IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i18p7833-d417534.html
   My bibliography  Save this article

Combine MCDM Methods and PSO to Evaluate Economic Benefits of High-Tech Zones in China

Author

Listed:
  • Xiaobing Yu

    (Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
    School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Xuejing Wu

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Tongzhao Huo

    (School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

High-tech zones (HTZs), as important economic growth poles, have played a key role in China’s economic boom. A method based on multi-criteria decision-making (MCDM) and particle swarm optimization (PSO) is proposed to evaluate economic benefits of HTZs. MCDM involves analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) as they are easy and simple to calculate. AHP is used to construct judgment matrix. Then, the judgment matrix is converted to a constraint optimization problem. PSO is adopted to optimize the problem and get weights of indicators. TOPSIS is used to make the evaluation. The results from 2012 to 2016 of 105 HTZs are obtained and hierarchical clustering analysis is applied to cluster results. The results have demonstrated that the rankings of Zhongguancun Technology Park and Wuhan East Lake HTZ have always been at the forefront, and the ranking of Kunshan New District has risen rapidly, while Shenyang HTZ has dropped significantly. According to the results, some targeted suggestions have been proposed for the development of HTZs.

Suggested Citation

  • Xiaobing Yu & Xuejing Wu & Tongzhao Huo, 2020. "Combine MCDM Methods and PSO to Evaluate Economic Benefits of High-Tech Zones in China," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7833-:d:417534
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/18/7833/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/18/7833/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Henriques, Iago Cotrim & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2018. "Science and technology park: Future challenges," Technology in Society, Elsevier, vol. 53(C), pages 144-160.
    2. Kuo-Feng Huang & Chwo-Ming Yu & Dah-Hsian Seetoo, 2012. "Firm innovation in policy-driven parks and spontaneous clusters: the smaller firm the better?," The Journal of Technology Transfer, Springer, vol. 37(5), pages 715-731, October.
    3. Bai, Xue-Jie & Yan, Wen-Kai & Chiu, Yung-Ho, 2015. "Performance evaluation of China's Hi-tech zones in the post financial crisis era — Analysis based on the dynamic network SBM model," China Economic Review, Elsevier, vol. 34(C), pages 122-134.
    4. Alberto Albahari & Andrés Barge‐Gil & Salvador Pérez‐Canto & Aurelia Modrego, 2018. "The influence of science and technology park characteristics on firms' innovation results," Papers in Regional Science, Wiley Blackwell, vol. 97(2), pages 253-279, June.
    5. Li, Lan-bing & Liu, Bing-lian & Liu, Wei-lin & Chiu, Yung-Ho, 2017. "Efficiency evaluation of the regional high-tech industry in China: A new framework based on meta-frontier dynamic DEA analysis," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 24-33.
    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. Alberto Albahari & Magnus Klofsten & Juan Carlos Rubio-Romero, 2019. "Science and Technology Parks: a study of value creation for park tenants," The Journal of Technology Transfer, Springer, vol. 44(4), pages 1256-1272, August.
    2. Hülya Ünlü & Serdal Temel & Kristel Miller, 2023. "Understanding the drivers of patent performance of University Science Parks in Turkey," The Journal of Technology Transfer, Springer, vol. 48(3), pages 842-872, June.
    3. Alberto Albahari & Andrés Barge-Gil & Salvador Pérez-Canto & Paolo Landoni, 2023. "The effect of science and technology parks on tenant firms: a literature review," The Journal of Technology Transfer, Springer, vol. 48(4), pages 1489-1531, August.
    4. Albahari, Alberto & Pérez-Canto, Salvador & Barge-Gil, Andrés & Modrego, Aurelia, 2017. "Technology Parks versus Science Parks: Does the university make the difference?," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 13-28.
    5. Steruska, Jana & Simkova, Nikola & Pitner, Tomas, 2019. "Do science and technology parks improve technology transfer?," Technology in Society, Elsevier, vol. 59(C).
    6. Eduardo Cadorin & Magnus Klofsten & Hans Löfsten, 2021. "Science Parks, talent attraction and stakeholder involvement: an international study," The Journal of Technology Transfer, Springer, vol. 46(1), pages 1-28, February.
    7. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "Estimating the multi-period efficiency of high-tech research institutes of the Chinese Academy of Sciences: A dynamic slacks-based measure," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    8. Tangwei Teng & Yi Zhang & Yuefang Si & Jiayi Chen & Xianzhong Cao, 2020. "Government support and firm innovation performance in Chinese science and technology parks: The perspective of firm and sub‐park heterogeneity," Growth and Change, Wiley Blackwell, vol. 51(2), pages 749-770, June.
    9. Bai, Xue-Jie & Yan, Wen-Kai & Chiu, Yung-Ho, 2015. "Performance evaluation of China's Hi-tech zones in the post financial crisis era — Analysis based on the dynamic network SBM model," China Economic Review, Elsevier, vol. 34(C), pages 122-134.
    10. Corrocher, Nicoletta & Lamperti, Francesco & Mavilia, Roberto, 2019. "Do science parks sustain or trigger innovation? Empirical evidence from Italy," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 140-151.
    11. Caloffi, Annalisa & Colovic, Ana & Rizzoli, Valentina & Rossi, Federica, 2023. "Innovation intermediaries' types and functions: A computational analysis of the literature," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    12. Li, Xiang & Cheng, Zhonghua, 2022. "Does high-speed rail improve urban carbon emission efficiency in China?," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    13. Shanwei Li & Yongchang Wu & Qi Yu & Xueyuan Chen, 2023. "National Agricultural Science and Technology Parks in China: Distribution Characteristics, Innovation Efficiency, and Influencing Factors," Agriculture, MDPI, vol. 13(7), pages 1-26, July.
    14. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    15. Laura Lecluyse & Mirjam Knockaert & André Spithoven, 2019. "The contribution of science parks: a literature review and future research agenda," The Journal of Technology Transfer, Springer, vol. 44(2), pages 559-595, April.
    16. DiMaria, charles-henri, 2024. "ESG principles: the limits to green benchmarking," MPRA Paper 120410, University Library of Munich, Germany, revised 2024.
    17. Wei Liu & Hong-Bo Shi & Zhe Zhang & Sang-Bing Tsai & Yuming Zhai & Quan Chen & Jiangtao Wang, 2018. "The Development Evaluation of Economic Zones in China," IJERPH, MDPI, vol. 15(1), pages 1-14, January.
    18. Yafen He & Zhenhong Zhu & Hualin Xie & Xinmin Zhang & Meiqi Sheng, 2023. "A case study in China of the influence mechanism of industrial park efficiency using DEA," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 7261-7280, July.
    19. Zaiwu Gong & Xiaoqing Chen, 2017. "Analysis of Interval Data Envelopment Efficiency Model Considering Different Distribution Characteristics—Based on Environmental Performance Evaluation of the Manufacturing Industry," Sustainability, MDPI, vol. 9(12), pages 1-25, November.
    20. Laura Lecluyse & Mirjam Knockaert & Annelore Huyghe, 2023. "It is not because it is offered that it is used: an investigation into firm-level determinants of use intensity of buffering services in science parks," Small Business Economics, Springer, vol. 61(1), pages 85-104, June.

    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:12:y:2020:i:18:p:7833-:d:417534. 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.