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Collaborative evolution of regional green innovation system under the influence of high-speed rail based on Belousov-Zhabotinsky reaction

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Abstract

As a green transportation mode and carrying factors with high knowledge-intensive characteristics, high-speed rail (HSR) has contributed to the improvement of regional green innovation. However, the relationship between HSR and regional green innovation is still limited. Based on the Belousov-Zhabotinsky (B-Z) reaction model, this study establishes a three-dimensional logistic dynamic evolution model for factor flow, knowledge spillover and green innovation performance. Through linear stability analysis, the threshold conditions for the evolution of the regional green innovation system under the influence of HSR are explored, and the impact of HSR policy on the regional green innovation system under four different initial states is examined. The results reveal four major points: (1) Factor flow is the order parameter and shows a significant collaborative relationship with green innovation performance. (2) The opening of HSR can effectively promote the evolution of the regional green innovation system, and the powerful stimulus of HSR contributes to shortening its evolutionary cycle. (3) The initial state of the regional green innovation system plays a crucial role in the green innovation performance. (4) In the process of collaborative evolution of regional green innovation system, factor flow and knowledge spillover serve as the premise and foundation, respectively, to jointly promote green innovation performance enhancement. Findings not only provide references for decision-makers to implement green innovation strategy and boost the green innovation performance, but also extend the theoretical system of HSR effect and collaborative evolution.

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Data availability

Data used in this manuscript is available from http://www.china-railway.com.cn, https://www.12306.cn, https://navi.cnki.net/knavi/yearbooks/YCJ-TJ/detail and http://www.stats.gov.cn/tjsj/ndsj.

References

  • Abramo G, D’Angelo CA, Di Costa F (2020) The role of geographical proximity in knowledge diffusion, measured by citations to scientific literature. J Informetr 14(1):9

    Article  Google Scholar 

  • Almeida P, Kogut B (1999) Localization of knowledge and the mobility of engineers in regional networks. Manage Sci 45(7):905–917

    Article  Google Scholar 

  • Bai J, Wang Y, Jiang F, Li J (2017) R&D element flow, spatial knowledge spillovers and economic growth. Econ Res J 52(07):109–123

    Google Scholar 

  • Bai Y (2009) Knowledge spillovers: a survey of the literature. Econ Res J 44(01):144–156

    Google Scholar 

  • Bi K, Yang C, Sui J (2015) Impact of MNCs’ technology transfer on green innovation performance: perspective of manufacturing green innovation system. China Soft Sci 11:81–93

    Google Scholar 

  • Bian Y, Wu L, Bai J (2019) Does high-speed rail improve regional innovation in China? J Financ Res 6:132–149

    Google Scholar 

  • Chen C (2012) Reshaping Chinese space-economy through high-speed trains: opportunities and challenges. J Transp Geogr 22:312–316

    Article  Google Scholar 

  • Chen C, Hall P (2011) The impacts of high-speed trains on British economic geography: a study of the UK’s InterCity 125/225 and its effects. J Transp Geogr 19(4):689–704

    Article  CAS  Google Scholar 

  • Chen Z, Haynes KE (2017) Impact of high-speed rail on regional economic disparity in China. J Transport Geogr 65:80–91

    Article  Google Scholar 

  • Corning PA (1995) Synergy and self-organization in the evolution of complex systems. Syst Res 12(2):89–121

    Article  Google Scholar 

  • Ding K (2009) Study on the structure and function of green innovation system. Sci Technol Progress Policy 26(15):116–119

    Google Scholar 

  • Du Q, Yu H, Yan C, Yang T (2020) Does high-speed rail network access enhance cities’ innovation performance? Sustainability 12(19):8239

    Article  Google Scholar 

  • Du X, Peng M (2017) Do high-speed trains motivate the flow of corporate highly educated talents? Bus Manag J 39(12):89–107

    Google Scholar 

  • Feng Z, Zeng B, Ming Q (2018) Environmental regulation, two-way foreign direct investment, and green innovation efficiency in China’s manufacturing industry. Int J Environ Res Public Health 15(10):2292

    Article  Google Scholar 

  • Garcia R, Wigger K, Hermann RR (2019) Challenges of creating and capturing value in open eco-innovation: evidence from the maritime industry in Denmark. J Clean Prod 220:642–654

    Article  Google Scholar 

  • Haken H (2004) Synergetics: introduction and advanced topics. Springer-Verlag, Berlin, Heidelberg, New York

    Book  Google Scholar 

  • Huang Y, Wang Y (2020) How does high-speed railway affect green innovation efficiency? A perspective of innovation factor mobility. J Clean Prod 265:121623

    Article  Google Scholar 

  • Jaffe AB, Trajtenberg M (1996) Flows of knowledge from universities and federal laboratories: Modeling the flow of patent citations over time and across institutional and geographic boundaries. Proc Natl Acad Sci USA 93(23):12671–12677

    Article  CAS  Google Scholar 

  • Jin Y, Hu H (2013) Stabilization of traffic flow in optimal velocity model via delayed-feedback control. Commun Nonlinear Sci Numer Simul 18(4):1027–1034

    Article  Google Scholar 

  • Krugman P (1992) Geography and trade. The MIT Press, Cambridge, pp 114–116

    Google Scholar 

  • Li B, Gao S (2019) Research on evolution of knowledge flow of enterprise collaborative original innovation. Stud Sci Sci 37(8):1506–1516

    Google Scholar 

  • Li B, Wang D, Zhao J, Zeng J (2019) The evolution of knowledge flow of community of practice in the enterprise based on the B-Z reaction. J Ind Eng Manag 33(3):84–92

    Google Scholar 

  • Li B, Yin S, Zeng J, Luo X (2020a) Cooperative innovation mechanism and dynamic evolution of integrated supply chain based on SEM and B-Z reaction-the perspective of the relationship quality of integrated supply chain. Chin J Manag Sci 28(2):166–177

    Google Scholar 

  • Li B, Zeng J, Wang D, Su Y (2020b) Research on co-evolution of enterprise green innovation system based on knowledge behavior. J Ind Eng Eng Manag 34(05):42–52

    Google Scholar 

  • Li G, Zhou Y, Liu F, Wang T (2021) Regional differences of manufacturing green development efficiency considering undesirable outputs in the Yangtze River Economic Belt based on super-SBM and WSR system methodology. Front Environ Sci 8:631911

    Article  Google Scholar 

  • Li W (2017) Spatial-temporal evolution and factors of industrial green technological innovation output in China’s Provinces: an empirical study of 30 provinces’ data. J Ind Eng Eng Manag 31(02):9–19

    CAS  Google Scholar 

  • Long Y, Zhao H, Zhang X, Li Y (2017) High-speed railway and venture capital investment. Econ Res J 52(04):195–208

    Google Scholar 

  • Michael F, Grit F (2004) Innovation, regional knowledge spillovers and R&D cooperation. Res Policy 33(2):245–255

    Article  Google Scholar 

  • Ministry of Transport of the People’s Republic of China (MOT) (2020) Statistical Bulletin of Transportation Industry Development in 2019. The National Bureau of Statistic of the People’s Republic of China, Beijing

    Google Scholar 

  • National Bureau of Statistics (2017a) China City Statistical Yearbook 2017. The National Bureau of Statistics of the People’s Republic of China, Beijing

    Google Scholar 

  • National Bureau of Statistics (2017b) China Statistical Yearbook 2017. The National Bureau of Statistics of the People’s Republic of China, Beijing

    Google Scholar 

  • Naveed A, Javakhishvili-Larsen N, Schmidt TD (2017) Labour mobility and local employment: building a local employment base from labour mobility? Reg Stud 51(11):1622–1634

    Article  Google Scholar 

  • Negro SO, Suurs RAA, Hekkert MP (2008) The bumpy road of biomass gasification in the Netherlands: explaining the rise and fall of an emerging innovation system. Technol Forecast Soc Chang 75(1):57–77

    Article  Google Scholar 

  • Nie Y, Lv T, Gao J (2017) Co-evolution entropy as a new index to explore power system transition: a case study of China’s electricity domain. J Clean Prod 165:951–967

    Article  Google Scholar 

  • Peng X, Wang J (2019) High-speed rail construction and green total factor productivity: based on factor allocation distortion. China Popul Resour Environ 29(11):11–19

    Google Scholar 

  • Su Y, Jiang X, Lei J, Lin Z (2016) Research on collaborative evolution of regional innovation system. China Soft Sci 03:44–61

    Google Scholar 

  • Wang F, Wei X, Liu J, He L, Gao M (2019) Impact of high-speed rail on population mobility and urbanisation: a case study on Yangtze River Delta urban agglomeration. China Transport Res Pol Pract 127:99–114

    Article  Google Scholar 

  • Wang J, Cai S (2020) The construction of high-speed railway and urban innovation capacity: based on the perspective of knowledge Spillover. China Econ Rev 63:101539

    Article  Google Scholar 

  • Wang M, Li Y, Cheng Z, Zhong C, Ma W (2021) Evolution and equilibrium of a green technological innovation system: simulation of a tripartite game model. J Clean Prod 278:123944

    Article  Google Scholar 

  • Wang Y, Ni P (2016) Economic growth spillover and spatial optimization of high-speed railway. China Ind Econ 02:21–36

    Google Scholar 

  • Wu J, Xia Q, Li Z (2022) Green innovation and enterprise green total factor productivity at a micro level: a perspective of technical distance. J Clean Prod 344:131070

    Article  Google Scholar 

  • Wurlod JD, Noailly J (2018) The impact of green innovation on energy intensity: an empirical analysis for 14 industrial sectors in OECD countries. Energy Econ 71:47–61

    Article  Google Scholar 

  • Xie HL, Zhu ZH, Wang BH, Liu GY, Zhai QL (2018) Does the expansion of urban construction land promote regional economic growth in China? Evidence from 108 Cities in the Yangtze River Economic Belt. Sustainability 10:4073

    Article  Google Scholar 

  • Xu C, Ye X, Luo Z, Shi Y, Gao C, Bai Y (2019) Effects of selenium species on the Belousov-Zhabotinsky reaction. J Phys Chem A 123(38):8148–8153

    Article  CAS  Google Scholar 

  • Yang X, Lin S, Li Y, He M (2019a) Can high-speed rail reduce environmental pollution? Evidence from China. J Clean Prod 239:118135

    Article  CAS  Google Scholar 

  • Yang X, Lin S, Zhang J, He M (2019b) Does high-speed rail promote enterprises productivity? Evidence from China. J Adv Transport 04:1–19

    Google Scholar 

  • Ye C, Chen Y (2018) Research on the influencing mechanism of high-speed railway on regional knowledge spillover based on the perspective of heat transfer. Foreign Econ Relat Trade 12:47–48

    Google Scholar 

  • Ye D, Pan S, Wu W, Zhou H (2020) Distance, accessibility and innovation: a study on the optimal working radius of high-speed railway opening for urban innovation. Finance Trade Econ 41(02):146–161

    Google Scholar 

  • Yin S, Zhang N, Li B, Dong H (2021) Enhancing the effectiveness of multi-agent cooperation for green manufacturing: dynamic co-evolution mechanism of a green technology innovation system based on the innovation value chain. Environ Impact Assess Rev 86:106475

    Article  Google Scholar 

  • Zhang J, Zhang Y (2020) Impact of high-speed railway on industrial green total factor productivity: a case of 11 provinces along the Yangtze River Economic Belt. Area Res Develop 39(4):24–28

    Google Scholar 

  • Zhao L, Zhang X, Zhao F (2021a) The impact of high-speed rail on air quality in counties: econometric study with data from southern Beijing-Tianjin-Hebei, China. J Clean Prod 278:123604

    Article  CAS  Google Scholar 

  • Zhao X, Shi L, Yu Q (2021b) Study on modeling and simulation of sequential synergetic evolvement in enterprise innovation system. Chin J Manag Sci 18(3):402–409

    Google Scholar 

  • Zhuo C, Deng F (2018) Interregional flow of innovative elements and upgrading of industrial structure. Inquiry Econ Issues 5:70–79

    Google Scholar 

Download references

Acknowledgements

Thanks to the efforts of anonymous reviewers and editors. This research has been supported by Soft Science Program of Shanghai Science and Technology Commission (No. 21692111300).

Funding

This research has been supported by the Soft Science Program of Shanghai Science and Technology Commission (No. 21692111300).

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  1. School of Management, Shanghai University, No.99, Shangda Road, Shanghai, 200444, People’s Republic of China

    Yanfei Zhou & Xueguo Xu

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  1. Yanfei Zhou
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Yanfei Zhou selected the topic and completed the writing. Xueguo Xu revised the logic and language of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yanfei Zhou.

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Table 3 Results of the numerical calculation of evaluation indicators
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Zhou, Y., Xu, X. Collaborative evolution of regional green innovation system under the influence of high-speed rail based on Belousov-Zhabotinsky reaction. Environ Sci Pollut Res 29, 69101–69116 (2022). https://doi.org/10.1007/s11356-022-20772-3

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