臺灣博碩士論文加值系統

Around the World, renewable energies are becoming one of the priority developments of the governments in power generation. This trend is carried by raising concerns on the negative environmental effects caused by the emission of greenhouse gases to the earth’s atmosphere. This study aims to measure the potentiality of the 40 countries in terms of carrying out renewable energy projects using the Data Envelopment Analysis (DEA) Window model integrated with the Fuzzy TOPSIS (Technique for Order Preference by Similarities to Ideal Solution) method. By using DEA, several variables were used for the analysis; population, total energy consumption, and total renewable energy capacity as inputs, while gross domestic product and total energy production as outputs. After this method, 24 countries were chosen as the highest efficiency countries that will be used for the next method. Fuzzy TOPSIS evaluates these countries under a fuzzy environment using five criteria which involves availability of resources, energy security, technological infrastructure, economic stability and social acceptance. Through qualitative analysis of the experts, the top 10 countries can be considered to have the most potential in terms of renewable energy developments. It turned out that Canada, New Zealand, and Norway are the top three with 0.58, 0.57, and 0.57 closeness coefficients respectively. While Germany, United Kingdom, and France are in the 8th to 10th places in which all the three garnered 0.53 score. This study can be a guide for energy investors, policymakers, and governments in making or crafting strategies for their country renewable energy development. The methods used can also be a support tool for them in terms of their research perspective in the area of renewable energy sustainability.

Contents
ABSTRACT i
ACKNOWLEDGEMENTS iii
List of Figures vi
List of Tables vii
Chapter 1: INTRODUCTION 1
1.1 Research background and motivation 1
1.2 The objectives of the study 3
1.3 Content of the Thesis 4
Chapter 2: LITERATURE REVIEW 5
2.1 Literature Review of Renewable Energy Problem 5
2.2 DEA Window Model 7
2.3 The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under fuzzy environment 7
Chapter 3: MATERIALS AND METHODS 9
3.1. The Process Flow Chart 9
3.2. A Proposal of Methodology Approach 10
3.2.1. A Hybrid Multi-Criteria Decision Making (MCDM) Model 10
3.2.2. Pearson Correlation Coefficient 13
3.2.3. DEA Window Model 13
3.2.4. Triangular Fuzzy Number 15
3.2.5. Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) 16
Chapter 4: RESULTS 19
4.1. Data Analysis and Pearson Correlation Results 19
4.2. Results of DEA Window Model 21
4.3 Results of Fuzzy TOPSIS 26
Chapter 5: DISCUSSIONS AND CONCLUSIONS 31
5.1 Conclusions 31
5.2 Research contribution 32
5.3 Recommendation for Future Research 33
References 34
Appendix 38

List of Figures
Figure 1. Estimated renewable share of total final energy consumption 1
Figure 2. The process flow chart of the study. 9
Figure 3. Proposal MCDM model. 11
Figure 4. Triangular Fuzzy Number. 15
Figure 5. Average efficiency score of 40 countries (2009-2018). 21
Figure 6. FPIS and FNIS from F-TOPSIS model. 30

List of Tables
Table 1. All criteria affect to renewable energy production. 12
Table 2. Pearson correlation index. 13
Table 3. Linguistic variables for the weight of the criteria. 18
Table 4. Linguistics variables for the rating. 18
Table 5. Name of the countries in their respective DMUs. 19
Table 6. Descriptive statistics and Pearson correlation coefficient (2018). 20
Table 7. The efficiency score of each country (2009-2018). 22
Table 8. Ranking countries over the periods (2009-2018). 23
Table 9. Average efficiency score and rank of 40 countries (2009-2018). 25
Table 10. The fuzzy decision matrix and fuzzy weights of 24 countries. 26
Table 11. The fuzzy normalized decision matrix of 24 countries. 27
Table 12. The fuzzy weighted normalized decision matrix of 24 countries. 28
Table 13. The distance of each alternative from FPIS and FNIS. 29

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