1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
Benchmarking
Benchmarking green green logistics
logistics performance with performance
a composite index
873
Kwok Hung Lau
School of Business Information Technology and Logistics, College of Business,
Royal Melbourne Institute of Technology University, Melbourne, Australia
Abstract
Purpose – This paper aims to discuss the development and use of a green logistics performance
index (GLPI) for easy comparison of performance among industries and countries. It uses the survey
data collected from the home electronic appliance industry in China and Japan as an example to
demonstrate the index development process and compare the performance of green logistics (GL)
practices between the two countries using the proposed index.
Design/methodology/approach – Two-sample t-test and one-way analysis of variance (ANOVA)
were used to analyse the data collected from a questionnaire survey. Principal component analysis
(PCA) was employed to derive the weights from the survey data for the GLPI.
Findings – The findings reveal that the GLPI derived using PCA is robust and gives similar results
as obtained through two-sample t-test and ANOVA of the dataset in the comparison of performance
among firms and between countries in the study.
Research limitations/implications – This study lends insight into the use of an objectively
derived composite index to measure and compare GL performance. To serve mainly as a proof of
concept and to enhance response rate in the questionnaire survey, the scope of the study is limited to
three major logistics functions in an industry in two countries.
Practical implications – Managers can use the GLPI to benchmark their performance in the
respective logistics areas and revise their supply chain strategy accordingly. The proposed index may
also assist governments in formulating policies on promoting their GL implementation.
Social implications – A comprehensive composite index to benchmark GL performance can
facilitate and encourage industries to invest in GL. This will help reduce negative impacts of logistics
activities on the environment.
Originality/value – Research in GL to date has largely focused on theory and management
approach. This paper fills the gap in the literature by empirically comparing GL performance among
firms and countries through the use of a composite index. It also contributes to a better understanding
of the association between GL performance and firm size as well as the driving factors behind it.
Keywords Benchmarking, Green logistics, Performance, Sustainable development,
Extended producer responsibility, Resource-based view, China, Japan, Distribution management
Paper type Research paper
Introduction
Environmental impact of business activities has become an important issue in recent
years due to growing public awareness of environmental conservation, increasing need
for sustainable development, and introduction of environmental legislations Benchmarking: An International
Journal
Vol. 18 No. 6, 2011
The author would like to sincerely thank the retailers for providing the information used in this pp. 873-896
q Emerald Group Publishing Limited
study. He also wishes to extend his gratitude to the two anonymous reviewers for providing 1463-5771
valuable comments and suggestions for improving the paper. DOI 10.1108/14635771111180743

2. BIJ and regulations in developed countries. Companies are redesigning their logistics
18,6 practices to make the activities more energy efficient and environment friendly. Green
supply chain initiatives in procurement, manufacturing, distribution, and recycling are
rapidly emerging as major trends (Mason, 2002). Consequently, green logistics (GL) have
become an important consideration and a big challenge to supply chain management
around the globe (Murphy and Poist, 2000; Rao and Holt, 2005; Vachon and Klassen, 2006).
874 The need to lessen the impact of business logistics activities on the environment is
constantly increasing. In a series of workshops organized by the University of Hull
involving academics and practitioners in supply chain management to investigate the
issues and challenges of the next generation supply chains, environmental issues with
cost effectiveness is always the major and most imminent concern identified (EPSRC,
2010). Generally speaking, GL refer to “attempts to measure and minimize the ecological
impact of logistics activities” (Reverse Logistics Executive Council, 2010). They include
green purchasing, green material management and manufacturing, green distribution
and marketing, as well as reverse logistics (Hervani et al., 2005). The overall objective is
to reduce impact on the environment, lower production cost, and improve product value.
GL can lead to lower inventory level, reduced logistics cost, increased revenue, improved
customer service, enriched information for reverse logistics, and enhanced corporate
image (Murphy et al., 1995). Effective management of GL activities not only affects an
organization’s operational and economic performance (Tooru, 2001; Alvarez et al., 2001)
but also increases its competitiveness in the long run (Bacallan, 2000; Rao, 2004).
From a broader perspective, GL can be regarded as part of green supply chain
management (GSCM) that aims at integrating environmental thinking into closed-loop
supply chain management. The activities involved include product design, supplier
selection and material sourcing, inbound transportation, manufacturing processes,
waste reduction, product packaging, distribution and delivery to customers, and
end-of-life product returns for recycling and reuse (Beamen, 1999; Linton et al., 2007;
Srivastara, 2007). With the growing concern of the public about the environment, GSCM
has moved to the top of the research agenda. There have been studies investigating the
various aspects of GSCM in recent years (Table I). For example, Zhu and Sarkis (2004)
explore the relationship between GSCM practices and firm performance in the
manufacturing industry of China. Hervani et al. (2005) develop a conceptual framework
and proposed some metrics to measure environmental performance. Kainuma and
Tawara (2006) apply the multiple attribute utility theory to assess a supply chain with
re-use and recycling throughout the life cycle of products and services. Simpson et al.
(2007) study the role of supply chain relationship in GSCM and the conditions for
positive response from supplier to customer’s environmental requirements. Walker et al.
(2008) investigate the drivers, such as regulations and customer preferences, and the
barriers, such as costs and poor commitment, that companies face in implementing
GSCM practices. Zhu et al. (2008) test the validity of including factors such as internal
environmental management, green purchasing, cooperation with customers, eco-design
practices, and investment recovery in the measurement models of GSCM practices
implementation. More recently, Sundarakani et al. (2010) measure the carbon footprints
across the supply chain using a mobile (logistics) emission diffusion model.
GL and GSCM are particularly important to developing countries such as China,
which has now become a global manufacturing base for many developed countries
because of cheap labour supply and other incentives offered to foreign investors

3. Benchmarking
Category Focus/theme Studies
green logistics
Theoretical Concept, definition, and overview of GSCM Linton et al. (2007), Srivastara (2007), performance
Van Hoek (1999)
Theory and approach to assessing green Handfield et al. (2002), Kainuma and
supply chain Tawara (2006)
GSCM strategies and decision framework Sarkis (2003), Sheu and Chen (2009) 875
GSCM drivers and barriers Testa and Iraldo (2010), Walker et al. (2008),
Zhu and Sarkis (2006)
Green supply chain design Beamen (1999)
Green supply chain modelling and Hui et al. (2007), Sheu et al. (2005)
simulation
Carbon management and measurement of Butner et al. (2008), Sundarakani et al.
carbon footprints in supply chain (2010)
Empirical Performance measurement of green supply Hervani et al. (2005), Zhu and Sarkis
chain (2004, 2007), Zhu et al. (2008) Table I.
GSCM practices in manufacturing Ferretti et al. (2007), Shang et al. (2010), GSCM studies conducted
industries Simpson et al. (2007), Zhu et al. (2007) in recent years
(Langley Jr et al., 2007). Nevertheless, comprehensive regulations in many developing
countries to protect the environment from heavy industrial and business activities
have yet to be introduced. GL and GSCM practices are relatively uncommon and
mostly initiated by large corporations with more resources to invest in these practices.
While there are studies investigating the emergent GSCM practices in several
manufacturing industries of China (Zhu and Sarkis, 2006; Zhu et al., 2007), research in
comparing GL or GSCM performance among industries or countries is limited. The
purpose of this study is to fill this gap by proposing the use of a Green Logistic
Performance Index?? (GLPI) to facilitate the comparison of GL performance across
industries or nations. The concept is similar to the logistics performance index (LPI)
developed by the The World Bank (2010) which can be used to assess and benchmark
performance of different countries using the same set of criteria. As an example to
illustrate the development and the application of the proposed index, the current GL
practices and performance of the home electronic appliance (HEA) manufacturers in
China and Japan are investigated and compared.
While a comprehensive GLPI should cover all the GL and GSCM practices in its
formulation, collection of data on all GL activities from companies in a pilot study to
help develop the index as proof of concept will be too ambitious and hence affect the
response rate. This is particularly so when GSCM practices are not fully adopted by
many firms especially the small- and medium-sized manufacturers. To serve as a
demonstration of feasibility and to simplify data collection, this study has focused
mainly on three categories of GL activities, namely, purchasing, packaging, and
transportation in the data collection. The rationale of choosing these three activities for
investigation is given in the next section.
GL activities
While all logistics activities affect the environment in one way or the other, activities in
certain areas tend to generate larger impacts and the adoption of GL would bring
relatively greater benefits (Guide, 2000; Wu and Dunn, 1995). For example,

4. BIJ using environment-friendly materials in production or recycled parts in
18,6 remanufacturing not only lessens the adverse effect on the environment but also
reduces manufacturing cost (Karpak et al., 2001). Similarly, the use of green or recycled
packaging materials, together with improved packaging designs and techniques,
help manufacturers reduce packaging waste and cost (Crumrine et al., 2004). In
transportation, consolidation of orders and optimisation of schedules and routes
876 decrease distribution frequency and cut fuel consumption (Rao et al., 1991). The use of
more fuel-efficient vehicles or alternative energy sources directly reduces greenhouse
gas emission (European Commission, 2001). Purchasing, packaging, and transportation
also broadly represent the major upstream and downstream logistics functions in a
supply chain. GL practices in these three functions can, to a certain extent, reflect the
state of GSCM in an industry. Table II summarizes the benefits of and challenges in
implementing the three categories of GL activities as reported in the literature.
Surveys also reveal an increasing awareness, interest, and emphasis in green
purchasing, packaging, and transportation. A survey of 527 US enterprises by Min and
Galle (2001) reveals that over 84 percent of the firms have participated in some form of
green purchasing initiatives. Involvement in green purchasing is found to be related
positively to firm size and attitude towards regulatory compliance. Similarly, a survey
of 1,225 packaging personnel by the sustainable packaging coalition and packaging
digest shows that 73 percent of the respondents report that their companies have
increased an emphasis on packaging sustainability (Kalkowski, 2007). Sustainability
innovators and early adopters of green packaging practices tend to be those who work
for larger organizations that have a high level of commitment at the corporate level,
and with staff dedicated to the sustainability function. This finding suggests that
green packaging may be related to firm size. Another study reveals that 72 percent
of the 235 transportation and logistics professionals surveyed are planning to improve
energy efficiency and 42 percent are planning to use vehicle re-routing to reduce
Activity Benefit Challenge Studies
Green Reduces waste and High set up cost Karpak et al. (2001),
purchasing liability cost Requires management Min and Galle (2001),
Builds a “green” image for commitment and Rao and Holt (2005)
the company company-wide standards
Green Reduces packaging cost High cost of using Crumrine et al. (2004),
packaging and solid waste alternative packaging Delaney (1992),
Maximizes environment materials and techniques Harrington (1994)
friendliness through the
use of alternative
packaging materials and
techniques
Green Reduces fuel consumption High investment cost of Rao et al. (1991),
transportation and cuts operating cost alternative fuel vehicles Vannieuwenhuyse et al.
Table II. Generates less noise, air (2003), Wu and Dunn (1995)
Benefits and challenges pollution, and traffic
of green purchasing, congestion
packaging, and Improves customer and
transportation public relationships

5. mileage (O’Reilly, 2008). Relative importance of green issues to a company is found to Benchmarking
be related positively to its annual revenue suggesting that larger firms accord higher green logistics
priority to green transportation and logistics.
performance
Green logistic performance index
Based on the same concept of the LPI developed by the The World Bank (2010), the GLPI
proposed in this study is designed to facilitate cross-industry or cross-country assessment 877
of GL performance and identification of gaps in GL practices. Similar to the LPI, the GLPI
and its underlying indicator variables constitute a dataset to measure GL performance
among industries or countries across several major categories of GL activities. The richer
the dataset is in terms of categories of GL activities investigated and the number of
industries or countries surveyed, the more robust the comparison and benchmarking
will be. While the LPI considers various attributes affecting the logistics performance of a
country such as infrastructure, information technology, service quality, government
regulations and policies, etc. the GLPI looks at investment of resources, adoption of latest
technology, and compliance with environmental regulations, etc. to determine the overall
performance of the industry or nation in GL activities.
The approach adopted in developing the GLPI is also similar to that of the LPI.
A five-point scale is used to gauge the performance of a surveyed firm in various GL
activities. These numeric outcomes, from 1 (worst) to 5 (best), serve as indicators to
indicate how bad or good a firm in the industry performs in the surveyed activities in
comparison with others. The GLPI is then aggregated as a weighted average of the
various performance scores using the principal component analysis (PCA) method to
derive the weights for the indicator variables thereby improving the statistical
confidence of the composite index.
Unlike the LPI which surveys the logistics companies and professionals trading
with the countries under study on the various dimensions of logistics performance, the
GLPI relies on the self-assessment of firms to report their performance in the surveyed
GL activities. There are reasons for taking this approach. First, unlike logistics
outsourcing, GL practices are still mainly in-sourced since the scale and the scope of
activities on many occasions are still relatively small. Second, as a pilot study to collect
data to prove the concept of the GLPI, limitation in resources has restricted the
opportunity of hiring an expert panel to perform the evaluation.
Research objective
This study attempts to use China, a developing country, and Japan, a developed country,
as case studies to illustrate how a GLPI can be developed and used to compare the overall
GL performance of the two nations. As a rapidly developing country, China has become
the world’s biggest manufacturing base for many developed nations (Langley et al.,
2007). Consequently, there is an urgent need to implement GL and GSCM in various
industry sectors to help reduce negative impact on the environment. In contrast, Japan as
a developed country has widely implemented GL and GSCM in many industries. For
many years, it has been the world’s leading country in the number of ISO 14001 certified
firms (ISO World, 2007). Using the HEA manufacturing industry as an example, this
study aims at developing a GLPI and revealing the differences in GSCM practices
between the two countries. The objective of this study is to answer the following
research questions:

6. BIJ RQ1. What is the current GL performance of the HEA manufacturing industry
18,6 in China and Japan?
RQ2. What are the differences in GL practices identified through the comparison
of performance?
RQ3. Can an overall GLPI be developed to simplify performance comparison with
878 reliable result?
Research methodology
To answer the above research questions, this paper reports the findings of a questionnaire
survey of 107 HEA manufacturing companies – 58 in China and 49 in Japan on their
current GL adoption and performance. The data collected are used to develop a GLPI for
comparison. Companies participated in the questionnaire survey were requested to
evaluate their own performance in 15 GL activities with reference to the industry
practices. The self-evaluation approach has been adopted in many studies on supply chain
and logistics performance (Carter, 2005; Lee et al., 2007; Lin and Ho, 2009; McCormack et al.,
2008; Zhu and Sarkis, 2004). Although there might be possibilities of under- or
over-assessment of performance on certain activities by individual respondents, the
aggregate findings should reflect more or less the current situation. The emphasis on
relative rather than absolute performance using a five-point scale will further lessen the
impact of any random assessment bias. In this survey, the focus is placed on three major
logistics areas in the HEA supply chain, namely, purchasing, packaging, and
transportation, where GL can bring significant benefits (Guide, 2000; Wu and Dunn, 1995).
Sample selection and survey instrument design
As successful GL implementation requires resources and experiences, it is more likely
that companies practicing GL are relatively large and well-established organizations.
Therefore, for the survey, only companies operating for at least five years in the
industry with 200 or more employees and an average annual sales volume greater than
US$30 million were selected. Based on these criteria, altogether 176 HEA
manufacturers in China and 165 in Japan were identified from the industry member
lists of the two countries compiled through internet search. These HEA manufacturers
cover a wide range of industry segments producing products such as television,
refrigerator, microwave oven, washing machine, air-conditioner, household audio and
video entertainment equipment, and communication devices.
A self-administered questionnaire was employed to collect data for analysis.
It focused on evaluating the performance of GL activities in the three areas under
investigation. Apart from providing information on company profile as to years of
establishment, number of employees, and annual sales, etc. respondents were also asked
if their companies had implemented GL. If affirmative, they were requested to evaluate
the GL performance of their companies in various activities with reference to the
industry practices. To encourage response, a relatively short questionnaire was designed
involving only 15 GL activities (Table III). They include the use of environment-friendly
raw materials, adoption of environment-friendly packaging design, and optimisation of
distribution process to reduce transportation hence carbon emission, etc. To standardize
replies so as to facilitate statistical analysis, closed-end questions with multiple-choice
answers in a five-point scale, ranging from worst (1) to best (5), were asked.

7. Benchmarking
Category Activity
green logistics
Green purchasing A1 – purchase of environment-friendly raw materials performance
A2 – substitution of environment harmful raw materials with friendly ones
A3 – purchase of recycled raw materials
A4 – use of suppliers that meet stipulated environmental criteria
A5 – compliance with international environmental regulations in purchasing 879
Green packaging A6 – use of environment-friendly materials in packaging
A7 – use of environment-friendly design in packaging
A8 – use of cleaner technology in packaging
A9 – use of recycled packaging materials purchased externally
A10 – taking back waste packaging materials from customers for recycling
Green transportation A11 – optimisation of efficiency through the use of energy efficient vehicles
A12 – optimisation of distribution process through better routing and
scheduling Table III.
A13 – use of integrated delivery to reduce transportation Green logistics activities
A14 – use of environment-friendly technology in transportation investigated in the
A15 – managing reverse material flows to reduce transportation questionnaire survey
The survey questions are developed from the literature of GL practices reviewed in the
previous sections. For example, the use of recycled packaging materials (A9) and
environment-friendly packaging design (A7) to reduce waste are based on the study
of Crumrine et al. (2004). The purchase of environment-friendly raw materials for
production (A1) and recycled parts for remanufacturing (A3) come from the findings
of Karpak et al. (2001). Also, the use of consolidation of orders (A13) and optimization
of schedules (A12) to reduce distribution frequency and to cut fuel consumption are
derived from the studies of Rao et al. (1991) and Wu and Dunn (1995). Many of the
activities investigated in this study also align with the actual practices of the industries
as well as the recommendations made by major logistics consulting companies. For
example, activities A6, A7, and A11-A14 are in agreement with the GL principles
adopted by the Italian automobile manufacturer Fiat. These principles include:
.
increased use of low-emission vehicles;
.
use of intermodal solutions to reduce road transportation;
. optimisation of transport capacity through consolidation and scheduling; and
.
reduced use of packaging and protective materials through lightweight design
(Fiat Group, 2010).
Similarly, the activities match well with some of the major GL opportunities
recommended by the global management consulting firm (Accenture, 2008) which
include:
.
network optimisation;
.
improvement inventory management;
.
improved vehicle fuel consumption;
.
reduced warehouse energy consumption; and
.
packaging reduction.

8. BIJ Data collection and tools of analysis
18,6 The questionnaires were e-mailed directly to the logistics managers of the companies
selected for the survey with a covering letter explaining the purpose of the study.
A reminder was sent to encourage response two weeks after the questionnaire was
dispatched. The mailing of survey questionnaires and reminders and collection of
returns were completed in October 2007. A total of 341 questionnaires – 176 to China
880 and 165 to Japan were sent using the e-mail addresses provided in the industry member
lists. A total of 107 valid returns – 58 from the Chinese and 49 from the Japanese
manufacturers were received (Table IV). Of the 107 companies, 69 reported that they
had implemented GL to various extents (36 in China and 33 in Japan).
As shown in Table V, the 107 responding HEA manufacturing companies were
divided into three groups:
(1) medium-sized firms;
(2) large-sized firms; and
(3) very large-sized firms
based on their number of employees following the European practice (European
Commission, 2003). Pearson’s x 2-test (Pearson, 1900) was used to investigate if there is
association between adoption of GL practices and firm size. Two-sample t-test
(Student, 1908) was used to test if there are significant differences between China and
Japan in the performance of various GL activities among the surveyed HEA
manufacturers. one-way analysis of variance (ANOVA) (Fisher, 1925) and Scheffe’s ´
(1953) test were used to test if there are significant differences in GL performance
among the surveyed HEA manufacturers of different firm size. PCA (Hotelling, 1933)
was used to obtain the weights to develop the GLPI used for an overall comparison of
GL performance between the two countries.
China Japan Total
Questionnaires sent 176 165 341
Questionnaires successfully delivered 172 159 331
Questionnaires returned 59 51 110
Valid returns 58 49 107
Table IV. Response rate (%) 33.7 30.8 32.3
Response rate of Manufacturers with GL adoption 36 33 69
questionnaire survey Manufacturers with no GL adoption 22 16 38
Group of firms Number of employees Count %
Table V.
Classification of 1. Medium sized ,250 38 35.5
responding companies 2. Large sized $250 and ,1,000 49 45.8
based on number 3. Very large sized $1,000 20 18.7
of employees Total 107 100

9. Results and discussions Benchmarking
Adoption of GL practices and firm size green logistics
Returns from the survey reveal that adoption of GL practices in the HEA
manufacturing industry is not particularly widespread. Only about 65 per cent of the performance
responding companies have reported GL adoption. Pearson’s x 2-test was applied to
determine if there is any association between GL adoption and firm size. The result is
shown in Table VI. 881
The x 2-test result suggests that there is a positive association between adoption of
GL practices and firm size. In other words, larger firm has a higher propensity to adopt
GL. The correlation coefficients C and V are both around 0.3 indicating that the
association is only a moderate one. Results of the Marascuilo (1966) procedure, which
allows a simultaneous testing of differences of all pairs of proportions when there are
several populations under investigation, indicate that the level of GL implementation of
medium-sized firms is significantly lower than that of the other two groups. On the
other hand, there is not enough evidence to suggest that large- and very large-sized
firms are different in the likelihood of adoption. The observed difference may be related
to the ability to invest in GL, the management support available, and the organization
structure of the companies. As GL requires additional resources for planning and
implementation, larger firms are more capable to invest in the area and use GL as a
competitive edge. This finding aligns with the literature that many big companies and
organizations are incorporating GL or GSCM as part of their corporate strategies
(Murray, 2000; Olson, 2008). The observation can be explained by the resource-based
view (RBV) theory, which advocates that to gain sustainable competitive advantage
large firms tend to use their resources to develop unique capability that is difficult
for their competitors to imitate or substitute (Barney, 1991; Conner, 1991; Grant, 1991;
Wernerfelt, 1984). In contrast, investment in environmental program may be a heavy
economic burden to smaller firms. Therefore, support from top management may not
Group of firms
(1) Medium sized (2) Large sized (3) Very large sized Total
Adoption of GL practices
GL practices adopted 17 35 17 69
GL practices not adopted 21 14 3 38
Total 38 49 20 107
Pearson’s x2-test
Calculated x 2-value 11.178
Degree of freedom 2
Critical x 2-value at a ¼ 0.05 5.992
[ Reject H0: GL adoption is independent
p-value 0.004 of firm size
Marascuilo procedure
Proportions Absolute difference Critical range
j Group 1-Group 2 j 0.267 0.253 [ Significant
j Group 1-Group 3 j 0.403 0.278 [ Significant
j Group 2-Group 3 j 0.136 0.251 [ Not significant Table VI.
Correlation coefficient Pearson’s x 2-test
Contingency coefficient 0.308 for independency of
C adoption of GL practices
´
Cramer’s V 0.323 from firm size

10. BIJ be readily available. The organization structure of smaller companies may also not be
18,6 able to provide proper management to support GL. Last but not least, economies of
scale can also play an important role. Larger firms tend to invest more in GL and are
more likely to benefit from economies of scale than their smaller counterparts (Min and
Galle, 2001). This in turn can provide additional incentive for larger companies to
further invest in GL practices.
882
GL performance between HEA manufacturers in China and Japan
For each sample, one-sample t-test was first used to determine if the mean performance
score of each GL activity surveyed is significantly different from the conjectured value
of three (i.e. average performance). Two-sample t-test was then used to determine if
there is any significant difference in average performance in the various GL activities
of the two countries. Results of Levene’s (1960) test for equality of variance show that
equal variance can be assumed in the analysis. Therefore, the pooled-t method can be
used to increase the power of the test if necessary. To be prudent, however, the
two-sample method with no pooling of variances was used as recommended in many
recently published statistics textbooks (Sharpe et al., 2010, p. 358). The results are
summarized in Table VII.
The findings reveal that in general HEA manufacturers in Japan perform better in GL
(with all of the mean scores above 3) than their counterparts in China (with majority of
the mean scores below 3). The two-sample t-test results show that, for more than half
of the surveyed activities, the differences in performance between the two samples
are significant at a ¼ 0.05 suggesting that there is room for improvement for the Chinese
manufacturers. Among the 15 activities investigated, the Chinese manufacturers
perform best (and on par with the Japanese manufacturers) in A3, A10, and A13.
This finding suggests that the Chinese manufacturers may be more concerned with the
cost reduction aspect of GL implementation. The use of recycled raw materials and
taking back waste packaging materials from customers for recycling can help reduce
purchasing and packaging costs. The use of integrated delivery to reduce transportation,
which requires little capital investment to implement, also lowers distribution cost.
For the more costly activities such as A1, A7, A8, and A11, the Japanese manufacturers
clearly excel in performance. This finding suggests that to the Japanese manufacturers
GL may be adopted for reasons other than sheer cost reduction. Considerations such
as extended producer responsibility (EPR), sustainable development, and long-term
competitive advantage, etc. may be equally important. In other words, the Chinese
manufacturers seem to focus more on the short-term cost benefit of GL and may not
appreciate the greater long-term benefit arising from environmental consideration as the
Japanese manufacturers do.
GL performance among different groups of HEA manufacturers
ANOVA was used to determine if the mean performance scores of the three groups of
´
manufacturers in the 15 GL activities surveyed are different. Scheffe’s test was then
employed for post hoc multiple comparisons to detect pairwise differences among
the groups. The analysis and test were applied to both the samples from China and
Japan for comparison and the results are given in Tables VIII and IX.
The mean performance scores of the different groups of HEA manufacturers in China
and Japan align with the earlier finding of the aggregate analysis using Chi-square test

11. Benchmarking
One-sample t-test Two-sample t-test
China Japan green logistics
(n ¼ 36) (n ¼ 33) Reject performance
Activity Mean p Mean p t-value p H0?
A1 – purchase of environment-friendly raw
materials 2.44 * 0.010 3.67 * 0.003 24.20 0.000 U 883
A2 – substitution of environment harmful raw
materials with friendly ones 2.81 0.352 3.39 0.062 22.03 0.047 U
A3 – purchase of recycled raw materials 3.31 0.196 3.27 0.247 0.10 0.921 X
A4 – use of suppliers that meet stipulated
environmental criteria 2.56 * 0.047 3.52 * 0.024 23.13 0.003 U
A5 – compliance with international
environmental regulations in purchasing 2.86 0.492 3.48 * 0.021 22.21 0.031 U
A6 – use of environment-friendly materials in
packaging 2.67 0.103 3.48 * 0.024 22.87 0.006 U
A7 – use of environment-friendly design in
packaging 2.69 0.155 3.55 * 0.010 22.93 0.005 U
A8 – use of cleaner technology in packaging 2.72 0.185 3.48 * 0.011 22.79 0.007 U
A9 – use of recycled packaging materials
purchased externally 3.00 1.000 3.45 * 0.030 21.60 0.116 X
A10 – taking back waste packaging materials
from customers for recycling 3.31 0.110 3.12 0.488 20.73 0.473 X
A11 – optimization of efficiency through the use
of energy efficient vehicles 2.56 0.051 3.52 * 0.030 23.04 0.003 U
A12 – optimization of distribution process
through better routing and scheduling 2.89 0.606 3.39 0.062 21.71 0.093 X
A13 – use of integrated delivery to reduce
transportation 3.47 * 0.042 3.21 0.344 0.83 0.412 X
A14 – use of environment-friendly technology in
transportation 2.64 0.074 3.06 0.786 21.43 0.157 X
A15 – managing reverse material flows to reduce
transportation 3.06 0.793 3.36 0.076 21.06 0.293 X Table VII.
Comparison of
Notes: *Significant at: a ¼ 0.05; H0: there is no difference in average performance in the GL activity differences in GL
concerned between China and Japan; performance score: 1 (worst)-5 (best), X – do not reject H0, performance between
U – reject H0 China and Japan
that GL adoption is related to firm size. In both cases, it can be seen that very large-sized
firms are performing better than large- and medium-sized firms in most of the GL
activities. The ANOVA results shown in Table IX indicate that there is significant
difference in performance among the three groups of HEA manufacturers in China in
eight activities, namely, A2, A5, A6, A7, A9, A11, A12, and A15. In contrast, the difference
among the three groups of Japanese manufacturers only exists in three activities,
namely, A1, A6, and A11. This suggests that the performance of different groups of
manufacturers in China is more diverse than that of the Japanese manufacturers. The
relative consistency in performance of the Japanese manufacturers may be due to greater
awareness of environmental protection, more stringent environmental regulations,
as well as longer history of GL adoption in developed countries.
´
Scheffe’s test results in Table IX indicate that very large-sized firms in China are
performing better than large- and medium-sized firms in A2, A5, A7, and A12.

12. BIJ
18,6
884
Table VIII.
Comparison of
performance in GL
of HEA manufacturers
activities among groups
between China and Japan
One-sample t-test on significance of mean performance score
China Japan
Medium Very large Medium Very large
sized (M) Large sized sized (VL) sized (M) Large sized sized (VL)
n ¼ 13 (L) n ¼ 16 n¼7 n¼4 (L) n ¼ 19 n ¼ 10
Activity Mean p Mean p Mean p Mean p Mean p Mean p
A1 – purchase of environment-friendly raw materials 1.85 * 0.000 2.75 0.483 2.86 0.788 2.25 0.319 3.37 0.110 4.80 * 0.000
A2 – substitution of environment harmful raw materials with
friendly ones 2.38 0.055 2.44 * 0.034 4.43 * 0.003 2.75 0.761 3.16 0.546 4.10 * 0.003
A3 – purchase of recycled raw materials 2.85 0.711 3.75 * 0.023 3.14 0.818 2.25 0.215 3.21 0.508 3.80 0.070
A4 – use of suppliers that meet stipulated environmental criteria 2.69 0.455 2.13 * 0.011 3.29 0.457 3.50 0.495 3.32 0.316 3.90 * 0.029
A5 – compliance with international environmental regulations in
purchasing 2.62 0.096 2.50 0.150 4.14 * 0.005 3.00 1.000 3.47 0.120 3.70 0.066
A6 – use of environment-friendly materials in packaging 1.85 * 0.000 3.25 0.388 2.86 0.788 2.25 0.319 3.21 0.331 4.50 * 0.000
A7 – use of environment-friendly design in packaging 2.08 * 0.004 2.44 * 0.034 4.43 * 0.003 2.75 0.761 3.42 0.119 4.10 * 0.003
A8 – use of cleaner technology in packaging 2.77 0.534 2.56 0.186 3.00 1.000 4.25 0.080 3.26 0.310 3.60 0.051
A9 – use of recycled packaging materials purchased externally 2.15 * 0.005 3.44 0.130 3.57 0.280 3.25 0.628 3.53 0.096 3.40 0.223
A10 – taking back waste packaging materials from customers for
recycling 2.92 0.673 3.38 0.252 3.86 0.143 3.00 1.000 2.95 0.826 3.50 0.138
A11 – optimisation of efficiency through the use of energy efficient
vehicles 1.85 * 0.001 2.75 0.483 3.43 0.407 2.25 0.319 3.26 0.331 4.50 * 0.001
A12 – optimisation of distribution process through better routing
and scheduling 2.54 0.165 2.50 0.088 4.43 * 0.003 2.75 0.761 3.16 0.546 4.10 * 0.003
A13 – use of integrated delivery to reduce transportation 3.08 0.861 3.75 * 0.023 3.57 0.280 2.25 0.215 3.16 0.578 3.70 0.132
A14 – use of environment-friendly technology in transportation 2.23 * 0.018 2.88 0.697 2.86 0.766 3.25 0.761 3.16 0.615 2.80 0.591
A15 – managing reverse material flows to reduce transportation 2.62 0.096 2.94 0.872 4.14 * 0.005 3.00 1.000 3.26 0.367 3.70 0.066
Notes: *Significant at: a ¼ 0.05; performance score: 1 (worst)-5 (best)

13. ´
ANOVA and Scheffe’s test on differences in mean performance scores
China Japan
Between Between Between Between Between Between
Activity F p Diff. M and L M and VL L and VL F p Diff. M and L M and VL L and VL
A1 – purchase of environment-friendly raw
materials 2.66 0.085 X X X X 14.86 0.000 U X U U
A2 – substitution of environment harmful
raw materials with friendly ones 12.30 0.000 U X U U 3.19 0.055 X X X X
A3 – purchase of recycled raw materials 1.63 0.211 X X X X 2.13 0.136 X X X X
A4 – use of suppliers that meet stipulated
environmental criteria 2.21 0.126 X X X X 0.70 0.505 X X X X
A5 – compliance with international
environmental regulations in purchasing 6.60 0.004 U X U U 0.52 0.602 X X X X
A6 – use of environment-friendly materials
in packaging 6.71 0.004 U U X X 10.13 0.000 U X U U
A7 – use of environment-friendly design in
packaging 15.63 0.000 U X U U 2.44 0.104 X X X X
A8 – use of cleaner technology in packaging 0.31 0.737 X X X X 1.66 0.208 X X X X
A9 – use of recycled packaging materials
purchased externally 6.48 0.004 U U X X 0.11 0.900 X X X X
A10 – taking back waste packaging
materials from customers for recycling 1.72 0.196 X X X X 1.05 0.361 X X X X
A11 – optimisation of efficiency through the
use of energy efficient vehicles 4.26 0.023 U X U X 7.03 0.003 U X U U
A12 – optimisation of distribution process
through better routing and scheduling 9.21 0.001 U X U U 3.19 0.055 X X X X
A13 – use of integrated delivery to reduce
transportation 0.92 0.407 X X X X 2.03 0.149 X X X X
A14 – use of environment-friendly
technology in transportation 1.25 0.301 X X X X 0.30 0.746 X X X X
A15 – managing reverse material flows to
reduce transportation 4.05 0.027 U X U X 0.70 0.505 X X X X
Notes: X – No difference; U- – difference exists; a – 0.05
green logistics
in GL performance
performance
Benchmarking
manufacturers between
Comparison of difference
China and Japan
Table IX.
885
among groups of HEA

14. BIJ This finding suggests that very large-sized firms are embracing GL to a greater
18,6 extent than their smaller competitors. Like their Japanese counterparts, very
large manufacturers in China (many are multinational corporations) may have
greater awareness of environmental protection, rigorous compliance with regulations,
and stronger sense of social responsibility (or EPR) as reported in the literature
(Khetriwal et al., 2009; Lee et al., 2000). The practice, which requires higher investment
886 in resources, can also be seen as a long-term strategy to sharpen competitiveness of the
company (Bacallan, 2000; Chan and Chan, 2008; Deshmukh et al., 2006). The mean
performance scores in Table VIII also indicate that medium-sized firms in China
are performing significantly below average in A1, A6, A7, and A9. This finding again
suggests that small firms may be more cost conscious as the use of environment-
friendly materials incurs higher cost (Thomas, 2008). Probably for the same reason,
medium-sized firms in China are also performing poorer than large- and very
large-sized firms in A11 and A14. The use of latest technology in green transportation
requires significant capital investment and is usually only affordable to larger
manufacturers.
Although for the Japanese manufacturers the differences in performance among
groups are not as big as that of their Chinese counterparts, the finding also supports
the view that a firm’s ability to invest in GL affects its performance. As shown in
Tables VIII and IX, very large-sized firms in Japan are performing better than the other
two groups of manufacturers in A1, A6, and A11. All these activities incur higher cost
or require significant capital investment that is more affordable to very large
corporations than smaller companies.
The differences in GL performance between firms of different sizes in China and
Japan revealed in the survey data suggest that there are basically two approaches to
GL implementation. As shown in Figure 1, GL practices can be just a reactive response
of smaller firms with limited resources to comply with environmental regulations and
to reduce production cost (as reflected in the case of China). In contrast, larger firms
may take a proactive approach in which GL is seen not only as sheer compliance with
laws and regulations or a mere cost saving measure but also unique capability that
adds value to product. Large firms tend to embrace GL in a fuller scale and invest
extensively to develop GL as a unique capability to enable the company to attain
long-term competitive advantage over their competitors (as reflected in both the cases
of China and Japan). In this regard, the RBV theory can be used to account for the
incorporation of GL as part of long-term business strategy by some large corporations
(Clendenin, 1997; Wells and Seitz, 2005).
PCA to generate GLPI
To generate a GLPI for overall comparison combining all the indicator variables
investigated in the survey, PCA is adopted to help determine the weights for the
variables that constitute the index. PCA as a multivariate statistical weighting approach
Firm Size Approach to GL Implementation GL Performance
- Amount of resources available affects 1. Reactive approach affects 1. Reactive approach
- Strength of corporate social responsibility - Law compliance and cost saving - Focuses mainly on low-cost activities
Figure 1. - Significance of company image 2. Proactive approach 2. Proactive approach
Different approaches - Level of pressure from stakeholders - Unique capability building - Invests in technologies and infrastructure
to GL implementation
Underpinned by the RBV theory

15. is often used in the development of composite index. Examples include Jollands et al. Benchmarking
(2004), Ali (2009), and Primpas et al. (2010). PCA weighs data by combining the indicator green logistics
variables into linear combinations that explain as much variation in the dataset as
possible. It provides a relatively objective approach to setting weights that is less biased performance
than other subjective weighting methods such as opinion polls. Another advantage of
PCA is that it reports the amount of variance in the data that is explained by the
resulting composite index indicating how representative the index is. Furthermore, PCA 887
is a data reduction method and may help reduce the dimensionality of the dataset if some
of the indicator variables are highly correlated. In this analysis, six components with
Eigenvalue greater than 1 are extracted and orthogonal rotation (varimax with Kaiser
normalization) is used to improve interpretability (Costello and Osborne, 2005).
Category labels are given to the components based on the indicator variables involved.
Table X shows the component loadings after rotation with the largest values in each
category highlighted for easy interpretation.
The determinant of the correlation matrix of all the indicator variables has a value of
0.000015, which is larger than the necessary value of 0.00001 suggesting that
multicollinearity is not a problem in this case. The Kaiser-Meyer-Olkin (KMO) measure
of sampling adequacy is 0.592 which exceeds the recommended acceptance value of
0.5 (Kaiser, 1974) suggesting that PCA can be applied. Bartlett’s test of sphericity
(Bartlett, 1950) is significant ( p , 0.001) suggesting that there are relationships between
variables. The six components obtained from the dataset together account for
81.3 per cent of the total variance. Albeit a good sign indicating the appropriateness
Principal component loading
PC 2 – PC 3 – PC 4 –
PC 1 – awareness of compliance cost PC 5 –
availabilityof environmental with reduction willingness PC 6 –
Variable (or activity) alternatives conservation regulations measures to invest EPR
A2 0.979 0.058 0.103 20.031 20.039 0.021
A12 0.974 0.053 0.079 20.049 20.036 2 0.025
A7 0.922 0.117 0.154 20.061 0.111 0.079
A1 0.145 0.924 20.015 0.021 0.057 2 0.005
A6 0.090 0.912 20.125 0.025 0.060 2 0.020
A11 2 0.021 0.777 0.240 20.065 20.034 0.185
A15 0.177 0.079 0.880 0.065 0.207 2 0.090
A5 0.147 0.144 0.862 0.021 0.250 0.031
A10 0.029 2 0.138 0.649 0.047 20.166 0.226
A3 2 0.043 0.016 0.055 0.951 20.017 2 0.017
A13 2 0.080 2 0.029 0.053 0.949 20.103 0.052
A14 2 0.036 2 0.036 0.010 20.005 0.864 0.198
A9 0.044 0.101 0.189 20.128 0.765 2 0.079
A8 2 0.007 0.089 0.041 20.098 20.079 0.842
A4 0.089 0.042 0.104 0.200 0.349 0.688
Total percentage of
variance explained 19.1 15.9 14.1 12.6 10.9 8.9
Cumulative (%) 19.1 34.9 49.0 61.6 72.4 81.3 Table X.
Principal component
Notes: KMO measure of sampling adequacy ¼ 0.592; Bartlett’s test of sphericity (approx. analysis of the survey
x 2 ¼ 690.74, df ¼ 105, p ¼ 0.000) dataset

16. BIJ of using PCA to obtain the weights for the variables, the figure has to be interpreted with
caution. While the natural randomness in the dataset may actually be low in this case, the
18,6 use of a coarse five-point measurement scale and a relatively small number of indicator
variables may also result in lower variability hence the relatively high percentage
of variance explained (Møller and Jennions, 2002). Based on the indicator variables or
activities included in each category, the components are labelled as availability of
888 alternatives, awareness of environmental conservation, compliance with regulations,
cost reduction measures, willingness to invest, and EPR. They indicate the distinct
dimensions in the measurement of GL performance of the firms in the dataset. Using the
dominant statistical weights (with values greater than 0.6) obtained from the PCA and
the performance scores A1-A15 of the 15 GL activities reported, the total performance
score S across the six components can be calculated using Equation (1) as follows:
S ¼ 0:924A1 þ 0:979A2 þ 0:951A3 þ 0:688A4 þ 0:862A5 þ 0:912A6 þ 0:922A7
þ 0:842A8 þ 0:765A9 þ 0:649A10 þ 0:777A11 þ 0:974A12 þ 0:949A13 ð1Þ
þ 0:864A14 þ 0:880A15
As the scale used for all the indicator variables are from one to five, the absolute
minimum and maximum values of S obtained using Equation (1) are Smin ¼ 12.94 and
Smax ¼ 64.69. Using these values, the total performance score S of each firm in the
survey can be converted to a composite index I between 0 and 100 using Equation (2).
Greater value of I implies a better performance on average across all measures:
ðS 2 S min Þ100
I¼ ð2Þ
S max 2 S min
Comparison of performance using the GLPI
By calculating a GLPI for each firm and an average value for China and Japan,
an objective comparison between the two countries can be made. The index-based
comparison among firms can also be made at a finer level in the areas of green
purchasing, packaging, and transportation by using the weights generated in the PCA
but including only a subset of the indicator variables. Also, focusing on the six
components identified, performance of firms based on the various drivers such as cost
reduction and regulation compliance can also be easily compared. Table XI gives a
summary of the comparison among firms of different size in China and Japan in different
logistics functions based on their GL performance indices.
It can be seen from Table XI that on the whole firms in Japan are performing better
than their counterparts in China regardless of firm size. The average GLPI for China
Green Green Green Overall
purchasing packaging transportation performance
China Japan China Japan China Japan China Japan
Table XI. Medium-sized firms 37 43 33 52 37 43 36 46
Average GLPI of firms Large-sized firms 44 57 50 57 49 55 47 57
in different Very large-sized firms 65 77 63 72 68 69 65 72
logistics functions All firms 45 62 46 61 48 58 47 60

17. and Japan for all firms are 47 and 60, respectively, indicting a big difference in Benchmarking
performance. Nevertheless, the performance gap is larger for medium- and large-sized green logistics
firms but relatively smaller for very large-sized companies. Looking at performance in
different logistics functions, the gap is largest in green packaging between the performance
medium-sized firms (33 against 52 – a difference of 19 points in the GLPI) and smallest
in green transportation between the very large-sized firms (68 against 69 – a difference
of only one point in GLPI) of the two countries. These results align with the outcome of 889
previous comparison using two-sample t-test as shown in Table VII that medium-sized
firms in China are performing poorly in costly activities such as the use of
environment-friendly materials and design in packaging. The alignment suggests that
the GLPI developed in this case is robust and the use of it for comparison is relatively
convenient. The outcome is also easier to interpret as the performance in various
activities of a GL function is now measured using a single index.
Applying the same approach but looking at performance in the six dimensions
identified in the PCA, another table of indices comparing the performance of firms of
difference size in China and Japan can be generated. It can be seen from Table XII that,
when all firms are considered, Japanese companies are having higher GLPI than their
Chinese counterparts in all components except cost saving. The exception is attributed
mainly to the high scores of the medium- and the large-sized Chinese firms in this
aspect. This suggests that many firms in China, particularly the medium- and large-
sized ones, are implementing GL for cost reduction purposes. This finding also aligns
with that of the previous analysis using ANOVA in Table IX. Again, it shows the
robustness of the index and hence the merit of using it as a simple and objective mean
to compare performance.
By applying the same technique in a larger survey covering more firms in different
countries, a list of indices can be produced similar to the one developed by The The
World Bank (2010) for comparison of logistics performance across developing and
developed nations. If deemed necessary, the survey can cover GL activities in areas
other than the three major GL functions investigated in this study. Repeated surveys,
similar to the annual third-party logistics study (Langley et al., 2007) can also be
conducted to reveal the trend of development in GL performance of the different
countries based on their respective indices.
Conclusions and implications
Summary of findings and implications
This paper has presented and compared the GL performance of some of the HEA
manufacturers in China and Japan in purchasing, packaging, and transportation. It has
also demonstrated the development and application of a GLPI for easy comparison of GL
Availability Awareness Compliance Cost saving Investment EPR
C J C J C J C J C J C J
Medium-sized firms 33 44 21 31 42 50 49 31 30 56 43 73
Large-sized firms 36 56 48 57 47 56 69 55 53 58 34 57 Table XII.
Very large-sized Average GLPI
firms 86 78 51 90 77 66 59 69 35 52 53 68 of firms in different
All firms 45 61 39 64 51 59 60 56 45 55 41 62 components or factors

18. BIJ performance between the two countries. The findings reveal that China – a developing
18,6 country – is still a distance behind Japan – a developed country – in GL implementation
particularly in the upstream of the supply chain, i.e. purchasing. While the HEA industry
of Japan has implemented GL throughout the whole supply chain with relatively
good performance in almost all activities surveyed, the Chinese HEA manufacturers,
particularly the small ones, are focusing mainly in certain downstream activities such
890 as packaging with recycled material and consolidation to reduce transportation. These
activities require relatively little investment in technology but the cost saving from GL is
readily achievable. The findings also suggest that the main drivers for GL implementation
in the HEA industry of China are still regulatory compliance and cost saving at this
stage. The Japanese manufacturers are implementing GL more for reasons of stronger
awareness, availability of alternative green materials and technologies, development of
unique capability for long-term competition, and EPR. The different approaches to GL
implementation by the small and the large firms can be accounted for using the RVB
theory. With these findings, the first two research questions are fully answered.
Although this study was not designed to investigate the barriers to GL practices
and GSCM, the findings have shed light on the challenges of GL implementation in
developing countries such as China. These challenges include:
.
relatively low public awareness of sustainability and environmental protection
hence weaker pressure on manufacturers to go green;
.
lack of comprehensive environmental policies, regulations, and directives such
as the restriction of hazardous substance and the Waste Electrical and Electronic
Equipment directives of the European Community (EU) (European Parliament
and Council, 2003a, b) to force compliance;
.
limited investment in green technology, research and development to enhance
efficiency and achieve economies of scale;
.
over-emphasis on low-cost production and short-term benefits than long-term
gains in order to maintain competitiveness in the global market; and
. lack of resources, expertises, and management experiences in GSCM particularly
for the small manufacturers.
These observations align with the comments made by some researchers in China that
both the country’s hardware and software for GL are lagging behind that of developed
countries (Liu, 2009; Zhou, 2009). To promote GL practices and GSCM in developing
countries, government can play a critical role in enhancing awareness through
public education and industrial workshops, encouraging implementation through tax
incentives and subsidies, enforcing compliance through legislations and regulations,
sponsoring academic research for long-term sustainable development, and investing
in infrastructure and technology to benefit the entire industry. Manufacturers,
particularly large corporations with more resources, can also take greater initiatives to
invest in green technology, environment-friendly product design, cleaner manufacturing
and distribution processes, and recycling. Strong collaboration among business partners
across the supply chain will put pressure on smaller manufacturers to follow suit and
help them develop their GL capabilities (Lau and Wang, 2009).
The paper has also demonstrated the development of a GLPI using PCA to obtain
the weights for the indicator variables involved in the equation. Results of comparison

19. among the surveyed firms in China and Japan using the GLPI align with the outcomes Benchmarking
obtained through other statistical analyses. The feasibility of using a single index for green logistics
GL performance evaluation is proved and the robustness of the index is established.
The use of the GLPI can simplify the GL performance comparison process and provide performance
a simple and objective mean to compare among industries and countries. Managers can
use the GLPI to benchmark the performance of their firms in the respective logistics
areas against those adopting best practices and revise their supply chain strategy 891
accordingly. The proposed index may also assist governments in formulating policies
on promoting GL implementation in various industry sectors. With the findings and
conclusions, the RQ3 is also satisfactorily answered.
Limitations and future research
This study has only covered three major GL functions involving 15 activities to help
develop a GLPI for easy comparison of performance in GL practices. While the study is
adequate as a pilot to prove the feasibility of the concept, the index developed may
need to include other GL activities in order to be comprehensive. A larger survey
covering more GL activities and industries would be needed for further investigation.
Further, a seven- or ten-point scale can be used in gauging performance of GL activities
in the survey so as to give a finer measurement. Also, self-appraisal of performance
may not be entirely objective. An expert panel or a study approach similar to the one
adopted by The World Bank in developing the LPI can be used. Restricted by the scope
of the study, findings from this research are also not able to disclose further details of
the GL implementation such as the various drivers and obstacles of GL implementation
and their correlations. To obtain a fuller picture of the situation, future research
may further investigate the drivers and the obstacles of GL implementation faced by
the industry in comparison with other industry sectors. In this regard, a more
sophisticated questionnaire survey design focusing on the relationships among
variables or the use of in-depth exploratory case studies may be appropriate. To
facilitate standardization of practices in the industry for higher efficiency, a study to
compare in detail the actual practices of firms of different size in adopting and
implementing GL is also recommended.
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About the author
Kwok Hung Lau is a Senior Lecturer in the School of Business Information Technology and
Logistics at the Royal Melbourne Institute of Technology (RMIT) University in Australia.
He holds a Bachelor’s degree in geography, Master’s degrees in business administration,
information systems, urban planning, and a PhD in geocomputation. He has papers published in
journals and conference proceedings such as Environment and Planning (Part B), Transactions
in GIS, Supply Chain Management: An International Journal, International Journal of Physical
Distribution & Logistics Management, International Journal of Information Systems & Supply
Chain Management, Australasian Transport Reform Forum, International Conference on City
Logistics, and Australian and New Zealand Academy of Management Conference. His research
interests include modelling and simulation in supply chain, e-supply chain management,
outsourcing, benchmarking, reverse logistics, and green logistics. Kwok Hung Lau can be
contacted at: charles.lau@rmit.edu.au
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