Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/142628
|
| Title: | 台灣再生能源指數型保險之研究
Essays on index-based renewable energy insurance for Taiwan |
| Authors: | 廖士傑
Liao, Shih-Chieh |
| Contributors: | 張士傑
Chang, Shih-Chieh
廖士傑
Liao, Shih-Chieh |
| Keywords: | 再生能源
指數型保險
離岸風力發電場
太陽能發電廠
天氣時機
天氣停工
Renewable energy
Index-based insurance
Offshore wind farm
Solar PV power plant
Weather window
Weather downtime |
| Date: | 2022 |
| Issue Date: | 2022-12-02 15:17:33 (UTC+8) |
| Abstract: | 本論文由三篇指數型保險的研究，運用於台灣再生能源風險管理相關議題所構成。台灣政府規劃於2025年，再生能源的發電量可以達到供應總發電需求的百分之二十。達成這個目標，需要加速發展運用再生能源發電，並達到一定的規模才可能實現。減少燃煤，增加天然氣與再生能源的發電比重，是台灣的能源政策，並完成非核家園的理想。離岸風力與太陽能發電對於台灣的再生能源發展，扮演至關重要的角色。離岸風力預計在2021至2025年間併網發電5.7GW，於2026至2035年間，另規劃再增加10GW併網發電量。太陽能規劃在2025年達到14.2GW發電量。
再生能源專案的發電機組壽命約20至25年，在整個專案過程中，會一直面臨不同的動態風險，所以其相對應的保險要求具專業且複雜度高。不論是開發商、承包商、投資者與貸款人都需要有相當程度的知識並了解其所面臨的風險特性與程度。指數型保險設計是多元的，可以運用在分散再生能源初期的融資風險，也可以擴及整個運營階段。再生能源所產生的發電量多寡，主要是依靠天然的可再生來源。因此，風速太弱或是太陽輻射度不足，皆會造成再生能源發電營收的波動。因為可再生資源有天然的間歇特性，投資者與貸款人一般評估再生能源專案的穩定獲利風險度較高，因此安排專案融資的難度也較高。
指數型保險的設計可以運用於管理再生能源發電量的波動風險，以第三方機構所提供天氣指數資料，按照離岸風力發電場的場址或太陽能發電廠的廠址與配置發電機組能量，利用歷史資料庫去模擬再生能源波動所造成的發電量波動風險，並進而訂出保險觸發等賠付條件。第一章的指數型保險設計，著重在承保離岸風力發電量的波動風險，而第二章的指數型保險內容，運用在管理太陽能發電量的波動風險。
運營階段在離岸風力發電專案的總成本支出，佔有相當大的比例。離岸風電工作船舶在執行運營活動時，必須考量可執行度與安全性。若受到氣候不佳因素的影響，例如浪高超過船舶的設計限制等，會導致運營作業無法執行，進而造成不同程度的費用負擔。第三章的研究內容關於浪高風險對離岸風電工作船舶所造成的停工損失，藉由指數型保險的設計，運用保險的安排，藉以分散因為天氣風險導致停工的費用損失。
This dissertation includes three essays regarding index-based insurance applications for renewable energy. Renewable energy is crucial to secure a clean energy transition and help to limit global warming. Taiwan plans to generate 20% of its total energy capacity from renewable energy by 2025, and the share of renewable sources in the power sector needs to be rapidly scaled up. The overall energy policy calls for significantly less coal, more LNG, increased renewables, and a homeland with nuclear-free. Offshore wind energy and solar PV power play an important role; Taiwan will add 5.7GW of allocated already offshore wind power to the grid between 2021 and 2025. Between 2026-2035, an additional 10GW of offshore wind will be added to the grid. For solar PV power, Taiwan will add 14.2 GW by 2025.
Renewable energy projects face dynamic risk exposure for different risks throughout their life cycle that can contribute to a complex insurance environment requiring detailed knowledge and understanding from stakeholders such as developers, contractors, investors, and lenders. Index-based insurance can provide coverage opportunities for the complete life cycle of renewable energy projects from the beginning financing stage to operational exposures.
Renewable energy generation is dependent upon natural resources. Therefore, excess or lack of wind speeds and solar radiation shortfalls can lead to revenue variability. In addition, because of intermittency, investors and lenders consider renewable energy projects risky investments and can face difficulties in securing financing. Index-based solutions can cover the renewable energy production volatility. Triggers based on objective and third-party data customized to the insured`s site, offshore wind farm or solar PV power plant generation technology, and historical index dataset, index insurance can protect against loss of energy production due to the volatility of natural resources. Chapters 1 and 2 design index insurance products to manage the volatility risk for offshore wind and solar PV power production.
Operation and maintenance (O&M) activities are a big part of the total costs for offshore wind farms. However, weather-related risks such as high waves can result in time spent waiting out unfavorable weather conditions until planned works can recommence for safety reasons. That causes a costly impact on O&M. Chapter 3 designs index insurance to manage the logistical and financial risks caused by the weather downtime for offshore wind farm O&M activities. |
| Reference: | Alderman, H. & Haque, T. (2007). Insurance against covariate shocks-the role of index-based insurance in social protection in lower income countries of Africa, Working paper, The World Bank, Washington, DC, USA.
Allen, L. & Jagtiani, J. (2000). The risk effects of combining banking, securities, and insurance activities. Journal of Economics and Business, 52, 485–497.
Alternative Energy Stocks. (2018). How weather risk transfer can help wind & solar development, http://www.altenergystocks.com/archives/2018/06/how-weather-risk-transfer-can-help-wind-solar-development/. Accessed date: August 9, 2021.
Bandyopadhyay, A., Bhadra, A., Raghuwanshi, N. S. & Singh, R. (2008). Estimation of monthly solar radiation from measured air temperature extremes, Agricultural and Forest Meteorology, 148(11), 1707–1718.
Bansal, R., Bati, T. S. & Kothari, D. P. (2002). On some of the design aspects of wind energy conversion systems. Energy Conversion and Management, 43, 2175–2187.
Barnett, B. J. & Mahul, O. (2007). Weather insurance index for agriculture and rural areas in lower income countries, American Journal of Agricultural Economics, 89(5), 1241–1247.
Barnett, B. J., Barrett, C. B. & Skees, J. R. (2008). Poverty traps and index-based risk transfer products, World Development, 36(10), 1766–1785.
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Schepers, J. G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E. & Neckelmann, S. (2010). Quantifying the impact of wind turbine wakes on power output at offshore wind farms, Journal of Atmospheric and Oceanic Technology, 27, 1302–1317.
Behera, M. K., Majumder, I. & Nayaka, N. (2018). Solar photovoltaic power forecasting using optimized modified extreme learning machine technique, Engineering Science and Technology, an International Journal, 21(3), 428–438.
Borgonovoa, E., Gattib, S. & Peccatia, L. (2010). What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions,
European Journal of Operational Research, 205(1), 227–236.
Box, G. E. P., Jenkins, G. M. & Reinsel, G. C. (1994). Time series analysis: Forecasting and control, 3rd ed., Englewood Cliffs: Prentice-Hall, NJ, USA.
Brustedt, G., Bokusheva, R. & Heidelbach, O. (2008). Evaluating the potential of index insurance schemes to reduce crop yield risk in an arid region, Journal of Agricultural Economics, 59(2), 312–328.
Calif, R. & Schmitt, F. (2012). Modeling of atmospheric wind speed sequence using a lognormal continuous stochastic equation, Journal of Wind Engineering and Industrial Aerodynamics, 109, 1–8.
Carter, M., Janvry, A. D., Sadoulet, E. & Sarris, A. (2014). Index-based weather insurance for developing country: A review of evidence and a set of propositions for up-scaling, Working paper, Fondation Pour les études et Recherches sur le Développement International (FERDI), Clermont-Ferrand Cedex, France.
Carter, M., Janvry, A. D., Sadoulet, E. & Sarris, A. (2017). Index insurance for developing country agriculture: A reassessment, Annual Review of Resource Economics, 9, 421–438.
Carter, M., Janvry, A. D., Sadoulet, E. & Sarris, A. (2014). Index-based Weather Insurance for Developing Country: A Review of Evidence and A Set of Propositions for Up-scaling, Working paper. Fondation Pour les études et Recherches sur le Développement International.
Chang, H. W., Maa, M. J. & Lin, M. C. (2017). Comparative analysis of wave weather windows in operation and maintenance of offshore wind farms at Hsinchu and Changhua, Taiwan, Journal of Marine Science and Technology, 25(5).
Chantarat, S., Barrett, C. B., Mude, A. G. & Turvey, C. G. (2007). Using weather index insurance to improve drought response for famine prevention, American Journal of Agricultural Economics, 89(5), 1262–1268.
Chantarat, S., Mude, A. G., Barrett, C. B. & Carter, M. M. (2013). Designing index-based livestock insurance for managing asset risk in Northern Kenya, The Journal of Risk and Insurance, 80, 205–237.
Charabi, Y., Gastli, A. & Al-Yahyai, S. (2016). Production of solar radiation bankable datasets from high-resolution solar irradiance derived with dynamical downscaling numerical weather prediction model, Energy Reports, 2, 67–73.
Clement, K. Y., Botzen, W. W., Brouwer, R. & Aerts, J. C. (2018). A global review of the impact of basis risk on the functioning of and demand for index insurance, International Journal of Disaster Risk Reduction, 28, 845–853.
Coimbra, C., Kleissl, J. & Marquez, R. (2013). Chapter 8, Overview of solar-forecasting methods and a metric for accuracy evaluation. In: Kleissl, J. (Ed.), Solar Energy Forecasting and Resource Assessment, Academic Press, San Francisco, USA.
Dalhaus, T. & Finger, R. (2016). Can gridded precipitation data and phenological observations reduce basis risk of weather index-based insurance? Weather, Climate and Society, 8(4), 409–419.
Dalhaus, T., Musshoff, O. & Finger, R. (2018). Phenology information contributes to reduce temporal basis risk in agricultural weather index insurance, Scientific Reports, 8, 46.
De Wit, A. J. & Van Diepen, C. A. (2008). Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs, International Journal of Applied Earth Observation and Geoinformation, 10, 414–425.
Dick, W., Stoppa, A., Anderson, J., Coleman, E. & Rispoli, F. (2011). Weather index-based insurance in agriculture development a technical guide, International Fund for Agricultural Development (IFAD), Rome, Italy.
Diebold, F. X. (2007). Elements of forecasting, 4th ed., Cincinnati: South-Western.
Dinwoodie, I. A., Catterson, V. M. & McMillan, D. (2013). Wave height forecasting to improve offshore access and maintenance scheduling, IEEE Power & Energy Society General Meeting, 2013.
Dobos, A., Gilman, P. & Kasberg, M. (2012). P50/P90 analysis for solar energy systems using the system advisor model, Conference paper, World Renewable Energy Forum, Denver, CO, USA.
Figueroa-Acevedo, A. L. & Irizarry-Rivera, A. A. (2014). Variability assessment of solar and wind resources in Puerto Rica, International Conference on Probabilistic Methods Applied to Power Systems (PAMPS), 1–6.
Garcia-Bernabeu, A., Mayor-Vitoria, F. & Mas-Verdu, F. (2015). Project finance recent applications and future trends: The state of the art, International Journal of Business and Economics, 14(2), 159–178.
Gatti, S. (2019). Project finance in theory and practice: Designing, structuring, and financing private and public projects, Third Edition, Academic Press, MA, USA.
Gatzert, N. & Kosub, T. (2016). Risks and risk management of renewable energy projects: The case of onshore and offshore wind parks, Renewable and Sustainable Energy Reviews, 60, 982–998.
Gbémou, S., Eynard, J., Thil, S., Guillot, E. & Grieu, S. (2021). A comparative study of machine learning-based methods for global horizontal irradiance forecasting, Energies, 14, 3192.
Ghofrani, M. & Alolayan, M. (2018). Chapter 6, Time series and renewable energy forecasting, Time Series Analysis and Applications, IntechOpen Limited, London, UK.
Giné, X., Townsend, R. & Vickery, J. (2007). Statistical analysis of rainfall insurance payouts in Southern India, American Journal of Agricultural Economics, 89(5), 1248–54.
Graham, C. (1982). The parameterization and prediction of wave height and wind speed persistence, Coastal Engineering, 6, 303–329.
Gueymard, C. A. (2014). A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects, Renewable and Sustainable Energy Reviews, 39, 1024–1034.
Hazel, P., Anderson, J., Balzer, N., Clemmensen, A. H., Hess, L. I. & Rispoli, F. (2010). The Potential for Scale and Sustainability in Weather Index Insurance for Agriculture and Rural Livelihood. International Fund for Agricultural Development and World Food Programme.
Hess, U., Richter, K. & Stoppa, A. (2002). Weather risk management for agriculture and agri-business in developing countries. Climate risk and the weather market, Financial Risk Management with Weather Hedges, Risk Books, London, UK.
HSB. (2020). Energy Shortfall Insurance, https://www.munichre.com/hsb/en/products/commercial-lines-agents-and-brokers/energy-insurance/energy-shortfall-insurance.html. Accessed date: August 9, 2021.
Hyndman, R. J. & Fan, Y. (1996). Sample quantiles in statistical packages, American Statistician 50, 361–365.
Iturrioz, R. (2009). Agricultural insurance, Primer series on insurance issue, The World Bank, Washington, DC, USA.
Janvry, A. de & Sadoulet, E. (2011). From Indemnity, Index-based, and Group Weather Insurance Contracts. Policy Brief Note 25. Fondation Pour les études et Recherches sur le Développement International.
Jerez, S., Tobin, I., Vautard, R., Monta´vez, J. P., Lo´pez-Romero, J. M. ,Thais, F., Bartok, B., Christensen, O. B., Colette, A., De´que´, M., Nikulin, G., Kotlarski, S., Meijgaard, E., Teichmann, C. & Wild, M. (2015). The impact of climate change on photovoltaic power generation in Europe, Nature Communications, 6, 10014.
Jordan, D. C. & Kurtz, S. R. (2014). Reliability and geographic trends of 50,000 photovoltaic systems in the USA, Conference paper, The National Renewable Energy Laboratory, CO, USA.
Judgea, F., McAuliffea, F. D., Sperstadb, I. B., Chestera, R., Flannerya, B., Lyncha, K. & Murphy, J. (2019). A lifecycle financial analysis model for offshore wind farms, Renewable and Sustainable Energy Reviews, 103, 370–383.
Jung, C. & Schindler, D. (2018). On the inter-annual variability of wind energy generation – A case study from Germany. Applied Energy, 230, 845–854.
Kaplanis, S. & Kaplani, E. (2007). A model to predict expected mean and stochastic hourly global solar radiation I(h;n_j) values, Renewable Energy, 32(8), 1414–1425.
Karlan, D. & Morduch, J. (2010). “Access to Finance.” Chapter 2 in Dani Rodrik and Mark Rosenzweig, eds., Handbook of Development Economics, vol. 5. Amsterdam: North Holland.
Kleissl, J. (2013). Solar energy forecasting and resource assessment, 1st ed., Academic Press: Waltham, MA, USA.
Langella, R., Proto, D. & Testa, A. (2014). A statistical model of solar radiation daily variability, International Conference on Probabilistic Methods Applied to Power Systems (PAMPS), 1–6.
Laveyne, J. I., Bozalakov, D., Eetvelde, G.V. & Vandevelde, L. (2020). Impact of solar panel orientation on the integration of solar energy in low-voltage distribution grids, International Journal of Photoenergy.
Leblois, A. & Quirion, P. (2013). Agricultural insurances based on meteorological indices: Realizations, methods and research challenges, Meteorological Applications, 20, 1–9.
Li, D. H. W. & Lam, T. N. T. (2007). Determining the optimum tilt angle and orientation for solar energy collection based on measured solar radiance data, International Journal of Photoenergy.
Liao, S. C., Chang, S. C. & Cheng, T. C. (2021). Managing the volatility risk of renewable energy: Index insurance for offshore wind farms in Taiwan, Sustainability, 13, 8985.
Liao, S. C., Chang, S. C. & Cheng, T. C. (2022). Index-based renewable energy insurance for Taiwan solar photovoltaic power plants, Risk Management and Insurance Review, 25, 145–172.
Lowder, T., Mendelsohn, M. & Speer, B. (2013). Continuing developments in PV risk management: Strategies, solutions, and implications, Technical report, National Renewable Energy Laboratory, USA.
Lumby, B. (2015). A project developer’s guide to utility-scale solar photovoltaic power plants, International Finance Corporation, World Bank Group, Washington, DC, USA.
Mahul, O. & Skees, J. (2007). Managing agricultural risk at the country level: The case of index-based livestock insurance in Mongolia, Working paper, Washington, DC: The World Bank.
Manuamorn, O. P. (2005). Scaling up microinsurance: The case of weather insurance for smallholders in India, Agriculture and rural development discussion paper, The World Bank, Washington, DC, USA.
Martins, D., Muraleedharan, G. & Soares, C. G. (2015). Weather window analysis of a site off Portugal, Maritime Technology and Engineering, Taylor & Francis Group, UK, pp. 1329–1337.
Matsui, R., Moore, J., Nunalee, C., Garcia da Fonseca, L., Vadhavkar, N., Crimmins, J., Dise, S., Ahmad, J., Gregory, I., Fort, J. & Corbitt, J. (2021). Solar Risk Assessment: 2020, kWh Analytics, CA, USA.
Miranda, M. J. (1991). Area-yield crop insurance reconsidered, American Journal of Agricultural Economics, 73, 233–242.
Mora, E. B., Spelling, J., Weijde, A. H. & Pavageau, E. (2019). The effects of mean wind speed uncertainty on project finance debt sizing for offshore wind farms, Applied Energy, 252, 113419.
Morduch, J. (2006). Micro-insurance: The next revolution?, A. Banerjee, R. Benabou, D. Mookherjee (Eds.), What have we learned about poverty?, Oxford University Press, Oxford and New York.
Ortega, C., Younes, A., Severy, M., Chamberlin, C. & Jacobson, A. (2020). Resource and load compatibility assessment of wind energy offshore of Humboldt county, California, Energies, 13, 1–27.
Osgood, D. & Shirley K. E. (2012). The value of information in index insurance for farmers in Africa. In: Laxminarayan R., Macauley M. (Eds), The Value of Information, Springer.
Osgood, D., McLaurin, M., Carriquiry, M., Mishra, A., Fiondella, F., Hansen, J., Peterson, N. & Ward, N. (2007). Designing weather insurance contracts for farmers in Malawi, Tanzania and Kenya, Final report to the commodity risk management group, ARD, Washington, DC: The World Bank.
Pacudan, R. (2016). Implications of applying solar industry best practice resource estimation on project financing, Energy Policy, 95, 489–497.
Pelka, N. & Musshoff, O. (2013). Hedging effectiveness of weather derivatives in arable farming – Is there a need for mixed indices?, Agricultural Finance Review, 73(2), 358–372.
Pelka, N. & Musshoff, O. (2013). Hedging effectiveness of weather derivatives in erable farming – Is there a need for mixed indices?, Agricultural Finance Review, 73, 358–372.
Pinker, R.T., Frouin, R. & Li, Z. (1995). A review of satellite methods to derive surface shortwave irradiance, Remote Sensing of Environment, 51(1), 108–124.
Polo, J., Téllezb, F. M. & Tapia, C. (2016). Comparative analysis of long-term solar resource and CSP production for bankability, Renewable Energy, 90, 38–45.
Polzin, F., Egli, F., Steffen, B. & Schmidt, T. S. (2019). How do policies mobilize private finance for renewable energy?—A systematic review with an investor perspective, Applied Energy, 236, 1249–1268.
Santos, F. P., Teixeira, A. P. & Soares, C. G. (2015). An age-based preventive maintenance for offshore wind turbines, Safety and Reliability: Methodology and Applications, UK: Taylor & Francis Group; pp. 1147–1155.
Santos, F. P., Teixeira, A. P. & Soares, C. G. (2013). Maintenance planning of an offshore wind turbine using stochastic petri nets with predicates. Proc. 32nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2013), Nantes, France, OMAE 2013–11639.
Sen, R. & Bhattacharyyab, S. C. (2014). Off-grid electricity generation with renewable energy technologies in India: An application of HOMER, Renewable Energy, 62, 388–398.
Skees, J. R. (2008). Innovation in index insurance for the poor in lower income countries, Agricultural and Resource Economics Review, 37(10), 1–15.
Skees, J. R., Black, J. R. & Barnett, B. J. (1997). Designing and rating an area-yield crop insurance contract, American Journal Agricultural Economics, 79, 430–438.
Skees, J., Gober, S., Varangis, P., Lester, R. & Kalavakonda, V. (2001). Developing rainfall-based index insurance in Morocco, Policy research working papers, Washington, DC: The World Bank.
Skees, J. R. (2008). Innovation in index insurance for the poor in lower income countries, Agricultural and Resource Economics Review, 37, 1–15.
Skees, J. R., Black, J. R. & Barnett, B. J. (1997). Designing and rating an area-yield crop insurance contract, American Journal Agricultural Economics, 79, 430–438.
Steffen, B. (2018). The importance of project finance for renewable energy projects. Energy Economics, 69, 280–294.
Stoffel, T., Renne, D., Myers, D., Wilcox, S., Sengupta, M., George, R. & Turchi, C. (2010). Concentrating solar power: Best practices handbook for the collection and use of solar resource data, National Renewable Energy Laboratory Technical Report NREL, Golden, CO, USA.
Suh. J. & Choi, Y. (2017). Methods for converting monthly total irradiance data into hourly data to estimate electric power production from photovoltaic systems: A comparative study, Sustainability, 9, 1234.
Tiwari, G. N. & Dubey, S. (2010). Fundamentals of Photovoltaic Modules and their Applications, Royal Society of Chemistry, Cambridge, UK.
Tomaselli, P. D., Dixen, M., Sanchez, R. B. & Sørensen, J. T. (2021). A decision-making tool for planning O&M activities of offshore wind farms using simulated actual decision drivers, Frontiers in Marine Science, 7, 588624.
Vahdatmanesh, M. & Firouzi, A. (2018). Price risk management in BOT railroad construction projects using financial derivatives, Journal of Financial Management of Property and Construction, 23(3), 349–362.
Vedenov, D.V. & Barnett, B. J. (2004). Efficiency of weather derivatives as primary corp insurance instruments, Journal of Agricultural and Resource Economics, 29(3), 387–403.
Vendrell, I., Korsakul, R., Verojporn, S., Smithtinand, P. & Indradesa, P. (2014). Solar PV technical guidelines for financiers, Techno-commercial risk mitigation for grid-connected PV systems in ASEAN, Deutsche Gesselchaft für Internationale Zusammenarbeit (GIZ) GmbH.
Vignola, F., Grover, C., Lemon, N. & McMahan, A. (2012). Building a bankable solar radiation dataset, Solar Energy, 86(8), 2218–2229.
Vrieling, A., Meroni, M., Shee, A., Mude, A. G., Woodard, J., de Bie, C. A. J. M. & Rembold, F. (2014). Historical extension of operational NDVI products for livestock insurance in Kenya, International Journal of Applied Earth Observation and Geoinformation, 28, 238–251.
Westbrook, O. (2021). Your P values are wrong, IEEE 48th Photovoltaic Specialists Conference (PVSC), Virtual conference, pp. 2351-2356.
Wijeratne, W. M. P. U, Yang, R. J., Too, E. & Wakefield, R. (2019). Design and development of distributed solar PV systems: Do the current tools work?, Sustainable Cities and Society, 45, 553–578.
Wit, A. J. W. de & Diepen, C. A. van. (2008). Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs, International Journal of Applied Earth Observation and Geoinformation, 10, 414-425.
Wu, Y. T. & Porte-Agél, F. (2015). Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm, Renewable Energy, 75, 945–955.
Zurita, A., Mata-Torres, C., Cardemil, J. M., Guedez, R. & Escobar, R. (2021). Multi-objective optimal design of solar power plants with storage systems according to dispatch strategy, Energy, 237, 121627. |
| Description: | 博士
國立政治大學
風險管理與保險學系
105358503 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0105358503 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201695 |
| Appears in Collections: | [風險管理與保險學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 850301.pdf | | 5631Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
