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題名 中國新能源汽車產業發展現狀及前景探析— DEA和神經網路模型
Analysis on the Current Development Status and Prospect of China`s New Energy Vehicle Industry — Based on DEA and Neural Network Model作者 黃舒平
Huang, Shu-Ping貢獻者 廖四郎
Liao, Szu-Lang
黃舒平
Huang, Shu-Ping關鍵詞 新能源汽車
生產效率
資料包絡分析法
神經網路模型
New Energy Vehicles
Productive Efficiency
DEA
Neural Network日期 2022 上傳時間 10-Feb-2022 12:54:37 (UTC+8) 摘要 在能源危機和環境污染的背景之下，新能源汽車憑藉其節能減排的優勢，在發展初期便得到了國家的大力扶持，隨著其市場規模的迅速擴張，國家補貼力度逐漸減小，且產業發展戰略已由“政策推動”逐漸轉變為“市場拉動”，但新能源汽車仍存在著相關基礎設施不完善、電池續航能力差等問題，還需要政府加強基礎設施建設，及相關企業加大研發力度。在政府的補貼扶持及企業的研發投入皆有限的情況下，該產業未來是否能可持續發展，則需要瞭解其投入-產出比。本文將基於三階段DEA-Malmquist和BP神經網路模型，以中國新能源汽車上市公司為研究對象進行實證分析。首先，利用資料包絡分析法分別從靜態和動態分析中國新能源汽車產業的發展現狀；繼而，利用神經網路模型進行場景類比，預測中國新能源汽車產業未來發展的可能趨勢；最後，針對研究結果進一步探討促進該產業可持續發展的相應對策。實證結果顯示，2016年為中國新能源汽車產業的轉折點，2013至2016年間，中國新能源汽車上市公司技術效率均值穩步提高，2017年後，則開始逐年降低，近幾年產業的規模報酬也呈現出遞減的狀態，產業動態效益亦是呈下降的趨勢。除此之外，中國新能源汽車產業未來五年的效率預測值從整體角度看，同樣是下跌的走向，可見近年來距離產業發展有效狀態的差距逐步增大，找出相應解決辦法改善現狀實為重中之重。
Under the background of energy crisis and environmental pollution, new energy vehicles are strongly supported by the state at the early stage of development by virtue of their advantages in energy conservation and emission reduction. With the rapid expansion of the market scale of the industry, the national subsidy has decreased gradually. The industrial development strategy has been gradually transformed from "Policy-driven" to "Market-driven". However, there are still some problems about new energy vehicles, such as imperfect infrastructure and poor battery life. In the case of limited government subsidies and enterprises` investment in research and development, the sustainable development of the industry is determined by the input-output ratio.The paper is based on the Three-stage DEA-Malmquist and the BP neural network model. Chinese listed companies about new energy vehicles are taken as the research subject for empirical analysis. Firstly, the development status of the Chinese new energy vehicle industry is analyzed through data envelopment analysis from static and dynamic aspects. Then, the neural network model is adopted to simulate the scene and predict the possible development trend of the Chinese new energy vehicle industry in the future. Finally, the corresponding countermeasures to promote the sustainable development of the industry are further discussed.The empirical results show that the year of 2016 was the turning point of the Chinese new energy vehicle industry. From 2013 to 2016, the average technical efficiency of Chinese listed companies about new energy vehicles grew steadily. It began to decrease year by year after 2017. In recent years, the return to scale and the dynamic benefits of the industry has also been in a decreasing state. In addition, the predicted efficiency of the Chinese new energy vehicle industry in the next five years is also on a downward trend from an overall perspective. It can be seen that the gap from the effective state of industrial development has gradually increased in recent years. It is vital to make corresponding solutions to improve the status.參考文獻 Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Vol. 30, No. 9, pp. 1078-1092.Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2 (1978), pp. 429-444.Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), Vol. 120, No. 3, pp. 253-290.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z.Y. (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. The American Economic Review, Vol. 84, No. 1, pp. 66-83.Fried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis. Journal of Productivity Analysis, 17, pp. 157–174.Jondrow, J., Lovell, C., Materov, I.S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, vol. 19, issue 2-3, pp. 233-238.Leibenstein, H. (1966). Allocative Efficiency vs. "X-Efficiency". The American Economic Review, Vol. 56, No. 3, pp. 392-415.McCulloch, W.S., & Pitts, W. (1966). A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biophysics, vol. 5, pp. 115–133.Widrow, B, & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE WESCON Convention Record, 1960, pp. 96-104.盛士能（2018）。新能源汽車發展概述與趨勢。科學技術創新，2018.23，167-168。李大元（2011）。低碳經濟背景下我國新能源汽車產業發展的對策研究。科學經濟縱橫，2011年第2期，72-75。李蘇秀、劉穎綺、王靜宇、張雷（2016）。基於市場表現的中國新能源汽車產業發展政策剖析。中國人口·資源與環境，第26卷第9期，158-166。孫紅霞、呂慧榮（2018）。新能源汽車後補貼時代政府與企業的演化博弈分析。戰略與決策，第32卷第2期，24-49。丁 芸、張天華（2014）。促進新能源汽車產業發展的財稅政策效應研究。稅務研究，第355期，16-20。李 磊（2018）。政府研發補貼對新能源汽車產業技術創新產出的影響研究。科技管理研究，第17期。李素梅、陳琛、徐繼明（2016）。我國新能源汽車產業融資效率評價與分析——基於DEA-Logit模型的實證研究。科技管理研究，第18期。程驍凡、楊凱雯、潘志洋、譚江龍（2021）。中國新能源汽車產業發展現狀及對策。合作經濟與科技，第11期，20-22。陳巍巍、張 雷、馬鐵虎、劉秋〇（2014）。關於三階段DEA模型的幾點研究。系統工程，第32卷第9期（總第249期），144-149。李娜（2020）。我國跨境電商產業可持續發展的效率評價研究—基於三階段DEA模型實證分析。科技與經濟，第33卷第1期（總第193期），106-110。宋馬林、王舒鴻、汝慧萍、張廷海（2010）。中國新興生物企業的生產效率及其不確定性—基於DEA和神經網路類比的面板資料分析。企業管理，2010.10，131-137。 描述 碩士
國立政治大學
金融學系
108352037資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108352037 資料類型 thesis dc.contributor.advisor 廖四郎 zh_TW dc.contributor.advisor Liao, Szu-Lang en_US dc.contributor.author (Authors) 黃舒平 zh_TW dc.contributor.author (Authors) Huang, Shu-Ping en_US dc.creator (作者) 黃舒平 zh_TW dc.creator (作者) Huang, Shu-Ping en_US dc.date (日期) 2022 en_US dc.date.accessioned 10-Feb-2022 12:54:37 (UTC+8) - dc.date.available 10-Feb-2022 12:54:37 (UTC+8) - dc.date.issued (上傳時間) 10-Feb-2022 12:54:37 (UTC+8) - dc.identifier (Other Identifiers) G0108352037 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138888 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 108352037 zh_TW dc.description.abstract (摘要) 在能源危機和環境污染的背景之下，新能源汽車憑藉其節能減排的優勢，在發展初期便得到了國家的大力扶持，隨著其市場規模的迅速擴張，國家補貼力度逐漸減小，且產業發展戰略已由“政策推動”逐漸轉變為“市場拉動”，但新能源汽車仍存在著相關基礎設施不完善、電池續航能力差等問題，還需要政府加強基礎設施建設，及相關企業加大研發力度。在政府的補貼扶持及企業的研發投入皆有限的情況下，該產業未來是否能可持續發展，則需要瞭解其投入-產出比。本文將基於三階段DEA-Malmquist和BP神經網路模型，以中國新能源汽車上市公司為研究對象進行實證分析。首先，利用資料包絡分析法分別從靜態和動態分析中國新能源汽車產業的發展現狀；繼而，利用神經網路模型進行場景類比，預測中國新能源汽車產業未來發展的可能趨勢；最後，針對研究結果進一步探討促進該產業可持續發展的相應對策。實證結果顯示，2016年為中國新能源汽車產業的轉折點，2013至2016年間，中國新能源汽車上市公司技術效率均值穩步提高，2017年後，則開始逐年降低，近幾年產業的規模報酬也呈現出遞減的狀態，產業動態效益亦是呈下降的趨勢。除此之外，中國新能源汽車產業未來五年的效率預測值從整體角度看，同樣是下跌的走向，可見近年來距離產業發展有效狀態的差距逐步增大，找出相應解決辦法改善現狀實為重中之重。 zh_TW dc.description.abstract (摘要) Under the background of energy crisis and environmental pollution, new energy vehicles are strongly supported by the state at the early stage of development by virtue of their advantages in energy conservation and emission reduction. With the rapid expansion of the market scale of the industry, the national subsidy has decreased gradually. The industrial development strategy has been gradually transformed from "Policy-driven" to "Market-driven". However, there are still some problems about new energy vehicles, such as imperfect infrastructure and poor battery life. In the case of limited government subsidies and enterprises` investment in research and development, the sustainable development of the industry is determined by the input-output ratio.The paper is based on the Three-stage DEA-Malmquist and the BP neural network model. Chinese listed companies about new energy vehicles are taken as the research subject for empirical analysis. Firstly, the development status of the Chinese new energy vehicle industry is analyzed through data envelopment analysis from static and dynamic aspects. Then, the neural network model is adopted to simulate the scene and predict the possible development trend of the Chinese new energy vehicle industry in the future. Finally, the corresponding countermeasures to promote the sustainable development of the industry are further discussed.The empirical results show that the year of 2016 was the turning point of the Chinese new energy vehicle industry. From 2013 to 2016, the average technical efficiency of Chinese listed companies about new energy vehicles grew steadily. It began to decrease year by year after 2017. In recent years, the return to scale and the dynamic benefits of the industry has also been in a decreasing state. In addition, the predicted efficiency of the Chinese new energy vehicle industry in the next five years is also on a downward trend from an overall perspective. It can be seen that the gap from the effective state of industrial development has gradually increased in recent years. It is vital to make corresponding solutions to improve the status. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景和意義 1第二節 研究目的和內容 2第三節 研究框架和方法 3第四節 研究的創新點 4第二章 文獻回顧 5第一節 中國新能源汽車產業相關文獻梳理 5第二節 效率理論與DEA理論基礎 7第三節 神經網路理論基礎 12第三章 中國新能源汽車產業現狀 15第一節 政策狀況 15第二節 市場狀況 16第三節 技術狀況 17第四節 充電基礎設施狀況 18第四章 研究方法 20第一節 評價模型—三階段DEA-Malmquist模型 20第二節 預測模型—BP神經網路模型 24第三節 DEA與神經網路模型的優缺點比較 26第五章 實證分析 28第一節 指標選取與數據處理 28第二節 靜態分析 30第三節 動態分析 34第四節 模擬預測 35第六章 總結與建議 37第一節 主要結論 37第二節 對策建議 38第三節 不足與展望 40參考文獻 41 zh_TW dc.format.extent 1403721 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108352037 en_US dc.subject (關鍵詞) 新能源汽車 zh_TW dc.subject (關鍵詞) 生產效率 zh_TW dc.subject (關鍵詞) 資料包絡分析法 zh_TW dc.subject (關鍵詞) 神經網路模型 zh_TW dc.subject (關鍵詞) New Energy Vehicles en_US dc.subject (關鍵詞) Productive Efficiency en_US dc.subject (關鍵詞) DEA en_US dc.subject (關鍵詞) Neural Network en_US dc.title (題名) 中國新能源汽車產業發展現狀及前景探析— DEA和神經網路模型 zh_TW dc.title (題名) Analysis on the Current Development Status and Prospect of China`s New Energy Vehicle Industry — Based on DEA and Neural Network Model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Vol. 30, No. 9, pp. 1078-1092.Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2 (1978), pp. 429-444.Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), Vol. 120, No. 3, pp. 253-290.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z.Y. (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. The American Economic Review, Vol. 84, No. 1, pp. 66-83.Fried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis. Journal of Productivity Analysis, 17, pp. 157–174.Jondrow, J., Lovell, C., Materov, I.S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, vol. 19, issue 2-3, pp. 233-238.Leibenstein, H. (1966). Allocative Efficiency vs. "X-Efficiency". The American Economic Review, Vol. 56, No. 3, pp. 392-415.McCulloch, W.S., & Pitts, W. (1966). A logical calculus of the ideas immanent in nervous activity. Bulletin of mathematical biophysics, vol. 5, pp. 115–133.Widrow, B, & Hoff, M. E. (1960). Adaptive Switching Circuits. IRE WESCON Convention Record, 1960, pp. 96-104.盛士能（2018）。新能源汽車發展概述與趨勢。科學技術創新，2018.23，167-168。李大元（2011）。低碳經濟背景下我國新能源汽車產業發展的對策研究。科學經濟縱橫，2011年第2期，72-75。李蘇秀、劉穎綺、王靜宇、張雷（2016）。基於市場表現的中國新能源汽車產業發展政策剖析。中國人口·資源與環境，第26卷第9期，158-166。孫紅霞、呂慧榮（2018）。新能源汽車後補貼時代政府與企業的演化博弈分析。戰略與決策，第32卷第2期，24-49。丁 芸、張天華（2014）。促進新能源汽車產業發展的財稅政策效應研究。稅務研究，第355期，16-20。李 磊（2018）。政府研發補貼對新能源汽車產業技術創新產出的影響研究。科技管理研究，第17期。李素梅、陳琛、徐繼明（2016）。我國新能源汽車產業融資效率評價與分析——基於DEA-Logit模型的實證研究。科技管理研究，第18期。程驍凡、楊凱雯、潘志洋、譚江龍（2021）。中國新能源汽車產業發展現狀及對策。合作經濟與科技，第11期，20-22。陳巍巍、張 雷、馬鐵虎、劉秋〇（2014）。關於三階段DEA模型的幾點研究。系統工程，第32卷第9期（總第249期），144-149。李娜（2020）。我國跨境電商產業可持續發展的效率評價研究—基於三階段DEA模型實證分析。科技與經濟，第33卷第1期（總第193期），106-110。宋馬林、王舒鴻、汝慧萍、張廷海（2010）。中國新興生物企業的生產效率及其不確定性—基於DEA和神經網路類比的面板資料分析。企業管理，2010.10，131-137。 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200102 en_US
