氣候變遷已經成為全球最重要的環境問題之一，隨著氣候的暖化，聯合國制定了相關條約要求各國提出減碳目標，然而結果卻往往不如預期。以往各國在經濟發展與減碳目標下無法兩者兼顧。主要因為經濟發展的來源多側重於碳排放密集的產業，也因此常常因為追求經濟發展而增加額外的碳排放量。此外兩者之間缺乏客觀的量化因素，使得兼顧二者的政策不易制定。同時、以非再生能源為主的能源業又為碳排放量最大的行業之一。
故本研究根據Leontief的投入產出均衡理論和Cobb-Douglas生產函數理論，建構一兩階段目標規劃模型，通過調整國家產業結構來解決這一困境。第一階段在考慮各產業所需的生產要素及需求的同時，提供各產業理想產值，以滿足政府設定的減碳與經濟成長目標。一旦確定了理想的能源產業結構，在第二階段針對電力產業做進一步分析。第二階段將確定再生能源與非再生能源的發電結構。透過此產業結構之產值以追求在經濟發展的同時達到減碳目標，並且政府可以在此基礎上制定相關政策，幫助產業轉型，以達到低碳經濟及永續經營。
本文實例研究乃根據台灣能源局統計資料，將提供帶有權重及優先級別偏好的機制，希望透過此工具為決策者提供參考依據，以促進相關政策的制定與評估程序。

Climate change has become one of the most important environmental issues in the world. With the increasing warming of the earth, the United Nations has formulated environmental treaties, which require countries to commit to carbon reduction targets, however, the results are not optimistic. In the past, countries could not implement economic development and carbon reduction goals simultaneously, because the sources of economic development are mostly focused on carbon emission-intensive industries. There was a lack of objective adjustment between the two.
Therefore, this study proposes a solution to this dilemma by adjusting the national industry structure based on the Leontief Input-Output Equilibrium Theory and Cobb-Douglas Production Theory, a two-stage goal programming module is established. At the first stage, while the required production factors and the demands of each industry are considered, a desirable production value for each industry is provided. Once the ideal supply for the power industry is determined with the desirable industry structure, the individual values of renewable and non-renewable energies are determined at the second stage. Through this production value for each industry, we can achieve the goal of carbon reduction while pursuing economic growth. Based on these results, relevant policies are suggested to assist in transforming industries.
A case study based on the statistics of the Taiwan Bureau of Energy, Ministry of Economic Affairs, is used to verify the model. A preference realization mechanism with weighting and priority tools is provided to facilitate the decision support and policy assessment procedure.

ACKNOWLEDGMENT I
ABSTRACT II
CONTENTS IV
LIST OF TABLES VIII
LIST OF FIGURES X
1.INTRODUCTION 1
2. LITERATURE REVIEW 4
2.1 Theoretical Foundation 4
2.1.1 Leontief I-O Equilibrium Theory 4
2.1.2 Cobb-Douglas Production Theory 6
2.2 Carbon Reduction Factors 7
2.2.1 Industry Structure 7
2.2.2 Energy Structure 8
2.3 Economic Tools of Carbon Reduction 10
2.3.1 Carbon Tax 10
2.3.2 Carbon Trading System 11
2.4 Quantitative Approach to Decision Support 13
2.4.1 Basic Goal Programming Model 13
2.4.2 Weighted Goal Programming Model 14
2.4.3 Lexicographic Goal Programming Model 14
2.4.4 General Goal Programming Model 15
2.5 Time Series Analysis and Prediction 16
2.6 Summary and Conclusion 17
3. RESEARCH METHOD 19
3.1 Problem Description 19
3.2 First Stage – Industrial Structure Model 21
3.2.1 The Proposed Goal Programming Model (GP1) 21
3.2.2 Scenario Analysis 24
3.3 Second Stage – Power Generation Model 25
3.3.1 The Proposed Goal Programming Model (GP2) 25
3.4 Summary and Conclusion 28
4. CASE STUDY 30
4.1 Data Analysis, Prediction and Forecasting 30
4.2 Numerical Results & Analysis 36
4.3 Discussion 43
4.4 Summary and Conclusion 44
5. PARAMETER ANALYSIS 45
5.1 Deviation Weight Analysis at the 1st Stage 45
5.2 Base Load Capacity Analysis at the 2nd Stage 48
5.3 Discussion and Summary 51
6. CONCLUSIONS AND FUTURE RESEARCH 55
7. REFERENCES 57
APPENDIX 1: GIVEN DATA ANALYSIS 63
Table A1-1 Investment Budget 63
Table A1-2 Total Labor Force 63
Table A1-3 Average Electricity Carbon Emission Factor 64
APPENDIX 2: PARAMETER PREDICTION 66
Table A2-a1 Energy Sector Carbon Emission Factor 66
Table A2-a2 Industrial Sector Carbon Emission Factor 67
Table A2-a3 Transportation Sector Carbon Emission Factor 67
Table A2-a4 Agricultural Sector Carbon Emission Factor 68
Table A2-a5 Service Sector Carbon Emission Factor 69
Table A2-a6 Other Sector Carbon Emission Factor 70
Table A2-b1 Energy Sector Additional Value Factor 71
Table A2-b2 Industrial Sector Additional Value Factor 72
Table A2-b3 Transportation Sector Additional Value Factor 73
Table A2-b4 Agricultural Sector Additional Value Factor 74
Table A2-b5 Service Sector Additional Value Factor 75
Table A2-b6 Other Sector Additional Value Factor 76
Table A2-c1 Energy Sector Production Cost Factor 77
Table A2-c2 Industrial Sector Production Cost Factor 77
Table A2-c3 Transportation Sector Production Cost Factor 78
Table A2-c4 Agricultural Sector Production Cost Factor 79
Table A2-c5 Service Sector Production Cost Factor 79
Table A2-c6 Other Sector Production Cost Factor 80
Table A2-d1 Energy Sector Labor Cost Factor 81
Table A2-d2 Industrial Sector Labor Cost Factor 82
Table A2-d3 Transportation Sector Labor Cost Factor 83
Table A2-d4 Agricultural Sector Labor Cost Factor 84
Table A2-d5 Service Sector Labor Cost Factor 85
Table A2-d6 Other Sector Labor Cost Factor 86
Table A2-e1 Energy Sector Final Demand 87
Table A2-e2 Industrial Sector Final Demand 88
Table A2-e3 Transportation Sector Final Demand 88
Table A2-e4 Agricultural Sector Final Demand 89
Table A2-e5 Service Sector Final Demand 90
Table A2-e6 Other Sector Final Demand 91
Table A2-f1 Solar Power Generation Conversion Coefficient 92
Table A2-f2 Wind Power Generation Conversion Coefficient 93
Table A2-f3 Other Renewable Energy Power Generation Conversion Coefficient 94
Table A2-f4 Coal Power Generation Conversion Coefficient 95
Table A2-f5 Gas Power Generation Conversion Coefficient 96
Table A2-f6 Other Non-renewable Energy Power Generation Conversion Coefficient 97
Table A2-g1 Solar Power Carbon Emission Factor 98
Table A2-g2 Wind Carbon Emission Factor 99
Table A2-g3 Other Renewable Energy Carbon Emission Factor 100
Table A2-g4 Coal Carbon Emission Factor 101
Table A2-g5 Gas Carbon Emission Factor 102
Table A2-g6 Other Non-renewable Energy Carbon Emission Factor 103
Table A2-h1 Solar Power Generation Cost Factor 104
Table A2-h2 Wind Power Generation Cost Factor 105
Table A2-h3 Other Renewable Energy Power Generation Cost Factor 106
Table A2-h4 Coal Power Generation Cost Factor 107
Table A2-h5 Gas Power Generation Cost Factor 108
Table A2-h6 Other Non-renewable Energy Power Generation Cost Factor 108

1. 劉基全, 廖述賢, & 陳凌焜. (2004). 多目標供應鏈生產規劃問題之研究.科技整合管理國際研討會.
2. 徐世輝. (2005). 應用統計學. 華泰文化事業股份有限公司.
3. 葉惠青,& 莊銘池.(2009). 台灣永續能源政策與 98 年全國能源會議.科技發展政策報導, (4), 3-16.
4. 蕭代基, 洪志銘, & 羅時芳. (2010). 碳稅與碳交易之比較與搭配. 台電工程月刊, (747), 59-66.
5. 左峻德. (2014). 我國減碳目標下之市場機制政策與配套措施設計及評估. 行政院原子能委員會委託研究計畫研究報告.
6. 郭瑾瑋, 周裕豐, 洪明龍, & 劉子衙. (2015). 應用台灣TIMES模型進行我國長期電力供需規劃.台灣能源期刊,2(4), 363-382.
7. 陳正杰, 馮正民, & 劉庭豪. (2015). 應用多目標規劃法於低碳運輸計畫之預算分配. 運輸計劃季刊, 44(4), 373-400.
8. 邱虹儒, & 王穎達. (2019). 國家能源政策評析報告:英國.
9. 經濟部能源局. (2019). 我國燃料燃燒二氧化碳排放統計與分析.
10. 莊明泰. (2020). 建築物設置太陽光電發電設備的趨勢分析與其能源政策探討-以彰化縣為例.
11. 中央研究院. (2020). 臺灣深度減碳政策建議書.
12. 國家委員會. (2020). 國家發展計畫.
13. 天下雜誌. (2020). 與地球和好, (700).
14. Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613. https://arxiv.org/abs/1302.6613
15. Allen, F., & Eze, P. (2019). Achieving Sustainable Development Goals in the Niger Delta: A Corporate Social Responsibility Pathway. European Journal of Sustainable Development Research, 3(4). https://doi.org/10.29333/ejosdr/5877
16. BAŞEĞMEZ, H. (2021). ESTIMATION OF COBB–DOUGLAS PRODUCTION FUNCTION FOR DEVELOPING COUNTRIES. Journal of Research in Business, 6(1), 54-68.
17. Billah, B., King, M. L., Snyder, R. D., & Koehler, A. B. (2006). Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2), 239-247.
18. Bodansky, D. (1993). The United Nations framework convention on climate change: a commentary. Yale Journal of International Law, 18, 451-473.
19. Cai, H., Qu, S., & Wang, M. (2020). Changes in China’s carbon footprint and driving factors based on newly constructed time series input–output tables from 2009 to 2016. Science of The Total Environment, 711, 134555.
20. Chen, J., & Wen, S. (2020). Implications of Energy Intensity Ratio for Carbon Dioxide Emissions in China. Sustainability, 12(17), 6925. https://www.mdpi.com/2071-1050/12/17/6925
21. Cobb, C. W., & Douglas, P. H. (1928). A theory of production. The American Economic Review, 18(1), 139-165.
22. Cramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J. P., Iglesias, A., ... & Penuelas, J. (2018). Climate change and interconnected risks to sustainable development in the Mediterranean. Nature Climate Change, 8(11), 972-980.
23. Crini, G., & Lichtfouse, E. (2019). Advantages and disadvantages of techniques used for wastewater treatment. Environmental Chemistry Letters, 17(1), 145-155.
24. Felipe, J., & Adams, F. G. (2005). " A theory of production" the estimation of the Cobb-Douglas function: A retrospective view. Eastern Economic Journal, 31(3), 427-445.
25. Flues, F., & Van Dender, K. (2020). Carbon pricing design: Effectiveness, efficiency and feasibility: An investment perspective. OECD Taxation Working Papers, 54.https://doi.org/10.1787/22235558
26. Greco, S., Figueira, J., & Ehrgott, M. (2016). Multiple Criteria Decision Analysis. New York: Springer.
27. Griffin, J. M. (1976). Energy input-output modelling: Problems and prospects. NASA STI/Recon Technical Report N, 77, 22679.
28. Hinson, S. (2020, August). House of Commons Library: Briefing paper Number 8980, Energy policy: an overview. https://commonslibrary.parliament.uk/research-briefings/cbp-8980/
29. Ignizio, J. P. (1983). Generalized goal programming an overview. Computers & Operations Research, 10(4), 277-289.
30. Ishikawa, A., Fujimoto, S., & Mizuno, T. (2021). Why does production function take the Cobb–Douglas form?. Evolutionary and Institutional Economics Review, 18(1), 79-102.
31. Keeney, R. L., Raiffa, H., & Meyer, R. F. (1993). Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Cambridge University press.
32. Krause, U. (1992). Path stability of prices in a nonlinear Leontief model. Annals of Operations Research, 37(1), 141-148.
33. Leontief, W. W. (1937). Interrelation of prices, output, savings, and investment. The Review of Economic Statistics, 109-132
34. Leontief, W. W. (1951). “Input-output economics.” Scientific American, October, 15–21.
35. Lewis, C.D. (1982). Industrial and Business Forecasting Methods. London: Butterworths.
36. Lin, P. P., Li, D. F., Jiang, B. Q., Wei, A. P., & Yu, G. F. (2019). Regional input–output multiple choice goal programming model and method for industry structure optimization on energy conservation and GHG emission reduction in China. International Journal of Computational Intelligence Systems, 12(2), 1311-1322.
37. Liu, J., Yang, Q., Zhang, Y., Sun, W., & Xu, Y. (2019). Analysis of CO2 emissions in China’s manufacturing industry based on extended logarithmic mean division index decomposition. Sustainability, 11(1), 226-253.
38. Liu, Y., Kong, J., Zhuang, Y., Xing, P., Yin, H., & Luo, X. (2019). Recycling high purity silicon from solar grade silicon cutting slurry waste by carbothermic reduction in the electric arc furnace. Journal of Cleaner Production, 224, 709-718.
39. Oosterhaven, J., & Hewings, G. J. (2021). Interregional input-output models. Handbook of Regional Science, 397-423.
40. Qi, J. (2020, July). Study on the threshold effect of China’s industrial structure on carbon emission. IOP Conference Series: Earth and Environmental Science, 546(2), 022053. IOP Publishing.
41. Raicharoen, T., Lursinsap, C., & Sanguanbhokai, P. (2003, May). Application of critical support vector machine to time series prediction. Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS'03., 5, IEEE.
42. Ramamurthy, P. (2007), Operations Research, New Age International.
43. Rogelj, J., Forster, P. M., Kriegler, E., Smith, C. J., & Séférian, R. (2019). Estimating and tracking the remaining carbon budget for stringent climate targets. Nature, 571(7765), 335-342.
44. Romero, C. (2004). A general structure of achievement function for a goal programming model. European Journal of Operational Research, 153(3), 675-686.
45. Shen, C. Y., & Wang, H. F. (2016). Electricity load forecasting in a smart grid system. Intelligent Data Analysis, 20(5), 1223-1242.
46. Strauss, B. H., Kulp, S., & Levermann, A. (2015). Carbon choices determine US cities committed to futures below sea level. Proceedings of the National Academy of Sciences, 112(44), 13508-13513.
47. Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240.
48. Tamiz, M., Jones, D., & Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research, 111(3), 569-581.
49. Tsou, Y. S., & Wang, H. F. (2012). Subsidy and penalty strategy for a green industry sector by bi-level mixed integer nonlinear programming. Journal of the Chinese Institute of Industrial Engineers, 29(4), 226-236.
50. Tvinnereim, E., & Mehling, M. (2018). Carbon pricing and deep decarbonisation. Energy Policy, 121, 185-189.
51. Uggetti, E., García, J., Álvarez, J. A., & García-Galán, M. J. (2018). Start-up of a microalgae-based treatment system within the biorefinery concept: from wastewater to bioproducts. Water Science and Technology, 78(1), 114-124.
52. UNFCCC, V. (2015). Adoption of the Paris Agreement. I: proposal by the president (Draft Decision). United Nations Office, Geneva (Switzerland).
53. Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56-66.
54. Visagie, J. C., Sibanda, V., & Coetzee, R. (2019). The Evolution and Models of Corporate Social Responsibility. The Journal of Social Sciences Research, 5(12), 1885-1892.
55. Wang, H. F., &Chou P. W. (2018). Optimal Combination of Renewable Energies for an Enterprise–A Case of Taiwan, International Journal of Operations and Quantitative Management,. 24(2), June, 117-138.
56. Wang, H. F., Sung, M. P., & Hsu, H. W. (2016). Complementarity and substitution of renewable energy in target year energy supply-mix planning–in the case of Taiwan. Energy Policy, 90, 172-182.
57. Wang, L. (2020, July). The Application of Douglas Production Function in Urban Local Economic Growth Management Under Computer Big Data. Journal of Physics: Conference Series, 1578(1), 012117. IOP Publishing.
58. Xiang, W., Zhang, X., Chen, J., Zou, W., He, F., Hu, X., ... & Gao, B. (2020). Biochar technology in wastewater treatment: A critical review. Chemosphere, 252, 126539.
59. Yang, H. L., Liu, I. T., Liu, C. E., Hsu, H. P., & Lan, C. W. (2019). Recycling and reuse of kerf-loss silicon from diamond wire sawing for photovoltaic industry. Waste Management, 84, 204-210.
60. Yang, W., Zhao, R., Chuai, X., Xiao, L., Cao, L., Zhang, Z., ... & Yao, L. (2019). China’s pathway to a low carbon economy. Carbon Balance and Management, 14(1), 1-12.
61. Yuan, C., Liu, S., & Wu, J. (2009). Research on energy-saving effect of technological progress based on Cobb–Douglas production function. Energy Policy, 37(8), 2842-2846.
62. Zhu, C., &Gao, D. (2019). A research on the factors influencing carbon emission of transportation industry in “the Belt and Road Initiative” countries based on panel data. Energies, 12(12), 2405. https://www.mdpi.com/1996-1073/12/12/2405