E3S Web Conf.
Volume 214, 20202020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|Number of page(s)||12|
|Section||Machine Learning and Energy Industry Structure Forecast Analysis|
|Published online||07 December 2020|
Factors Influencing Regional Economic Vitality Based on Regression Analysis
School of Business, Shandong Normal University, Jinan, China
The urban economic vitality refers to capacity and potential of urban economic development. At present, Chinese cities are in a period of rapid growth, the vitality of urban economy has become a driving force for attracting talents, urban construction and sustainable regional development. Based on multiple stepwise regressions, this paper quantitatively analyzes the current regional economic vitality and future development from different regions and different perspectives. This paper models and analyzes the economic vitality of different provinces of China from six aspects: population change, corporate development vitality, regional development, regional environment, industry development, and income level. The results show the increase of urban population and population density is conducive to the enhancement of regional economic vitality, and the absorption of enterprises can drive the regional economic growth. We use propensity score matching to calculate the change of the growth rate of the number of enterprises after the implementation of the policy, and analyze the short-term and long-term effects of changes in economic policy factors on regional economic vitality. This paper quantifies the key factors affecting the survival of urban enterprises, and provides theoretical support for promoting urban economic vitality. Finally, we tested the model, verified the rationality of the results, and extended the scope of application of the model, which has certain reference value in practical applications.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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