Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research on Countermeasures of Rural Revitalization Assisted by Science and Technology Finance

Download as PDF

DOI: 10.23977/accaf.2023.040104 | Downloads: 10 | Views: 426

Author(s)

Yang Liu 1

Affiliation(s)

1 Department of Economics, Shenyang Institute of Science and Technology, Shenyang, Liaoning, 110167, China

Corresponding Author

Yang Liu

ABSTRACT

For the past few years, the development of rural areas has been lagged behind, and the gap between urban and rural areas has been widening. For solving the problems of economic backwardness and inadequate public services in rural areas, the Chinese government has launched the rural rejuvenation strategy. For this purpose, the development of science and technology finance is an important part of the rural revitalization strategy, which plays a key role in promoting the sustainable development of rural areas. This paper makes an in-depth analysis of the current situation of science and technology finance in China's rural areas and puts forward countermeasures for rural science and technology finance to facilitate the revitalization of rural areas, such as strengthening government support, improving the legal system, and promoting the application of financial technology.

KEYWORDS

Rural rejuvenation, science and technology finance, countermeasures, agricultural intelligence, rural revitalization strategy

CITE THIS PAPER

Yang Liu, Research on Countermeasures of Rural Revitalization Assisted by Science and Technology Finance. Accounting, Auditing and Finance (2023) Vol. 4: 23-30. DOI: http://dx.doi.org/10.23977/accaf.2023.040104.

REFERENCES

[1] Zhao L, Ruan J, Shi X. Local industrial policies and development of agricultural clusters: a case study based on a tea cluster in China. International Food and Agribusiness Management Association, 2021, 24 (2): 1-22.
[2] Yang Q. Study on The Industrial Cluster of Tropical Bananas Based on Gem Model. Acta Universitatis Cibiniensis, 2017, 21 (1): 69-74.
[3] Ukibayeva G K, Kocherbayeva A A, Temirbaeva G R, Daukenova G A, Kurmanova D S. Cluster management technologies as the tendency for development of the agricultural industry. Journal of Environmental Management and Tourism, 2018, 9 (5): 895-906.
[4] Zhao N, Bai F. Development Status and Policy Path of Cultivating Marine Bio-industry Cluster in Zhanjiang City. Asian Agricultural Research, 2018, 10 (09): 27-32.
[5] Guo H, Woodruff A, Yadav A. Improving Lives of Indebted Farmers Using Deep Learning: Predicting Agricultural Produce Prices Using Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (8): 13294-13299.
[6] Chohan M, Khan A, Chohan R, Hassan S, Mahar M S. Plant Disease Detection using Deep Learning. International Journal of Recent Technology and Engineering, 2020, 9 (1): 909-914.
[7] Zou W, Shen C, Yin G. Application of image recognition technology in agricultural production process. International Agricultural Engineering Journal, 2018, 27 (2): 318-326.
[8] Ramirez M, Clarke I, Klerkx L. Analysing intermediary organisations and their influence on upgrading in emerging agricultural clusters. Environment and planning, 2018, 50 (6): 1314-1335.
[9] Cvijanovic D, Stanisic T, Lekovic M, Kostic M. Indicators of agricultural and rural development in the East Central and South-East European countries. Agriculture and Forestry, 2020, 66 (2): 19-32.

Downloads: 3572
Visits: 35002

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.