## 5 Empirical Analysis
### Analysis of the Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**DMU** & \(R_{j}^{*1}\) & \({R_{j}^{*2}}\) & \({R_{j}^{*3}}\) & \({R_{j}^{*1}}/{R_{j}^{1}}\) & \({R_{j}^{*2}}/{R_{j}^{2}}\) & \({R_{j}^{*3}}/{R_{j}^{3}}\) & \({R_{j}^{*}}\) & \({R_{j}^{*}}/{R_{j}}\) \\ \hline Heilongjiang & 1410.22 & 12,113.03 & 4808.62 & 1.00 & 0.91 & 0.78 & 18,331.87 & 0.87 \\ Shanghai & 84.43 & 14,022.98 & 8740.16 & 1.00 & 1.07 & 1.00 & 22,847.57 & 1.04 \\ Jiangsu & 1035.46 & 57,531.02 & 10,012.72 & 1.00 & 1.00 & 1.00 & 68,579.20 & 1.00 \\ Zhejiang & 727.82 & 36,645.42 & 6789.41 & 1.19 & 1.27 & 1.01 & 44,162.66 & 1.22 \\ Anhui & 366.66 & 24,180.58 & 4676.35 & 0.76 & 1.04 & 0.97 & 29,223.59 & 1.02 \\ Fujian & 371.96 & 23,178.09 & 4962.81 & 1.00 & 1.16 & 1.27 & 28,512.86 & 1.17 \\ Jiangsci & 229.58 & 17,535.92 & 3749.89 & 0.79 & 1.00 & 0.91 & 21,515.39 & 0.98 \\ Shandong & 1315.58 & 78,277.87 & 9957.18 & 1.00 & 1.00 & 0.96 & 89,550.63 & 1.00 \\ Henan & 967.74 & 34,005.57 & 7185.79 & 0.95 & 1.00 & 0.92 & 42,159.10 & 0.98 \\ Hubei & 739.01 & 22,413.64 & 7174.58 & 0.98 & 1.16 & 1.09 & 30,327.24 & 1.14 \\ Hunan & 1008.68 & 18,489.62 & 6532.78 & 0.84 & 1.13 & 0.96 & 26,031.08 & 1.07 \\ Guangdong & 1089.82 & 38,158.48 & 13,409.46 & 1.08 & 1.00 & 1.00 & 52,657.76 & 1.00 \\ Guangxi & 289.23 & 51,485.34 & 3650.96 & 0.91 & 1.00 & 1.24 & 55,425.53 & 1.01 \\ Hainan & 269.50 & 2923.80 & 1791.42 & 1.00 & 1.00 & 1.00 & 4984.73 & 1.00 \\ Chongqing & 175.67 & 10,274.48 & 3792.68 & 1.00 & 0.99 & 1.21 & 14,242.82 & 1.04 \\ Sichuan & 353.63 & 26,246.11 & 6046.00 & 1.00 & 1.50 & 1.50 & 32,645.74 & 1.49 \\ Guizhou & 346.26 & 11,283.82 & 5727.82 & 0.74 & 1.05 & 0.99 & 17,357.90 & 1.03 \\ Yunnan & 417.52 & 18,713.56 & 3997.92 & 0.87 & 1.50 & 1.36 & 23,129.00 & 1.45 \\ Shaanxi & 385.54 & 19,069.25 & 4377.22 & 1.00 & 1.00 & 1.00 & 23,832.01 & 1.00 \\ Gansu & 347.89 & 12,053.75 & 2497.29 & 1.02 & 1.27 & 1.26 & 14,898.94 & 1.26 \\ Qinghai & 24.03 & 3525.22 & 1087.86 & 1.00 & 1.00 & 1.50 & 4637.11 & 1.08 \\ Ningxia & 143.25 & 17,060.94 & 1003.21 & 0.73 & 1.00 & 1.00 & 18,207.40 & 1.00 \\ Xinjiang & 1188.75 & 31,797.70 & 2908.24 & 1.00 & 1.00 & 0.81 & 35,894.69 & 0.98 \\ Total & 16,741.21 & 814,096.68 & 159,062.11 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(Cont.\)  
Figure 3: Change in industrial carbon emission ratio of each province before and after allocation. Table 4 and Figure 3 indicate that the resulting reductions for provinces or municipalities show different features. In the primary industry, Beijing has the largest proportion of incremental emissions, followed by Zhejiang, Guangdong, and Gansu; 14 provinces, including Ningxia and Shanxi, need to reduce emissions, with Shanxi having the largest proportion of emission reductions. In the secondary industry, only Jilin, Heilongjiang, and Chongqing need to reduce emissions, and 13 provinces, such as Sichuan and Yunnan, need to increase emissions, with Sichuan and Yunnan having the largest proportion of emissions increase. In the tertiary industry, 10 provinces, such as Qinghai and Sichuan, need to increase emissions, and Qinghai and Sichuan have the largest proportion of increased emissions; and 13 provinces, such as Tianjin and Inner Mongolia, need to reduce emissions, of which Inner Mongolia, Jilin, and Heilongjiang have the largest proportion of reductions. It should be noted that the overall allocation scheme is basically in line with the current situation of regional industrial development (for example, heavy industrial regions such as Shanxi, Inner Mongolia, and Liaoning, whose carbon quota allocation is still dominated by the secondary industry).