Economic Assessment of Natural Disasters in China:
A Dynamic Spatial CGE Approach
Nian LI, Hiroyuki SHIBUSAWA, and Yuzuru MIYATA
Department of Architecture and Civil Engineering
Toyohashi University of Technology, Japan
Abstract

On July 28, 1976 at 3:42 am local time, a moment magnitude 7.5 earthquake
occurred below Tangshan city in the Hebei Province of northern China, causing
extensive damage to the industrial city and its surroundings. The earthquake ruptured
100 km (62 mi) of the Tangshan fault, a right-lateral strike-slip fault trending in thenorth-northeast direction. The area experienced 10 km of extensive surface faulting
running through downtown Tangshan with horizontal displacements up to 1.5 m.
Many buildings collapsed as a result of the intense ground shaking. Due to theearthquake’s occurrence in the middle of the night, this damage led in turn to very
high casualties among the inhabitants of the city and the surrounding communities.
The event also caused major damage in the city of Tianjin located 100 km southwestof Tangshan, and moderate damage in Beijing approximately 140 km to the west. The
1976 Great Tangshan Earthquake remains the deadliest earthquake in modern times
with an official death count of approximately 242,400.
With over 2,000 years of chronicled history, the spatial and temporal occurrence ofhistorical earthquakes is particularly well studied in northern China. Since 1000 C.E.,
strong crustal earthquakes have occurred in this region over four distinct high activity
time periods, separated by decades of quiet seismic activity. The active periods span
1011 to 1076 C.E., 1290 to 1368 C.E., 1484 to 1730 C.E., and 1815 to the present.
Research has found that over the four active periods, seismicity was concentrated in
one or more distinct areas and the strongest earthquakes appeared to have beenclustered. For example, during the fourth period, which is still active in 2006, one
concentration of strong earthquakes occurred along the north-northeast trend from
Tianjin to Tangshan. A series of major events also occurred between 1966 and 1976:
the 1966 Xingtai earthquakes, the 1970 Tonghai Earthquake, the 1975 Haicheng
Earthquake, and the 1976 Tangshan Earthquake.
In this paper, The CGE model is be used dynamic process of the Ms=7.8 Tangshan
earthquake.China is subdivided into 30 cities/regions. All the cities/regions areconnected by transportation networks.Our model is of a decentralized economy withutility-maximizing consumers and value-maximizing firms in a dynamic context.The

model is calibrated for the regional economy using a multi-regional input-output tablefor China.As a case study,we estimate the impacts of a hypothetical earthquake in the
Bohai Economic Rim on the regional economy.
We develop a spatial computable general equilibrium model to investigate the regional
economic impacts of an earthquake. In our spatial model, China is subdivided into 30
cities/regions. All the cities/regions are connected by transportation networks. Our model is ofa decentralized economy with utility-maximizing consumers and value-maximizing firms in a
dynamic context. The model is calibrated for the regional economy using a multi-regional
input-output table for China. As a case study, we estimate the impacts of a hypotheticalearthquake in the Bohei Economic Rim on the regional economy.
Keywords: Disaster Evaluation, Indirect Economic Impacts, Large-scale Earthquake, Spatial
CGE Modeling, China
1. Introduction
We develop a dynamic spatial computable general equilibrium model to investigate theregional economic impacts of earthquakes in China. Our model is of a decentralized economy
with utility-maximizing consumers and value-maximizing firms in a dynamic context. The
model embodies both the spatial interactions among regions and the dynamics of regional
investments. A numerical simulation model is developed of an inter-regional inter-sectoral
economy in which China is subdivided into 30 regions. All the regions are connected bytransportation networks. The model is calibrated for the regional economy in China.
The economic impacts of disasters have been analyzed by computational modelingapproaches, such as the input-output model, mathematical programming, CGE, andeconometric models. Those studies mostly focused on assessing the economic impacts ofdamage to the public infrastructure such as transportation links, electric utility lifelines, water
facilities, and telecommunications networks.
The CGE model gives us an excellent framework for analyzing disaster impacts and policy
responses both across and between industries, households, and government. To assess the
distribution impacts of a disaster in multi-regional settings, the spatial CGE model approach,which disaggregates the world or a country into a number of regions or counties, has also
been developed. The models were characterized by the optimizing behavior of individual
consumers and firms, subject to market balances and resource constraints in a static
framework. The spatial interactions between regions are internalized by the transportation
networks and trade costs. The spatial CGE (or CGE) model is widely recognized as a
powerful tool.
China is located in the continent of Asia and is the world’s second-large country in terms ofland area. In China, major disasters are drought, flood, earthquake, hails and typhoon. Several
disasters have been assessed and empirical studies have adequately estimated direct and someindirect economic losses based on actual data (Tamura et al. 1982, Yaoxian 2002, Huixian etal.2002). Direct damage induced by catastrophic earthquake has significant impacts not only
on the disaster-affected region’s economy but also on other regions. Wu et al.(2012)
developed an interregional model and evaluated the indirect losses in and outside the disasterarea caused by the Wenchuan earthquake. The distributional impacts across economic
institutions and between regions caused by the direct economic loss in specific regions after
an earthquake were estimated.
The paper is organized as follows. We outline the model in Section 2. In section 3 and 4,
we provide scenarios and simulation results. Two cases are compared to a base case, which isa steady state solution for a 51-year period. Section 5 summarizes the paper and offers someconcluding remarks.
2. Outline of the Model
The model is finitely set up in discrete time. The world is divided into a home country anda foreign country. These are subdivided by region. There are three kinds of industries, i.e.
general, transportation and distribution industries. The general industry involves domestic andforeign trade between regions. All the regions interact with each other via the transportation
networks. A transportation network is defined by nodes and links. A transport path connecting
two regions is fixed and the transport link distance is exogenously given.
The model is based on dynamic macroeconomic theory with a multi-region andmulti-sector specification. Each region has production and household sectors. Commodity
trade flows are determined by the trade and modal share coefficients. We characterize theproblems related to the maximization of the production and household sectors in thiseconomy.
In the simulation model, the world is subdivided into 30 regions, which cover China’s
cities/provinces. General industry is divided into three sectors, i.e. agriculture, manufacturing,and services. There are four kinds of transportation networks: road, railway, sea, and air. Thenthe transportation industry is also divided into four sectors. The network structure, which isdefined by the distance between an origin and a destination, is given for each period. The
simulation period is set at t F = 51 . Population growth and technological progress are also
fixed over time. Utility, production, and investment functions are specified for the simulation
analysis. Our simulation model is calibrated using the multi-regional input-output table in
China (ref. Shi and Zhang 2012).
3. Simulation Cases and Numerical Examples
Our target area is the Bohai Bay Economic Rim (BER). It is the economic hinterland
surrounding Beijing and Tianjin. In this study, we consider a situation where a large scale
earthquake is occurred in the BER. In the past, there have been strong earthquakes in this area.

Since 1966, there have been four earthquakes of magnitude greater than 7.0: Hsingtai 1966,
Bohai 1969, Haicheng 1975, Tangshan 1976. According to the history of earthquake in China,
after 1000 B.C. four cycles of earthquake occurrence in the North China have been identified
by Chinese seismologists. Each cycle spans roughly 300 years (Huixian et al. 2002).
Three cases are examined to evaluate the dynamic impacts of an earthquake in the Bohai
Bay Economic Rim region. The primary physical damage is simply assumed in terms of the
reduction in the industrial capital stock in each region.
(a) Base Case
The base case is the business as usual case where there is no earthquake. In this case, asteady state solution is derived. The population growth rate and the technical progress growth
are also both given as zero percent.
(b) Case 1
We assume that an unpredicted earthquake occurs suddenly and hits the target area. In thissimulation, the earthquake occurs in the 25th period. The level of physical damage is also
assumed to comprise a reduction in capital stocks. In this situation, no industry can make a
new investment to protect itself from the disaster before the earthquake.
(c) Case 2
In this case, it is assumed that an earthquake is accurately predicted. Here, the earthquake
occurs in the 25th period. Then the capital stocks are reduced in the 25th period as shown in
Table 1. The amount of the reduction is the same as in Case 1. In Case 2, each industry canmake an additional investment to protect itself before the earthquake.
3. The Results of the Simulation(a) Impacts on Capital Stock
The dynamic solutions for the capital stocks and the value of the capital stocks (i.e., thecostate variable) of the manufacturing sector are examined. The BER region is directly
affected by an earthquake and suffers great losses, especially Hebei province. Other regions
are also indirectly influenced by the earthquake through the transportation network. Figure 2
shows the dynamic impacts of the earthquake on the capital stock and its value in Hebei
province. In our dynamic simulation, the capital stock is accumulated by forward calculation,
while the value of the capital stock (the costate variable) is solved by backward calculation.
Beijing and Tianjin are located inside Hebei province. In Heibei province, the capital stock
suddenly decreases during the 25th period and is gradually restored after the earthquake inCase 1. The capital stock value is unchanged before the earthquake, and it increases
unexpectedly during the 25th period due to the capital stock damage in Case 1. In Case 2, thecapital damage seems to be more rapidly repaired after the earthquake by an increase ininvestment to protect against the earthquake. In Case 2, the capital stock value would increasemore before the earthquake than in the Base case and Case 1. This implies that industrial
sector would exactly estimate the value of the capital stock before the earthquake since it canaccurately predict the occurrence of an earthquake.
Provinces which are not directly affected are severely influenced by the earthquake and areindirectly affected by the quake through the inter-trade between cities/provinces. In Case 1,
some provinces experience an increase in investment due to the recovery activities in the BER
region after the earthquake. By contrast, in Case 2, before the earthquake some provinces
experience a decrease in investment and output, since the investments in the BER region
increase to protect from the disaster. After the earthquake, similar changes can be seen.
(b) Impacts on GRPs
Figure 3 shows the results of the impacts of the earthquake on the GRP (Gross Regional
Products) in all the regions (cities and provinces). The change in the GRP is defined as∆=x ( xCase − xBase ) / xBase ×100% . In both cases, the earthquake occurs in the BER region
during the 25th period. This figure shows the dynamic impacts of the earthquake in all theregions. In addition, the bold line depicts the change in the total GRP of China and it implies
the average impact of the earthquake. It may be useful to compare a regional impact and anational impact. The industrial capital stocks in the BER region Heibei, Tianjin and Beijing,
are directly reduced by the earthquake. In both cases, the BER region suffers damage from theearthquake and the percentage of damage in Hebei province appears to be greater than that of
the whole of China.
It is assumed that the disaster hits in the 25th period, and the earthquake is accurately
predicted. In Case 2, the production sectors can make an additional investment before theearthquake. Before the earthquake, the BER region experiences a positive impact owing to theincrease in investment designed to protect the region from earthquake damage. In particular,
the GRP in Heibei province is largely influenced by the additional investment before thedisaster and the change in the GRP would be greater than that of China. After the disaster, theBER region in Case 2 is more rapidly restored by the investment from other provinces thanCase 1.
(c) Impacts on Commodity Flows
Here, we examine the dynamic impacts of the earthquake on the commodity flows between
regions. The changes in investment before and after the earthquake inevitably involve
changes in the intra- and inter-trade commodity flows through the transportation networks.
The intra- and inter-trade of manufactured goods during the 24th and 26th periods are shown in
Figure 4.

It shows the changes in the intra-and-inter trade commodity flows ofmanufactured goods for all the transport modes before and after the earthquake in Case 2. Thechange is defined as ∆=x ( xCase − xBase ) .

The diagonal line in the figure depicts the intra-trade commodity flows. The intra-tradeflows increase before and decrease after the earthquake. Before the earthquake, the increases
in the BER region are particularly noticeable. On the other hand, after the earthquake, thedecreases in in the BER region are noticeable. In Case 1, after the earthquake, similar changes
can be seen, i.e. there is a decrease in intra-trade commodity flows in the BER region.
The figure also presents the changes in the inter-trade commodity flows of manufactured
goods for all the transport modes between provinces in Case 2. We observe that the changes in
the commodity flows before the earthquake are smaller than those after the earthquake. Twokinds of major change can be seen in the figures. The first is a noticeable increase incommodity inflows to the BER region from other provinces. The second is a noticeable
decrease after the earthquake in the inflows to the BER region.
6. Concluding Remarks
In this paper, we described a dynamic spatial general equilibrium model. A decentralized
economic system, which linked with the transportation networks, was constructed in a
dynamic framework. The main purpose of the paper is to assess the impacts of an earthquake
in the BER area on the regional economy in China. We presented the results of threesimulations: no earthquake, unpredicted earthquake and predicted earthquake in terms of theoccurrence of an earthquake. We estimated dynamic and spatial impacts, i.e. industrial
investments and commodity flows between regions before and after the earthquake. The
indirect effects before and after a disaster were simultaneously solved. Our simulation results
suggest the importance of investment in terms of protecting the regional economy in the eventof a disaster.