IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i17p10661-d898904.html
   My bibliography  Save this article

Empirical Study on the Green Transformation of the Sports Industry Empowered by New Infrastructure from the Perspective of the Green Total Factor Productivity of the Sports Industry

Author

Listed:
  • Yanmei Dong

    (College of Sports Industry and Leisure, Nanjing Sport Institute, Nanjing 210014, China)

Abstract

In this research, under the guidance of scientific and available principles, an evaluation index system for the green total factor productivity of new infrastructure construction and the sports industry was constructed. The evaluation was conducted using Stata16, the DEA-Solver PRO13 software, and GIS technology using the entropy weight method, super-efficiency SBM model, and other methods. The results indicated the following: First, the overall level of new infrastructure in China is low (mean 0.255), being slightly higher than that of information infrastructure (mean 0.230), innovation infrastructure (mean 0.190), and convergence infrastructure (mean 0.555). The level of information infrastructure, especially innovation infrastructure, in eastern China is much higher than that in central and western China, especially western China. Second, the sports industry in most Chinese provinces is effective in terms of technology and scale and is in a constant stage of scale return, while the remaining provinces are in a rising stage of scale return. The mixed efficiency in the sports industry of eastern China is at a higher level than its scale efficiency and pure technical efficiency, while the mixed efficiency levels in the sports industries of central and western China are greater than those of the pure technical efficiency but less than the scale efficiency. Meanwhile, the level of mixed efficiency in the sports industry of northeast China is far lower than that of its pure technology and scale efficiency. There is still room for improvement in the discharge of pollutants and labor practices in the sports industry, especially in the sports service industry. Third, the impact of the new infrastructure and its three subsystems on the sports industry is significantly positive at the 1% level. By region, the marginal effect of information infrastructure in eastern China is the largest (2.469), while the effect of innovation infrastructure in central China (5.113), western China (4.866), and northeast China (3.251) is the largest.

Suggested Citation

  • Yanmei Dong, 2022. "Empirical Study on the Green Transformation of the Sports Industry Empowered by New Infrastructure from the Perspective of the Green Total Factor Productivity of the Sports Industry," Sustainability, MDPI, vol. 14(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10661-:d:898904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/17/10661/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/17/10661/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohsin, Muhammad & Hanif, Imran & Taghizadeh-Hesary, Farhad & Abbas, Qaiser & Iqbal, Wasim, 2021. "Nexus between energy efficiency and electricity reforms: A DEA-Based way forward for clean power development," Energy Policy, Elsevier, vol. 149(C).
    2. Jari Lyytimäki & Riina Antikainen & Joonas Hokkanen & Sirkka Koskela & Sirpa Kurppa & Riina Känkänen & Jyri Seppälä, 2018. "Developing Key Indicators of Green Growth," Sustainable Development, John Wiley & Sons, Ltd., vol. 26(1), pages 51-64, January.
    3. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    4. Amoako, Samuel & Insaidoo, Michael, 2021. "Symmetric impact of FDI on energy consumption: Evidence from Ghana," Energy, Elsevier, vol. 223(C).
    5. Liu, Hongwei & Yang, Ronglu & Wu, Jie & Chu, Junfei, 2021. "Total-factor energy efficiency change of the road transportation industry in China: A stochastic frontier approach," Energy, Elsevier, vol. 219(C).
    6. 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)

    Citations

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


    Cited by:

    1. Xuefeng Tan & Chenggen Guo & Pu Sun, 2023. "Study on Rationality of Public Fitness Service Facilities in Beijing Based on GIS," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    2. Yina Zhang & Wu Zhao & Haiman Liu, 2024. "Effect of Digital Transformation in Sports Companies on Green Innovation: Evidence from Listed Companies in China," Sustainability, MDPI, vol. 16(19), pages 1-31, September.
    3. Mei Yang & Hongling Zhou & Yali Li & Jinyu Zhang, 2023. "Efficiency Evaluation and Influencing Factors of Sports Industry and Tourism Industry Convergence Based on China’s Provincial Data," Sustainability, MDPI, vol. 15(6), pages 1-23, March.

    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. Yanmei Dong & Yingming Zhu, 2023. "Exploring the Coupling Coordination of Green Transformation of Industry and Novel Infrastructure in the Context of Low-Carbon Economy," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    2. Ling Bai & Tianran Guo & Wei Xu & Kang Luo, 2022. "The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    3. 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).
    4. Shuangjie Li & Hongyu Diao & Liming Wang & Chunqi Li, 2021. "Energy Efficiency Measurement: A VO TFEE Approach and Its Application," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    5. Wang, Xipan & Song, Junnian & Xing, Jiahao & Duan, Haiyan & Wang, Xian'en, 2022. "System nexus consolidates coupling of regional water and energy efficiencies," Energy, Elsevier, vol. 256(C).
    6. Harald Dyckhoff, 2018. "Multi-criteria production theory: foundation of non-financial and sustainability performance evaluation," Journal of Business Economics, Springer, vol. 88(7), pages 851-882, September.
    7. Yunbo Xiang & Wen Shao & Shengyun Wang & Yong Zhang & Yaxin Zhang, 2022. "Study on Regional Differences and Convergence of Green Development Efficiency of the Chemical Industry in the Yangtze River Economic Belt Based on Grey Water Footprint," IJERPH, MDPI, vol. 19(3), pages 1-19, February.
    8. Zhiqiang Zhu & Xuechi Zhang & Mengqing Xue & Yaoyao Song, 2023. "Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    9. Wang, Hui & Li, Rupeng & Zhang, Ning & Zhou, Peng & Wang, Qiang, 2020. "Assessing the role of technology in global manufacturing energy intensity change: A production-theoretical decomposition analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    10. Kao, Chiang, 2020. "Decomposition of slacks-based efficiency measures in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 283(2), pages 588-600.
    11. 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.
    12. Xiaoke Zhao & Xuhui Ding & Liang Li, 2021. "Research on Environmental Regulation, Technological Innovation and Green Transformation of Manufacturing Industry in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 13(18), pages 1-15, September.
    13. Keliang Wang & Yajing Bian & Yunhe Cheng, 2022. "Exploring the Spatial Correlation Network Structure of Green Innovation Efficiency in the Yangtze River Delta, China," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    14. Yigang Wei & Yan Li & Meiyu Wu & Yingbo Li, 2020. "Progressing sustainable development of “the Belt and Road countries”: Estimating environmental efficiency based on the Super‐slack‐based measure model," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 521-539, July.
    15. Victoria Wojcik & Harald Dyckhoff & Sebastian Gutgesell, 2017. "The desirable input of undesirable factors in data envelopment analysis," Annals of Operations Research, Springer, vol. 259(1), pages 461-484, December.
    16. Wang, Jiexin & Wang, Song, 2023. "The effect of electricity market reform on energy efficiency in China," Energy Policy, Elsevier, vol. 181(C).
    17. Lee, Junghwan & Kim, Jinsoo, 2023. "Are electric vehicles more efficient? A slacks-based data envelopment analysis for European road passenger transportation," Energy, Elsevier, vol. 279(C).
    18. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    19. Dong Tian & Fengtao Zhao & Weisong Mu & Radoslava Kanianska & Jianying Feng, 2016. "Environmental Efficiency of Chinese Open-Field Grape Production: An Evaluation Using Data Envelopment Analysis and Spatial Autocorrelation," Sustainability, MDPI, vol. 8(12), pages 1-13, November.
    20. Marcel Clermont & Julia Schaefer, 2019. "Identification of Outliers in Data Envelopment Analysis," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 71(4), pages 475-496, October.

    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:14:y:2022:i:17:p:10661-:d:898904. 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.