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題名 國際貿易能否促進永續發展？世界貿易數據的網路分析
Can international trade facilitate sustainable development? A network analysis of world trade data作者 廖其興
Liao, Chi-Hsing貢獻者 何靜嫺
廖其興
Liao, Chi-Hsing關鍵詞 全球貿易網絡
中心性
外溢效果
環境保護
Global trade network
Centrality
Spillover
Environmental Protection日期 2024 上傳時間 5-八月-2024 13:38:07 (UTC+8) 摘要 本文利用聯合國貿易數據庫的進出口實際數據，構建1996年至2022年間涵蓋144個國家的27個全球貿易網絡。研究方法包括採用閾值法建立網絡，利用網絡中心性(度中心性、緊密中心性、介數中心性)與網絡指標（網絡密度、同配性和模組度）分析全球貿易網絡的演變。並進一步探討環境保護的程度是否能透過國際貿易在國家之間產生溢出效應，並研究這種溢出效應的規模如何受網絡結構影響。此外，我們還分析了需求拉動和供給推動兩種溢出效應的具體表現。本研究發現，主要進口國和出口國能透過貿易網絡對其他國家的環境保護程度有顯著影響。依研究結果顯示，全球貿易網絡結構的變化會影響國家的環境保護水平。此外，本文進一步發現，主要出口大國對於貿易聯繫較少的國家存在顯著的供給推動外溢效果，而主要進口大國並無顯著的需求拉動外溢效應存在。這些發現對於政策制定者在推動環保政策和國際貿易政策時提供了寶貴的實證依據。未來的研究可以納入更多指標，如碳排放、空氣質量和水資源利用，以提供更全面的分析。同時，擴展數據範圍，收集和分析更多國家和更長時期的數據，將有助於提高研究結果的普遍性和穩健性。
Our paper constructs 27 global trade networks covering 144 countries from 1996 to 2022. The research methods include using the threshold method to establish networks and analyzing the evolution of global trade networks through network centrality and other indicators. Furthermore, it explores whether environmental protection can generate spillover effects between countries through international trade and how network structures influence these spillover effects. The paper also analyzes the specific manifestations of demand-pull and supply-push spillover effects. The research indicates that major importing and exporting countries can significantly influence environmental protection in other countries through trade networks. Additionally, notable spillover effects from major exporting countries are observed in countries with fewer trade connections. These findings provide valuable empirical evidence for policymakers in promoting environmental protection and international trade policies. Future research could incorporate more indicators such as carbon emissions, air quality, and water resource utilization to provide a more comprehensive analysis. Expanding the data scope to include more countries and longer periods will also help enhance the generalizability and robustness of the research results.參考文獻 1. Abduraimova, K. (2022). Contagion and tail risk in complex financial networks. Journal of Banking & Finance, 143, 106560. 2. Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. 3. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7), 107-117. 4. Ciaschini, M., Pretaroli, R., Severini, F., & Socci, C. (2012). Regional double dividend from environmental tax reform: An application for the Italian economy. Research in Economics, 66(3), 273-283. 5. De Benedictis, L., & Tajoli, L. (2010). Comparing sectoral international trade networks. Aussenwirtschaft. 6. De Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34(8), 1417-1454. 7. Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1-15. 8. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134. 9.Ferrier, G. D., Reyes, J., & Zhu, Z. (2016). Technology diffusion on the international trade network. Journal of Public Economic Theory, 18(2), 291-312. 10. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5), 2223-2261. 11. Freeman, L. C. (2002). Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge, 1, 238-263. 12. He, J., & Deem, M. W. (2010). Structure and response in the world trade network. Physical review letters, 105(19), 198701. 13. Ilori, A. E., Paez-Farrell, J., & Thoenissen, C. (2022). Fiscal policy shocks and international spillovers. European Economic Review, 141, 103969. 14. Kali, R., & Reyes, J. (2010). Financial contagion on the international trade network. Economic Inquiry, 48(4), 1072-1101. 15. Liang, S., Feng, Y., & Xu, M. (2015). Structure of the global virtual carbon network: revealing important sectors and communities for emission reduction. Journal of Industrial Ecology, 19(2), 307-320. 16. Liu, B. Y., Fan, Y., Ji, Q., & Hussain, N. (2022). High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system. Energy Economics, 105, 105749. 17. Ligthart JE (1999) g-fiscalmacro-imf.pdf. IMF Work Pap WP/98/75:1--35 18. Miller, S., & Vela, M. (2013). Are environmentally related taxes effective?Inter-American Dev Bank IDB-WP-467. 19. Nobi, A., Lee, T. H., & Lee, J. W. (2020). Structure of trade flow networks for world commodities. Physica A: Statistical Mechanics and its Applications, 556, 124761. 20. Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. 21. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS 2005: 20th International Symposium, Istanbul, Turkey, October 26-28, 2005. Proceedings 20 (pp. 284-293). Springer Berlin Heidelberg. 22. Shahzad, U. (2020). Environmental taxes, energy consumption, and environmental protection: Theoretical survey with policy implications. Environmental Science and Pollution Research, 27(20), 24848-24862. 23. Tong, C., Chen, J., & Buckle, M. J. (2018). A network visualization approach and global stock market integration. International Journal of Finance & Economics, 23(3), 296-314. 24. Wen, F., Yang, X., & Zhou, W. X. (2019). Tail dependence networks of global stock markets. International Journal of Finance & Economics, 24(1), 558-567. 25. Wu, B., Zhu, P., Yin, H., & Wen, F. (2023). The risk spillover of high carbon enterprises in China: Evidence from the stock market. Energy Economics, 126, 106939. 26. Xia, L., Li, Y., & Ma, X. (2023). Identification of key carbon emitters from the erspective of network analysis. Ecological Indicators, 150, 110284. 27. Yanquen, E., Livan, G., Montanez-Enriquez, R., & Martinez-Jaramillo, S. (2022). Measuring systemic risk for bank credit networks: A multilayer approach. Latin American Journal of Central Banking, 3(2), 100049. 28. Zhang, X., Yang, X., Li, J., & Hao, J. (2023). Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach. Journal of Futures Markets, 43(6), 705-733. 29. Mahanti, A., Carlsson, N., Mahanti, A., Arlitt, M., & Williamson, C. (2013). A tale of the tails: Power-laws in internet measurements. IEEE Network, 27(1), 59-64. 描述 碩士
國立政治大學
經濟學系
111258031資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111258031 資料類型 thesis dc.contributor.advisor 何靜嫺 zh_TW dc.contributor.author (作者) 廖其興 zh_TW dc.contributor.author (作者) Liao, Chi-Hsing en_US dc.creator (作者) 廖其興 zh_TW dc.creator (作者) Liao, Chi-Hsing en_US dc.date (日期) 2024 en_US dc.date.accessioned 5-八月-2024 13:38:07 (UTC+8) - dc.date.available 5-八月-2024 13:38:07 (UTC+8) - dc.date.issued (上傳時間) 5-八月-2024 13:38:07 (UTC+8) - dc.identifier (其他 識別碼) G0111258031 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152707 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 111258031 zh_TW dc.description.abstract (摘要) 本文利用聯合國貿易數據庫的進出口實際數據，構建1996年至2022年間涵蓋144個國家的27個全球貿易網絡。研究方法包括採用閾值法建立網絡，利用網絡中心性(度中心性、緊密中心性、介數中心性)與網絡指標（網絡密度、同配性和模組度）分析全球貿易網絡的演變。並進一步探討環境保護的程度是否能透過國際貿易在國家之間產生溢出效應，並研究這種溢出效應的規模如何受網絡結構影響。此外，我們還分析了需求拉動和供給推動兩種溢出效應的具體表現。本研究發現，主要進口國和出口國能透過貿易網絡對其他國家的環境保護程度有顯著影響。依研究結果顯示，全球貿易網絡結構的變化會影響國家的環境保護水平。此外，本文進一步發現，主要出口大國對於貿易聯繫較少的國家存在顯著的供給推動外溢效果，而主要進口大國並無顯著的需求拉動外溢效應存在。這些發現對於政策制定者在推動環保政策和國際貿易政策時提供了寶貴的實證依據。未來的研究可以納入更多指標，如碳排放、空氣質量和水資源利用，以提供更全面的分析。同時，擴展數據範圍，收集和分析更多國家和更長時期的數據，將有助於提高研究結果的普遍性和穩健性。 zh_TW dc.description.abstract (摘要) Our paper constructs 27 global trade networks covering 144 countries from 1996 to 2022. The research methods include using the threshold method to establish networks and analyzing the evolution of global trade networks through network centrality and other indicators. Furthermore, it explores whether environmental protection can generate spillover effects between countries through international trade and how network structures influence these spillover effects. The paper also analyzes the specific manifestations of demand-pull and supply-push spillover effects. The research indicates that major importing and exporting countries can significantly influence environmental protection in other countries through trade networks. Additionally, notable spillover effects from major exporting countries are observed in countries with fewer trade connections. These findings provide valuable empirical evidence for policymakers in promoting environmental protection and international trade policies. Future research could incorporate more indicators such as carbon emissions, air quality, and water resource utilization to provide a more comprehensive analysis. Expanding the data scope to include more countries and longer periods will also help enhance the generalizability and robustness of the research results. en_US dc.description.tableofcontents 1 Introduction 1 2 Literature Review 2 3 Global Trade Networks 6 3.1 Data Source 6 3.2 Network Architecture Construction 8 3.3 Determine The Threshold 9 3.4 Network and Country-wise Properties 11 3.4.1 Network-wise Indicators 12 3.4.2 Country-wise Indicators 14 4 Evolution of Global Trade Networks 18 4.1 Evolution of Network Structures 19 4.2 Evolution of Countries Influences 24 5 Spillover of Environmental Protection 31 5.1 Regression Models and Variables 32 5.2 Regression Results 36 6 Concluding Remarks 48 7 References 51 zh_TW dc.format.extent 2063477 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111258031 en_US dc.subject (關鍵詞) 全球貿易網絡 zh_TW dc.subject (關鍵詞) 中心性 zh_TW dc.subject (關鍵詞) 外溢效果 zh_TW dc.subject (關鍵詞) 環境保護 zh_TW dc.subject (關鍵詞) Global trade network en_US dc.subject (關鍵詞) Centrality en_US dc.subject (關鍵詞) Spillover en_US dc.subject (關鍵詞) Environmental Protection en_US dc.title (題名) 國際貿易能否促進永續發展？世界貿易數據的網路分析 zh_TW dc.title (題名) Can international trade facilitate sustainable development? A network analysis of world trade data en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. Abduraimova, K. (2022). Contagion and tail risk in complex financial networks. Journal of Banking & Finance, 143, 106560. 2. Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social networks, 44, 153-159. 3. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems, 30(1-7), 107-117. 4. Ciaschini, M., Pretaroli, R., Severini, F., & Socci, C. (2012). Regional double dividend from environmental tax reform: An application for the Italian economy. Research in Economics, 66(3), 273-283. 5. De Benedictis, L., & Tajoli, L. (2010). Comparing sectoral international trade networks. Aussenwirtschaft. 6. De Benedictis, L., & Tajoli, L. (2011). The world trade network. The World Economy, 34(8), 1417-1454. 7. Demirer, M., Diebold, F. X., Liu, L., & Yilmaz, K. (2018). Estimating global bank network connectedness. Journal of Applied Econometrics, 33(1), 1-15. 8. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134. 9.Ferrier, G. D., Reyes, J., & Zhu, Z. (2016). Technology diffusion on the international trade network. Journal of Public Economic Theory, 18(2), 291-312. 10. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5), 2223-2261. 11. Freeman, L. C. (2002). Centrality in social networks: Conceptual clarification. Social network: critical concepts in sociology. Londres: Routledge, 1, 238-263. 12. He, J., & Deem, M. W. (2010). Structure and response in the world trade network. Physical review letters, 105(19), 198701. 13. Ilori, A. E., Paez-Farrell, J., & Thoenissen, C. (2022). Fiscal policy shocks and international spillovers. European Economic Review, 141, 103969. 14. Kali, R., & Reyes, J. (2010). Financial contagion on the international trade network. Economic Inquiry, 48(4), 1072-1101. 15. Liang, S., Feng, Y., & Xu, M. (2015). Structure of the global virtual carbon network: revealing important sectors and communities for emission reduction. Journal of Industrial Ecology, 19(2), 307-320. 16. Liu, B. Y., Fan, Y., Ji, Q., & Hussain, N. (2022). High-dimensional CoVaR network connectedness for measuring conditional financial contagion and risk spillovers from oil markets to the G20 stock system. Energy Economics, 105, 105749. 17. Ligthart JE (1999) g-fiscalmacro-imf.pdf. IMF Work Pap WP/98/75:1--35 18. Miller, S., & Vela, M. (2013). Are environmentally related taxes effective?Inter-American Dev Bank IDB-WP-467. 19. Nobi, A., Lee, T. H., & Lee, J. W. (2020). Structure of trade flow networks for world commodities. Physica A: Statistical Mechanics and its Applications, 556, 124761. 20. Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556. 21. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS 2005: 20th International Symposium, Istanbul, Turkey, October 26-28, 2005. Proceedings 20 (pp. 284-293). Springer Berlin Heidelberg. 22. Shahzad, U. (2020). Environmental taxes, energy consumption, and environmental protection: Theoretical survey with policy implications. Environmental Science and Pollution Research, 27(20), 24848-24862. 23. Tong, C., Chen, J., & Buckle, M. J. (2018). A network visualization approach and global stock market integration. International Journal of Finance & Economics, 23(3), 296-314. 24. Wen, F., Yang, X., & Zhou, W. X. (2019). Tail dependence networks of global stock markets. International Journal of Finance & Economics, 24(1), 558-567. 25. Wu, B., Zhu, P., Yin, H., & Wen, F. (2023). The risk spillover of high carbon enterprises in China: Evidence from the stock market. Energy Economics, 126, 106939. 26. Xia, L., Li, Y., & Ma, X. (2023). Identification of key carbon emitters from the erspective of network analysis. Ecological Indicators, 150, 110284. 27. Yanquen, E., Livan, G., Montanez-Enriquez, R., & Martinez-Jaramillo, S. (2022). Measuring systemic risk for bank credit networks: A multilayer approach. Latin American Journal of Central Banking, 3(2), 100049. 28. Zhang, X., Yang, X., Li, J., & Hao, J. (2023). Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach. Journal of Futures Markets, 43(6), 705-733. 29. Mahanti, A., Carlsson, N., Mahanti, A., Arlitt, M., & Williamson, C. (2013). A tale of the tails: Power-laws in internet measurements. IEEE Network, 27(1), 59-64. zh_TW
