Environmental Engineering Reference
In-Depth Information
Simulated 2005
Simulated 2010
Kilometer
80
Kilometer
80
0
20
40
0
20
40
FIGURE 24.3
The predicted urban growth from SUGAM in the years of 2005 and 2010 (the yellow patches are land uses in
1990).
resource constraints. They combine the top-down approach
(the government policy) with the bottom-up momentum (local
development opportunity) to decide on locations and rate of
urban growth.
As shown, the agent-based models are not only well suited to
disaggregate systems but can also be used to integrate different
levels and scales essential to simulating what at first sight appears
to be bottom-up phenomenon such as desakota. Of course,
enabling agents to search and to extract information beyond their
locality is a challenging task. Our handling of the agents at the local
neighborhoods and at the level of the townships in this chapter is a
considerate attempt to understand the interactions of geographic
agents at different scales. However, as we pointed out earlier, the
consideration of agents acting in a competitive environment both
from regional and national perspectives in a world of economic
globalization leads to more realistic simulations.
On the other hand, there are several areas where further
explorations and improvements are needed. The most challeng-
ing one is the verification of the simulation results. Currently,
the verification of the simulation results is carried out between
the simulation results and the regression analysis. Further ver-
ifications are needed to check errors of the total count and
sub-counts of land development by land categories. Attentions
should be paid to the verification and validation of spatial dis-
tribution and patterns and the statistics derived from landscape
metrics (Xie, Yu and Bai, 2006) and computation geometry (Xie
and Ye, 2007).
Finally, the simulation results raise a number of questions
on rural urbanization in China. An urbanization policy that
emphasizes rural areas continues to be successful, because rural
urbanization shows a very positive response to economic pros-
perity. Less restrictive migration policies and less parochial town
and county governance are leading tomore efficient allocations of
resources and greater economic growth in the long run. However,
the rural industrialization is facing many challenges. Compared
with large cities, lack of high-tech and trained workers often lead
to the quality concerns and thus slowing demand for rural town-
ship and village enterprise (TVE) produced goods. Moreover,
due to relatively loosened environmental protection or con-
trol in rural areas, environmental consequences of uncontrolled
rural industrialization are serious. Furthermore, new compet-
itive demands are putting pressure on China's industries that
may eventually reward the economies of scale and locational
advantages of large metropolitan areas. Perhaps continued rural
urbanization is neither economically sound, nor environmentally
wise. The pattern changes of land use identified in these agent-
based simulations support some of these concerns. For instance,
hilly and plain dry-lands, environmentally sensitive ecosystems,
were almost wholly converted to urban land use. In other word,
these concerns of sustainable development and environmental
quality should be integrated in agent-based urban modeling.
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