IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8067-d1147868.html
   My bibliography  Save this article

Research on the Spillover Effect of Different Types of Technological Innovation on New Energy Industry: Taking China’s Solar Photovoltaic as an Example

Author

Listed:
  • Hua Gao

    (School of Economics and Management, China University of Geosciences (Wuhan), Wuhan 430074, China)

  • Zhenghao Meng

    (School of Economics and Management, China University of Geosciences (Wuhan), Wuhan 430074, China)

Abstract

Technological innovation has always played a very important role in the development of new energy industries. This paper takes the solar photovoltaic industry as an object of study, taking into account the diffusion of technological advances and the different roles of different technological innovations, and uses a spatial econometric SDM model to analyze the spillover effects of different types of technological advances on the solar industry in China. It is found that for the PV industry, efficiency-enhancing technological advances have the most significant impact, with efficiency-enhancing technologies contributing significantly to the annual electricity production of the PV industry; safety-enhancing technological advances having the second highest impact on the industry’s development; and cost-reducing technological advances have no significant impact on the industry. The study also found that due to the positive externalities of technological innovation, the spillover effect of technological innovation between regions has a significant impact on the development of the regional solar PV industry. In the long term, the direct effect of efficiency-enhancing technological innovation on the development of the PV industry is significantly positive, while the direct effect of safety-enhancing technological innovation on the development of the PV industry is significantly negative. Therefore, in the future, China’s solar energy industry should combine the capital investment of different types of science and technology into research and development, fully consider the impact of regional and technological spillover on industrial development, use technological innovation spillover to promote technological exchange and progress, and continuously improve the level of equipment operation safety, output efficiency, and electricity cost.

Suggested Citation

  • Hua Gao & Zhenghao Meng, 2023. "Research on the Spillover Effect of Different Types of Technological Innovation on New Energy Industry: Taking China’s Solar Photovoltaic as an Example," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8067-:d:1147868
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8067/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8067/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kiani Mavi, Reza & Saen, Reza Farzipoor & Goh, Mark, 2019. "Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 553-562.
    2. Cowan, Robin & Zinovyeva, Natalia, 2013. "University effects on regional innovation," Research Policy, Elsevier, vol. 42(3), pages 788-800.
    3. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, vol. 32(8), pages 1365-1379, September.
    4. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2022. "The power of innovation diffusion: How patent transfer affects urban innovation quality," Journal of Business Research, Elsevier, vol. 145(C), pages 414-425.
    5. Liu, Wei & Shen, Yedan & Razzaq, Asim, 2023. "How renewable energy investment, environmental regulations, and financial development derive renewable energy transition: Evidence from G7 countries," Renewable Energy, Elsevier, vol. 206(C), pages 1188-1197.
    6. Kumar, Rajesh & Agarwala, Arun, 2016. "Renewable energy technology diffusion model for techno-economics feasibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1515-1524.
    7. Abbott, M. & Doucouliagos, C., 2003. "The efficiency of Australian universities: a data envelopment analysis," Economics of Education Review, Elsevier, vol. 22(1), pages 89-97, February.
    8. Furman, Jeffrey L. & Porter, Michael E. & Stern, Scott, 2002. "The determinants of national innovative capacity," Research Policy, Elsevier, vol. 31(6), pages 899-933, August.
    9. Wang, Zheng-Xin & Zheng, Hong-Hao & Pei, Ling-Ling & Jin, Tong, 2017. "Decomposition of the factors influencing export fluctuation in China's new energy industry based on a constant market share model," Energy Policy, Elsevier, vol. 109(C), pages 22-35.
    10. Charoenrat, Teerawat & Harvie, Charles, 2014. "The efficiency of SMEs in Thai manufacturing: A stochastic frontier analysis," Economic Modelling, Elsevier, vol. 43(C), pages 372-393.
    11. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Li, Xibao, 2009. "China's regional innovation capacity in transition: An empirical approach," Research Policy, Elsevier, vol. 38(2), pages 338-357, March.
    2. Edquist , Charles & Zabala-Iturriagagoitia , Jon Mikel, 2015. "The Innovation Union Scoreboard is flawed: The Case of Sweden – not the innovation leader of the EU – updated version," Papers in Innovation Studies 2015/27, Lund University, CIRCLE - Centre for Innovation Research.
    3. Hamidi, Shima & Zandiatashbar, Ahoura & Bonakdar, Ahmad, 2019. "The relationship between regional compactness and regional innovation capacity (RIC): Empirical evidence from a national study," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 394-402.
    4. Barbero, Javier & Zabala-Iturriagagoitia, Jon Mikel & Zofío, José L., 2021. "Is more always better? On the relevance of decreasing returns to scale on innovation," Technovation, Elsevier, vol. 107(C).
    5. Marina Van Geenhuizen & Pieter Stek, 2015. "Mapping innovation in the global photovoltaic industry: a bibliometric approach to cluster identification and analysis," ERSA conference papers ersa15p697, European Regional Science Association.
    6. Fei, Jinhua & Pu, Zhengning & Tavera, Christophe, 2024. "The power of patent transfer: The impact of green technology acquisition on non-residential CO2 emissions under the intervention of government actions," Journal of Asian Economics, Elsevier, vol. 94(C).
    7. Rakesh B. Sambharya & Jooh Lee, 2014. "Renewing Dynamic Capabilities Globally: An Empirical Study of the World’s Largest MNCs," Management International Review, Springer, vol. 54(2), pages 137-169, April.
    8. Tae Hoon Oum & Katsuhiro Yamaguchi & Yuichiro Yoshida, 2011. "Efficiency Measurement Theory and its Application to Airport Benchmarking," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 13, Edward Elgar Publishing.
    9. Zare Ahmadabadi, Habib & Zamzam, Fatemeh & Emrouznejad, Ali & Naser Sadrabadi, Alireza & Morovati Sharifabadi, Ali, 2024. "Measuring sustainable performance of OECD countries considering CO2 emissions: A new optimistic-pessimistic distance friction Minimization Model," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    10. Lesław Rządziński & Anna Sworowska, 2016. "Parametric and Non Parametric Methods for Efficiency Assessment of State Higher Vocational Schools in 2009-2011," Entrepreneurial Business and Economics Review, Centre for Strategic and International Entrepreneurship at the Cracow University of Economics., vol. 4(1), pages 95-112.
    11. Xiafei Chen & Zhiying Liu & Chaoliang Ma, 2017. "Chinese innovation-driving factors: regional structure, innovation effect, and economic development—empirical research based on panel data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 59(1), pages 43-68, July.
    12. Kristof De Witte & Laura López-Torres, 2017. "Efficiency in education: a review of literature and a way forward," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(4), pages 339-363, April.
    13. Fatih KARASAÇ, 2020. "Efficiency Analysis of Turkish Higher Education Institutions: Stochastic Frontier Model," Sosyoekonomi Journal, Sosyoekonomi Society.
    14. Ji Wu & Xian Cheng & Stephen Shaoyi Liao, 2020. "Tourism forecast combination using the stochastic frontier analysis technique," Tourism Economics, , vol. 26(7), pages 1086-1107, November.
    15. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2018. "Does econometric methodology matter to rank universities? An analysis of Italian higher education system," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 104-120.
    16. Gavilan, José M. & Ortega, Francisco J., 2020. "Productive efficiency analysis of quantitative economics journals through Stochastic Frontier Analysis using panel data || Análisis de eficiencia productiva de revistas de economía cuantitativa a trav," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 30(1), pages 297-311, December.
    17. Agasisti, Tommaso & Barra, Cristian & Zotti, Roberto, 2016. "Evaluating the efficiency of Italian public universities (2008–2011) in presence of (unobserved) heterogeneity," Socio-Economic Planning Sciences, Elsevier, vol. 55(C), pages 47-58.
    18. Mei-Chih Hu & Ching-Yan Wu & Jung Hoon Lee & Yun-Chu Lu, 2014. "The influence of knowledge source and ambidexterity in the thin film transistor and liquid crystal display industry: evidence from Japan, Korea, and Taiwan," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 233-260, May.
    19. Jie Wu & Ganggang Zhang & Qingyuan Zhu & Zhixiang Zhou, 2020. "An efficiency analysis of higher education institutions in China from a regional perspective considering the external environmental impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 57-70, January.
    20. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2018. "Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model," Journal of Informetrics, Elsevier, vol. 12(1), pages 10-30.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:15:y:2023:i:10:p:8067-:d:1147868. 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.