IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p8251-d1483193.html
   My bibliography  Save this article

The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-Differences Models and Double Machine Learning

Author

Listed:
  • Ruiyu Hu

    (Business School, Ningbo University, Ningbo 315211, China)

  • Zemenghong Bao

    (Business School, Ningbo University, Ningbo 315211, China)

  • Zhisen Lin

    (Business School, East China University of Science and Technology, Shanghai 200237, China)

  • Kun Lv

    (Business School, Ningbo University, Ningbo 315211, China
    Ningbo Urban Civilization Research Institute, Ningbo 315211, China)

Abstract

Based on the theory of regional innovation niches, this study calculates the resilience of regional innovation ecosystems and constructs a comprehensive evaluation index system for regional artificial intelligence development, resulting in a panel dataset for 30 provinces in China from 2009 to 2021 (excluding Tibet, Hong Kong, Macao, and Taiwan). Within the framework of the construction of innovative provinces, regional artificial intelligence, and the resilience of regional innovation ecosystems, spatial double-difference and double machine learning models are employed for a quasi-natural experiment. The main research conclusions are as follows: (1) Both the construction of innovative provinces and artificial intelligence have a significant positive impact on the resilience of regional innovation ecosystems. (2) However, regional artificial intelligence exhibits a negative spatial spillover effect on the resilience of regional innovation ecosystems. (3) The construction of innovative provinces can positively moderate the effect of artificial intelligence on the resilience of regional innovation ecosystems. (4) Through the promotion of regional artificial intelligence, the construction of innovative provinces can indirectly enhance the diversity, evolutionary potential, buffering capacity, fluidity, and coordination of regional innovation ecosystems, thereby driving a leap in resilience. (5) The mechanisms by which the construction of innovative provinces stimulates regional intelligent input, application, innovation, and market dynamics to further enhance the resilience of regional innovation ecosystems are effective not only in the treatment group but also in the control group.

Suggested Citation

  • Ruiyu Hu & Zemenghong Bao & Zhisen Lin & Kun Lv, 2024. "The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-D," Sustainability, MDPI, vol. 16(18), pages 1-37, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:8251-:d:1483193
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/8251/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/18/8251/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liang, Lin & Li, Yan, 2023. "How does government support promote digital economy development in China? The mediating role of regional innovation ecosystem resilience," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Nathan Nunn & Nancy Qian, 2014. "US Food Aid and Civil Conflict," American Economic Review, American Economic Association, vol. 104(6), pages 1630-1666, June.
    4. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    5. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Zhang, Shaopeng & Wang, Xiaohong, 2022. "Does innovative city construction improve the industry–university–research knowledge flow in urban China?," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    7. Liao, Kaicheng & Liu, Juan, 2024. "Digital infrastructure empowerment and urban carbon emissions: Evidence from China," Telecommunications Policy, Elsevier, vol. 48(6).
    8. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    9. Irfan, Muhammad & Razzaq, Asif & Sharif, Arshian & Yang, Xiaodong, 2022. "Influence mechanism between green finance and green innovation: Exploring regional policy intervention effects in China," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    10. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    11. Shaw, Duncan R. & Allen, Tim, 2018. "Studying innovation ecosystems using ecology theory," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 88-102.
    12. Enji Li & Qing Chen & Xinyan Zhang & Chen Zhang, 2023. "Digital Government Development, Local Governments’ Attention Distribution and Enterprise Total Factor Productivity: Evidence from China," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    13. Adam Stecyk & Ireneusz Miciuła, 2023. "Harnessing the Power of Artificial Intelligence for Collaborative Energy Optimization Platforms," Energies, MDPI, vol. 16(13), pages 1-20, July.
    14. Li, Chengming & Xu, Yang & Zheng, Hao & Wang, Zeyu & Han, Haiting & Zeng, Liangen, 2023. "Artificial intelligence, resource reallocation, and corporate innovation efficiency: Evidence from China's listed companies," Resources Policy, Elsevier, vol. 81(C).
    15. Song Wang & Jiexin Wang & Chenqi Wei & Xueli Wang & Fei Fan, 2021. "Collaborative innovation efficiency: From within cities to between cities—Empirical analysis based on innovative cities in China," Growth and Change, Wiley Blackwell, vol. 52(3), pages 1330-1360, September.
    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. Zemenghong Bao & Zhisen Lin & Tiantian Jin & Kun Lv, 2024. "Regional Breakthrough Innovation Change Strategies, Ecological Location Suitability of High-Tech Industry Innovation Ecosystems, and Green Energy," Energies, MDPI, vol. 17(16), pages 1-34, August.
    2. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    3. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    4. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2023. "Design-Based Identification with Formula Instruments: A Review," NBER Working Papers 31393, National Bureau of Economic Research, Inc.
    5. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    6. Fengting Zhang & Yang Lv & Md Nazirul Islam Sarker, 2022. "Spatio-Temporal Evolution and Development Path of Industry–University–Research Cooperation and Economic Vulnerability: Evidence from China’s Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    7. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    8. Wang, Weilong & Xiao, Deheng & Wang, Jianlong & Wu, Haitao, 2024. "The cost of pollution in the digital era: Impediments of air pollution on enterprise digital transformation," Energy Economics, Elsevier, vol. 134(C).
    9. Chen, Aizhen & Zhao, Dongyan & He, Chengying, 2024. "Firm digital investments and capacity utilisation: A perspective of factor inputs," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Zhou, Qin & Cheng, Changgao & Fang, Zhou & Zhang, Hengquan & Xu, Yining, 2024. "How does the development of the digital economy affect innovation output? Exploring mechanisms from the perspective of regional innovation systems," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 1-17.
    11. Wang, Heting & Wang, Huijuan & Guan, Rong, 2024. "Digitalization of industries and labor mobility in China," China Economic Review, Elsevier, vol. 87(C).
    12. Lee, Chien-Chiang & Yan, Jingyang & Wang, Fuhao, 2024. "Impact of population aging on food security in the context of artificial intelligence: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    13. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    14. Yuan, Ning & Li, Meijuan, 2024. "Research on collaborative innovation behavior of enterprise innovation ecosystem under evolutionary game," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    15. Zou, Jing & Deng, Xiaojun, 2022. "To inhibit or to promote: How does the digital economy affect urban migrant integration in China?," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    16. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    17. Xu, Ye & Wang, Zhi-Chao & Tao, Chang-Qi, 2024. "Can innovative pilot city policies improve the allocation level of innovation factors? – Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    18. Yipeng Zhang, 2023. "The Sustainability of Regional Innovation in China: Insights from Regional Innovation Values and Their Spatial Distribution," Sustainability, MDPI, vol. 15(13), pages 1-42, June.
    19. Zhang, Xinchun & Sun, Murong & Liu, Jianxu & Xu, Aijia, 2024. "The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    20. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(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:18:p:8251-:d:1483193. 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.