Page 406 - 360.revista de Alta Velocidad - Nº 5

Zhenhua Chen

The aggregate regional impacts of rail infrastructure development in China for the period 2002 –
2013 are summarized in Table 4. The real GDP impacts were found to be the largest in the north-
coast region, whereas the smallest one was in the northwest. The impacts on the aggregated
employment are similar as most of the jobs were added in the north-coast regions as a result
of rail infrastructure development, but southwest has the second highest number of jobs being
created due to rail development. The regional economic contributions of rail development
during 2002 and 2013 were found to be the highest in the north-coast region if measured in
GDP multiplier. The overall multipliers for gross output (real terms) and real GDP are 1.01 and
0.09, which suggests that a one-dollar investment in rail sector is likely to generate one-dollar
increase in gross output and 0.09 dollar increase in real GDP.

7. Discussions

China has built the largest HSR system in the world with the strong support from its central
government. While more people began enjoying the convenience of intercity travel since the
opening of numerous HSR services, the understanding of its regional economic impacts remains
unclear. This study introduces for the first time, a comprehensive modeling framework to
evaluate the long-term regional economic impacts of rail infrastructure development in China.
By applying the state-of-the-art approach to economic impact assessment using a dynamic
SCGE model, we developed a detailed modeling procedure to reflect both the short-run effect
from rail investment and the long-run effect from the operations of new HSR services. Such
a modeling procedure is expected to provide a more reliable estimate than the traditional
approach that often evaluated from an ex ante perspective.
After incorporating the four types of effects including land use, capital investment, change of
transportation cost and productivity into the modeling framework, the results indicate that rail
infrastructure development in China, which is dominated by HSR investment, demonstrates a
positive long-term impacts on regional economic growth with a gross output multiplier of 1.01
and a GDP multiplier of 0.09. The aggregate impacts were found to be much significant in the
in the southwest region, whereas the impacts are relatively small in developed eastern regions.
One should note that the aforementioned empirical results are preliminary in the sense that
they only reflect the feasibility of the modeling framework for the evaluation of the long-
term regional economic impacts of HSR development. Hence, the assessment outcomes should
be read with caution. Limitations still need to be clarified so that further endeavors can be
made to improve the assessment outcomes. The first limitation is that since the detailed
regional level data that reflect the change of inter-regional transport cost and productivity
is not available, the existing empirical assessment doesn’t fully capture the regional impacts
that caused by other factors, such as a reduction of interregional transportation cost and a
productivity increase brought by HSR. Similarly, due to the lack of travel demand statistics at
the regional level, the results are also limited as the induced demand effect and effect of the
substitution among different transportation modes ignored.
Second, some of the direct impact drivers for CGE simulation need to be further improved. For
instance, the productivity change as an outcome of passenger rail system improvement was
currently measured in labor productivity. Although such a consideration captures the dynamics
of operational efficiency in rail sector, the indicator also has a limitation in that it inevitably
included other factors, such as influences from economic performance, regulatory changes and
etc. This also explains the negative consequence of a productivity decrease on the regional
economy. Hence, in order to reflect the trend of productivity change as a response to the
infrastructure and technology improvement, these aforementioned disturbing factors should be
removed from the existing indicators or better indicator should be considered.
Third, some key parameters of the SCGE modeling system, such as the elasticity of substitution

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