Challenges of diffusion and commercialization of bioenergy in developing countries

Renewable Energy 145 (2020) 1780e1798

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Challenges of diffusion and commercialization of bioenergy in developing countries Asieh Bakhtiar a, Alireza Aslani b *, Seyed Mohsen Hosseini b a b

Department of Technology Management, Faculty of Management, Shaid Beheshti University, Iran Department of Renewable Energy and Environment, Faculty of New Sciences and Technologies, University of Tehran, Iran

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 September 2018 Received in revised form 16 May 2019 Accepted 21 June 2019 Available online 10 July 2019

Increasing environmental awareness accompanied by the recent fluctuations in the fossil fuels underline the need to focus more on the management of energy mix and turn the attention towards renewable energies (REs). Diffusion and commercialization of renewable energy technologies are of paramount importance, particularly for developing countries. However, there are serious challenges and barriers that can be discussed in different aspects. This paper presents an applied and novel framework to tackle the challenges of diffusing and commercializing biomass energy technologies (BET) in developing countries. As a good example, our study focuses on the commercialization of the BETs in Iran. For this purpose, we provide an overview of the importance of commercialization of BETs. Then, by analysis of the literature and best cases, we identify crucial elements for commercialization of the BETs. Conducted surveys and theme analysis have resulted in a framework with 8 major dimensions and a total of 73 diverse components. The dimensions are technical and infrastructural; R & D and technology transfer; investment; social, cultural and behavioral; promotion and distribution; marketing and market building; macroeconomics; and support policy. Findings suggest that ‘investment' and ‘technical and infrastructural' dimensions are the two major pillars of the framework for commercialization of the BETs for developing countries. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Biomass Commercialization Renewable energies Bioenergy

1. Introduction Energy as one of the main inputs of product/services plays a major role in the development and economic growth of the countries [1]. Energy resources come to prominence as a key element in sustainable development [2,3]. The growth of environmental awareness in conjunction with fluctuations in fossil fuels prices has caused the necessity for effective management of energy systems around the world [4e6]. Diffusion and adaptation of REs are one of the important strategies to respond to the necessity [7,8]. REs have great potential to supply a large proportion of energy demand in developing countries. They could be served as a stimulus for economic growth, job creation, and development of manufacturing and service industries [9]. Among different RE sources, biomass plays a vital role due to the long history of utilization [10]. Biomass is compounded of a bio

* Corresponding author. Department of Renewable Energy and Environment, Faculty of New Sciences and Technologies, University of Tehran, Iran E-mail address: [email protected] (A. Aslani). https://doi.org/10.1016/j.renene.2019.06.126 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

residue of agricultural products, organic wastes (including plant and animal material), forests and related industries, as well as industrial and urban decomposable wastes [11,12]. Today, Biomass is regarded as the main source of REs for heat, electricity, and transportation fuels production [13]. Growing utilization of BETs ameliorate the drastically high global CO2 emissions and facilitate the way to low-carbon societies [14,15]. Commercialization takes a leading role in the diffusion of REs. Successful diffusion of the technologies is highly contingent upon a variety of aspects from economic and technical proficiency to environmental and social efficiency [16]. Inattention to these aspects can simply compound the situation and slow down on-going RE development programs. This can further adversely affect the resilience and security of REs in developing countries [17]. As a result, commercialization efforts at the distribution of sustainable energies remain the most challenging problem in this field [18]. Recognition of obstacles to commercialization of the RETs, as well as, efficient commercialization approaches help the executive bodies to address the challenges in the way of developing innovative and low-carbon energy systems [19]. Moreover, feasibility

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assessment of alternative commercialization approaches helps the policy-makers to ascertain about the strategic variables. The main purpose of this paper is to provide an appropriate framework for commercialization and diffusion of the BETs in developing countries and Iran as the case study. For this purpose, we start with a brief overview of the biomass resources and potentials in Iran. The second section deals with the review of pertinent papers on the commercialization of the RETs. Next, a theme analysis will be performed in order to identify prominent dimensions and factors for commercialization of the BETs in the literature. From the produced results, we performed a questionnaire survey in order to determine the most decisive dimensions and factors and prioritize them in order of priority based on expert opinion. Finally, based on the outcoming results, a framework for commercialization of the BETs is suggested and discussed. 2. Bioenergy in Iran Iran has a wide operational capacity to utilize bioenergy for a wide range of applications. Iran's biomass resources have been estimated to be 132.5 million barrels of crude oil equivalent (Mboe) [20]. These resources include agricultural wastes, livestock losses, urban wastes, industrial wastes, and wastewater. They are evaluated in terms of exploitation systems and management systems in Iran in three parts of the forest, pasture, and desert. Diversity of natural resources in different regions is regarded as the main upside of the country for sustainable development that should be considered during all of the energy-related decision-making processes [21]. Fortunately, due to the specific climatic and geographical conditions and the presence of forests in parts of the north, west, and center of Iran, the country has a good potential for utilizing bioenergy in some parts of its economy. About 7% of the country is covered with forest, which are good sources for biofuel production. Additionally, climate and land diversity in Iran has provided an opportunity for the cultivation of diverse agricultural products. During the wet years, there is a potential to cultivate about 30% of Iran total land area; this is while only around 12% of the total land area is under cultivation at the moment [22]. Ganjali and Khaksefidi [23] have identified the major resources of biomass in Iran as follows:






Agricultural and forest wastes (74 Mboe); Solid waste (15 Mboe); Livestock exports (36 Mboe); Urban sewage (2 Mboe); Industrial sewage (5.5 Mboe).

3. Literature review There is a large number of studies in this field approaching the issue from many different angles. This section provides an overview of the literature on the commercialization of BETs. Balachandra et al. [18] carried out an assessment over the procedure of commercializing RETs in India. They analyzed the dynamics of the energy market to identify potential issues, barriers, and partners for commercialization of RETs and suggested a private-sector business model for the successful dissemination of RETs. Jagoda et al. [24] conducted research into the commercialization potential of RETs in Canada. The study offers an innovative framework for diffusion and development of RETs. The framework opens up opportunities for small and medium enterprises (SMEs) to survive and flourish in the RE market. Sambeek et al. [25] evaluated the feasibility of procuring biofuel from non-food biomass resources in China. The study emphasizes the importance of some

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particular factors in the way of commercializing RETs. The factors include: continuum industrial feedstock supply, support for research and development (R&D) in the field of RETs, support for the private sector, and private enterprises access to the government subsidies. Hamzeh et al. [26] evaluated the capabilities of the BETs for the greening of Iran energy system. They insist on the potential of agricultural remnants, animal manures, municipal solid wastes, forest debris, and algae for the production of biofuels in this country. Cortez et al. [27] have scrutinized policies of the Brazilian federal government for diffusion and development of BETs through R&D, commercialization, and support for private venture capital. They found the key role of the federal government in the successfulness of the BETs in Brazil. Government-funded research agencies are pivotal to build up awareness and human capacity. They mentioned the necessity of proportionally raising investments in human resources for R&D. Aslani [17] have assessed the major factors for the commercialization of RETs. The study has identified market competitiveness, government incentives, renewable portfolio standard, infrastructure capability, and economic stability as the decisive factors in the way of commercializing the RETs. Manoukian et al. [28] emphasized the importance of collaboration and partnership synergy in the commercialization of the RETs. They believe that the partnership synergy would provide an efficient combination of internal/external resources which is essential for successful commercialization. Adam et al. [29] examined the accelerating the process of commercializing RETs through a strategic alliance approach and concluded that the emerging models provide a collaborative climate which involves all of the relevant players in this way, such as clean technology firms, support systems, and potential customers. Shakeel et al. [30] investigated the feasibility of commercialization of RETs in Finland through a ladder building methodology. The study argues that the leading elements, in this regard, are certainly the market dynamism, potent R&D infrastructure, technical knowledge, environmental awareness, and supportive policies.

4. Research methodology The purpose of current research is to present a theoretical framework to assess the commercialization potential of BETs in the case study, Iran. To achieve the purpose, the combination of the thematic analysis with the questionnaire survey has been implemented. BETs are still in their introduction level in Iran. There are a few small-scale enterprises working in this field. Consequently, there is a faint possibility of implementing in-depth technical and economic analysis over the BETs. Moreover, there is no centralized database containing detailed information on active enterprises and their operational activities. The importance of our study can be discussed from different aspects: First of all, a few studies have been done for commercialization of RE technologies in the developing countries. In particular, there is not any research to focus on commercialization of bioenergy in such cases. On the other hand, despite fast growth in R&D deployment of different bioenergy generations, commercialization and adaptation are the main challenges. Therefore, analysis of the successful experiences of the best practices can help technology developers and policymakers to understand better the requirements to have a successful technology/product [31]. The best practices are Argentina, the Netherlands, and Portugal as they are among pioneers in bioenergy utilization based on the latest published data source [32]. Table 1 describes the methodology.

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Table 1 Research methodology. Step

Methodology

Strategy

Results

1

-

Documentary-secondary research

2

Qualitative

Thematic analysis

3

Quantitative

Field study











Assessment of previous studies on the commercialization of BETs. Conceptual analysis of commercialization Introduction of different BETs. Assessment of leading countries in the field of BET. Assessment of operational mechanisms of diffusing and commercializing BETs in chosen countries. Investigating the role of influential institutions in chosen countries (text mining). Gathering information and compilation for the conceptual framework (text interpretation). Verification of framework

Amirkabir University of Technology, University of Tehran, and Shahid Beheshti University.

4.1. Thematic analysis Thematic analysis is a technique for detecting, analyzing, and recording themes within qualitative datasets. The methodology constantly analyzes the entire dataset, the codes extracted from data, and provided data analysis [33]. We have analyzed and recorded the themes related to commercialization and diffusion of RETs, in particularly the BETs. The following table describes the steps of thematic analysis. Table 2-a, 2-b shows the identified themes and codes. 4.2. Experts survey This step requires asking the leading experts to fill in a questionnaire about the identified themes and codes. The selection and sampling of the experts have been carried out through a theoretical sampling methodology. The criterion used to select the experts is bioenergy knowledge and experience. The experts have been chosen from a variety of backgrounds, including qualified biomass engineers, biomass researchers, and highly-involved participants in bioenergy projects. Therefore, through a non-probability sampling methodology and involving personal judgment, 31 experts have been selected as follows:
7 senior managers and experts from policy-making organizations, including Renewable Energy Technology Development Council (Vice Presidency for Science and Technology) and Energy Efficiency Organization.
10 senior managers and experts from biofuel companies, including Arya Parto Pars, ITech, GhodsNiroo Engineering Company, MAPNA Group, TTS Group, and Panah Sanat Pars.
14 faculty members, students, and researcher from Institute for International Energy Studies, Niroo Research Institute,

The designed questionnaire encompasses 73 items regarding the importance of derived codes. The questions have been scored based upon a 5-point Likert Spectrum (very high, high, medium, low, very low). ‘Very high’ refers to key codes and ‘very low’ refers to insignificant codes. The validity of the questionnaire has been assessed through a content validity approach. Moreover, a Cronbach's Alpha approach has been applied to measure the reliability of the questionnaire. a coefficient obtained 0.97 for all of the 73 items which imply a high covariance between the designed items. The items have been classified into 8 different categories and Separate Cronbach's alpha tests also have been performed for each category. Table 3 contains the relevant results. Questionnaires have been analyzed through descriptive and inferential statistics using SPSS software. The descriptive approach applies a percentage frequency distribution analysis, while the inferential approach applies t-test and Friedman test.

5. Data analysis Table 4 contains the demographic characteristics of respondents to the questionnaire. Before implementation of any statistical test, it needs to check if the given scores are normally distributed. As this study uses the Likert scale, Skewness and Kurtosis coefficients are used to assess the distribution normality. The data are considered normally distributed if the Skewness coefficient is between 3 and 3, and the Kurtosis be between 5 and 5. Table 5 contains the Skewness and Kurtosis coefficients for each theme. Results emphasize the normality of the sample data.

Table 2a Theme analysis steps. No.

Phase

Description

1

Familiarizing with data

2

Initial coding

3 4

Generating theme Validity and Reliability of themes

5

Defining and naming themes

An initial search in the literature with specific keywords including “development”, “commercialization and diffusion of bioenergy”, “biomass”, “biogas”, and “biofuel” in the title, abstract, and keywords. The body of literature encompasses all the relevant papers and reports, as well as, documents from selected countries. The authors conduct a thorough search for the articles in ScienceDirect, Emeraldinsight, Scopus, Springer, Magiran, Noormags, and ISC databases. Moreover, they consider content saturation and repetitiveness issues for identification of the themes. At this stage, after familiarizing with the data, we started creating codes to identify the concepts, their specifics and their dimensions in the data. At the beginning of the work, 193 codes were extracted from the texts of the articles and reports. At this point, all data summaries are coded and sorted in the form of each code. The codes introduce a data feature that the analyst finds interesting. Data coded from analysis units (themes) are different. By combining the same concepts which differ superficially, the number of retrieved codes has declined to 73. The identified themes are reviewed and refined through two stages. The stage one reviews the extracted codes; while, stage two reviews the whole dataset and examines the validation of identified themes with respect to the dataset. Afterward, the codes have been sorted into 8 potential themes. This is a stage for defining and naming the themes. Researcher scrutinizes the themes, as well as, the data within them for further refinement at this stage. The authors finally extracted 8 themes from the identified codes. The themes include the technical and infrastructural; R & D and technology transfer; investment; social, cultural and behavioral; promotion and distribution; marketing and market building; macroeconomics; and support policy. The themes have been verified through expert opinions.

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Table 2b Categorization of the identified themes and codes. No.

Theme

Code

Index

Reference

1 2 3 4 5 6

Technical and infrastructural dimension

Improvement of the diversity and quality of cultivated species. Optimization of production technology and process through R&D contribution. Installation of biofuel plants, waste incinerators plants, and new production facilities. Designing efficient logistics systems. Role of research centers. Strengthening R&D (In the field of low carbon technologies and development of raw materials for energy efficiency). Implementation of agriculture and forestry biomass projects, new bioenergy projects, and green gas projects. Development of innovation center clusters in the vicinity of research centers. Establishment of committees for development and promotion of biofuels and increase of the domestic production rate. Government support for bioenergy (through innovation grants, loans, discount taxes, long-term and stable financing, and regulatory reform). Technical know-how in project cycle management. Development of effective communication strategies. Strong knowledge infrastructure. Proximity and accessibility to resources. Close relationships between universities, research centers, industrial sector, public sector, investors, consultant companies, certification bodies, social groups etc. Development of international cooperation. Roadmap preparation. Stimulation of investment in bioenergy technology projects. Financial cooperation and effective participation with investors Financing and allocating funds to R&D projects for research on biofuels of the first, second, and third generations. Investment in manufacturing infrastructure. Investment in environmentally-friendly projects through tax plans. Promotion of bioenergy technologies. Protocols on green patents. Secure and reliable energy supply through a moderate combination of national and international green energy resources. Utilization of biofuels in the road and public transport. Effective usage of bioenergy in different sectors (heating and cooling, electricity and transportation); switch to biogas resources to meet the natural gas demand. Provision of Guidelines and instructions for the introduction of new technologies Formulating policies, regulations, and legislation in support of bioenergy. Financial assistance program (feed in tariff). Formulating stringent regulations on biogas injection into the natural gas grids. Indirect incentives (Ownership of land, access to R&D credits, incentives for new agricultural-industrial projects). Certificate Issuance (a scheme to simply licensing process). allocation of specific subsidies for innovative technologies. Support for renewable fuels supply chain. Financial support for the installation of energy production facilities. R&D grants Proper tax system (eliminating the tax on biofuels, adding tax on fossil fuels and tax exemptions). Utilization of domestic resources for the production of bioenergy. Involvement with science and technology parks, incubators, and accelerators. White certificates and tax deduction. Tax subsidies to encourage the development and use of bioenergy. Support for research and green deals. Performance-based production subsidy. Feed in premium. Subsidies for low-cost technologies. Discount Tax on biofuel consumption. Allocation of a mandatory percentage of biofuels in transport fuels. Allocation of funds to universities, research centers, and industrial partners. Carbon tax. Support for research clusters. Development of Entrepreneurship and SMEs in bioenergy. Strategic planning for enterprises. Market-based approaches for technology development. Applying multidisciplinary skills and knowledge in management and marketing. Identification of new business and market opportunities. Development of new sales channels. Commercialization through major commercial companies. Creation of agricultural markets. Introducing environmental markets such as public transportation markets, and etc. Introduction of advanced biofuels. Large-scale marketing for sustainable biomass resources. Promotion of biofuel-compatible vehicles.

C1 C2 C3 C4 C5 C6

[34e43] [25,41,44e48] [7,36,39,41,49e51] [7,38,52e54] [30,44,51,52,55,56] [41,49,57]

C7

[50,58,59]

C8 C9

[25] [36]

C10

[30,40]

C11 C12 C13 C14 C15

[25] [25,30,60] [25,49,52,61] [25,30,38,40,44,46,49,52,53,58,60e65] [18,30,40,45,51,56,57,61,62,65e67]

C16 C17 C18 C19 C20

[30,41,45] [67] [30,38,41,44,45,49e51,53,67e70] [39,62] [30,41,45,49,51,56,59,62,67]

C21 C22 C23 C24 C25

[30,36,60,69] [50] [44] [45,51,56,71] [49]

C26 C27

[36,38,39,45,48,49,57,68] [7,38,39,44,49,56,69]

C28 C29 C30 C31 C32

[40] [18,25,30,38,45,48,65,72] [43,44,46,49,53,55,56,69,70,73,74] [41,44,71,75] [36,41,74]

C33 C34 C35 C36 C37 C38

[40,41,44,48,51,53,54,57,69,72,76] [30,36,37,41,45,50,51,63,68,70,71,76,77] [51,67,68] [57] [14,41,54,78] [36,37,39,40,43,45,50,55,59,69,74,77e81]

C39 C40 C41 C42 C43 C44 C45 C46 C47 C48 C49 C50 C51 C52 C53 C54 C55 C56 C57 C58 C59 C60 C61 C62 C63

[44] [30] [56,69,70] [30,35,48,54,59] [7] [36] [45,49,62] [62] [40,70] [36,67,69] [82] [73] [25] [18,48] [30,48,51,66,81] [25,45,46,52,67] [25,67] [25,45,61,62] [30,77] [60] [58,81] [40] [41] [41] [36]

R&D and technology transfer dimension

7 8 9 10 11 12 13 14 15

Investment dimension

16 17 18 19 20 21 22 23 24 25

promotion and distribution dimension

26 27 28 29 30 31 32

Support policy dimension

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

Marketing dimension

(continued on next page)

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Table 2b (continued ) No. 64 65 66 67 68 69 70 71 72 73

Theme

Socio-economic dimension

Social, cultural and behavioral dimension

Code

Index

Reference

Attention to product quality, brand, design, packing, labeling, and after-sale service. Consideration for customer issues. Identification of proper markets for biofuels. Job creation and innovation promotion. Cost efficiency. An open economy with attention to distribution and logistics systems. Competing with foreign technology suppliers; maintaining trade and exchange balance. Proper behavioral patterns for social acceptance. Participation of citizens, companies, local councils, and others in the bioenergy project. Development of innovation culture.

C64 C65 C66 C67 C68 C69 C70 C71 C72 C73

[60] [40,57,60,67] [40] [50] [83,84] [25,83,84] [23,58] [7,30,48] [7] [30]











Table 3 Results of Cronbach's alpha tests for each category.

a coefficient

Category Category Category Category Category Category Category Category Category

1 2 3 4 5 6 7 8

0.5 0.9 0.81 0.79 0.94 0.92 0.88 0.86

Technological infrastructure dimension: 4 questions; R&D and technology transfer dimension: 13 questions; Investment dimension: 5 questions; Diffuse and promotional dimension: 6 questions; Support policy dimension: 24 questions; Marketing dimension: 14 questions; Socio-economic dimension: 4 questions; Social, cultural and behavioral dimension: 3 questions.

Lower bounds and upper bounds of the 95% confidence interval have been positive for each question on the questionnaire (see

Table 4 Demographic profile of respondents to the questionnaire. Percentage frequency distribution of respondents based on work experience’s years in the field of bioenergy Frequency percentage

Frequency

60% 17% 13% 10%

19 5 4 3

Less than 5 years Between 5 and 10 years Between 10 and 15 years More than 15 years

Frequency percentage distribution of respondents based on the field of activity 23% 32% 45%

7 10 14

Energy policy making Industry Universities and research centers

Frequency percentage distribution of respondents based on the academic major 38% 45% 17%

12 14 5

Energy and chemistry engineering Other engineering majors Non-engineering majors

Table 5 Skewness and Kurtosis coefficients of the identified themes. Theme

Sample size

maximum

minimum

Mean importance

Standard deviation

Kurtosis Coeff.

Skewness Coeff.

Technological and infrastructural dimension R&D and technology transfer dimension Investment dimension Diffuse and promotional dimension Support policy dimension Marketing dimension Socio-economic dimension Social, cultural and behavioral dimension

31 31 31 31 31 31 31 31

2.75 1.23 1.4 2.17 1.83 1.86 1 1

5 5 5 5 5 5 5 5

4.0161 3.7667 4.0065 3.8602 3.7459 3.6866 3.8226 3.9355

0.59838 0.72337 0.77371 0.67645 0.76028 0.75878 0.8972 0.95615

0.767 3.946 3.713 0.328 0.987 0.07 2.677 1.532

0.346 1.407 1.644 0.611 1.079 0.364 1.428 1.151

5.1. The conceptual framework for commercialization of BETs After receiving the views of experts on crucial measures for commercialization of BETs in Iran, a viewpoint analysis has been performed in order to prioritize the identified dimensions and codes. Using one sample t-test and Friedman test, the identified themes have been scored on a specific scale. Themes with scores above 3 (medium) have been considered influential. The questionnaire has been designed with the following items:

Appendix). Moreover, all of the identified themes and constructing codes have an importance coefficient above 3 (Test Value). Therefore, these 8 dimensions and 73 codes assume great importance for commercialization of the BETs in Iran. Table 6 contains the importance coefficient of the identified themes. Findings suggest that‘investment dimension’ exceed all other dimensions in importance. Fig. 1 shows the proposed framework for the commercialization of BETs in Iran. The framework has been formed based on the

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of primary importance according to the questionnaire survey. And then, respectively, C6, C16, C5, C14, C7, C13, C17, C12, C15, C11, C8, C9 have the other priorities respectively. ‘Investment’ theme achieved the highest importance among the identified themes. The high average importance coefficient of the investment codes addresses the critical role of investment issues for diffusion and commercialization of the BETs in Iran. This paper has found C12 as the determining code in this regard. A number of studies also have attached a great significance to this code. For example, Ferreira [44] considers the investment issues as one of the major success factors for commercialization of the BETs in Portugal. As well, Suurs & Hekkert [81] regards investment incentives as a solution to market creation and guaranteed demand for BETs. Experts have identified C27 as the most important code among the ‘diffuse and promotional’ codes. The high growth potential of biogas in Iran and numerous ongoing biogas projects in this country could be the reasons behind this choice. A study by Monterio et al. [78] also mentions the importance of this dimension in this case. The thematic analysis resulted in 24 codes about support policies. However, among them, experts have attached a great significance to C38, C30, C47, and C29, as the most supportive measures for commercialization of the BETs. Indeed, high taxes are one of the main sources of unwillingness for investors to invest in bioenergy projects. From this perspective, therefore, commercialization of the BETs seems unprofitable in the majority of cases. Importance of the ‘support policy’ theme, in this regard, has been verified by Albrecht et al. [85]. According to this, there are various support tools in the core of bioenergy development policies, including investment

Table 6 The influential dimensions in the commercialization of BETs in order of priority. Dimension

Importance coefficient

Investment dimension Technological & infrastructural dimension Social, cultural and behavioral dimension promotion and distribution dimension Socio-economic dimension R&D and technology transfer dimension Support policy dimension Marketing and market building dimension

5.58 5.26 4.66 4.47 4.35 4.11 3.82 3.74

identified dimensions and constructing codes. All the relationships in this framework are non-causal explanatory. 6. Results Analysis of ‘technological & infrastructural’ dimension reveals that C3 is of primary importance. C2, in this regard, is of secondary importance according to the experts' opinions. C4 and C3 rank third and fourth among the identified codes. However, their high average importance coefficient gives prominence to all of the four contingent codes. Importance of ‘technical and infrastructural’ dimension also came to prominence in the studies of van de Kaa et al. [48] and Sue and Yang [67]. Findings emphasize the importance of R&D dimension for commercialization of the BETs. A Study by Shakeel et al. [30] also proves this point. From their viewpoint, particular attention should be given to strengthening R&D and research centers for commercializing the BETs. Notably, C10, in this regard, is

C27 C26 C25 C23 C24

C18 C20 C19 C21 C22

C68 C67 C70 C69

C71 C72 C73

C3 C2 C4 C1

Social, cultural and behavioral theme

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Diffuse and promotional theme

Socioeconomic theme

Technological infrastructure theme

C10, C6 C16, C5, C14, C7 C13, C17 C12, C15 C11, C8 C9

C38 ,C30 C47, C29 C31, C42 C44, C48 C39, C41 C43, C36 C50, C46 C35, C32 C40, C51 C37, C52 C34, C33 C45,C49 C58 C66 C62 C54 C63 C53

R&D and technology transfer theme Support policy theme

Commercialization

Investment theme

Marketing theme

Outcomes

Energy independence and security of supply

Environmental sustainability

Economic and Social Development

Utilization and mobilization of local resources

Fig. 1. A framework for commercialization of the BETs in Iran (dimensions and codes have been listed in order of importance).

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grants, tax exemptions, etc., which are of great importance in order to increase the number of bioenergy projects. In the marketing dimension, the most important component of the action, according to the experts’ opinion, is commercialization through major commercial companies (C58). They believe that the supply barriers will be removed to a large extent in this way. C66 is of secondary importance in this case. Findings of this paper, in this regard, bear a close resemblance to a study by van de Kaa et al. [48], who believe that marketing plays a major role in the commercialization of the BETs. A Study by García-Maroto et al. [60] also confirm the findings of this paper. Socio-economic dimension is one of the important pillars of the proposed framework. From this perspective, a variety of variables, such as capital, labor, Gross Domestic Product (GDP), inflation rate, and interest rate significantly affect the technical efficiency of the bioenergy industry. In this case, experts found C68 and C67 the most important codes. Approvingly, Alsaleh & Abdul-Rahim [83] have mentioned that an assessment of the macroeconomic factors, as well as, productivity and costeffectiveness of bioenergy market is crucial for commercialization of the BETs. The last identified theme is the social, cultural and behavioral dimension. The most important code, in this regard, is C71. Social acceptance is a crucial factor as regards the commercialization of the BETs. In addition, the emergence of innovative behaviors in a society is highly contingent upon the development of innovation culture at all levels. A number of studies [30,48]also prove the point. 7. Conclusions and suggestions for future works Growing energy demand and population, as well as, high dependency on fossil fuels become a matter of concerns in different countries. Limited fossil fuel resources and growing greenhouse gas emission emphasizes the necessity to take a paradigm shift to the clean sources of energy. To achieve this shift and due to the high potentials of BETs, development, and commercialization of BETs can be one of the strategies. The current work analyzed the leading countries in the field of biomass energy and accordingly provided an appropriate framework for the commercialization of BETs in the case study, Iran. The proposed framework encompasses 73 key codes in 8 different themes. The framework has been verified through a questionnaire survey. The experts believe that all of the identified themes and codes are crucial to successful commercialization of the BETs in Iran. The results show that investment is the most important factor for commercialization of the BETs in the developing countries. High dependency on the public and governmental financial resources, is the main barriers for investment success of BETs. Technological and infrastructural dimension is the second on the list. Implementation of new technology needs a proper technological infrastructure and readiness in society. The importance of this component is that if the technology is not technically feasible to implement and develop, then other components of concept and application will not be found. The third prominent dimension is the social, cultural and behavioral dimension. Transmission to sustainable energy systems to solve the climate change problem requires a change in the sociotechnical systems and change acceptance among various stakeholders. Raise awareness about the climate change issue, as a matter of great urgency, is of great importance in this regard. The next dimension is the promotion and distribution dimension. From this perspective, the widespread use of the BETs is highly dependent on the promotion of these technologies, as clean sources of energy, in the society. The socio-economic dimension also plays a

strategic role in the commercialization of the BETs. The economy and biomass market are mutually related to each other. On one hand, commercialization of the BETs is a strong positive influence for the economy; on the other hand, a biomass-based economy is one of the pillars of the bioenergy development. Investment costs, production costs, and competitive selling prices are economic indicators related to the feasibility of biomass production. Therefore, the production of economical biomass in the market is essential for the successful implementation of biodegradable projects. R&D, as a key element of successful commercialization, has been placed the sixth among the identified dimension for commercialization of the BETs. In effect, the experts think that the local production technology and international technology transfer process have become mature enough for large-scale commercialization the BETs in Iran, however, the lack of interest for private investment still is a barrier for R&D. They recommend financial asset allocations on R&D activities after the commercialization processes. Support policy dimension has been identified as one of the other dimensions of the proposed framework. The government should provide strong incentives and support policies to lower the production costs and develop crucial infrastructures for commercialization of the BETs. Importantly, biomass suppliers need support policies to seamlessly enter the energy markets and survive the crises. The last point is the marketing and market building dimension. A large number of previous studies on the commercialization of the BETs have pointed to the importance of marketing. As the last phase of a commercialization process, marketing plays a crucial role in the success or failure of a technology. But, as the bioenergy is still in its infancy in Iran, the experts have not paid significant attention to the last phase of the production chain in this paper. This paper developed a commercialization roadmap for the BETs in Iran and identified numerous dimensions and codes for this purpose. However, each dimension needs an in-depth study of the potential effects on the commercialization process. Feasibility studies on the commercialization of biomass conversion technologies are certainly worthwhile for promotion and diffusion of the bioenergy in Iran. Moreover, the impact of political, technological, and social changes on the diffusion of bioenergy, optimization of the supply chain, and assessment of the biomass market may be interesting subjects for future studies. Acknowledgement The authors acknowledge the University of Tehran for financial support of the research. Appendix. A. Data analysis of technological and infrastructural dimension Lower bounds and upper bound of questions related to the technological & infrastructural dimension are all positive. This means that for any constructed confidence interval, the importance coefficient lies between the lower and upper bound. Therefore, the results are reliable and valid. The average importance coefficient for this dimension is 4.0161 which is higher than the test value. Thus, it is noteworthy to consider the identified codes in the commercialization framework. Table 7 and Table 8 contain the T-test and onesample test results respectively.

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Table 7 Results of T-test analysis of responses to technological and infrastructural questions. No.

Code

N

Mean

Standard deviation

Std. Error Mean

1 2 3 4 Mean value

Improvement of the diversity and quality of cultivated species. Optimization of production technology and process through R&D contribution Installation of biofuel plant, waste incinerators plant, and new production facilities. Designing efficient logistics systems -

31 31 31 31 31

3.94 3.97 4.16 4 4.0161

1.093 1.048 0.898 0.931 0.598

0.196 0.188 0.161 0.167 0.10747

Mean

Standard devia on

4.5 4

4.16

3.97 1.048

3.94 1.093

Std. Error Mean 1.2

4.0161

4

1

3.5

0.931

0.898

0.8

3 2.5

0.6

0.598

2

0.4

1.5 1 0.5

0.196 0

0.188 0

0

0.2

0.167

0.161 0

0.10747

0

0

0 1

2

3

4

5

6

Fig. 1. Results of T-test analysis of responses to technological and infrastructural questions.

Table 8 Respondents' scores to the importance of technological and infrastructural codes (one-sample test). No.

T-value

1 2 3 4 Whole questions

df

4.736 5.140 7.200 5.981 9.455

30 30 30 30 30

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

0 0 0 0 0

0.935 0.968 1.161 1.000 1.016

1.34 1.35 1.49 1.34 0.235

0.53 0.58 0.83 0.66 0.79

Table 9 contains the results of the Friedman test. According to the analysis, installation of biofuel plant, waste incinerators plant,

and new production facilities is of primary importance.

Table 9 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3 4

Installation of biofuel plant, waste incinerators plant, and new production facilities Optimization of production technology and process through R&D contribution Designing efficient logistics systems Improvement of the diversity and quality of cultivated species

2.66 2.55 2.42 2.37

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Appendix. B. Data analysis of R&D and technology transfer dimension Results show positive lower and upper bounds for questions related to this dimension. This implies that the results are reliable and valid. The average importance coefficient of R&D and technology transfer dimension is 3.7667. Therefore, it can be inferred

that this theme is one of the important dimension of commercialization and diffusion. Table 10 and Table 11 contains T-test and one-sample test results for the R&D and technology transfer theme respectively.

Table 10 Results of T-test analysis of R&D and technology transfer theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

5 6 7 8 9 10

Role of research centers Strengthening R&D Implementation of agriculture and forestry biomass projects, new bioenergy projects, and green gas projects Development of innovation centers cluster in the vicinity of research centers Establishment of committees for development and promotion of biofuels, as well as, increase in domestic production state support for bioenergy (through innovation grants, loans, discount taxes, long-term and stable financing, and regulatory reform Technical know-how in project cycle management Development of effective communication strategies Strong knowledge infrastructure Proximity and accessibility The close relationship between universities, research centers, industry sector, public sector, investors, consultant companies, certification bodies, social groups, etc. Development of international cooperation Roadmap preparation

31 31 31 31 31 31

3.97 4.06 3.90 3.39 3.16 4.06

1.016 0.892 1.076 0.882 1.214 1.289

0.182 0.160 0.193 0.158 0.218 0.232

31 31 31 31 31

3.68 3.71 3.81 3.81 3.68

0.702 1.071 1.014 1.046 1.013

0.126 0.192 0.182 0.188 0.182

31 31 31

4.03 3.71 3.76

1.197 1.346 0.723

0.215 0.242 0.129

11 12 13 14 15 16 17 Mean value

Chart Title Mean

Standard devia on

Std. Error Mean

4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Fig. 2. Results of T-test analysis of R&D and technology transfer theme.

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Table 11 Respondents' scores to the importance of R&D and technology transfer codes (one-sample test). No.

5 6 7 8 9 10 11 12 13 14 15 16 17

T-value

5.303 6.644 4.675 2.443 0.740 4.597 5.375 3.691 4.429 4.292 3.724 4.802 2.935 5.902

df

30 30 30 30 30 30 30 30 30 30 30 30 30 30

Sig. (2-tailed) P-value

Mean Difference

0 0 0 0.21 0.465 0 0 0.001 0 0 0.001 0 0.006 0

0.968 1.065 0.903 0.387 0.161 1.065 0.677 0.710 0.806 0.806 0.677 1.032 0.710 1.016

Table 12 contains the results of the Friedman test. The results indicated that state support for bioenergy through innovation grants, loans, discount taxes, long-term and stable financing, and regulatory reform is the most important code for commercialization in this regard.

95% Confidence Interval of the Difference Upper

Lower

1.34 1.39 1.30 0.71 0.61 1.54 0.93 1.10 1.18 1.19 1.05 1.47 1.20 0.235

0.60 0.74 0.51 0.06 0.28 0.59 0.52 0.32 0.43 0.42 0.31 0.59 0.22 0.501

Appendix. C. Data analysis of investment theme This section provides results related to the investment theme. Results indicate positive lower and upper bounds for all of the relevant questions. This emphasizes the reliability and validity of the outcomes. The average importance coefficient of the investment theme is 4.0065 which is higher than the defined test value. Therefore, the investment theme would be of value in the process of commercialization. Table 13 and Table 14 contains the T-test and one-sample test results respectively.

Table 12 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3 4 5 6 7 8 9 10

State support for bioenergy through innovation grants, loans, discount taxes, long-term and stable financing, and regulatory reform Strengthening R&D Development of international cooperation Role of research centers Proximity and accessibility Implementation of agriculture and forestry biomass projects, new bioenergy projects, and green gas projects Strong knowledge infrastructure Roadmap preparation Development of effective communication strategies The close relationship between universities, research centers, industry sector, public sector, investors, consultant companies, certification bodies, social groups, etc. Technical know-how in project cycle management Development of innovation centers cluster in the vicinity of research centers Establishment of committees for development and promotion of biofuels, as well as, increase in domestic production

8.52 8.37 8.24 7.69 7.39 7.37 7.05 7.05 6.61 6.58

11 12 13

6.40 5.05 4.68

Table 13 Results of T-test analysis of the investment theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

18 19 20

Stimulation of investment in bioenergy technology projects Financial cooperation and effective participation of investors Financing and allocating funds to research and development projects and renewable energy for research on biofuels of the first, second and third generation Investment in manufacturing infrastructure Investment in environmentally friendly projects through tax plans

31 31 31

4.26 4.03 4.06

0.855 0.948 1.153

0.154 0.170 0.207

31 31 31

4 3.68 4.0065

1.033 1.107 0.773

0.185 0.199 0.138

21 22 Mean value

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Mean

Standard devia on

Std. Error Mean

5

1.4 4.26

4.5

4.06 1.153

4.03

4

4

1.2 3.68 1.107

1.033

3.5

1

0.948 0.855

3

0.8

2.5 0.6

2 1.5

0.4

1 0.5

0.154

0

0.207

0.17 0

0

0.2

0.199

0.185 0

0

0

0 18

19

20

21

22

Mean value

Fig. 3. Results of T-test analysis of the investment theme.

Table 14 Respondents' scores to the importance of investment codes (one-sample test). No.

T-value df

Sig. (2-tailed) Mean 95% Confidence Interval P-value Difference of the Difference

18 19 20 21 22 Whole questions

8.192 6.062 5.141 5.391 3.407 7.243

0.00 0.00 0.00 0.00 0.00 0.00

30 30 30 30 30 30

1.258 1.032 1.065 1.000 0.677 1.006

Upper

Lower

1.57 1.38 1.49 1.38 1.08 1.29

0.94 0.68 0.64 0.62 0.27 0.72

Appendix. D. Data analysis of the diffuse and promotional theme As the previous themes, the lower bounds and upper bounds are all positive for relevant questions to the diffuse and promotional theme and the results are reliable and valid. Moreover, the average importance coefficient is 3.86. Thus, this theme could be considered as one of the significant dimensions of commercialization. Table 16 and Table 17 contains the T-test and one-sample test results respectively.

Table 15 lists the investment codes in order of priority. According to the results, the stimulation of investment in BET projects is of utmost significance for commercialization from this perspective.

Table 15 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3

Stimulation of investment in bioenergy technology projects Fund allocation to R&D projects Financing and allocating funds to research and development projects and renewable energy for research on biofuels of the first, second and third generation Investment in manufacturing infrastructure Investment in environmentally friendly projects through tax plans

3.39 3.21 2.94

4 5

2.94 2.53

Table 16 Results of T-test for the diffuse and promotional theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

23 24 25

Promotion of bioenergy technologies Protocols on green patents Secure and reliable energy supply through a moderate combination of national and international green energy resources Utilization of biofuels in the road and public transport Effective usage of bioenergy in different sectors(Heating and cooling, electricity and transportation); switch to biogas resources to meet the natural gas demand Provide Guidelines and instructions for the introduction of new technologies

31 31 31

3.87 3.26 3.90

0.991 1.064 0.831

0.178 0.191 0.149

31 31

4.03 4.32

0.912 0.871

0.164 0.156

31 31

3.77 3.86

1.087 0.676

0.195 0.121

26 27 28 Mean value

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Mean

Standard devia on

1791

Std. Error Mean

5

1.2

4.5

1.064

3.87 0.991

4

4.32 4.03

3.9

1

3.86

3.77

0.912

3.26

3.5

1.087

0.871

0.831

0.8

3

0.676

2.5

0.6

2 0.4

1.5 1 0.5

0.191

0.178 0

0

0.164

0.149 0

0

0

0.2

0.195

0.156

0.121

0

0

0 23

24

25

26

27

28

Mean value

Fig. 4. Results of T-test for the diffuse and promotional theme.

Table 17 Respondents' scores to the importance of diffuse and promotional codes (one-sample test). No.

T-value

df

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

23 24 25 26 27 28 Whole questions

4.892 1.351 6.053 6.300 8.452 3.967 7.080

30 30 30 30 30 30 30

0.000 0.187 0.000 0.000 0.000 0.000 0.000

0.871 0.258 0.903 1.032 1.323 0.774 0.860

1.23 0.65 1.21 1.37 1.64 1.17 1.10

0.51 0.13 0.60 0.70 1.00 0.38 0.61

Appendix. E. Data analysis of support policy theme Table 18 shows the results of the Friedman Test. The results indicate that effective usage of bioenergy in different sectors and switch to biogas resources to meet the natural gas demand is of primary importance among the derived codes.

The analysis shows positive lower bounds and upper bounds for the responses to the questions. Thus, the results are reliable. The average importance coefficient of the support policy theme is 3.74. As a result, support policies can play a major role in the

Table 18 Results of Friedman ranking test. Rank

Code

Mean rank

1

Effective usage of bioenergy in different sectors(Heating and cooling, electricity and transportation); switch to biogas resources to meet the natural gas demand Utilization of biofuels in the road and public transport Secure and reliable energy supply through a moderate combination of national and international green energy resources Promotion of bioenergy technologies Protocols on green patents Provide Guidelines and instructions for the introduction of new technologies

4.42

2 3 4 5 6

3.79 3.58 3.56 3.37 2.27

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commercialization of the BETs in Iran. Table 19 and Table 20 contains the T-test and one-sample test results respectively. Table 19 Results of T-test for support policy theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

29 30 31 32

Formulating policies, regulations, and legislation in support of bioenergy Financial assistance program (feed-in tariff) Formulating stringent regulations on biogas injection into the natural gas grid Indirect incentives (Ownership of land, access to research and development credits, incentives for new agricultural-industrial projects). Certificate Issuance (The purpose of this scheme is to increase the speed of the licensing process). The offer of specific subsidies for innovation Support for renewable fuels supply chain Donations to support the installation of energy production facilities R & D grants Proper tax system (Eliminating tax on biofuels, adding tax on fossil fuels and tax exemptions) Utilization of domestic resources for the production of bioenergy Linkage to science and technology parks, incubators, and accelerator programs White certificates and tax deduction Tax subsidies to encourage the development and use of biomass Support for research and green deals Performance-based production subsidy Feed in premium Subsidies for low-cost technologies Discount Tax on biofuel consumption Allocation of a mandatory percentage of biofuels in transport fuels. Allocation of funds to universities, research centers, and industrial partners Carbon tax Support for research clusters Development of Entrepreneurship SMEs in bioenergy

31 31 31 31

3.97 4.23 3.94 3.71

1.048 0.884 1.031 1.131

0.188 0.159 0.185 0.203

31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31

3.39 3.45 3.77 3.81 3.58 4.16 3.84 3.61 3.81 3.84 3.77 3.81 3.42 3.68 4.00 3.94 3.42 3.74 3.61 3.48 3.74

1.202 1.207 1.194 1.108 0.886 0.860 1.068 1.022 1.014 1.036 1.055 1.167 0.848 1.166 1.095 1.181 1.285 1.237 0.989 1.313 0.760

0.216 0.217 0.218 0.199 0.159 0.154 0.192 0.184 0.182 0.186 0.190 0.210 0.152 0.209 0.197 0.212 0.231 0.222 0.178 0.236 0.136

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 Mean value

Mean

Standard devia on

Std. Error Mean

4.5

1.4

4

1.2

3.5 1 3 2.5

0.8

2

0.6

1.5 0.4 1 0.2

0.5 0

0 Fig. 5. Results of T-test for support policy theme.

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Table 20 Respondents' scores to the importance of support policy codes (one-sample test). No.

T-value

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 Whole questions

5.140 7.725 5.053 3.493 1.793 2.084 3.516 4.052 3.649 7.517 4.374 3.338 4.429 4.508 4.084 3.848 2.755 3.235 5.083 4.409 1.817 3.338 3.450 2.051 5.462

df

30 30 30 30 30 30 29 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

0.000 0.000 0.000 0.002 0.083 0.046 0.001 0.000 0.001 0.000 0.000 0.002 0.000 0.000 0.000 0.001 0.010 0.003 0.000 0.000 0.079 0.002 0.002 0.049 0.000

0.968 1.226 0.935 0.710 0.387 0.452 0.767 0.806 0.581 1.161 0.839 0.613 0.806 0.839 0.774 0.806 0.419 0.677 1.000 0.935 0.419 0.742 0.613 0.484 0.745

1.35 1.55 1.31 1.12 0.83 0.89 1.21 1.21 0.91 1.48 1.23 0.99 1.18 1.22 1.16 1.23 0.73 1.11 1.40 1.37 0.89 1.20 0.98 0.97 1.02

0.58 0.90 0.56 0.29 0.05 0.01 0.32 0.40 0.26 0.85 0.45 0.24 0.43 0.46 0.39 0.38 0.11 0.25 0.60 0.50 0.05 0.29 0.25 0.00 0.46

Table 21 ranks the relevant codes in order of importance. Findings emphasize the importance of tax issues in this regard.

Table 21 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Proper tax system (Eliminating tax on biofuels, adding tax on fossil fuels and tax exemptions) Financial assistance program (feed-in tariff) Discount Tax on biofuel consumption Formulating policies, regulations, and legislation in support of bioenergy Formulating stringent regulations on biogas injection into the natural gas grid Tax subsidies to encourage the development and use of biomass Performance-based production subsidy Allocation of a mandatory percentage of biofuels in transport fuels. Utilization of domestic resources for the production of bioenergy White certificates and tax deduction Support for research and green deals Donations to support the installation of energy production facilities Carbon tax Subsidies for low-cost technologies Support for renewable fuels supply chain Indirect incentives(Ownership of land, access to research and development credits, incentives for new agricultural-industrial projects). Linkage to science and technology parks, incubators, and accelerator programs Support for research clusters Donations to R&D Development of Entrepreneurship SMEs in bioenergy The offer of specific subsidies for innovation Certificate Issuance (The purpose of this scheme is to increase the speed of the licensing process). Feed in premium Allocation of funds to universities, research centers, and industrial partners

15.85 15.60 14.80 14.18 13.95 13.67 13.42 13.35 13.12 13.10 13.08 12.78 12.55 12.53 12.48 12.15 11.50 11.17 10.80 10.65 10.13 10.07 9.08 9.98

Appendix. F. Data analysis of marketing theme Results indicate positive lower and upper bounds for questions related to the marketing theme. This implies the validity of the

questionnaire survey. The average importance coefficient of marketing theme is 3.68. Therefore, the marketing theme definitely deserves consideration in the process of commercialization. Table 22 and Table 23 show T-test and one-sample test results respectively.

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Table 22 Results of T-test for marketing theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

53 54 55 56 57 58 59 60 61 62 63 64 65 66 Mean value

Strategic plans of enterprises Market-based approaches to technology development Applying multidisciplinary skills and knowledge in management and marketing Identification of new business and market opportunities Development of new sales channels Commercialization through large establishments major commercial companies Creation of agricultural markets Introducing environmental markets such as public transportation markets, and etc Application of advanced biofuels Large-scale commercialization marketing for sustainable biomass resources Promotion of biofuel-compatible vehicles Attention to product quality, brand, design, packing, labeling, and after-sale service Consideration for customer issues Identification of proper markets for biofuels

31 31 31 31 31 31 31 31 31 31 31 31 31 31 31

3.68 3.74 3.52 3.48 3.65 3.97 3.42 3.58 3.74 3.87 3.84 3.58 3.68 3.87 3.68

1.077 0.930 1.061 1.122 1.050 0.912 1.089 1.232 1.094 0.991 1.128 1.025 1.166 1.024 0.758

0.193 0.167 0.190 0.201 0.189 0.164 0.196 0.221 0.197 0.178 0.203 0.184 0.209 0.184 0.136

Mean

Standard devia on

Std. Error Mean

4.5

1.4

4

1.232

3.5

1.122

1.077

1.061

1.094

1.089

1.05

1.025

0.991

3

0.93

1.2

1.166

1.128

1.024

1

0.912

2.5

0.8

2

0.6

1.5 0.4 1 0.2

0.5 0

0 53

54

55

56

57

58

59

60

61

62

63

64

65

66

Fig. 6. Results of T-test for marketing theme.

Table 23 Respondents' scores to the importance of marketing codes (one-sample test). No.

T-value

df

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

53 54 55 56 57 58 59 60 61 62 63 64 65 66 Whole questions

3.503 4.443 2.710 2.402 3.420 5.906 2.145 2.624 3.774 4.892 4.139 3.153 3.235 4.734 5.038

30 30 30 30 30 30 30 30 30 30 30 30 30 30 30

0.001 0.000 0.011 0.023 0.002 0.000 0.040 0.014 0.001 0.000 0.000 0.004 0.003 0.000 0.000

0.677 0.742 0.516 0.484 0.645 0.968 0.419 0.581 0.742 0.871 0.839 0.581 0.677 0.871 0.686

1.07 1.08 0.91 0.90 1.03 1.30 0.82 1.03 1.14 1.23 1.25 0.96 1.11 1.25 0.96

0.28 0.40 0.13 0.07 0.26 0.63 0.02 0.13 0.34 0.51 0.42 0.20 0.25 0.50 0.40

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Table 24 contains the results of the Friedman Test. Commercialization through large establishments topped the list of influential codes in the marketing theme.

Table 24 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Commercialization through major commercial companies Identification of proper markets for biofuels Large-scale marketing for sustainable biomass resources Market-based approaches to technology development Promotion of biofuel-compatible vehicles Strategic plans of enterprises Consideration for customer issues Development of new sales channels Introducing environmental markets such as public transportation markets, and etc Application of advanced biofuels Applying multidisciplinary skills and knowledge in management and marketing Attention to product quality, brand, design, packing, labeling, and after-sale service Identification of new business and market opportunities Creation of agricultural markets

8.55 8.27 8.08 8.05 8.05 7.76 7.52 7.50 7.35 7.34 6.77 6.77 6.68 6.31

Appendix. G. Data analysis of the socio-economic theme Analysis reveals positive lower bounds and upper bounds for responses to the questions in this section. Hence, the results are valid and reliable. The average importance coefficient (3.82) is higher than the test value. Thus, the socio-economic theme can be

considered as one of the important dimensions of commercialization. Table 25 and Table 26 show T-test and one-sample test results respectively.

Table 25 Results of T-test for the socio-economic theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

67 68 69 70 Mean value

Job creation and innovation promotion Cost efficiency Open economy and sharp focus on distribution and logistics system Competing with foreign technology suppliers; maintaining trade and exchange balance

31 31 31 31 31

3.97 4.00 3.61 3.71 3.82

1.048 1.033 1.054 1.039 0.897

0.188 0.185 0.189 0.187 0.161

Mean

Standard devia on

Std. Error Mean

4.1 4

1.2 3.97 1.048

4

1.054

1.033

1.039

3.9

1 0.8

3.8 3.71

0.6

3.7 3.61

0.4

3.6 0.188

3.5

0.189

0.185

0.187

0.2 0

3.4 67

68

69

Fig. 7. Results of T-test for the socio-economic theme.

70

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A. Bakhtiar et al. / Renewable Energy 145 (2020) 1780e1798

Table 26 Respondents' scores to the importance of socio-economic codes (one-sample test). No.

T-value

67 68 69 70 Whole questions

df

5.140 5.391 3.236 3.803 5.105

30 30 30 30 30

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

0.000 0.000 0.003 0.001 0.000

0.968 1.000 0.613 0.710 0.822

1.35 1.38 1.00 1.09 1.15

0.58 0.62 0.23 0.33 0.49

Table 27 contains the results of the Friedman Test. According to the findings, cost efficiency is of primary importance among the derived codes in this section. Table 27 Results of Friedman ranking test. Rank Code

Mean rank

1 2 3

2.76 2.66 2.31

4

Cost efficiency Job creation and innovation promotion Competing with foreign technology suppliers; maintaining trade and exchange balance Open economy and sharp focus on distribution and logistics system

Appendix. H. Data analysis of the social, cultural and behavioral theme Lower and upper bounds also are positive for the responses to the relevant questions in this section. This implies the reliability and validity of the outcomes. The average importance coefficient for this theme is 3.93. Therefore, this theme could have a vital role in the process of commercialization. Table 28 and Table 29 show Ttest and one-sample test results respectively.

2.27

Table 28 Results of T-test for the socio-economic theme. No.

Code

N

Mean

Standard deviation

Std. Error Mean

71 72 73 Mean Value

Proper behavioral patterns in order to gain social acceptance Community participation to offer state support for bioenergy projects Development of innovation culture

31 31 31 31

4.10 3.90 3.81 3.93

0.908 1.106 1.195 0.956

0.163 0.199 0.215 0.171

Mean 4.15

Standard devia on

Std. Error Mean 1.4

4.1

4.1 1.195 1.106

4.05 4

1.2 1

0.908 3.9

3.95

0.8

3.9 3.85

3.81

0.6

3.8 0.4

3.75 3.7

0.215 0.1630.199

3.65 3.6

0.2 0

71

72

73 Fig. 8. Results of T-test for the socio-economic theme.

A. Bakhtiar et al. / Renewable Energy 145 (2020) 1780e1798

1797

Table 29 Respondents' scores to the importance of social, cultural and behavioral codes (one-sample test). No.

71 72 73 Whole questions

T-value

6.729 4.546 3.758 5.447

df

30 30 30

Sig. (2-tailed) P-value

Mean Difference

95% Confidence Interval of the Difference Upper

Lower

0.000 0.000 0.001 0.000

1.097 0.903 0.806 0.935

1.43 1.31 1.24 1.28

0.76 0.50 0.37 0.58

Table 30 listed the relevant codes in order of priority.

Table 30 Results of Friedman ranking test. Rank

Code

Mean rank

1 2 3

Proper behavioral patterns in order to gain social acceptance Participation of citizens, companies, local councils and others in proposing a bioenergy project Development of innovation culture

2.21 1.97 1.82

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