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研究生：	葉宗震 Ye, Zong-Jhen
論文名稱：	台灣鋼鐵業二氧化碳減量評估-時間序列及多目標規劃模式之應用 CO2 Reduction Assessment for Steel Industry in Taiwan - Application of Time Series and Multi-Objective Models
指導教授：	林素貞 Lin, Sue-Jane
學位類別：	碩士 Master
系所名稱：	工學院 - 環境工程學系 Department of Environmental Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	99
中文關鍵詞：	鋼鐵工業、時間數列分析、目標規劃、CO2減量
外文關鍵詞：	steel industry, time series analysis, goal programming, CO2 reduction
相關次數：	點閱：100 下載：5
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全球暖化的議題在近十年相當受到重視，而二氧化碳是造成全球暖化最常見的溫室氣體。台灣的二氧化碳排放量在2010年占全球0.96%且排第19名，造成台灣二氧化碳排放量位居世界第19名的原因可能是能源效率不佳導致。因為台灣的能源政策限制能源價格低於鄰近各國，所以可能導致較低的能源效率。為了減少二氧化碳的排放，台灣政府於2008年提出了二氧化碳減量目標：永續能源政策綱領(以下簡稱綱領)。因此本研究選擇台灣製造業中主要二氧化碳排放源－鋼鐵業進行二氧化碳減量評估。
本研究先使用時間序列模式評估二氧化碳減量目標是否低於理論最低鋼鐵業的二氧化碳排放量，再使用多目標規劃模式評估在GDP年成長率2%下，考慮科技改善、能源稅或碳稅課徵的情況後，逐漸減少二氧化碳會對鋼鐵業經濟造成的衝擊。時間序列模型結果顯示綱領的二氧化碳減量目標並不低於理論最低鋼鐵業的二氧化碳排放量；減量成本在多目標模式規劃之下顯示：在科技改善的策略下平均減量成本為4.04 103NTD / tCO2；在能源稅及碳稅的平均減量成本介於6.62 ~8.31 103NTD/t CO2；在混合科技進步及能源稅，最低減量成本將出現在2015年的1.70 103NTD/t CO2；在混和科技進步及碳稅平均減量成本最低將降至 3.44 103NTD/t CO2。
本研究結果可供其他相關機構及產業進行二氧化碳減量政策及策略的制定。

Global warming issue has been an important issue for decades. CO2, one of the most common greenhouse gases, contributes the most to global warming. In 2010, Taiwan generated 0.96% CO2 and ranked 19th in the world. Because the energy policy in Taiwan requests that the energy price should be lower than nearby countries, this may lead to lower energy efficiency and emit higher CO2. To reduce CO2 emission, the Taiwan government has set CO2 reduction goal based upon Sustainable Energy Policy Convention (SEPC) of 2008. Thus, we chose one of the major CO2 emission sources in industry, the iron and steel industry to assess the CO2 reduction potential.
In this research, we first use time series model to see if the CO2 reduction goal can meet the lowest theoretical CO2 emission. Further, we use a multi-objective model with a 2% GDP annual growth rate to evaluate the economic impact of ISI, based on gradual reduction planes of CO2 emission under several scenarios including technology improvement, energy and carbon tax. Results showed that the CO2 reduction goals of SEPC are achievable and within the limitation of the theoretical CO2 emission. The average CO2 reduction cost for technology is 4.04 103NTD/t CO2. For energy and carbon tax, the average reduction cost range from 6.62~8.31 103NTD/t CO2. To combined the technology improvement with energy tax, the lowest reduction cost will down to 1.70 103 NTD/t CO2 in 2015. If we combined the technology with carbon tax, the lowest average cost could be 3.44 103 NTD/t CO2.
Results of this study can be helpful for related agency and industry for planning CO2 reduction strategies and relevant policy-makings.

Chinese Abstract .......................................................................................... I
Abstract ..................................................................................................... II
Acknowledgment ..................................................................................... III
Contents .................................................................................................... IV
List of Tables ............................................................................................ VI
List of Figures ....................................................................................... VIII
Chapter 1 Introduction ............................................................................... 1
Chapter 2 Background and literature review ............................................. 3
1 Background and scope ....................................................................... 3
1.1 Scope on iron and steel industry .............................................. 3
1.2 ISI global view ......................................................................... 4
1.3 Economy, energy and environmental impact ........................... 5
2 Greenhouse gas global issues ............................................................. 9
2.1 Sustainable energy policy convention ................................... 10
2.2 CO2 scope ............................................................................... 11
2.3 CO2 data ................................................................................. 12
3 Literature review .............................................................................. 15
3.1 Time series ............................................................................. 15
3.2 Multi-objective model ............................................................ 17
3.3 Energy tax .............................................................................. 18
3.4 Carbon tax .............................................................................. 20
3.5 Energy saving technology ...................................................... 22
Chapter 3 Methodology............................................................................ 25
1 Time series analysis.......................................................................... 25
1.1 Autoregressive (AR) .............................................................. 25
1.2 Moving average (MA) ........................................................... 27
1.3 Decision of the ARMA model ................................................ 29
1.4 Statistical tests ........................................................................ 29
1.5 Software ................................................................................. 32
2 Multi-objective programming method ............................................. 33
2.1 Multi-objective programming model ..................................... 33
2.2 Elasticity................................................................................. 38
2.2.1 Modified energy consumption ............................................ 38
2.2.2 Modified added value rate .................................................. 40
2.3 Software ................................................................................. 42
3 Data collection.................................................................................. 42
Chapter 4 Results ..................................................................................... 44
1 Time series prediction ...................................................................... 44
1.1 ARMA model ......................................................................... 44
1.2 ARMA prediction ................................................................... 45
1.3 Limitations on CO2 emission ................................................. 46
2 Multi-objective model ...................................................................... 47
2.1 Business as usual (BAU)........................................................ 48
2.2 Technology development ....................................................... 52
2.3 Energy tax .............................................................................. 57
2.4 Energy tax and technology ..................................................... 65
2.5 Carbon tax .............................................................................. 71
2.6 Carbon tax and technology .................................................... 79
Chapter 5 Discussion ................................................................................ 84
1 ARMA model ................................................................................... 84
2 Multi-objective model ...................................................................... 86
Chapter 6 Conclusion ............................................................................... 88
References ................................................................................................ 91
Appendix 1 Ratio constrain ...................................................................... 95
Appendix 2 Energy consumption coefficients limitation ........................ 96
Appendix 3 Details of the multi-objective model .................................... 97
Appendix 4 Results of the multi-objective method for each scenarios ... 98
                                    

Journal and Book
1.A. L. Bristow, M. Wardman, A. M. Zanni, & P. K. Chintakayala. (2010). Public acceptability of personal carbon trading and carbon tax. Ecological Economics, 69, 1824-1837.
2.A. Markandya, R. A. Ortiz, S. Mudgal, & B. Tinetti. (2009). Analysis of tax incentives for energy-efficient durables in the EU. Energy Policy, 37(12), 5662-5674. 3.Agung, G. N. (2009). Time Series Data Analysis Using Eviews: John Wiley & Sons(Asia)Pte Ltd.
4.Aki, H., Oyama, T., & Tsuji, K. (2006). Analysis of energy service systems in urban areas and their CO2 mitigations and economic impacts. Applied Energy, 83(10), 1076-1088.
5.ArcelorMittal. (2011). ArcelorMittal CSR2010
6.B. Lin, & X. Li. (2011). The effect of carbon tax on per capita CO2 emissions. Energy Policy, 39, 5137–5146.
7.Box, G., & Jenkins, G. (1970). Time series analysis: Forecasting and control.
8.Chang, Y. F. (1997). Model Development and Application of Linkage Effects between Industry, Energy and CO2 Reduction. Ph. D thesis, National Cheng Kung University.
9.Chen, T. Y. (2001). The impact of mitigating CO2 emissions on Tjohnaiwan's economy. Energy Economics 23, 141-151.
10.Chen, Y. C. (2010). Co-relation between the Unemployment Rate and CO2 Emission and other Economic Development Indicators in Taiwan. National Taipei University of
Technology.
11.China Steel. (2010). China Steel Corporate Social Responsibility.
12.Cryer, J. D., & Chan, K. S. (2008). Time Series Analysis with Applications in R: Springer.
13.Daniel Peña, George C. Tiao, & Ruey S. Tsay. (2008). A course in Time Series Analysis: Wiley-Interscience.
14.Dylan, J., & Mehrdad, T. (2010). Practical Goal Programming: Springer.
15.Dylan, J., Mehrdad, T., & Jana, R. (2010). New Developments in Multiple Objective and Goal Programming: Springer.
16.F. Biermann, & R. Brohm. (2005). Implementing the Kyoto Protocol without the USA: the strategic role of energy tax adjustments at the border. Climate Policy, 4, 289–302.
17.Fang, J. M., & Chen, J. C. (2007). Comparative Analysis of CO2 Emissions and Economic Growth Relationships in Taiwan and Sweden. 4, p.16-33.
18. G. H. Bjertnæs, & T. Fæhn. (2008). Energy taxation in a small, open economy Social efficiency gains versus industrial concerns. Energy economics, 30(4), 2050-2071.
19.G. R. Timilsina, S. Csordás, & S. Mevel. (2011). When does a carbon tax on fossil fuels stimulate biofuels? Ecological Economics, 70, 2400–2415.
20.Gaur, V., Giloni, A., & Seshadri, S. (2005). Information sharing in a supply chain under ARMA demand. Management Science, 51 (6), 961-969.
21.Gustavsson, L., Holmberg, J., Dornburg, V., Sathre, R., Eggers, T., Mahapatra, K., & Marland, G. (2007). Using biomass for climate change mitigation and oil use reduction.
Energy Policy, 35(11), 5671-5691.
22.H. Hennessy, & R. S. J. Tol. (2011). The impact of tax reform on new car purchases in Ireland. ENERGY POLICY, 39(11), 7059-7067.
23.H. R.J. Vollebergh. (2008). Lessons from the polder: Energy tax design in The Netherlands from a climate change perspective. Ecological economics, 64, 660-672.
24.Henriques, M. F., Dantas, F., & Schaeffer, R. (2010). Potential for reduction of CO2 emissions and a low-carbon scenario for the Brazilian industrial sector. Energy Policy,
38(4), 1946-1961.
25.Hsu, C.-C., & Chen, C.-Y. (2004). Investigating strategies to reduce CO2 emissions from the power sector of Taiwan. International Journal of Electrical Power & Energy
Systems, 26(6), 455-460.
26.Hsu, G. J. Y., & Chou, F. Y. (2000). Integrated planning for mitigating CO2 emissions in Taiwan a multi-objective programming approach. Energy Policy 28 (2000), 519-523.
27.Huang, L. L. (1996). Assessment and Analysis of Carbon Dioxide Reduction Potentiality for Steel Industry in Taiwan. Master thesis, National Cheng Kung University.
28.Hung, C. M., & Wu, P. H. (2010). Greenhouse gas reduction tool assessment and case application. Paper presented at the 2010 Conference of Environmental and Energy
29.IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Chapter 6, reference approach.
30.Jeong, C., Chu, Y.-H., Han, C., & Yoon, E. S. (2010). Gasholder level control based on time-series analysis and process heuristics. Korean Journal of Chemical Engineering, 28(1), 16-21.
31.JFE. (2011). JFE annual report 2011.
32.Johnston, J. and DiNardo J. (1997). Econometric Methods, McGraw-Hill/Irwin; 4 edition
33.K. A. Hassett, A. Mathur, & G. E. Metcalf. (2009). The Incidence of a U.S. Carbon Tax: A Lifetime and Regional Analysis. Energy Journal, 30(2), 155-177.
34.Lee, C. F. (2000). Model Construction and Impacts Assessment of CO2 Reduction in the Petrochemical Industries - an Application of Grey and Fuzzy Theory. Ph.D. thesis,
Nationcal Cheng Kung University.
35.Lee, C. F., Lin, S. J., Lewis, C., & Chang, Y. F. (2007). Effects of carbon taxes on different industries by fuzzy goal programming: A case study of the petrochemical-related
industries, Taiwan. Energy Policy, 35(8), 4051-4058.
36.Li, H., Cang, D., Guo, Y., & Bai, H. (2011). the analysis of energy structure of coal using and CO2 emission of boasteel. Paper presented at the Materials for Renewable Energy & Environment (ICMREE), 2011 International Conference on Shanghai.
37.Liang, C. Y. (2001). The Impact of Energy Conservation Under Petrol and Electricity Price Changes Bureau of Energy, Ministy of Economic Affairs, R.O.C. .
38.Liu, K. C. (2009, 10/19/2009). Energy tax data collection, analysis and recommendations. Paper presented at the CTCI 2009 Environment and Energy International Seminar.
39.Ministry of Economic. (2010). Policy Impact Assessment on Iron and steel Industry.
40.Ministry of Economic Affairs. (2008). Sustainable Energy Policy Programme.
41.Nippon Steel. (2010). Nippon Steel Sustainable Report.
42.Preez, J. d., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting, 19, 435–451.
43.R. Hart. (2008). The timing of taxes on CO2 emissions when technological change is endogenous. Journal of Environmental Economics and Management, 55, 194–212.
44.Ruth, M. (1995). Technology change in US iron and steel production. Resources Policy, 21 No.3, 199-214.
45.Saxén, H., Pettersson, F., & Gunturu, K. (2007). Evolving Nonlinear Time-Series Models of the Hot Metal Silicon Content in the Blast Furnace. Materials and Manufacturing Processes, 22(5), 577-584.
46.Shaw, D. G., Huang, Y. H., Chen, M. J., Hung, C. M., Chen, P., & Huang T. H. (2009). Research of Green Tax Reform: Chung-Hua Institution for Economic Research.
47.Stavins, R. (2011). Assessing the Climate Talks — Did Durban Succeed? Harvard Kennedy school.
48.Taiwan Steel. (2010). Steel Statistics Taiwan Steel & Iron Industries Association.
49.Tseng, F. M., Tzeng, G. H., & Yu, H. C. (1999). Fuzzy Seasonal TimeSeries for Forecasting the Production Value of the Mechanical Industry in Taiwan. Technological
Forecasting and Social Change, 60, 263-273.
50.Tzeng, Y. J. (2007). The Impact of Greenhouse Gas Mitigation Strategies on Taiwanese Industries Application of Multiple Objectice Decision Method. Master thesis, National
Taiwan University.
51.Vanderbei, R. J. (2008). Linear Programming: Foundations and Extensions. Springer.
52.Wang, C., Ryman, C., & Dahl, J. (2009). Potential CO2 emission reduction for BF–BOF steelmaking based on optimised use of ferrous burden materials. International Journal of
Greenhouse Gas Control, 3(1), 29-38.
53.Wang, W. C., Chau, K. W., Cheng, C. T., & Qiu, L. (2009). A comparison of performance of several artificial intelligence methods for forecasting monthly discharge
time series. Journal of Hydrology, 374(3-4), 294-306.
54.Wang, Y. T. (2009). Multiobjective Decision Making on CO2 Emission Reduction in Taiwan. Master, National Chung Hsing University.
55.World Resources Institute. (2005). The Greenhouse Gas Protocol: a Corporate Accounting and Reporting Standard (M. H. Huang, Trans.): Business Council for Sustainable Development of the Republic of China.
56.Wu, R. H. (2006). Energy Saving Technology Handbook: Bureau of Energy, Ministy of Economic Affairs.
57.Wu, Z. H. (2009). Combining Material Flow Analysis and System Dynamic to Asscess CO2 emissions of Taiwan Iron and Stell Industry. Master Thesis, National Taiwan University.
58.Yang, J. Y., & Fan, P. H. (2007). Study on the Industrial Added Value Ministry of Economic Affairs,R.O.C.: Ministry of Economic.
59.Yang, Y. N. (2009). Time Series Analysis in Economics and Finance: Yeh Yeh Book Gallery.

Website ans other references
1.AREMOS database.
2.Energy Bureau. (2007).
3.Energy statistics.
4.UCL. (2011). University College London website.
5.World Bank website.
6.World Steel.
7.World Steel. (2011). Top steel producers 2010

校內：2015-09-07公開
校外：2015-09-07公開

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