*Article* **Environmental Assessment of Innovative Paper Recycling Technology Using Product Lifecycle Perspectives**

#### **Yuya Ono 1, Masaaki Hayashi 2, Koichiro Yokoyama 2, Takehiko Okamura <sup>2</sup> and Norihiro Itsubo 1,\***


Received: 31 January 2020; Accepted: 27 February 2020; Published: 29 February 2020

**Abstract:** Paper can be reused to efficiently manage biomass consumption, meaning that it has potential as an environmentally friendly material. On the other hand, because of high energy usage during the recycling process and transportation inefficiencies, there is a call for the development of technologies that can mitigate this environmental burden. This study evaluated, from a lifecycle perspective, a new technology that can collect and recycle paper within the office. This technology can reduce by over 90% the amount of water used compared with the conventional recycled paper that is pulped and bleached once by the dry process. It also eliminates transportation from paper collection facilities to recycling factories, reducing greenhouse gas emissions. This new technology is already in use in Japan, and analyses by user data indicate that evaluation results differ greatly depending on the utilization rate of the machine. In the future, environmental information should be shared by both users and manufacturers, so that users could increase their utilization rate, and manufacturers could develop alternative bonding agents in order to further reduce the total environmental burden.

**Keywords:** LCA; paper production; CO2 emission; water consumption; water footprint; Japan; recycle

#### **1. Introduction**

Forests mitigate climate change, conserve biodiversity, lessen the risk of natural disasters, and conserve soil, thus providing diverse functionality and value. These roles that forests play are essential assets and services to living things, and thus international efforts are underway to promote sustainable forest management as well as to prevent global warming. According to the Food and Agriculture Organization of the United Nations (FAO) [1], forests covered a total of 4 billion hectares worldwide in 2015, approximately 31% of the world's land area. In the five years from 2010 to 2015, the area of forested land has increased significantly through planting in China, Australia, and other countries; however, countries, such as Brazil and Indonesia, have seen a decrease in areas covered by tropical forests—this has given a net annual reduction of 3.31 million hectares [2]. This decrease is attributable to problems, such as felling of forests to make farmland, illegal logging, and forest fires. Focusing on forest fires in particular, a total of approximately 19,900 forest fires were confirmed in Brazil's tropical rainforest along the Amazon River basin as of September 2019 with serious damage, including the loss of 43,500 km<sup>2</sup> of forest between January and August [3]. Despite the situation of global deforestation, issues with marine plastic pollution in recent years mean we have seen a focus on using paper as a replacement, with an attendant increase in demand. Many companies in Japan are using slogans that urge reductions in the usage of plastics, thus promoting the development and usage

of paper-based products. However, from the perspective of ever-decreasing forested areas, the effective usage, reuse, and recycling of paper are also important points of consideration.

Paper has a long history as a medium for transmitting information, and with printers becoming widespread, offices are using increasingly large quantities of paper. At present, Japan produces around 7.87 million tons of paper for printing and for communication paper, and around 800,000 tons of PPC (plain paper copier) paper [4]. A characteristic of paper is that as a medium, it is easier to read, understand, and find errors in information than with electronic media. Even in recent years, these characteristics have resulted in a minimal change in the amount of PPC paper production in spite of the prevalence of electronic media and the move towards a paperless society [5]. Paper used in the marketplace is actively recycled so that it can be used more effectively. The paper collection rate in Japan is around 81.6% [6], which is high when compared to other countries, but this high collection rate is primarily due to the recycling of cardboard, magazines, and newspaper, and the collection rate for shredded paper and office paper is low, at under 60% [6]. The reason for this is that office paper often has confidential information printed on it, which needs to be securely disposed of. Additionally, shredding paper reduces its transportation efficiency, and if it is shredded too finely, reuse of the paper itself becomes difficult. Recent years have seen an increase in the use of processing of paper by dissolving, after which this material is mainly reused as cardboard, with only a low proportion of it reused as paper for printing. However, in terms of energy usage, the pulp and paper industry is focusing on energy reductions, and is investing in the development of manufacturing processes that are efficient over the long term, triggered by increasing energy prices, as well as to maintain competitiveness [7,8]. In addition to the above, achieving efficiencies in energy usage is also considered to be the most cost-effective way to reduce CO2 emissions [9]. However, it is important that we recognize not only the impact of the paper manufacturing process but also that of the overall lifecycle, from the procurement of materials through to their disposal. In view of this, up until now, we have actively been using an LCA (lifecycle assessment) for paper [10–21].

LCA is a methodological tool for assessing the environmental impact associated with a process, product, or services by identifying and quantifying the energy and materials used, as well as the waste products released into the environment. Many academic papers in the early 2000s discussed energy usage during the production stage while many recent studies tend to focus on waste processes and technical innovations. Furthermore, China had not formerly carried out proper LCA until this point, but given the increased paper consumption there, we are seeing an increase in paper-related academic papers [17]. When focusing on evaluation targets, there are a range of types of evaluations, not just for paper products but also for printing paper, newsprint, and for the paper industry as a whole. Similarly, some evaluation scopes cover only the paper production stage, but there are also articles covering everything from raw materials, production, and transport, through to sales and disposal [10–12,14,17,18]. While the majority of these academic papers used the literature to determine activity data, there are some [10] that also conducted interviews with multiple factories, and have highly reliable data. Most of the annual activity data comes from the late 1990s to early 2000s. There is some variance in the results from these academic papers, but this is due to differences in the evaluation scope and selections of energy source at the manufacturing stage, as well as in the disposal methods. In addition, there are academic papers that focus not only on greenhouse gas substances but also on water consumption [22], indicating that there is a large increase in paper-related water consumption. However, simply utilizing recycled paper will not in itself necessarily reduce greenhouse gas emissions. This is because the production stage of recycled paper uses large amounts of water, and other main causes include the energy required during drying processing, the high energy consumption of air blowing during the de-inking process, and greenhouse gas emissions during the collection process.

As mentioned above, research has been underway worldwide into the environmental burden of paper, and research and development is underway into reducing this environmental burden. However, the issue of the trade-off between greenhouse gas emissions and water consumption has not yet been resolved. Given this, Seiko Epson Corporation has developed a new dry-type paper recycling

technology. This technology consists of three technologies, "defibration technology" for decomposing used paper into each one pulp fiber, "sheet forming technology" for forming fibers again into a uniform sheet, and "pressing and binding technology" for increasing the fiber density and bonding pulp fibers to each other to create new paper. As a specific aspect, it is possible to reduce CO2 emission and water consumption by this technology. Using this technology not only eliminates the need for both water disposal and drying processing, but because the machine using this technology can produce paper within the office, it also reduces the environmental burden from thte transport required during collection. The aim of this study was to use an LCA to analyze the environmental performance of this paper recycling technology.

#### *1.1. Innovative Paper Recycling Technology (Development of the Dry Paper Recycling Technology that Realizes a New O*ffi*ce Papermaking System)*

This chapter describes this newly developed dry-type used paper recycling technology. Figure 1 shows a schematic of this technology. This technology can be broadly categorized into three processes.

(1) "Defibration processing" that degrades used paper into pulp fiber.

(Figure 1 (A) Paper feeding section, (B) shredding section, (C) defibration section, and (D) selection section)

(2) "Binding processing" that mixes a bonding agent to increase strength, and then forms sheets.

(Figure 1 (E) Mixing section, (F) binding section)

(3) "Forming processing" that uses pressure and heat to form sheets of paper.

(Figure 1 (G) Pressurization section, (H) heating section, (I) cutting section)

Furthermore, processes other than those detailed above are classified as "others."

**Figure 1.** Dry-type used paper recycling technology process diagram ((1) Defibration process, (2) Binding process, (3) Forming process); (A) Paper feeding section, (B) Shredding section, (C) Defibration section, (D) Selection section, (E) Mixing section, (F) Binding section, (G) Pressurization section, (H) Heating section, (I) Cutting section, (a) Insertion section, (b) Discharge section, (o) Rotating sieve, (p) Mesh belt, (q) Suction mechanism, (x) Following process path, (y) Return path.

#### 1.1.1. Defibration Processing

First, the used paper raw material is fed from the paper feeding section (A) to the shredding section (B). Next, in the shredding section (B), this is cut to a size of several millimeters to several centimeters, and then carried to the defibration section (C). In the defibration section, the cut paper is mechanically impacted to weaken the links between the fibers without shredding them. The aim of this procedure is to ensure the strength of the final paper product, and ensure it is uniform and free from unevenness. The defibration section (C) ensures that most of the fiber is evenly flocculated, but some remains uneven. Accordingly, there is a selection section (D) after the defibration section, and the fibers are selected by passing these through a sieve. The uneven fiber is returned to the defibration section (C) using the return path (y), and then is reprocessed to make it even. This minimizes fiber degradation, enabling continuous defibration and feeding to the binding processes.

#### 1.1.2. Binding Processing

In the mixing section (E), material degraded into fiber in Section 1.1.1 is combined with fiber, and pneumatically fed to the binding section (F). In the binding section (F), the material is dispersed using a rotating sieve (o) comprising a cylindrical mesh, and the fiber is discharged at a constant speed and then deposited on a moving mesh belt (p), thus allowing the continuous formation of sheets. In order to continuously form sheets, it is important to ensure good dispersion of the fiber discharged from the rotating sieve (o) so that there is no difference in the density of the fibers on the belt. Additionally, reducing the size of the mesh in the rotating sieve (o) will make it is possible to prevent the discharge of fiber that is still clumped. However, a mesh size that is too small will make it difficult for the material to pass through the sieve, resulting in the sieve becoming clogged by the fiber. The machine was designed to set the selection section (D) sieve mesh size smaller than the rotating sieve (o) mesh size, enabling the continuous production of quality sheets of paper.

#### 1.1.3. Forming Processing

Forming processing increases the density of materials formed into sheets in Section 1.1.2, forming sheets of paper with the fibers bound together. In wet process paper manufacturing, hydroxyls in the cellulose form hydrogen bonds in the process that squeezes out water and dries the paper, thus binding together the fibers in the paper. For this dry-type technology, a powdered bonding agent was developed. Before binding processing, the bonding agent is mixed with the fiber through the mixing section (E), with the fibers in the sheet formed in the binding process having bonding agent applied. This has 1 to 3 tons of pressure applied in the pressurization section (G), increasing its density. After this, the heating section (H) as a whole applies approximately 3600 J of heat, fusing the bonding agent, and bonding the fibers together. We can see that this pressure means that paper manufactured with this method (Figure 2a) has a higher density with the pulp fibers bonded together when compared with conventional wet-type paper (Figure 2b). This technology that uses the dry-type process is recognized as providing the functionality required of PPC paper. Furthermore, the strength of the paper differs depending on the amount of pressure applied. Tensile testing of paper produced using this method showed results of 12 to 15 MN/m<sup>2</sup> (density of 0.7 to 0.8/cm3), which has been confirmed as a sufficient strength performance required for PPC paper.

**Figure 2.** Comparison image from scanning microscope. (**a**) Dry fiber paper (DFP) produced with this method (SEM), (**b**) Commercial plain paper copier (PPC) paper (SEM).

#### **2. Materials and Methods**

#### *2.1. Scope of Evaluation and Functional Units*

This study applied the fundamentals of the LCA methodology to evaluate the environmental impact of dry fiber paper (DFP) made by a dry-type office paper-making machine (Figure 3 and Table 1) in Japan. LCA can handle hundreds of inputs and outputs at different stages, "cradle-to-grave", and provides for a means of comparing the impact of different products. In LCA, it is important to define the system being studied, and to determine the system boundaries to aid in narrowing down the elements of the lifecycle inventory. The lifecycle inventory consists of flows into and out of the system boundary. This section describes the functional unit, system boundaries, and data collection method used in this project.

This study covers paper produced using this newly developed dry-type used paper recycling technology. Substances evaluated were CO2 emissions and water consumption. The main reasons for this are that CO2 has been well covered by research up until now, and large quantities of water are used as raw materials and in the production of paper. The functional unit in this study was 1 ton of DFP, and raw materials, energy, manufacture, transportation, and waste treatment were based on this functional unit. Although the ISO 14040 and ISO 14044 standards define the LCA methodology, some necessary flexibility is left to practitioners during implementation, especially regarding the allocation methods and definition of the system boundary.

**Figure 3.** Dry-type office paper-making machine's external view.


**Table 1.** Dry-type office paper-making machine's main specifications.

#### *2.2. System Boundary*

Figure 4 shows the system boundary. This study includes in its evaluation scope the flow from raw materials' procurement and manufacturing through to disposal. Details for each flow are as follows. Raw materials' procurement and manufacturing includes parts, unit replacement parts, and cartridge parts required in order to manufacture the main unit. Transport includes transport of the main unit, cartridges, and replacement parts, and usage includes consumption of electricity and water during the paper-making process. Disposal includes the environmental burden incurred from disposal of the main unit, and of the waste generated during the paper production process. However, it does not include the environmental burden from transportation for sales locations and transportation to disposal units, PPC paper being used to feed into the unit, or DFP manufactured by this product being re-fed into the unit.

The system boundary considers the upstream processes associated with DFP production, transportation, and disposal. Figure 5 shows a schematic representation of the system boundary used in this analysis. DFP production requires chemicals, including polypropylene (PP), calcium carbonate, adhesion bond, liquefied, electricity, and water, at various stages in its production.

**Figure 4.** System boundary of the dry office paper making system. This system includes defibrillation, binding, and forming. Making the producing system, usage, and disposal of this system were considered in LCA.

**Figure 5.** Bonding agent structure.

#### *2.3. Database and Activity Data*

CO2 and water consumption were calculated using the following formula:

$$\text{Enviornmental burden} = \sum \{ \text{Activity}\_{i} \times \text{Intersity}\_{i,s} \}, \tag{1}$$

where "i" refers to articles, and "s" to substances that impact the environment (CO2, water consumption).

In order to obtain the CO2 emissions and water consumption results, we obtained basic units for the activity data and for the environmental burden.

The inventory analysis sums the emissions and calculates the consumption of energy, raw materials, water, chemicals, transport, wastewater, and solid waste treatment.

The inventory database used as the environmental burden basic units is as follows. CO2 emissions by sectors were obtained from Embodied Energy and Emission Intensity Data for Japan Using Input-Output Tables (3EID) developed by National Institute for Environmental Studies (NIES) [23], CO2 emissions by processes from Inventory Database for Environmental Analysis (IDEA) developed by the National Institute of Advanced Industrial Science and Technology [24], and finally the power generation inventory from the Agency for Natural Resources and Energy [25]. In the power generation results [25], the power company basic units and the amount of power generation are disclosed. This study used a weighted average of actual values from major power companies, creating and using power consumption basic units. Water consumption used the water consumption basic unit database developed by Ono et al. [26].

These databases were used to simulate the environmental burden. A generic database based on an input-output table was used to estimate the contributions of unavailable data. These databases were applied to the Japanese input-output table. 3EID covers the greenhouse gas emission intensity (CO2, CH4, N2O, etc.) and the water footprint inventory database covers the water consumption intensity (total water consumption, rain, surface and ground water). Both databases have about 400 sectors. As the databases applied in input-output analysis are generally based on monetary data, we used the unit price list released by the Japanese government to convert these data into quantitative data for 3571 sectors.

The activity data for this study were provided by Seiko Epson Corporation, which is the largest DFP producer in Japan. The data year was 2018. Including components in the product body would increase the number of activity data items to several thousand, therefore these individual components are not listed individually. However, important items (power consumption for office paper-making machines, for the production of bonding agent, and for paper making) are listed below.

#### 2.3.1. Office Paper-Making Machine

Information regarding the office paper-making machine main unit is categorized by the process (defibration, binding, forming), exterior, and common parts (Table 2). Furthermore, parts information for each unit is based upon that from the manufacturer of the office paper-making machine, with activity data obtained per part.


Unitnamesandnumbersofpartsbyprocess,exterior,andcommonparts.

#### 2.3.2. Bonding Agent

As detailed above, a bonding agent is used to bind paper fibers together, thus creating the paper. The bonding agent is a powder mainly consisting of a thermoplastic resin. For its structure, the binder contains pigments, with a surface treatment agent applied to the exterior surface of the powder (Figure 5). Its composition is shown in Table 3.

The manufacturing process for the bonding agent fully agitates and mixes together its raw materials, and then temporarily forms these into a mass. This mass is again pulverized, and then a functional surface treatment agent (for fluidity) as well as pigments (as necessary) are applied to the exterior surface. Figure 6 shows the bonding agent manufacturing process. Energy consumption and input/output data for all substances in all processes shown in Figure 6 were obtained from the primary supplier.

**Table 3.** Bonding agent composition.

**Figure 6.** Bonding agent manufacturing flow.

#### 2.3.3. Power Consumption at the Paper-Making Stage

This study measured the amount of power consumed as the basic unit for processes in the paper-making operation, from start-up and paper production through to shut-down, and applied these units for evaluation. As mentioned before, the electrical power consumption basic unit was calculated based upon data disclosed by the Agency for Natural Resources and Energy.

Figure 7 shows an example of the power consumption at the paper-making stage. Both start-up and shut-down take approximately 12 min, consuming a total of 0.74 kWh electricity. In total, 250 min of paper production produces 3040 sheets, consuming 21.75 kWh of electricity. As shown in Figure 7, variances in the power consumption in paper production are because of differences in the quantities of paper fed into the defibration section as well as in the quantities of materials returned from the selection section (separator drum unit) to the defibration section. From this, we can see that this series of processes consume a total of 22.49 kWh of electricity. Power consumption per sheet of paper is 7.40 Wh. Furthermore, these measurements were repeated three times, confirming their reproducibility (1st time: 22.50 kWh, 2nd time: 21.97 kWh, 3rd time: 22.62 kWh). The weight of DFP manufactured using this technology is 5.7 g per sheet, giving a power consumption of 1298.25 kWh (7.40 Wh/5.70 g × 1,000,000.00 g) per ton as the functional unit.

**Figure 7.** Example measurement results for power consumption.

#### **3. Results**

#### *3.1. CO2 Emissions*

Figure 8a shows CO2 emissions throughout the whole lifecycle. These results show 1449 kg-CO2 per ton of paper. Looking at these emissions through each stage of the lifecycle, the discharge quantity in the usage stage had the largest influence on the results, comprising approximately 80% of the total. The next largest was the manufacturing of office paper-making machines, comprising approximately 10% of the total. In comparison to these, there was a low environmental burden for assembly and disposal, with each of these at below 5% of the total. CO2 emissions for transportation were also relatively low, because the implementation of this technology means used paper within the office can be used to produce DFP, without the need to transport it to an external facility from the office. Focusing on the usage stage, there was a high environmental burden from the power consumption and the production of cartridges including adhesives, comprising 50% and 30% of the total, respectively.

Accordingly, Figure 8b shows a breakdown of the CO2 emissions in the usage stage. Among the defibration, binding, and forming processes, the defibration process had the highest emissions, taking up approximately half of the total, because this process requires time to break down paper into fiber, and thus takes longer than the other processes. Additionally, binding processing and forming processing each comprise under 20% of the total, with a large impact from the binding section heater and from the heater used during forming. This study assumes usage within Japan, and therefore uses CO2 emissions basic units corresponding to Japanese power generation. Accordingly, power structures and generation efficiency differ between countries and regions in which the product is used, meaning that CO2 emissions will also differ widely depending on these parameters.

Next, Figure 8c shows a breakdown of the CO2 emissions from the bonding agent cartridge. These results show a large proportion of CO2 emissions from the production of polyester, a major component in the bonding agent. Accordingly, when looking towards further future reductions in the environmental burden, the important parameters are the efficiency of the defibration section and the reduction of the bonding agent quantity used.

Finally, Figure 8d shows a breakdown of the CO2 emissions in the production stage. By process, this is defibration (9.7%), binding (25.7%), and forming (36.7%), with forming comprising the largest proportion. The reason that the forming process has the highest ratio is the large sizes of the pressurization and heating units used during manufacturing, with a corresponding large environmental burden from the procurement of these materials.

Next, the results from this study were compared with the case of recycled paper (Figure 9). To calculate the CO2 emissions for recycled paper, CO2 emissions until production used data from the Japan Paper Association [27], and emissions from transport and sales used data from Environmental

Hotspot Analysis (EHSA) [28]. It was shown that utilization of the dry-type paper recycling technology enabled a total reduction of 500 kg, or 26% in CO2 emissions. In particular, this technology produced reductions in the environmental burden up until the procurement of pulp, and in the delivery and sales of the product. However, CO2 emissions through the use of this technology in the production stage of DFP were comparatively high. Therefore, as shown previously, further study is required in how to reduce the environmental burden by reducing power consumption and the amount of bonding agent used.

**Figure 8.** Office paper-making machine's CO2 emissions calculation results: (**a**) Whole lifecycle CO2 emissions and breakdown, (**b**) Power consumption breakdown at the usage stage, (**c**) CO2 emissions breakdown with a focus on cartridges, (**d**) CO2 emissions breakdown during the production stage.

**Figure 9.** Comparative results in CO2 emissions between the office paper-making machine (left) and recycled paper (right).

#### *3.2. Water Consumption*

In addition to CO2 emissions, this study also focused on water consumption. Figure 10a shows the water consumption results when using this technology. Water consumption per ton of product is approximately 9 m3, and as with CO2 emissions, the usage stage was responsible for more than half of the total. However, the production of cartridges had high water consumption, rather than power consumption. Figure 10b shows a breakdown of the water consumption focusing on cartridges. As with CO2 emissions, there was high water consumption (approximately 75%) until the production of materials with polyester as a main ingredient, with packaging and external additives around 10% each.

**Figure 10.** Water consumption for office paper-making machines (**a**) and with a focus on cartridges (**b**).

*Resources* **2020**, *9*, 23

We compared the results above with existing studies (Figure 11). This comparison used data from the Japan Paper Association [27] and the Water Footprint Network (WFN) [22]. The WFN [22] has results for three types of printing paper (broadleaf, softwood, and eucalyptus), but as Japan mainly uses domestic and imported broadleaf, this study used the figures for broadleaf. Water consumption for the production of 1 ton of paper comes to 965 m3. The water consumption from the distribution of PPC paper from Ono et al. [24] was added to this, giving a total of 983 m3. Compared to the water consumption for PPC paper, that for producing 1 ton of DFP was approximately 9.15 m3, roughly 1% of that for PPC paper. The reason for this is that printing paper requires large amounts of rainwater in order to grow wood and to produce pulp, whereas DFP reduces the consumption of virgin pulp. Furthermore, because it uses a dry-type production process, the water used during the production process is also significantly reduced. There is high water consumption during the production stage of wood and pulp, which comprise the raw materials used to make paper as well as during the paper-making stage, but this technology obviates the need for this production, thus limiting water consumption during the paper-making stage.

**Figure 11.** Comparative results in water consumption between the office paper-making machine and recycled paper.

#### **4. Discussion**

This study used the production of 1 ton of paper as its functional unit. Note that the environmental burden per unit will differ depending on the utilization rate of the office paper-making machines. Fundamentally, evaluations based on actual specification data are desirable; however, since this system has only just entered the market, there is insufficient data to set usage scenarios. Therefore, this evaluation was based on 8 h of operation per day.

This section evaluates the sensitivity of CO2 emissions to variances in the product utilization rate. With 100% utilization set at 8 h per day, we ran simulations at between 10% to 100%, with results of the comparisons of CO2 emissions for the manufacture of recycled paper (Japan Paper Association) shown in Figure 12. The lowest CO2 emissions were obtained for the 100% utilization rate scenario, at 1449 kg-CO2, whereas emissions for the 10% scenario were 2.8 times higher, at 3975 kg-CO2. A breakdown of CO2 emissions shows the environmental burden from cartridges as 31% and that of electrical power usage as 46% in the 100% utilization rate scenario, comprising approximately 80% of the total emissions in the usage stage. On the other hand, in the 10% utilization rate scenario, the manufacture of the main unit comprised 32%, transport 26%, and disposal 13%, with emissions in other than the usage stage comprising more than half of the total.

The reason for this is while the per-unit environmental burden for the production of paper at the usage stage is unchanged, that for manufacturing and transport changes, and becomes relatively higher as the utilization rate lowers. From the above, it is clear that effective reduction strategies will differ depending on how this technology is utilized by users. Usage of the office paper-making machine at the 100% utilization rate is expected to provide a reduction in CO2 emissions of roughly 500 kg. However, lower utilization rates have a corresponding increase in per-unit CO2 emissions, giving less of a reduction in the environmental burden than with recycled paper. The environmental burdens intersect at a utilization rate of 36%. Results showed that with a utilization rate above this, the office paper-making machine has a lower environmental burden, but with a utilization rate below 36%, the environmental burden increases. A utilization rate of 36% corresponds to approximately 3 h of usage daily. This should act as a guide for users who are anticipating using this product to reduce their environmental burden.

The study above clarified the following. Firstly, increasing the utilization rate can increase the per-unit environmental burden reduction effect. Therefore, it is important to promote usage of this technology as an alternative. There is a need to visualize environmental information and convey this to a wide range of stakeholders, including manufacturers, users of paper-making machines, and users of paper, and to strive towards increasing the collection rate for used paper. We need to communicate to not only manufacturers but also users of the office paper-making machines and of paper the fact that the utilization rate has a large impact on the overall results, and to increase the collection rate for used paper. Next, given that environmental burden reduction measures differ depending on the utilization rate, it is absolutely necessary that we fully understand the usage conditions of users. If there is a high utilization rate, there will also be high power consumption and burden from the usage of the bonding agent. Therefore, there is a need to make further energy savings and increase usage efficiency for the bonding agent, as well as to prioritize the usage of renewable energy. On the other hand, a low utilization rate means that the environmental burden from the manufacture of paper-making machines and from transport becomes relatively higher; therefore, this becomes an issue of reviewing raw materials, costs, and parts, as well as achieving efficiencies in delivery.

**Figure 12.** Sensitivity analysis and comparison when changing the utilization rate of the office paper-making machine.

#### **5. Conclusions**

Recycled paper has been widely used to reduce the land usage required for forestry; however, substantial consumption of energy and water is still required in the production of recycled paper, and this requires the development of further measures in order to reduce the environmental burden. This study focused on an innovative technology for the dry-type production of paper as developed by Seiko Epson Corporation. Using this technology not only eliminates the need for both water disposal and drying processing, but also by producing paper within the office, reduces the environmental burden from transport required for paper collection. The aim of this study was to analyze from an LCA perspective the environmental performance of this innovative paper recycling technology.

The study showed that the use of this technology enables a reduction of 26% in CO2 emissions, and a 99% reduction in water consumption over similar general PPC paper. Focusing on CO2 emissions, when compared to PPC paper, this shows a large contribution in reductions in the production and transport stages, and a large contribution to decreased water consumption attributable to reduced usage of raw materials. The CO2 emissions results show that there is a high environmental burden from power consumption in the production and usage of cartridges. Therefore, further studies will need to look at making this technology more environmentally friendly, including considerations of the used quantities of and materials selection for the bonding agent used within the cartridge, as well as energy savings during usage. Additionally, evaluation results differ significantly depending on users' utilization rates. Therefore, it is necessary to convey information to the users of office paper-making machines and paper, and to improve the collection rate of paper. As a limitation of this study, this study was evaluated assuming that all processes were performed in Japan. Therefore, it is not right to compare with other countries' PPC paper. In case of comparisons, it is necessary to make evaluations using primary data, usage conditions, and an environmental load database in specific countries and regions.

**Author Contributions:** Conceptualization, Y.O. and N.I.; methodology, Y.O. and N.I.; software, Y.O.; validation, Y.O.; formal analysis, Y.O.; investigation, Y.O.; resources, Y.O., M.H., K.Y. and T.O.; data curation, Y.O.; writing—original draft preparation, Y.O. and N.I.; writing—review and editing, Y.O. and N.I.; visualization, Y.O.; supervision, N.I.; project administration, Y.O. and N.I.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Dominant Consumer Attitudes in the Sharing Economy—A Representative Study in Hungary**

**Gabriella Buda 1,\*, Barbara Pethes <sup>2</sup> and József Lehota <sup>1</sup>**


Received: 24 November 2019; Accepted: 23 December 2019; Published: 27 December 2019

**Abstract:** As a result of the digital revolution, new business models are emerging, and one of the most dynamic is the sharing economy. In many cases, the strategic communication of sharing economy firms is linked to current socio-economic trends, such as digital innovation, consumers' empowerment, experience gaining (instead of stock), environmental awareness, and community building. In our research (a nationwide representative sample of 3520), we aimed to determine how open the Hungarian population is toward sharing economy services. Furthermore, we explored the relationship between openness and consumers' socio-demographic factors, attitudes related to the current consumer trends and Internet usage habits. As a result, we found that 38.4% of the Hungarian population is open toward sharing economy services. From a socio-demographic point of view, wealthy, metropolitan, family-oriented, educated, and younger people are more open toward sharing activities. In terms of consumer attitudes, people who take risks, like having a social life, are environmentally and health conscious, spend their leisure time actively, enjoy quality things, and have a positive attitude toward digitalization are more open to using the sharing economy services. As a final result of the regression modeling, we found that the examined consumer attitudes and Internet usage habits determine openness, but socio-demographic factors largely lose their significant effect, except for generation and wealth, in the case of the integrated model. Our results show that a well-defined and relatively large segment is open to the sharing economy, and sharing economy companies could target them directly to achieve a more sustainable environment.

**Keywords:** sharing economy; consumer behavior; consumer attitudes; sustainability

#### **1. Introduction**

The sharing economy is playing an increasingly important role in our daily lives, and there is a blurring of lines between the personal and commercial assets, consumers and producers [1]. Centuries ago, sharing activities could be found in society [2], and the question now is why this phenomenon began to grow dynamically. Many factors are contributing to this growth, but the development of digital technologies must be emphasized [3,4]. As a result of the digital revolution, people in the online space can find, pay for, and value each other's activities easily and quickly.

The sharing economy is present in every part of our lives, be it work or leisure. About a decade ago, sharing activities emerged that were later classified by the literature as part of new business activity. The best-known examples are Airbnb [5], which appears in the accommodation market, Uber in the passenger transport market [6], crowd-funding in the financing area [7], and TaskRabbit in the labor hire sector [8]. The phenomenon of the sharing economy or collaborative consumption can support sustainable consumption [8–12] which could have also long-term impacts. One of the benefits of sustainable consumption is the reduction of waste, and this is one way of effectively feeding back to consumers the direct relationship between consumption and waste production [13].

Many factors must contribute to the success of a new business model. In this study, we are looking for the reasons why consumers are increasingly choosing these types of services, and the factors that influence the consumers' openness to these new, alternative business models. Consumer behavior is most influenced by external impacts. These are part of marketing origin and are determined by the company's strategy. On the other hand, the wider environment also influences consumer decisions, such as cultural and social factors, reference groups, and personal factors. Thirdly, there are socio-economic trends related to as sustainability, social networks, digitalization, and globalization that also influence consumer behavior [14–16].

In our research, we looked for the consumer segments that are open toward sharing-based services. We analyzed openness in socio-demographic terms, consumer attitudes, and Internet usage habits. We assumed that the more sensitive segments of the socio-demographic trends would be more open toward sharing economy services, and we aimed to prove this hypothesis.

#### **2. Literature Background**

A few years ago, the sharing economy was defined the following way: "In the sharing economy users share with each other their idle capacities and resources (e.g., fixed assets, services, money), on an on-demand basis (as and when the consumer need arises), usually via an IT platform, on the basis of trust, ascribing particular importance to personal interaction and the community experience, with an eye on sustainability" [17].

In recent years, new elements or expressions of the sharing economy have emerged, highlighting different dimensions or major features of the phenomenon. It was written about by Botsman and Rogers firstly in a widespread book in 2010 called 'Collaborative Consumption' [18]. They wrote about sharing and redistribution activities among individuals. In 2011, Gansky wrote about a new corporate model called the 'mesh economy', in which he encouraged companies to share instead of selling (for example, in the automotive industry) [19]. Bardhi and Ekhard [20] formulated the spread of 'access-based consumption', where consumers prefer access to goods and are willing to pay for the possibility of temporary access rather than buying and owning the good. The term 'sharing economy' was first used by Friedman in 2013 [21]. Curtis and Lehner concluded the following characteristics, or semantic properties, of the sharing economy for sustainability: "ICT-mediated, non-pecuniary motivation for ownership, temporary access, rivalrous and tangible goods" [22]. Since 2015, the concept of the circular economy has come to the attention of European Union policymakers, and one of the model solutions to achieving this could be the sharing economy. Models of western Europe and east-central Europe may differ in several factors, but the size of the EU provides an opportunity to revise circular processes [23]. The phenomenon is constantly evolving and changing and therefore different dimensions can be distinguished. The first dimension is the subject of sharing, which could be physical goods (car, apartment) or non-physical goods (time, knowledge, money). The second dimension: differentiate between C2C (or peer to peer), B2C, or C2B models. The third dimension states that from monetization's point of view, we can observe, barter, or use business models in financial exchange [8]. In various combinations of these dimensions, we can find businesses that place themselves under the auspices of the sharing economy.

The sharing economy can be analyzed from a variety of scientific perspectives. From an economics' point of view, the sharing economy has a stimulating effect on competition and can be seen as a form of economic innovation [24,25]. From a business economics' point of view, the phenomenon is mentioned as a new business model [26,27], which is a kind of competitor to the enterprises operating in the traditional business model. In the new business model, the value proposition of the company who is operating the platform is to effectively combine supply and demand (a peer-to-peer business model). The value proposition is one of the main elements of the business models, next to the partner network, resources, distribution network, market segment, and value configuration [28]. Further studies have

referred to the sharing economy as a new innovative business model that could be used as a potential tool for corporate sustainability [11,29] or as a resource-saving potential that which can change consumer patterns [12]. Most of the criticisms of the sharing economy come from the tax and legal perspective. Most experts agree that new, innovative activities should also be regulated, but there are differences of opinion regarding the depth of regulation. Some experts favor unified regulation for companies in a similar industry [30], while others argue that regulation should distinguish between traditional and new models [31–33]. From a human resource management perspective, we are also seeing a new phenomenon that is increasingly being called the 'gig economy' [34]. Within the gig economy, employees are not employing full-time (or even part-time) employees in the traditional, long-term contracted way, but are engaging freelancers, typically through an online platform, occasionally. Freelancers typically share their knowledge and/or time (as a graphic designer, web-designer, etc.). The advantages include flexible working hours and the possibility of working from home. [35,36], while the disadvantages are a lack of advocacy and social networking [37]. Kallenberg and Dunn [38] make the points that the gig economy opens up new opportunities. It is thought that casual workers still make up only a small percentage of the total workforce, but the gig economy may have important implications for the future. In a knowledge-based economy, intellectual capital is one of the most important factors that can help a company grow and be a success [39], and the gig economy supports the free flow of intellectual capital. Additionally, many experts believe the basic elements of social security (minimum wage, health care, retirement, and unemployment insurance) should be also available to gig economy workers. In many countries, there are critical issues of human resource management in the central and non-central regions [40], and the gig economy can potentially solve at least one part of the problem. From a sociological point of view, the changing behavior of consumers can be analyzed [24], and there is already a proposal for transformation towards sustainable consumption, called the sustainable consumption and production (SCP) transformation model [41]. A further suggestion is to engage users in innovation to develop a user integrated sustainable product service system (PSS) [42]. One of the biggest challenges today is to convince society to change its habits, to achieve growth to be sustainable, from an economic, social, and environmental point of view. This is an interesting challenge from a marketing point of view as well. Several marketing studies have identified the preferences and motivation of consumers who participate in the sharing economy, which include, among others, economic gains, enjoyment of the activity, sustainability, utility, familiarity [43,44].

Schor's study summarizes the critiques areas of the sharing economy, including sustainability, building a social community, taxation, insurance, and labor conditions, but she also notes that critics are too cynical sometimes and there are many opportunities in this new business model that are gaining ground [45].

#### *2.1. The Relationship between Consumer Behavior and the Sharing Economy*

This study aimed to investigate the extent to which the sharing economy affects consumer behavior and/or how expectations arising from changing consumer behavior meet the perceived or real characteristics of the sharing economy. In several cases, companies in the sharing economy have used communication keywords that are in line with current socio-economic trends (local space, environmental protection, experience, community, sustainability, etc.). We assume that companies in the sharing economy can succeed, among other things, because related services support current consumer expectations that are driven by megatrends. We assume that those people who are more open to using sharing economy services are also more sensitive to megatrends. Megatrends are trends related to global phenomena that have a significant impact on our daily lives over a long time horizon of 10–15 years [46]. Trends could be related to social, technology, and economy changes. From the perspective of the research topic, the following trends can influence consumer behavior: ICT (Information and Communication Technology) trends (empowering consumers), well-being society (consuming experiences instead of materials, need for self-realization), the eco-paradigm and sustainability (environmental sensitivity), globalization and urbanization.

#### 2.1.1. ICT Trends

The information revolution enabled the rapid flow of information and ideas. The number of digital platforms and devices is exponentially growing [17]. There is not only one-way communication between companies and consumers anymore, but also two-way communication (more interactivity from the consumers side), and furthermore, consumers can communicate with each other on social networking sites [47]. According to Prahalad and Ramaswamy [48], several aspects can be observed in terms of the spread of the Internet and these also influence consumer behavior, for example, wide access to information, global vision, networking, and experimentation (product development, knowledge sharing). The progression of the sharing economy is based on the existence of the digital platforms and, within that, both on the demand and supply side, consumers can easily interact with each other. Due to the digital revolution, people are operating both in real and virtual space: consumer participation is growing, and consumer collaboration is gaining ground [49,50]. The possibility of virtual connection leads to the creation of new communities, allowing them to think together without face-to-face meetings. This growth in consumer power is also important from the sharing economy's point of view, and the digital community is gaining strength. A good example is crowd-funding, which is a new form of financing. In these cases, the implementation of a start-up company is not funded by a financial institution but by individuals. Furthermore, we can highlight another aspect of community power by developing open-source software and/or products. In the case of this activity, individuals share their knowledge. Knowledge and money sharing are usually classified as a sharing economy if there is an economic interest in the activity.

#### 2.1.2. Impact of the Well-Being Society

To understand consumer behavior, it is important to recognize the level of the target groups using Maslow's pyramid (physiological needs, safety and security, love and belonging, self-esteem, and self-realization). Experiences lie at the top of Maslow's pyramid of need [51,52]. Due to the ever-changing environmental, technological, and sociological conditions, the significance of experiences is changing; the experience is becoming more and more important in the lives of consumers [53]. We can identify different areas of experience: entertainment, education, desire to escape, and esthetic experience [54] Furthermore, Uriely [55] notes the blurring of the perception of the differences between work and leisure. Typical motivational factors for traveling in a welfare society are: widening horizons, learning something new, enjoying communication with others, promoting creativity and openness, individual risk-taking, and experimentation [32]. Interpersonal sharing activities can be a new experience for many consumers, and this is something we are exploring in our research.

#### 2.1.3. Eco-Trends and Sustainability

The focus was on sustainable development in 1987, when the United Nations World Commission on the Environment and Development published their work entitled 'Our Common Future' [56]. Here we find the definition that is still used today by many: "Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs". There are three pillars to sustainable development, namely the economic, environmental, and social pillars. [57] Consumers sensitive to sustainable development are striving to become sustainable consumers, which has created the concept of conscious consumption. This may be related to the consumer's self-interest (price awareness, quality awareness, health awareness), or the interests of the public and society (environmental awareness, social awareness). Within this, we can identify the LOHAS target group, which is an environmentally and health conscious group (LOHAS = lifestyle of health and sustainability) [58], they are playing an increasingly dominant segment in many markets [59]. Sustainable consumption is increasingly important, including understanding the needs of consumers and persuading consumers. In the case of a vehicles' purchase, for example, it is an important factor that the consumer centric total cost of ownership could be cheaper compared to internal combustion

engine vehicles and hybrid electric vehicles. [60] It is important to point out that several studies have confirmed that there is a difference between an eco-friendly attitude and real action [43,61]. Activities in the sharing economy basically might be a solution that can both support the right business model towards eco-friendly activities, and support the prevention of overproduction and/or overconsumption. We assume there is a connection between the conscious consumer's behavior, and the same consumer's openness toward the sharing economy.

#### 2.1.4. Globalization and Urbanization

The globalization of markets has now become a reality including for standardized consumer products, multinational commercial cooperation, and distribution [62]. It has also impacted the tourism industry; tourists receive standardized services at the hotels in most places, and standardized products in many cities [63]. It affects consumer behavior as a counter-trend; sooner or later consumers will need individual, non-standardized products and services, and they will want to learn about local culture and local customs.

To sum up, there are some typical characteristics of consumers who have more of an affinity for social, technology, and economic trends: they like to be broadly informed, to be in the community (even virtually), focused on experiences, to be environmentally aware, and they prefer uniqueness and local characteristics.

#### *2.2. Strategies and Communication Messages of Companies Operating in the Sharing Economy*

In the following, we present the activities of some companies involved in the sharing economy and their communication strategies. Our aim is to show that companies in the sharing economy emphasize features in their marketing strategy and, consequently, in their communication that attract the attention of trend-sensitive consumers and thus make them more open to trying out new/alternative services. In recent years, there has been intense competition between companies in the sharing economy and those who are operating in the traditional industry. Typical examples could be the hotels versus Airbnb competition in the accommodation market, and the taxi companies versus Uber in the passenger carrier market. It has to be emphasized because, due to the reduction in transaction costs, a significant number of companies in the sharing economy are able to provide their services at a more affordable price (versus companies in the same industry), and this is the primary consumer motivation to use them [43,64], however communication strategy typically does not call attention to discount pricing, but to other attributes that fit consumer trends. Because of the combined effect of many factors, the sharing economy can be very successful, and technology-driven development is only one of many factors.

Airbnb is focusing on an authentic, local experience. Adventure tourism is becoming more widespread, and tourists, more and more, are seeking unique impressions. In 2015, a survey confirmed the fact, with 86% of Airbnb users saying they had used Airbnb because of encounters with locals [65]. Uber is focusing on human relations, personal stories, and trying to influence people's emotions. Lime is a vehicle sharing company in many countries, and their communication message is "sustainable—spanning countries & communities". In one sentence, they target three different trends: the environment, community, and globalization. Starterkit is a crowd-funding association, but they never explain that they lend money to start-up companies. Their stated mission is to help bring creative projects to life. Kaptár is a co-working place for freelancers in the capital of Hungary. They use the following keywords: community, inspiration and freedom, and central location. They do not rent offices, but rather an opportunity to build relationships, experiences, and inspiration.

#### **3. Material and Methods**

We had multiple aims for this study: firstly, we examined the openness of Hungarian residents towards sharing economy services and how customers' openness is affected by socio-demographic factors, different consumer attitudes (in particular, attitudes related to megatrends), and Internet usage habits. Based on our definition, those consumers who would use or definitely use or have already used sharing economy services are open toward sharing economy services; openness is willingness to participate. Secondly, we aimed to construct a logistic regression model, taking into account the factors identified above, to determine which attributes most influence openness toward sharing economy services, if we examine the effect of factors in a common model.

#### *3.1. Consumers' Openness; Correlation between Openness and Di*ff*erent Socio-Demographic, Attitudinal, and Internet Usage Patterns*

The survey was conducted on a nationwide representative sample of 3520 people in December 2017 in Hungary. Data were collected through personal interviews with interviewers. Key examined demographic factors that were asked were gender, economic status, marital status, age (generation), education level of the respondents, place of residence, and financial status. A total of 47.1% of respondents were male and 52.9% were women. According to the economic status of respondents, 56.2% were active workers, 27.9% were retired, 8.5% were students, and 7.4% had an inactive and unemployed economic status. A total of 17.1% of respondents have lived in capital city (Budapest), 21% of the respondents live in the county seat or county town, 33.1% live in another town, and 28.8% of the respondents live in the municipality. The respondents' financial situation was identified based on their assets and income. According to the classification, respondents were examined along with the following lines: lower (19.6%), lower-middle (20.7%), upper-middle (39.4%), and upper (20.2%). We also looked at the marital status of respondents. Here we have distinguished two categories, family and non-family status. Namely, the respondents with a child (ren) under 18 years were of family status. Based on this, 35.2% of respondents were the family category, while 64.8% of respondents fell into the non-family category. We also investigated the age of respondents: 3% of the respondents belonged to the Z generation (age 14–25), 37% to the Y generation (age 26–39), 31% to the X generation (age 40–59), and 28% to the Baby Boomers (age above 60). In terms of respondents' educational qualifications: 21.5% of them had a maximum primary school education, 26.9% had vocational qualifications, 31.7% had graduated from grammar school, and 19.9% had a university or college diploma (see in Table A1 in Appendix A).

Openness towards the sharing economy was examined as follows. We listed six different sharing economy services, and since the familiarity with these types of services is not necessarily specific, we described for each service what and under what conditions the service is provided, and then named the most typical companies for that activity. We then asked if he/she had ever heard of the service or considered using it if it was available to them. The detailed questionnaire is attached in Appendix B.

Services in the questionnaire:


The services were chosen arbitrarily, but we also relied on the results of our previous, non-representative study [43], in which the listed services were relatively well known.

In the next step in the analysis, we combined the responses and focused on how many people in Hungary are generally familiar with sharing economy services and how many would use these services. Based on this aggregated data, we formed two groups: (1) Acceptors; if respondent said 'considers to use', or 'would definitely use' or 'have already used'—at least one service. In our study, 'acceptors' are those consumers who are open toward sharing economy services. (2) Refusers; who said in the case of each service that they 'would definitely not use it' or 'probably not use it'.

We then examined whether there was a correlation between the respondents' openness and different socio-demographic, attitudinal, and Internet usage patterns. We analyzed the effect of socio-demographic factors and attitudinal differences on openness to services by using a cross-table method and examined the effect of different Internet usage patterns on openness. The data were analyzed using SPSS software, version 23 (IBM Corp., Armonk, NY, USA).

**Hypothesis 1 (H1).** *From the socio-demographic data point of view, we assumed was that there is a correlation between examined socio-demographic factors and consumers' openness toward sharing economy services.*

From the *consumer attitudes* point of view, we assumed that those people who are more sensitive to current trends that impact consumer behavior are more open toward sharing economy services.

**Hypothesis 2 (H2).** *Environmentally and health conscious persons are more open toward sharing economy services.*

**Hypothesis 3 (H3).** *Those people—who like to be in the community—are more open toward sharing economy services.*

**Hypothesis 4 (H4).** *Those people who enjoy traveling while gathering experience are more open toward sharing services.*

**Hypothesis 5 (H5).** *Those people who are willing to pay for quality things are more open toward sharing economy services.*

**Hypothesis 6 (H6).** *Those people who believe that the digital world is a positive thing are more open to sharing economy services.*

**Hypothesis 7 (H7).** *Finally, we examined the habits of Internet users more narrowly. Our hypothesis was that those people who use the Internet more often are more open to sharing economy services.*

#### *3.2. Logistic Regression Model*

We constructed a *logistic regression model*. In this model, the dependent variable was openness. This assumes two values, that is, we classified people according to whether they are open (acceptor) or not. The explanatory variables (independent variables) were divided into three broad groups of the *Internet user population*: socio-demographic variables, attitude type variables, and a group describing Internet usage habits. We assumed that these various factors each have a significant effect on openness. At the current status, we did not test the correlation among the independent variable. In the final model, we examined if when these factors were taken into account together, which factors remained significant. This approach may, however, exclude potentially irrelevant factors from the model. The result will be a reduced version of the explanatory variables, which are the most important features of openness (Figure 1).

The national representative sample was reduced to the population using the Internet during the construction of the logistic regression model, because in most cases, sharing economy services can only be accessed using the Internet. The sample of Internet users is also representative of Hungarian Internet users. Within the total population, 49% of Internet users and only 11% of non-Internet users are open to this new type of service. If we had undertaken regression modeling for the entire population, then Internet users would have been most open to services and other attitudes would be been pushed into the background. Based on this, the demographic pattern of the Internet population was as follows: 2513 responded that they were engaged in activities on the Internet. Respondents were 48% male and 52% female. According to the economic status of the respondents, 70% were active workers, 11% were retired, 11% were students, and 7% had an inactive economic status (e.g., unemployed). A total of

35% of the respondents belong to the family category and 65% to the non-family category. We also looked at the age of the respondents; 24% of respondents belonged to Generation Z, 29% to Generation Y, 35% to Generation X, and 13% to the Baby Boomers. We also asked about the education of the respondents, according to which 12% of the respondents had primary education qualifications, 27% had a vocational education, 46% had graduated from grammar school, and 15% had a university or college diploma. A total of 19% of the respondents lived in Budapest, 23% in the county seat, 32% in the city, and 27% in the village. Their financial situation, based on their assets or their income, classified the respondents into four categories (lower, lower-middle, upper-middle, and upper). According to the classification, respondents were surveyed according to the following ratios: lower (16%), lower-middle (18%), upper-middle (43%), and upper (23%). This data is also available in Appendix A.

**Figure 1.** Structure of the logistic regression model, own editing.

During the regression modeling, we distinguished two phases. In the first phase, three separate models were constructed: one examining demographic factors exclusively, one examining consumer attitudes, and one examining Internet usage habits. Throughout the modeling, we worked with the ENTER method (all independent variables are entered into the equation in (one step), also called "forced entry"), so we did not filter for significant factors. Finally, in the second phase, to compare the effect of each group of independent variables, we constructed a complex final logistic regression model involving all independent variables, the results of which are presented below.

#### **4. Results**

#### *4.1. Openness*

Analyzing the responses of the Hungarian nationally representative sample, we found that at least 12% of people are open to one of the sharing economy services. A total of 12.3% of respondents said they were open to borrowing and lending household appliances. The ratio of the respondents who are open toward different sharing economy services (respondents who said 'would use', or 'would definitely use', or 'have already used') is: 12.3% for borrowing or lending household appliances; 15.8% for public car-sharing (e.g., DriveNow, Munich, Germany); 23.7% for public car ride-sharing (e.g., BlaBlaCar, Paris, France); 21.5% for private car-sharing within the city (e.g., Uber, San Francisco, CA, USA); 23.2% for public bike-sharing; and 20.1% for private flat-sharing (e.g., Airbnb, San Francisco, CA, USA).

We then aggregated the data according to the methodology described above, into the 'group of acceptors' and 'group of refusers'. Those who, for each question, answered that they would not use the service or may not use it, fell into the group of 'refusers'. Everyone else fell into the 'acceptors' group. Based on this, 38.4% of the Hungarian population is open toward sharing economy services

(11.3% open to one thing, 8.5% open to two things, 6% open to three things, 12.6% open to at least four), while 61.6% of the respondents are not open to sharing economy services.

#### 4.1.1. Socio-Demographic Data versus Openness

The following socio-demographic features were examined: gender, economic status, marital status, age (generation), education, settlement type, and financial status of the respondent. Cross-table and pairwise correlation analyses were performed to determine whether socio-demographic factors influence openness (tested based on groups of acceptors and refusers).

Gender: the gender of respondents did not influence openness towards shared services. A total of 38.4% of the total sample was open toward sharing economy services (as acceptors), this included 39.8% of the men, and 37.1% of the women. The effect of gender is not significant (*p* = 0.095).

Economic status: Openness is overrepresented among active workers and students, with a significant relationship (*p* = 0.000, Chi2 = 318.4 df = 3, Cramer's V = 0.301). While 38.4% of the total sample was open to sharing economy services, 46.7% of active employees and 56.8% of students were open to sharing economy services, meaning they were proportionally over-represented compared to the total sample. By contrast, only 15.5% of retirees were open to sharing economy services. Among the demographic factors examined in this study, the impact of this economic status was one of the strongest elements.

Family status: The family status of the respondent influences openness. Here, two categories were distinguished, namely, those respondents who had a minor child were considered as family subjects. The relationship is significant (*p* = 0.000, Chi<sup>2</sup> = 48.346 df = 1, Cramer's V = 0.117). We found that families are more open toward sharing economy services. While 38.4% of respondents in the full sample were open to the sharing economy services, 46% of respondents with a family were open to these service.

Generation: Belonging to particular generation influences openness. The correlation is significant, and it is the strongest influencing factor among the examined factors (*p* = 0.000, Chi2 = 361.001 df = 3, Cramer's V = 0.320). The Baby Boomers is negative, 84% of this generation refuse sharing economy services (significantly overrepresented), compared to a rejection rate of the entire sample of 61.6%. Furthermore, we found that while 38.4% of the population in the total sample is open to shared services, the proportion of those showing openness within the Y and Z generations is higher (Y: 53.9%, Z = 52.4%), which means that these generations are much more open to using sharing economy services.

Education: Educational level influences openness. The effect is significant, though the relationship is weaker than the previous indicators (*p* = 0.000, Chi<sup>2</sup> = 144.715, df = 3, Cramer's V = 0.203). Within the group who are open to sharing economy services (38.4%), those who have a graduation or university diploma are overrepresented (group with graduation: 45.4%, group with a diploma: 50.8%).

Residence: We found that the type of place of residence of the respondent influences openness; the relationship is significant but weak (*p* = 0.000, Chi2 = 26.077 df = 3, Cramer's V = 0.086). A total of 38.4% of respondents were open to shared services, in which 37% of Budapest residents, 45.5% of residents of towns and cities with county seats, 38.9% of residents of smaller towns and villages, and 33.6% of residents of villages are open. Based on this, residents of county seats and cities with county rights are the most open to using the sharing economy services.

Financial situation: We found that the financial situation influences openness. The respondents were classified into four categories (lower, lower-middle, upper-middle and upper) based on their financial position. As a result of the cross-table analysis, we found that the higher the income category of the respondent, the more open they were to sharing economy services. The effect is significant, and the association is moderately strong compared to the other demographic factors examined in the study (*p* = 0.000, Chi2 = 227.786 df = 3, Cramer's V = 0.254). More than half (56.3%) of those in the upper class, 43% of the upper-middle class, 28.9% of the lower-middle income group, and only 20.8% of the lower income group were open to sharing economy services.

In conclusion, the socio-demographic factors examined (economic status, marital status, age (generation), educational attainment, type of settlement, and financial status) do indeed influence openness to shared services, and only the gender of the respondent (male/female) does not affect openness. Based on this, our original Hypothesis 1 was rejected because we assumed that all the examined socio-demographic data would influence the openness.

#### 4.1.2. Consumer Attitudes versus Openness

Nearly forty questions related to consumers' attitude were asked on the following topics: socio-relationships (extrovert vs. introvert, health and/or environmental awareness, risk-taking), leisure activities (frequency and type), product/service purchase attitude (price vs. quality), and attitudes toward the digital world. Factor analysis was performed on each of these four topics.

#### (1) Social Behaviors

The factor analysis resulted in thirteen observed variables aggregated into four factors. We identified the following factors: risk-taking factor, social factor, conscious factor, and recycling factor (Table 1).



Extraction method: rotated component matrix. The bold indicates which variables belong to which factor.

In future analyses, we will use these factors in relation to social behavior.

#### (2) Leisure Activity

We identified the following factors: the simpler daily leisure factor (friends, entertainment, computer games), and the higher quality leisure factor (e.g., museums, traveling, wellness programs, gastronomy tours). The results are shown in Table 2.


**Table 2.** Factors of leisure activity attitudes.

Extraction method: rotated component matrix. The bold indicates which variables belong to which factor.

#### (3) Attitudes Related to Willingness to Pay

We identified the following factors: the quality-sensitive factor and price-sensitive factor (Table 3). Quality-sensitive factor means that people are willing to pay for quality, while the price-sensitive factor means that people compare the prices of products and the possibilities, and may not always choose the better quality.

**Table 3.** Factors of attitudes related to willingness to pay.


Extraction method: rotated component matrix. The bold indicates which variables belong to which factor.

#### (4) Openness to the Internet

Finally, we looked at how people relate to the digital world and computers. Five questions were asked and only one factor was obtained using the factor analysis method. Related variables are presented in Table 4; we named this the digital factor.

**Table 4.** Attitudes toward the digital world.


Extraction method: factor analysis, component matrix.

After dimension reduction, the factors were specified, and we examined the relationship between factors and openness to the sharing economy using an independent sample *t*-test. After generating and naming the factors, we examined whether there was a difference in factor scores between acceptors versus refusers. To do this, we measured the average of each group and looked for significant differences. In the case of the original variables, a higher numerical value means that someone was using the given function and a lower numerical value means that someone does not use that function. In this case, a lower average value indicates that the given factor is less typical for the group. Similarly, a high average value in a group indicates that the group is characterized by the use of elements belonging to that factor. The openness variable classifies people into two categories, so we tested the significance of the difference in means with two-sample *t*-tests. When presenting the results, we indicate the average of the factors in parentheses.

From the social relationship point of view, those people who are more open toward sharing economy services are:


We obtained a special result in terms of the recycling factor: average of acceptors' group: −0.01; average of refusers' group: −0.05. This means that the attitude toward recycling is similar for both groups. Here, we have to highlight that recycling attitude is only one element of environmentally and health conscious people's attitudes.

Taking into consideration all the results Hypothesis 2 was accepted, environmentally and health conscious persons are more open toward sharing economy services.

From the point of view of *leisure activity*, those people who are more open toward sharing economy services are:


Based on the results, Hypothesis 3 was accepted: those people—who like to be in the community and relax with friends—are more open toward sharing economy services. Furthermore, Hypothesis 4 was also accepted: those people who enjoy traveling and collecting experiences are more open toward sharing services.

Concerning the pricing of products/services, those people who are more open toward sharing economy services are:


Based on these results, Hypothesis 5 was accepted: those people—who are willing to pay for quality things—are more open toward sharing economy services.

Regarding the perception of the digital world, those people who are more open toward sharing economy services (among Internet users) are:


Based on the results, Hypothesis 6 was accepted: those people—who believe that the digital world is a positive thing—are more open to sharing economy services.

Taking into consideration all the consumers' attitudes which were examined in the questionnaire, we identified that the characteristics of the group of acceptors are similar and parallel to the specific features of current megatrends. There is one interesting exception: the attitude toward recycling is

similar for both groups. The price sensitivity attitude is more typical of the refuser group, but this does not contradict our basic hypothesis, price sensitivity is not a feature of current megatrends. The summary diagram is shown in Figure 2.

**Figure 2.** Different consumer attitudes versus openness toward the sharing economy, *Source: Own data collection and processing*, 2017.

#### 4.1.3. Different Types of Internet Activities versus Openness (Subgroup, Analysis among Internet Users)

Internet activities could include simpler or more complex activities. We looked at the relationship between different Internet activities and openness (within the same two groups of acceptors and refusers). Of the total sample, 2534 used the Internet, and their answers were considered in the factor analysis.

In the questionnaire, 23 questions were asked about Internet activity. From these 23 variables, we created factors, by exploration, and there was no specified factor structure that we could confirm. Four factors were generated and the following indices were obtained: KMO (Kaiser-Meyer-Olkin) value is 0.909, which is above the expected value of 0.7, so the result is acceptable. The next item to consider was communality, where the value of each variable was above the threshold of 0.25, so no variables needed to be subtracted from the initial set of variables. The combined explanatory power is 49.36%, well above the expected level of 30%, so we consider the result acceptable.

The following names were given to the resulting factors:

Internet activities for entertainment—Related to the following Internet activities: on-line movie, streaming of films and series; downloading of films and series; downloading music; online radio listening; games; posts in forums.

Complex Internet activities—Related to the following Internet activities: on editing own blog; designing your website; home-based work; online photo hosting; on-line web hosting; e-learning.

Social Internet activities—Related to the following Internet activities: on online social sites; Internet chat, instant messaging programs; on-line video sharing; Internet phones, videophone.

Browsing, e-mail, purchase—Related to the following Internet activities: on work-related or private; browsing of websites (for information, entertainment); purchasing.

The results of the T-tests for the factors of Internet activity:


After generating and naming the factors, we examined whether there was a difference in factor scores between acceptors and refusers. To do this, we measured the mean of each group, as before, and looked for significant differences. The results are presented in Figure 3.


**Figure 3.** Internet usage habits versus openness toward sharing economy, *Source: Own data collection and processing*, 2017.

There is a significant difference in the means for all four variables. In each of the four cases, it can be seen that the acceptors' groups achieves a higher average, that is, all four activities are more typical for the acceptors' group. Observing the averages, it is worth pointing out that there is the smallest difference between groups in the case of complex Internet activities. Based on these results, Hypothesis 7 was accepted.

#### *4.2. Logistic Regression Model*

Finally, regression modeling was used to determine which of the various socio-demographic, attitudinal, and Internet activity characteristics had the greatest impact on openness. The use of the Internet greatly influences the openness towards the sharing economy, therefore we used only the population using the Internet in the regression model study.

First, we constructed the regression models one by one in the following order: socio-demographic, consumer attitudes, and Internet usage patterns.

#### 4.2.1. Socio-Demographic Regression Model

We looked at gender, economic status, generational affiliation, settlement type, wealth segment, education, and family status. Based on this, *generational a*ffi*liation, financial status, and educational qualification* have a significant effect on openness, the results of which are also shown in Appendix C.1. The regression model, which is based on socio-demographic factors, has 60% explanatory power. That is, if we know the generational affiliation, income level, and educational background, we can

determine with 60% good faith whether or not a person is open to sharing economy services. Within the generation factor, the Baby Boomers is the least open, and the X generation twice as open (exp (B): 2.048), the Y generation three times as open (W: 28.5, exp (B): 2.929), and the Z generation four times as open (exp (B): 4.121) towards sharing economy services, relative to the Baby Boomers. In terms of income level, respondents in the lowest income category are the least open, with the lower-middle 1.7 times, upper-middle 1.8 times, and upper income respondents 2.6 times more open. The third independent variable in the case of socio-demographic factors is education, which has a significant impact on openness. People with a primary education level are the least open, followed by vocational graduates (exp (B): 1.532), high school graduates (exp (B): 1.986), and university or college graduates (exp (B): 2.155). All the results are linked in Appendix C.1.

The aim of the regression model, in this case, was to find the most open target population along with socio-demographic factors. The results show that Generation Z people with a high income and college education are the most open, meaning they should be targeted by various marketing tools.

#### 4.2.2. Regression Model Based on Consumer Attitudes

In our basic research, we investigated different consumer attitudes and, from the answers given to a significant number of behavioral questions, we identified the following factors: (1) social behaviors: risk-taking factor, social factor, conscious factor, recycling factor; (2) leisure activity: daily leisure factor and quality leisure factor; (3) attitudes related to willingness to pay: quality-sensitive factor and price-sensitive factor; and (4) openness to the Internet: we have only one factor. Examined individually, these factors showed a significant association with openness, and we now present the results of regression modeling. The aim was to determine which factor has an effect and how strong it is in this model. The regression model, which is based on consumer attitude factors, has 67.9% explanatory power.

Taking into consideration all the factors, in the case of regression modeling, the following factors show significant correlation with openness: social factor (exp (B): 1.256), product quality sensitive factor (exp (B): 1.271), both leisure factors (higher quality activities: exp (B): 1.738, and simpler activities: exp (B): 1.615), and Internet usage factor (exp (B): 1.514). This means that people who engage in more leisure time activities (travel, cultural programs, meeting friend, etc.) are more open to sharing economy services, and this is an even more important indicator than the frequency of Internet usage. The results are linked in Appendix C.2.

#### 4.2.3. Regression Model Based on Internet Usage Patterns

In the previous factor analysis, we obtained four different factors for analyzing Internet use activities: (1) entertainment factor (watching movies online, downloading music, playing games); (2) complex activity factor (own blog, website editing, e-learning); (3) social factor (social networking sites, video sharing); (4) email, browsing, and shopping. Based on these factors, we have found that the more frequently respondents conduct these Internet activities, the more open they are to using sharing economy services. According to the results of the regression model, all four factors show a significant correlation with openness. Entertainment factor (exp (B)): 1.575), social factor (exp (B)): 1.457), and e-mail/browsing factor (exp (B)): 1.477) show similar strong openness. The regression model, which is based on Internet usage patterns factors, has 65% explanatory power. The detailed results are linked in Appendix C.3.

#### 4.2.4. Integrated Regression Model

After examining separately the socio-demographic, consumer attitudes, and Internet usage patterns, we investigated which factors have the strongest effect in consideration towards sharing economy services. The explanatory power of all three models was above 60%, and several independent variables were significant in each model. To compare the effect of each group of independent variables, we built a large final model involving all the independent variables. The explanatory power of the integrated regression model is 69%.

Although in the first phase, many demographic factors and almost all attitude-type factors significantly explained openness, by putting all variables into one model, we can see that demographic factors lose most of their effect, whereas attitude-type independent variables retain it. Overall, it is more important to know people's attitudes and Internet habits if we would like to estimate openness, than to know their socio-demographic data. However, it is important to note that the two demographic factors (generation and financial status) that remain in the final model have a stronger impact than attitudes in general. Based on this, the following factors show a significant correlation with an openness toward the sharing economy: generation, financial status, and attitudes toward social events, quality sensitivity factor, both leisure activities factors and frequency of Internet usage factor. Within this, the most open target group is generation Z. Within generation Z, those who are the more open who like to travel, go to museums, do wellness programs, and enjoy gastronomic tours. The results are presented in Figure 4, and further detailed results are in Appendix C.4.

**Figure 4.** Results of the integrated logistic regression model.

#### **5. Discussion and Conclusions**

Sharing economy is a relatively new phenomena and brings up novelties in many scientific areas. Although we have carefully defined the hypotheses of our research and reviewed the related literature, our study has some limitations. At the time of the survey (end of 2017), some of the respondents had not even heard of these type of activities. In the questionnaire, we explained the different type of services in details, but it is still possible that someone responded to their intention to participate in sharing economy without fully understanding the nature of the services. Furthermore, to our knowledge, there is no uniformly accepted list of consumer trends, there are many changes that may become trends over time. The presented trends and the related attitudes have been arbitrarily selected. We have selected the trends that we believe are currently the most influential on consumer behavior.

Taking into consideration the limitation, in conclusion, there is a relatively high degree of openness among consumers around the use of sharing economy activities (38.4% of the Hungarian population). Among the motivations of consumers, preference is given to low prices, which would suggest that price-sensitive, less well-off consumers are the primary target group, and that they are more open

to this type of service. However, the following elements appeared among the users as secondary motivational factors: experience gathering, digital payment opportunity, personal human relations, and sustainability aspects. These elements drew our attention to the fact that future users will not necessarily choose the service because of the price, but because they are more receptive to the present megatrends. In our national representative research, we wanted to support this hypothesis, which we succeeded in doing. Cross-table methods were used to investigate the correlation between openness and different socio-demographic and attitudinal correlations. Among other things, we found that it is not the price-sensitive and less affluent consumers who are most open to shared services, but rather well-off people. We also found that consumers who are more sensitive to megatrends are more open to sharing economy services. Further, people who are more environmentally conscious, like to spend leisure time with friends, for whom traveling is important to them, who like to gather experience in the local community, are willing to pay for quality things, and consider the opportunities offered by the digital world to be positive are more likely to be open to sharing economy services. Finally, we were curious as to which of the many socio-demographic, attitudinal, and Internet usage habits are the key elements that truly determine who are the most open to the sharing economy. The result of a logistic regression model showed that the strongest determinant is the consumer's attitude towards spending leisure time. The most open consumers are those who spend their free time in active recreation. We distinguished between quality and simpler forms of recreation that can be done daily. Both factors show a very strong correlation with openness. Additionally, generational affiliation, financial status, and Internet use frequency have become the most important determinants. That is, people of generation Z who are otherwise well-off and who like to spend their free time actively, and also who use the Internet more often than their peers, are the most open segment.

In recent years, some research has been looking for which factors may influence participation in sharing economy. Important statement that there is a gap between attitude and behavior related to participation in collaborative consumption [43]. Hamari et al. identified that participation in collaborative consumption is motivated by many factors such as its sustainability, enjoyment of activity, and economic gains [43]. Albinson et al. identified respondents' perceived sustainability as the strongest predictor of participation in collaborative consumption. Further factors are "trust, generosity, risk-seeking, materialism, power distance, long-term orientation, and collectivism" [66]. We have confirmed that perceived sustainability and the risk-seeking factor are relevant, and completed several factors related to leisure activities', social relationship's, price- and qualitative sensitive's, and digital behavior's attitudes.

Recognizing changes in consumer behavior is one of the most important factors in the long-term success of a company. The success of the sharing economy, among other things, can be achieved by offering opportunities and/or solutions that attract the consumer. With Airbnb, it is worth offering travel experiences instead of just accommodation, and sharing a community bike should be promoted not as a means of transport, but as an opportunity to protect the environment. Our results can also be used by companies operating in the traditional business model. There are some industries where traditional companies are threatened by the sharing economy firms (accommodation, travelling, creative agencies, financial sector, etc.). From one side, based on our results, they can identify the most endangered segments, from the other side, they can also use some elements of the mentioned success factors. There are already examples where traditional companies are taking over an innovation from a sharing economy company. Evaluation of the services from the users' side is already available not only at Airbnb and Uber, but currently several hotels are evaluated at least in some market places. This is related to consumers' empowering. Application was used firstly by Uber, where passenger could follow the ordered car, now a lot of taxi companies also use a similar application. This is related to the digital innovation.

In addition, one of the most important trends today is to do more to achieve a sustainable world. Several elements of the sharing economy offer opportunities for this, and it is our responsibility to make the most of this opportunity.

**Author Contributions:** Conceptualization, G.B.; data curation, G.B.; methodology G.B.; validation, G.B.; funding acquisition, G.B.; formal analysis, G.B. and B.P.; investigation, G.B.; writing—Original draft preparation G.B.; writing—Review and editing, G.B. and B.P.; visualization, G.B.; supervision, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors wish to express their gratitude to the Szent István University for providing access to the necessary databases (e.g., Scopus). The authors are grateful to Manolisz Karajánnisz for valuable contributions to the questionnaire. The authors are grateful to Magyar Telekom Ltd. for the contribution of the questionnaire.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Socio-Demographic Data of Representative Sample, Total Base, 3520 and Internet User Base, 2513.


#### **Appendix B. Questionnaire**

**Q1 In the Following, I Would Like to Ask if You Have Heard of Certain Types of Services. So, Have You Heard of the Possibility that** ... **?**



**Q3 Now I Read Statements that Others Have Made about Themselves. To What Extent Do You Agree with These Statements? There Is No Good Answer or Bad Answer, We Are Curious about Your Opinion.**





#### **Appendix C**


*Appendix C.1. Results of Regression Modeling of Socio-Demographic Characteristics versus Openness to the Sharing Economy, Own Editing*






#### *Appendix C.4. Results of Integrated Regression Modeling*

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Example of a German Free-Float Car-Sharing Company Expansion in East-Central Europe**

#### **Árpád Tóth and Cecília Szigeti \***

Kautz Gyula Faculty of Economics and Business Administration, Széchenyi István University, Egyetem Square 1, 9026 Gy˝or, Hungary; totha@sze.hu

**\*** Correspondence: szigetic@sze.hu or szigetic@gmail.com

Received: 15 October 2019; Accepted: 4 November 2019; Published: 8 November 2019

**Abstract:** This study examines the expansion of a German free-float car-sharing company in Hungary from financial and sustainability perspectives. BMW and Daimler recently created the joint ventures ShareNow, ChargeNow, ReachNow, FreeNow, and ParkNow, which are having a significant global impact, as their services are now available in 14 different countries. We also expect further market development, since ShareNow started to operate in Hungary in May 2019. The whole EU market is just one step away from being covered by the same professional service, and the future might bring a real globally available free-float car-sharing service provider. Our review used a combination of two methodologies: financial statement-based business analysis and sustainability analysis. On the basis of this study, we concluded that these companies are primarily operated for profit and not on a sustainable operation basis. Additionally, it was also found that the current statistical data collection method does not measure precisely these activities. Financial reporting and sustainability reporting are connected, but they cover different areas. As a subject of further research, we suggest examining whether it is possible to establish a clear connection between these methodologies in the foreseeable future.

**Keywords:**Car2Go; DriveNow; GreenGo; MOL LIMO; sustainability; economies of scale; sharing economy

#### **1. Theoretical Background**

The objective of this study was to examine the performances of free-float car-sharing entities in Hungary and compare them to those of their German counterparts from financial analysis and sustainability perspectives. On the basis of actual financial results in Hungary, they appear to be less profitable businesses compared to other rental service companies. Recently, Car2Go and DriveNow created joint ventures, which generated significant competition because they entered the Hungarian market in May 2019.

In Hungary, free-float car-sharing companies might follow different business models, which can cause unusual results. We also reviewed the available sustainability reports to define a possible connection to financial statements. Additionally, we tried to evaluate these companies from the sustainability perspective.

#### *1.1. Business Model Review*

The free-float car-sharing business model was categorized, defined, and described in a car-sharing business model review by Deloitte [1]. Since then, other studies reviewed the model and the markets itself, for example that of Munoz and Cohen [2]. Several studies raised sustainability-related questions regarding sharing economy models.

Reitmann and Lieven [3] examined how policy measures succeeded in promoting electric mobility in 20 countries by measuring the influence of monetary incentives, regulations, and charging infrastructure. Hartl et al. [4] addressed the gap between business-to-consumer (B2C) and peer-to-peer (P2P) car-sharing services from the customer's perspective. Overall, these previous studies on free-float car-sharing businesses support the initial assumption that these entities are profit-oriented, and their operations can be questioned from a sustainability perspective. From the business model perspective, in Hungary, there is a unique situation for free-float car-sharing companies, considering the impact of the international lease regulation changes. A wide range of studies, such as those of Wheeler and Webb [5] and Barone et al. [6], have provided summaries on the expected impact of lease capitalization and its effect on profitability and leverage ratios. Giner and Pardo [7] reviewed the value relevance of operating lease liabilities.

#### *1.2. Sustainability Reviews*

Sustainable business model (SBM) types were introduced to describe groupings of mechanisms and solutions that may contribute to building a business model for sustainability. Examples are: Maximize material and energy efficiency; Create value from 'waste'; Substitute with renewables and natural processes; Deliver functionality rather than ownership; Adopt a stewardship role; Encourage sufficiency; Re-purpose the business for society/environment; and Develop scale-up solutions [8].

Geissinger et al. [9] described and classified the sustainability connotation of sharing-economy platforms for Sweden. Indeed, sharing economy can be considered as a path towards sustainability [10]. Bernardi and Diamantini [11] explored how sharing economy, adopted by an increasing number of cities, may be integrated into the urban agenda, fostering its positive aspects (like decreased carbon emissions [12]), while avoiding its negative externalities, and focused, as examples, on Milan and Seoul. Ma et al. [13] proposed an alternative governance model to improve the effectiveness of a collaborative governance regime towards urban sustainability. Albinsson et al. [14] developed a two-dimensional sharing economy matrix for sustainability reviews, which focuses on collaborative consumption users vs. non-users in the US and Indian markets. Ma et al. [15] argued that the two-level transformations, triggered by the disruptive innovation of the sharing economy and led by urban change towards sustainability, mutually influence each other in the fast-changing urban context in Shanghai.

#### *1.3. Sharing Economy Reviews*

The emergence and rapid spread of the 'sharing' or 'collaborative' economy is one of the most significant social-economic challenges of our time. The success of the concept can be traced back to the economic crisis. It focuses on usage and not on owning goods. The debate over the regulation of the sharing economy has become polarized between those who are radically opposed to any intervention and those who favor some form of regulation (Table 1).




Sharing economy platforms can be represented in a two-dimension matrix. The first dimension of the matrix classifies sharing platforms into for-profit (FP) and not-for-profit (NFP) activities. The second dimension follows the B2C–P2P axis [30]. Car-sharing business models are for-profit, B2C sharing economy platforms and therefore belong to group 4. (Figure 1).

**Figure 1.** Two-dimensional sharing-economy matrix [31]. P2P: peer-to-peer, B2C: business-to-consumer, NFP: not-for-profit, FP: for-profit.

#### **2. Materials and Methods**

From the available financial and legal information, the following elements were reviewed:


Sustainability was reviewed on the basis of Penz et al. [32], exploring and explaining how, why, and when a sustainable operation is adopted and participation in the sharing economy becomes key, as well as how sharing economy models and sustainability (sustainable sharing economy, SSE) correspond conceptually in the collected articles. Seven sustainability aspects were addressed, of which four refer to car-sharing (Table 2).


**Table 2.** Sustainability aspects of car sharing.

#### **3. Results and Discussion**

#### *3.1. Free-Float Car-Sharing Business Models in Hungary and Germany*

Specific free-float service providers are defined as companies offering the service of car-sharing, i.e., the use of vehicles that can be rented and parked freely throughout the entire business area without having to determine the start and the end of the rental period in advance. The beginning and end of the rent are established for all vehicles through a specific smartphone application. Payment is based on usage and according to a fixed minute rate.

Comparing this market to the sharing economy review models, according to Codagnone and Martens [30] (Figure 1), free-float car-sharing entities are B2C entities focused on profitable operation, and this requires strict regulation (Table 1). This business model represents a different resource utilization with respect to P2P-based common sharing, which motivated us to perform a parallel profitability and sustainability review.

To accurately identify all key free-float companies, the complete database of the firm registry was reviewed, considering the defined principal operational activity of each company. This classification (TEÁOR'08) is "identical and fully harmonized with the European one, NACE Rev.2. Statistical Classification of Economic Activities in the European Community, 2008 (Nomenclature des activités économiques dans les Communautés européennes) [37]. Based on Regulation 1893/2006/EC, with effect from 1 January 2008, TEÁOR'08 is used to determine the principal activities of enterprises, in the calculation of economic and social indicators as well as for the publication of statistical data." The car-sharing activities are classified under Section "N" as administrative and support service activities, in division 77, group 77.1, and class 77.11 "renting and leasing of cars and light motor vehicles". From the registered Hungarian companies' database, 362 companies were identified. This analysis covers all Hungarian operational entities. In order to include recently established objects, all companies above 10 staff headcounts were investigated, according to the EU commission-defined categories. On the basis of a detailed review, 28 companies were identified, as presented in Appendix A (Figure A1).

According to the Hungarian Accounting Regulation Act C of 2000, in Hungary [38], companies need to file a financial statement by the end of the fifth month after the fiscal year. Consequently, the latest reports available were for 2017.

From Appendix A, on the basis of their financial statements, as of April 2019, only 2 companies out of the total 28 entities, i.e., #11 GreenGo Car Europe Korlátolt Felel˝osség ˝u Társaság (hereinafter: GreenGo) and #20 MOL Limitless Mobility Korlátolt Felel˝osség ˝u Társaság (hereinafter: MOL LIMO), were real flee-float car-sharing companies, and both operate in Budapest. This list contained all free-float service providers but did not represent the total lease market, because financial lease activities are classified in a different statistical segment, in section K Financial and insurance activities, divisions 64–66. It did, however, represent all non-micro-level free-float car-sharing companies. This is the consequence of the unclear current statistical data, which do not identify specific lease, rental, or free-float services. In the case of a larger population, it would be challenging to sort out such companies manually; sub-sections could be created to evaluate lease and rental services accurately in the statistical classification. In 2017 for Hungary, free-float car-sharing represented a 110.7 million Hungarian forint (HUF) (€358,300) market.

In the analyzed group from the profitability perspective, it was visible that the free-float car-sharing service providers delivered significantly worse results in Hungary compared to lease and rental service companies in 2017, as shown in Figure 2.

**Figure 2.** Changes in asset structure, GreenGo (2014–2017 in EUR) [39].

To gain a better understanding of the situation, each Hungarian free-float service was separately examined and later compared to German service providers.

#### 3.1.1. Financial Statement Analysis and Review of the Financing Model

GreenGo was established in 2014 as the first free-float car-sharing service in the Hungarian market, where it was the only market participant until 2017. The first day of real operation, when the company started to provide services, was in November 2016, with 45 electric cars.

From the financial perspective, the assets and liabilities of the company looked as follows. Assets: The long-term assets value continuously increased from HUF 69 M in 2019 to HUF 102 M on 2017, which consists of intangible assets of HUF 43 M, tangible assets of HUF 58 M, and other investments of HUF 1 M. This breakdown would give the reader important information if we included the published data from January 2018 when GreenGo reported 168 vehicles, which in case of purchase, should be recorded as property, plant, and equipment (PPE). It appears that HUF 58 M/168 vehicles = HUF 0.34 M (approx. €1060) per car is a very unreasonable figure. The only reasonable explanation is if the company applied operational leases, and these assets are off-balance-sheet financed items. Later in this review, this business model will be compared to that of the other Hungarian competitor. Below in Figure 3 is a summary table related to the asset items for the period 2014–2017:

*Resources* **2019**, *8*, 172

**Figure 3.** Changes in asset structure, GreenGo (2014–2017 in EUR) [39].

Liabilities, equity: The equity value remained relatively the same over 2016–2017, i.e., HUF 43 M; however, the generated loss increased significantly from HUF 18 M (€59,000) to HUF 158 M (€512,600), which was compensated by the equity contribution from owners. The debt/equity ratio also significantly increased in relation to the liabilities increase by HUF 129.3 M, mainly as a result of the short-term shareholders' loans of HUF 115 M and the long-term related parties' credit of HUF 16 M. Profit and loss statement: The realized revenue increased from the 2016 value of HUF 8 M (€26,000) to the 2017 value of HUF 111 M (€358,000), while the expenses increased from HUF 27 M to HUF 275 M. This was the principal reason for the generated loss as the company did not realize enough revenue to compensate for the increased material expenditures. Below in Figure 4 is a summary of the statement of profit and loss of GreenGo for the period of 2014–2017.

**Figure 4.** Comparison of assets and liabilities of MOL Limo and GreenGo (2017) [39]

In 2017, MOL Limo entered the market with secured funding from the listed Hungarian Oil-and-Gas Company (whereas GreenGo owners are private investors). MOL Limo market presence did not cause the reported increasing loss of GreenGo, because, in 2017, it did not realize any revenue. In Table 3, a comparison between the profit and loss statements of these two entities is presented.


**Table 3.** Comparison of the profit and loss statement for MOL Limo and GreenGo (2017) [39].

MOL Limo generated a significantly higher loss compared to GreenGo, but 2017 was the year of its establishment, with a large scale of operation and considerable fleet investment, as presented in Table 3. The difference in asset value is related to a specific accounting regulation difference in lease accounting. MOL Limo prepared an IFRS-based financial statement, and GreenGo prepared a simplified national accounting-based financial report.

From the operation perspective, it is essential to mention that GreenGo only uses electric vehicles differently from MOL Limo. The total number of 400 electric vehicles operated by these two companies represents approx. 10% of the registered fully electric (excluding hybrids) cars in Hungary, as presented in Table 4. It should also be highlighted that hybrid vehicles increased more significantly in Hungary compared to fully electric ones from 2017 to 2018. This trend seems to continue and could be a subject of future investigation.

**Table 4.** Registered electric vehicles in Hungary and comparison MOL LIMO and GreenGo fleets [39].


#### 3.1.2. Lease Accounting Differences

Lease accounting is significantly different in the C Act of 2000 compared to IFRS. According to Hungarian Accounting Law (HAL) and IFRS, the definition of lease is different, and other fundamental accounting difference regard, for example, operating leases, which are not required by HAL to be recorded in the balance sheet, as shown in Table 5. Also, in the disclosure requirements, as in the HAL-based financial statements, operational leases only appear in the profit and loss statement.

**Table 5.** Comparison of operational lease accounting between the Hungarian Accounting Law and IFRS 16 from the lessee perspective.


IFRS 16 key objective was to record the operational lease committed rights (rights of use, ROU) as assets and committed liabilities to reduce the off-balance sheet items. For the entities reporting under HAL regulation, this is not a requirement, and in case of an independent financial analysis or a credit strength testing, they can be invisible. The recorded off-balance sheet value can be significant from a creditor's or financial analysis' point of view. GreenGo reported under HAL regulation, where the operational leases as off-balance sheet items might create a business advantage from the presentation perspective because the leverage ratio does not show the total minimum of liabilities from the lease obligations.

#### 3.1.3. Comparison to German Entities

Germany has the most significant car-sharing market in Europe, with several service providers and over 30,000 registered users, as summarized below in Table 6 in comparison to Hungary.


**Table 6.** Comparison of German and Hungarian entities' published users, fleet size, and serviced cities.

From this table, it can be concluded that German free-float car-sharing companies operate significantly larger fleets and have a substantially larger number of registered users in absolute terms. Hungarian companies operate only in one city, namely, Budapest, with a total of 750 vehicles for a 525 km2 city area, where the population is approx. 1.75 M. In contrast, only one company, ShareNow, operates approx. 4000 cars in Berlin for an 891 km<sup>2</sup> city area with a 3.6 M population. For additional comparison, in the capital city in the region with the most similar population, Vienna, only ShareNow operates, with 2000+ vehicles for a 1.8 M population and a 415 km2 city area.

The service fees can also be compared, because in April 2019, ShareNow announced to extend the operation in Budapest as well, with approx. 240 vehicles (of which, 40 electric BMW i3). Table 7 shows the fee and car type comparison.

**Table 7.** Comparison of free-float service costs between ShareNow, MOL Limo, and GreenGo (2019) [39–41].


ShareNow provides services across the EU and, in 2019, established the most significant European fleet; additionally, it published a plan to invest further €1 billion. With 20,000+ vehicles, joint companies operate in 24 countries globally. It is only a matter of time to utilize the economies-of-scale advantage and provide service in all European countries. A coverage map for Car2Go and DriveNow is shown in Figure 5.

From the operation and financial analysis perspectives, an apparent market concentration is happening now in Europe, which is a successful business model. Without doubts, it supports sustainability; however, there is no core sustainability element in this business model. The more effective utilization of the resources has an impact on sustainability, but it is based on a usual corporate profit model.

**Figure 5.** Car2Go and DriveNow joint coverage.

#### *3.2. Sustainability*

From the sustainability perspective (Table 2), three statements (out of a total of seven) appeared in the official communications of the reviewed companies, presented in Table 8.


**Table 8.** Sustainability-related aspects of car-sharing [32,39–41].

\* Electric cars have two main advantages: unlike gasoline, electricity can be generated from various sources including renewable ones, and electric vehicles can reduce urban air pollution from road transportation. "However, while electric cars can reduce gasoline use, they increase electricity consumption. Depending on how the electricity is generated, emissions of particular air pollutants may reduce or increase" [42]. In Appendix B (Table A1), we list the vital sustainability-related statements from car2go and DriveNow sustainability reports; the reviewed sustainability reports are all related to Corporate Social Responsibility (CSR) orientation.

#### *3.3. Analytic Hierarchy Process*

To resolve the lack of reconciliation between financial and sustainability reporting, potential decision-support models, such as the analytic hierarchy process model, can be utilized to present the connection between the different reporting systems. It is crucial to determine the factors and to apply proper weights for the specific items. To measure impacts, the method of the analytic hierarchy process (AHP) was used, where the weights of the factors were identified in order from the most to the least significant from the investor decision's perspective.

When constructing the decision-making environment, it is crucial to identify issues or attributes that may be helpful [43,44], which brings the disharmony of traditional financial performance measuring attributes and sustainability aspects into perspective. The AHP theory aims to find the preferable alternative by weighing the priorities of the involved factors on a 1–9 scale (1: equal importance, 9: higher importance with respect to another component) and carrying out pairwise comparisons and standardization of the results to validate the overall ranking of factors [43,44]. Considering the findings of the current study, six elements were selected and weighed (w), as shown in Figure 6.

**Figure 6.** Decision factors and assumed weights.

In the analysis process, pairwise comparisons were developed for each criterion using linear integer scaling, summarized in a 6 × 6 matrix, which was then normalized using natural logarithms (ln(*A*)) [45]. Using the AHP template and methodology of Goepel [46], the results were then averaged by rows, and the impacts were measured by the Eigenvector method (EVM). The summary matrix is presented in Figure 7.

**Figure 7.** Summary of the analytic hierarchy process (AHP) matrix.

Additionally, the Eigenvalue (or λ, consistency measure), the consistency index (CI), the mean relative error (MRE) of the weights, and the consistency ratio (CR) were calculated [47]. If the Eigenvalue (the matrix product of normalized principal Eigenvectors) equals the sample size (6), perfect consistency can be identified (λ = *n*), which in our case corresponds to the value of 6.091.

The priorities pi in the input matrix were transformed into a near-consistent model using the EVM. In the pairwise *n* × *n* comparison matrix *A* = *aij*, where Ω1, Ω2, ... , Ω*<sup>n</sup>* are comparable elements with a positive numerical value, the transformation procedure is as follows:

$$\left( \begin{array}{c} \text{measurement} \\ \dots \\ \Omega\_{n} \end{array} \right) \xrightarrow{\text{procedure}} \left( \begin{array}{c} w\_{1}^{(1)}, \dots, w\_{1}^{(n)} \\ \dots \\ w\_{n}^{(1)}, \dots, w\_{n}^{(n)} \end{array} \right) \tag{1}$$

with the use of EVM, the measuring procedure can be adapted to pairwise comparisons: *<sup>n</sup> k*=1 *aikwk* = λ*maxwi*, *i* = 1, ... , *n*, where λ*maxwi* are the principal Eigenvectors [48].

The normalization process is as follows:

$$p\_i = r\_1 / \sum\_{i=1}^{N} r\_i \tag{2}$$

The CI was calculated by:

$$\text{CI} = \frac{(\lambda - n)}{n - 1} = 0.18\% \tag{3}$$

Error calculation of the priority vector *wi* with the used EVM followed:

$$
\Delta w\_{i} = \sqrt{\frac{1}{n-1} \sum\_{k=1}^{n} \left(\frac{n}{\lambda} a\_{ik} w\_{k} - w\_{i}\right)^{2}}, \; i = 1, \ldots, n = 19.0\% \tag{4}
$$

In the CR, the Alonson/Lamata linear fit was used: CR <sup>=</sup> <sup>λ</sup>−*<sup>n</sup>* 2,7699*n*−4,3513−*<sup>n</sup>* <sup>=</sup> 1.4% [47].

From the hierarchical structure and from the potential AHP model presented in Figure 7, profitability remains the most significant factor in an investor company valuation with a normalized principal Eigenvector of 41.3%, followed by the cash flows (22.3%) and total assets (18.2%). From the investor decision's perspective, as long as sustainability reporting does not harmonize with financial reporting, the sustainability aspects tend to have a low impact factor (4.4%). In conclusion, the AHP statistical method is usable for the prioritization of factors, but it should be emphasized that the applied weights of the factors can be depend on subjective evaluations.

#### **4. Conclusions**

From the financial and sustainability reports, the following conclusions can be made related to the Hungarian free-float car-sharing market:


Sustainability reports in the examined sample cannot be connected to the financial statements, whereas harmonization is essential and should be a subject of future studies.


**Author Contributions:** A.T. and C.S. initiated the study and performed the conceptualization, A.T. prepared the literature review, designed the methodology and collected the data, C.S. validated the results, A.T. and C.S. wrote the paper.

**Funding:** This research received no external funding.

**Acknowledgments:** Data collection, analysis, and administrative support received from the "Research Center of Vehicle Industry" at Széchenyi István University (JKK).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**


**Figure A1.** TEÁOR 77.11 main renting or lease activity of companies with at least 10 staff headcounts in Hungary [25].

#### **Appendix B**



#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Circular Economy and its Comparison with 14 Other Business Sustainability Movements**

#### **Gergely Tóth**

Department for Alternative Economics, Institute of Finance and Accounting, Faculty of Economic Science, Kaposvár University, 7400 Kaposvár, Hungary; toth.gergely@ke.hu

Received: 24 July 2019; Accepted: 18 September 2019; Published: 25 September 2019

**Abstract:** Circular economy is not the first, and probably not the last "movement" in the arena of sustainability macroeconomic and business solutions. In this article we produce a—not full—list of similar movements from the 1990s, publish a comparative table and propose a simple framework to decide the significant points of the life cycle of such a kind of movement. For significant points and statistics, we use simplified content analysis from normal and scientific research engines. Finally, we use this framework to make a forecast about time for the circular economy approach "to stay on the top" and conclude if these movements are "Much Ado about Nothing" or they help us on our way to a sustainable planetary, social and economic system.

**Keywords:** business sustainability movements; circular economy; life cycle; sustainable development; human economics

#### **1. Introduction: Hypes, Movements, Scientific Schools**

Circular economy and sustainable development goals of the UN are far the most popular topics in the last two years in the business sustainability arena. This was not the case five years ago. It is an interesting question to try to forecast whether it will be the same in 2–5 years' time. To decide that, in this paper we will look at the popularity of other similar movements. We will examine the hypothesis that these movements come and go as fashion, or they keep up the interest for sustainable development, as a whole approach, to prepare a paradigm change from unlimited growth to sustainable development.

The first sentence of the Book of Genesis (and the whole Bible) is: "In the beginning God created the heaven and the earth". If once the "bible" of the modern business-environmental movements would be written, it could start with such a sentence: "In the beginning environmentalist created Recycling!"

Indeed, recycling is a very old approach. Written sources mention paper recycling from 1031 from Japan [1,2], and the second utilization of resources were used as nation-wide strategies in World War 2 UK and USA. However, recycling, as an "environmental movement" or techno-scientific approach is much younger. The first mention of recycling in *Google Scholar* is from the early 1800s, and we have altogether 1730 records till 1900. Currently it is about 20–60 thousand in a single year (32,500 Google Scholar hits between 1 January 2018 and 26 June 2019, 22,800 between 1 January and 26 June 2019, 308,000 since 1 January 2015.) and 2.8 million in total.

If we look at (normal) *Google* hits, recycling is a very popular topic. Its product life cycle is similar to Coca-Cola from marketing (*Life cycle* and *life cycle assessment* is used in two meaning in this article. The main meaning is from marketing science: How long a product can stay in the market, before it gets technically or fashionwise obsolete. Some exceptional): The product does not get obsolete, it does not go out of fashion, it stays on the top. If we hit the research phrase "recycling" to Google, we get approximately 332 million results (This article heavily relies on normal Google searches, Google Science hits and time series of hits etc. from *Google Trends*. It is necessary to deduce the "noise" and severe short-term time fluctuation of results. For this reason, all hits are from a period of three days:

27–29 June 2019, unless otherwise indicated). With these numbers, recycling is far the most popular movement among the 15 we are considering in this article, its dominance *in everyday use* is "oppressive" and irreversible, *in scientific publications* it is only highly outstanding and unquestionable.

This is the point to explain why we use the term *movement*. We could call these fifteen things *hype*, as they have characteristics of fashion, people are enthusiastic about them, but then they go out of fashion. However, they are too well supported and scientific to be called a hype. We could also call them *scientific schools*, as they are well defined, we have scientific evidence behind them in forms of monographs [1,3], primary research [4,5], journal articles [6–8]. For example, literature supports that circular economy can contribute to the energy [9] and material [10,11] perspectives, embracing topics from residential photovoltaic systems [9] to sewage sludge biogas solutions [12]. New movements are widely documented with systematic literature reviews [13,14].

However, most of these studies come from semi-scientific sources like the consultancy sphere [15] or the European Commission [16]. These institutions—although making excellent and reliable research with hard work—have a primary interest to spread what they consider good politically, and these forecasts are often positively biased. So, these things do not show the characteristics of scientific schools in the long run, they might be called one scientific school *(the business sustainability school)* in the long run. It is also often the case that a thing has a look of a scientific school or looks like a hype, but then another characteristic of it becomes more dominant. Marxism is an example for that: If it did not turn into a social movement with the aim to change the world very pragmatically, we would probably consider Marxism as one of the most elaborate schools of economics. However, the political movement faded this characteristic of being a scientific school.

We could also look for other expressions like *paradigm*, *meme*, *program*, etc., but we find that the connotation of the world of *movement* is the most proper for our purposes. This is the strongest common term but saying that we do not ignore that the 15 movements have different characteristics. For instance, *recycling*—apart from being a movement—is a very practical approach to waste management, *zero emission* is mostly known in the car industry, *cleaner production* is a very highly ranked scientific school with an excellent dedicated journal, and so on. For the sake of simplicity and our intention to compare these things, we call them movements. So, let us see, what similar movements can we consider as predecessors of the circular economy.

#### **2. Dataset: A Catalogue of 15 Business Sustainability Movements**

We can pick a list of 15 movements showing similar characteristics to circular economy. They often have common fields, so in order to define them we describe them shortly, to have a common understanding. In the list below (Figure 1) we use the most widespread definition and one-paragraph description of the movement, if it is not available from secondary source, we produce a short summary. In some cases we put the most well-known symbol or "founding father" (namely 1. *Recycling* logo, 3. *Cleaner Production*—UN logo, 10. *Corporate Social Responsibility* explaining graphic, 11. Günther Pauli with *Blue economy*, 12. Michael Porter with *Creating shared value*, and a 15. *Circular economy* explaining chart, referring back to the beginning of the list: product life cycle). This list could be extended with phrases like eco-efficiency or eco-design, but a list of 15 significant movements is strong enough to see differences, commonalities, and most of all meet our primary goal: To depict the life cycles.

**Figure 1.** The 15 business sustainability movements in our focus.

#### *2.1. Recycling*

Recycling is a procedure to convert waste materials into useful objects again, that is to produce new products from old (vs. so-called virgin) material. Most common examples are paper, glass, and metal recycling. Compound products are harder to recycle, cars or electronics are made of a number of carefully combined materials, which does not ease detachment and reutilization. Recycling is normally considered as an environmentally friendly solution opposite to waste disposal (dumping), incineration (utilization of the energy content) is half-way. The waste mitigation hierarchy or the three 'Re' are often cited [3,16] that is Reduce-Reuse-Recycle. In this sense the best environmental solution is (i.) not to produce and consume, than (ii.) to use things for the same purpose without an energy-intensive de- and remanufacturing (e.g., selling mineral water again in the same glass bottles), and (iii.) finally convert material through handicraft or industrial processes into new products. As we will see, recycling is the first and far the best known movement in our list.

#### *2.2. Waste Minimization (WM)*

Waste minimization is a systematic approach to reduce, and if possible, to prevent the "production" of unintended by-products and other waste material, including fluent and gaseous emissions. Ojovan and Lee [17] defines waste minimization as a process of reducing the amount and activity of waste materials to a level as low as reasonably achievable. WM strongly relies on the waste mitigation hierarchy: reduce-reuse-recycle (as shown in Figure 2). Sometimes other 'Re's are added like *rethink*, *redesign*, *refuse*, *replace*, *reengineer*—but the point is the same, this is mostly playing with the words. As Rosenfeld [18] states, the objective of WM is to decrease the amount of hazardous waste bound for energy recovery, treatment, and disposal facilities. Utilization for the same purpose in the same form (reuse), in a modified form (remanufacture) and in a new form (recycle) is sought instead. Although waste minimization is already mentioned in 1974 [19], it became a massive movement from the 1990s, propagated by prestigious organizations like the US EPA, specialized UN agencies, etc. 1984 the US Congress passed amendments to the Resource Conservation and Recovery Act (RCRA) declaring waste minimization to be national policy [20].

**Figure 2.** The 3 'Re's—the waste avoidance/utilization hierarchy.

#### *2.3. Cleaner Production (CP)*

The methodology, earlier also termed pollution prevention, is based on preventive solutions as opposed to end-of-pipe technologies. Besides being logical it has also been proved by several studies that if a procedure is originally formulated so as not to create pollution or waste it is not only environmentally positive, but also financially advantageous. This way materials and energy obtained at high costs are not wasted by low efficiency. In contrast, end-of-pipe solutions leave production processes unchanged, but add supplementary devices, e.g., filters, cleaners, to them. These supplements have extra cost on the one hand, and on the other many times just transform one type of pollution into another (e.g., Sludge, energy-plant ash). They are of course needed and handy in everyday practice, but our main perspective should be prevention. Cleaner production is propagated through the international network of CPCs, Cleaner Production Centers.

The promotion of energy efficiency can be taken as a special manifestation of cleaner production. Here our aim is to keep wasted energy at the lowest possible level at an organization or in a building. As a result of CIPEC (Canadian Industry Program for Energy Conservation), for example, 5000 companies, responsible for 98% of the total industrial energy consumption, decreased their energy intensity by 9.1% between 1990 and 2004. Energy conservation is usually attained by the combination of two types of measures: "hard" measures are technological changes (like recuperative devices, installation and reuse of thermal energy waste), while the "soft" ones request behavioral or administrative modifications only. Experience shows that at least half of the environmental problems would be prevented by responsible behavior. Looking at it from another angle, the development of technologies will never be an answer to mankind's environmental problems by itself, to reach this goal we have to change our own behavior [21].

#### *2.4. Zero Emission*

Zero emission is a well-researched topic and its connection to other movements like CP or LCA are apparent in the literature [8,22]. Some even assume that this approach could be a holistic tool to bring about a sustainable society [23]. Nevertheless, the most well-spread use of the term is in the automotive industry, hinting that zero emission is a narrow focused methodology referring to an industrial or mechanical process, motor, or engine, emitting no waste products of any kind that pollute the environment or contribute to climate change. Nieminen [5] shows that this approach is very closely linked to best available technologies (BATs), eco-efficiency and LCA. A complex approach

to zero emissions was first published in 2002 [24] (Dixon, Porche and Kulick), but much earlies it gave birth to ZERI—Zero Emissions Research and Initiatives in 1994. The movement than was reborn in the Blue Economy movement by the same think-tank, Günter Pauli.

#### *2.5. Zero Growth, Decroissanse*

Actors of the business sphere are more practical minded than to be easily put off by some conceptual obscurity about how to define sustainability in every-day use. Especially because from the 60's they have been susceptible to strong attacks first in the name of environmental protection, then sustainable development. Some even started talking about zero growth as the practical realization of sustainable development [25,26]. Zero growth is obviously contrary to the growth myth running in the blood of both micro and macro level decision makers in economy [21,27].

#### *2.6. Green Economy (GE)*

The green economy can be defined "as economy that aims at reducing environmental risks and ecological scarcities, and that aims for sustainable development without degrading the environment" [28,29]. GE is closely related with environmental and ecological economics, but it has a more politically applied focus. Although the UN Environmental Program adapted the idea, its concept is at least more than two decades older: David Pearce, a prominent environmental economist published his report entitled "Blueprint for a Green Economy" already in 1989 [30]. The book had been prepared by the London Environmental Economics Centre (LEEC), a joint venture by the International Institute for Environment and Development (IIED) and the Department of Economics of University College London (UCL). The Pearce Report demonstrated models where environmental elements in threat of being polluted can be costed. The green economy concept urges systems of taxation which would both reduce pollution by making it too costly and generate revenue for cleaning up the damage. A central GE concept is therefore "the polluter pays" principle.

#### *2.7. Triple-Bottom-Line, Alias 3P*

Big enterprises made up their own well operationalized concept of sustainable development ("Triple bottom line" also used as TBL, 3BL, People, Planet, Profit, originates from John Elkington, the influential English founder of SustainAbility, from 1994 [31]). As a matter of fact—though not to the satisfaction of all—consensus is about to be reached on the basis of "something is better than nothing". According to this corporate sustainability is the outcome of a triple optimization, or "triple bottom line". It is a three-legged model in which the foundations are the three columns of ecological, social and economic sustainability. The operationalization of corporate sustainability usually means that eco-efficiency is taken for ecological responsibility, keeping to basic norms (such as improving working conditions, giving financial aid, not using child labor and abuse) stands for social sustainability and economic sustainability is clearly understood as the enterprise's long term profitability [21,31].

#### *2.8. Life Cycle Assessment (LCA)*

The method of Life Cycle Assessment embraces environmental impacts of the product during all stages of its life-cycle. Such an assessment contains all the in- and outgoing material and energy flows separately in the phases of the production of raw materials, processing/manufacturing, usage and becoming waste, not forgetting to consider the transportation linking these phases. Once we have drawn the "boxes" representing these processes (which might amount to thousands within a somewhat more complicated industrial framework like that of manufacturing automobiles) and their input-output flows, we can proceed to summarize the impacts using natural indicators, ending up with an eco-balance. Here we can apply different methods to adapt the different measures into comparable measurement units. Available software (e.g., Gabi) can be of great help, especially because of their evaluation methods in the background (e.g., BUWAL). The major steps of LCA are setting the system limits, inventory analysis and, finally, impact assessment. A number of ISO 14,000 standards deal with LCA [21].

#### *2.9. Sustainable Consumption*

Sustainable consumption and production aim to promote resource and energy efficiency, sustainable infrastructure. Its strategic goal is to provide access to basic services, green and decent jobs and a better quality of life for all. It is one of the 19 Sustainable Development Goals (SDGs) of UN by 2030, under the name "responsible production and consumption" [32]. Already in 1992, at the United Nations Conference on Environment and Development (UNCED) the concept of sustainable consumption was established in chapter 4 of the Agenda 21. In 2002 a ten-year program on sustainable consumption and production was created at the World Summit on Sustainable Development in Johannesburg. The definition proposed by the 1994 Oslo Symposium on Sustainable Consumption [33] defines it as "the use of services and related products which respond to basic needs and bring a better quality of life while minimizing the use of natural resources and toxic materials as well as emissions of waste and pollutants over the life cycle of the service or product so as not to jeopardize the needs of future generations" [33].

#### *2.10. Corporate Social Responsibility (CSR)*

It is written in the EU Green Paper on CSR [34] "most definitions of corporate social responsibility describe it as a concept whereby companies integrate social and environmental concerns in their business operations and in their interaction with their stakeholders on a voluntary basis." The Commission recognizes that CSR "can play a key role in contributing to sustainable development while enhancing Europe's innovative potential and competitiveness" [34–36]. According to EU initiatives enterprises "over comply" legislation in collaboration with their stakeholders.

According to the WBCSD "Corporate social responsibility is the continuing commitment by business to behave ethically and contribute to economic development while improving the quality of life of the workforce and their families as well as of the local community and society at large" [37] (p. 6).

The so-called "deep" definition for CSR is the following: "The Truly Responsible Enterprise (i) sees itself as a part of the system, not a stowaway concerned only about maximizing its own profit, (ii) recognizes unsustainability (the destruction of natural environment and the increase of social injustice) as the greatest challenge of our age, (iii) accepts that according to the weight they carry in economy, governments and enterprises have to work on solutions, (iv) honestly evaluates its own weight and part in causing the problems (it is best to concentrate on 2–3 main problems), and (v) takes essential steps—systematically, progressively and focused—towards a more sustainable world" [21].

#### *2.11. Blue Economy*

The Blue Economy concept was officially laid down in the same titled book of Günther Pauli in 2010 [38], but it refers back to the Zero Emission movement by the same author. It began as a project to find 100 of the best nature-inspired technologies that could affect the economies of the world, with the condition of providing basic human needs—potable water, food, jobs, and habitable shelter—in a strictly sustainable way. Hundreds of technical innovations were found and described, that could be bundled into systems functioning similar to ecosystems.

#### *2.12. Creating Shared Value (CSV)*

Creating shared value is the latest "hype" in our catalogue, it was first introduced in an often-cited Harvard Business Review article *The Link between Competitive Advantage and Corporate Social Responsibility* [39]. The business concept was proposed by Michael E. Porter, a leading authority on competitive strategy and head of the Institute for Strategy and Competitiveness at Harvard Business School, and Mark R. Kramer, Kennedy School at Harvard University and co-founder of FSG, "a mission-driven consulting firm". The main premise behind CSV is that of "extended CSR". Authors are very ambitious about their concept: They promise CSV has the power to unleash the next wave of global growth and to redefine capitalism [39]. On the other hand, critics say that "Porter and Kramer

basically tell the old story of economic rationality as the one and only tool of smart management, with faith in innovation and growth, and they celebrate a capitalism that now needs to adjust a little bit". They regard CSV as a "one-trick pony approach" with very little chance that an increasingly critical civil society would buy into such a story [40]. It is not clearly explained, if the current income from products in the market are not shared in a moral, just way, why would this happen in the case of "creating more value" (basically increasing retail prices due to more value added). This is not Porter's first approach, he basically connects competitiveness with many trendy approaches, like efficiency, the environmental cause or CSR [41].

#### *2.13. Industrial Ecology*

Industrial ecology aspires further than cleaner production since its ambition is not the optimization of a specific process, but the creation of a certain industrial eco-system. Here the waste produced by a process or a factory is the base material for another. Its tool kit does not contain too many new elements though, but, besides recycling, is made up of the same as that of the forenamed cleaner production, life cycle assessment and eco-design [21].

#### *2.14. Sharing Economy*

In the sharing economy, persons rent or "share" things like their cars, rooms, houses or apartments to other people. Also, personal time is not sold, but shared in a peer-to-peer fashion [42,43]. Sharing economy is a basically new approach to the ownership, use and marketing of products and services and has the highest chance to turn the current form of market economy into something slightly or dramatically different. The term is used to describe distributing goods and services differently from the traditional business models via hiring employees and selling products to consumers (as depicted in Figure 3). Others call it "access-economy", which might be a more proper, but less used term [7]. Uber and Airbnb are just two iconic examples of the sharing economy, generating massive and fierce debate among professionals, regulators, and researchers. Sharing economy is not fully dependent, but in its current form heavily relies on Internet-based social networks: a feeling of trusting—formerly unknown people through—the Network substitutes the traditional feeling of trusting your friend, group member or other peer in the local, personal society.

**Figure 3.** The bright future of sharing economy by PWC—from 5% market share in 2013 to 50% in 2025.Source: PricewaterhouseCoopers, 2014 [15].

#### *2.15. Circular Economy*

According to the definition of Merli, Peziosi and Acampora [13] circular economy "aims to overcome the take-make-dispose linear pattern of production and consumption, proposing a circular system in which the value of products, materials and resources is maintained in the economy as long as possible". Kirchherr, Reike and Hekkert [14] analyses 114 CE definitions and conclude that it is most frequently associated with a combination of reduce, reuse and recycle activities, which they held a mistake, lacking a systemic shift towards social equity and sustainable development. I agree with their conclusion that CE must aim at far beyond mainstream goals of economic prosperity, at a paradigm shift towards sustainable and human development. The concept of CE can be traced back to the works of David Pearce 1989 [30], Kenneth Boulding 1965 [44], and Tim Jackson 1993 [45].

After discussing the fifteen movements in detail, let us turn our attention to their common life cycle.

#### **3. Method: A Proposed Life Cycle**

According to our hypothesis, a hypothetical life cycle of the business sustainability movements can be constructed. They are known and practiced long in history, for example [1,2] mention *paper recycling* from 1031 Japan, *waste minimization* was probably a practice—although not under this name —in all historic times, due resource scarcity and common sense. William Foster Lloyd in 1833 [46], and Garrett Hardin [47], popularizing him in 1968 described the sharing economy in the *Tragedy of Commons*, which was rather the mainstream and not the exception before the massive enclosure in the 18th century England. However, waste minimization and sharing economy did not appear as a comprehensive and broad movement until the recent decades. So "historic times" on Figure 4 can take centuries or millennia, but as a movement, hype, widely spreading business initiative or public policy instrument by the UN, EC and other respected international agencies is normally taking place from the 1990s, when global environmental problems have been commonly understood and accepted. The historic (latent) period and the fashion (explicit) period is depicted on Figure 4 with red turning to green respectively. The figure proposes a life cycle as well: Steady and slowly accelerating growth, peak and going out of fashion, where the horizontal axis is a logarithmic scale.

**Figure 4.** A hypothetical life cycle of a business sustainability movement, time (horizontal) and popularity (vertical).

Compering historic practices and modern renaissance of these approaches we could conclude that modern societies keep on reinventing the wheel. What is worse, from the catalogue of the previous section we could conclude that we have reinvented at least 15 different wheels. As we emphasized, these movements we consider one wheel, although varying in shape, material and other important characteristics. Only the business sustainability movement is a wheel, with slight variations.

However, the main purpose of this article is not to create a catalogue of business sustainability movements, but to look at their respective life cycle. Is it true that they really emerge, fly high and disappear? Do they add new peaks and keep up public interest for business sustainability? In the next session we will see, that this hypothesis is only partly true, at least with our methodology: It is easy to be present on the Internet, it is hard to top the hit lists, but what is really impossible to disappear from there.

On Figure 5 we tried to depict a somewhat pessimistic hypothesis: business sustainability movements come, flourish and go. The thin color curves represent recycling, waste minimization, cleaner production, blue economy etc., the heavy grey curve represents the business sustainability movement in general. Colors extend a bit the total life cycle, but unless new hypes come, public interest will turn to other topics, in this accelerated and pulsing era of big data and mass information.

**Figure 5.** The individual LCs and consolidated hypothetical life cycle of business sustainability movements, time (horizontal) and popularity (vertical).

This would mean that we have to produce hypes in every 5–10 years, and repeat the tedious efforts of defining, finding positive examples, publishing handbooks, case studies, technical guides and policy documents, etc. This would also mean that these approaches hardly come to the boardrooms and university textbooks or they disappear very quickly. In the next section we use statistics of Google searches and hits in both the public pages and the scientific arena, in the last fifteen years. Google trends gives and excellent tool to produce time series in all different combinations.

Basically, our method is relatively simple: as we look at fashion and popularity AND presence in scientific publications in parallel, we look at 1) overall Google hits [this is what we call "normal", without any screening] AND 2) hits in qualified scientific databases. The latter is twofold: Google Scholar and Science Direct. Normal and scientific hits normally correlate, but not necessarily: sometimes they show fairly different results (as seen on Figures 6 and 7, e.g., sharing economy is very popular in normal Internet, but not visible in Google Scholar).

#### **4. Analysis: The Comparative Table and Citations**

In the table below (Table 1) we summarized the main characteristic points of fifteen sustainability movements of business and economics. In column II is the oldest paper in Google Scholar. Columns III–V are calculated values from Google Trends (as of 28 June 2019), showing the respective hits in Google and Google Scholar. We show highest and lowest values and their time (year and month). Columns III–V consider a 15-year period between January 2004 and June 2019. Column VI is again a somewhat anecdotal piece of information, but it is mostly agreed upon and easy to check. In column VII we cross-check Google trends and choose the scientific database over the common one: We decide the approximate length of the movements' fashion based on hits in Science Direct (as of 20 July 2019. We consider a movement "on top", if Science Direct lists minimum 100-300-1000 papers per annum, in relation to the total hits, to keep a balance and add a positive discrimination to less visible movements).


**Table 1.** The comparative table of 15 business sustainability movements—simplified content analysis.

<sup>1</sup> This date is mistyped in Google Scholar, as 1874. In reality it refers to the foundation of the Kroll Institute for Extractive Metallurgy (KIEM) at the Colorado School of Mines. KIEM focus areas included minerals processing, extractive metallurgy, *recycling* and *waste minimization*. <sup>2</sup> People-Planet-Profit (or **Profit** ... people ... planet?).

Composing the comparative table of the business sustainability movements in a precise way is harder than expected. In column II, should we specify the first historic example? The first proven use of the expression? The first scientific book or article solely devoted to the topic? We used a mixed

approach. For example, even Wikipedia denotes that Platon spoke about recycling 2500 years ago. However, most of much of Platon's and Aristotle's work was lost, the latter for example only survived in Arabic translations and were later translated back to Greek and Latin. Most of the movements, as we keep on emphasizing, refers back to some ancient and modern wise philosophers, scientists. A good example is the last line in the table, where Kenneth Boulding [44], David Pearce [30] and Tim Jackson [45] are referred to as "founding fathers", but also the Tragedy of the Commons (and herewith Hardin 1968 [46] and Lloyds 1833 [47]) are specified as theoretical basics. Anyway, roots and exact "who said first" is not so important, we could refer this question to monographs dealing with the specific movements (e.g., in CSR [21]).

What is more important from our special perspective, is the recent "web-footprint" and scientific records of the movements in question. The first we approximated with the (normal) Google hits of the last 15 years, the second with the hits in Google Scholar and we made a cross check through Science Direct. We specified some characteristics of these time series in the comparative table.

Our analysis also has some deficiencies: for example, in cell 5/III–IV it is hard to believe that *Zero growth* is on the peak and in its lowest mention in a period of three months. The French term *decroissanse* has a more profound, every day meaning—in English *degrowth* is devoted to the movement, the French *decroissanse* also means *decay, decreasing, reduction*. This means we cannot look for Google searches for *decroissanse* without being extremely biased with our results.

Google Trends is an excellent tool for time series analysis (from intervals of days and hours to a maximum period of 15 years), it gives area specific and detailed geographical information. Its main disadvantage that it is primarily for marketing, not for scientific purposes, its main advantage is that it normalizes hits on a scale of 100. This is the scale we used in the comparative table in columns III–V.

The first result is very apparent from the table: Recycling is far the oldest and most searched referred term of all 15 movements. If we put it to the comparative analysis, other movements become almost invisible (although in scientific articles the difference is much smaller). For this reason, we put the five less known and newer approaches on a joint graph (Figure 6). It is obvious, that single prophets (like Günter Pauli behind the *blue economy* or Michael Porter behind CSV) can have a huge added value in marketing, but this is still a short-term and relatively small push. If it is a long-term strategy and a giant agency as UNEP and UNIDO behind *cleaner production*, the effect is harder and longer. Nevertheless, general, easy-to-understand and appealing approaches like *sharing economy* and *circular economy* are the most successful in the evolution of business sustainability movements. Even the whole business sphere with all pioneering multinationals and their sustainability reports can have a relatively small leverage effect compered to this general appeal to the public. In the case of *circular economy,* Google Trends show us another interesting aspect: at one point around 2004–2005, 2 of the 5 related search terms included *Ellen MacArthur*, a champion yachtswoman from England. After retirement from professional sailing (at the age of 34) she established the *Ellen MacArthur Foundation*, a charity that works with business and education to accelerate the transition to a circular economy. One famous individual can do a lot to popularize the public good.

**Figure 6.** Frequency of normal Google searches for the terms *circular economy*, *sharing economy*, *waste minimization*, *cleaner production* and *zero emission.* Data and graph from Google Trends, as of 27 June 2019 (Hits normalized on a scale of 100).

On Figure 6 we see a limited effect of fashion: *Sharing economy* was very popular around 2015, but interest is significantly lost in the last 2–3 years. *Circular economy* was almost invisible till 2013, since that time its carrier is boosting. In science, however, the picture is slightly different. Sharing economy is very little discussed, and cleaner production keeps its positions much better.

We can observe significant regional differences in different countries. As apparent from Figure 8, sharing economy is still almost more popular in Germany, than circular economy. In the USA, the latter has clearly taken over. As well, in a new market economy, like Russia, where sustainability is probably less on the top of the agenda than in the EU or US, we see basically no evaluable activity. *Zero emission* is a more well-known term in the USA than in other countries, probably due to the fact that car development is more regulated by the market in the US, and more by the EC in the European Union (emission standards for passenger cars).

One major conclusion we can already draw here is that instead of competing movements, we should concentrate on strengths of each: *Cleaner production* has a very high scientific literature and technical background through the *best available technics* (BATs), *circular economy* is the newest concept with the contemporarily strongest appeal, *sharing economy* has the highest community (social network and 'apps') support, also there is the most fight around it taking the form of market regulation (Uber vs. taxi companies, Airbnb vs. hotel chains, pirate music sharing vs. traditional recording companies and Amazon, etc.). These fights create significant losses and some bankruptcies but are beneficial for the somewhat halted evolution of modern business towards a sustainable economy.

**Figure 7.** Frequency of Google Science searches for the terms *circular economy, sharing economy, waste minimization, cleaner production* and *zero emission.* Data and graph from Google Trends, as of 27 June 2019 (Hits normalized on a scale of 100).

**Figure 8.** Frequency of normal Google searches for the terms *circular economy, sharing economy, waste minimization, cleaner production* and *zero emission* in Germany, the USA and Russia. Data and graph from Google Trends, as of 27 June 2019 (Hits normalized on a scale of 100).

On Figure 9 we can see that *circular economy* is the strongest in Scandinavian countries, South America, South Africa, sharing economy is strongest in Russia, US, core of the EU, Australia. However, a new finding is that cleaner production has very strong support and leads the poll in Brazil and Iran. Instead of looking at these selected pictures, I strongly recommend putting these five phrases to Google Trends, select the 15-year period, and look at individual, interactive maps and charts. If we look at the five individual world maps, one major learning is that the US is strongest in everything, which is connected to the Internet.

On Figure 10 we disclose one of these individual world maps, namely for *circular economy*. Apart from the spatial distribution we also see the most common connected terms, which (including the other 14 movements) could be the topic of further investigation.

**Figure 9.** Geographical representation of normal Google searches for the terms *circular economy, sharing economy, waste minimization, cleaner production* and *zero emission*. Data and graph from Google Trends, as of 27 June 2019.

**Figure 10.** Google searches (hits) for the term *circular economy* by regions, and most frequent connected terms. Data and graph from Google Trends, as of 27 June 2019.

In Figures 11 and 12 we compared hits for another set of five of our selected business sustainability movements. As already pointed out, recycling is far most the winner, although its lead is less obvious in Google Scholar than in normal WWW content. In the normal arena, even the second sustainable development is hardly visible (see averages on the left), in science in rare cases it takes over recycling. Recycling is with no question the most technical and least scientific general approach of all.

**Figure 11.** Frequency of normal and scientific Google searches for the terms *recycling, sustainable development, corporate social responsibility, green economy,* and *blue economy*. Data and graph from Google Trends, as of 27 June 2019.

**Figure 12.** Geographical representation of normal and scientific Google searches for the terms *recycling, sustainable development, corporate social responsibility, green economy,* and *blue economy*. Data and graph from Google Trends, as of 27 June 2019.

We could create other graphs and maps, but Google trends has two severe numeric restrictions: It cannot compare more than 5 search phrases on the one hand, and cannot produce logarithmic axis on the other hand, to screen out the powerful dominance of *recycling*. However, we showed a comparative table and massive statistics to justify, modify or falsify our original hypothesis.

At last we can produce a top list of business sustainability movements and draw conclusions (the top 5-6 movements are highlighted in the first column of Table 1). It is remarkable, that we have to use exactly the 5 right search phrases from the 15 potential, and it is also important in what order we type them in to the statistical analyzer. It would be obvious to put the five top terms, but then *recycling* (whose gold medal is not questioned) would fade the other four. So, we look at the comparative table and look for ranks number 2–6, based on the last column: most recent Science Direct hits. We got a slightly different list from Figures 6–9 (*sharing economy* and *waste minimization* are omitted, LCA and CSR are added). Although circular economy is ranked only sixth in the list of total Science Direct hits, if we consider time—apart from recycling—it leads the list. *Cleaner production* takes the second place now, but it was leading at the beginning of the period (after 2004). It is clear from Figure 13, that they changed place.

Finally, we have to put our vote whether we consider general Google searches or the scientific realm more important. We should decide about the second, but if we decided about normal Google hits, CSR would dominate the whole ranking.

**Figure 13.** Frequency of scientific Google searches for the terms *circular economy, corporate social responsibility, life cycle assessment, cleaner production* and *zero emission*. Data and graph from Google Trends, as of 23 July 2019.

Our analysis can create a basis for a new tool for ranking different business sustainability movements (to be proposed as a future work). If we employ a critical analysis of our results, we can say that Google hit time series is a good first approximation of fashionableness, but does not provide deep scrutiny, compare content of movements, or assess their contribution to sustainability.

#### **5. Conclusion: Little Competition, Much Synergy**

Google trends is not a 100% precise analytic tool calibrated for scientific analysis, but due to its comprehensive nature, enormous access to data, and ease to use, it is an optimal tool to make quick analyses about an arbitrarily chosen to set of research phrases. Hereby we used it to see the popularity of fifteen business and economic sustainability movements and their change over time.

This approach is fresh but not unprecedented, for instance Denise Reike, Walter J.V. Vermeulen, and Sjors Witjes very recently published an article [56], looking at the Scopus hits of 12 movements similar to circular economy between 1970 and 2016. There is some overlap between the two studies, but apart from *recycling* and *cleaner production*, there are no common terms in the analysis. The reason for that is that we looked at circular economy from a broader perspective of sustainability, the Reika 2018 article is more precise and technology focused. They also used AND analysis, e.g., *circular economy* AND *reverse logistics*. Trends are very similar but focus of the study is also a bit different: we tried to look at life cycle of the business sustainability movements, and whether they can be seen as independent, competing, or symbiotic and mutually reinforcing concepts.

Another line of research does not take such a wide scope but tries to find common and differential points among some of the movements we proposed, for example between the *blue economy* and *circular economy* [57,58], *cleaner production* [59], *environmental accounting* [60] or specific areas of (nonsustainability) management [56]. A very popular line of papers deploys the concept of circular economy for a certain industrial application, like a factory or a domestic industry [58,61].

One of our basic questions were whether the 15 analyzed business sustainability movements are *independent*, *competing*, or *symbiotic and mutually reinforcing*? We have enough evidence to say that they are symbiotic. If we look at the number of publications in Science Direct, we see that all movements are on steep rise in the last 5 years. In other words, our presumed life cycle (on Figure 4) is valid to 80–85% only: till the absolute maximal point on the figure. In reality after that point the trend does not drop, only its acceleration is slower, the curve might level-off or keep on rising, but at a more moderate pace. The last phase of the trend line does not resemble the falling tail of a Gauss-curve, but a sigmoid curve. In a non-mathematical language: the business sustainability movements live in harmony, they refer to older movements as predecessors, the common field is much bigger, than the differences. I think, this is good news for all, who do not only seek publication credentials, but hope to contribute to make the economic system more ecologically and socially sustainable! We have a strong basis to hope that we do "much ado about SOMEthing".

**Funding:** This research has been supported by the Hungarian National Research, Development and Innovation Office, from the NKFI Fund (grant number K-120044). The APC was funded by the EFOP 3.6.1-16-2016-0007, "Intelligens szakosodási program a Kaposvári Egyetemen" project.

**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**


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