## 3 Results and Discussion
### Operational Efficiency Evaluation of Power Grid Enterprises
Next, a company's operational efficiency was analyzed and some improvements made based on the evaluation results. First, actual data were collected from 2008 to 2013. The mixed distance function and bidirectional priority of input-output were used in the calculation. The result showed that annual efficiency evaluations were 1 from 2008 to 2013, and the scale returns were stable. Thus, the technical efficiency and scale benefit scores of this enterprise were 1, which meant it was well controlled in input-output proportion. At the same time, it also showed that input decision and post management were reasonable and had achieved good operating efficiency. (2) Operational efficiency comparison among enterprises  
As the contrast of operational efficiency can improve the investment decision, we selected 14 provincial companies to calculate their efficiency based on data from 2013. The mixed distance function and bidirectional priority of input-output were also selected, and the operation results are shown in Table 6 and Figure 6.  
Figure 5: Comprehensive trend of operational performance driving index and achievement index. Where, DMU is abbreviation of Decision Making Unit; DEA is abbreviation of Data Envelopment Analysis; D-DEA is abbreviation of Dynamic Data Envelopment Analysis; SFM is abbreviation of Stochastic Frontier Model. From Table 6 and Figure 6, we observed that the results were similar to the improved DEA and SFM methods when calculating technical efficiency over one year; therefore, the two improved methods could provide a good solution when the efficiency of the DMU was 1. To better compare the two improved methods, we designed an efficiency linkage evaluation to account for many years, which considered the return delay of power grid investment. Through the technical efficiency calculation of each region, the results of the static efficiency and dynamic efficiency were compared, as shown in Table 7 and Figure 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**DMU**} & \multicolumn{2}{c}{**Technical Efficiency**} & \multicolumn{2}{c}{**Allocative**} & \multirow{2}{*}{**Return of**} \\ \cline{3-4} \cline{6-7}  & & **Traditional DEA** & & & **D-DEA** & & **SFM** \\ \hline
1 & Anhui & 0.636429 & 0.615978 & 0.621734 & 0.736429 & Growing \\
2 & Henan & 0.651479 & 0.751509 & 0.745261 & 0.651479 & Growing \\
3 & Heilongjiang & 0.88382 & 0.815186 & 0.802847 & 0.88382 & Growing \\
4 & Hubei & 0.590821 & 0.798534 & 0.817264 & 0.50821 & Growing \\
5 & Hunan & 0.842656 & 0.932699 & 0.918274 & 0.842656 & Growing \\
6 & Jilin & 0.763904 & 0.788218 & 0.872734 & 0.63904 & Growing \\
7 & Northern Hebei & 0.94921 & 0.855103 & 0.873212 & 0.794921 & Growing \\
8 & Liaoning & 0.484767 & 0.75323 & 0.783621 & 0.64923 & Growing \\
9 & Sichuan & 0.380624 & 0.664676 & 0.683762 & 0.80624 & Growing \\
10 & Chongqing & 0.745368 & 0.636271 & 0.657261 & 0.745368 & Growing \\
11 & Beijing & 1 & 0.965293 & 0.912837 & 0.738273 & Stable \\
12 & Tianjin & 1 & 0.931343 & 0.942812 & 0.682763 & Stable \\
13 & Shanghai & 0.937281 & 0.923472 & 0.932321 & 0.652734 & Growing \\
14 & Fujian & 1 & 0.964716 & 0.945627 & 0.789321 & Stable \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Regional efficiency assessment. Figure 6: A comparison of the efficiency evaluation results. From Table 7 and Figure 7, when considering the delay of grid investment, the dynamic and static evaluation results changed with the improved DEA method, and was relatively reasonable for the certain particularities of power grid investment, which include the large scale of fixed assets, long investment cycle, influence of grid structure, and regional economy. Therefore, it was not scientific to evaluate the static efficiency of power grid enterprises, and instead, dynamic efficiency evaluation should be adopted. Results with a high efficiency declined and increased with low assessment results.