IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i7p2990-d1369709.html
   My bibliography  Save this article

A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China

Author

Listed:
  • Panpan Liu

    (School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China)

  • Guanghui Han

    (School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China)

  • Haichao Yang

    (School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China)

  • Xiaobo Li

    (School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China)

Abstract

The level of development of the innovation factor plays a crucial role in supporting the high-quality sustainable development of China’s economy. In order to advance the sustainable development of regional innovation factor allocation efficiency along the Belt and Road this study introduces the super-efficient slacks-based measure (SBM)-data envelopment analysis (DEA)-Malmquist model for static and dynamic analyses of innovation factor allocation efficiency in 17 provinces along the Belt and Road from 2012 to 2021. This study used the Moran index model to analyze spatial correlation. The results show the following: (1) The overall innovation factor allocation efficiency along the Belt and Road is not high, and there are obvious differences among different regions. The eastern region’s efficiency is the highest compared to other regions. (2) According to the efficiency decomposition results, pure technical efficiency (PTE) is the main reason for the low innovation factor allocation efficiency. (3) Through the Malmquist index and decomposition index, it was found that pure technical efficiency (PECH) and scale efficiency (SECH) are key factors in improving technical efficiency (TECH). (4) The analysis of spatial correlation revealed a strong spatial agglomeration feature among the provinces along the Belt and Road. Innovation factor allocation efficiency is mainly manifested in the third quadrant. Finally, drawing on the results of the analysis, suggestions and policies are put forward to improve innovation factor allocation efficiency in the regions along the Belt and Road. This study is of great significance for promoting the sustainable development of the regional innovation level along the Belt and Road in China.

Suggested Citation

  • Panpan Liu & Guanghui Han & Haichao Yang & Xiaobo Li, 2024. "A Sustainable Development Study on Innovation Factor Allocation Efficiency and Spatial Correlation Based on Regions along the Belt and Road in China," Sustainability, MDPI, vol. 16(7), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2990-:d:1369709
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/7/2990/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/7/2990/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leonid Kogan & Dimitris Papanikolaou & Amit Seru & Noah Stoffman, 2017. "Technological Innovation, Resource Allocation, and Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 665-712.
    2. 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.
    3. Lauren A. Johnston, 2019. "The Belt and Road Initiative: What is in it for China?," Asia and the Pacific Policy Studies, Wiley Blackwell, vol. 6(1), pages 40-58, January.
    4. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    5. Tom Broekel & Nicky Rogge & Thomas Brenner, 2018. "The innovation efficiency of German regions – a shared-input DEA approach," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 38(1), pages 77-109, February.
    6. Gao, Qiang & Cheng, Changming & Sun, Guanglin, 2023. "Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    7. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    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. Pastor, Jesus T. & Lovell, C.A. Knox & Aparicio, Juan, 2020. "Defining a new graph inefficiency measure for the proportional directional distance function and introducing a new Malmquist productivity index," European Journal of Operational Research, Elsevier, vol. 281(1), pages 222-230.
    2. Liu, Fuh-Hwa Franklin & Wang, Peng-hsiang, 2008. "DEA Malmquist productivity measure: Taiwanese semiconductor companies," International Journal of Production Economics, Elsevier, vol. 112(1), pages 367-379, March.
    3. Cai, Jinyang & Zheng, Huanyu & Vardanyan, Michael & Shen, Zhiyang, 2023. "Achieving carbon neutrality through green technological progress: evidence from China," Energy Policy, Elsevier, vol. 173(C).
    4. Yuanying Chi & Situo Xu & Xiaolei Yang & Jialin Li & Xufeng Zhang & Yahui Chen, 2023. "Research on Beijing Manufacturing Green-Oriented Transition Path under “Double Carbon” Goal-Based on the GML-SD Model," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    5. Mengchao Yao & Yihua Zhang, 2021. "Evaluation and Optimization of Urban Land-Use Efficiency: A Case Study in Sichuan Province of China," Sustainability, MDPI, vol. 13(4), pages 1-22, February.
    6. Irena Lacka & Lukasz Brzezicki, 2021. "The Efficiency and Productivity Evaluation of National Innovation Systems in Europe," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 471-496.
    7. Marinko Škare & Danijela Rabar, 2016. "Measuring Economic Growth Using Data Envelopment Analysis," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 18(42), pages 386-386, May.
    8. Teng, Xiangyu & Zhuang, Weiwei & Liu, Fan-peng & Chang, Tzu-han & Chiu, Yung-ho, 2023. "China's path of carbon neutralization to develop green energy and improve energy efficiency," Renewable Energy, Elsevier, vol. 206(C), pages 397-408.
    9. Muliaman D. Hadad & Maximilian J. B. Hall & Wimboh Santoso & Karligash Kenjegalieva & Richard Simper, 2009. "Productivity Changes in Indonesian Banking: Application of a New Approach to Estimating Malmquist Indices," Discussion Paper Series 2009_13, Department of Economics, Loughborough University, revised Sep 2009.
    10. Stepan Zemtsov & Maxim Kotsemir, 2019. "An assessment of regional innovation system efficiency in Russia: the application of the DEA approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 375-404, August.
    11. Shen Zhong & Aizhi Li & Jing Wu, 2023. "Eco-efficiency of freshwater aquaculture in China: an assessment considering the undesirable output of pollutant emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3555-3576, April.
    12. Chia-Nan Wang & Hector Tibo & Van Thanh Nguyen & Duy Hung Duong, 2020. "Effects of the Performance-Based Research Fund and Other Factors on the Efficiency of New Zealand Universities: A Malmquist Productivity Approach," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    13. Phi-Hung Nguyen & Thi-Ly Nguyen & Hong-Quan Le & Thuy-Quynh Pham & Hoang-Anh Nguyen & Chi-Vinh Pham, 2023. "How Does the Competitiveness Index Promote Foreign Direct Investment at the Provincial Level in Vietnam? An Integrated Grey Delphi–DEA Model Approach," Mathematics, MDPI, vol. 11(6), pages 1-30, March.
    14. Chia-Nan Wang & Minh Nhat Nguyen & Anh Luyen Le & Hector Tibo, 2020. "A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam," Mathematics, MDPI, vol. 8(7), pages 1-24, July.
    15. Yung-Ho Chiu & Yu-Chuan Chen & Xue-Jie Bai, 2011. "Efficiency and risk in Taiwan banking: SBM super-DEA estimation," Applied Economics, Taylor & Francis Journals, vol. 43(5), pages 587-602.
    16. Muliaman Hadad & Maximilian Hall & Karligash Kenjegalieva & Wimboh Santoso & Richard Simper, 2011. "Banking efficiency and stock market performance: an analysis of listed Indonesian banks," Review of Quantitative Finance and Accounting, Springer, vol. 37(1), pages 1-20, July.
    17. Guangdi Zhang & Yaojun Ye & Mengya Sun, 2023. "Assessing the Static and Dynamic Efficiency of Digital Economy in China: Three Stage DEA–Malmquist Index Based Approach," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    18. Chia-Nan Wang & Hector Tibo & Duy Hung Duong, 2020. "Renewable Energy Utilization Analysis of Highly and Newly Industrialized Countries Using an Undesirable Output Model," Energies, MDPI, vol. 13(10), pages 1-21, May.
    19. Sun, Yu & Yang, Feng & Wang, Dawei & Ang, Sheng, 2023. "Efficiency evaluation for higher education institutions in China considering unbalanced regional development: A meta-frontier Super-SBM model," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    20. Huang, Hongyun & Wang, Fengrong & Song, Malin & Balezentis, Tomas & Streimikiene, Dalia, 2021. "Green innovations for sustainable development of China: Analysis based on the nested spatial panel models," Technology in Society, Elsevier, vol. 65(C).

    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:gam:jsusta:v:16:y:2024:i:7:p:2990-:d:1369709. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.