Mirakyan / De Guio Three Domain Modelling and Uncertainty Analysis

Applications in Long Range Infrastructure Planning
1. Auflage 2015
ISBN: 978-3-319-19572-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 222 Seiten

Reihe: Energy Systems

This book examines in detail the planning and modelling of local infrastructure like energy systems, including the complexities resulting from various uncertainties. Readers will discover the individual steps involved in infrastructure planning in cities and territories, as well as the primary requirements and supporting quality factors. Further topics covered concern the field of uncertainty and its synergies with infrastructure planning. Theories, methodological backgrounds and concrete case studies will not only help readers to understand the proposed methodologies for modelling and uncertainty analysis, but will also show them how these approaches are implemented in practice.

Atom Mirakyan studied engineering at the Technical University in Erevan/Armenia (Dipl.-Ing.) and Energy economics (Dipl.-Energy economics) at University of apply science in Darmstadt/Germany. He works at Technical University in Darmstadt as scientist in the field of energy planning and modelling for 5 years. As energy consultant he does energy planning and regional development consultancy for cities and territories for 4 years. In 2007 he joined the European Institute for Energy Research working on energy planning and modelling. His research focus is techno-economic and ecological modelling and planning of energy systems, uncertainty analysis and life cycle assessment. He has also developed methods for innovative support of planning and system design. He has done his PhD about Methodological frameworks for uncertainty analysis in long range integrated energy planning for cities and territories (IEPCT) at University of Strasbourg in 2014. In his PhD frame developed uncertainty analysis approaches have been successfully implemented in megacity studies, in context of energy planning and modelling.Roland De Guio is professor of Industrial and Systems Engineering at I.N.S.A Graduate School of Science and Technology, Strasbourg France. Since 2000, he manages research activities about applications of theory of inventive problem solving on technical and non-technical multidisciplinary problems. Among his activities he worked on long run technological forecast since 2004 and started his collaboration with EIFER in the area of energy planning in 2010.

Autoren/Hrsg.

Mirakyan, Atom

De Guio, Roland

Inhaltsverzeichnis

1;Preface;7
2;Contents;9
3;List of Figures;13
4;List of Tables;15
5;Abbreviations and Symbols;17
6;1 Introduction;21
6.1;1.1 Scope and Structure of the Book;21
6.2;1.2 Main Questions Addressed and the Purpose of the Book;23
6.3;1.3 Overall Definitions and Theoretical Backgrounds;25
6.3.1;1.3.1 Defining Planning, Scenarios, Strategies and Initiatives;25
6.3.2;1.3.2 Systems from the System Science Point of View;28
6.3.3;1.3.3 Models and Modelling;30
6.3.4;1.3.4 Mixed Method Methodologies, a Pragmatic View;32
6.3.4.1;1.3.4.1 Introduction;32
6.3.4.2;1.3.4.2 Aspects for Designing Mixed Methods;33
6.3.5;1.3.5 Pre-existing Concepts of Uncertainty in Planning and Modelling;35
6.3.6;1.3.6 Planning and Decision Making in Different Information Availability Conditions;36
6.3.7;1.3.7 Theories for Uncertainty Analysis and Representation;37
6.3.7.1;1.3.7.1 Basic Notions of Probability Theory;37
6.3.7.2;1.3.7.2 Basic Notions of Fuzzy Set and Possibility Theory;38
6.3.7.3;1.3.7.3 Basic Notion of Evidence Theory;40
6.4;References;41
7;2 Energy Infrastructure Planning in Cities and Territories, Quality Factors of Methods for Infrastructure Planning;45
7.1;2.1 Introduction;45
7.2;2.2 Integrated Energy Planning in Cities and Territories;46
7.3;2.3 Energy Systems in City and Territory, a Sociotechnical Infrastructure;47
7.4;2.4 Defining Typology of Application or Use Cases;48
7.4.1;2.4.1 Use Case I: Decentralised Multi-model Based IEPCT;48
7.4.2;2.4.2 Use Case II: Integrated-Model Based IEPCT;49
7.5;2.5 Modelling in IEPCT;49
7.5.1;2.5.1 Models and Different Degrees of Formalisation;49
7.6;2.6 Overall Requirements and Quality Factors of Energy Planning and Modelling Methods;51
7.7;2.7 Summary and Open Problems;54
7.8;References;55
8;3 3-Domain Modelling;58
8.1;3.1 Introduction;58
8.2;3.2 3-Domain Metasystem;59
8.3;3.3 3-Domain Modelling: Different Approaches for Different Domains;62
8.3.1;3.3.1 Introduction;62
8.3.2;3.3.2 Data-Driven Modelling;63
8.3.3;3.3.3 Process-Driven Modelling;64
8.3.3.1;3.3.3.1 Comparison of Complex System Modelling Approaches;64
8.3.4;3.3.4 Judgmental-Driven Modelling;65
8.4;3.4 Defining Modelling Approaches for Different Modelling Domains and Use Cases;66
8.4.1;3.4.1 General;66
8.4.2;3.4.2 Modelling Approaches for Targeted Domain;67
8.4.2.1;3.4.2.1 Selecting the Modelling Methods;67
8.4.2.2;3.4.2.2 Selected Process Driven Models for Targeted Domain;67
8.4.2.2.1;System Dynamic (SD) Approach to Model the Targeted Domain in Use Case II—Mexico;67
8.4.2.3;3.4.2.3 Judgment-Driven Modelling Methods;69
8.4.2.3.1;MICMAC Approach to Model the Targeted Domain in Use Case I—Singapore;69
8.4.3;3.4.3 Data Driven Modelling Approaches for Neighbouring and Distant Domains;70
8.4.3.1;3.4.3.1 Selecting the Modelling Methods;70
8.4.3.2;3.4.3.2 Selected Data-Driven Modelling Methods;72
8.4.3.2.1;Linear Regression;72
8.4.3.2.2;Theta Model;73
8.4.3.2.3;ARIMA Models;73
8.4.3.2.4;Robust Trend and Random Walk;74
8.4.3.2.5;Artificial Neural Network (ANN);75
8.4.3.2.6;S-shaped Curve Methods to Forecast Technology Evolution and Substitution;77
8.4.4;3.4.4 Modelling the Distant Domain and Its Impact to Other Domains;78
8.4.4.1;3.4.4.1 Reference Impact Matrix Method (RIM);78
8.5;3.5 Summary of Modelling Approches for Different Use Cases and Domains;81
8.6;3.6 3-Domain Modelling in Context of Multi Method Research;82
8.7;References;82
9;4 Conceptual Basis of Uncertainty in IEPCT;86
9.1;4.1 Why Be Explicit About Uncertainty in IEPCT?;86
9.2;4.2 Typology of Uncertainty;87
9.2.1;4.2.1 Linguistic Uncertainty;88
9.2.2;4.2.2 Epistemic Uncertainty;88
9.2.3;4.2.3 Variability Uncertainty;89
9.2.4;4.2.4 Decision Making Uncertainty;89
9.2.5;4.2.5 Procedural Uncertainty;89
9.2.6;4.2.6 Levels of Uncertainty;90
9.3;4.3 Incorporating Uncertainty in Current IEPCT Studies;90
9.4;4.4 Conclusion;90
9.5;References;91
10;5 Multi-method Approaches for Uncertainty Analysis;92
10.1;5.1 Introduction;92
10.1.1;5.1.1 IEP in Cities and Territories, Specific Conditions;93
10.2;5.2 Analysis Sophistication Degrees;93
10.2.1;5.2.1 Introduction;93
10.2.2;5.2.2 Appropriate Analytical Degrees in IEPCT Context;95
10.3;5.3 Quality Factors of Methods for Uncertainty Analysis;96
10.3.1;5.3.1 Technical Quality Factors;96
10.3.2;5.3.2 Organisational Capability;96
10.3.3;5.3.3 Satisfaction by Planning Participants;97
10.4;5.4 Methods and Methodologies for Uncertainty Assessment: A Review;98
10.4.1;5.4.1 Evaluation Criteria;98
10.4.2;5.4.2 List of the Reviewed Methods and Methodologies;99
10.4.3;5.4.3 Summary of Evaluation Results of Reviewed Methods;99
10.5;5.5 Multi Method Approaches for Uncertainty Analysis;100
10.5.1;5.5.1 Introduction;100
10.5.2;5.5.2 Fuzzy Scenario Based Uncertainty Analysis for Use Case-I;100
10.5.2.1;5.5.2.1 Analysis Procedure and Steps, Functional View;100
10.5.2.2;5.5.2.2 Model Context Uncertainty Analysis;101
10.5.2.3;5.5.2.3 Methods for Model Structure Uncertainty Analysis;101
10.5.2.3.1;Method for the Analysis of Judgmental-Driven Model Structure Uncertainty in a Targeted Domain;101
10.5.2.3.2;Method for the Analysis of Model Structure Uncertainty for Data-Driven Models in Neighbouring or Distant Domain;103
10.5.2.3.3;Methods of Model Structure Uncertainty Analysis of Judgmental Driven Model in Distant Domain;103
10.5.2.4;5.5.2.4 Identification of Main Drivers (Key Descriptors);104
10.5.2.5;5.5.2.5 Methods for Uncertainty Analysis of Models’ Inputs;104
10.5.2.5.1;Methods for the Uncertainty Analysis of Process-Driven Models’ Inputs in Targeted Domain;104
10.5.2.5.2;Methods for Uncertainty Analysis of Data-Driven Models’ Inputs in Neighbouring Domain;105
10.5.2.5.3;Methods for Uncertainty Analysis of Judgmental Driven Models’ Inputs in Distant Domain;105
10.5.2.6;5.5.2.6 Methods for Uncertainty Analysis of Model Outputs;106
10.5.2.6.1;Methods for Uncertainty Analysis of Process-Driven Models Output in Targeted Domain;106
10.5.2.6.2;Methods for Uncertainty Analysis of Data-Driven Models Outputs in Neighbouring Domain;106
10.5.2.6.3;Methods for the Uncertainty Analysis of Judgmental Driven Models Outputs in Distant Domain;107
10.5.2.7;5.5.2.7 Model Technical Uncertainty;107
10.5.2.8;5.5.2.8 Uncertainty Communication;107
10.5.2.9;5.5.2.9 Assignment FSUA Methods According Planning and Modelling Steps for Addressing Different Typologies of Uncertainties;108
10.5.3;5.5.3 Probabilistic, Random Sampling Based Uncertainty Analysis (PRSUA) Approach for Use Case-II;109
10.5.3.1;5.5.3.1 Analysis Procedure and Steps, Functional View;109
10.5.3.2;5.5.3.2 Model Context Uncertainty Analysis;109
10.5.3.3;5.5.3.3 Methods for Model Structure Uncertainty Analysis;111
10.5.3.3.1;Methods for the Analysis of Process-Driven Model Structure Uncertainty in a Targeted Domain;111
10.5.3.3.2;Methods for the Analysis of Model Structure Uncertainty for Data-Driven Models in Neighbouring or Distant Domain;112
10.5.3.3.3;Methods of Model Structure Uncertainty Analysis of Judgmental Driven Model in Distant Domain;112
10.5.3.4;5.5.3.4 Identification Main Model Drivers (Key Descriptors);112
10.5.3.5;5.5.3.5 Methods for the Uncertainty Analysis of Models’ Inputs;113
10.5.3.5.1;Methods for the Uncertainty Analysis of Process-Driven Models’ Inputs in a Targeted Domain;113
10.5.3.5.2;Methods for Uncertainty Analysis of Data-Driven Models’ Inputs in Neighbouring Domain;113
10.5.3.5.3;Methods for Uncertainty Analysis of Judgmental Driven Models’ Inputs in Distant Domain;114
10.5.3.6;5.5.3.6 Methods for Uncertainty Analysis of Model Outputs;114
10.5.3.6.1;Methods for the Uncertainty Analysis of Process-Driven Model Outputs in Targeted Domain;114
10.5.3.6.2;Methods for the Uncertainty Analysis of Data-Driven Model Outputs in Neighbouring Domain;114
10.5.3.6.3;Methods for the Uncertainty Analysis of Judgmental Driven Model Outputs in the Distant Domain;115
10.5.3.7;5.5.3.7 Model Technical Uncertainty;115
10.5.3.8;5.5.3.8 Uncertainty Communication;115
10.5.3.9;5.5.3.9 Assignment PRSUA Methods According to Planning and Modelling Steps for Addressing Different Typologies of Uncertainty;116
10.6;5.6 A Review of Methods and Methodologies for Uncertainty Analysis;117
10.6.1;5.6.1 Correlations and Copulas;117
10.6.1.1;5.6.1.1 Description;117
10.6.1.2;5.6.1.2 Typology of Uncertainty Addressed;118
10.6.1.3;5.6.1.3 Potential, Main Rationales;118
10.6.1.4;5.6.1.4 Performance According to Some Quality Factors;118
10.6.1.4.1;Technical Quality Factors;118
10.6.1.4.2;Organisational Capability;118
10.6.1.4.3;Satisfaction by Planning Participants;119
10.6.1.5;5.6.1.5 Future Reading;119
10.6.2;5.6.2 Expert Elicitation;119
10.6.2.1;5.6.2.1 Description;119
10.6.2.2;5.6.2.2 Typology of Uncertainty Addressed;120
10.6.2.3;5.6.2.3 Potential, Main Rationales;120
10.6.2.4;5.6.2.4 Performance According to Quality Factors;120
10.6.2.4.1;Technical Quality Factors;120
10.6.2.4.2;Organisational Capability;120
10.6.2.4.3;Satisfaction by Planning Participants;120
10.6.2.5;5.6.2.5 Future Reading;121
10.6.3;5.6.3 Fuzzy Inference;121
10.6.3.1;5.6.3.1 Description;121
10.6.3.2;5.6.3.2 Potential, Main Rationales;122
10.6.3.3;5.6.3.3 Typology of Uncertainty Addressed;122
10.6.3.4;5.6.3.4 Performance According to Quality Factors;122
10.6.3.4.1;Technical Quality Factors;122
10.6.3.4.2;Organisational Capability;123
10.6.3.4.3;Satisfaction by Planning Participants;123
10.6.3.5;5.6.3.5 Future Reading;123
10.6.4;5.6.4 Innovative Multimethod Approach (IMMA);123
10.6.4.1;5.6.4.1 Description;123
10.6.4.2;5.6.4.2 Typology of Uncertainty Addressed;124
10.6.4.3;5.6.4.3 Potential, Main Rationales;124
10.6.4.4;5.6.4.4 Performance According to Quality Factors;124
10.6.4.4.1;Technical Quality Factors;124
10.6.4.4.2;Organisational Capability;124
10.6.4.4.3;Satisfaction by Planning Participants;124
10.6.4.5;5.6.4.5 Future Reading;125
10.6.5;5.6.5 Inverse Modelling;125
10.6.5.1;5.6.5.1 Description;125
10.6.5.2;5.6.5.2 Potential, Main Rationales;125
10.6.5.3;5.6.5.3 Typology of Uncertainty Addressed;125
10.6.5.4;5.6.5.4 Performance According to Quality Factors;125
10.6.5.4.1;Technical Quality Factors;125
10.6.5.4.2;Organisational Capability;126
10.6.5.4.3;Satisfaction by Planning Participants;126
10.6.5.5;5.6.5.5 Future Reading;126
10.6.6;5.6.6 Interval Prediction (IP) in Data Driven Models;126
10.6.6.1;5.6.6.1 Description;126
10.6.6.2;5.6.6.2 Potential, Main Rationales;127
10.6.6.3;5.6.6.3 Typology of Uncertainty Addressed;128
10.6.6.4;5.6.6.4 Performance According to Quality Factors;128
10.6.6.4.1;Technical Quality Factors;128
10.6.6.4.2;Organisational Capability;128
10.6.6.4.3;Satisfaction by planning participants;128
10.6.6.5;5.6.6.5 Future Reading;129
10.6.7;5.6.7 Monte Carlo Simulation;129
10.6.7.1;5.6.7.1 Description;129
10.6.7.2;5.6.7.2 Potential, Main Rationales;129
10.6.7.3;5.6.7.3 Typology of Uncertainty Addressed;129
10.6.7.4;5.6.7.4 Performance According to Quality Factors;129
10.6.7.4.1;Technical Quality Factors;129
10.6.7.4.2;Organisational Capability;130
10.6.7.4.3;Satisfaction by Planning Participants;130
10.6.7.5;5.6.7.5 Future Reading;130
10.6.8;5.6.8 Multiple Model Simulation (MMS) of Process Driven Models;130
10.6.8.1;5.6.8.1 Description;130
10.6.8.2;5.6.8.2 Potential, Main Rationales;131
10.6.8.3;5.6.8.3 Typology of Uncertainty Addressed;131
10.6.8.4;5.6.8.4 Performance According to Some Quality Factors;131
10.6.8.4.1;Technical Quality Factors;131
10.6.8.4.2;Organisational Capability;131
10.6.8.4.3;Satisfaction by Planning Participants;132
10.6.8.5;5.6.8.5 Future Reading;132
10.6.9;5.6.9 Multiple Model Simulation (MMS) of Data Driven Models;132
10.6.9.1;5.6.9.1 Description;132
10.6.9.2;5.6.9.2 Potential, Main Rationales;133
10.6.9.3;5.6.9.3 Typology of Uncertainty Addressed;133
10.6.9.4;5.6.9.4 Performance According to Some Quality Factors;133
10.6.9.4.1;Technical quality factors;133
10.6.9.4.2;Organisational Capability;133
10.6.9.4.3;Satisfaction by planning participants;134
10.6.9.5;5.6.9.5 Future Reading;134
10.6.10;5.6.10 Scenario Analysis and Fuzzy Clustering;134
10.6.10.1;5.6.10.1 Description;134
10.6.10.1.1;Step 1;135
10.6.10.1.2;Step 2;135
10.6.10.1.3;Step 3;135
10.6.10.1.4;Step 4;135
10.6.10.1.5;Step 5 Scenario Selection;137
10.6.10.2;5.6.10.2 Potential, Main Rationales;139
10.6.10.3;5.6.10.3 Typology of Uncertainty Addressed;139
10.6.10.4;5.6.10.4 Performance According to Some Quality Factors;139
10.6.10.4.1;Technical Quality Factors;139
10.6.10.4.2;Organisational Capability;139
10.6.10.4.3;Satisfaction by Planning Participants;140
10.6.10.5;5.6.10.5 Future Reading;140
10.6.11;5.6.11 Sensitivity Analysis;140
10.6.11.1;5.6.11.1 Description;140
10.6.11.2;5.6.11.2 Potential, Main Rationales;141
10.6.11.3;5.6.11.3 Typology of Uncertainty Addressed;141
10.6.11.4;5.6.11.4 Performance According to Some Quality Factors;141
10.6.11.4.1;Technical Quality Factors;141
10.6.11.4.2;Organisational Capability;141
10.6.11.4.3;Satisfaction by Planning Participants;142
10.6.11.5;5.6.11.5 Future Reading;142
10.6.12;5.6.12 Tests of Complex Models for Model Uncertainty;142
10.6.12.1;5.6.12.1 Description;142
10.6.12.2;5.6.12.2 Potential, Main Rationales;143
10.6.12.3;5.6.12.3 Typology of Uncertainty Addressed;143
10.6.12.4;5.6.12.4 Performance According to Some Quality Factors;144
10.6.12.4.1;Technical Quality Factors;144
10.6.12.4.2;Organisational Capability;144
10.6.12.4.3;Satisfaction by Planning Participants;144
10.6.12.5;5.6.12.5 Future Reading;144
10.6.13;5.6.13 NUSAP and PRIMA Methodologies;144
10.6.13.1;5.6.13.1 Description;144
10.6.13.2;5.6.13.2 Potential, Main Rationales;145
10.6.13.3;5.6.13.3 Typology of Uncertainty Addressed;145
10.6.13.4;5.6.13.4 Performance According to Quality Factors;145
10.6.13.4.1;Technical Quality Factors;145
10.6.13.4.2;Organisational Capability;146
10.6.13.4.3;Satisfaction by Planning Participants;146
10.6.13.4.4;Future Reading;146
10.7;5.7 Summary;146
10.8;References;147
11;6 Implementation of Discussed Uncertainty Analysis Approaches in Case Studies;150
11.1;6.1 Selection of Application Studies;150
11.2;6.2 An Example of Use Case I: Singapore;151
11.2.1;6.2.1 Development of the “Singapore Sustainable Growth” Model;151
11.2.1.1;6.2.1.1 Historical and Current Situation;151
11.2.1.2;6.2.1.2 Modelling the Targeted Domain and Identification of Key Descriptors;152
11.2.1.3;6.2.1.3 Modelling the Neighbouring Domain;156
11.2.1.4;6.2.1.4 Modelling the Distant Domain;157
11.2.2;6.2.2 Uncertainty Analysis;157
11.2.2.1;6.2.2.1 Context and Framing Uncertainty Analysis;157
11.2.2.2;6.2.2.2 Model Structure Uncertainty Analysis;157
11.2.2.2.1;Model Structure Uncertainty in the Targeted Domain;157
11.2.2.2.2;Model Structure uncertainty in the Neighbouring Domain;161
11.2.2.2.3;Model Structure Uncertainty in the Distant Domain;161
11.2.2.3;6.2.2.3 Model Inputs Uncertainty Analysis;162
11.2.2.3.1;Model Inputs Uncertainty Analysis in the Targeted Domain;162
11.2.2.3.1.1;Uncertainty of Individual Model Driving Forces;162
11.2.2.3.1.2;Uncertainty Because of Interdependency Among Different Model Inputs and Linguistic Uncertainty;163
11.2.2.3.2;Model Inputs Uncertainty Analysis of the Neighbouring Domain;165
11.2.2.3.3;Model Inputs Uncertainty Analysis of the Distant Domain;166
11.2.2.4;6.2.2.4 Model Output Uncertainty;166
11.2.2.4.1;Model Output Uncertainty in the Neighbouring Domain;166
11.2.2.4.2;Model Output Uncertainty in the Targeted Domain;166
11.2.2.4.3;Model Output Uncertainty in the Distant Domain;171
11.3;6.3 An Example of Use Case II: Mexico City;171
11.3.1;6.3.1 Modelling Mexico City’s Waste-to-Energy System;171
11.3.1.1;6.3.1.1 The Waste Management System in Mexico City;172
11.3.1.2;6.3.1.2 Modelling the Targeted Domain and Identification of Key Descriptors;174
11.3.1.3;6.3.1.3 Modelling Neighbouring Domain;174
11.3.1.4;6.3.1.4 Modelling Distant Domain;174
11.3.2;6.3.2 Uncertainty Analysis;176
11.3.2.1;6.3.2.1 Context and Framing Uncertainty Analysis;176
11.3.2.2;6.3.2.2 Model Structure Uncertainty Analysis;176
11.3.2.2.1;Introduction;176
11.3.2.2.2;Model Structure Uncertainty Analysis in Targeted Domain;177
11.3.2.3;6.3.2.3 Model Inputs Uncertainty Analysis;178
11.3.2.3.1;Model Inputs Uncertainty Analysis of the Neighbouring Domain;178
11.3.2.3.2;Model Input Uncertainty Analysis in the Targeted Domain;178
11.3.2.3.2.1;Uncertainty of Individual Model Driving Forces;178
11.3.2.3.2.2;Uncertainty Because of Interdependency Among Different Model Inputs and Linguistic Uncertainty;178
11.3.2.3.3;Model Input Uncertainty Analysis in the Distant Domain;179
11.3.2.4;6.3.2.4 Model Output Uncertainty Analysis;179
11.3.2.4.1;Model Output Uncertainty Analysis in Neighbouring Domain;179
11.3.2.4.2;Model Output Uncertainty of Process Driven Models of the Targeted Domain;180
11.3.2.4.3;Model Output Uncertainty Analysis in Distant Domain;180
11.4;References;180
12;7 Evaluation and Discussion;182
12.1;7.1 Evaluation and Discussion of the 3-Domain Modelling Concept and Different Modelling Approaches;182
12.1.1;7.1.1 General;182
12.1.2;7.1.2 Modelling Approaches for Targeted Domain;183
12.1.2.1;7.1.2.1 Technical Quality;183
12.1.2.2;7.1.2.2 Organisational Capability;183
12.1.2.3;7.1.2.3 Satisfaction by Planning Participants;183
12.1.3;7.1.3 Modelling Approaches for Neighbouring Domain;184
12.1.3.1;7.1.3.1 Technical Quality;184
12.1.3.2;7.1.3.2 Organisational Capability;184
12.1.3.3;7.1.3.3 Satisfaction by Planning Participants;184
12.1.4;7.1.4 Modelling Approaches for Distant Domain;185
12.1.4.1;7.1.4.1 Technical Quality;185
12.1.4.2;7.1.4.2 Organisational Capability;185
12.1.4.3;7.1.4.3 Satisfaction by Planning Participants;185
12.2;7.2 Evaluation and Discussion of Uncertainty Analysis Approaches;185
12.2.1;7.2.1 General;185
12.2.2;7.2.2 Evaluation of FSUA Multi Method Approach and Discussion;186
12.2.2.1;7.2.2.1 Technical Quality of FSUA;186
12.2.2.2;7.2.2.2 Organisational Capability;187
12.2.2.3;7.2.2.3 Satisfaction by Planning Participants;187
12.2.3;7.2.3 Evaluation of PRSUA Multi Method Approach and Discussion;188
12.2.3.1;7.2.3.1 Technical Quality of PRSUA;188
12.2.3.2;7.2.3.2 Organisational Capability;189
12.2.3.3;7.2.3.3 Satisfaction by Planning Participants;189
12.2.4;7.2.4 Comparative Assessment of Proposed Approaches;191
12.3;References;191
13;8 Overall Conclusion and Future Research;192
13.1;8.1 Overall Synthesis and Conclusions;192
13.2;8.2 Synthesis and Conclusions of Chaps. 1 and 2;193
13.4;8.4 Synthesis and Conclusion of Chap. 4;194
13.5;8.5 Synthesis and Conclusions of Chaps. 5, 6