## 5 The Driving Mechanism and Key Driving Factors of Green Residences in China
### Regression Analysis
Every unit of change in the other indicators had an impact on the G\({}^{\prime}\)-score of less than 0.4%, indicating that their effects on the two-star rating were weak and negligible. Third, the numbers of positive and negative regression coefficients for three-star G\({}^{\prime}\)-scores were 12 and 8, respectively. The average marginal effect values of UR, UCHA, UCDI, CRCNA, GDP, and GPBR indicated that each unit of change in them increased the proportion of green residences in newly started housing by 9.91, 3.65, 2.18, 1.59, 0.99, and 0.76%, respectively. It should be noted that the influences of UR, UCHA, and UCDI on the three-star rating were much stronger than those on the two-star rating. Meanwhile, the new housing scale and economic factors also had a significant positive driving effect on the three-star rating. This paper also noticed that PPEU, ICR, and AQI had significant negative effects on the three-star green residences development, and if they increased by 1%, then the G\({}^{\prime}\)-stores would change by \(-\)2.92, \(-\)2.29, and \(-\)1.43%, respectively. It can be seen that the education level of residents and real estate investment could not effectively promote three-star green residential development, and cities with high air quality had insufficient motivation to develop three-star green residences. Fourth, the climate condition was significantly correlated with green residences' development, especially those of a three-star rating. When compared to CRA.1, the G\({}^{\prime}\)-store of one-star residences in the division CRA.3 increased by 0.94%, two-star in the division CRA.2 increased by 0.02%, and three-star in the divisions CRA.4 and CRA.5 changed by 0.78, and 0.56%, respectively. All others were negative. It can be seen that one-star green residences are mainly distributed in areas with hot summers and cold winters, two-star ratings are chiefly located in severe cold or cold regions, and three-star rating tend to be distributed in hot summers and warm winters, or mild areas. By observing the regression indexes and average marginal effect values of the same factor on G\({}^{\prime}\)-stores with the same star ratings, this study found that there are differences in their sizes, and this difference is irregular. This proves that the development of green residences is the result of the combined action of multiple driving factors. Further comparing the influence of the same factor on G\({}^{\prime}\)-stores with different stars, this paper found that both the regression indexes and average marginal effect values are inconsistent in terms of sign, magnitude, and significance. This shows that the key driving factors vary between green residences with different star levels. Concretely, the economic factors played positive roles in promoting the green residences development. GDP had the strongest driving force for one-star green residences, while cities with strong economic strength also have a higher three-star green residence development index. The influence of environmental factors on green residences decreased with the improvement of evaluation grade, but the demographic factors and living standards increased with the improvement of evaluation level. Residents' education level was negatively correlated with the development of green residences, and the change of the coefficients was not regular with the improvement of evaluation level. The city's construction technology level and commercial housing price had different trends with the increase of green label grade. The higher the technical level of construction, the better the development of high-star green residences, but the sales price of commercial housing was just the opposite. The changing trend of the real estate market size index was not significant with the improvement of the green residence evaluation grade. Except for economic incentives, government measures showed a trend of weakening influence with the improvement of green residence evaluation level.