## 4 Data Testing and Analysis
### Training and Analysis  
We adopt the data from 1996 to 2001 to train our neural networks. The input variables include CO\({}_{2}\), oil consumption, population, and power consumption, and the predicted measures include the morbidity rate and GDP. A sensitivity analysis is conducted to determine the critical factors and put forward advice that would help the government to establish policy. In Table 7, we list the sensitivity of the input factors and predicted measures from 1997 to 2001.  
From Table 7, we could draw a conclusion that the CO\({}_{2}\) content has been the critical factor. In our model, CO\({}_{2}\) stands for the environmental change.  
In 1997, CO\({}_{2}\) was the main factor behind the rapid increase of GDP, and also strengthened the morbidity rate. This means that the GDP increasing is based on environmental deterioration. Meanwhile, the deteriorating environment has caused an increase in the morbidity rate.  
From 1998 to 1999, CO\({}_{2}\) has also been the prominent factor in the rapid increasing of GDP, and exacerbated the morbidity rate. However, the increase has plateaued, which means that the government has recognized the problem and taken slight measures to tackle it.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**The Real, Measured Data**} \\ \hline
**Year** & **Morbidity Rate (100\%)** & **GDP (10 per Unit)** \\ \hline
1996 & 0.406 & 0.4520 \\
1997 & 0.411 & 0.4660 \\
1998 & 0.438 & 0.4750 \\
1999 & 0.454 & 0.4811 \\
2000 & 0.442 & 0.4950 \\
2001 & 0.437 & 0.5031 \\
2002 & 0.435 & 0.5061 \\
2003 & 0.433 & 0.5062 \\
2004 & 0.411 & 0.5063 \\
2005 & 0.407 & 0.5151 \\
2006 & 0.405 & 0.5280 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The real, measured data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **CO\({}_{2}\) (1e3 Billion Ton)** & **Oil Consumption** & **Population (10 Billion)** & **Power Consumption (1e5 _kw h_)** \\ \hline
1996 & 0.36236 & 0.429 & 0.57962 & 0.136714 \\
1997 & 0.36347 & 0.445 & 0.5878 & 0.139623 \\
1998 & 0.3665 & 0.455 & 0.9599 & 0.143186 \\
1999 & 0.36814 & 0.46 & 0.60386 & 0.147216 \\
2000 & 0.3694 & 0.475 & 0.61181 & 0.154169 \\
2001 & 0.37107 & 0.48 & 0.61957 & 0.155235 \\
2002 & 0.37317 & 0.482 & 0.62725 & 0.161266 \\
2003 & 0.37578 & 0.483 & 0.63492 & 0.167138 \\
2004 & 0.37752 & 0.49 & 0.64261 & 0.175049 \\
2005 & 0.37976 & 0.51 & 0.65032 & 0.182721 \\
2006 & 0.38185 & 0.515 & 0.65805 & 0.189788 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The local factor data. 1e3 means ten to the power of 3.  
In 2000, CO\({}_{2}\) shifted from a positive role to a negative factor in relation to the morbidity rate, but meanwhile it strengthened the GDP. This means that the government has taken measures to prevent some diseases. However, GDP increasing is also based on environmental deterioration.  
In 2001, CO\({}_{2}\) was a negative factor in relation to the GDP and morbidity rate. By a sensitivity analysis of morbidity rate, we found that environmental deterioration has alleviated the positive influence of the government policies that prevent some diseases. Even more, the environmental deterioration has hindered the increasing of GDP.