## 4 Empirical Analysis  
### Evaluation Index Selection and Data Sources  
This study focuses on the integrated transport efficiency of China's regions based on capital, manpower and resource inputs. Considering the limitations of data availability, we further selected three indexes as the inputs and five indexes as the outputs to evaluate the integrated transport efficiency of 31 provinces in China during 2009-2016, as shown in Figure 1.  
Figure 1: Super-efficiency slack-based measure model.  
The amount of fixed capital investment in transport and the number of employees were used as the capital and the manpower input values, respectively. The volume of energy consumed by transport was used as the energy input value; this is one of the major inputs in many production and related processes, and is especially needed in the transport sector. The value added by the transport industry, passenger turnover and freight turnover were selected as the desirable outputs and reflected production values. The volume of carbon dioxide emissions and traffic accident property losses were selected as the undesirable outputs.  
The fixed assets investment, number of employees, added value in transport industry, passenger turnover, freight turnover and traffic accident property loss were directly derived from the _China Statistical Yearbook_[55] (2009-2016), and the energy consumption indicator was derived from the _China Energy Statistical Yearbook_[56] (2009-2016). However, there were no official statistics available on regional carbon dioxide emissions in China. Therefore, transport CO\({}_{2}\) emissions were estimated using the method provided by the Intergovernmental Panel on Climate Change (IPCC) [57]. The specific formula is as follows:  
\[C=\sum_{i}C_{i}=\sum_{i}E_{i}F_{i}\quad\left(i=1,2,3\cdot\cdot\cdot\cdot\cdot 8\right) \tag{12}\]  
where \(C\) represents total carbon emissions from various types of energy and \(C_{i}\) refers to carbon emissions from category \(i\) energy. \(E_{i}\) and \(F_{i}\) represent energy consumption of category \(i\) and the CO\({}_{2}\) emission factor, respectively. The standard coal coefficient and CO\({}_{2}\) emission factor for each type of energy conversion are shown in Table 1.  
The calculation of carbon dioxide emissions by the integrated transport of the 31 studied regions of China is shown in Figure 2. In recent years, carbon emissions as a negative external transport output has shown a significant growth trend, with emissions reaching a peak in 2015. Although China's transport carbon emissions declined in 2016, in this year, emissions were still 28.7% higher than in 2009. Carbon emissions are one of the undesirable outputs of transport which seriously restrict improvements to integrated transport efficiency. Moreover, according to the changing trend that can be observed in Figure 2, transport carbon emissions are predicted to continue increasing to some extent.  
The statistical descriptions of the indexes are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Energy Type** & **Raw Coal** & **Coke** & **Crude** & **Fuel Oil** \\ \hline Standard Coal Conversion Factor & & & & \\ (kgce/kg or m\({}^{3}\)) & & & & \\ CO\({}_{2}\) emission factor & & & & \\ (kg CO\({}_{2}\)/kg or m\({}^{3}\)) & & & & \\ \hline
**Energy Type** & **Petrol** & **Kerosene** & **Diesel** & **Natural Gas** \\ \hline Standard Coal Conversion Factor & & & & \\ (kgce/kg or m\({}^{3}\)) & & & & \\ CO\({}_{2}\) emission factor & & & & \\ (kg CO\({}_{2}\)/kg or m\({}^{3}\)) & & & & \\ \hline \hline \end{tabular} Note: Standard coal conversion factors of energy were derived from the _China Energy Statistical Yearbook_. CO\({}_{2}\) emission factors were from the _China Energy Statistical Yearbook and National Greenhouse Gas Emission Inventory Guide_ (2006).  
\end{table}
Table 1: Standard coal coefficient and CO\({}_{2}\) emission factor.