Evaluation of the coordination between China’s technology and economy using a grey multivariate coupling model
Abstract
As extremely complex interactions exist in the process of economic research and development, a novel grey multivariable coupling model called CFGM(1,N) is proposed to evaluate the coordination degree between China’s technology and economy with limited information. This proposed model improves the aggregation in GM(1,N) model through the Choquet integral among λ-fuzzy measure, which can reflect interactions among factor indexes. Meanwhile, it can estimate the coordinate parameters via the whale optimization algorithm and obtains the coupling coordination degree combining with grey comentropy. To verify the proposed model, a case study using a dataset from China’s technology and the economic system is conducted. The CFGM(1,N) model has a better performance in the convergence and interpretability, as compared to the three heuristic algorithm and two classical approaches. Our finding suggests that China’s technology and the economic system is still relatively coordinated. Results also reveal that there exists strong negative cooperation between the comprehensive human input and the comprehensive capital investment in this system.
First published online 19 November 2020
Keyword : technology and economic system, Choquet integral, GM(1,N) model, whale optimization algorithm, coordination degree
How to Cite
Xiao, Q., Shan, M., Gao, M., Xiao, X., & Guo, H. (2021). Evaluation of the coordination between China’s technology and economy using a grey multivariate coupling model. Technological and Economic Development of Economy, 27(1), 24-44. https://doi.org/10.3846/tede.2020.13742
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Wang, Y., & Xu, Q. (2009). Comparison analysis on the coordination degree between science & technology and economy in different regions in China. Science & Technology Progress and Policy, 26(20), 29–31.
Wang, Z. (2017). Multivariable GM(1,N) model with interaction effects. Control and Decision, 32(3), 515–520.
Wang, Z. X., He, L. Y., & Li, D. D. (2019). Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on Shannon entropy. Energy Policy, 132, 429–439. https://doi.org/10.1016/j.enpol.2019.06.002
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Xiao, Q., Shan, M., Gao, M., & Xiao, X. (2020a). Grey information coverage interaction relational decision making and its application. Journal of Systems Engineering and Electronics, 31(2), 359–369. https://doi.org/10.23919/JSEE.2020.000013
Xiao, Q., Shan, M., Gao, M., Xiao, X., & Goh, M. (2020b). Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction. Applied Soft Computing, 95, 106538. https://doi.org/10.1016/j.asoc.2020.106538
Xiao, X., & Duan, H. (2020). A new grey model for traffic flow mechanics. Engineering Applications of Artificial Intelligence, 88, 103350. https://doi.org/10.1016/j.engappai.2019.103350
Xiao, X., Duan, H., & Wen, J. (2020d). A novel car-following inertia grey model and its application in forecasting short-term traffic flow. Applied Mathematical Modelling, 87, 546–570. https://doi.org/10.1016/j.apm.2020.06.020
Yuan, Y., Yang, J., Li, Y., & Li, W. (2020). Necessary conditions for coordination of dual-channel closedloop supply chain. Technological Forecasting and Social Change, 151, 119823. https://doi.org/10.1016/j.techfore.2019.119823
Zeng, B., Luo, C., Liu, S., Bai, Y., & Li, C. (2016). Development of an optimization method for the GM(1, N) model. Engineering Applications of Artificial Intelligence, 55, 353–362. https://doi.org/10.1016/j.engappai.2016.08.007
Zhang, W., Ju, Y., & Liu, X. (2017). Multiple criteria decision analysis based on Shapley fuzzy measures and interval valued hesitant fuzzy linguistic numbers. Computers & Industrial Engineering, 105, 28–38. https://doi.org/10.1016/j.cie.2016.12.046
Zhang, Y. (2014). A study of dynamic relationship between science & technology investment and economic growth in China. Science Research Management, 35(9), 58–68. https://doi.org/10.19571/j.cnki.1000-2995.2014.09.008
Block, J., Miller, D., Jaskiewicz, P., & Spiegel, F. (2013). Economic and technological importance of innovations in large family and founder firms: An analysis of patent data. Family Business Review, 26(2), 180–199. https://doi.org/10.1177/0894486513477454
Chiang, J. H. (1999). Choquet fuzzy integral-based hierarchical networks for decision analysis. IEEE Transactions on Fuzzy Systems, 7(1), 63–71. https://doi.org/10.1109/91.746311
Coccia, M. (2014). Driving forces of technological change: The relation between population growth and technological innovation: Analysis of the optimal interaction across countries. Technological Forecasting and Social Change, 82, 52–65. https://doi.org/10.1016/j.techfore.2013.06.001
Deng, J. (2002). The basis of grey theory. Huazhong University of Science & Technology Press.
Ding, S., Dang, Y., Xu, L., & Wang, J. (2018). Multivariable grey forecasting model based on interaction effect and its application. Systems Engineering & Electronics, 40(3), 595–602. https://doi.org/10.3969/j.issn.1001-506X.2018.03.17
Feng, Y., Zhou, M., Tian, G., Li, Z., Zhang, Z., Zhang, Q., & Tan, J. (2018). Target disassembly sequencing and scheme evaluation for CNC machine tools using improved multiobjective ant colony algorithm and fuzzy integral. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(12), 2438–2451. https://doi.org/10.1109/TSMC.2018.2847448
Fu, Z., & Zheng, R. (2018). Time delay multivariable GM(1,N) coordination model and its application. Statistics & Decision, 34(13), 77–80. https://doi.org/10.13546/j.cnki.tjyjc.2018.13.017
Grabisch, M. (1996). The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research, 89(3), 445–456. https://doi.org/10.1016/0377-2217(95)00176-X
Hong, J. P. (2017). Causal relationship between ICT R&D investment and economic growth in Korea. Technological Forecasting and Social Change, 116, 70–75. https://doi.org/10.1016/j.techfore.2016.11.005
Hu, Y. C. (2008). A novel fuzzy classifier with Choquet integral-based grey relational analysis for pattern classification problems. Soft Computing, 12(6), 523–533. https://doi.org/10.1007/s00500-007-0224-5
Ishida, H. (2015). The effect of ICT development on economic growth and energy consumption in Japan. Telematics and Informatics, 32(1), 79–88. https://doi.org/10.1016/j.tele.2014.04.003
Jiang, Y. (2011). Construction of forecasting and optimizing control model for coordinated development of regional science and technology systems. Statistics & Decision, 6, 60–62. https://doi.org/10.13546/j.cnki.tjyjc.2011.06.006
Kadaifci, C., Asan, U., & Bozdag, E. (2020). A new 2-additive Choquet integral based approach to qualitative cross-impact analysis considering interaction effects. Technological Forecasting and Social Change, 158, 120131. https://doi.org/10.1016/j.techfore.2020.120131
Khan, M. S. A., & Abdullah, S. (2018). Interval – valued Pythagorean fuzzy GRA method for multipleattribute decision making with incomplete weight information. International Journal of Intelligent Systems, 33(8), 1689–1716. https://doi.org/10.1002/int.21992
Krammer, S. M. (2017). Science, technology and innovation for economic competitiveness: The role of smart specialization in less-developed countries. Technological Forecasting & Social Change, 123, 95–107. https://doi.org/10.1016/j.techfore.2017.06.028
Marichal, J. L. (2004). Tolerant or intolerant character of interacting criteria in aggregation by the Choquet integral. European Journal of Operational Research, 155(3), 771–791. https://doi.org/10.1016/S0377-2217(02)00885-8
Meng, F., Zhang, Q., & Zhan, J. (2015). The interval-valued intuitionistic fuzzy geometric Choquet aggregation operator based on the generalized Banzhaf index and 2-additive measure. Technological and Economic Development of Economy, 21(2), 186–215. https://doi.org/10.3846/20294913.2014.946983
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
National Bureau of Statistics of China. (2020). Retrieved October 26, 2020, from https://data.stats.gov.cn
Neokosmidis, I., Avaritsiotis, N., & Ventoura, Z. (2013). Modeling gender evolution and gap in science and technology using ecological dynamics. Expert Systems with Applications, 40(9), 3481–3490. https://doi.org/10.1016/j.eswa.2012.12.056
Pradhan, R. P., Arvin, M. B., Norman, N. R., & Bele, S. K. (2014). Economic growth and the development of telecommunications infrastructure in the G-20 countries: A panel-VAR approach. Telecommunications Policy, 38(7), 634–649. https://doi.org/10.1016/j.telpol.2014.03.001
Qu, G., Qu, W., Zhang, Z., & Wang, J. (2017). Choquet integral correlation coefficient of intuitionistic fuzzy sets and its applications. Journal of Intelligent & Fuzzy Systems, 33(1), 543–553. https://doi.org/10.3233/JIFS-162131
Sugeno, M. (1974). Theory of fuzzy integral and its applications. Tokyo Institute of Technology.
Sun, J. (1996). Research of that science and technology economy society coordination development model. Chinese Journal of Management Science, 2, 13–18.
Takahagi, E. (2008). A fuzzy measure identification method by diamond pairwise comparisons and Φ_s transformation. Fuzzy Optimization and Decision Making, 7(3), 219–232. https://doi.org/10.1007/s10700-008-9032-3
Tian, G., Hao, N., Zhou, M., Pedrycz, W., Zhang, C., Ma, F., & Li, Z. (2019). Fuzzy grey choquet integral for evaluation of multicriteria decision making problems with interactive and qualitative indices. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–14. https://doi.org/10.1109/TSMC.2019.2906635
Wang, Y., & Xu, Q. (2009). Comparison analysis on the coordination degree between science & technology and economy in different regions in China. Science & Technology Progress and Policy, 26(20), 29–31.
Wang, Z. (2017). Multivariable GM(1,N) model with interaction effects. Control and Decision, 32(3), 515–520.
Wang, Z. X., He, L. Y., & Li, D. D. (2019). Assessment of the degree of order in the organisational structure of electricity regulatory institution in China based on Shannon entropy. Energy Policy, 132, 429–439. https://doi.org/10.1016/j.enpol.2019.06.002
Xiao, Q., Gao, M., Xiao, X., & Goh, M. (2020c). A novel grey Riccati-Bernoulli model and its application for the clean energy consumption prediction. Engineering Applications of Artificial Intelligence, 95, 103863. https://doi.org/10.1016/j.engappai.2020.103863
Xiao, Q., Shan, M., Gao, M., & Xiao, X. (2020a). Grey information coverage interaction relational decision making and its application. Journal of Systems Engineering and Electronics, 31(2), 359–369. https://doi.org/10.23919/JSEE.2020.000013
Xiao, Q., Shan, M., Gao, M., Xiao, X., & Goh, M. (2020b). Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction. Applied Soft Computing, 95, 106538. https://doi.org/10.1016/j.asoc.2020.106538
Xiao, X., & Duan, H. (2020). A new grey model for traffic flow mechanics. Engineering Applications of Artificial Intelligence, 88, 103350. https://doi.org/10.1016/j.engappai.2019.103350
Xiao, X., Duan, H., & Wen, J. (2020d). A novel car-following inertia grey model and its application in forecasting short-term traffic flow. Applied Mathematical Modelling, 87, 546–570. https://doi.org/10.1016/j.apm.2020.06.020
Yuan, Y., Yang, J., Li, Y., & Li, W. (2020). Necessary conditions for coordination of dual-channel closedloop supply chain. Technological Forecasting and Social Change, 151, 119823. https://doi.org/10.1016/j.techfore.2019.119823
Zeng, B., Luo, C., Liu, S., Bai, Y., & Li, C. (2016). Development of an optimization method for the GM(1, N) model. Engineering Applications of Artificial Intelligence, 55, 353–362. https://doi.org/10.1016/j.engappai.2016.08.007
Zhang, W., Ju, Y., & Liu, X. (2017). Multiple criteria decision analysis based on Shapley fuzzy measures and interval valued hesitant fuzzy linguistic numbers. Computers & Industrial Engineering, 105, 28–38. https://doi.org/10.1016/j.cie.2016.12.046
Zhang, Y. (2014). A study of dynamic relationship between science & technology investment and economic growth in China. Science Research Management, 35(9), 58–68. https://doi.org/10.19571/j.cnki.1000-2995.2014.09.008