# Analysis of Potential Factors Influencing China's Regional Sustainable Economic Growth
## 5 Comparative Analysis of the Variables Selected Based on the Four Methods  
Common to the results of least squares estimation, stepwise regression, ridge regression, and Lasso regression variable selection are the significant positive contributions of the domestic consumption level, tertiary industry, and import and export trade to China's economic growth. Among these variables, the growth rate of total retail sales of consumer goods is the most important positive contributor to China's economic growth, which indicates that residential consumption demand is a long-term sustainable pulling force for China's economic development. The positive impact of the tertiary industry on China's economic development is second only to the total retail sales of consumer goods, indicating that China's tertiary sector has become a new economic growth area. The tertiary industry mainly includes knowledge-intensive industries and various service industries. Accelerating the development of the tertiary industry eases the pressure on natural resources and land resources, reduces environmental costs to achieve sustainable development, and helps expand employment and improve comprehensive national power. The international environment also influences domestic economic growth, and within the context of economic globalization, import and export trade can also drive China's economic growth. Studies had shown that international trade has become one of the factors driving China's economic growth. The volume of imports of capital and technology-intensive goods has contributed most strongly to China's economic growth [109]. Therefore, to expand the economy, capital and technology-intensive goods need to be emphasized.  
The differences in the results of the least squares estimation, stepwise regression, ridge regression, and Lasso regression variable selection are as follows. First, there is a significant difference in the degree of influence of the financial sector and industry on economic growth. Financial sector development and industrialization have a significant adverse effect on economic growth in the least squares regression estimation. Still, the impact is not substantial in the ridge regression or Lasso regression, which can be censored according to the BIC score. Second, foreign investment, fiscal expenditure, education expenditure, money supply, and the financial sector and industry have insignificant effects on the development of the domestic economy in the stepwise regression.  
Comparing the variable selection results revealed that least squares regression can only be used for parameter estimation and cannot achieve variable selection. Stepwise regression suffers from variable over-censoring. Only the variables with highly significant effects are kept, and those ranging between substantial and insignificant variables are not supported, resulting in a significant bias in parameter estimation. Both ridge regression and Lasso regression can be used for variable selection without over-censoring. Comparing the mean square error values, it is clear that the Lasso regression results are closer to the standard level. Therefore, based on the above empirical study, the following regression equation can be obtained.  
China's economic growth rate = 0.32174 + 0.00882 import and export trade growth rate +0.02303 growth rate of FDI \(-\)0.18662 growth of fiscal expenditure +0.00372 growth of money supply +0.65418 total retail sales of social consumer goods growth rate +0.07189 expenditure on education +0.693368 tertiary industry growth \(-\)0.01650 financial industry added value changes \(-\)0.00703 industrial growth rate.