Environment-adjusted total-factor energy ef
ficiency
of Taiwan's service sectors
Chin-Yi Fang
a, Jin-Li Hu
b,n, Tze-Kai Lou
b aGraduate Institute of Sport, Leisure, and Hospitality Management, National Taiwan Normal University, Taiwan
bInstitute of Business and Management, National Chiao Tung University, Taiwan
H I G H L I G H T S
The technical efficiency and energy-saving target of service sectors are assessed.
The pre-adjusted and environment-adjusted total-factor energy efficiency scores in services are assessed. The industrial characteristic differences are examined by the panel-data, random-effects Tobit regression model. Labor, capital, and energy and an output (GDP) are included in the DEA model.
Future new capital investment should also be accompanied with energy-saving technology in the service sectors.
a r t i c l e i n f o
Article history: Received 15 August 2012 Accepted 29 July 2013
Available online 5 September 2013 Keywords:
Data envelopment analysis
Environment-adjusted total-factor energy efficiency (EATFEE)
Panel random-effects Tobit regression
a b s t r a c t
This study computes the pure technical efficiency (PTE) and energy-saving target of Taiwan's service sectors during 2001–2008 by using the input-oriented data envelopment analysis (DEA) approach with the assumption of a variable returns-to-scale (VRS) situation. This paper further investigates the effects of industry characteristics on the energy-saving target by applying the four-stage DEA proposed byFried et al. (1999). We also calculate the pre-adjusted and environment-adjusted total-factor energy efficiency (TFEE) scores in these service sectors. There are three inputs (labor, capital stock, and energy consumption) and a single output (real GDP) in the DEA model. The most energy efficient service sector isfinance, insurance and real estate, which has an average TFEE of 0.994 and an environment-adjusted TFEE (EATFEE) of 0.807. The study utilizes the panel-data, random-effects Tobit regression model with the energy-saving target (EST) as the dependent variable. Those service industries with a larger GDP output have greater excess use of energy. The capital–labor ratio has a significantly positive effect while the time trend variable has a significantly negative impact on the EST, suggesting that future new capital investment should also be accompanied with energy-saving technology in the service sectors.
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1. Introduction
Energy is one of the critical resources for economic development in a country as well as one of the most important input production factors for driving business growth. In accordance with the Kyoto Protocol, which is the international treaty to mitigate global warming, each country is required to reduce its greenhouse gas emissions down to 1990 levels. As energy consumption is the main
source of carbon dioxide emissions, the energy efficiency issue has
been addressed by both developed and developing economies. Because Taiwan is a small open and developing economy, it relies heavily on overseas supplies for its energy needs, and as a
result the international economic environment has continuously impacted the country's energy prices. For example, in 2010 the percentage of Taiwan's imported energy accounted for 99.4% of total energy supplies, in contrast to 0.6% of indigenous energy, with total energy supplies in Taiwan hitting 145,561 thousands of kiloliter of oil equivalent (KLOE). In the same year, Taiwan's total energy domestic consumption achieved a record high of 120,308
thousands of KLOE (Energy Statistics, 2012). Saving energy has
thus become an extremely important issue in Taiwan, with
existing research studies addressing energy efficiency or
produc-tivity on the region level or country level (for exampleBian and
Yang (2010), Greening et al. (1997), Guo et al. (2011), Honma and Hu (2008, 2011), Howarth et al. (1991), Hu and Kao (2007), Shi et al. (2010), Worrell et al. (1997)). Some researches focused on the fossil fuel power plants with considering the undesirable outputs (Sueyoshi and Goto, 2012). Furthermore, the capital–labor ratio as the proxy of technology level is one of the important indicators in
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the energy economics.Wu (2012)proposed that the capital–labor
ratio reduces inefficient energy use, because new capital utilizes
energy-saving technology. However, Blomberg et al. (2012)
indi-cated that even bigger companies with high capital in Sweden
might have challenges to improving energy efficiency because of
possible risks for production disruptions and the associated costs. However, there is a paucity of research investigating the energy
issue in the service sectors as well as the impact of the capital–labor
ratio toward the energy efficiency even though the relative share of
services in total gross domestic production (GDP) has been increas-ing and accounts for nearly 70% in Taiwan. Especially, the growth rate of energy consumption in the service sectors from 2001 to 2008 is 20.44% in contrast to the 19.67% as of the industrial sectors in Taiwan. Hence, this paper's objective is to bridge the gap in the
literature by measuring the energy efficiency for different
subsec-tors under the service aggregation secsubsec-tors in Taiwan and to validate
the hypothesis of the impact of capital–labor ratio on the energy
efficiency in the service sectors.
Ang (2006)defined that energy efficiency is a relative concept.
Hu and Wang (2006) indicated that other inputs (for example, labor and capital) together with energy consumption ought to be
considered in assessing the energy efficiency. Furthermore,Hu and
Wang (2006)developed the index of total-factor energy efficiency
(TFEE) to analyze energy efficiencies of 29 administrative regions
in China during 1995–2002. Their paper employed data
envelop-ment analysis (DEA), using labor, capital stock, energy consump-tion, and total sown area of farm crops as the four inputs and real
GDP as the single output in order tofind the target energy input of
each region in China for each particular year. A U-shape relation between an area's TFEE and per capita income in the areas of
China empirically confirms the scenario that energy efficiency
eventually improves with economic growth.
Hu and Kao (2007) further used constant-returns-to-scale (CRS) DEA by incorporating three inputs (energy, labor, and capital) and a single output (GDP) to establish the energy-saving target (EST) and then measured energy-saving target ratios
(ESTRs) for 17 APEC economies during 1991–2000. The empirical
results indicate a U-shape relation between per capita EST and per capita GDP. ESTR has a positive relation with the value-added percentage of GDP of the industry sectors and a negative relation with that of the service sectors.
Researchers have also focused on energy efficiency in the
energy and manufacturing sectors (for exampleBlomberg et al.
(2012)), though few studies have looked at the energy efficiency of
specific industrial sectors. Gouyette and Perelman (1997) took
input-oriented DEA and the Malmquist index, including GDP as the single output and labor and capital inventory as two inputs, to
measure efficiency and productivity of the manufacturing and
service sectors for 13 OECD countries over the 1970–1987 period.
The results indicate that the productivity of the service sectors in OECD countries slightly increased, which is mostly caused by an
increase in efficiency change.Boyd and Pang (2000)examined the
differences in plant-level electricity and fossil fuel intensity in the glass industry. Productivity differences between plants are
statis-tically significant in explaining differences in plant energy
inten-sity. Productivity has a significantly positive impact on the energy
efficiency for flat glass, but not for container glass.
Honma and Hu (2013) used DEA with the assumption of variable-return-to-scale (VRS) by incorporating three inputs (energy consumption, labor, and capital stock) and a single output (the value added in each sector) to estimate the TFEE of 17 sectors
in Japan during 1998–2005. The empirical result presents that the
TFEE is relatively higher in the mining, general machinery, real
estate and housing service, and thefinancial and insurance and
service sectors in Japan, in contrast to the relatively lower energy intensity for the agricultural sector, as well as the transportation
and communication sector. Honma and Hu (2011) further
com-puted and analyzed the TFEE of 11 industries in 14 developed
countries during 1995–2005 using the DEA approach and by
considering four inputs (labor, capital stock, intermediate inputs other than energy, and energy) and one output (the value added).
The most inefficient industry is the metal industry, with an
average TFEE of 40.6%. The results also identify the most efficient
countries in each different time period of 1995–1998, 1999–2002,
and 2003–2005. Shi et al. (2010) also used fixed asset, energy
consumption, and labor as the inputs of the DEA model to assess
the efficiency of 28 different regions in China.
Many studies have criticized energy intensity (EI), which is a
commonly used indicator of energy efficiency in the past. EI stands for
the energy consumption for producing every unit of real GDP within a
certain time frame.Renshaw (1981)andPatterson (1996)suggested that
EI considers only partial factors of energy consumption without embra-cing capital and labor factors. Another critic noted that this partial factor index is inappropriate for investigating the impact of changing energy
use over time (Asia Pacific Energy Research Center (APERC), 2002).
Hence, the objective of this paper is three-fold. First, this paper analyzes
the pure technical efficiency (PTE) and assesses the energy-saving target
of Taiwan's service sectors by employing VRS–DEA. Second, the paper
computes the energy-saving target ratio and TFEE for each service
subsector developed byHu and Wang (2006). Third, this study further
investigates the effects of industry characteristics on the energy-saving
target by applying the four-stage DEA proposed byFried et al. (1999).
We also calculate the pre-adjusted and environment-adjusted
total-factor energy efficiency (TFEE) scores in these service sectors.
This paper is organized as follows. The next section describes
the theoretical model, which briefly introduces VRS–DEA, TFEE, and
the four-stage DEA along with the data collected and variables used.
The section following that applies VRS–DEA, and the four-stage DEA
to measure pre-adjusted TFEE and environment-adjusted TFEE indices on Taiwan's service sectors. We also examine the hypothesis
of capital–labor ratio toward the energy efficiency by applying the
panel random-effects Tobit regression. Thefinal section then presents
some concluding remarks and future research direction.
2. Efficiency model and TFEE
2.1. VRS–DEA
The paper uses VRS–DEA to determine the input targets for
each service sector by comparing the efficiency frontier that is
established by all service sectors in Taiwan. The paper utilizes
input-oriented measures followingFarrell's (1957)work. In order
to control the effects of scale, this study adopts the VRS–DEA
model (Banker et al., 1984).
A higher efficiency score means that the decision making units
(DMUs) use fewer inputs to obtain a given level of outputs (Charnes et al., 1978). All DMUs at the same time constitute the
reference set for constructing the efficiency frontier for each DMUi.
Banker et al. (1984) developed the so-called BCC-DEA model by expanding the CRS-DEA model into a VRS situation as follows:
Min θi θi; λ1; :::; λN s:t: ym i þ ∑ N j ¼ 1λ jymj Z0; m ¼ 1; :::; M; θixki ∑ N j ¼ 1λ jxkjZ0; k ¼ 1; :::; K; ∑N j ¼ 1λ j¼ 1; λ1; :::; λNZ0; ð1Þ
whereθ is a scalar and the PTE for the ith DMU, with 0rθr1; there are K inputs and M outputs for each of the DMU, respectively.
The ith DMU is represented by the column vectors xiand yi, andλ
is an N 1 vector of constants.
The value θ¼1 indicates a point on the efficient frontier and
hence presents technically efficient sectors, in accordance with
Farrell's (1957) definition. The frontier is a piecewise linear isoquant, determined by the observed data points of the same
year. The sectors that construct the frontier are the efficient sectors
among those observed sectors in that year. The weight vectorλ
forms a convex combination of observed inputs and outputs. The set on the frontier is the production of best practices among the observed sectors. For the ith sector, the distance (amount) from the projected point on the frontier by radial
reduction without reducing the output level, (1 θ)xi, is called
the‘radial adjustment’.
2.2. Four-stage DEA Models & TFEE index
Technical efficiency reflects the ability of firms to use as little
input as possible to obtain a given level of output. Fried et al.
(1999)introduced a four-stage DEA. The management component
of inefficiency is separated from the influences of the external
environment, because the management level is not able to control
these influences. The result is a radial measurement of managerial
efficiency. It is indeed the assessment of managerial competence
on running a business. The first stage calculates a DEA frontier
using the observable inputs and outputs according to the VRS
model in Eq.(1).
The summation of slack and radial adjustments is the total
amount (‘target’) that can be reduced without decreasing the
output levels. With respect to energy input, the above summation
is called the‘energy-saving target’ (EST), and the formula is
ESTði;tÞ¼ Nonradial Slack Adjustment for Energyði;tÞ
þRadial Adjustment for Energyði;tÞ; ð2Þ
where EST(i, t)refers to the EST in the ith sector and the tth year.
An inefficient sector can reduce EST in energy use without
reducing real economic growth. The DEA model suggests that the input slack and radial adjustments of any individual input for all
objectives are efficient. The actual energy consumption is larger
than or equal to the ideal energy input, because the actual practice is able to improve to become the best practice.
Efficiency is generally defined as the ratio of the value of the
best practice compared to that of the actual practice. The energy-saving target ratio (ESTR) index is therefore the ratio of the
aggregate energy-saving target from Eq. (2) to actual energy
consumption. The total adjustments in energy input are regarded
as the inefficient portion of actual energy consumption. Hu and
Wang (2006)indicated that the ESTR in Eq.(3)can be measured based on the slack and radial adjustments of energy obtained from the DEA model
ESTRði;tÞ¼ ESTði;tÞ
Actual Energy Inputði;tÞ; ð3Þ
where ESTR(i,t)refers to the ESTR in the ith sector and the tth year.
As Eq. (3)indicates, the ESTR represents each sector's inef
fi-cient level of energy consumption. Since the minimal value of EST is zero, the value of ESTR lies between zero and unity. The
total-factor energy efficiency (TFEE) index originally developed byHu
and Kao (2007)andHonma and Hu (2008)is related to the ESTR as
in Eq.(4)
TFEEði;tÞ¼ 1ESTRði;tÞ; ð4Þ
where TFEE(i, t)refers to the TFEE in the ith sector and the tth year.
A zero ESTR value means a sector is on the frontier with the best TFEE (up to one) among the observed sectors and also indicates that no redundant or over-consumed energy use exists
in this sector; otherwise, an inefficient sector with the value of
ESTR larger than zero shows that energy needs to be saved at the same economic growth level. A higher ESTR and lower TFEE imply
higher energy inefficiency and a higher energy-saving amount,
and vice versa.
The different industry characteristics generate different
impacts on the EST. In order to incorporate these industry
characteristics, the second stage of Fried et al. (1999) used a
cross-sectional Tobit regression to adjust these environmental impacts. This study estimates the energy consumption equation
by implementing a panel Tobit regression in Eq.(5). The
depen-dant variables are radial plus slack input movement for energy consumption; the independent variables are measures of environ-mental variables applicable to this particular input. The objective is to quantify the effect of external conditions on the excessive use of inputs.
ESTði;tÞ¼ f ðEði;tÞ; βði;tÞ; uði;tÞÞ; i ¼ 1; :::; N; t ¼ 1; :::; T: ð5Þ
where ESTði;tÞis the total radial plus slack movement for the energy
input of service sector I on time T based on the DEA results from stage 1; Eði;tÞis a vector of variables characterizing the operating environ-ment for different service i that may affect the utilization of the input; βði;tÞis a vector of coefficients and uði;tÞis a disturbance term.
The third stage uses the estimated coefficients from the
above-mentioned equations to predict the total input slack for each service sector based on its industry characteristic difference: ESTði;tÞ¼ f ðEði;tÞ; βði;tÞÞ; i ¼ 1; :::; N; t ¼ 1; :::; T ð6Þ These predictions are used to adjust the primary energy data for each service sector based on the difference between maximum predicted total energy slack and predicted total energy slack: AEIði;tÞ¼ Actual Energy Inputði;tÞþ ½MaxfESTði;tÞg ^EðESTði;tÞjEði;tÞÞ;
i ¼ 1; :::; N; t ¼ 1; :::; T: ð7Þ
This study uses the concept of using the least favorable
operating environment as the basis fromFried et al. (1999). The
notation AEI means the adjusted energy input. This generates a new projected dataset where the inputs are adjusted for the
influence of external conditions.
Thefinal stage uses the adjusted dataset to re-compute the DEA
model under the initial output data and adjusted input data. The
result generates new radial and slack measures of inefficiency.
These radial and slack scores measure the inefficiency that is
attributable to environmental characteristics.
ADJ_ESTði;tÞ¼ Non radial Slack Adjustment for Energyði;tÞ
þRadial Adjustment for Energyði;tÞafter the final stage DEA;
ð8Þ
where ADJ_ EST(i, t)refers to the adjusted EST in the ith sector and
the tth year after incorporating the industrial characteristics. This study yields the adjusted energy-saving target (ADJ_EST)
and adjusted energy-saving target ratio (ADJ_ESTR) in Eq. (9),
incorporating the different industrial characteristics
ADJ_ESTRði;tÞ¼ADJ_ESTði;tÞ
AEIði;tÞ ; ð9Þ
where ADJ_ESTR(i,t)refers to the ADJ_ESTR in the ith sector and the
tth year.
The environment-adjusted total-factor energy efficiency
(EAT-FEE) index is related to the ADJ_ESTR as in Eq.(10):
where EATFEE(i,t)refers to the environment-adjusted TFEE in the
ith sector and the tth year.
Existing research studies have criticized the commonly used
indicator of energy inefficiency, which is the EI, as a direct ratio of
energy consumption to GDP. The ratio is only a partial factor of the
energy efficiency index without considering the capital and labor
inputs. Hence, this paper measures energy efficiency using the
TFEE index by a total-factor framework, extending to include EATFEE after incorporating the industry characteristic differences using the four-stage DEA in order to provide more information and a more realistic comparative base to examine the de facto situation across sectors.
3. Empirical results and analysis 3.1. Data and variables
Thefirst major objective of this section is to derive PTE and
TFEE in Taiwan's service sectors over the 2001–2008 periods.
Service is defined as the aggregation of four service sectors in
Taiwan including: (1) wholesale and retail trade sector; (2) transportation and storage sector; (3) lodging and catering sector;
and (4)finance, insurance and real estate sector.
We apply the DEA to a dataset of these 4 service subsectors
during the 2001–2008 periods. The paper uses three inputs
(capital input, labor employment, and energy consumption) and a single output (real GDP) to assess the PTE of each sector.
Data regarding real GDP (NT$ million) were collected from the Directorate General of Budget, Accounting and Statistics of the Executive Yuan in Taiwan. Two data inputs (capital input and labor employment) were obtained from the database of Advanced Retrieval and Econometric Modeling System (AREMOS) and a third data input, energy consumption, from the Bureau of Energy (Bureau of Energy, Ministry of Economic Affairs, 2013). All nominal variables are transformed into real variables at the 2006 price
level by Taiwan's GDP deflators. The units of real GDP,
labor employment, real capital, and energy consumption are NT$
million, 1000 persons, NT$ million, and millions of tons of oil
equivalent (Mtoe), respectively. Table 1 shows the summary
statistics of these inputs and output.Table 2presents the
correla-tion coefficients of the input and output variables. The isotonicity
property—that an output should not decrease with an increase in
an input—is not violated.
3.2. Empirical results and analysis 3.2.1. PTE from BCC-DEA
This study uses the software DEAP 2.1 provided byCoelliet al.
(2005)to assess the annual PTE from Eq.(1). The average PTEs for
these 4 service sectors during 2001–2008 are 0.84, 0.84, 0.83, 0.83,
0.84, 0.89, 0.95, and 0.95 inTable 3, respectively. Even though there
is an increasing trend in terms of PTE after 2004, the result shows
that there is a 5–18% improvement in input resource savings.
Table 3also reveals that the PTEs of the two sectors offinance, insurance and real estate and transportation and storage are
higher than the other two sectors. The financial service sector
has been apparently continuous working on business process
reengineering (BPR). Thefinancial holding companies established
since 2002 have also created capital efficiency in the financial
service sector. The transportation and storage sector has the
second highest efficiency score among these four service sectors
over the period 2001–2008. The wholesale and retail trade sector
obtains the lowest efficiency in the service sectors during 2001–
2008. This result indicates that the financial service and
Table 1
Description and summary statistics of variables.
Variables N Mean Std dev Maximum Minimum
Real GDP (NT$ million) 32 1,112,970 843,781 2,345,685 202,018
Wholesale and retail trade 8 2,038,974 230,703 2,345,685 1,757,734
Transportation and storage 8 379,052 26,704 414,210 344,197
Lodging and catering 8 220,420 17,126 243,592 202,018
Finance, insurance and real estate 8 1,813,434 178,896 2,047,154 1,615,800
Capital (NT$ million) 32 2,732,851 1,617,469 5,149,814 683,808
Wholesale and retail trade 8 4,507,343 422,392 5,149,814 3,954,886
Transportation and storage 8 3,995,252 410,556 4,523,873 3,414,821
Lodging and catering 8 761,472 57,750 849,419 683,808
Finance, insurance and real estate 8 1,667,336 277,015 2,054,554 1,272,692
Labor (Thousand persons) 32 804 550 1782 407
Wholesale and retail trade 8 1729 38 1782 1679
Transportation and storage 8 415 4 421 407
Lodging and catering 8 622 55 687 532
Finance, insurance and real estate 8 451 29 485 413
Energy consumption (Mtoe) 32 892,979 551,674 1,605,268 273,620
Wholesale and retail trade 8 1,491,469 108,389 1,605,268 1,312,870
Transportation and storage 8 438,732 50,388 508,718 373,240
Lodging and catering 8 1,351,341 181,981 1,548,068 1,110,760
Finance, insurance and real estate 8 290,376 10,588 300,847 273,620
Note: The base year for real GDP and real capital is 2006.
Table 2
Correlation coefficients of inputs and output.
Real GDP (y1) Capital (x1) Labor (x2) Energy consumption (x3)
y1 1.0000
x1 0.3530 1.0000
x2 0.5956 0.5545 1.0000
transportation and storage sectors have a competitive capability for using less capital, labor, and energy to yield a certain GDP.
From the statistics of the Directorate General of Budget, Accounting and Statistics of the Executive Yuan in Taiwan, total employment in the wholesale and retail trade sector is the highest among the service sectors, accounting for 1.77 million persons (17%), in contrast to 0.69 million persons (6.6%) in the lodging and catering sector, and 74,000 persons (0.7%) in the real estate sector.
Thisfinding shows that the wholesale and retail trade sector needs
much improvement in the input of resources, especially on labor savings. The PTE of the lodging and catering sector obviously deceases after 2003. The severe acute respiratory syndrome (SARS) outbreak in 2003 weakened Taiwan's economy as well as its tourism industry. This event also proved that the slowdown in the economy had a great impact on lodging and catering businesses.
3.2.2. Four-stage DEA, TFEE, and environment-adjusted TFEE indices
This study further utilizes Eqs.(2)–(4)to measure TFEE for each
service sector in Table 5. The result of TFEE presents a similar
pattern with that of PTE. The financial service sector has the
highest TFEE among the service sectors herein. Particularly, energy
in thefinancial service sector might be consumed less than in the
other service sectors.Honma and Hu (2013)also indicated that the
financial and insurance sector has the high level TFEE in Japan. They pointed out that the tertiary industries, except for the transportation and communication sector, have higher TFEE scores
in Japan, which is consistent with this paper's finding. The
government needs to developfinancial service businesses to yield
a relatively higher GDP under the given labor and energy.Weber
(2005) further indicated that banks have pursued cost saving effects by reducing their consumption of energy, water, and resource materials. Banks also have demonstrated their
environ-mental strategy of‘going for green’ to attract more customers. The
TFEE in the wholesale and retail trade sector dramatically increases to a record high in 2008. The 3R (recycling, reuse, and
refill) policy in Taiwan has resulted in obvious progress in the
wholesale and retail trade sector. The popular recycle containers in hypermarkets have helped promote the concept of energy con-servation, while the severe price-cutting competition among wholesalers and retailers in Taiwan have also indirectly had a positive impact on the energy-saving action.
The TFEE of the transportation and storage sector has deterio-rated since 2003. In contrast with a GDP growth rate of 17% in this sector, the input resource consumption growth rate of capital and energy in this sector increased 27.2% and 30%, respectively. This finding is a reminder to the management of this sector to pay
attention to resource efficiency. This finding is consistent with the
work in Japan byHonma and Hu (2013), as they indicated that the
transportation and communication sector in Japan had high fuel
consumption.Chen et al. (2009)suggested that the efficient use of
energy, the introduction of non-fossil fuels, and the development of innovative technologies are essential strategies for establishing a robust renewable energy technology portfolio plan.
To understand the industrial variation in EST and its determi-nants, we compute four industrial characteristics indices, includ-ing GDP shares, labor use shares, energy consumption shares, and
capital–labor ratio, by using the panel random-effects Tobit
regression for EST in Eq.(6).Metcalf (2008)indicated that energy
and technology might have a substitution effect.
Wu (2012) used the capital–labor ratio as the proxy of the
technology level and hypothesized that the capital–labor ratio
reduces inefficient energy use, because new capital utilizes
energy-saving technology. This study also includes a time trend
variable to capture the trend of change over time.Table 4shows
that a (positive) negative coefficient on these environmental
variables suggests that the environment is (un)favorable for a DMU, since it is associated with (greater) less excess use of energy.
Several findings can be drawn from the estimation results of
the random-effects Tobit regression. A GDP share variable has a
significantly positive effect on the EST, which indicates that those
service industries with more GDP output have greater excess use
of energy. The capital–labor ratio also has a significantly positive
effect on the EST, which is not consistent with Wu'sfindings on
regions in China. This may be because more high-tech service industries in Taiwan use more energy-consuming facilities that are
not so energy-efficient.Blomberg et al. (2012)also indicate that
even bigger companies in Sweden may face significant barriers to
improving energy efficiency because of possible risks for
produc-tion disrupproduc-tions and the associated costs. Meanwhile, the medium sized companies also have the lower priority to increase the
capital turnover in order to address on the energy efficiency issue
(Blomberg et al., 2012). Therefore, the service industries in Taiwan
should pay attention to energy efficiency of newly acquired
facilities. A time trend variable has a significantly negative impact
on the EST and indicates less excess use of energy over 2001–2008
among the service industries in Taiwan.
Table 5 compares the TFEE and environment-adjusted TFEE
(EATFEE) for the individual services industry in Taiwan. Fig. 1
indicates the comparisons of TFEE and EATFEE for the wholesale
and retail trade sector during 2001–2008. The graph illustrates
that the EATFEE and TFEE have a similar increasing pattern for this sector, with the EATFEE obviously higher than the TFEE after incorporating the industrial characteristics. The GDP real growth Table 3
Pure technical efficiency (PTE) for the service sectors in Taiwan during 2001–2008.
Service sectors 2001 2002 2003 2004 2005 2006 2007 2008
Index PTE PTE PTE PTE PTE PTE PTE PTE
Wholesale and retail trade 0.38 0.39 0.39 0.42 0.52 0.73 0.98 1.00
Transportation and storage 0.97 1.00 0.98 0.97 0.99 0.98 0.98 0.98
Lodging and catering 1.00 0.99 0.94 0.92 0.90 0.88 0.85 0.82
Finance, insurance and real estate 1.00 1.00 1.00 1.00 0.97 0.98 1.00 1.00
Mean 0.84 0.84 0.83 0.83 0.84 0.89 0.95 0.95
Table 4
Panel random-effects Tobit regression for EST.
Variable Coefficient Standard error
GDP share for each service industry 3,467,059n 1,803,212
Labor share for each service industry 3,895,604 2,798,850
Energy share for each service industry 7,004,991nn 3,244,409
Capital–labor ratio 143.03nn 69.62
Time trend 37,735.13n 19,657.99
Constant 1,810,437nn 815,727.50
Wald statistic 81.15
Log likelihood 437.73
nSignificant at the 10% level. nnSignificant at the 5% level.
rate in Taiwan was 3.67% in 2003, and had increasingly to 6.19% in
2004, respectively (Directorate General of Budget, Accounting and
Statistics of the Executive Yuan, 2013), which led to the dramati-cally jump for the TFEEs in the wholesale and retail trade sector. Meanwhile, the Bureau of Energy, Ministry of Economic Affairs in
Taiwan had firstly established in 2004 leading to the possible
positive impact on the energy efficiency since 2004. However, this
finding suggests that there is much room for energy-saving improvement among wholesalers and retailers after incorporating
the unfavorable factors (GDP, energy consumption, capital–labor
ratio) compared to capital and labor. Hence, thisfinding also offers
a recommendation for wholesale and retail trade to keep up their cost savings through energy-saving actions even though there is a
significant movement toward the efficient frontier for the TFEE
and EATFEE in 2008.
Fig. 2illustrates the comparisons of TFEE and EATFEE in the transportation and storage sector. The empirical results reveal a
worsening trend in TFEE in this sector over 2005–2008. As a result
of controlling for the industrial characteristics, the EATFEE has decreased in the sector since 2003 and indicates that without controlling for the industrial difference, the penalty to this sector
operating under favorable factors is less than the benefit to this
sector operating under favorable industrial characteristics. These favorable environments (less energy consumption and lower
capital–labor ratio in the transportation and storage sector)
provide much benefit for the TFEE of this sector.
Fig. 3illustrates that the TFEE and EATFEE in the lodging and
catering sector have a decreasing trend in the 2003–2008 period.
During the same period, the TFEE and EATFEE in this sector
present a similar pattern with the transportation and storage sector. These favorable environments (less energy consumption
and lower capital–labor ratio in the lodging and catering sector)
Table 5
TFEE and environment-adjusted TFEE (EATFEE) for the service sectors in Taiwan during 2001–2008.
Service sectors 2001 2002 2003 2004 2005 2006 2007 2008
Index TFEE EATFEE TFEE EATFEE TFEE EATFEE TFEE EATFEE TFEE EATFEE TFEE EATFEE TFEE EATFEE TFEE EATFEE
Wholesale and retail trade 0.23 0.48 0.22 0.52 0.21 0.35 0.20 0.45 0.29 0.65 0.55 0.82 0.89 1.00 1.00 1.00
Transportation and storage 0.97 1.00 1.00 1.00 0.96 0.45 0.94 0.47 0.90 0.50 0.80 0.49 0.80 0.43 0.76 0.38
Lodging and catering 1.00 1.00 0.99 0.89 0.91 0.55 0.84 0.56 0.76 0.55 0.73 0.52 0.72 0.49 0.71 0.45
Finance, insurance and real estate 1.00 1.00 1.00 0.66 1.00 0.39 1.00 0.64 0.97 0.86 0.98 1.00 1.00 1.00 1.00 0.90
Mean 0.80 0.87 0.80 0.77 0.77 0.43 0.74 0.53 0.73 0.64 0.76 0.71 0.85 0.73 0.87 0.68
Note: TFEE and EATFEE stand for total factor of energy efficiency and environment-adjusted energy efficiency, respectively.
Fig. 1. The trend of TFEE and EATFEE in the wholesaler and retailer sector during 2001–2008.
Fig. 2. The trend of TFEE and EATFEE in the transportation and storage sector during 2001–2008.
Fig. 3. The trend of TFEE and EATFEE in the lodging and catering sector during 2001–2008.
provide much benefit for the TFEE of this sector. Even though Taiwan's environmental law has restricted the use of non-washable dining utensils in restaurants since 2006, which possibly led to the slightly drop for the TFEE and EATFEE in the sector of
lodging and catering. This finding shows that energy efficiency
also needs improvement in the lodging and catering sector.
Fig. 4 shows the trend of TFEE and EATFEE in the financial
service sector in the 2001–2008 periods. The results show that
TFEE and EATFEE hit the efficient frontier in the financial service
sector during 2006–2007. The comprehensive conclusion in the
National Energy Conference in 2005 alleged that the energy service company (ESCO) establishment in Taiwan had led the
energy efficiency improvement for the peripheral banking and
leasing business because of the increasing loan amounts for procurements of energy-saving equipment since 2005. Aside from
the synergy creation in financial holding companies since 2003,
merger and acquisition activities in this sector have also promoted
cost savings, capital efficiency, and cross selling to yield overall
efficiency. The unfavorable industrial characteristics (second
high-est GDP share, second highhigh-est capital–labor ratio) benefit the TFEE
in this sector.
Being a small open economy highly dependent on imported energy, Taiwan needs to put forth a lot of effort and a better policy execution on energy saving and conservation. To achieve better
performance in improving energy efficiency, those service sectors
with worsening and/or poor energy efficiency should receive more
attention from policy makers. Particularly, the Taiwan Tourism Bureau (TTB) needs to offer incentives for lodging and catering businesses to offer green hotels or green restaurants. In order to
accomplish energy-saving benefits in the lodging industry, TTB
could include “green” criteria in TTB's hotel star-rating system.
Offering incentives to use solar water heaters and green furniture in the service sectors could also minimize the gap between the PTE and TFEE.
Önüt and Soner (2006)suggest that the lodging industry could install solar energy systems for different departments such as swimming pools and laundry. Meanwhile, the hoteliers could control and repair all water related equipment as soon as possible and it was also suggested monitoring water consumption
periodically. To encourage water saving programs by a permanent promotion program for employees and customers, and using low flow shower heads are the way to save the energy consumption.
Thefinancial services sector has the best performance among
the four service sectors in terms of TFEE and EATFEE. This result is
consistent with the work fromHonma and Hu (2013)on Japan's
financial industries. Their empirical result indicates that the TFEE is relatively higher in the real estate and housing service sector as
well as thefinancial and insurance service sector in Japan.
From the statistics of the Directorate General of Budget, Accounting and Statistics of the Executive Yuan in Taiwan, total employment in the wholesale and retail trade sector is the highest among the service sectors, accounting for 1.77 million persons (17%). The TFEE and EATFEE in the group sector of lodging and catering show a deteriorating trend, as the severe acute respiratory syndrome (SARS) in 2003 weakened Taiwan's economy. In fact, the tourism industry experienced the highest stock price decline (Chen et al., 2007) during this time.
4. Concluding remarks and future research
This research utilizes the VRS–DEA to assess the PTE, TFEE, and
EATFEE of service sectors in Taiwan over the period 2001–2008.
In contrast to the traditional EI, which considers only the direct
ratio of energy input to GDP for assessing energy efficiency
without embracing capital and labor factors, we measure TFEE and EATFEE by incorporating the industrial characteristics and
extending the four-stage DEA proposed by Fried et al. (1999),
which includes inputs such as energy, labor, and capital. A better
comprehensive indicator for energy efficiency provides more
information for improvement and more comparative suggestions for different service subsectors.
The results herein show that there is a 5–18% potential
improvement on input resource savings in Taiwan's service sec-tors, although there is an increasing trend in terms of PTE after
2004. The PTE of two sectors (the finance, insurance and real
estate sector and the transportation and storage sector) is higher
than that of the other two sectors. Thefinancial service sector has
been dramatically working on BPR and organizational reshuffling
in order to establishfinancial holding companies or push mergers
among financial institutions, causing consolidation synergy to
gradually appear. The empirical result also indicates that TFEE
and EATFEE of thefinance, insurance and real estate sector have
the highest score among all service sectors during 2001–2008.
Both the wave of mergers and acquisitions and the establishment
offinancial holding companies during 2002–2004 helped motivate
energy efficiency in the financial service arena. This result also
confirms that 3C (cross-selling, capital-efficiency, and cost-saving)
synergy in the financial service sector was created, because
financial holding companies were established. At the same time,
thefinancial institutions pursued cost savings effects by reducing
their consumption of energy, water, and materials and
demon-strated their environmental strategy of‘going for green’ in order to
attract more customers (Weber, 2005).
The PTE of the wholesale and retail trade sector is the worst, and this group sector needs a lot of improvement on input resources, especially labor savings. The PTE of the lodging and catering sector had an obvious decreasing trend after 2003. The
SARS outbreak in 2003 and the globalfinancial turmoil in 2008
weakened Taiwan's economy as well as its tourism industry. These events also prove that a slow economy has a great impact on lodging and catering businesses.
This study further utilizes the panel-data, random-effects Tobit regression model with the EST as the dependent variable. Those service industries with more GDP output have greater excess use
Fig. 4. The trend of TFEE and EATFEE in thefinancial service sector during 2001–
of energy. The capital–labor ratio has a significantly positive effect,
while the time trend variable has a significantly negative impact
on the EST, indicating that more high-tech service industries in Taiwan use more energy-consuming facilities that may not be
more energy-efficient. Therefore, the service industries in Taiwan
should pay attention to energy efficiency of their newly acquired
facilities. A time trend variable has a significantly negative impact
on the EST, indicating less excess use of energy over 2001–2008
among the service industries in Taiwan.
The EATFEE is obviously higher than the TFEE after incorporating the industrial characteristics in the wholesale and retail trade sector.
This finding suggests that there is much room for energy-saving
improvement among wholesalers and retailers after incorporating
the unfavorable factors (GDP, energy consumption, capital–labor
ratio) compared to capital and labor. Hence, thisfinding also offers
a recommendation for wholesalers and retailers to keep up their cost savings through energy-saving actions even though they show a
significant achievement toward the efficient frontier for the TFEE and
EATFEE in 2008.
The TFEE of the transportation and storage sector has been decreasing since 2003, showing that this sector has relatively high
fuel consumption. This finding is consistent with the results in
Japan (Honma and Hu, 2013). Establishing a robust renewable
energy technology portfolio plan should yield an efficient use of
energy (Chen et al., 2009). After controlling for the industrial
characteristics, the EATFEE also has been decreasing in the trans-portation and storage sector since 2003 and indicates that without controlling for the industrial difference, the penalty to this sector for
operating under favorable factors is less than the benefit to this
sector for operating under favorable industrial characteristics. The TFEE in the wholesale and retail trade sector dramatically increased to a record high in 2008. The 3R (recycling, reuse, and
refill) policy in Taiwan has resulted in obvious progress for this
sector. For example, popular recycle containers in hypermarkets help promote the concept of energy conservation. The most energy
efficient service sector is finance, insurance and real estate. It has
an average TFEE of 0.994 and environment-adjusted TFEE (EAT-FEE) of 0.807.
Being a small open economy highly dependent on imported energy, Taiwan has to put forth a lot of effort and a better policy execution on energy savings and conservation. In fact, Taiwan's indigenous energy supply has fallen so much that it has only accounted for less than 1% of total energy supply since 2003 (Energy Statistics, Bureau of Energy, Ministry of Economic Affairs) in contrast to more than 99% of energy supply being imported. Policy makers should pay more attention to those service sectors
with poor energy efficiency. For example, the Taiwan Tourism
Bureau (TTB) could offer incentives for green hotels or green
restaurants in order to improve energy efficiency in the catering
and lodging sector. The TTB could further combine‘green’ criteria
into the on-going hotel star-rating system. Taiwan's government should encourage restaurateurs to measure, list, and mitigate their carbon footprint on each menu item. Even though Taiwan
government alleges main six promotions for energy saving:“(1)
Promote energy saving light bulb; (2) Apply thermostat timer on water dispenser and water fountain machine; (3) Set up one degree more for the temperature in summer time and clean the filter; (4) Be sure to power off the computer, instead of idling for a while; (5) Promote unplugging the electric appliance; (6) Pro-mote turning off the light for an hour during lunch break for
governmental and regular business office buildings (Bureau of
Energy, Ministry of Economic Affairs),” offering financial
incen-tives to use solar water heaters, green furniture, and other energy-saving equipment in the service sectors could also
enhance energy efficiency through electricity, gas, and other
energy-saving plans.
The government in Taiwan at the same time needs to address the issue of whether new capitals bring energy-saving technology into the rapidly expanding service sectors. According to this
study's empirical findings on the service industries in Taiwan, a
high capital–labor ratio tends to have excess energy consumption
during the period of 2001–2008. Hence, the replacement of old
equipment and infrastructure and new capital inflow with
energy-saving technology are two very important issues among the service sectors in Taiwan.
Future research can focus on assessing energy efficiency in a
specific service industry, such as banking, securities, insurance, etc.
Measuring energy efficiency by a total-factor framework and
extending it to include more input resources will enhance the
comprehensiveness of energy efficiency and offer policy makers
further industry structure suggestions to improve a country's overall
energy efficiency.
Acknowledgments
The authors thank the chief editor and two anonymous referees of this journal for their valuable comments. Financial support from the Taiwan's National Science Council is gratefully acknowledged (NSC100-2410-H-009-051 and NSC101-2410-H-003-003).
References
Ang, B.W., 2006. Monitoring changes in economy-wide energy efficiency: from energy–GDP ratio to composite efficiency index. Energy Policy 34, 574–582.
Asia Pacific Energy Research Center (APERC), 2002. Energy efficiency indicators and potential energy savings in APEC economies. APERC, Tokyo.
Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30, 1078–1092.
Bian, Y., Yang, F., 2010. Resource and environment efficiency analysis of provinces in China: a DEA approach based on Shannon's entropy. Energy Policy 38, 1909–1917.
Blomberg, J., Henriksson, E., Lundmark, R., 2012. Energy efficiency and policy in Swedish pulp and paper mills: a data envelopment analysis approach. Energy Policy 42, 569–579.
Boyd, G.A., Pang, J.X., 2000. Estimating the linkage between energy efficiency and productivity. Energy Policy 28, 289–296.
Bureau of Energy, Ministry of Economic Affairs, 2013, The main six promotions for energy saving. Available from: 〈http://web3.moeaboe.gov.tw/ECW/english/con-tent/Content.aspx?menu_id ¼ 1533〉. (accessed 30.05.13).
Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operation Research 2, 429–444.
Chen, M.H, Jane, S.C., Kim, W.G., 2007. The impact of the SARS outbreak on Taiwanese hotel stock performance: an event-study approach. International Journal of Hospitality Management 26, 200–212.
Chen, T.Y., Yu, O.S., Hsu, G.J.Y., Hsu, F.M., Sung, W.N., 2009. Renewable energy technology portfolio planning with scenario analysis: a case study for Taiwan. Energy Policy 37, 2900–2906.
Coelli, T.J., Rao, D.S.P., O'Donnell, C.J., Battese, G.E., 2005. An Introduction to Efficiency and Productivity Analysis, 2nd ed. Springer, New York.
Directorate General of Budget, Accounting and Statistics of the Executive Yuan, 2013. National statistics Taiwan. Available from: 〈http://eng.stat.gov.tw/mp. asp?mp ¼ 5〉. (accessed 30.05.13).
Energy Statistics, 2012. Bureau of Energy, Ministry of Economic Affairs. Available from: 〈http://www.moeaboe.gov.tw/promote/publications/PrPubMain.aspx? PageId ¼ pr_publist〉. (accessed 30.04.12).
Farrell, M.J., 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A 129, 253–351.
Fried, H.O., Schmidt, S.S., Yaisawarng, S., 1999. Incorporating the operating environment into anonparametric measure of technical efficiency. Journal of Productivity Analysis 12, 249–267.
Gouyette, C., Perelman, S., 1997. Productivity convergence in OECD service industries. Structure Change and Economic Dynamics 8, 279–295.
Greening, L.A., Davis, W.B., Schipper, L., Marta, K., 1997. Comparison of six decomposition methods: application to aggregate energy intensity for manu-facturing in 10 OECD countries. Energy Economics 19, 375–390.
Guo, X.D., Zhu, L., Fan, Y., Xie, B.C., 2011. Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy 39, 2352–2360.
Honma, S., Hu, J.L., 2008. Total-factor energy efficiency of regions in Japan. Energy Policy 36, 821–833.
Honma S. and Hu J.L., 2011. Industry-level total-factor energy efficiency in devel-oped countries. In: Proceedings of the International Atlantic Economic Con-ference, Athens.
Honma, S., Hu, J.L., 2013. Total-factor energy efficiency for sectors in Japan. Energy Sources, Part B: Economics, Planning, and Policy 8, 130–136.
Howarth, R.B., Schipper, L., Duerr, P.A., Strom, S., 1991. Manufacturing energy use in eight OECD countries: decomposition the impacts of changes in output, industry structure and energy intensity. Energy Economics 13, 135–142.
Hu, J.L., Kao, C.H., 2007. Efficient energy-saving targets for APEC economies. Energy Policy 35, 373–382.
Hu, J.L., Wang, S.C., 2006. Total-factor energy efficiency of regions in China. Energy Policy 34, 3206–3217.
Metcalf, G.E., 2008. An empirical analysis of energy intensity and its determinants at the state level. Energy Journal 29, 1–26.
Önüt, S., Soner, S., 2006. Energy efficiency assessment for the Antalya Region hotels in Turkey. Energy and Buildings 38, 964–971.
Patterson, M.G., 1996. What is energy efficiency? concepts, indicators and meth-odological issues. Energy Policy 24, 377–390.
Renshaw, E.F., 1981. Energy efficiency and the slump in labour productivity in the USA. Energy Economics 3, 36–42.
Shi, G.M., Bi, J., Wang, J.N., 2010. Chinese regional industrial energy efficiency evaluation based on a DEA model offixing non-energy inputs. Energy Policy 38, 6172–6179.
Sueyoshi, T., Goto, M., 2012. DEA radial measurement for environmental assess-ment and planning: desirable procedures to evaluate fossil fuel power plants. Energy Policy 41, 422–432.
Weber, O., 2005. Sustainability benchmarking of European banks andfinancial service organizations. Corporate Social Responsibility and Environmental Management 12, 73–87.
Worrell, E., Price, L., Martin, N., Farla, J., Schaeffer, R., 1997. Energy intensity in the iron and steel industry: a comparison of physical and economic indicators. Energy Policy 25, 727–744.
Wu, Y., 2012. Energy intensity and its determinants in China's regional economies. Energy Policy 41, 703–711.
