IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3920-d249510.html
   My bibliography  Save this article

A Complex Network Method in Criticality Evaluation of Air Quality Standards

Author

Listed:
  • Yongchang Wei

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Lei Chen

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Yu Qi

    (School of Public Finance and Taxation, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Can Wang

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Fei Li

    (Research Center for Environment and Health, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Haorong Wang

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Fangyu Chen

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

In recent years, poor air quality has brought serious threats to public health and sustainable development. The air quality standard is an effective prerequisite to ensure the quality of the air. The citation relationships between air quality standards at a certain time point, which reflect technological development and knowledge transition, form a complex network. In this study, an integrated multi-criteria decision making method is proposed to measure the criticality of standards based on a dynamic citation network model. In particular, the Entropy Weight (EW) method is used to set the weights of each node measurement to avoid subjectiveness, while the TOPSIS method is employed to measure the criticality for each air quality standard. A case study based on the data of 444 of China’s national air quality standards reveals that the complex network method facilitates the identification of critical standards effectively. In addition, we found that there exist some structural problems in China’s air quality standard system: the connectivity between standards is insufficient; critical standards are lacking; and the critical standards change over time following the issue of national policies. Finally, policy suggestions are recommended on strengthening inter-standard citation, attaching importance to the revision of critical standards, and the dynamics of critical standards.

Suggested Citation

  • Yongchang Wei & Lei Chen & Yu Qi & Can Wang & Fei Li & Haorong Wang & Fangyu Chen, 2019. "A Complex Network Method in Criticality Evaluation of Air Quality Standards," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3920-:d:249510
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3920/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/14/3920/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Calero-Medina, Clara & Noyons, Ed C.M., 2008. "Combining mapping and citation network analysis for a better understanding of the scientific development: The case of the absorptive capacity field," Journal of Informetrics, Elsevier, vol. 2(4), pages 272-279.
    2. Jose A. García & Rosa Rodriguez-Sánchez & Joaquín Fdez-Valdivia, 2015. "Social impact of scholarly articles in a citation network," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 117-127, January.
    3. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    4. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    5. Naoki Shibata & Yuya Kajikawa & Ichiro Sakata, 2012. "Link prediction in citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 78-85, January.
    6. Yongli Zhang & Sanggyun Na, 2018. "Research on the Topological Properties of Air Quality Index Based on a Complex Network," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    7. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    8. Shiu-Wan Hung & An-Pang Wang, 2010. "Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 121-134, January.
    9. Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
    10. Xiaodan Wang & Zhengyu Yang, 2019. "Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    11. Wang, Jue & Zhang, Liwei, 2018. "Proximal advantage in knowledge diffusion: The time dimension," Journal of Informetrics, Elsevier, vol. 12(3), pages 858-867.
    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. Weiwei Sun & Xueli Zhang & Min Yuan & Zheng Zhang, 2023. "Complex Network Analysis of China National Standards for New Energy Vehicles," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
    2. Suo Qi & Wang Liyuan & Yao Tianzi & Wang Zihao, 2021. "Promoting Metro Operation Safety by Exploring Metro Operation Accident Network," Journal of Systems Science and Information, De Gruyter, vol. 9(4), pages 455-468, August.

    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. Adilson Vital & Diego R. Amancio, 2022. "A comparative analysis of local similarity metrics and machine learning approaches: application to link prediction in author citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 6011-6028, October.
    2. Yichi Zhang & Zhiliang Dong & Sen Liu & Peixiang Jiang & Cuizhi Zhang & Chao Ding, 2021. "Forecast of International Trade of Lithium Carbonate Products in Importing Countries and Small-Scale Exporting Countries," Sustainability, MDPI, vol. 13(3), pages 1-23, January.
    3. Sasaki, Hajime & Sakata, Ichiro, 2021. "Identifying potential technological spin-offs using hierarchical information in international patent classification," Technovation, Elsevier, vol. 100(C).
    4. Wang, Feifei & Dong, Jiaxin & Lu, Wanzhao & Xu, Shuo, 2023. "Collaboration prediction based on multilayer all-author tripartite citation networks: A case study of gene editing," Journal of Informetrics, Elsevier, vol. 17(1).
    5. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
    6. Martin Ho & Henry CW Price & Tim S Evans & Eoin O'Sullivan, 2023. "Order in Innovation," Papers 2302.13076, arXiv.org.
    7. Zhi Li & Qinke Peng & Che Liu, 2016. "Two citation-based indicators to measure latent referential value of papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1299-1313, September.
    8. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    9. Raf Guns & Ronald Rousseau, 2014. "Recommending research collaborations using link prediction and random forest classifiers," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1461-1473, November.
    10. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    11. Jiancheng Guan & Yuan Shi, 2012. "Transnational citation, technological diversity and small world in global nanotechnology patenting," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 609-633, December.
    12. Xiaowen Xi & Jiaqi Wei & Ying Guo & Weiyu Duan, 2022. "Academic collaborations: a recommender framework spanning research interests and network topology," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6787-6808, November.
    13. Flavia Filippin, 2021. "Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6443-6477, August.
    14. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    15. Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Satyaki Roy & Ahmad F. Al Musawi & Preetam Ghosh, 2023. "Inferring links in directed complex networks through feed forward loop motifs," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    17. Yan, Erjia & Guns, Raf, 2014. "Predicting and recommending collaborations: An author-, institution-, and country-level analysis," Journal of Informetrics, Elsevier, vol. 8(2), pages 295-309.
    18. Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    19. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).
    20. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.

    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:11:y:2019:i:14:p:3920-:d:249510. 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.