Content uploaded by Eric Grosse
Author content
All content in this area was uploaded by Eric Grosse on Jul 09, 2021
Content may be subject to copyright.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
1
Green warehousing: systematic literature review and bibliometric analysis
Maicol Bartolini, Eleonora Bottani, Eric H.Grosse
Abstract
Warehouses are major contributors to the rise of greenhouse gas emissions in supply chains. Thus, it
is not surprising that the attention of academic research to green and sustainable warehousing has
been growing in recent years. This attention has led to an increasing number of publications in this
field, which is why a systematic literature review on the topic of green warehousing is proposed in
the paper at hand. This work provides a comprehensive overview and classification of the existing
research on green warehousing, summarizes and synthesizes the available knowledge on this topic,
and identifies key trends. Based on the evaluation of the literature, promising ideas for future research
are proposed. Citation and network analyses are carried out to evaluate the relationships among the
topics covered. The results show an increasing interest in sustainability topics within the warehousing
literature, where energy saving has been the most frequently studied objective, followed by
environmental impact of warehouse buildings, and green warehouse management in general. The
green warehousing literature, however, lacks case studies and empirical data. The main contribution
of this paper is an exhaustive summary of the state of knowledge on green warehousing in terms of
the macro-themes addressed, the specific topics investigated and the methodological approaches,
including a comprehensive and systematic classification of the relevant literature. An outline of
managerial guidelines about green warehouse management and the propositions of future research
ideas contribute to the further development of this emerging research field.
Keywords: emissions; energy consumption; green warehousing; literature survey; material
handling; sustainability.
1 Introduction
Warehouses are important nodes in each supply chain and almost every industry. The expanding e-
commerce sector and the growing demand for mass customization have even led to an increasing
need for warehouse space and buildings (Angel et al., 2006), particularly for serving the uninterrupted
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
2
customer demand in the business-to-consumer market (Boysen et al., 2018). For example, JingDong,
the 2nd largest e-commerce company in China, has built 166 warehouses up to 2015 due to the
widespread increment of e-commerce business (Yu et al., 2017). In the time span between 2007 and
2017 the average size of newly built warehouses in the United States has increased by 143%, which
means an increment of 10,095 square metres (CBRE, 2017). These developments have determined
the need for larger regional hubs and smaller urban warehouses for enhancing the efficiency of
logistics space and facilitating the movement of goods to customers who require shorter delivery time
(Deloitte, 2014). E-commerce retailers increasingly focus on improving last mile delivery by
reshaping their warehouse network to be closer at their customers, who more and more demand for a
fast delivery (McKinsey, 2016). This development becomes visible, for example, by the 90 million
US people who used the two-day delivery service of Amazon in 2018 (Wall Street Journal, 2017).
As each logistic activity, warehouses contribute to the generation of greenhouse gases (GHG) and
their impact on global warming can no longer be disputed. Warehousing activities contribute roughly
11% of the total GHG emissions generated by the logistics sector across the world (Doherty & Hoyle,
2009). Hence, companies are broadening their attention, besides operational and economic objectives,
to environmental and social issues of warehouses. These two aspects of sustainability have
traditionally been overlooked as important key performance indicators by many companies (cf.
Elkington, 1998). In general, governments and companies have more and more realized the need for
environmental awareness. The Paris climate agreement signed in December 2015 for example, with
its purpose to give strength responses to the climate change effects, has achieved a global compromise
among countries towards a net reduction of GHG emissions with the pursue of keeping global
warming below two degrees Celsius above pre-industrial levels (Rogelj et al., 2016). Furthermore,
2016 has been registered as the hottest year from 1880 with an average temperature of 0.99 Celsius
degree warmer than the mid-twentieth century (NASA, 2016). Some European governments have
also set specific targets for energy efficiency. The German government, for example, has set out a
target to be achieved within 2020, declaring that the primary energy consumption of private
households, industrial companies and local authorities must be cut by 20% compared to 2008 (Federal
Ministry for Economic Affairs and Energy, 2016). Outside Europe, the Chinese government is also
engaged to promote climate change strategies by providing more research funds in this field
(Kitagawa, 2017).
Following this global trend, an increasing attention to green and sustainable warehousing processes
has led to many new research results regarding management concepts, technologies and equipment
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
3
to reduce warehouses’ carbon footprint, i.e. the total emissions of GHG in carbon equivalents directly
caused by warehouses’ activities (Carbon Trust, 2007; Wiedmann & Minx, 2008). Some authors have
recently included ‘green warehousing’ among the environmentally sustainable processes of a supply
chain (Rostamzadeh et al., 2015; Kumar et al., 2015), although a formal definition of this concept has
not been provided so far. In this paper, the term ‘green warehousing’ (GW) is used to denote a
managerial concept integrating and implementing environmental friendly operations with the
objective of minimizing energy consumption, energy cost and GHG emissions of a warehouse.
Sustainability, as a general theme, has received an increased consideration in supply chain
management literature since the late 1990s (Rajeev et al., 2018). The development of a managerial
approach that incorporates sustainable practices into the companies’ strategies (de Oliveira, 2018)
has led to the definition of green supply chain management (GSCM), i.e. the integration of
“environmental thinking into supply-chain management, including product design, material sourcing
and selection, manufacturing processes, delivery of the final product to the consumers as well as the
end-of-life management of the product after its useful life” (Srivastava, 2007). The literature on the
various topics of GSCM is huge and widespread and deals with miscellaneous aspects, including
green logistics, reverse logistics, green building, product life cycle assessment, or environmental and
operational performance (de Oliveira et al., 2018). Several review papers on GSCM have also been
published. The first one has been by Srivastava (2007), who considered the literature on GSCM
published between 1990 and 2006. Ahi & Searcy (2013) developed a review paper focusing on the
different definitions for green and sustainable supply chain management. More recently, Fahimnia et
al. (2015) completed a bibliometric and network analysis on GSCM.
Despite the fact that the concept of GSCM has increasingly gathered attention from researchers and
practitioners (Jabbour et al., 2014), the topic of GW has not been addressed in a self-standing
literature review so far. As this review addresses an emerging topic, the analysis aims to achieve a
preliminary conceptualization of GW rather than a reconceptualization of previous frameworks (cf.
Torraco, 2005). The paper at hand thus reviews and classifies the literature focusing on GW and
summarizes the state of knowledge within this specific research field, excluding other (more general)
aspects of GSCM. Based on the evaluation of the literature, we identify patterns and research streams
as well as green warehousing topics, macro-themes and employed research methods. A network
analysis of the GW literature is also conducted to substantiate the research outputs, identify the most
influential researchers, organizations and journals, as well as to determine the value and impact of
the reviewed papers. The ultimate aim is to help researchers and practitioners to identify specific
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
4
areas of interest within GW, to point out research gaps and to highlight potential directions for further
activities in the field.
The remainder of the paper is structured as follows. The next section describes the methodology used
for conducting the review. Section 3 provides some descriptive statistics about the GW studies.
Section 4 classifies these studies in terms of macro-theme and topic(s) analysed, and research
methodology used, describes the key results available in literature and shows the results of a citation
analysis. Section 5 provides a critical discussion of the findings from the literature review and
highlights its key contributions. Section 6 concludes by highlighting the limitations of this review
and proposing directions for future research.
2 Research methodology
2.1 Databases, keywords and inclusion criteria
The methodology used for identifying the studies relevant to this review is based on the work of
Rhoades (2011), Hochrein & Glock (2012) and Kable et al. (2012). Three different databases, namely
EBSCOhost, Google Scholar and Scopus were used to search for relevant literature, so as to ensure
that all pertinent papers are included (Crossan & Apaydin, 2010).
A fundamental issue to carry out queries in databases is the determination of keywords that allow
identifying all papers that are relevant to the research objectives (Aveyard, 2010). One way to address
this issue is to study the most frequently keywords used in peer-reviewed literature for the topic under
examination. For the GW theme, the most frequently used keywords were analysed by Ries et al.
(2017); this study was therefore taken as the basis for identifying the keywords to be used in this
paper. These keywords were split into two groups as follows:
• Group A keywords include general terms related to warehousing and its key activities and
processes, i.e.: “warehous*”, “automated storage”, “material handling”, “order- picking”,
and “AS/RS”
• Group B keywords includes terms linked to environmental sustainability, i.e.: “sustain*”,
“carbon”, “green”, “energy”, “environment*”, “emission”, “eco”, “CO2”, and “life
cycle”.
To run the queries, each keyword from group A was combined with each keyword from group B and
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
5
papers were considered relevant if they contain at least one search term from both groups in the title,
abstract or keywords. No constraints were set for the publication time span. By carrying out the
queries as described above, we retrieved 118 papers in total from EBSCOhost, 131 from Google
Scholar, and 174 from Scopus (all numbers effective September 2018). These papers were first
checked to eliminate duplicates (i.e. studies that were retrieved from more than one database or that
resulted from the use of different keyword combinations). We then applied the following criteria for
inclusion of papers in the final sample:
1. Only papers written in English and published in peer-reviewed academic journals and
conference proceedings were retained;
2. Only papers that focused expressively on GW were considered relevant. Conversely, papers
that just mentioned topics related to GW in different areas (e.g. GSCM) were excluded from
the review.
After applying the inclusion criterion #1, the relevance of the papers found was evaluated by checking
all titles and abstracts of the remaining papers, to eliminate those papers that did not meet the
inclusion criterion #2. A working sample of 37 papers was obtained after applying the inclusion
criteria. These papers were all retrieved and completely read. After this check, six papers were further
excluded due to a lack of relevance, leading to 31 relevant papers. In the last step, a snowball approach
was conducted by analysing the references cited in the working sample to identify papers that might
be relevant for the review at hand and could not be found with the database search (cf. Glock &
Hochrein, 2011). This approach led to seven additional papers and a final sample of 38 works.
2.2 Analysis tools
The relevant data of the 38 papers in the final sample was saved in a Microsoft ExcelTM spreadsheet
to conduct the descriptive statistics shown in Section 3. We used two software packages (i.e. Gephi
and R) to support the analysis of the sampled papers. Gephi 0.9.1 (https://gephi.org/), a software
useful to develop network analyses and display the results in graph format, was used to identify the
network of collaborations among authors (Section 3.3) and the relationships among the different
topics of GW (see Sections 4.4 and 4.5). R (https://www.r-project.org/), a programming language for
developing statistical analyses, was used to build graphs in Section 4.3 and coupled with Gephi to
perform the analysis described in Section 4.4.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
6
3 Descriptive results
3.1 Trend of publications in time
The publication trend of the papers on GW is shown in Figure 1. Although the publication time span
was not restricted, the first relevant paper was published in 2006 (Figure 1), which renders GW a
relatively young a research field. The publication trend has been quite stable around one-three works
per year in the period 2006-2014, while it has started to increase strongly since 2015 (ten papers
published in that year). Moreover, 27 papers out of 38 (71% of the sample) were published between
2014 and 2018. This significant number of publications could probably be attributed to the increased
attention around the integration of environmental sustainability and logistics activities, which has
forced entrepreneurs to find new opportunities to reduce the carbon footprint of warehouse facilities
(Tambovcevs & Tambovceva, 2012; Satolo et al. 2013).
These results indicate that the interest towards GW has risen considerably in recent years and confirm
the suitability of a literature review to analyse and summarize the key findings about this topic and
point out future research ideas that can further develop this research field.
Figure 1: Number of publications per year.
3.2 Publication outlet
Out of the 38 papers reviewed, 32 were published in scientific journals (84.2%) and the remaining 6
in international conference proceedings (15.8%). In general, these journals focus on the energy and
industrial engineering area, while only few focus on other disciplines, such as building and
architectural science. Most of the journals (19) published only one paper on GW. The journals that
published more than one paper are: International Journal of Production Economics (4), Energy (2),
0
2
4
6
8
10
12
2006 2010 2011 2012 2013 2014 2015 2016 2017 2018
Number of papers
Years
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
7
International Journal of Advanced Manufacturing Technology (2), International Journal of Logistics
Research and Applications (2), International Journal of Production Research (2) and Production
Planning & Control (2).
3.3 Authorship and collaborations
In total, 107 different authors contributed to the GW literature. The majority of authors (84.4%)
published one paper on GW, whereas 17 authors (15.6%) published more than one. Two authors
published five papers in the field of GW. Table 1 reports the number of authors per paper and shows
that most papers (34.2%) were written by three authors, followed by collaboration of four authors
(23.7%) and two authors (21.1%). The collaborations of more authors are likely to be motivated by
the increased number of researchers interested in the GW field and by the fact that this research field
is multidisciplinary in nature and needs exchanges and complementary knowledge across different
research fields. This is also confirmed by the network of international collaborations (Figure 2), which
was determined on the basis of authors’ origin using the Fruchterman & Reingold’s (1991) layout in
Gephi. In Figure 2, each node represents a country and its size reflects the amount of papers
contributed by authors from that country. European countries (Italy, United Kingdom and Germany
in particular) published most of the research on GW, but a relevant number of contributions also came
from China and USA. The links in Figure 2 denote the collaboration among countries and the
thickness of each link denotes the collaboration strength between two countries. For example, UK
researchers have established a network of collaborations with nine countries across the world (most
of which in Europe), while Germany and Austria have activated collaborations with two countries
each.
Table 1: Authorships per paper.
Authorship
Frequency
Percent
Single author
3
7.9%
Two authors
8
21.1%
Three authors
13
34.2%
Four authors
9
23.7%
Five authors
3
7.9%
Six authors
1
2.6%
> six authors
1
2.6%
Total
38
100%
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
8
Figure 2: Collaboration ranks and strengths among different countries.
4 Systematic literature review
This section provides a classification of the GW literature according to the macro-theme highlighted
and topics analyses (see
Figure 3), methodological approach followed and study keywords. Citation and modularity analysis
are also carried out to corroborate the results of the classification.
4.1 Framework and literature classification
The framework shown in
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
9
Figure 3 was used to classify the GW literature, as it allows to draw up a systemic assessment about
how warehouse management and operations entail on GHG emissions, without focusing on the
depiction of a specific warehouse. This framework was deductively adapted from Fichtinger et al.
(2015) and then inductively refined based on the results of the literature review.
Figure 3: Classification framework.
With respect to the triple bottom line (TBL) approach to sustainability, whose function is to go beyond
the traditional measure of business with the aim to include social and environmental dimensions
(Elkington, 1998; Slaper & Hall, 2011), the framework in
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
10
Figure 3 targets expressively the environmental aspect of sustainability relating to warehouses and
their management. Three different macro-themes can be identified to categorize the GW literature:
• the green warehouse management macro-theme covers both environmental guidelines and the
analysis of available initiatives and environmental certifications within warehouse facilities,
which endeavour to appraisal the issues that affect the performance of the warehouse (Horvat
& Fazio, 2005);
• the environmental impact of warehouse building investigates the key warehouse
characteristics that contribute to environmental emissions and energy consumption. These
characteristics include warehouse size, space utilisation, illumination, heating, ventilation and
air conditioning (HVAC) (Fichtinger et al., 2015), and building structure in terms of roof
insulation, wall and doors (James & James, 2009). Complementary aspects, such as building
manufacturing, or maintenance, are sometimes mentioned in those papers, although they do
not form the main topics treated;
• The energy saving in warehousing macro-theme includes various initiatives whose general
aim is to achieve energy efficiency in a warehouse. Energy usage can be converted into GHG
emissions (Ries et al., 2017). Specific material handling systems, which usage in warehouses
requires a substantial amount of energy, are also evaluated. Examples of these systems are
fixed material handling equipment (FMHE) and mobile material handling equipment
(MMHE).
The classification above was used to structure the discussion of the GW literature (Section 4.2). Table
2 shows the categorization of the papers per macro-theme and specific topic(s) addressed. As a
general rule, papers that address more than one theme were assigned to the category that best reflects
their core content; this is, for instance, the case for papers that propose a framework or develop models
for GW and therefore embrace several topics. The same table also shows the research methodology
used in each paper, according to the following definitions (Staudt et al., 2015; Glock et al., 2017):
i) case study paper: examination of a phenomenon within its real-life context, investigating
the results in practice;
ii) conceptual paper: conceptual distinctions while exploring a specific topic, framework and
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
11
theory development, practical applications often lack;
iii) analytical paper: mathematical modelling, optimisation model, heuristics development,
numerical examples.
iv) simulation paper: analysis and test on the response of a model, software programs.
Table 2: Classification of the literature on GW.
GW topic
Macro-theme
Methodology
Building features
Sustainability indicators and guidelines
TBL
Environmental certification
Cap-and-trade emission policy
Energy end-
use types
Green warehouse management
Environmental impact of warehouse building
Energy saving in warehousing
Case study
Conceptual
Analytical
Simulation
Author
Lighting
HVAC
MHE
Makris et al. (2006)
X
X
X
X
Đukić et al. (2010)
X
X
X
X
X
X
Tan et al. (2010)
X
X
X
X
X
Daheng (2010)
X
X
X
Cook & Sproul (2011)
X
X
X
X
Rai et al. (2011)
X
X
X
X
Zajac (2011)
X
X
X
X
Dhooma & Baker (2012)
X
X
X
X
X
Amjed & Harrison (2013)
X
X
X
X
X
X
X
Bank & Murphy (2013)
X
X
X
X
Meneghetti & Monti (2013)
X
X
X
X
Fekete et al. (2014)
X
X
X
X
Lerher et al. (2014)
X
X
X
Meneghetti & Monti (2014)
X
X
X
Boenzi et al. (2015)
X
X
X
X
Carrano et al. (2015)
X
X
X
X
Dadhich et al. (2015)
X
X
X
X
X
Fichtinger et al. (2015)
X
X
X
X
Meneghetti & Monti (2015)
X
X
X
X
X
Meneghetti et al. (2015a)
X
X
X
Meneghetti et al. (2015b)
X
X
X
Pudleiner et al. (2015)
X
X
X
X
Tappia et al. (2015)
X
X
X
Żuchowski (2015)
X
X
X
X
X
X
Chen et al. (2016)
X
X
X
X
Ene et al. (2016)
X
X
X
Facchini et al. (2016)
X
X
X
Fikiin et al. (2016)
X
X
X
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
12
Freis et al. (2016)
X
X
X
X
X
Rüdiger et al. (2016)
X
X
X
X
X
Wang et al. (2016)
X
X
X
Accorsi et al. (2017)
X
X
X
X
X
X
Bortolini et al. (2017)
X
X
X
X
Nia et al. (2017)
X
X
X
X
Ries et al. (2017)
X
X
X
X
X
Burinskiene et al. (2018)
X
X
X
X
X
Seifhashemi et al. (2018)
X
X
X
Stöhr et al. (2018)
X
X
X
Total
20
8
2
1
1
14
10
25
7
11
20
4
7
14
13
4.2 Description of the macro-themes
4.2.1 Green warehouse management
One of the first papers that addressed GW from a management point of view is the one of Đukić et
al. (2010), who studied how different technologies and order picking methods such as routing, storage
assignment, and order batching can improve energy efficiency and operational performance within
warehouses. A simulation considering order size and routing distance was conducted comparing the
energy efficiency of three warehouse layouts (basic traditional, traditional with one cross-aisle and
fishbone). Tan et al. (2010) developed a relationship among social, economic and environmental
issues through a modelling tool that supports system dynamics creating key sustainability indicators.
Data from a case company that provides storage and transportation services was used to test the
applicability of the model. Similarly, Amjed & Harrison (2013) focused on the definition of best
practices with the aim to develop a model for evaluating the main issues of GW considering
environmental, social and economic perspectives. Each dimension of the TBL approach was analysed
focusing on those areas that need to be modernized in order to implement sustainable actions within
warehouses. The authors provided a list of best practices for each issue including a roadmap for
enhancing the trade-off among these dimensions towards GW.
Another issue in this macro-theme is the adoption of sustainability standards aiming to reduce GHG
emissions. Bank & Murphy (2013) highlighted the motivation to develop and introduce a
sustainability standard for warehouse processes. The authors identified metrics and measurements for
expanding sustainability standards for warehouses through the introduction of so-called Sustainable
Logistics Initiatives (SLI). This program was developed by the International Warehouse Logistics
Association (IWLA) to demonstrate that the adoption of green actions within warehouse facilities
brings both a reduction of GHG emissions and an improvement of monetary performance indicators.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
13
Electricity usage, recycling, liquid fuel usage, and water consumption were considered as the main
environmental metrics of the SLI. Similarly, Rüdiger et al. (2016) studied a method for assessing
GHG emissions of warehousing and transhipment activities considering a set of environmental
performance indicators (EPI). The authors presented a holistic perspective of the energy consumption
within logistics facilities divided by energy, maintenance, and packaging/waste. To highlight
strengths and weaknesses, the authors evaluated the developed method in a case warehouse.
Besides standards, green building certifications, often incentivised by government, can support the
adoption of sustainable solutions (Colicchia et al., 2013). In warehousing, some certifications have
been developed up to now. Żuchowski (2015), for example, presented sustainable factors about
reducing harmful emissions and consumption of resources within warehouses, describing
certification methodologies that may be used to assess the impact of the various environmental factors
in terms of their effects in a warehouse building. First, the author provided three sustainable solutions
that may be applied in a warehouse and then proposed a comparison of four certification
methodologies, i.e. Building Research Establishment Environmental Assessment Methodology
(BREEAM), Haute Qualité Environnementale (HQE), Deutsche Gesellschaft für Nachhaltiges Bauen
(DGNB), Leadership in Energy and Environmental Design (LEED), to evaluate the warehouse
sustainability performance that should have a warehouse in order to respect one of the certifications
cited above.
A further issue investigated in this macro-theme is the adoption of sustainability standards and the
respect of cap-and-trade emission policy, namely the overall control policy schemes implemented
across the world leading towards a better environmental warehouse performance. Chen et al. (2016)
discussed the impact of a cap-and-trade emission policy on warehouse management decisions
exploring the role of green technology investments in achieving the sustainability objectives of
warehouse operations. The authors used case study data to assess the GHG emissions of a retailer’s
warehouse. The results underlined that cap-and-trade prices of carbon emissions as well as related
control policies can successfully reduce the carbon footprint of warehouses.
4.2.2 Environmental impact of warehouse building
The environmental impact of warehouse building macro-theme is divided into two sub-themes, i.e.:
a) warehouse building; and b) lighting and HVAC. They are detailed in the following sub-sections.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
14
4.2.2.1 Warehouse building
The warehouse building is one of the factors that contribute most to the consumption of energy and
natural resources, since buildings need energy throughout their life cycle, from the construction up to
demolition (Rai et al, 2011). Energy usage is also related to climate due to the connection between
outside condition and warehouse temperature; in summer, the warehouse building energy
consumption can increase for over 100% during the daytime (Huang & Gurney, 2016). Papers
focusing on warehouse building have evaluated issues related to GHG emissions reductions, building
design and optimal use of material installed. In this context, Daheng (2010) proposed the use of grey
relational analysis, a distinct method for multiple decision problems, to optimize the design scheme
of a warehouse building to achieve energy savings. Eleven quantitative Green Building Evaluation
(GBE) indexes were evaluated, including building orientation, roof insulation and natural ventilation.
In addition, as these indicators were found insufficient to evaluate the whole sustainability of the
warehouse building, four economic and management factors were added. The grey relation analysis
evaluated and weighted the value of each factor giving as output a ratio that means how that factor
influences the energy savings in the warehouse. Cook & Sproul (2011) examined a retail warehouse
building defining techniques through the adoption of a simulation software for assessing the energy
requirements of a warehouse. The simulation results showed that lighting is the main contributor to
energy consumption. Applying various changes to the warehouse building, such as insulation,
sawtooth roof, selective glazing, natural ventilation, renewal of lamps, and lighting controls, the
simulation demonstrated a reduction of energy consumption of up to 73%. Similarly, Rai et al. (2011)
studied operational solutions to reduce the life cycle emissions of a distribution warehouse and
evaluated how different design decisions and building materials, in particular insulation, affect GHG
emissions. A conventional distribution centre located in the UK was taken as example to assess the
building emissions in a time span of 25 years for three different scenarios: low, medium and high
insulation. Pudleiner & Colton (2015) developed a model to quantify the effects of design parameters
on warehouse building energy consumption using the Net-Zero Energy building design process. This
means for every unit of energy consumed by the building it must, in turn, create a unit of energy. The
parameters studied were external wall insulation, window properties, infiltration and natural
ventilation, lighting and equipment. Their relative importance has been investigated through a case
study in a vaccine warehouse in which a set of building control (evaporator fan, lighting, defrost, plug
loads, thermostat) and architectural design parameters were studied through a Monte-Carlo
simulation and sensitivity analysis. Using multivariate regression, the sensitivity of the building
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
15
energy consumption to each design parameter was evaluated. It was shown that the building control
parameters have a greater impact on reducing the energy consumption than architectural design
parameters in this case. Fikiin et al. (2016) outlined Renewable Energy Sources (RES) integration
opportunities for the food refrigeration sector, explaining how a conventional refrigerated warehouse
for chilled and frozen products may be converted into a smart energy hub by employing an innovative
cryogenic energy storage technology that allows the sustainability of industrial food refrigeration to
be enhanced. Accorsi et al. (2017) proposed a multi-objective model for warehouse building design
applied to a warehouse for a food and beverage company, with the aim to define the most efficient
sizing (width, length and height) and minimize the cycle time, total cost, and carbon footprint of the
warehouse during its lifetime. The model tried to balance building dimensions, storage capacity and
material handling performance by Pareto frontier analysis. The final solution was achieved with an
empirical rule to select the Pareto points, which leads to a specific building dimension that pointed
out a global cost rise of 0.23% and 0.03% carbon footprint rise against their specific single-objective
optimum. Ries et al. (2017) used empirical data from the US to analyse how different warehouse
design factors, such as building features and technologies, affect GHG emissions. A simulation study
of three different warehouses was proposed. A factorial long-term analysis identified specific
scenarios that allow reducing warehouse emissions. Seifhashemi et al. (2018) paid attention to retail
buildings that contribute to 35% of energy use in Australia. Many of these buildings are single-storey
warehouses, in which the prevailing heat load comes through the roof. Thus, the authors developed a
computational method to appraise how the adoption of cool roofs influence warehouse energy
consumption. The results showed that this technology has multiple benefits in terms of reduction of
energy consumption and energy cost saving, enhances the thermal comfort of the building, and
decreases the cooling energy demand of warehouses across all climatic zones evaluated.
4.2.2.2 Lighting and HVAC
The materials installed is a further element affecting the GHG emissions of warehouse buildings. In
an eco-friendly perspective, one of the primary energy efficiency initiatives is the adjustment of
lighting and heating systems of warehouses (Colicchia et al., 2013), not forgetting air condition and
ventilation in warmer environments (Fichtinger et al., 2015). Strategies for lighting and HVAC
tailored to reduce the environmental impact of warehouses are also mentioned.
Dhooma & Baker (2012), for example, provided a case study of energy usage by warehousing end-
use consumption types (i.e. lighting, equipment, HVAC and plug loads), designed a framework to
identify energy saving opportunities and applied it to four warehouses operating in the same sector.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
16
The outcomes of the application highlighted how energy may be stored in the end-use consumption
type particularly for HVAC and lighting. Similarly, Dadhich et al. (2015) identified direct and indirect
lifecycle GHG emissions within a plasterboard warehouse using a hybrid life cycle assessment (LCA)
technique. The results showed that the highest carbon emissions of warehousing activities arise from
handling operations, due to the use of diesel forklifts to load, unload and store the plasterboard, and
from electricity consumption due to lighting. Two different options were proposed for reducing
warehouse related GHG emissions. First, the implementation of cross-docking principles was
suggested, in which the plasterboard would not be stored in the warehouse for more than 24 hours.
Second, the adoption of housekeeping (namely turning off lights when not needed), renewable energy
sources (especially wind energy), LPG forklifts rather than diesel ones and lamp renewal, was
identified to reduce warehouse cost and GHG emissions as well. Freis et al. (2016) introduced a
systemic approach that can be used to evaluate the energy demand and GHG emissions of logistics
facilities considering different parameters, such as material handling, heating system and insulation.
The authors developed mathematical models to derive the energy demand and presented the results
of a case study grounded on three different warehouse types, i.e. manual, semi-automated and fully
automated. The results showed that in a manual warehouse, heating and cooling systems require most
of the energy, while in a semi- and fully automated warehouse the most significant quota of energy
comes from the material handling equipment.
4.2.3 Energy saving in warehousing
The energy saving in warehousing macro-theme is split into two sub-themes, i.e.: a) manual
warehouses and material handling equipment; and b) automated storage and retrieval systems. The
specific issues addressed are detailed in the sub-sections that follow.
4.2.3.1 Manual warehouses and material handling equipment
Manual warehouses, often known as “man-to-goods” warehouses, are equipped with storage and
racking systems. Human workers are employed and responsible for warehouse operations. Even if
these systems are denoted as “manual”, these warehouses may use conveyors and forklifts. In this
context, Makris et al. (2006) studied an order picking problem focusing on the trade-off between
energy consumption and travel time and developed a routing algorithm based on the Travelling
Salesman Problem (TSP) that minimizes the energy consumption. Zajac (2011) developed a
mathematical model to enhance the energy efficiency for the process of transporting goods within
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
17
warehouses considering the energy consumption for lifting and transporting pallets as well as for
forklifts travelling. Similarly, Ene et al. (2016) proposed a genetic algorithm (GA) to solve the order
batching and routing problem with a bi-objective approach minimizing travel time and energy
consumption in a manual warehouse. As input, the picking lists, warehouse layout features and
picking equipment’s features (speed, unit energy consumption) are required. To evaluate the
performance of the model, different numerical examples were evaluated. The results of the GA
showed that both batching and routing optimization as well as energy savings can be achieved; the
latter is higher in case of an increasing number of orders. Recently, Burinskiene et al. (2018) identified
the main areas of energy and time waste in a warehouse by first creating an illustrative waste
generation pyramid and then developing a mathematical model to improve the efficiency of
warehouse processes, with a particular attention to replenishment and order picking The authors used
simulation to study the effectiveness of the developed model. The results revealed that the location
of high-volume products should be as closest as possible to their storage area to achieve the highest
total distance savings, meaning a noticeably reduction of energy needed.
Besides the order picking, material handling equipment such as forklifts used to lift and transport
materials are typical elements of manual warehouses. The carbon footprint generated by forklift
activities depends on energy consumption and on the time required to complete a process. Fekete et
al. (2014) proposed an applicable process of energy monitoring in material handling processes
developing key performance indicators for technological, organizational and economic perspectives.
The paper follows the scheme of SECA (Standardised Energy Consuming Activities) about the
energy monitoring of material handling processes. The results of a case study indicated that the major
consumptions in warehouses come from illumination (36%) and material handling equipment (32%),
and that the cost of energy consumption affects the total cost of the material handling process for up
to 7%. Boenzi et al. (2015) developed a decision support system based on an iterative non-linear
integer programming model to minimize the GHG emissions of material handling activities in
warehouses. The model considered the emissions of forklifts in transport, picking, storing and
retrieving processes and derived the storage configuration as well as the forklift type that minimize
the energy required during the material handling operations. In another study, Facchini et al. (2016)
built a simulation model for forklift selection taking into account the technical characteristics of the
main forklift models and their GHG emissions to prove the statement of Johnson (2008) (“the carbon
footprints of electric and LPG fork-lifts are, in principle, about equal, while in actual practice, LPG
footprint is smaller than that of electricity”). Two forklift fuel supplies were considered, i.e. fossil
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
18
fuel and electricity. The simulation model was used to evaluate the energy and time required by a
forklift in each phase of the inbound logistics activities. To evaluate the effectiveness of the developed
tool, the model was applied to a full scale numerical case. Simulation results showed that electric
powered forklifts are preferable for low/mid weight units and with lower carbon footprint than the
fuel powered forklifts.
The way in which pallets are used and managed throughout their life-cycle is a further aspect that can
influence the integration of environment into strategic decision-making (Bilbao et al., 2011) and
warehouse carbon footprint as well. In this regard, Carrano et al. (2015) provided an overview of
three different pallet management strategies and the resulting GHG emissions. They developed a
mathematical model that determines the optimal pallet management strategy and load duty batch
policy mix to reduce the carbon footprint. The authors evaluated three strategies, i.e. single-use
expendable pallet, buy-sell program and leased pallet pooling. They provided a detailed comparison
among these strategies in terms of their GHG impact. The results suggest that for shorter distribution
distances the best strategy is pallet pooling while for longer distances the lighter pallet under the
single-use expendable strategy is optimal.
4.2.3.2 Automated storage and retrieval systems (AS/RSs)
Due to global awareness of climate change and the corresponding increase of electricity prices in the
late years (Nia et al., 2015), the consideration of energy efficiency of AS/RSs has more and more
become a turning point towards sustainable warehousing.
4.2.3.2.1 Control of AS/RS
Papers falling in this category deal with the control of AS/RSs from a sustainable perspective.
Meneghetti & Monti (2015) suggested an optimization model for the design of a sustainable cold-
storage AS/RS considering investment cost, operating cost, energy usage, and GHG emissions.
Specific characteristics such as maintaining the desired storage temperature and rack structure based
on surfaces and volumes of the cold cell were optimized to minimize the total yearly cost, energy use
and GHG emissions of the warehouse. More specifically, Fichtinger et al. (2015) developed an
integrated simulation model for a multi-item inventory-warehouse system for evaluating the
interactions between inventory management, warehouse management, and the related GHG
emissions. The results demonstrated that some decisions, such as time or reorder quantities have a
notable impact on costs and emissions, and, for the scenarios investigated, AS/RS have lower GHG
emissions compared to wide- and narrow-aisle warehouses. Otherwise, Wang et al. (2016) focused
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
19
on reducing energy consumption and optimizing cable force for a cable-driven parallel mechanism
used in AS/RS. An optimization method was developed to achieve the minimal cable force
distribution and thus also the minimum overall energy consumption. By means of a kinematic model,
the authors demonstrated that cable-driven parallel mechanism has better performance such as high
speed, high space usage and low energy consumption than the traditional stacker which, for instance,
cannot move towards horizontal and vertical directions simultaneously. Nia et al. (2017) developed
a simulation model for GHG efficiency dealing with dynamic sequencing in unit-load multiple-rack
AS/RS system, considering energy consumption by MMHE and extra costs (penalty and tax) based
on the GHG consumption produced by the whole equipment in the warehouse. The model is an integer
programming which embodies two meta-heuristic algorithms, i.e. an ant colony optimization,
employed to identify a near-optimum to minimize the energy cost, and a GA, used to validate the
result achieved as no benchmark was available in the literature. Twenty numerical examples were
used to prove the developed algorithms. The results showed that GA performs better in terms of total
cost, time and GHG cost in order to reduce tax and penalty cost.
4.2.3.2.2 AS/RSs and storage assignment
There is also literature available that derives storage assignment methods for AS/RSs considering
energy consumption. One example is the work of Meneghetti & Monti (2013), who studied how the
movements of a crane in an AS/RS can be designed to be energy efficient. By means of simulation,
the authors evaluated how performance levels change when the traditional minimum picking time
strategy is replaced by the minimum energy strategy. The authors focused on the different behaviours
of picking time and energy recorded when the position of the crane is idle. The results demonstrated
that for AS/RSs with dedicated storage assignment and replenishment, the movement of the crane to
the gravity centre of the rack during idle time leads to reduced picking time but also to increased
energy consumption comparing with staying at the I/O point. Otherwise, Meneghetti & Monti (2014)
investigated how adopting proper storage assignment policies in AS/RSs can lead to energy
efficiency, highlighting the role of weight unit loads through the introduction of assignment
strategies. The results displayed how multiple weight unit loads affect potential energy savings with
respect to single-weight systems. As far as in multiple weight unit load racks heavy products are
indicated to fill the lower places, whereas light products increase their gravitational energy through
filling upper places. In a follow-up paper, Meneghetti et al. (2015b) studied a time-based and an
energy-based AS/RS control policy model, paying attention to the rack shapes which play a relevant
role in terms of energy consumption. The authors conducted a simulation study for both storage
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
20
assignment and operation sequencing with multiple weight loads comparing how five different rack
shapes affected energy and time performance. The results showed that the energy requirement of a
crane is related to the height of the rack, where for a given storage capacity the best energy efficiency
in the simulation was achieved by intermediate height class rack shapes. Bortolini et al. (2017) solved
a storage assignment problem using a time and energy bi-objective model for a single-deep stationary
rack in a unit-load AS/RS. The model minimizes the energy consumed by cranes and the travel time
simultaneously, considering a unit-load size capacity. A case study was carried out to redesign the
assignment strategy in the automatic warehouse of a beverage company.
4.2.3.2.3 Comparison of different AS/RSs
Another issue investigated in the literature refers to the different kinds of AS/RSs. One of this is the
paper of Lerher et al. (2014), who proposed an energy efficiency model for mini-load AS/RS. The
authors considered throughput capacity, velocity profile and engine power of the S/R machine with
the hoisted carriage and energy consumption including related GHG emissions. Comparing these
various models of mini-load AS/RS, the results showed that energy consumption as well as GHG
emissions increase with raising velocity of the AS/RS. Similarly, Meneghetti et al. (2015a) proposed
a constraint programming model for evaluating the impact of different rack shapes in a single unit-
load aisle-captive AS/RS with one crane per aisle considering warehouse cost and carbon footprint.
Two distinct models were proposed to point out the optimal rack configuration based on both cost
and GHG emissions minimization. The evaluation of these two models allowed to recognize potential
trade-offs referring to sustainable and cost perspectives. Tappia et al. (2015), for example, proposed
an evaluation of energy consumption in terms of GHG emissions of automated warehousing solutions
through a comparison between autonomous vehicle storage and retrieval systems (AVS/RSs) and
AS/RSs. Their results underlined that AVS/RS technology has lower costs compared to AS/RSs.
However, the authors also pointed out that there is no assessment method in the literature available
for evaluating the emergent trade-off between the environmental and economic perspectives in
warehousing. Differently, Stöhr et al. (2018) noted that benchmarking procedures of material flow
systems, such as conveyors, have been investigated in detail providing methods and procedures to
rate energy efficiency, while for AS/RSs not. For this purpose, the authors provided a benchmarking
method to rate the energy efficiency of three AS/RSs, i.e. miniload-crane, horizontal carousel and
shuttle systems.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
21
4.3 Keyword analysis
A keyword analysis was carried out to substantiate the findings about the key research topics of GW.
To this end, the keywords were exported during the database search. The 38 papers of the sample
contain 161 different keywords in total. It is usual, however, that authors use slightly different
keywords to express a similar (or the same) concept. Thus, the keywords were manually screened to
identify similarities and were classified into groups, which are shown in
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
22
Table 3 together with the author’s keywords and the relating frequency.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
23
Table 3 highlights that the most recurring groups of keywords deal with carbon emissions, green
logistics, material handling and energy management.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
24
Table 3: Topics, authors’ keywords and frequency.
Keyword group
Authors’ keywords (frequency)
Total
Carbon footprint
carbon (1), carbon emission (3), carbon equivalent emissions (1), carbon
footprint (4), CO2 emission (1), CO2 emissions (1), CO2 emission reduction (1),
cap-and-trade emission policy (1), low carbon operations (1), low-carbon
economy (1), greenhouse gas emissions (2), greenhouse gases (1), GHG
protocol (1)
19
Green logistics
green logistics (3), leased pallet pool (1), logistics (3), logistic center (1),
logistic facilities (1), logistics planning (1), sustainable logistics (3), transport
(1), transhipment terminal (1), unit load weight (1), wood pallets (1)
17
Material handling
cable-drive parallel mechanism (1), cranes (1), horizontal carousel (1), material
flow system (1), material handling (4), material handling equipment (1),
material handling process (1), mini-load (1), mini-load system (1), rack shape
(1), shuttle (1), S/R machines (1), warehouse operations (1)
16
Warehousing
distribution warehouses (1), refrigerated warehousing (1), warehouse (3),
warehouse management (3), warehousing (7)
15
Energy efficiency
energy efficiency (8), energy efficient (1), energy saving (1), energy
conservation (1), minimization of energy consumption (1), net-zero energy (1),
renewable energy (1)
14
Energy
energy (2), energy audit (1), energy consumption (2), energy monitoring (1),
energy plus (2), energy storage (1), standardized energy consuming activity (1),
energy load profile (1), energy model (1)
12
Storage
allocation scheme (1), cycle time (1), dwell-point policies (1), dual command
cycle (1), dual command cycles (1), inventory management (3), storage
assignment (2), storage location assignment (2)
12
Automated
warehouses
automated storage and retrieval system (2), automated storage and retrieval
systems (2), automated warehouses (3), automated warehouse system (1),
automatic storage/retrieval system (1), AS/RS (2)
11
Warehouse
building design
acoustics (1), building simulation (1), retail building (1), construction industry
(1), cool roof (1), design (1), design builder (1), natural ventilation (1), green
design (1), life cycle assessment (1), warehouse building design (1)
11
Sustainability
environmental sustainability (2), green technology investment (1), sustainable
development (1), sustainable modelling (1), sustainable solutions (1),
sustainability (4)
10
Green warehousing
green warehouse (1), green warehouse management (1), green warehousing (4),
sustainable warehouse (1), sustainable warehouse management (1)
8
Green supply chain
food supply chain (1), green supply chain (1), green supply chain management
(1), supply chain (1), sustainable supply chain (1), sustainable supply chain
management (1)
6
Order picking
order picking (1), order-picking (1), order-picking methods and technologies
(1), picking time (1), routing (1)
5
Environmental
performance
indicators
Environmental performance indicators (1), key performance indicators (1),
performance analysis (1)
3
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
25
4.4 Citation, PageRank and co-citation analysis
Citation analysis is a way to assess scientific and academic productivity (Price, 1965). The number
of citations that one paper receives usually reflects its influence and impact, as well as the quality of
the paper cited (Wade, 1975). Citation analysis has been used in research to determine the stage of
development of a research field. Indeed, citations provide insight into how an author’s work is
positioned in relation to others and indicate how a research topic is developing (Bontekoning et al.,
2004).
To this end, local citations (LCs) and global citations (GCs) were evaluated in this paper. LCs refers
to the number of citations a paper has received by other papers within the sample of papers reviewed.
GCs, instead, refers to the total citations received by a paper by other papers indexed in a given
database, e.g. Scopus (Fahimnia et al., 2015). As a complementary aspect, the number of GCs or LCs
per year was evaluated; this is especially relevant for recently published papers that have not received
a high number of citations so far (Fahimnia et al., 2015). These results are shown in Table 4. In terms
of GCs, it can be seen that a few publications (i.e. Rai et al., 2011; Dadhich et al., 2015; Lerhrer et
al., 2014) have been more cited than others; however, the number of GCs is still limited. Moreover,
a relevant gap exists between LCs and GCs, with a peak for the papers by Lerher et al. (2014) for
which the difference between the GCs and LCs totals 20. This implies that GW papers have frequently
been cited by researchers working on more general disciplines such as GSCM, while they have been
less frequently referenced by researchers in the specific GW field. This, in turn, could suggest that if
excluding international research collaborations, in general GW researchers are not totally aware of
each other’s work yet, which is in line with the newness of the GW field. Similar considerations hold
true for the number of GCs or LCs per year. From an aggregated perspective, energy saving in
warehousing is the macro-theme that received the highest number of GCs (214) and LCs (43),
followed by environmental impact of warehouse building (97 GCs and 12 LCs) and green warehouse
management (45 GCs and 9 LCs).
A citation analysis might sometimes be biased by authors that cite their own papers, or by citations
that are actually negative and thus criticize a paper rather than underlying its relevance (Geng et al.,
2017); hence, it could not always reflect the exact value of a paper. Moreover, simply measuring the
number of citations does not take into account the relevance of the citing papers (Ding et al., 2009).
To overcome these issues, it is advisable to distinguish between the popularity of a paper, i.e. the
number of citations received, and the prestige, i.e. the number of times a paper is cited by highly-
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
26
cited papers. Although it is not always true that a highly-cited paper is also a prestigious paper, in
many cases these aspects turn out to be correlated (Fahimnia et al., 2015). The PageRank algorithm
(Brin & Page, 1998), originally conceived to evaluate the importance of a hyperlink on websites,
considers both measures. A PageRank measure for the papers reviewed was obtained using Gephi
and R software packages. Gephi was used to build a citation analysis network composed of 356 links
and 275 nodes, reflecting the papers that cited at least one of the 38 papers reviewed, and to evaluate
the importance of the connection between each pair of nodes in the network. The weight of each node
was set at the number of citations received by the paper. The PageRank score was obtained with R,
using a matrix composed by the cited papers in Gephi. The main reason for the use of R grounds on
its greater accuracy in defining the PageRank score compared to Gephi. Table 5 lists in the last
column the PageRank score of each paper. For instance, the PageRank score shows almost the same
results as the citation analysis for the paper by Rai et al. (2011). On contrary, the paper by Lerhrer et
al. (2014) is frequently cited (highest GC/Y scores) but has a lower PageRank score than the study
by Rai et al. (2011), which instead has lower GC/Y. Similar considerations can be derived by
comparing the PageRank score and GC/Y for the papers by Fichtinger et al. (2015) and Meneghetti
and Monti (2013). To sum up, papers with highest PageRank scores are not always the ones with the
most citations. More probably, these papers received citations from highly-cited papers, which
increase their prestige. The highest PageRank was observed for two papers categorised into the
environmental impact of warehouse building macro-theme. As PageRank takes into account the
prestige of a paper, it could be argued that these papers are particularly prominent in the GW field.
From an aggregated perspective, the average PageRank value is similar for the environmental impact
of warehouse building and energy saving in warehousing macro-themes (≈0.010), while it is lower
for the green warehouse management macro-theme. In terms of authors, Meneghetti & Monti appear
as the most influential researchers in the GW field, with three papers in the top-10 by PageRank.
Table 4: Classification of the papers based on their PageRank.
#
Authors
GCª
GC/Yᵇ
LCc
LC/Yd
PageRank
GW macro-theme
1
Rai et al. (2011)
33
4.13
6
0.75
0.0362122
Environmental impact of
warehouse building
2
Dadhich et al. (2015)
29
7.25
1
0.25
0.0325672
Environmental impact of
warehouse building
3
Meneghetti & Monti (2013)
20
3.33
10
1.67
0.0308710
Energy saving in warehousing
4
Lerhrer et al. (2014)
34
6.80
4
0.80
0.0300426
Energy saving in warehousing
5
Makris et al. (2006)
11
0.85
4
0.31
0.0198320
Energy saving in warehousing
6
Meneghetti & Monti (2015)
25
6.25
2
0.50
0.0171868
Energy saving in warehousing
7
Meneghetti & Monti (2014)
12
2.40
6
1.20
0.0146662
Energy saving in warehousing
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
27
8
Tan et al. (2010)
11
1.22
5
0.56
0.0144180
Green warehouse management
9
Fichtinger et al. (2015)
17
4.25
4
1.00
0.0135826
Energy saving in warehousing
10
Zajac (2011)
16
2.00
2
0.25
0.0135213
Energy saving in warehousing
11
Carrano et al. (2015)
12
3.00
1
0.25
0.0113799
Energy saving in warehousing
12
Chen et al. (2016)
10
3.33
0
0.00
0.0105936
Green warehouse management
13
Ene et al. (2016)
7
2.33
0
0.00
0.0105936
Energy saving in warehousing
14
Đukić et al. (2010)
13
1.44
1
0.11
0.0092694
Green warehouse management
15
Dhooma & Baker (2012)
8
1.14
3
0.43
0.0081279
Environmental impact of
warehouse building
16
Meneghetti et al. (2015b)
13
3.25
3
0.75
0.0080169
Energy saving in warehousing
17
Bortolini et al. (2017)
10
5.00
2
1.00
0.0075590
Energy saving in warehousing
18
Pudleiner et al. (2015)
5
1.25
1
0.25
0.0068662
Environmental impact of
warehouse building
19
Fekete et al. (2014)
6
1.20
1
0.20
0.0067451
Energy saving in warehousing
20
Tappia et al. (2015)
15
3.75
3
0.75
0.0066041
Energy saving in warehousing
21
Cook & Sproul (2011)
6
0.75
1
0.13
0.0062593
Environmental impact of
warehouse building
22
Meneghetti et al. (2015a)
6
1.50
1
0.25
0.0054209
Energy saving in warehousing
23
Daheng (2010)
2
0.22
0
0.00
0.0052968
Environmental impact of
warehouse building
24
Żuchowski (2015)
5
1.25
2
0.50
0.0047588
Green warehouse management
25
Amjed & Harrison (2013)
5
0.83
1
0.17
0.0046347
Green warehouse management
26
Freis et al. (2016)
4
1.33
0
0.00
0.0044140
Environmental impact of
warehouse building
27
Boenzi et al. (2015)
4
1.00
0
0.00
0.0039726
Energy saving in warehousing
28
Fikiin et al. (2016)
5
1.67
0
0.00
0.0039726
Environmental impact of
warehouse building
29
Rüdiger et al. (2016)
1
0.33
0
0.00
0.0039726
Green warehouse management
30
Ries et al. (2017)
1
0.50
0
0.00
0.0039726
Environmental impact of
warehouse building
31
Accorsi et al. (2017)
3
1.50
0
0.00
0.0033105
Environmental impact of
warehouse building
32
Stöhr et al. (2018)
1
1.00
0
0.00
0.0030898
Energy saving in warehousing
33
Bank & Murphy (2013)
0
0.00
0
0.00
0.0026484
Green warehouse management
34
Facchini et al. (2016)
3
1.00
0
0.00
0.0026484
Energy saving in warehousing
35
Wang et al. (2016)
1
0.33
0
0.00
0.0026484
Energy saving in warehousing
36
Nia et al. (2017)
1
0.50
0
0.00
0.0026484
Energy saving in warehousing
37
Burinskiene et al. (2018)
0
0.00
0
0.00
0.0026484
Energy saving in warehousing
38
Seifhashemi et al. (2018)
1
1.00
0
0.00
0.0026484
Environmental impact of
warehouse building
(note: ª Global citations: Scopus citations; ᵇ GC/Y: Global citations divided by the number of years from publication; c Local citations: citations within the 38
papers reviewed; ᵈ LC/Y: Global citations divided by the number of years from publication)
Co-citation analysis is a tool used for studying the relationship between different papers based on
their citations. According to Hjørland (2013), the papers that are often cited together in other papers
are likely to be semantically related and belong to the same field of research. This analysis is therefore
useful to strengthen the classification of the studies proposed previously. Moreover, co-citation
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
28
analysis is typically used to identify the number of authors and key authors in a discipline, based in
the citations of their works (Garfield, 1979). To carry out the co-citation analysis, we checked if two
or more papers in the sample analysed were cited together in the reference list of each paper in the
sample. Overall, 23 out of 38 papers were co-cited by other papers. The co-citation analysis of these
papers was again carried out using Gephi, to obtain a visual representation of the co-citation map.
The positions of the nodes (papers), which all have identical size, in the citation map (Figure 4) was
generated randomly. The links represent the co-citations. For visualization purpose, the most
connected nodes, which represent the most influential papers in the GW field, move to the centre of
the figure, while the less connected ones move to the borders (Fahimnia et al., 2015). The most
influential papers mainly belong to the energy saving in warehousing macro-theme, while only one
paper belongs to the green warehouse management macro-theme. Moreover, the papers at the centre
of the figure analysed similar topics, relating to material handling equipment, lighting and HVAC.
Figure 4: Co-citation map.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
29
4.5 Modularity analysis
A data clustering tool called “modularity” was finally used to group the GW topics and to position
the GW research field with respect to the existing literature. With this tool, the nodes of a network
can be decomposed into communities for underlining which nodes are highly interconnected among
them in a research area (Blondel et al., 2008). The modularity algorithm was applied to the 23 nodes
network resulted from the co-citation mapping with Gephi. Starting from the consideration that
modularity assigns a number to each paper so that highly interconnected papers receive the same
number, a modularity index in Gephi defined four clusters (Figure 5) representing four different
research fields based on the co-citation network. The composition of each cluster varies from 4 papers
in cluster 4 to 7 papers in cluster 2, which is the largest group identified. Table 5 lists the different
macro-themes investigated and the PageRank average scores by each cluster.
Table 5: Macro-themes and PageRank’s average scores.
Macro-themes
Publications
PageRank Average
Green warehouse management
Environmental impact of
warehouse building
Energy saving in warehousing
Cluster 1
6
0.0133362
2
3
1
Cluster 2
7
0.0142385
1
-
6
Cluster 3
6
0.0127455
2
1
3
Cluster 4
4
0.0174138
-
1
3
As a first outcome, the analysis confirms that GW research is widely multidisciplinary, as can be seen
in the presence of different macro-themes in each cluster. Clusters 1 and 3 have closer
interconnection, being both focused on building features, lighting and MHE. Cluster 2, on the other
hand, focuses strictly on MHE and only thereafter on HVAC. Cluster 4 is the smallest group and
connections with the remaining clusters are limited; nonetheless, it is also the cluster with the highest
PageRank average score. As a second point, the modularity analysis shows that the four clusters share
at least one topic and confirms that the top GW research topic is material handling equipment,
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
30
followed by building features, sustainability indicators and guidelines, lighting and HVAC. Overall,
the research topics and macro-themes investigated in GW research can be positioned at the crossroads
among GSCM practices, GHG management, decision support systems in green practices within
supply chains and TBL.
Figure 5: Modularity map.
5 Discussion
5.1 Critical analysis of the GW literature
The review of the 38 papers on GW and the analyses made in the previous sub-sections led to the
following implications. Firstly, the relevance of GW has grown in time, moving from a first decade
where the number of annual publications was very limited, to the last years (i.e. since 2014) where a
significant increase in the number of publications has been observed. This trend reflects the increasing
attention paid by different stakeholders to concerns about climate change worldwide and shows that
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
31
initiatives aiming to reduce GHG emissions in the warehousing field are gaining attention in the
scientific community.
In line with this consideration, energy saving in warehousing emerged as the macro-theme that has
most frequently been studied (52.6% of the papers reviewed – see Table 2), followed by
environmental impact of warehouse building (30.0%) and green warehouse management (18.4%).
The evolution in time of the three macro-themes, shown in Figure 6, highlights that energy saving in
warehousing is also the single macro-theme analysed since 2006. Environmental impact of
warehouse building and green warehouse management were studied since 2010, probably motivated
by the need of green practices relating to the structural development of warehouse building. A further
relevant point is that green warehouse management was not considered in research papers published
in the last two years, meaning that the most recent research was not so focused on sustainability
indicators and guidelines within warehouses. Probably, authors were more focused on energy
efficiency of end-use types.
Figure 6: Macro-themes pattern over the years.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
32
In terms of relevance, energy saving in warehousing also got the highest number of citations per year,
probably due to the fact that this macro-theme encompasses the majority of sample papers and
therefore a greater number of researchers put efforts into this GW macro-theme. This insight is
confirmed by citations (global and local) and the average PageRank value (see Table 4).
Looking at the methodology adopted, analytical models are the most frequently employed tools in
GW literature (36.8%), followed by simulation methods (34.2%) and conceptual studies (18.4%);
only 10.5% of the papers are case studies. By correlating the methodology with the macro-theme
addressed, as done in Figure 7, it emerges that energy saving in warehousing has been evaluated
primarily through analytical and simulation models; conversely, green warehouse management was
studied mainly by means of conceptual research (more than 70% of the papers). The few case studies
available in literature refer to the environmental impact of warehouse building macro-theme.
Moreover, case study papers are rather recent, as the first paper was published in 2012 (cf. Table 2);
this might be due to the fact that up to few years ago there were not so many concrete examples of
GW to be examined in-depth. At the same time, the presence of some case studies indicate that
influential considerations can be derived from the direct analysis of in-field applications of GW
principles (when available). Indeed, the results from case study papers (e.g. Dhooma & Baker, 2012;
Rüdiger et al., 2016) demonstrate that GW is becoming increasingly important for the competitive
success of companies, as the market pays more and more attention to environmental aspects. This
achieved awareness is a sign of maturity of companies which have started a path focused on the
reduction of warehouse carbon footprint.
Figure 7: Methodology used vs. macro-theme.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
33
According to the classification in Table 2, the topics most frequently debated in literature are material
handling equipment and warehouse building features (30.9% and 24.7% of the reviewed papers
respectively). Further topics of interest are lighting (17.3%), HVAC (12.3%), general sustainability
indicators and guidelines (9.9%) and TBL (2.5%). The keyword analysis (
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
34
Table 3) and modularity map (Figure 5) confirm the role of material handling equipment as key GW
topic. Keyword analysis, at the same time, highlights carbon emissions as the most recurring subject.
This outcome suggests that researchers have begun to put a spotlight on warehouse emissions, quality
of environmental management alongside with the operations development within warehouses.
Similarly, the modularity map indicates building features, sustainability indicators and guidelines,
lighting and HVAC as key topics of GW literature, addressed by different clusters of papers.
Warehouse building design in particular was often discussed with respect to the adoption of
environmental guidelines.
Among the less investigated GW topics, instead, cap-and-trade emission policies and environmental
certifications (1.2% of the reviewed papers each) received very limited attention. The reason for this
trend might be that the knowledge about these two issues has raised only in the last years.
Nonetheless, cap-and-trade emission policy affecting warehouse decisions and performance with its
carbon emissions cap and the trading prices of carbon emissions may help companies undertake
optimal operational and investment decisions on GW towards greater economic and environmental
performance (Chen et al., 2016).
5.2 Contribution of this work
From a scientific perspective, the primary goal of any literature review is to support readers in
assessing the available research and the state of knowledge on a specific topic, summarizing the
existing research, analysing the content of the papers and underlining strengths and weaknesses of
the selected literature. In line with this consideration, this review classifies the literature on GW,
summarizes the key research topics and creates the basis for future research activities in this field.
Moreover, this review delineates the emerging research field of GW, which was not covered in a
dedicated literature analysis so far. As the number of publications on GW has grown significantly in
the last years, it can be argued that research in this field will certainly become mature and more
knowledgeable in the next years and will offer useful outcomes for greener supply chains. Therefore,
the data provided in this review are expected to consolidate the available knowledge about GW and
enhance researchers’ and practitioners’ awareness about warehouse sustainability.
From a more practical point of view, the outcomes of this review show that the warehouse carbon
footprint cannot be overlooked anymore and suggest reducing waste of resources such as fuel,
electricity, water and land. Some sustainable warehouses opportunities towards low-carbon
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
35
environments already exist and can be achieved without significant capital investments. For instance,
researchers have provided simulation models and sustainability guidelines aimed to reduce the GHG
emissions of warehouses, acknowledging various structural interventions, ranging from the
optimization of warehouse building design and equipment installed, up to the design of automated
solutions and procedures for an effective energy usage. Moreover, many papers have also examined
AS/RSs in order to enhance the energy efficiency derived from their usage in combination with the
reduction of energy consumption for lighting and HVAC, or with the improvement of material
handling, order picking and storage processes. A further point is that sustainability actions need to
encompass wider economic, social and environmental aspects. Cost optimization is a recurring
agenda in the sample papers as underlined by the correlations between sustainable targets,
warehousing activities and economic performance. In fact, business results should increasingly be
integrated within the relationship between the adoption of green practices and warehouse impacts
seeking either the best economic and environmental performance.
6 Future research directions and conclusion
Considering the impacts of carbon tax on company competitiveness (Lin & Li, 2011) in the next
years, green warehouse management will become an unavoidable requirement that can potentially
lead to significant improvements both in operating performance and energy efficiency,
simultaneously fostering the adoption of renewable energy. Moving from this consideration, this
paper presented a systematic literature review of 38 papers on GW, with the aim to delineate this
research field, classify the published studies and summarize available knowledge in the GW area.
Being GW a relatively recent research field, there are various opportunities for future research. Based
on the results of this review the following research recommendations (RR) can be formulated as
promising areas for research on GW:
• RR1: Looking at the macro-themes investigated in the literature, green warehouse
management seems to be less researched than the remaining two. More attention to
sustainability indexes is needed to highlight the benefits of sustainable policies within
warehouses.
• RR2: Further research is needed to encourage new investments in GW facilities taking into
account a full lifecycle approach. As only 10.5% of the sample papers developed case study
based research, more applications on real cases and data may prove how sustainable actions
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
36
can decrease the carbon footprint and energy efficiency of warehousing, thus encouraging
investments. Simulation is also a suitable method to assess energy saving opportunities in real
warehouses.
• RR3: From a methodological point of view, analytical models available in the GW literature
are rarely supported by empirical data; this might be a promising area for future research.
• RR4: In terms of research topic, many papers investigated issues limited to the field of energy-
end use types only. It might be useful to study the overall energy consumption of a warehouse,
including all end-use types. In addition, very few papers have addressed environmental
certifications and cap-and-trade emission policies of warehouses. Moreover, starting from the
available models that simulate and evaluate lighting consumption and its weight on the GHG
emissions, it might be interesting to evaluate the benefits of smart lighting systems and their
interactions with the warehouse processes (e.g. order picking routing or batching). The role
of warehousing-related emissions within the whole supply chain could also benefit from a
more accurate evaluation.
One limiting factor of this review are the criteria used to select relevant papers. Papers published in
non-peer reviewed journals, books and papers written in languages other than English were excluded,
which might have led to the case that few relevant studies have been missed. Similarly, the systematic
literature review was restricted to papers that focused on GW as the main topic covered, excluding
studies on interrelated topics (e.g. GSCM), which could, however, provide useful insights also for
GW. Similarly, the keywords used for the queries were chosen based on published research and the
authors’ assessment; although these keywords are expected to cover the various aspects of the GW
field, the use of other keywords might have led to slightly different findings. Finally, the classification
of papers according to the developed framework required some amount of judgement, which was
resolved through a discussion within the research team.
References
Accorsi, R., Bortolini, M., Gamberi, M., Manzini, R., & Pilati, F. (2017). Multi-objective warehouse
building design to optimize the cycle time, total cost, and carbon footprint. The International
Journal of Advanced Manufacturing Technology, 92(1-4), 839-854.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
37
Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable
supply chain management. Journal of Cleaner Production, 52, 329-341.
Amjed, T.W., & Harrison, N.J. (2013). A Model for sustainable warehousing: from theory to best
practices. Proceedings of the International Decision Sciences Institute and Asia Pacific DSI
Conference, 1-28.
Angel, B.W.F., Van Damme, D.A., Ivanovskaia, A., Lenders, R.J.M., & Veldhuijzen, R.S. (2006).
Warehousing space in Europe: meeting tomorrow’s demand. Prologis/Cap Gemini, Utrecht.
Aveyard, H. (2014). Doing a literature review in health and social care: A practical guide. McGraw-
Hill Education (UK).
Bank, R., & Murphy, R. (2013). Warehousing Sustainability Standards Development. IFIP
International Conference on Advances in Production Management Systems, 294-301. Springer,
Berlin, Heidelberg.
Bilbao, A., Carrano, A.L., Hewitt, M., & Thorn, B.K. (2011). On the environmental impacts of pallet
management operations. Management Research Review, 34(11), 1222-1236.
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of
communities in large networks. Journal of Statistical Mechanics: Theory and
Experiment, 2008(10), P10008.
Boenzi, F., Digiesi, S., Facchini, F., Mossa, G., & Mummolo, G. (2015). Greening activities in
warehouses: a model for identifying sustainable strategies in material handling. Annals of
DAAAM & Proceedings, 26(1), p0980-0988.
Bontekoning, Y.M., Macharis, C., Trip, J.J. (2004). Is a new applied transportation research field
emerging? - A review of intermodal rail–truck freight transport literature. Transportation
Research Part A, 38, 1-34.
Bortolini, M., Faccio, M., Ferrari, E., Gamberi, M., & Pilati, F. (2017). Time and energy optimal unit-
load assignment for automatic S/R warehouses. International Journal of Production
Economics, 190, 133-145.
Boysen, N., de Koster, R., & Weidinger, F. (2018). Warehousing in the e-commerce era: A
survey. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.08.023
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
38
Brin, S, & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer
Networks and ISDN Systems, 30, 107-117.
Burinskiene, A., Lorenc, A., & Lerher, T. (2018). A simulation study for the sustainability and
reduction of waste in warehouse logistics. International Journal of Simulation Modelling, 17(3).
Carbon Trust, (2007). Carbon footprint measurement methodology, version 1.3. The Carbon Trust,
London, UK. Retrieved October 18, 2018 from https://semspub.epa.gov/work/09/1142519.pdf.
Carrano, A.L., Pazour, J.A., Roy, D., & Thorn, B.K. (2015). Selection of pallet management
strategies based on carbon emissions impact. International Journal of Production
Economics, 164, 258-270.
CBRE, (2017). U.S. MarketFlash | Going Large: Warehouse Sizes Increase for Modern Logistics.
Retrieved January 22, 2018 from https://www.cbre.us/research-and-reports/US-MarketFlash-
Going-Large-Warehouse-Sizes-Increase-for-Modern-Logistics.
Chen, X., Wang, X., Kumar, V., & Kumar, N. (2016). Low carbon warehouse management under
cap-and-trade policy. Journal of Cleaner Production, 139, 894-904.
Colicchia, C., Marchet, G., Melacini, M., & Perotti, S. (2013). Building environmental sustainability:
empirical evidence from Logistics Service Providers. Journal of Cleaner Production, 59, 197-
209.
Cook, P., & Sproul, A. (2011). Towards low-energy retail warehouse building. Architectural Science
Review, 54(3), 206-214.
Crossan, M.M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational
innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154-
1191.
Dadhich, P., Genovese, A., Kumar, N., & Acquaye, A. (2015). Developing sustainable supply chains
in the UK construction industry: a case study. International Journal of Production
Economics, 164, 271-284.
Daheng, Y. (2010). Optimizing design scheme of energy saving in warehouse building based on grey
relational analysis. International Conference on Logistics Systems and Intelligent Management,
2, 1090-1092.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
39
de Oliveira, U.R., Espindola, L.S., da Silva, I.R., da Silva, I.N., & Rocha, H.M. (2018). A systematic
literature review on green supply chain management: research implications and future
perspectives. Journal of Cleaner Production, 187, 537-561.
Deloitte, (2014). The shed of the future – E-commerce: Its impact on warehouses. Deloitte Insight
Report. Retrieved January 19, 2018 from
https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/real-estate/deloitte-uk-shed-of-
the-future.pdf
Dhooma, J., & Baker, P. (2012). An exploratory framework for energy conservation in existing
warehouses. International Journal of Logistics Research and Applications, 15(1), 37-51.
Ding, Y., Yan, E., Frazho, A., & Caverlee, J. (2009). PageRank for ranking authors in co‐citation
networks. Journal of the Association for Information Science and Technology, 60(11), 2229-
2243.
Doherty, S., & Hoyle, S. (2009). Supply Chain Decarbonisation: The Role of Logistics and Transport
in Reducing Supply Chain Carbon Emissions. World Economic Forum, Geneva.
Đukić, G., Česnik, V., & Opetuk, T. (2010). Order-picking methods and technologies for greener
warehousing. Strojarstvo, 52(1), 23-31.
Elkington, J. (1998). Partnerships from cannibals with forks: The triple bottom line of 21st‐century
business. Environmental Quality Management, 8(1), 37-51.
Ene, S., Küçükoğlu, İ., Aksoy, A., & Öztürk, N. (2016). A genetic algorithm for minimizing energy
consumption in warehouses. Energy, 114, 973-980.
Facchini, F., Mummolo, G., Mossa, G., Digiesi, S., Boenzi, F., & Verriello, R. (2016). Minimizing
the carbon footprint of material handling equipment: Comparison of electric and LPG
forklifts. Journal of Industrial Engineering and Management, 9(5), 1035.
Fahimnia, B., Tang, C.S., Davarzani, H., & Sarkis, J. (2015). Quantitative models for managing
supply chain risks: A review. European Journal of Operational Research, 247(1), 1-15.
Federal Ministry for Economic Affairs and Energy (BMWi), (2016). Green paper on energy
efficiency. Retrieved April 17, 2018 from
https://www.bmwi.de/Redaktion/EN/Publikationen/green-paper-on-energy-
efficiency.pdf?__blob=publicationFile&v=4.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
40
Fekete, P. L., Martin, S., Kuhn, K., & Wright, N. (2014). The status of energy monitoring in science
and industry by the example of material handling processes. Business, Management and
Education, 12(2), 213-227.
Fichtinger, J., Ries, J.M., Grosse, E.H., & Baker, P. (2015). Assessing the environmental impact of
integrated inventory and warehouse management. International Journal of Production
Economics, 170, 717-729.
Fikiin, K., Stankov, B., Evans, J., Maidment, G., Foster, A., Brown, T., ... & Alvarez, G. (2017).
Refrigerated warehouses as intelligent hubs to integrate renewable energy in industrial food
refrigeration and to enhance power grid sustainability. Trends in Food Science &
Technology, 60, 96-103.
Freis, J., Vohlidka, P., & Günthner, W.A. (2016). Low-Carbon Warehousing: Examining Impacts of
Building and Intra-Logistics Design Options on Energy Demand and the CO2 Emissions of
Logistics Centers. Sustainability, 8(5), 448.
Fruchterman, T.M., & Reingold, E.M. (1991). Graph drawing by force‐directed placement. Software:
Practice and Experience, 21(11), 1129-1164.
Garfield, E. (1979). Citation Indexing. Wiley, New York.
Geng, Y., Chen, W., Liu, Z., Chiu, A. S., Han, W., Liu, Z., ... & Cui, X. (2017). A bibliometric review:
Energy consumption and greenhouse gas emissions in the residential sector. Journal of Cleaner
Production, 159, 301-316.
Glock, C.H., & Hochrein, S. (2011). Purchasing Organization and Design: a literature
review. Business Research, 4(2), 149-191.
Glock, C.H., Lange, A., Grosse, E.H., & Das, A. (2017). Celebrating the 10th volume of IJISM: a
bibliographic review and outlook. International Journal of Integrated Supply
Management, 11(4), 332-353.
Hjørland, B. (2013). Citation analysis: A social and dynamic approach to knowledge
organization. Information Processing & Management, 49(6), 1313-1325.
Hochrein, S., & Glock, C.H. (2012). Systematic literature reviews in purchasing and supply
management research: a tertiary study. International Journal of Integrated Supply
Management, 7(4), 215-245.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
41
Horvat, M., & Fazio, P. (2005). Comparative review of existing certification programs and
performance assessment tools for residential buildings. Architectural Science Review, 48(1), 69-
80.
Huang, J., & Gurney, K. R. (2016). The variation of climate change impact on building energy
consumption to building type and spatiotemporal scale. Energy, 111, 137-153.
Jabbour, A.B., Jabbour, C., Govindan, K., Kannan, D., & Arantes, A.F. (2014). Mixed methodology
to analyze the relationship between maturity of environmental management and the adoption of
green supply chain management in Brazil. Resources, Conservation and Recycling, 92, 255-267.
James, S.J., & James, C. (2010). The food cold-chain and climate change. Food Research
International, 43(7), 1944-1956.
Johnson, E. (2008). Disagreement over carbon footprints: A comparison of electric and LPG
forklifts. Energy Policy, 36(4), 1569-1573.
Kable, A.K., Pich, J., & Maslin-Prothero, S.E. (2012). A structured approach to documenting a search
strategy for publication: a 12 step guideline for authors. Nurse Education Today, 32(8), 878-886.
Kitagawa, H. (2017). Environmental Policy Under President Xi Jinping Leadership: The Changing
Environmental Norms. Environmental Policy and Governance in China, 1-15. Springer Japan.
Kumar, N., Agrahari, R.P., Roy, D. (2015). Review of green supply chain processes. IFAC-
PapersOnLine, 48(3), 374–381.
Lerher, T., Edl, M., & Rosi, B. (2014). Energy efficiency model for the mini-load automated storage
and retrieval systems. The International Journal of Advanced Manufacturing Technology, 70(1-
4), 97-115.
Lin, B., & Li, X. (2011). The effect of carbon tax on per capita CO2 emissions. Energy Policy, 39(9),
5137-5146.
Makris, P.A., Makri, A.P., & Provatidis, C.G. (2006). Energy-saving methodology for material
handling applications. Applied Energy, 83(10), 1116-1124.
McKinsey (2016). How customer demands are reshaping last-mile delivery. Retrieved February 2,
2019 from https://www.mckinsey.com/industries/travel-transport-and-logistics/our-
insights/how-customer-demands-are-reshaping-last-mile-delivery.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
42
Meneghetti, A., & Monti, L. (2013). Sustainable storage assignment and dwell-point policies for
automated storage and retrieval systems. Production Planning & Control, 24(6), 511-520.
Meneghetti, A., & Monti, L. (2014). Multiple-weight unit load storage assignment strategies for
energy-efficient automated warehouses. International Journal of Logistics Research and
Applications, 17(4), 304-322.
Meneghetti, A., & Monti, L. (2015). Greening the food supply chain: an optimisation model for
sustainable design of refrigerated automated warehouses. International Journal of Production
Research, 53(21), 6567-6587.
Meneghetti, A., Borgo, E.D., & Monti, L. (2015a). Decision support optimisation models for design
of sustainable automated warehouses. International Journal of Shipping and Transport
Logistics, 7(3), 266-294.
Meneghetti, A., Dal Borgo, E., & Monti, L. (2015b). Rack shape and energy efficient operations in
automated storage and retrieval systems. International Journal of Production Research, 53(23),
7090-7103.
NASA, (2016). NOAA Data Show 2016 Warmest Year on Record Globally. Retrieved January 22,
2018 from https://www.nasa.gov/press-release/nasa-noaa-data-show-2016-warmest-year-on-
record-globally.
Nia, A.R., Far, M.H., & Niaki, S.T.A. (2015). A hybrid genetic and imperialist competitive algorithm
for green vendor managed inventory of multi-item multi-constraint EOQ model under
shortage. Applied Soft Computing, 30, 353-364.
Nia, A.R., Haleh, H., & Saghaei, A. (2017). Dual command cycle dynamic sequencing method to
consider GHG efficiency in unit-load multiple-rack automated storage and retrieval
systems. Computers & Industrial Engineering, 111, 89-108.
Price, D.J.D.S. (1965). Networks of scientific papers. Science, 149(3683), 510-515.
Pudleiner, D., & Colton, J. (2015). Using sensitivity analysis to improve the efficiency of a Net-Zero
Energy vaccine warehouse design. Building and Environment, 87, 302-314.
Rai, D., Sodagar, B., Fieldson, R., & Hu, X. (2011). Assessment of CO2 emissions reduction in a
distribution warehouse. Energy, 36(4), 2271-2277.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
43
Rajeev, A., Pati, R.K., Padhi, S.S., & Govindan, K. (2017). Evolution of sustainability in supply chain
management: A literature review. Journal of Cleaner Production, 162, 299-314.
Rhoades, E. A. (2011). Literature Reviews. Volta Review, 111(1), 61-71.
Ries, J.M., Grosse, E.H., & Fichtinger, J. (2017). Environmental impact of warehousing: a scenario
analysis for the United States. International Journal of Production Research, 55(21), 6485-6499.
Rogelj, J., Den Elzen, M., Höhne, N., Fransen, T., Fekete, H., Winkler, H., ... & Meinshausen, M.
(2016). Paris Agreement climate proposals need a boost to keep warming well below 2
C. Nature, 534(7609), 631-639.
Rohde, R.A., & Muller, R.A. (2015). Air pollution in China: Mapping of concentrations and
sources. PloS ONE, 10(8), e0135749.
Rostamzadeh, R., Govindan, K., Esmaeili, A., Sabaghi, M. (2015). Application of fuzzy VIKOR for
evaluation of green supply chain management practices. Ecological Indicators, 49, 188–203.
Rüdiger, D., Schön, A., & Dobers, K. (2016). Managing greenhouse gas emissions from warehousing
and transshipment with environmental performance indicators. Transportation Research
Procedia, 14, 886-895.
Satolo, E.G., Lima, C.R.C., & Simon, A.T. (2013). Sustainability within Logistics Operations: a
Brazilian experience. International Refereed Journal of Engineering and Science, 2(12), 23-28.
Seifhashemi, M., Capra, B.R., Milller, W., & Bell, J. (2018). The potential for cool roofs to improve
the energy efficiency of single storey warehouse-type retail buildings in Australia: A simulation
case study. Energy and Buildings, 158, 1393-1403.
Slaper, T.F., & Hall, T.J. (2011). The triple bottom line: What is it and how does it work. Indiana
Business Review, 86(1), 4-8.
Srivastava, S.K. (2007). Green supply‐chain management: a state‐of‐the‐art literature
review. International Journal of Management Reviews, 9(1), 53-80.
Staudt, F.H., Alpan, G., Di Mascolo, M., & Rodriguez, C.M.T. (2015). Warehouse performance
measurement: a literature review. International Journal of Production Research, 53(18), 5524-
5544.
Stöhr, T., Schadler, M., & Hafner, N. (2018). Benchmarking the energy efficiency of diverse
automated storage and retrieval systems. FME Transactions, 46(3), 330-335.
This is the author’s version. Please cite as: “Bartolini, M., Bottani, E., Grosse, E.H. (2019). Green
warehousing: Systematic literature review and bibliometric analysis. Journal of Cleaner Production
226, 242-258. The final version is available at: https://doi.org/10.1016/j.jclepro.2019.04.055
________________________________________________________________________________
44
Tambovcevs, A., & Tambovceva, T. (2012). Logistic system integration with environmental
management system, a case study of international company. World Academy of Science,
Engineering and Technology, 6(8), 1-11.
Tan, K. S., Daud Ahmed, M., & Sundaram, D. (2010). Sustainable enterprise modelling and
simulation in a warehousing context. Business Process Management Journal, 16(5), 871-886.
Tappia, E., Marchet, G., Melacini, M., & Perotti, S. (2015). Incorporating the environmental
dimension in the assessment of automated warehouses. Production Planning & Control, 26(10),
824-838.
Torraco, R.J. (2005). Writing integrative literature reviews: Guidelines and examples. Human
Resource Development Review, 4(3), 356-367.
Wade, N. (1975). Citation analysis: a new tool for science administrators. Science, 188(4187), 429-
432.
Wall Street Journal (2017). Last mile package delivery must fulfill online shoppers’s quest for
convenience. Retrieved February 2, 2019 from https://partners.wsj.com/ups/last-mile-package-
delivery-must-fulfill-online-shoppers-quest-convenience/.
Wang, W., Tang, X., & Shao, Z. (2016). Study on Energy Consumption and Cable Force
Optimization of Cable-Driven Parallel Mechanism in Automated Storage/Retrieval System.
In Soft Computing and Machine Intelligence (ISCMI), 2015 Second International Conference
on (pp. 144-150). IEEE.
Wiedmann, T., & Minx, J. (2008). A definition of ‘carbon footprint’. Ecological Economics Research
Trends, 1, 1-11.
Yu, Y., Wang, X., Zhong, R.Y., & Huang, G.Q. (2017). E-commerce logistics in supply chain
management: Implementations and future perspective in furniture industry. Industrial
Management & Data Systems, 117(10), 2263-2286.
Zając, P. (2011). The idea of the model of evaluation of logistics warehouse systems with taking their
energy consumption under consideration. Archives of Civil and Mechanical Engineering, 11(2),
479-492.
Żuchowski, W. (2015). Division of environmentally sustainable solutions in warehouse management
and example methods of their evaluation. LogForum, 11(2).
