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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 童慶斌 | zh_TW |
dc.contributor.advisor | Ching-Pin Tung | en |
dc.contributor.author | 林暐盛 | zh_TW |
dc.contributor.author | Wei-Sheng Lin | en |
dc.date.accessioned | 2023-07-20T16:04:18Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-07-19 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-05-17 | - |
dc.identifier.citation | 1. Ramstein, C., Dominioni, G., Ettehad, S., Lam, L., Quant, M., Zhang, J., ... & Trim, I. (2021). State and trends of carbon pricing 2021. The World Bank.
2. European Commission. (2021). Proposal for a regulation of the European parliament and of the Council establishing a carbon border adjustment mechanism 3. Josh Burke, Luca Taschini, Stuart Evans, Karishma Gulrajani and Aaron Tam.(2020). Carbon pricing options for Taiwan: Report prepared for Taiwan Environmental Protection Administration. London School of Economics and Political Science 4. 行政院環境保護署國家溫室氣體登錄平台(2020年版)。臺北市:行政院。取自網址https://ghgregistry.epa.gov.tw/ghg_rwd/Main/Index 5. Board, F. S. (2017). Recommendations of the task force on climate-related financial disclosures. 6. Eis, J., Schafer, J., Carr, B., Clawson, F., Borduas, T., Léveillée, M., ... & Block, M. (2019). Changing Course: A comprehensive investor guide to scenario-based methods for climate risk assessment, in response to the TCFD. UNEP Finance Initiative. 7. Bank for International Settlements (2021). Climate-related financial risks – measurement methodologies. 8. GHG Protocol. (2013). Technical Guidance for Calculating Scope 3 Emissions. First edition. 9. PCAF (2020). The Global GHG Accounting and Reporting Standard for the Financial Industry. First edition. 10. Statistical Office of the European Communities. (1990). Greenhouse gas emissions by source sector. Luxembourg: Eurostat. Retrieved from https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sdg_13_10&lang=en 11. Stadler, K., Wood, R., Bulavskaya, T., Södersten, C. J., Simas, M., Schmidt, S., ... & Tukker, A. (2018). EXIOBASE 3: Developing a time series of detailed environmentally extended multi‐regional input‐output tables. Journal of Industrial Ecology, 22(3), 502-515. 12. Wood, R., Stadler, K., Simas, M., Bulavskaya, T., Giljum, S., Lutter, S., & Tukker, A. (2018). Growth in environmental footprints and environmental impacts embodied in trade: resource efficiency indicators from EXIOBASE3. Journal of Industrial Ecology, 22(3), 553-564. 13. Teske, S., Niklas, S., Nagrath, K., Talwar S., Atherton, A., Guerrero Orbe, J., (2020), Sectoral pathways and Key Performance Indicators: aluminium, chemical, cement, steel, textile & leather industry, power utilities, gas utilities, agriculture, forestry, the aviation and shipping industry, road transport, and the real estate & building industry. Report prepared by the University of Technology Sydney for the UN-convened Net Zero Asset Owners Alliance. 14. IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Masson-Delmotte V, Zhai P, Pirani A., Connors SL, Péan C, Berger S, Caud N, Chen Y, Goldfarb L, Gomis MI, Huang M, Leitzell K, Lonnoy E, Matthew JBRs, Maycock TK, Waterfield T, Yelekçi O, Yu R, and Zhou B (eds). Cambridge University Press. 15. Network for Greening the Financial System. (2021). NGFS Climate Scenarios for central banks and supervisors 16. Devulder, A., & Lisack, N. (2020). Carbon Tax in a Production Network: Propagation and Sectoral Incidence. 17. Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015), "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production", Review of International Economics., 23: 575–605 18. Bun, M. (2018). The economic impact of pricing CO2 emissions: Input output analysis of sectoral and regional effects. The price of transition: An analysis of the economic implications of carbon taxing. DNB Occasional Studies, 1608. 19. Guo, Z., Zhang, X., Zheng, Y., & Rao, R. (2014). Exploring the impacts of a carbon tax on the Chinese economy using a CGE model with a detailed disaggregation of energy sectors. Energy Economics, 45, 455-462. 20. Hu, H., Dong, W., & Zhou, Q. (2021). A comparative study on the environmental and economic effects of a resource tax and carbon tax in China: analysis based on the computable general equilibrium model. Energy Policy, 156, 112460. 21. 吳榮華(2016)。應用產業關聯線性規劃模型於臺灣電力配置之研究。科技部補助計畫(報告編號:MOST 105-2410-H-006-076-),未出版。 22. 孫雅彥(2012)。觀光發展為無煙囪產業? 觀光產業溫室氣體排放趨勢之研究。行政院國家科學委員會補助計畫(報告編號:NSC101-2410-H006-128),未出版。 23. 綠色和平(2021)。淨零賽局來臨: 國際碳邊境稅臺灣衝擊報告。取至網址https://www.greenpeace.org/static/planet4-taiwan-stateless/2021/08/9268d16a-%E6%B7%A8%E9%9B%B6%E8%B3%BD%E5%B1%80%E4%BE%86%E8%87%A8%EF%BC%9A%E5%9C%8B%E9%9A%9B%E7%A2%B3%E9%82%8A%E5%A2%83%E7%A8%85%E8%87%BA%E7%81%A3%E8%A1%9D%E6%93%8A%E5%A0%B1%E5%91%8A-compressed.pdf 24. Bank of England. (2021). Guidance for participants of the 2021 Biennial Exploratory Scenario: Financial risks from climate change. 25. Bataille, C., & Melton, N. (2017). Energy efficiency and economic growth: A retrospective CGE analysis for Canada from 2002 to 2012. Energy Economics, 64, 118-130. 26. Liang, Q. M., Fan, Y., & Wei, Y. M. (2009). The effect of energy end-use efficiency improvement on China’s energy use and CO 2 emissions: a CGE model-based analysis. Energy Efficiency, 2(3), 243-262. 27. OECD (2021), OECD Inter-Country Input-Output Database, http://oe.cd/icio 28. Carrico, C., Corong, E., & van der Mensbrugghe, D. (2020). The GTAP 10A Multi-Region Input Output (MRIO) Data Base. 29. Bhatia, P., & Ranganathan, J. (2004). The Greenhouse Gas Protocol. 30. 臺灣電力公司 (2016)。歷年行業別用電按月統計資料(2021年6月更新版本) 碼。取自網址https://data.gov.tw/dataset/31966 31. Bertram, C., van Ruijven, B., Hilaire, J., Kriegler, E., Clarke, L., Cui, R., ... & Hurst, I. (2021). NGFS Climate Scenarios Data Set. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87847 | - |
dc.description.abstract | 碳定價相關制度會對產業營運帶來額外支出,且產業中的企業若為因應碳定價費用而調漲產品價格,將進一步透過產業供應鏈間的成本轉嫁,以及下游產業的替代效應而影響供應鏈供需關係。因此,一個產業所受之碳定價制度衝擊程度,除了取決於直接的費用支出外,也可能受其所在產業鏈之位置影響。此外,在未來有許多動態的變化因子,包含碳價格、經濟與產能的成長、能源使用效率改善,也將近一步改變產業供需結構以及碳定價的影響程度。
臺灣為出口導向的國際電子與科技產品的代工重鎮,其中的產業除了可能遭受臺灣政府可能於未來實行的國內碳費政策影響外,也可能受到未來國際以歐盟為首的碳邊境調整機制措施衝擊,影響整體營運與財務表現。因此,找出可能會受到碳定價政策顯著負面影響的產業,將對於產業/企業利害關係人之決策調整有巨大幫助。 本研究將使用可計算一般均衡(Computable General Equilibrium, CGE) 模型建構產業之間的供需變化,產出每個產業的價格與產量等指標在不同情境下的變化,同時依據所設定的四種不同情境調整動態因子,呈現政策、經濟以及能源效率在未來不同時期對整體碳定價影響程度的改變,在產出結果上主要聚焦於臺灣各產業的相關指標變化。本研究範疇涵蓋67個國家(整合為五大地區)以及每個地區中的45項產業,模擬時間依據不同情境分別涵蓋2025與2035年。 研究結果呈現出在有序轉型-2030年情境(全球循序漸進進行氣候轉型發展至2030年之情境)下,臺灣各產業受碳定價費用而產生相對基準情境的價格變化範圍自0.60%至37.73%,產量變化範圍則自0.24%至-13.34%。此結果展現出各產業間所受衝擊程度的顯著差距,且所受衝擊程度仍主要取決於自身所受的碳定價費用支出,產業鏈所帶來的間接影響相對輕微。而從情境間的差距比較,結果顯示越高的碳價格以及越短的轉型時間可能會對各產業帶來越顯著的衝擊,尤其是本身受碳定價影響大之產業差距更顯著,故無序轉型-2030年情境(全球以較急遽且無秩序之氣候轉型持續至2030年之情境)將帶來最大的影響。而從有序轉型-多地區碳價上升-2030情境(在有序轉型之假設下調整歐洲與中國等地之區域碳定價價格同步上升並持續至2030年之情境)則可以觀察到臺灣的製造業相關產業會因來自中國、歐洲地區的中間投入成本上升而受到更大衝擊,相對而言服務業相關產業則會相對受益於替代效應。 | zh_TW |
dc.description.abstract | Carbon pricing policy can bring operational cost for industries. In addition, an industry may adjust their product prices in response to these costs, and this could further affect the supply-demand relationship through cost pass-through and substitution effect from each industry, causing the total production amount of the industries to change. Moreover, there are several dynamic factors, such as carbon price level, production growth and improvement of energy efficiency, which will further change the industry structure and the level of impact by carbon pricing policy in different period. As a result, the industry sector could be affected by carbon price, and some industries could suffer more severe impact due to their GHG emission or their positions in production chain.
Taiwan is an export-oriented territory whose industries plays an important role in the international value chains. The relevant industries and other carbon-intensive industries may be impacted by not only the domestic carbon fee regulation by Taiwanese government, but also the carbon border tax policy such as EU’s Carbon Border Adjustment Mechanism (CBAM). These regulations will bring effect to the financial and marketing performance of the industries. By finding the most affect industries in Taiwan, not only the companies can design strategies early to adapt, but also the government and even the financial institutes can obtain more information to make decisions. The research adopts the computational general equilibrium (CGE) model to simulate the change of supply-demand structure raised by carbon pricing policy and carbon border tax between industries of different countries/regions in the world. Under this research, 4 sets of simulation are conducted. Each one is set in different period, scenario and number of regions implementing carbon price, then the dynamic factors are adjusted correspondingly as the input for simulation. The main outputs of this research are (1) the product price and (2) the amount of production for each industry under simulation, including 5 countries/regions and 45 industries in each region, and this research focuses on the impact of the industries in Taiwan. In this research, the simulated periods are 2030 and 2035, depends on the scenario adopted. The result of this research shows that under the orderly transition - 2030 scenario, the rises of product/service price from each industry in Taiwan range from +0.60% to 37.73% due to carbon pricing policy, and the amount of production changes from +0.24% to -13.34%, relative to the baseline scenario. This result demonstrates high difference of impact among the industries, which mainly depends on the carbon pricing expenditure of each industry comparing to the effects of industry chain. As for different scenarios, the result shows more severe impacts for high sensitivity industries under scenarios with higher level of carbon price and shorter time for transition. Therefore, the disorderly transition – 2030 scenario brings the most serious impact for the Taiwanese industrial sector. In addition, under orderly transition- carbon price for multiple region – 2030 scenario, the Taiwanese manufacturing industries suffers from higher costs due to the rising price of Intermediate inputs from China and Europe, whereas the service industries relatively benefits from the substitute effects. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-07-20T16:04:17Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-07-20T16:04:18Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審訂書 ... I
謝辭 ... II 摘要 ... III Abstract ....V 目錄 ... VII 圖目錄 ... IX 表目錄 ... XI 第一章、緒論 ... 1 1.1 研究動機 ... 1 1.2 研究目的 ... 3 1.3 研究架構 ... 4 第二章、文獻回顧 ... 5 2.1 碳定價制度對企業 / 產業之直接影響評估: 現況評估方法 ... 5 2.2 碳定價制度對產業鏈所造成風險之評估方法 ... 7 2.3 未來調整因子於模型中之考量 ... 10 2.3 文獻分析總結 ... 10 第三章、研究方法 ... 12 3.1 現況輸入資料 .... 12 3.2 未來情境調整因子 ... 17 3.3 模型 ... 20 3.4 碳定價費率計算 ... 25 第四章、研究案例設定與資料概述 ... 31 4.1 研究資料來源 ... 31 4.2 情境設定 ... 38 第五章、研究案例結果分析 ... 43 5.1 有序轉型-2030 年情境: 影響結果分析 ... 43 5.2 情境差距比較 ... 66 第六章、結論與建議 ... 79 6.1 研究結論 ... 79 6.2 研究建議 ... 82 參考文獻 ... 84 附錄一、產業代號與名稱對照表 ... 88 | - |
dc.language.iso | zh_TW | - |
dc.title | 碳定價對於產業鏈之供需影響評估之情境分析: 以臺灣產業鏈為例 | zh_TW |
dc.title | The Effect of Carbon Pricing Policy on Industry Chain and Its Future Change: The Case of Taiwan Production Network | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 胡明哲;李明旭;趙家緯 | zh_TW |
dc.contributor.oralexamcommittee | Ming-Che Hu;Ming-Hsu Li;Chia-Wei Chao | en |
dc.subject.keyword | 氣候變遷,轉型風險,碳定價,產業鏈,可計算一般均衡模型, | zh_TW |
dc.subject.keyword | climate change,transition risk,carbon pricing,industry chain,computational general equilibrium model, | en |
dc.relation.page | 90 | - |
dc.identifier.doi | 10.6342/NTU202300801 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2023-05-18 | - |
dc.contributor.author-college | 理學院 | - |
dc.contributor.author-dept | 氣候變遷與永續發展國際學位學程 | - |
顯示於系所單位: | 氣候變遷與永續發展國際學位學程(含碩士班、博士班) |
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