Evaluation and application of local fiscal science and technology expenditure efficiency in the Yangtze River Delta urban agglomeration based on AHP-TOPSIS and SOM clustering

Ai Mu; Biying  Huang

doi:10.54691/29de0p62

Authors

Ai Mu
Biying Huang

DOI:

https://doi.org/10.54691/29de0p62

Keywords:

Evaluation of science and technology expenditure efficiency; Yangtze River Delta urban agglomeration; AHP-TOPSIS model; SOM clustering.

Abstract

This study selected 18 indicators from four dimensions: the scale of regional fiscal science and technology expenditure, the social impact of science and technology, the efficiency of science and technology expenditure, and the environment for scientific and technological innovation, and constructed an evaluation index system for the efficiency of local fiscal science and technology expenditure in the Yangtze River Delta urban agglomeration. The AHP-TOPSIS model was used to determine the weights of evaluation indicators, and the efficiency ranking of fiscal science and technology expenditures in various cities of the Yangtze River Delta urban agglomeration was quantified. Furthermore, using the SOM algorithm for clustering analysis, the level division of innovation capability in the Yangtze River Delta urban agglomeration was clarified. This study provides a scientific tool and method for evaluating the efficiency of local financial science and technology expenditure in the Yangtze River Delta urban agglomeration, and provides solid theoretical support and practical guidance for promoting the coordinated development of regional science and technology innovation.

Downloads

Download data is not yet available.

References

Wu Yun. Research on the impact of government investment in science and technology on scientific and technological innovation: an empirical test based on panel data from 40 countries from 1982 to 2010 [J]. Science and Science and Technology Management, 2014, 35(1): 16-22 .

Li Zheng, Yang Siying. Fiscal decentralization, government innovation preferences and regional innovation efficiency [J]. Management World, 2018, 34(12): 29-42.

CHARNES A, COOPER W W, RHODES E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1979, 2(6): 429 − 444.

AIGNER D, LOVELL C A K, SCHMIDT P. Formulation and estimation of stochastic frontier production function models[J]. Journal of Econometrics, 1977, 6(1): 21 − 37.

Wang Qian, Dong Yue, Dong Yanling. Evaluation of the efficiency of China’s fiscal science and technology expenditure under the innovation-driven development strategy—based on the three-stage super-efficiency SBM-DEA model [J]. Science and Technology Management Research, 2020, 40(05): 23- 33.

Luo Weiping, Chen Zhijian. Performance evaluation of fiscal science and technology investment in 21 prefectures and cities in Guangdong Province based on DEA [J]. Science and Technology Management Research. 2007(3):38-46

Miao Hui. Research on the Efficiency Evaluation of Local Financial Science and Technology Investment - Taking Liaoning Province as an Example [D]. Dalian: Dalian University of Technology, 2013

Pan Juan, Zhang Yuxi. PP-SFA analysis of China's R&D investment in scientific and technological innovation efficiency - based on an empirical study of 30 provinces in China [J]. Systems Engineering, 2019, 37(2): 12-20.

Xu Yingqi, Liu Yuyin, Liu Yu. Evaluation of regional scientific and technological innovation efficiency and analysis of external environment influencing factors based on PP and Translog-SFA combination model - taking Sichuan Province as an example [J]. Western Economic Management Forum, 2022, 33( 02):16-26.

Zhu Mengfei, Chen Shouming, Shao Yuexin. Evaluation of regional innovation source capabilities based on AHP-TOPSIS and SOM clustering [J]. Scientific Research Management, 2020, 41(02): 40-50.

Guo Chong. Research on comprehensive evaluation of high-quality development of national new areas based on entropy weight TOPSIS - taking 19 national new areas as an example [J]. Science and Technology and Industry, 2023, 23(16): 37-43.

Kuang Haibo, Liu Tianshou, Liu Jiaguo, Deng Shunjiang. Research on measuring the openness level of free trade zones based on PCA-TOPSIS [J]. Scientific Research Management, 2018, 39(3): 69-79.

Ning Lianju, Zhang Shasha. Mobile communication product innovation evaluation model based on analytic hierarchy process [J]. Scientific Research Management, 2010, 31(3): 176-182.

Wang Xiaoning, Yan Ling, He Qincheng. Research on paper evaluation based on TOPSIS method and SOM neural network [J]. Science and Technology Progress and Countermeasures, 2009, 26 (24): 155-161.

TWISK W, NOORDERVLIET M A W, TER KEURS W J. The nature value of the ditch vegetation in peat areas in relation to farm management[J]. Aquatic Ecology, 2003, 37(2): 191-209.

KOHONEN T. Self-organized formation of topologically correctfeature maps[ J]. Biological Cybernetics, 1982, 43:59-69.

Zou Wei, Wang Xingyu, Wan Fengjiao, et al. Research on the coupled and coordinated development of urban resilience and technological innovation level - Taking the Yangtze River Delta urban agglomeration as an example [J]. Ecological Economy, 2024, 40(01): 78-87.