Spatiotemporal Evolution of The Coupling Coordination Degree Between Financial Agglomeration and Green Technology Innovation in China

With China's transition toward high-quality development, financial agglomeration (FA) and green technology innovation (GTI) have become key drivers of green transition and sustainable development. Existing studies mainly focus on the relationship between FA and general technological innovation, while the coordinated development of FA and GTI has received limited attention. Theoretically, FA promotes GTI through capital agglomeration, optimised resource allocation, knowledge spillover, and specialised labour division. GTI enhances FA by generating new financing demands and improving capital allocation efficiency. Through these mechanisms, FA and GTI form a mutually reinforcing and coordinated development relationship. This study investigates their coordinated development from the perspective of coupling coordination. Using panel data of 30 Chinese provinces from 2007 to 2022, the assessment index systems for FA and GTI are constructed, and the coupling coordination degree (CCD) model, data visualisation techniques, Moran’s I index and spatial visualisation methods are employed to explore the spatiotemporal evolution characteristics of the CCD between FA and GTI. The results indicate that the CCD steadily increased during the period of this study, while the coordination level remained relatively low. Significant regional disparities exist, where Eastern China performs better than the other three regions. In the analysis of development types of the CCD, GTI is found to play a more important role as the restrictive factor than FA. In the spatial autocorrelation analysis, the CCD exhibits significant positive spatial autocorrelation, with High–High mainly in Eastern China and Low–Low mainly in Western and Northeastern China. This study extends the literature by shifting the focus from general technological innovation to GTI and examining its coordinated development with FA from a coupling coordination perspective, thereby enriching the theoretical understanding of finance–innovation coordination under the sustainability transition and providing policy implications for integrating financial and innovation resources to promote balanced regional development and high-quality economic growth.

Financial Agglomeration; Green Technology Innovation; Coupling Coordination Degree; Spatiotemporal Evolution.

1. Ang, J. B. (2011). Financial development, liberalization and technological deepening. European Economic Review, 55(5), 688–701. [Google Scholar] [Crossref]

2. Anselin, L. (2003). Spatial externalities, spatial multipliers, and spatial econometrics. International Regional Science Review, 26(2), 153–166. [Google Scholar] [Crossref]

3. Bai, C. Q., Huang, Y. B., Song, W. X., & He, Y. Q. (2014). Coupling coordination development between provincial financial agglomeration and ecological efficiency in China. Journal of Arid Land Resources and Environment, 28(9), 1–7. [Google Scholar] [Crossref]

4. Beise, M., & Rennings, K. (2005). Indicators for lead markets of environmental innovations. In Indicator Systems for Sustainable Innovation (pp. 71–94). Physica-Verlag. [Google Scholar] [Crossref]

5. Berger, A. N. (2003). The economic effects of technological progress: Evidence from the banking industry. Journal of Money, Credit and Banking, 35(2), 141–176. [Google Scholar] [Crossref]

6. Braun, E., & Wield, D. (1994). Regulation as a means for the social control of technology. Technology Analysis & Strategic Management, 6(3), 259–272. [Google Scholar] [Crossref]

7. Buera, F. J., & Shin, Y. (2013). Financial frictions and the persistence of history: A quantitative exploration. Journal of Political Economy, 121(2), 221–272. [Google Scholar] [Crossref]

8. Chen, Y. (2022). Research on the coupling relationship between financial agglomeration and regional innovation capability: A case study of Hebei Province. Hebei Enterprise, (1), 140–142. [Google Scholar] [Crossref]

9. Cheng, X., Wu, J., & Liao, S. S. (2017). A study of contagion in the financial system from the perspective of network analytics. Neurocomputing, 264, 42–49. [Google Scholar] [Crossref]

10. Chung, Y. & Fare R.(1997). Productivity and Undesirable Outputs:A Directional Distance Function Approach.Microeconomics.51(3),229-240 [Google Scholar] [Crossref]

11. Das, M., & Ghosh, S. K. (2016, July). A cost-efficient approach for measuring Moran’s index of spatial autocorrelation in geostationary satellite data. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 5913–5916). IEEE. [Google Scholar] [Crossref]

12. Ding, Y., Li, S., & Li, L. (2009). Analysis on financial aggregation degree evaluation of China. Soft Science, 23(6), 9–13. [Google Scholar] [Crossref]

13. Faber, A., & Frenken, K. (2009). Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics. Technological Forecasting and Social Change, 76(4), 462–470. [Google Scholar] [Crossref]

14. Fang, H., Yang, Q., Wang, J., & Liu, X. (2021). Coupling coordination between technology transfer in universities and high-tech industries development in China. Complexity, 2021, 1809005. [Google Scholar] [Crossref]

15. Feng, R. (2022). Financial agglomeration, green technological innovation, and green economic efficiency. Economic Survey, 39(4), 150–160. [Google Scholar] [Crossref]

16. Glassman, R. B. (1973). Persistence and loose coupling in living systems. Behavioral Science, 18(2), 83–98. [Google Scholar] [Crossref]

17. Goldsmith, R. W. (1969). Financial structure and development. Studies In Comparative Economics, 70(4),31-45 [Google Scholar] [Crossref]

18. Haggett, P. (1965). Locational analysis in human geography. Edward Arnold. [Google Scholar] [Crossref]

19. Hall, B. H., & Helmers, C. (2013). Innovation and diffusion of clean/green technology: Can patent commons help? Journal of Environmental Economics and Management, 66(1), 33–51. [Google Scholar] [Crossref]

20. He, Y. Q., Wang, X. Z., Zhou, Y. F., & Bai, C. Q. (2015). An empirical analysis of the coupling coordination among financial agglomeration, economic growth, and eco-efficiency in the Yangtze River Economic Belt. Journal of Finance and Economics, (9), 13–19. [Google Scholar] [Crossref]

21. Herfindahl, O. C. (1997). Concentration in the steel industry. Columbia University Press. [Google Scholar] [Crossref]

22. Horbach, J., Rammer, C., & Rennings, K. (2012). Determinants of eco-innovations by type of environmental impact—The role of regulatory push/pull, technology push and market pull. Ecological Economics, 78, 112–122. [Google Scholar] [Crossref]

23. Hu, Y., Wang, C., Zhang, X., Wan, H., & Jiang, D. (2023). Financial agglomeration and regional green innovation efficiency from the perspective of spatial spillover. Journal of Innovation & Knowledge, 8(4), 100434. [Google Scholar] [Crossref]

24. Jiang, Y. H., & Chen, Y. (2023). Pathways through which the digital economy influences green technological innovation. Science and Management, 43(6), 1–10. [Google Scholar] [Crossref]

25. Jin, F., Qi, Z. H., & Liang, Y. L. (2021). Big data, financial agglomeration, and green technological innovation. Review of Economy and Management, 37(4), 97–112. [Google Scholar] [Crossref]

26. Kindleberger, C. P. (1974). The formation of financial centers: A study in comparative economic history (Princeton Studies in International Finance No. 36). International Finance Section, Princeton University. [Google Scholar] [Crossref]

27. Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499. [Google Scholar] [Crossref]

28. Lei, X., Xie, Y., & Chen, X. (2023). Does fiscal mechanism affect the promotion of outward foreign direct investment in green innovation? Discrete Dynamics in Nature and Society, 2023, 6630603. [Google Scholar] [Crossref]

29. Levine, R. (1997). Financial development and economic growth: Views and agenda. Journal of Economic Literature, 35(2), 688–726. [Google Scholar] [Crossref]

30. Li, J., & Bai, J. (2014). Measurement of the level of regional financial congregation in China. Seeking Truth, 41(4), 52–58. [Google Scholar] [Crossref]

31. Li, J. S., & Hu, Y. (2017). The influence of financial agglomeration on technological innovation: Evidence from Chinese provinces. Finance Forum, 22(7), 39–52. [Google Scholar] [Crossref]

32. Li, N. N., & Zhang, B. J. (2017). Study on the coupling coordination relationship between technological innovation and entrepreneurship: Empirical evidence from 25 countries. On Economic Problems, (12), 65–71. [Google Scholar] [Crossref]

33. Liao, C. B. (1999). Quantitative evaluation and classification system of the coordinated development between environment and economy: A case study of the Pearl River Delta urban agglomeration. Tropical Geography, (2), 76–82. [Google Scholar] [Crossref]

34. Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145–151. [Google Scholar] [Crossref]

35. Lin, T., Zhang, L., & Li, J. (2024). Coupling and coordinated development of green finance and renewable energy industry in China: Spatiotemporal differentiation and driving factors. Renewable Energy, 235, 121255. [Google Scholar] [Crossref]

36. Lu, C. (2021). The impact of financial agglomeration on green innovation efficiency (Master’s thesis, Shandong University). [Google Scholar] [Crossref]

37. Luo, Z. Y., He, Y. Q., & Mao, H. (2013). Study on the coupling relationship between financial agglomeration and economic development in East China. Enterprise Economy, 32(8), 135–138. [Google Scholar] [Crossref]

38. Miao, C. L., Duan, M. M., Zuo, Y., & Wu, X. Y. (2021). Spatial heterogeneity and evolution trend of regional green innovation efficiency: An empirical study based on panel data of industrial enterprises in China’s provinces. Energy Policy, 156, 112370. [Google Scholar] [Crossref]

39. Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society: Series B (Methodological), 10(2), 243–251. [Google Scholar] [Crossref]

40. Paas, T., & Schlitte, F. (2008). Regional income inequality and convergence processes in the EU-25. Scienze Regionali: Italian Journal of Regional Science, 7(Suppl. 2), 29–49. [Google Scholar] [Crossref]

41. Pandit, N. R., Cook, G. A. S., & Swann, G. M. P. (2001). The dynamics of industrial clustering in British financial services. The Service Industries Journal, 21(4), 33–61. [Google Scholar] [Crossref]

42. Pang, Q., Zhou, M., & Li, M. (2020). Spatiotemporal coupling coordination of financial agglomeration, regional innovation, and ecological efficiency in Jiangsu Province. East China Economic Management, 34(5), 33–41. [Google Scholar] [Crossref]

43. Porter, M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77–90. [Google Scholar] [Crossref]

44. Quan, T., & Quan, T. (2023). A study of the spatial mechanism of financial agglomeration affecting green low-carbon development: Evidence from China. Sustainability, 15(2), 965. [Google Scholar] [Crossref]

45. Ramanathan R. (2005). An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy, 30(15):2831-2842 [Google Scholar] [Crossref]

46. Schumpeter, J. A. (1934). The theory of economic development. Harvard University Press. [Google Scholar] [Crossref]

47. Song, D. J. (2014). Research on the coupling effect between technological innovation and high-tech product exports in China. Statistics & Decision, (4), 134–139. [Google Scholar] [Crossref]

48. Song, Q., Zhou, N., Liu, T., Siehr, S. A., & Qi, Y. (2018). Investigation of a “coupling model” of coordination between low-carbon development and urbanization in China. Energy Policy, 121, 346–354. [Google Scholar] [Crossref]

49. Xu, J.-Z., & Wang, M.-M. (2017). Empirical research on green innovation capability evaluation of China's manufacturing enterprises based on principal component and cluster analysis. Proceedings of the 2017 International Conference on Management Science and Engineering (ICMSE), 304–312. IEEE. [Google Scholar] [Crossref]

50. Wan, J., Li, C., Yang, Z., Wu, L., & Yue, M. (2025). Dynamic spatial spillover effects of financial agglomeration on CO₂ emissions: The case of China. Humanities and Social Sciences Communications, 12(1), 178. [Google Scholar] [Crossref]

51. Wang, W., & Liu, S. (2020). Financial agglomeration and the coupling coordination of regional innovation capability. Inquiry into Economic Issues, (1), 147–155. [Google Scholar] [Crossref]

52. Wang, R. X., & Yang, M. (2015). The coupling relationship between technological innovation and financial innovation and its impact on economic efficiency: Empirical evidence from 35 countries. Soft Science, 29(1), 33–36, 41. [Google Scholar] [Crossref]

53. Wang, S. J., Kong, W., Ren, L., Zhi, D. D., & Dai, B. T. (2021). Research on misuses and modification of coupling coordination degree model in China. Journal of Natural Resources, 36(3), 793–810. [Google Scholar] [Crossref]

54. Wang, Y. M., & Ma, Y. F. (2011). Analysis of the coupling coordination degree between urban tourism economy and transportation development: A case study of Xi’an. Journal of Shaanxi Normal University (Natural Science Edition), 39(1), 86–90. [Google Scholar] [Crossref]

55. Wen, Y., Zhao, M., Zheng, L., Yang, Y., & Yang, X. (2023). Impacts of financial agglomeration on technological innovation: A spatial and nonlinear perspective. Technology Analysis & Strategic Management, 35(1), 17–29. [Google Scholar] [Crossref]

56. Yao, L., & Wang, S. H. (2022). The interactive effects of financial agglomeration and regional economic growth and their spatial spillover effects: An empirical study based on a spatial panel simultaneous equation model. Journal of Statistics, 3(1), 27–39. [Google Scholar] [Crossref]

57. Ye, Q., & Cheng, C. (2019). Green technological innovation efficiency and financial ecological environment. Open Journal of Social Sciences, 7(12), 132–151. [Google Scholar] [Crossref]

58. Yu, L. P. (2013). An empirical study on the interaction between interprovincial finance and technological innovation. Science of Science and Management of S&T, 34(4), 88–97. [Google Scholar] [Crossref]

59. Zhang, D., & Wang, H. J. (2021). Financial agglomeration, spatial spillovers, and regional industrial green innovation efficiency. Economic Survey, 38(1), 134–142. [Google Scholar] [Crossref]

60. Zhang, H., Ding, D., & Ke, L. (2019). The effect of R&D input and financial agglomeration on the growth of private enterprises: Evidence from the Chinese manufacturing industry. Emerging Markets Finance and Trade, 55(10), 2298–2313. [Google Scholar] [Crossref]

61. Zheng, M., Wang, S., Du, Q., & Zhang, X. (2026). Digital dividends across space: How the digital economy fuels green innovation. Asia & the Pacific Policy Studies, 13(3), e70084. [Google Scholar] [Crossref]

62. Zhou, D. C., Wang, Y. Y., & Zhou, Y. F. (2015). Study on the coupling relationship between financial agglomeration and ecological efficiency in five coastal provinces and cities of East China. Commercial Economics Research, (28), 133–134. [Google Scholar] [Crossref]

• Impact of Foreign Direct Investment in India
• Issues Involved in Digitalisation Special Reference to Indian Tourism Growth
• Relationship Marketing and Customer Loyalty in the Fast-Moving Consumer Goods (FMCG) Industry in Nairobi County
• Financial Literacy or Financial Inclusion? Which is Which, What is What—To Achieve Uganda’s 10-Fold Economic Growth By 2040
• Harnessing Natural Gas for Economic Transformation: Overcoming the Regulatory and Infrastructural Bottlenecks in Nigeria