IDEAS home Printed from https://ideas.repec.org/a/eee/streco/v62y2022icp72-84.html
   My bibliography  Save this article

Industrial production evaluation with the consideration of technology accumulation

Author

Listed:
  • Chen, Hao

Abstract

In actual industrial production, technology shows an accumulative effect that exceeds the corresponding factor endowment in each period. This accumulation effect is accompanied by the whole industrial production and plays an important role in the evolution of industrialization as a bridge. In order to identify the status quo of industrial production more comprehensively, explore the role of technological accumulation in industrial production, and further promote industrialization, this paper constructs a new multi-period dynamic data envelopment analysis model reflecting the effect of technological accumulation. Furthermore, a spatial autocorrelation analysis is conducted to determine the spatial correlation of regional industrial production and to provide a reference for the coordinated development of industry among regions. A case study of China's industries was conducted, and the main results show the following: (1) The average industrial production efficiency is 0.806, and 17% of provinces have a score of less than 0.65. (2) There is an obvious positive spatial autocorrelation evolution trend in regional industrial production; the eastern coastal area is dominated by High-High clustering, and Low-Low clustering is concentrated in the northwest. (3) Technology accumulation aggravates the dispersion degree of regional industrial production efficiency. Finally, policy implications are put forward to promote industrialization and the coordinated development of interregional industry.

Suggested Citation

  • Chen, Hao, 2022. "Industrial production evaluation with the consideration of technology accumulation," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 72-84.
    • Handle: RePEc:eee:streco:v:62:y:2022:i:c:p:72-84
    DOI: 10.1016/j.strueco.2022.05.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0954349X22000686
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.strueco.2022.05.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    2. Xiang, Dong & Chen, Jiakui & Tripe, David & Zhang, Ning, 2019. "Family firms, sustainable innovation and financing cost: Evidence from Chinese hi-tech small and medium-sized enterprises," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 499-511.
    3. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    4. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    5. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    6. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Merges, Robert P. & Nelson, Richard R., 1994. "On limiting or encouraging rivalry in technical progress: The effect of patent scope decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 25(1), pages 1-24, September.
    8. Mukoyama, Toshihiko, 2003. "Innovation, imitation, and growth with cumulative technology," Journal of Monetary Economics, Elsevier, vol. 50(2), pages 361-380, March.
    9. Chen, Kun & Song, Yao-yao & Pan, Jiao-feng & Yang, Guo-liang, 2020. "Measuring destocking performance of the Chinese real estate industry: A DEA-Malmquist approach," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    10. Zhao, Pengjun & Zeng, Liangen & Li, Peilin & Lu, Haiyan & Hu, Haoyu & Li, Chengming & Zheng, Mengyuan & Li, Haitao & Yu, Zhao & Yuan, Dandan & Xie, Jinxin & Huang, Qi & Qi, Yuting, 2022. "China's transportation sector carbon dioxide emissions efficiency and its influencing factors based on the EBM DEA model with undesirable outputs and spatial Durbin model," Energy, Elsevier, vol. 238(PC).
    11. Yu, Anyu & Shi, Yu & You, Jianxin & Zhu, Joe, 2021. "Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 292(1), pages 199-212.
    12. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    13. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    14. Wang, Bin & Yu, Minxiu & Zhu, Yucheng & Bao, Pinjuan, 2021. "Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective," Energy Policy, Elsevier, vol. 158(C).
    15. Li, Nan & Liu, Cengceng & Zha, Donglan, 2016. "Performance evaluation of Chinese photovoltaic companies with the input-oriented dynamic SBM model," Renewable Energy, Elsevier, vol. 89(C), pages 489-497.
    16. Tone, Kaoru & Tsutsui, Miki, 2010. "Dynamic DEA: A slacks-based measure approach," Omega, Elsevier, vol. 38(3-4), pages 145-156, June.
    17. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    18. Li, Nan & Jiang, Yuqing & Mu, Hailin & Yu, Zhixin, 2018. "Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA)," Energy, Elsevier, vol. 164(C), pages 1145-1160.
    19. Meng, Lei & Guo, Ju'e & Chai, Jian & Zhang, Zengkai, 2011. "China's regional CO2 emissions: Characteristics, inter-regional transfer and emission reduction policies," Energy Policy, Elsevier, vol. 39(10), pages 6136-6144, October.
    20. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    21. Veiga, Gabriela Lobo & Pinheiro de Lima, Edson & Frega, José Roberto & Gouvea da Costa, Sérgio Eduardo, 2021. "A DEA-based approach to assess manufacturing performance through operations strategy lenses," International Journal of Production Economics, Elsevier, vol. 235(C).
    22. Wan, Qunchao & Chen, Jin & Yao, Zhu & Yuan, Ling, 2022. "Preferential tax policy and R&D personnel flow for technological innovation efficiency of China's high-tech industry in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    23. Mahdiloo, Mahdi & Ngwenyama, Ojelanki & Scheepers, Rens & Tamaddoni, Ali, 2018. "Managing emissions allowances of electricity producers to maximize CO2 abatement: DEA models for analyzing emissions and allocating emissions allowances," International Journal of Production Economics, Elsevier, vol. 205(C), pages 244-255.
    24. Song, Malin & Li, Hui, 2019. "Estimating the efficiency of a sustainable Chinese tourism industry using bootstrap technology rectification," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 45-54.
    25. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    26. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    27. Eguchi, Shogo & Takayabu, Hirotaka & Lin, Chen, 2021. "Sources of inefficient power generation by coal-fired thermal power plants in China: A metafrontier DEA decomposition approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    28. Hahn, G.J. & Brandenburg, M. & Becker, J., 2021. "Valuing supply chain performance within and across manufacturing industries: A DEA-based approach," International Journal of Production Economics, Elsevier, vol. 240(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Cheng & Yao, Yangyang & Zhou, Han, 2023. "External technology dependence and manufacturing TFP: Evidence from China," Research in International Business and Finance, Elsevier, vol. 64(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    2. Svetlana V. Ratner & Artem M. Shaposhnikov & Andrey V. Lychev, 2023. "Network DEA and Its Applications (2017–2022): A Systematic Literature Review," Mathematics, MDPI, vol. 11(9), pages 1-24, May.
    3. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    4. Helu Xiao & Na Wang & Shanping Wang, 2023. "Dynamic sustainability assessment of poverty alleviation in China: evidence from both novel non-convex global two-stage DEA and Malmquist productivity index," Operational Research, Springer, vol. 23(2), pages 1-40, June.
    5. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Liu, Haiyue & Zhang, Ruchuan & Zhou, Li & Li, Aijun, 2023. "Evaluating the financial performance of companies from the perspective of fund procurement and application: New strategy cross efficiency network data envelopment analysis models," Energy, Elsevier, vol. 269(C).
    7. Chen, Xiaoqing & Liu, Xinwang & Zhu, Qingyuan, 2022. "Comparative analysis of total factor productivity in China's high-tech industries," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    8. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).
    9. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    10. Wang, Qian & Ren, Shuming, 2022. "Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Hosseini, Keyvan & Stefaniec, Agnieszka, 2019. "Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure," Energy, Elsevier, vol. 189(C).
    12. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    13. Mohd Chachuli, Fairuz Suzana & Ahmad Ludin, Norasikin & Md Jedi, Muhamad Alias & Hamid, Norul Hisham, 2021. "Transition of renewable energy policies in Malaysia: Benchmarking with data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    14. Zhou, Xiaoyang & Chen, Hao & Chai, Jian & Wang, Shouyang & Lev, Benjamin, 2020. "Performance evaluation and prediction of the integrated circuit industry in China: A hybrid method," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    15. Puertas, Rosa & Carracedo, Patricia & Garcia−Mollá, Marta & Vega, Virginia, 2022. "Analysis of the determinants of market capitalisation: Innovation, climate change policies and business context," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    16. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    17. Chaogao An & Polat Muhtar & Zhenquan Xiao, 2022. "Spatiotemporal Evolution of Tourism Eco-Efficiency in Major Tourist Cities in China," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    18. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.
    19. Shiping Mao & Marios Dominikos Kremantzis & Leonidas Sotirios Kyrgiakos & George Vlontzos, 2022. "R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era," Agriculture, MDPI, vol. 12(11), pages 1-19, November.
    20. Jorge Antunes & Abdollah Hadi-Vencheh & Ali Jamshidi & Yong Tan & Peter Wanke, 2022. "Bank efficiency estimation in China: DEA-RENNA approach," Annals of Operations Research, Springer, vol. 315(2), pages 1373-1398, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:streco:v:62:y:2022:i:c:p:72-84. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/525148 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.