IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p178-d1011719.html
   My bibliography  Save this article

Smart Elderly Care Services in China: Challenges, Progress, and Policy Development

Author

Listed:
  • Jason Hung

    (Department of Sociology, The University of Cambridge, Cambridge CB3 0SZ, UK)

Abstract

In 2017, the State Council of China published an action plan for the construction of a smart and healthy elderly care industry (2017–2020). The action plan designed and implemented by the State Council of China demonstrates the Central Government’s determination to informationalise and digitalise the Chinese society. Therefore, the market of smart home care services should expectedly mushroom in the coming decades, as the demand for smart home care increase. However, there are a range of barriers to achieving the massification of smart home care services, which will be discussed in the following sections. In addition to the shortage of family care and nursing services, elders being physically and psychologically vulnerable also engenders the Central Government to accelerate the provision of smart home care services to the Chinese elderly population. Here, smart home investment and delivery are necessary when building a sustainable elderly care system. The investment in smart home elderly care can lessen the long-term burden on China’s healthcare system as more elders would be able to self-manage their everyday life and minor physical and psychological problems. In this article, the author would critically analyses China’s implementation of smart home elderly care services, particularly on the benefits and challenges of technological advancement in elderly care and the advantages and problems of relevant policy development. The author also highlights how the informationalisation and digitalisation in elderly care and policy development enhance the convenience of the elderly populations’ everyday life when family care is limited or absent. Additionally, the author assesses what the gaps are in existing smart home elderly care technologies and policy development that need to be addressed by Chinese policymakers to further advance the safety and convenience of the elderly cohorts’ living.

Suggested Citation

  • Jason Hung, 2022. "Smart Elderly Care Services in China: Challenges, Progress, and Policy Development," Sustainability, MDPI, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:178-:d:1011719
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/178/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/1/178/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui Zhang & Yongyi Wang & Dan Wu & Jiangping Chen, 2018. "Evolutionary Path of Factors Influencing Life Satisfaction among Chinese Elderly: A Perspective of Data Visualization," Data, MDPI, vol. 3(3), pages 1-20, September.
    2. Yu, Biying & Sun, Feihu & Chen, Chen & Fu, Guanpeng & Hu, Lin, 2022. "Power demand response in the context of smart home application," Energy, Elsevier, vol. 240(C).
    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. Tim Arlinghaus & Kevin Kus & Patricia Kajüter Rodrigues & Frank Teuteberg, 2023. "Visualizing Benefits of Case Management Software Using Utility Effect Chains," Sustainability, MDPI, vol. 15(6), pages 1-14, 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. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    2. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    3. Sridhar, Araavind & Honkapuro, Samuli & Ruiz, Fredy & Stoklasa, Jan & Annala, Salla & Wolff, Annika & Rautiainen, Antti, 2023. "Residential consumer preferences to demand response: Analysis of different motivators to enroll in direct load control demand response," Energy Policy, Elsevier, vol. 173(C).
    4. Shi, Renwei & Jiao, Zaibin, 2023. "Individual household demand response potential evaluation and identification based on machine learning algorithms," Energy, Elsevier, vol. 266(C).
    5. Ruben Barreto & Calvin Gonçalves & Luis Gomes & Pedro Faria & Zita Vale, 2022. "Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response," Energies, MDPI, vol. 15(7), pages 1-18, March.
    6. Junpei Nan & Jieran Feng & Xu Deng & Chao Wang & Ke Sun & Hao Zhou, 2022. "Hierarchical Low-Carbon Economic Dispatch with Source-Load Bilateral Carbon-Trading Based on Aumann–Shapley Method," Energies, MDPI, vol. 15(15), pages 1-17, July.
    7. Fupeng Zhang & Lei Shi & Simian Liu & Jiaqi Shi & Mengfei Cheng & Tansheng Xiang, 2022. "The Ancient Town Residential Environment of the Elderly in Xiangxi Tujia: Survey, Questions, and Recommendations," IJERPH, MDPI, vol. 19(17), pages 1-25, August.
    8. Jieran Feng & Junpei Nan & Chao Wang & Ke Sun & Xu Deng & Hao Zhou, 2022. "Source-Load Coordinated Low-Carbon Economic Dispatch of Electric-Gas Integrated Energy System Based on Carbon Emission Flow Theory," Energies, MDPI, vol. 15(10), pages 1-24, May.
    9. Ma, Nan & Waegel, Alex & Hakkarainen, Max & Braham, William W. & Glass, Lior & Aviv, Dorit, 2023. "Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces," Applied Energy, Elsevier, vol. 332(C).
    10. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    11. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    12. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).

    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:2022:i:1:p:178-:d:1011719. 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.