IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v172y2021ics0040162521004625.html
   My bibliography  Save this article

Selection of carbon emissions control industries in China: An approach based on complex networks control perspective

Author

Listed:
  • Hu, Ying
  • Yu, Yang
  • Mardani, Abbas

Abstract

To achieve the goal of reaching the peak of carbon emissions by 2030, in the absence of conditions to implement carbon emissions target control of the whole industry at this stage, it is important to select carbon emissions control industries. Carbon emissions will transfer amongst industries with the flow of production factors, forming a complex linkage network structure. Based on the theory of controllability complex networks, we built a carbon emissions control industry selection model and proposed an exactly controllable minimum control industry set search algorithm for minimising carbon emissions reduction cost (MCMCS algorithm) to select the carbon emissions control industries of China's inter-industry carbon emissions transfer network in 2017. The results show that most of the carbon emissions control industries avoid high-degree industries, most of which are less affected by upstream industries, and have greater impact on downstream high-degree industries and intermediary industries through carbon emissions transfer. Setting carbon emissions reduction targets for these industries for direct control will indirectly control the high-degree industries with high-emission reduction cost, great control difficulty, and great economic impact in the short term, and they can make the whole carbon emissions transfer network achieve the expected carbon emissions control target in a limited time.

Suggested Citation

  • Hu, Ying & Yu, Yang & Mardani, Abbas, 2021. "Selection of carbon emissions control industries in China: An approach based on complex networks control perspective," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
  • Handle: RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004625
    DOI: 10.1016/j.techfore.2021.121030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162521004625
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2021.121030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yongke Yuan & Yixing Wang & Yuanying Chi & Feng Jin, 2020. "Identification of Key Carbon Emission Sectors and Analysis of Emission Effects in China," Sustainability, MDPI, vol. 12(20), pages 1-19, October.
    2. Wang, Yafei & Liang, Sai, 2013. "Carbon dioxide mitigation target of China in 2020 and key economic sectors," Energy Policy, Elsevier, vol. 58(C), pages 90-96.
    3. Seo, Seongwon & Kim, Junbeum & Yum, Kwok-Keung & McGregor, James, 2015. "Embodied carbon of building products during their supply chains: Case study of aluminium window in Australia," Resources, Conservation & Recycling, Elsevier, vol. 105(PA), pages 160-166.
    4. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    5. Zhengzhong Yuan & Chen Zhao & Zengru Di & Wen-Xu Wang & Ying-Cheng Lai, 2013. "Exact controllability of complex networks," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
    6. Liyin Shen & Yingli Lou & Yali Huang & Jindao Chen, 2018. "A driving–driven perspective on the key carbon emission sectors in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(1), pages 349-371, August.
    7. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2012. "Control Centrality and Hierarchical Structure in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    8. Baležentis, Tomas & Butkus, Mindaugas & Štreimikienė, Dalia & Shen, Zhiyang, 2021. "Exploring the limits for increasing energy efficiency in the residential sector of the European Union: Insights from the rebound effect," Energy Policy, Elsevier, vol. 149(C).
    9. Wang, Zhen & Wei, Liyuan & Niu, Beibei & Liu, Yong & Bin, Guoshu, 2017. "Controlling embedded carbon emissions of sectors along the supply chains: A perspective of the power-of-pull approach," Applied Energy, Elsevier, vol. 206(C), pages 1544-1551.
    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. Wang, Zhenshuang & Xie, Wanchen & Zhang, Chengyi, 2023. "Towards COP26 targets: Characteristics and influencing factors of spatial correlation network structure on U.S. carbon emission," Resources Policy, Elsevier, vol. 81(C).
    2. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
    3. An Cheng & Xinru Han & Guogang Jiang, 2023. "Decomposition and Scenario Analysis of Factors Influencing Carbon Emissions: A Case Study of Jiangsu Province, China," Sustainability, MDPI, vol. 15(8), pages 1-16, April.
    4. Xu, Xinkuo & Li, Jingsi, 2023. "Can green bonds reduce the carbon emissions of cities in China?," Economics Letters, Elsevier, vol. 226(C).
    5. Chandrarin, Grahita & Sohag, Kazi & Cahyaningsih, Diyah Sukanti & Yuniawan, Dani, 2022. "Will economic sophistication contribute to Indonesia's emission target? A decomposed analysis," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    6. Yuan Yuan & Xintong Sun & Ning Liu, 2022. "Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 159-180, November.

    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, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    3. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    4. Nie, Sen & Wang, Xuwen & Wang, Binghong, 2015. "Effect of degree correlation on exact controllability of multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 98-102.
    5. Han, Fangyuan & Zio, Enrico, 2019. "A multi-perspective framework of analysis of critical infrastructures with respect to supply service, controllability and topology," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 1-13.
    6. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    7. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    8. Yan Zhang & Antonios Garas & Frank Schweitzer, 2019. "Control Contribution Identifies Top Driver Nodes In Complex Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-15, December.
    9. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    10. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Berryhill, Benjamin & Yazdani, Alireza, 2016. "Characterizing the topological and controllability features of U.S. power transmission networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 84-98.
    11. Ding, Jie & Wen, Changyun & Li, Guoqi, 2017. "Key node selection in minimum-cost control of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 251-261.
    12. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.
    13. Liu, Suling & Xu, Qiong & Chen, Aimin & Wang, Pei, 2020. "Structural controllability of dynamic transcriptional regulatory networks for Saccharomyces cerevisiae," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    14. Yang, Qing-Lin & Wang, Li-Fu & Zhao, Guo-Tao & Guo, Ge, 2020. "A coarse graining algorithm based on m-order degree in complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    15. Pang, Shaopeng & Hao, Fei, 2017. "Optimizing controllability of edge dynamics in complex networks by perturbing network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 217-227.
    16. Rinaldi, Marco, 2018. "Controllability of transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 381-406.
    17. Sun, Peng Gang & Ma, Xiaoke & Chi, Juan, 2017. "Dominating complex networks by identifying minimum skeletons," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 182-191.
    18. Wang, Pei & Xu, Shuang, 2017. "Spectral coarse grained controllability of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 168-176.
    19. Ding, Jin & Lu, Yong-Zai & Chu, Jian, 2013. "Studies on controllability of directed networks with extremal optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6603-6615.
    20. Wen, Wen & Feng, Cuiyang & Zhou, Hao & Zhang, Li & Wu, Xiaohui & Qi, Jianchuan & Yang, Xuechun & Liang, Yuhan, 2021. "Critical provincial transmission sectors for carbon dioxide emissions in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(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:eee:tefoso:v:172:y:2021:i:c:s0040162521004625. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.