## 4 Results and Discussions
### Spatial Pattern Analysis of the Coordination Degree
#### 4.3.2 Local Spatial Autocorrelation Analysis  
The present study selected the years 2009, 2012, 2015, and 2018 to further conduct a local spatial autocorrelation analysis in terms of the level of China's coordination degree regarding carbon emissions, economic development, and environmental protection, and we also calculated the local Getis-Ord G\({}^{*}\) index to reflect the coordination degree. China's provinces were categorized into seven types of regions, including 99% hot spot aggregation regions, 95% hot spot aggregation regions, 90% hot spot aggregation regions, non-significant regions, 90% cold spot aggregation regions, 95% cold spot aggregation regions, and 99% cold spot aggregation regions (Figure 3). In 2009, Shanghai, Liaoning, and Jiangsu belonged to the category of 99% hot spot aggregation regions, whereas Hebei, Beijing, and Shandong belonged to the category of 95% hot spot aggregation regions. There were no provinces belonging to the category of 90% hot spot aggregation regions. By contrast, Qinghai and Sichuan belonged to the category of 99% cold spot aggregation regions, Gansu, Chongqing, and Yunnan belonged to the category of 95% cold spot aggregation regions, Xinjiang belonged to the category of 90% cold spot aggregation regions, and the rest of the provinces belonged to the category of non-significant regions. The number of provinces belonging to the category of hot spot aggregation regions accounted for 33% of the total, those belonging to the category of cold spot aggregation regions accounted for 20% of the total, and the number of those belonging to the category of hot spot aggregation regions was higher than those belonging to the category of cold spot aggregation regions. In 2012, Shanghai, Liaoning, and Jiangsu  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Moran’s I** & **Z Value** & **\(P\) Value** \\ \hline
2  
evided into the category of 95% hot spot aggregation regions, although the spatial scope of the hot spot aggregation regions remained the same as that for the original provinces. Xinjiang evolved into the category of 95% cold spot aggregation regions, and Chongqing evolved into the category of 90% cold spot aggregation regions, although the overall spatial scope of the cold spot aggregation regions remained the same as that for the original provinces. In 2015, Inner Mongolia was withdrawn from the list of hot spot aggregation regions, and some provinces such as Hebei and Beijing evolved into the category of 90% hot spot aggregation regions. The spatial scope of the hot spot aggregation regions showed a significant contraction trend, and the number of relevant provinces kept decreasing. Gansu evolved into the category of 99% cold spot aggregation regions, and Xinjiang and Yunnan evolved into the category of 90% cold spot aggregation regions. However, the spatial scope of cold spot aggregation regions remained the same as that for the original provinces. In 2018, Hebei, Beijing, and Liaoning were withdrawn from the list of hot spot aggregation regions. The spatial scope of hot spot aggregation regions further shrank, and the number of relevant provinces decreased rapidly. Only four provinces, namely, Anhui, Jiangsu, Shanghai and Zhejiang, belonged to the category of hot spot aggregation regions. Yunnan and Chongqing were withdrawn from the list of cold spot aggregation regions, and the spatial scope of cold spot aggregation regions also showed a significantly shrinking trend, as the number of relevant provinces kept decreasing. Only Qinghai, Gansu, Sichuan and Xinjiang remained in the category of cold spot aggregation regions. Figure 3: China’s cold versus hot spot spatial evolution on coordination degree in regard to carbon emissions, economic development, and environmental protection. Overall, the level of China's coordination degree in regard to carbon emissions, economic development, and environmental protection across all provinces exhibited spatial aggregation patterns, with an obvious spatial dependence and heterogeneity.