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impacts. Our scenarios involve two mechanisms for distributing impacts across
regions. First, we use the standard MRIO procedure to distribute calculated region-
specific final demand impacts across states.
F
0
NIEMO
¼
T
NIEMO
F
NIEMO
ð
4
:
5
Þ
Distributing final demand losses resulting from the complete elimination of
activity in a given sector is more complicated. This requires modifying the com-
modity trade coefficients matrix
T
NIEMO
to delete domestic exports from the
quarantine area, for example, California in one of our case studies [see Lee
et al. (
2012
)]. We set the entries for the California row vector, for example,
describing USC Sector 1
Live animals and fish, meat, seafood
in the matrix
T
NIEMO
to zero. In addition, outbound state flows from this sector are redistributed to origins
in other states based on existing flow proportions. This defines a modified matrix
F
MDFNIEMO
that is used to allocate final demand losses.
F
0
MDFNIEMO
¼
T
MDFNIEMO
F
MDFNIEMO
ð
:
Þ
4
6
This provides three types of direct impacts: region-specific direct impacts
Δ
F
NIEMO
; and, via Eqs. (4.5) and (4.6), regionally distributed impacts
Δ
Y
'
NIEMO
and
Δ
F
'
MDFNIEMO
. Total economic impacts may be estimated as
Δ
X
NIEMO
¼
LINV
NIEMO
Δ
F
NIEMO
þ
Δ
F
0
NIEMO
þ
Δ
F
0
MDFNIEMO
ð
4
:
7
Þ
4.3
TransNIEMO
TransNIEMO involves three sub-models (Fig.
4.4
), a national highway network
model, a transportation cost impact model, and NIEMO (a demand-driven multi-
regional input-output model as described above). Applications of TransNIEMO
also require appropriate substantial data preparation. The model is applied to
generate 1-year results, but because NIEMO is linear it is a simple matter to
down-scale the results to shorter periods. The three major research steps associated
with the three sub-models are discussed below.
The highway network model can be applied to problems such as combining the
highway networks with bridge or tunnel disruption scenarios. A user equilibrium
(UE) model is applied twice for each test: first to develop a baseline and second by
applying the scenario. The user equilibrium approach is appropriate when there is
significant congestion on the network. When dealing with freight flows on highway
networks among metropolitan regions, applying the UE algorithm is reasonable.
The results from applying the UE algorithm include the times and the distances
from origin regions to destination regions. We assume that trip durations are related
to truckers' labor costs and distance is associated with the other variable costs
besides labor. The results from the network model simulations are used as inputs
