental Model Specification.	33
5.1 “Treating Outputs as Inputs.”	35
6 Weak Disposability Assumptions.	36
6.1 The Null Joint and Proportionality Hypotheses.	37
6.2 The Disproportionality Hypothesis.	39
7 The Latent Variable Uses Weak Disposability.	41
7.1 The Preferred Environmental Specification: Step by Step.	45
7.2 The Reduction Plane.	48
8 The Slacks Based Models.	48
8.1 The Reverse Slacks Based Model and Optimality Testing.	51
9 MPSS: Most Productive Scale Size.	53
10 Scale Efficiency, Congestion and Frontier Validity.	53
11 Environmental Estimation Models.	55
11.1 Pure Environmental Technologies.	55
11.2 The Mixed Environmental Index.	57
12 The Malmquist Productivity Index.	58
12.1 Structure of the Malmquist Index.	58
12.2 CO2 Decomposition Methods in DEA.	60
13 Aggregated Data Structures.	61
14 Three Preliminary Data Tests.	62
14.1 Sufficient Degrees of Freedom.	62
14.2 Sufficient Correlation.	62
14.3 Regression Analysis for Causality.	62
15 The Dispersion Index.	63
CHAPTER FOUR RESEARCH DESIGN AND EMPIRICAL RESULTS	65
1 Research Design & Results.	65
2. Precedent Study on Pure Environmental Indices.	65
3 Application Study One.	67
3.1. Study Overview.	67
3.2 Data Set.	68
3.3 The Disjoint CCR Models.	69
3.4 Study Anomalies Examined.	70
3.5 Integrating the Disjoint Model.	71
4 Application Study Two: Decomposition of OECD Using the LVM.	75
4.1 Dataset with Observations on Aggregated Data.	75
4.2 Decomposition Results.	76
4.3 Factor Reversals.	78
4.4 Statistical Analysis of the Models.	80
4.5 Scale Efficiency Comparisons for Simultaneous Reductions.	83
4.6 The Reduction Plane.	85
5 Other Reduction Plane Examples	88
6 DIR2 Proposed as a New Determination Coefficient	92
7 Testing Optimality with Reverse SBM	92
8 Conclusion of Empirical Results	95
CHAPTER FIVE RESEARCH IMPLICATIONS AND RAMIFICATIONS	97
1 Dissertation’s Primary Contribution.	97
2 Derivation and Proof of the Latent Variable Model.	98
3 Generalization of the Latent Variable Model.	101
3.1 The GLVM Technology Set.	103
3.2 General Disproportionality and the Nemesis of Proportionality.	105
3.3 The Preferred Model: GLVM.	106
CHAPTER SIX   CONCLUSIONS, OBJECTIVES ATTAINED AND THE FUTURE OF THE GLVM	110
1 Research Objectives Obtained.	110
2 Research Conclusion.	111
3 Future Studies Using the Latent Variable Model.	113
4 Limitations of this Study.	116
REFERENCES	117
APPENDICES	122
Appendix 1: Table of Abbreviations.	122
Appendix 2: Omega Publication.	122


                                    

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校內：2013-02-01公開
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