Open Access
SHS Web of Conf.
Volume 92, 2021
The 20th International Scientific Conference Globalization and its Socio-Economic Consequences 2020
Article Number 07061
Number of page(s) 11
Section Regions and Economic Resilience
Published online 13 January 2021
  1. He, Y., Sheng, P., Vochozka, M. (2017). Pollution caused by finance and the relative policy analysis in China. Energy & Environment, 28(7), 808-823. [CrossRef] [Google Scholar]
  2. Vrbka, J., Šuleř, P., Machová, V., Horák, J. (2019). Considering seasonal fluctuations in equalizing time series by means of artificial neural networks for predicting development of USA and Peoplés Republic of China trade balance. Littera Scripta, 12(2), 178-193. [Google Scholar]
  3. Machová, V., Mareček J. (2019). Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks. In J. Horák (Ed.), SHS Web of Conferences: Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018). Les Ulis, France: EDP Sciences. [Google Scholar]
  4. Liang, X. (2019). Study on the impact of industrial structure on GDP and economic growth in China based on multiple regression. In C. Huang, Y. Zhong & Z. Wang (Eds.), Proceedings of the 4th International Conference on Financial Innovation and Economic Development (ICFIED 2019) (pp. 350-354). Paris, France: Atlantis Press. [Google Scholar]
  5. Hašková, S., Volf, P., Machová, V. (2019). Economic convergence of Czech regions in terms of GDP and unemployment rate in response to FDI flows: Do businesses and regions flourish? Ad Alta: Journal of Interdisciplinary Research, 9(1), 326-329. [Google Scholar]
  6. Ji, D. (2019). Unusual patterns in China’s prefectural GDP growth rates. Applied Economics Letters, 26(4), 331-334. [CrossRef] [Google Scholar]
  7. Wang, J., Xin, L., Wang, Y. (2019). Economic growth, government policies, and forest transition in China. Regional Environmental Change, 19(4), 1023-1033. [CrossRef] [Google Scholar]
  8. Zheng, W., Walsh, P. P. (2019). Economic growth, urbanization and energy consumption – A provincial level analysis of China. Energy Economics, 80, 153-162. [CrossRef] [Google Scholar]
  9. Li, C., Li, J., Wu, J. (2018). What drives urban growth in China? A multi-scale comparative analysis. Applied Geography, 98, 43-51. [CrossRef] [Google Scholar]
  10. Mikheev, V. V., Lukonin, S. A. (2019). New stage in China’s development amid the “trade war” with the U.S. Mirovaya Ekonomika i Mezhdunarodnye Otnosheniya, 63(2), 56-65. [Google Scholar]
  11. Li, C. (2018). China’s 2009-2050 economic growth: A new projection using the Marxian optimal growth model. World Review of Political Economy, 9(4), 429-450. [CrossRef] [Google Scholar]
  12. Liu, M. H., Margaritis, D., Zhang, Y. (2019). The global financial crisis and the export-led economic growth in China. The Chinese Economy, 52(3), 232-248. [CrossRef] [Google Scholar]
  13. Rowland, Z., Šuleř, P., Vochozka, M. (2019). Comparison of neural networks and regression time series in estimating the Czech Republic and China trade balance. In J. Horák (Ed.), SHS Web of Conferences: Innovative Economic Symposium 2018 - Milestones and Trends of World Economy (IES2018). Les Ulis, France: EDP Sciences. [Google Scholar]
  14. Rawski, T. G. (2001). What is happening to China’s GDP statistics? China Economic Review, 12(4), 347-354. [CrossRef] [Google Scholar]
  15. Holz, C. A. (2014). The quality of China’s GDP statistics. China Economic Review, 30, 309-338. [CrossRef] [Google Scholar]
  16. Kerola, E. (2019). In search of fluctuations: Another look at China’s incredibly stable GDP growth rates. Comparative Economic Studies, 1-22. [Google Scholar]
  17. Henderson, J. V., Storeygard, A., Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review, 102(2), 994-1028. [CrossRef] [Google Scholar]

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