IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v99y2025ics0038012125000448.html
   My bibliography  Save this article

Scale measurement and economic effect evaluation of smart agriculture in China

Author

Listed:
  • Zhang, Shaohua
  • Chen, Rentao
  • Wu, Jian
  • Zhu, Ning

Abstract

In this paper, input‒output analysis is applied to assess the value-added scale of smart agriculture, with an emphasis on industrial linkages and final demand. The results reveal that smart agriculture constitutes 5.58 % of the agricultural sector and only 0.47 % of GDP in China. Although the value-added contribution of smart agriculture remains modest in comparison with that of traditional agriculture, it demonstrates strong integration with digital industrialization sectors and low dependency on inputs or demand growth from other industries. In terms of final demand, sensitivity analysis shows that smart agriculture is a consumption-dependent industry; however, the promotion of smart agriculture associated with various final demands remains limited, with the expansion of final demand predominantly benefiting traditional agriculture. The transformation of the industrial structure has been a key driver of the growth of smart agriculture since 2012. Between 2012 and 2020, changes in the structure of intermediate goods and final demand collectively contributed to a 44.9 % increase in the scale of smart agriculture.

Suggested Citation

  • Zhang, Shaohua & Chen, Rentao & Wu, Jian & Zhu, Ning, 2025. "Scale measurement and economic effect evaluation of smart agriculture in China," Socio-Economic Planning Sciences, Elsevier, vol. 99(C).
  • Handle: RePEc:eee:soceps:v:99:y:2025:i:c:s0038012125000448
    DOI: 10.1016/j.seps.2025.102195
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012125000448
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.seps.2025.102195?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiang, Song & Zhou, Jie & Qiu, Shuang, 2022. "Digital Agriculture and Urbanization: Mechanism and Empirical Research," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    2. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Guo, Zhengquan & Su, Shuai, 2021. "Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province," Energy, Elsevier, vol. 231(C).
    3. Wolsky, Alan Martin, 1984. "Disaggregating Input-Output Models," The Review of Economics and Statistics, MIT Press, vol. 66(2), pages 283-291, May.
    4. Siti Fatimahwati Pehin Dato Musa & Khairul Hidayatullah Basir & Edna Luah, 2022. "The Role of Smart Farming in Sustainable Development," International Journal of Asian Business and Information Management (IJABIM), IGI Global Scientific Publishing, vol. 13(2), pages 1-12, August.
    5. Chin-Ling Lee & Robert Strong & Kim E. Dooley, 2021. "Analyzing Precision Agriculture Adoption across the Globe: A Systematic Review of Scholarship from 1999–2020," Sustainability, MDPI, vol. 13(18), pages 1-15, September.
    6. Yixin Hu & Mansoor Ahmed Koondhar & Rong Kong, 2023. "From Traditional to Smart: Exploring the Effects of Smart Agriculture on Green Production Technology Diversity in Family Farms," Agriculture, MDPI, vol. 13(6), pages 1-19, June.
    7. Liu, Lirong & Huang, Charley Z. & Huang, Guohe & Baetz, Brian & Pittendrigh, Scott M., 2018. "How a carbon tax will affect an emission-intensive economy: A case study of the Province of Saskatchewan, Canada," Energy, Elsevier, vol. 159(C), pages 817-826.
    8. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
    9. Jie Guo & Jiahui Lyu, 2024. "The Digital Economy and Agricultural Modernization in China: Measurement, Mechanisms, and Implications," Sustainability, MDPI, vol. 16(12), pages 1-25, June.
    10. Xiance Sang & Chen Chen & Die Hu & Dil Bahadur Rahut, 2024. "Economic benefits of climate-smart agricultural practices: empirical investigations and policy implications," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 29(1), pages 1-21, January.
    11. Dongpo Li & Teruaki Nanseki, 2023. "Practice, Promotion and Perspective of Smart Agriculture in China," Springer Books, in: Teruaki Nanseki (ed.), Agricultural Innovation in Asia, chapter 0, pages 183-203, Springer.
    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. Liu, Lirong & Huang, Guohe & Baetz, Brian & Huang, Charley Z. & Zhang, Kaiqiang, 2019. "Integrated GHG emissions and emission relationships analysis through a disaggregated ecologically-extended input-output model; A case study for Saskatchewan, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 97-109.
    2. Zhang, Jinbo & Liu, Lirong & Xie, Yulei & Han, Dengcheng & Zhang, Yang & Li, Zheng & Guo, Huaicheng, 2023. "Revealing the impact of an energy–water–carbon nexus–based joint tax management policy on the environ-economic system," Applied Energy, Elsevier, vol. 331(C).
    3. Liu, Lirong & Huang, Guohe & Baetz, Brian & Guan, Yuru & Zhang, Kaiqiang, 2020. "Multi-Dimensional Hypothetical Fuzzy Risk Simulation model for Greenhouse Gas mitigation policy development," Applied Energy, Elsevier, vol. 261(C).
    4. Liu, Lirong & Huang, Guohe & Baetz, Brian & Zhang, Kaiqiang, 2018. "Environmentally-extended input-output simulation for analyzing production-based and consumption-based industrial greenhouse gas mitigation policies," Applied Energy, Elsevier, vol. 232(C), pages 69-78.
    5. Liu, Lirong & Huang, Gordon & Baetz, Brian & Cheng, Guanhui & Pittendrigh, Scott M. & Pan, Siyue, 2020. "Input-output modeling analysis with a detailed disaggregation of energy sectors for climate change policy-making: A case study of Saskatchewan, Canada," Renewable Energy, Elsevier, vol. 151(C), pages 1307-1317.
    6. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    7. da Silveira, Franco & da Silva, Sabrina Letícia Couto & Machado, Filipe Molinar & Barbedo, Jayme Garcia Arnal & Amaral, Fernando Gonçalves, 2023. "Farmers' perception of the barriers that hinder the implementation of agriculture 4.0," Agricultural Systems, Elsevier, vol. 208(C).
    8. Lin Xie & Biliang Luo & Wenjing Zhong, 2021. "How Are Smallholder Farmers Involved in Digital Agriculture in Developing Countries: A Case Study from China," Land, MDPI, vol. 10(3), pages 1-16, March.
    9. Oliver Falck & Johannes Koenen, 2020. "Rohstoff „Daten“: Volkswirtschaflicher Nutzen von Datenbereitstellung – eine Bestandsaufnahme," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 113.
    10. Hrosul, Viktoriia & Kruhlova, Olena & Kolesnyk, Alina, 2023. "Digitalization of the agricultural sector: the impact of ICT on the development of enterprises in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 9(4), December.
    11. Ascui, Francisco & Ball, Alex & Kahn, Lewis & Rowe, James, 2021. "Is operationalising natural capital risk assessment practicable?," Ecosystem Services, Elsevier, vol. 52(C).
    12. Huo, Dongyang & Malik, Asad Waqar & Ravana, Sri Devi & Rahman, Anis Ur & Ahmedy, Ismail, 2024. "Mapping smart farming: Addressing agricultural challenges in data-driven era," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    13. Pigford, Ashlee-Ann E. & Hickey, Gordon M. & Klerkx, Laurens, 2018. "Beyond agricultural innovation systems? Exploring an agricultural innovation ecosystems approach for niche design and development in sustainability transitions," Agricultural Systems, Elsevier, vol. 164(C), pages 116-121.
    14. Tianyu Qin & Lijun Wang & Yanxin Zhou & Liyue Guo & Gaoming Jiang & Lei Zhang, 2022. "Digital Technology-and-Services-Driven Sustainable Transformation of Agriculture: Cases of China and the EU," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    15. Theodoros Petropoulos & Lefteris Benos & Patrizia Busato & George Kyriakarakos & Dimitrios Kateris & Dimitrios Aidonis & Dionysis Bochtis, 2025. "Soil Organic Carbon Assessment for Carbon Farming: A Review," Agriculture, MDPI, vol. 15(5), pages 1-33, March.
    16. Alexandros Gkatsikos & Konstadinos Mattas, 2021. "The Paradox of the Virtual Water Trade Balance in the Mediterranean Region," Sustainability, MDPI, vol. 13(5), pages 1-14, March.
    17. Zhang, Tianyuan & Tan, Qian & Cai, Yanpeng, 2024. "General equilibrium analysis of carbon tax policy on water-energy-food nexus efficiency," Energy, Elsevier, vol. 304(C).
    18. Viet, Nguyen Quoc & Behdani, Behzad & Bloemhof, Jacqueline, 2018. "Value of Information to Improve Daily Operations in High-Density Logistics," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 9(01), January.
    19. repec:ags:areint:342110 is not listed on IDEAS
    20. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    21. Li, Lei & Lin, Jiabao & Ouyang, Ye & Luo, Xin (Robert), 2022. "Evaluating the impact of big data analytics usage on the decision-making quality of organizations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General

    Statistics

    Access and download statistics

    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:soceps:v:99:y:2025:i:c:s0038012125000448. 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.elsevier.com/locate/seps .

    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.