Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

Energy Demand Forecast for Motor Freight Transportation in Taiwan:

An Application of the Dynamic Interregional Input-Output Model1
By
Gwo-Hshiung Tzeng* and Sheng-Hshiung Tsaur**

Abstract

The structure of energy demand for motor freight transportation in response to in

dustrial development and changes in the industrial structure in Taiwan is evaluated forvarious forecast years. An inter-industry interdependence model is used to estimate the total amount of freight transport for various transportation modes. This dynamic structure ischaracterized by mutual effects among industries between regions and also by an analyticalequilibrium, due to the capability of capturing the progress of fast economic growth andglobal industrial development. A dynamic interrgional input-output model is appropriate
for estimating the total amount of freight transport by mode and its corresponding end-user
energy demand by vehicle type.

1.

Introduction

Taiwan is very deficient in domestic energy resources.

Energy consumption for motor freighttransportation has increased rapidly, due to rapid economic development during the past two
decades. Thus, a strategic model is needed for forecasting the energy demand of motor freighttransportation in response to economic development and changes in the industrial structure.
In this paper, a dynamic interregional input-output model, which could be characterized byregional inter-industry, interdependence and analytical equilibrium within a global economic
system, is used to estimate the total amount of freight transport by transportation mode.

Meyer (1971) introduced the concept of an interregional input-output model for transport
planning. Sakashita (1973) applied this concept to road investment and regional allocation in
Japan.

Generally, most previous research applied multiple regression techniques [e.g., Kaya

(1983)] and econometric analysis [e.g., Daughty (1979), Winston (1983) and Zlatoper et al(1989)] to examine the relationship between gross domestic product (GDP), the total amount offreight transport and energy demand. A major disadvantage of this forecasting technique is that

it identifies static relationship between variables and ignores global end-user industrial energy

demand. To overcome these disadvantages, the structure of energy demand for motor freighttransportation in response to industrial development and changes in the industrial structure are
evaluated for various forecast years.

Manuscript received July 19, 1993. Revised July 17, 1995.

* Energy and Environmental Research Group and Institute of Traffic and Transportation, National Chiao Tung
University, Taiwan, R.O.C.

** Department of Tourism, Chinese Culture University, Taiwan, R.O.C.

2The authors wish to thank the Energy Committee, MOEA for providing financial support for special surveyof energy consumption in motor freight transportation, and the Department of Statistics, MOCA for providing
the data. We also thank Mr. K. H. Lee for providing the regional I-O table and professor D. S. Dendrinos forcommenting and correcting the manuscript.

42 Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

2.

Model Formulation

Four stages are described in preparing an energy demand forecast for motor freight transporta
tion. During the first stage, commodity flow modeling is done by using a dynamic interregional

input-output model [e.g., Chardson (1972), Hewings (1986) and Leontief (1986)] for regionalcommodity flow. At the second stage, a relationship is developed between product value anddelivery weight in each sector to transform the regional commodity flow from product value

units (NTS) into delivery (tons) units. The third stage encompasses a mode-split model for
allocating transportation flow Qr-S by modes that depend on the characteristics of commodities

and delivery distances and for estimating total ton-kilometers for each commodity. Finally, atthe fourth stage, an energy-use model is constructed to estimate fuel demand for motor freighttransportation based on the total number of vehicle-kilometers from ton-kilometers for truck

utilization factors, such as usable loading capacity and actual loading, empty truck ratio, andfuel efficiency for each truck type and commodity.
2.1.

Commodity flow modeling

The principal purpose of an interregional input-output model is to analyze input, output, final
demand by regions and industries and their relationships. Final demand by regions and indus
tries can then be determined. Financial flow can also be determined based on the quantificationof transactions.

The framework for commodity flow modeling is illustrated by the dynamic

interrelationship between input-output and final demand, as shown in Figure 1. Major steps formodeling this process are as follows:
1. Transportation facilities in the last period (k — 1) are assumed to be an exogenous input

variable; i.e., cost or travel time between regions by transportation mode is used to quantify
transportation facilities.

2. Freight distribution shipped via each transportation mode represents a potential demandfor corresponding transportation facilities. Therefore, this factor could be used to verify
the capacity of transportation facilities respectively.
3. Average transportation costs between regions for commodities is determined by using the

distribution ratio as a weighted factor.
4. Regional transaction coefficients are determined by using regional average transportation
costs.

5. Final demands in the present period (k) are determined based on value-added components
in each industry for the last period (k — 1).
6. Inter-industrial interdependence is determined by using transaction coefficients, input andfinal demand.

7. Total amount of production, value-added components and transactions by regions and in

dustries are then determined using the inter-industrial interdependent input-output table.
8. The total amount of freight transported by industry is forecasted.
9. An iterative process is used to estimate the total amount of freight transported by industry
during the various forecast years.

t-1

ITraffic
components

Transport

facility

facility

Transport

Transport

Transport

cost & time

cost & time

cost & time
Traffic

Transportation farecomponents

facility

Traffic
Transportation farecomponents

Transportation fare

i

±

±

Input-output
coefficients

Transaction

Input-output

Transaction

coefficientscoefficientscoefficientsInterregional

Traffic

Transport

t+1

Transport

input-output model

capacity

Interregional

Final
demandTraffic

input-output modelTransaction

coefficientscoefficientsInterregional

Final
demandInput-output

Traffic

input-output model

Added value

Added value

Added value

Population

Population

Population±

traffic volume

Finaldemandcapacity

capacity

Interregional

D

Interregional

Interregionaltraffic volume

traffic volume

Figure 1 Dynamic interregional input-output model5-

44 Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

The fundamental formulation for a competitive transfer-in model is shown below:
On the demand side

Df(k)

=

IDi

^

Y

k)

(1)

where

Df(k) :
IDi(k) :TDj(k) :FD\{k) :total demand of ith sector (industry) in rth region at time k]intermediate demand of ith sector in rth region at time k\transfer out of ith sector in rth region at time k\
final demand of ith sector in rih region at time k]ari;j(k) :direct 1-0 coefficient (aj^fc) = XT.{k)/X]{k)) ; i.e., the technical coefficient(including transportation cost and technology) for industry i to industry jof region r at time 4; and «/(&) = Xlf(k)/Xj(k),X*(k) = EiEr *#(*);

Xlf(k) denotes the production (output) value of jth sector in sth region by

input value from ith sector in rth region at time k;
Xj(k) :
i™(k) :

total production (output) value of jth sector in rth region at time k\
regional transaction coefficient; i.e., ith sector in rth region share, it is the ratioof total demand of ith sector in sth region from rth region input to sth region
at time k,

On the supply side

where

Sf(£) :
TIl{k) :M[(k) :

total supply of ith sector rth region at time k;transfer-in of ith sector in rth region at time k\import of ith sector in rth region at time k;

Xf(Ar),^5(Ar),D[(fc) :the same definition as those in equation (1) at time k.

The equilibrium of the demand side and supply side

It is clear that at equilibrium, demand and supply must be equal for each product and eachregion from equation (1) and (2), i.e.
D\{k) = S\{k)This implies that the following equation must hold:(3)

Energy Demand Forecast for Motor Freight Transportation in Taiwan

45

Assuming that the vector of total production value X(k) satisfies demand D(k), equation.
(4) can be shown in matrix form with R regions and I sectors:

X(k) = [(I + IT(K) - A{k) - T{k)]-'[FD{k) - M{k)\(5)where IT(k) is a diagonal matrix at time k whose ith sector rih region component is' M\k)and M(k) =

A\k)

Mr{k)

,X(k) =

,A(k) =

MR{k) . RIxl

FD(k) =

,T(k) =_ FR(k) _ RIxl

Ar(k)

RIxl

'(k)

,D(k) =

RIxRI

Dr(k)

J RIxRI-I RIxl

Further, the total production value (Xj(k)) for each sector is equal to the input from othersectors and the value-added (Vj{k)) of its own:k) + Vj(k)(6)

where national 1-0 coefficient aij(k) — Xij(k)/Xj(k)]Xij(k) denotes the production value

from ith sector (input) to jth sector (output) at time k and Xj(k) = YliXij(k). In this model,
we assume 1-0 coefficient (^-(k) and transacation coefficient tf(k) are constants in time k (i.e.,in the short-run), because a^(k) and t^s(k) are exogenous variables depending on government
policy and social behavior, due to improving transportation costs, developing new technologies,
and so on.

Based on human behavior, the building of dynamic final demand Fj(k) at time

period k is influenced by total value-added V(k — 1) at previous time period k — 1. Thus we

can estimate final demand Fj(k) by using the regression between final demand Fj(k) and total

value added V(k — 1) at previous time period k — 1, as shown in equation (7).

(7)
where

Based on this model, the regional commodity flow X^(k) over year k can be obtained by thegiven initial value added Vj(k — 1) and the coefficient of ar^{k) in each period k. This iterative
process may result in chaotic movements under certain specification, according to Dendrinos

and Sonis (1990).
2.2.

Relationship model between value unit and quantitative weight

By using empirical survey data, the regression relationship can be obtained between the com

modity flow of product value (Xj(k)) and the commodity flow of delivery weight (Mj); i.e.

46 Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

Mj(k) = f(Xj(k))(8)

Then the total ton-kilometers of transportation flow (Qy(k)) in each sector can be estimated,
i.e.,

where

dy : delivery distance between r and s in ith industrial sector;
Xjs(k) -> Mjs(k) by using equation (8).
2.3.

Model split model

The model split model also uses empirical behavior data to calibrate the allocations of transportation flow Qy(k) by modes (m).

The modal share PJ^ in each sector is dependent on

the characteristics of commodities and delivery distribution and is estimated by using empiricalsurvey data:

TKim(k) = ££ P^QYik)
2.4.

(10)

Energy-use model

Truck demand is, in general, a function of the total amount of commodities carried and utilization
factors, such as efficiency, the loading factor, and the percentage of empty trucks. In reality,
truck fuel consumption is affected both by exogenous variables and human factors. These factors

may include road slope, pavement material, vehicle velocity and the road condition, and driving
habits. In this subsection, fuel demand for motor freight transportation is estimated using thetotal number of ton-kilometers carried by truck and truck utilization factors. Equation (10) is
used for this purpose.

Vjml ;
where™jm(k)

LTm x ALFjm x FEjm(k) x (1 - ERjm)(n)

Qjm(k)'.

fuel demand in forecast year for mth truck mode used by jth commodity typeTMjm(k):delivered quantity at forecast year for mth truck type use by jth commodity

at time k\
type at time k\

LTm\
ALFjm:

the average usable loading capacity (ton) of mth truck type;the average loading factor (%) for mth truck type carrying jth commodity typeFEjm(k):the average fuel efficiency (km/liter) for mth truck type carrying jth commodity

ERjm'-

percentage of empty truck for mth truck type carrying jth commodity type.

(excluding empty trucks);
type at time k;

3.

Empirical Study and Discussions

In this section, an empirical test of the above models is carried out for Taiwan. To simplify theusage of existing data, Taiwan is divided into northern, central, southern, and eastern regions(R=4); industrial uses are classified into 30 categories (1=30); and trucks are classified into 4types (m=4): private and business light trucks, and private and business heavy trucks.

Energy Demand Forecast for Motor Freight Transportation in Taiwan 47

3.1.

Data description and analysis

A. Structure of freight transportation and industry

National input-output tables from 1971 to 1984, five-year regional input-output tables (1966,
1971, 1976, and 1981) and monthly industrial production statistical data were collected. Data
analysis reveals that manufactured products constitute the mainstream of the domestic econ
omy in Taiwan. Compared with the industrial structure in developed countries, in which profit
is mainly derived from marketing, the ratio of the GDP in services in relation to that for

all industries is low in Taiwan.

Between 1976 and 1984, the ratio of the real GDP for civil

industries, such as food, textile, lumber and furniture, and printed products, to that for allindustries decreased, while the proportion of the real GDP in high-technology industries, suchas information, electronic and automatic products increased significantly. This phenomenon
may have resulted from the government's encouragement of investment in high-technology. In
the export sector, the total amount of metal products, such as non-metallic, transportationequipment, and information, electronic and automatic products increased significantly over thisperiod. High value-added, technology-intensive industries, such as information, electronic andautomatic products and machinery, will probably continue to grow under government policy.
The industrial structural changed gradually from agricultural to manufacturing between 1963
and 1973. Completion of six five-year economic plans, resulted in the growth of business andindustrial establishments, as well as shipping.

Bulk commodities, such as lumber, grain, fer

tilizer and coal, are transported by railroad and smaller size and high-value commodities aretransported by road. The advantages of accessibility in motor freight transportation and thehigh density of freeways allows this mode of transportation to advance hand in hand with thepromotion and adjustment of the industrial structure.
B. Vehicle operation characteristics of motor freight transportation

O-D data on the characteristics of motor freight transportation include delivery value (NTdollars) delivery quantity (tons), model split rates, deliver-distance distribution , usable loading
capacity (tons) and actual loading (tons), empty truck ratio (%), and fuel efficiency for eachtruck type and commodity.

All these variables were included in a special survey carried out

jointly by the Department of Statistics of the Ministry of Communication Affairs (MOCA),
the Energy Committee of the Ministry of Economic Affairs (MOEA) and the Energy ResearchGroup of the National Chiao Tung University (Tzeng et al.) in 1984.

In ton-kilometers, the modal share by vehicle type for different commodities shows little
variation. Share for business heavy trucks is the highest, followed by private heavy trucks. Modal
shares for private and business light trucks were small. The principal reason for the intensive useof business heavy trucks may be its long-distance delivery characteristics for intercity movement,
fixed routes, and high percentage of utilization. Light trucks, on the other hand, are used forshort-distance deliveries. In tons, the modal share by vehicle type is similar to that for tonkilometers for most commodity types. Except for non-alcoholic beverages, miscellaneous foods,
electrical machinery and equipment, and machinery products, other commodity types are heavily
dependent on business heavy trucks for delivery.

Average delivery distances for canned food, plywood, rubber and rubber products, andindustrial chemicals are longer than those of other commodities. The average delivery distances
for cement, chemical fertilizers, and rice are found to be the shortest because those that areconsigned for longer delivery distance are delivered by railway. High value-added products are

generally transported to harbors by freeway for export.

Therefore, they usually require longdelivery distances. By vehicle type, the average delivery distance is greater for business heavy
trucks; private heavy trucks come next, and private light trucks are the shortest. For canned

foods, petroleum products, and machinery, the average delivery distance for light commercial

48 Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

trucks is much longer than that for private trucks.

This finding may be because it is mosteconomical to use business light trucks to deliver small quantities of the aforementioned products
over long distances.

In general, it is economical and efficient for a truck to be fully loaded to its maximum

allowable size and weight limits.

On the average, the loading factor for business light trucks
is 58.27%, for private trucks 26.79%, for business heavy trucks 37.44%, and for private heavy

trucks 28.68%. By commodity type, trucks which carry rice (41.91%), coal products (37.3%),
cement (37.29%), petroleum products (36.73%), and industrial chemicals (36.68%) have highloading factors; those that carry electrical products (22.11%), and rubber products (23.26%),
conversely, have low loading factors.
Generally speaking, private trucks have better fuel efficiency than business trucks in terms ofkilometers per liter (km/liter). Average fuel efficiency for a private light truck is 13.58 km/liter;and for a business heavy truck it is 4.77 km/liter. By commodity type, trucks that carry crops(10.03 km/liter) or electrical machinery (9.33 km/liter) have better fuel efficiency. Those thatcarry cigarettes and wines (5.24 km/liter), coal and coal products (5.38 km/liter), petroleum
products (5.69 km/liter), and forest products (5.72 km/liter), on the other hand, are less fuelefficient.

3.2.

Model calibration

The process of model calibration are divided into three steps below:(a) The regressions in the relationship between transaction coefficients and transportation

cost (#2=0.60 - 0.85), and between value-added and final demand (J?2=0.75 - 0.99)
in each industry were built first.

(b) Combining the first step of the regression model, the dynamic interregional inputoutput model from four years data (1966, 1971, 1976, 1981) was calibrated for annual

estimates (1971-1984).

Then, the results of summation from the dynamic interregional input-output model can be checked with the figures in the national input-

output tables (1971-1984). Errors are controlled to be less than 5% in each industry
sector.

(c) The regression relationship was built (iZ2=0.64 - 0.99) for commodity flow between
product value and delivery weights (equation(7)) in each industry.

Then the ton-kilometers (1971-1984) were estimated according to equation(8).
Finally, the energy consumption for motor freight transportation (1971-1984) was estimatedby using equations (9) and (10) and the special survey data in 1984. The results were comparedwith transportation and energy statistics (1986). Errors were also controlled to be less than 5%
in each year.
3.3.

Forecast

Basic assumption: The following assumptions are necessary for forecasting the commodity flow,transportation flow and energy demand based on the above calibrated dynamic interregional
input-output model.

Energy Demand Forecast for Motor Freight Transportation in Taiwan 49

A. Commodity flow model - input-output model(a) input output coefficient (ar^(k)) and transaction coefficients (*™(&)) over periods are
given from the calibrated model.

(b) Average operating costs are aggregated by vehicle type and, therefore are insensitive
to the commodity types carried.

(c) Based on the goals and strategies of mid-long-range economic development [Prospectsof socioeconomic changes to 2000 (1984) and Tzeng (1989)], the transportation sector
will be improved by reducing transportation costs and adjusting the inter-industry
interdependence structure between regions in each period2.
B. Energy use(a) Model share of ton-kilometers by vehicle type has the same ratios.
(b) Maximum allowable truck size and weight limits are unchanged.
(c) Loading factor by vehicle type is unchanged.

(d) Since it is difficult to predict fuel efficiency for each representative truck type, withoutchanges, a five percent improvement in fuel efficiency and in the rate of empty trucks3
is used for the scenarios.

Thus, in this paper, for mid-long-range economic development planning to 2000, as modified
by transaction coefficients in 1996 and 2001, and using 1984 as a basic year for forecasting to
2001, the above assumptions were combined to conduct estimates for four scenario cases:
Case 1:

fuel efficiency and empty truck ratio are based on the year 1984.

Case 2:

fuel efficiency and an empty truck ratio of 5% to improve annually to the year

Case 3:

infrastructure investment, i.e., building a second freeway and establishing a(improvement plan 1).

distribution center for commodity transshipment between intra-city and intercity to reduce annual transportation costs (improvement plan 2).
Case 4:
3.4.

carrying out cases 2 and 3 simultaneously (improvement plan 3).

Results and discussions

After applying model results, fuel demands by vehicle types are summarized in Tables 1 to
4.

Basically, motor freight transportation is a derived demand.

Fuel consumption for freighttransportation is influenced by economic development and growth in the GDP and in freight

demand. Over the past decade, the total number of trucks on the road increased by 155%
(9.80% annually); tonnage increased by 118%; ton-kilometers increased by 124% (8.38% annually); gasoline increased by 174% (10.85% annually); and diesel consumption increased by 180%
(10.85% annually). These findings also show that :(a) Both gasoline and diesel fuel consumption increased by 10% annually, especially after
the opening of the Sun Yat-Sen Freeway.
2 For example, a second freeway will be finished from north to south, which will reduce annual transportation
costs.

3 In line with the goal of formulating the "Energy Consumption Standard Regulation" for trucks for about,five percent improvement to the year 2001 by the Energy Committee, MOEA in 1986.

50 Journal of Applied Input-Output Analysis, Vol.2, No.2, 1995

Table 1: Fuel Demands by Truck Type at Various Forecast Years-Case 1

units: kl

Modes

Fuel

Base year 1984

1991

1996

2001

privategasoline

308,070

478,513615,841

780,761

light truck

diesel129,670

201,411

266,843

334,010businessgasoline

9,082

14,833

19,176

24,537light truck

diesel9,269

15,13819,66525,082

3,558

5,695

7,329

9,292

204,913324,973

426,054

543,480

privategasoline

heavy truck

dieselbusinessgasoline

16,00323,50730,25338,355heavy truck

diesel

957,943

1,407,172

1 ,827,988

2 ,331,806

total

gasoline

336,713522,548

672,599852,945

diesel

1,301,795

1,951,694

2 ,535,550

3 ,234,381

Table 2: Fuel Demands by Truck Type at Various Forecast Years-Case 2

units: kl

Modes

Fuel

Base year 1984

1991

1996

2001

privategasoline

308,070

437,761554,727

694,937

light truck

diesel129,670

184,258

230,389284,642

13,573

17,54722,45022,913businessgasoline

9,082

light truck

diesel9,269

13,852

17,908

privategasoline

3,558

5,205

6,596

8,263

heavy truck

diesel204,913299,774

374,826

463,092

businessgasoline

16,003

21,91827,77434,794heavy truck

diesel657,943

1,312,038

1,640,521

2,026,841

total

gasoline

336,713478,457

606,644760,444diesel

1,301,795

1,809,922

2,263,644

2,797,488(b) Heavy trucks generally consume diesel fuel and light trucks consume gasoline.

In

Taiwan, both the number of heavy trucks in use and their ton-kilometer rates arelarger than those for light trucks; therefore, diesel fuel was the major fuel consumed
in motor freight transportation. Moreover, the government discoverages the use ofgasoline by raising its prices, and limiting the availability of diesel fuel for passenger
cars.

(c) Truck fuel consumption increases at a rate higher than that for the total number ofregistered trucks. This finding indicates that strategies introduced to improve thefuel consumption of trucks over the past decade were not very effective.

(d) The ton-kilometer rate for trucks increased at a rate lower than the total numberof registered trucks, which indicates that the current utilization of trucks is also
ineffective.

From these findings, the following conclusions may be drawn:(a) For case 1, energy demands for freight transportation will increase because of theincreased commodity flows resulting from economic growth and the changes in transaction coefficients, industrial structure, etc. within various regions.

(b) The beneficial effects of improvements in energy demand for motor freight transportation can be achieved if the government takes certain strategic steps:

Energy Demand Forecast for Motor Freight Transportation in Taiwan 51

Table 3: Fuel Demands by Truck Type at Various Forecast Years-Case 3

units: kl

Modes

Fuel

Base year 1984

1991

1996

2001

privategasoline

308,070402,015

487,469

600,531

light truck

diesel129,670

169,212204,392

255,442businessgasoline

9,082

12,46615,116

18,622light truck

diesel9,269

12,724

16,451

21,048

privategasoline

heavy truck

diesel

3,558

4,776

5,791

7,134

204,913275,065

332,252

415,236businessgasoline

16,003

20,492

24,87530,644heavy truck

diesel

957,943

1,226,649

1,489,007

1,860,403

total

gasoline

336,713439,749

533,251

656,931

diesel

1,301,795

1,683,650

2,042,102

2,552,129

Table 4: Fuel Demands by Truck Type at Various Forecast Years-Case 4units: kl

Modes

Fuel

Base year 1984

1991

1996

2001

privategasoline

308,070

379,403437,488

501,043

light truck

diesel129,670

161,111

186,953

216,204

businessgasoline

9,082

11,491

13,25015,175light truck

diesel9,269

11,727

13,60815,175

privategasoline

heavy truck

diesel

3,558

4,395

5,071

5,808

204,913254,598

295,436

341,661businessgasoline

16,003

19,70822,72326,024heavy truck

diesel

957,943

1,190,212

1,381,122

1,597,217

total

gasoline

336,713415,000

478,532

548,050

diesel

1,301,795

1,617,648

1,877,119

2,170,819

Table 5: Annual Growth Rate of Energy Demand and Elasticity
Annual growth rate (%)
Modes

Fuel

privateElasticity to GDP*

1984-1991

1991-2001

1984-1991

1991-2001

gasoline

6.48

5.02

0.80

0.67

light truck

diesel

5.96

5.18

0.73

0.69

businessgasoline

5.15

4.74

0.63

0.63

light truck

diesel

4.82

4.45

0.59

0.59

privategasoline

3.89

4.10

0.48

0.55

heavy truck

diesel

3.74

4.20

0.46

0.56

businessgasoline

3.02

2.82

0.37

0.38

heavy truck

diesel

3.15

2.99

0.39

0.40

* Annual growth rates of GDP: 8.1% in 1984-1991 and 7.5% in 1991-2001.

52 Journal of Applied Input-Output Analysis, Vol.2? No.2, 1995

i. building new freeways to improve transportation systems for reducing transportation cost and energy (because of mitigating traffic congestion);
ii. building intracity and intercity distribution centers to facilitate transshipments;
iii. revising regulation to reduce the level of empty truck trips and to increase theefficiency; andiv. planning for mid-long-ranged economic development on the basis of changing theregional structure.

(c) The energy elasticities (to GDP) will be substantially below 1.0, because the freighttransportation systems will improve to a high efficiency level and the industrial structure will also change continually to high value-added technology-intensive industries.

4.

Conclusions

A model was described of energy demand forecast for motor freight transportation in responseto economic development and changes in the industrial structure.

A dynamic inter-industry

interdependent regional input-output model presented was used to estimate the total amountof freight transport for the various transportation modes.
The model is based on strategic plans, and it can be used to estimate energy demands andreductions in energy-use in motor freight transportation.
The purpose of this study was to illustrate that a regional input-output model can be extended to address problems in transportation, allowing for factors in regional structure andsocioeconomic development to be taken into consideration for transportation policy.

Energy Demand Forecast for Motor Freight Transportation in Taiwan 53

References[1] Chardson, Harry W. (1972), Input-Output and Regional Economics, John Wiley &: Sons.
[2] Council for Economic Planning and Development of Executive Yuan (1984), Mid-Long-Range Economic Development Planning: Prospects of Socioeconomic Changes to 2000.

[3] Daughty, A. F. (1979), "Freight Transport Demand Revisited: A Microeconomic View of Multimodal, Multicharacteristic Service Uncertainty and the Demand for Demand for Freight Transport," Transportation Research, Vol.l3B, No.2, pp.281-288.

[4] Dendrinos, D. S. and M. Sonis (1990), Turbulence and Socio-Spatial Dynamics, Springer-Verlag,
Applied Mathematics Series, New York.

[5] Department of Housing and Urban Development, Council for Economic Planning and Development
Executive Yuan (1986), Interregional Input-Output Table in Taiwan (1966, 1971, 1976, 1981).

[6] Department of Statistics, Ministry of Economic Affairs (1985), Industrial Production Statistics
Monthly in Taiwan.

[7] Department of Statistics, Ministry of Communication Affairs (1972-1985), Survey Report on Motor
Vehicle Freight Traffic: 1971-1984.

[8] Directorate-General of Budget, Accounting and Statistics, Executive Yuan, R. O. C. (1986), Taiwan
Input-Output Table: 1971-1984.

[9] Energy Committee, MOEA (1986), Taiwan Energy Statistics: 1985.
[10] Hewings, G. (1986), Input-Output Economics, Oxford University Press.

[11] Institute of Transportation, Ministry of Communication Affairs (1985), Transportation Informa
tion.

[12] Kaya, Yoichi (1983), "Transportation and Energy in Japan," Energy, Vol.8, No.l, pp.15-27.

[13] Leontief, Wassily W. (1986), Input-Output Economics, Oxford University Press.
[14] Meyer, John R. (1971), Techniques of Transport Planning: System Analysis and Models (Volume
II), The Booking Institution.

[15] Sakashita, Noboru (1973), The Research of Road Investment and Regional Allocation, Institute of
Metric Planning.

[16] Tzeng, Gwo-Hshiung (1989), "Modeling Energy Demand and Socioeconomic Development of Tai
wan," The Energy Journal, Vol.10, No.2, pp.135-151.

[17] Tzeng, Gwo-Hshiung (1986), "Optimum Distribution System for Improving Energy Efficiency on
Highway Truck," IAEE of the Eighth Annual International Conference, Tokyo, Japan, June 5-7.

[18] Tzeng, Gwo-Hshiung and W-J Jang (1986), "The Strategies of Vehicle Occupancy and Fuel Con
sumption," IAEE of the Eighth Annual International Conference, Tokyo, Japan, June 5-7.

[19] Winston, C. (1983), "The Demand for Freight Transportation: Models and Applications," Transportation Research, Vol.l7A, No.6, pp.416-427.

[20] Zlatoper, Thomas J. and Ziona Austrian (1989), "Freight Transportation Demand: A Survey of
Recent Econometric Studies," Transportation, Vol.16, No.l, pp.27-46.