## 2 Index System Construction, Research Methods and Data Sources
### Spatio-Temporal Characteristics and Spatial Differences of the Coupling Coordination
#### 3.1.2 Regional Differences  
Dagum Gini coefficient and its decomposition are used to reveal the regional differences and sources of the coupling coordination degree between the digital economy and industrial green high-quality development.  
1. Overall difference analysis: Figure 2 confirms that the overall difference of the whole nation is commonly on the decline, thereby indicating that the coupling coordination degree difference is shrinking nationwide with the continuous support for industrial green development, but the overall spatial difference is still relatively prominent.  
Figure 1: Spatial Trend of the Coupling Coordination Degree.  
Figure 2: Regional Differences of the Coupling Coordination Degree.  
* Intra-regional differences: There are obvious spatial differences in the four regions. Firstly, the Intra-regional difference in the eastern region is the largest, with a Gini coefficient of around 0.16. Beijing, Shanghai, Zhejiang, Guangdong, and Jiangsu display strong economic strength, as these cities have substantially promoted industrial green development and a digital economy. Besides, the degree of coupling and coordination is significantly higher for these regions than that of Tanjin, Hebei, Fujian, Shandong, and Hainan. The intra-regional difference exhibited an expanding trend during the period from 2008 to 2020. Secondly, the intra-regional difference in the western region is smaller, although its growth rate is higher. For instance, the digital transformation of green industries such as Chongqing, Sichuan, and Shaanxi is high. In particular, Chongqing and Sichuan have formed a pole of China's digital economy. The coupling coordination's growth rate in both regions is far higher than that of other provinces in the western region. Thirdly, the intra-regional difference in the central region is the lowest with an upward trend. Fourthly, the Gini coefficient value in the Northeast is below 0.1 with a declining trend which indicates that since the development of all provinces in the Northeast is balanced, therefore the gap is small. The major reason behind this is the similarity of industrial development of all provinces in Northeast China.
* Inter-regional differences: The east-west region represents the largest regional difference, with an average of 0.153, followed by the east-northeast region and the east-central region. The Gini coefficient of the west-northeast, middle-west, and middle-northeast regions is relatively small, with mean values of 0.12, 0.11 and 0.1, respectively. This indicates that inter-regional differences in China are majorly driven by the spatial differences in the east-west, east-northeast, and east-Northeast, although the western, central, and northeastern regions have advantages in terms of resources and energy endowments compared to the eastern region. Meanwhile, the supporting conditions in several aspects are slightly insufficient, thereby leading to the relative backwarmness of the digital economy and industrial green development. Since there are more similarities in the development of the western, central, and northeast regions, there are therefore small differences between the regions.
* Sources and contributions of differences: Table 3 shows that the contribution rate of the three sources of the difference is highly stable. Hypervariable density serves as the major source of spatial differences. The intra-regional contribution rate fluctuates at 22%, while the inter-regional contribution rate is the lowest, displaying a downward trend. Conversely, the contribution of hypervariable density exhibits an upward trend, with the contribution rate up to 64.69%. This means that the coupling coordination degree of the four regions has a certain intersection. In addition, the industrial development of certain provinces in different regions is similar. As a result, a province with a lower coupling coordination degree in the higher rank region may be lower than a province with a higher value in the lower rank region.