A Scientometric Review of the Research: Biodiesel and Petrodiesel Fuels

Ozcan Konur

Introduction

Crude oils have been primary sources of energy and fuels, such as petrodiesel. However, significant public concerns about their sustainability, price fluctuations, and adverse environmental impact of crude oils have emerged since the 1970s (Ahmadunet al., 2009; Atlas, 1981; Babich and Moulijn, 2003; Haritash and Kaushik, 2009; Kilian, 2009; Leahy and Colwell. 1990, Olah, 2005; Perron, 1989). Thus, biooils (Bridgwater and Peacocke, 2000; Czernik and Bridgwater. 2004; di Blasi, 2008; Gallezot, 2012; Mohan et al., 2006; Mortensen et al., 2011) and biooil-based biodiesel fuels (Agarwal, 2007; Chisti, 2007; Hill et al., 2006; Lapuerta et al., 2008; Ma and Hanna. 1999; Mata et al., 2010; Meher et al.. 2006; Zhang et al., 2003a-b) have emerged as an alternative to crude oils and crude oil-based petrodiesel fuels in recent decades. Nowadays, although petrodiesel fuels are used extensively, biodiesel fuels are being used increasingly in the transportation and power sectors (Konur, 2021a-ag).

However, for the efficient progression of the research in this field, it is necessary to develop efficient incentive structures for the primary stakeholders and to inform these stakeholders about the research (Konur, 2000, 2002a-c, 2006a-b, 2007a-b; North, 1991a—b).

Scientometric analysis offers ways to evaluate the research in a certain field (Garfield. 1955, 1972). This method has been used to evaluate research in a number of fields (Konur, 2011, 2012a-n, 2015, 2016a-f, 2017a-f, 2018a-b. 2019a-b). However, there has been no current scientometric study of the research on both biodiesel and petrodiesel fuels in general.

This chapter presents a study of the scientometric evaluation of the research on both biodiesel and petrodiesel fuels using two datasets. The first dataset includes the 100-most-cited papers (n = 100 sample papers) whilst the second includes population papers (n = over 121,000 population papers) published between 1980 and 2019.

The data on the indices, document types, authors, institutions, funding bodies, source titles, ‘Web of Science’ subject categories, keywords, research fronts, and citation impacts are presented and discussed.

Materials and Methodology

The search for the literature was carried out in the ‘Web of Science’ (WOS) database in February 2020. It contains the ‘Science Citation Index Expanded' (SCI-E), the ‘Social Sciences Citation Index’ (SSCI), the "Book Citation Index-Science’ (BCI-S), the ‘Conference Proceedings Citation Index-Science’ (CPCI-S), the ‘Emerging Sources Citation Index’ (ESCI), the ‘Book Citation Index-Social Sciences and Humanities’ (BCI-SSH), the ‘Conference Proceedings Citation Index-Social Sciences and Humanities’ (CPCI-SSH). and the ‘Arts and Humanities Citation Index’ (A&HCI).

The keywords for the search of the literature were collated from the screening of the abstract pages for the first 1,000 highly cited papers in the related 11 research fields. These keyword sets are provided in the appendices of the related chapters (Konur, 2021e-ag).

Two datasets were used for this study. The 100-most-cited papers comprise the first dataset (n = 100 sample papers) whilst all the papers form the second dataset (n = over 121,000 papers).

The data on the indices, document types, publication years, institutions, funding bodies, source titles, countries, ‘Web of Science’ subject categories, citation impact, keywords, and research fronts are collated from these datasets. The key findings are provided in the relevant tables and one figure, supplemented with explanatory notes in the text. The findings are discussed and a number of conclusions are drawn, along with recommendations for further study.

Results

Indices and Documents

There are over 155,000 papers in this field in the 'Web of Science’ as of February 2020. This original population dataset is refined by document type (article, review, book chapter, book, editorial material, note, and letter) and language (English), resulting in over 121,000 papers comprising over 77.9% of the original population dataset.

The primary index is the SCI-E for both the sample and population papers; 92.7% of the latter are indexed by this database. Additionally 3.7, 3.8, and 3.1% of these papers are indexed by the CPCI-S, ESCI, and BCI-S databases, respectively. The papers on the social and humanitarian aspects of this field are relatively negligible with only 1.9 and 0.1% of the population papers indexed by the SSCI and A&HCI, respectively.

Brief information on the document types for both datasets is provided in Table 2.1. The key finding is that articles are the primary documents for both sample and population papers, whilst reviews form 68% of the sample papers.

Authors

Brief information about the 13-most-prolific authors with at least two sample papers each is provided in Table 2.2. Around 330 authors contributed sample papers.

The most-prolific author is ‘Yusuf Chisti’ with three sample papers working primarily on ‘algal biodiesel production'. The other authors have two papers each.

On the other hand, a number of authors have a significant presence in the population papers: ‘Hassan H. Masjuki', ‘Paul T. Williams’, ‘Jo-Shu Chang’, ‘Roger R. Ruan’, ‘Johan Sjoblom’, ‘Javier Bilbao’, ‘Paul Chen’, ‘M. Abul Kalam’,

TABLE 2.1

Document Types

Document Type

Sample Dataset (%)

Population Dataset (%)

Difference (%)

1

Article

32*

93.7

-61.7

2

Review

68*

3.1

64.9

3

Book chapter

0

1.6

-1.6

4

Proc, paper

4

5.3

-1.3

5

Editorial mat.

0

1.9

-1.9

6

Letter

0

0.5

-0.5

7

Book

0

0.1

-0.1

8

Note

0

0.7

-0.7

*Originally there were 53 articles and 47 reviews as classified by the database.

TABLE 2.2 Authors

Authors

Sample Papers (%)

Population

Papers (%)

Surplus (%)

Institution

Country

Research Front 1

Research Front II

1

Chisti. Yusuf

3

0.1

2.9

Massey Un iv.

New Zealand

Biodiesel fuels

Algal biodiesel production, algal biomass production

2

Bernard. Olivier

2

0.1

1.9

INRIA

France

Biodiesel fuels

Algal biodiesel production

3

Bridgwater. Antony V.

2

0.1

1.9

Aston Univ.

UK

Biodiesel fuels

Biooil production

4

Cass. Glen R.

2

0.1

1.9

CALTECH

USA

Petrodiesel fuels

Emissions

5

Demirbas, Ayhan

2

0.1

1.9

Sila Sei.

Turkey

Biodiesel fuels

Biodiesel production

6

Dube. Marc A.

2

0.1

1.9

Univ. Ottawa

Canada

Biodiesel fuels

Biodiesel production-waste oils

7

Kates, Morris

2

0.1

1.9

Univ. Ottawa

Canada

Biodiesel fuels

Biodiesel production-waste oils

8

Knothe, Gerhard

2

0.1

1.9

Dept. Agric.

USA

Biodiesel fuels

Biodiesel properties

9

Mclean. David D.

2

0.1

1.9

Univ. Ottawa

Canada

Biodiesel fuels

Biodiesel production-waste oils

10

Sialve. Bruno

2

0.1

1.9

INRIA

France

Biodiesel fuels

Algal biodiesel production

11

Simoneit, Bernd R.T.

2

0.1

1.9

Oregon State Univ.

USA

Petrodiesel fuels

Emissions

12

Van Gerpen. Jon

2

0.1

1.9

Iowa State Univ.

USA

Biodiesel fuels

Biodiesel production

13

Source:

Zhang. Yongkui 2 0.1 1.9

'Highly Cited Researchers’ in 2019 (Clarivate Analytics, 2019).

Univ. Ottawa

Canada

Biodiesel fuels

Biodiesel production-waste oils

Biodiesel Fuels

‘Young-Kwon Park'. ‘Chang Sik Lee', ‘Jorge Ancheyta’. ‘Martin Olazar’, ‘Rene H. Wijffels’, ‘Riayz Kharrat’, ‘Tayfun Babadagli’, ‘Constantine Rakopoulos’, ‘Shahab Ayotollahi’, ‘Mohamed G. El-Din’, ‘Yun Hin Taufiq-Yap’, ‘Robert C. Brown’, ‘Avinash K. Agarwal’, ‘John V. Headley’, ‘Kefa Cen’, ‘G. Nagarajan’, ‘Rolf D. Reitz’, ‘Merv Fingas’, ‘Umer Rashid’, ‘Akwasi A. Boateng’, ‘Ajay K. Dalai’, ‘A. Mandal’, ‘Suzana Yusup', ‘Manuel Garcia-Perez’, ‘Eilhann E. Kwon', ‘Magin Lapuerta’, ‘Kerry M. Peru', ‘Chun Shun Cheung’, ‘Christopher M. Reddy’, ‘Mustafa V. Kok’, ‘Ryan P. Rodgers’, ‘Gartzen Lopez’, ‘Antono Marcilla’, ‘Wei Du’, ‘Rafael Font’, ‘Mohammad H. Gazanfari’, ‘Hajime Takano’, ‘Barat Ghobadian’, ‘Hwai C. Ong’, ‘OliverC. Mullins’, ‘RaulPayri’, ‘BassimH. Hameed’, ‘AmirH. Mohammadi’, ‘Phillip M. Fedorak’, ‘Stephen R. Latter’, and ‘Masaru Sagai’ with at least 0.55% of the population papers each.

The most-prolific institutions for these top authors are the ‘University of Ottawa’ of Canada and ‘INRIA’ of France with four and two sample authors, respectively. Thus, in total, nine institutions house these top authors.

It is notable that none of these top researchers is listed in the ‘Highly Cited Researchers’ (HCR) in 2019 (Clarivate Analytics, 2019; Docampo and Cram, 2019).

The most-prolific countries for these top authors are the USA and Canada with four authors each. These top countries are followed by France with three authors. In total, 6 countries contribute to these top papers.

There are two key topical research fronts for these top researchers: ‘biodiesel fuels’ and ‘petrodiesel fuels’, with 11 and 2 authors, respectively. At the secondary level, there are four and three authors with papers on ‘biodiesel production from waste oils’ and ‘biodiesel production from algae’, respectively. There are also two authors each with papers on ‘biodiesel production in general’ and ‘petrodiesel exhaust emissions’. The other authors have papers on ‘biooil production’, ‘algal biomass production’, and ‘biodiesel properties’.

It is further notable that there is a significant gender deficit among these top authors as all of these researchers are male (Lariviere et al., 2013; Xie and Shauman, 1998).

The author with the most impact is ‘Yusuf Chisti ’ with a 2.9% publication surplus. The other authors have the same publication surplus of 1.9%.

Publication Years

The information about publication years for both datasets is provided in Figure 2.1. This figure shows that 6, 14, 64, and 16% of the sample papers and 7.5, 10.5, 19.1, and 63.4% of the population papers were published in the 1980s, 1990s, 2000s, and 2010s, respectively.

Similarly, the most-prolific publication years for the sample dataset are 2008, 2009,2003, 2005, and 2010 with 11,11,9,7, and 7 papers, respectively. On the other hand, the most-prolific publication years for the population dataset are 2019, 2018, 2017, 2016, and 2015 with 9.3, 8.2, 8.0, 7.6, and 6.9% of the population papers, respectively. It is notable that there is a sharply rising trend for population papers in the 2000s and 2010s.

The research output between 1980 and 2019

FIGURE 2.1 The research output between 1980 and 2019.

Institutions

Brief information on the top 24 institutions with at least 2% of the sample papers each is provided in Table 2.3. In total, around 160 and 27.200 institutions contribute to the sample and population papers, respectively. Additionally, 3.3% of the population papers have no institutional information on their abstract pages.

These top institutions publish 57 and 11.3% of the sample and population papers, respectively. The top institutions are the US ‘Department of Energy’ and ‘Department of Agriculture’ with five and four sample papers, respectively. These top institutions are followed by the ‘Indian Institute of Technology’ of India, ‘Massey University’ of New Zealand, the ‘National Renewable Energy Laboratory’ of the USA. and the ‘University of Ottawa’ of Canada with three sample papers each.

The most-prolific country for these top institutions is the USA with 11 institutions. The other prolific countries are France, Germany, and Italy with two institutions each. In total, 11 countries house these top institutions.

The institutions with the most impact are the US ‘Department of Agriculture’ and ‘Department of Energy’ with 3.5 and 3.3% publication surpluses, respectively. These top institutions are followed by the ‘University of Ottawa’, ‘Massey University’, and the ‘National Renewable Energy Laboratory’ with a 2.8% publication surplus each.

On the other hand, the institutions with the least impact are ‘Rio de Janeiro Federal University’ of Brazil, the ‘Chinese Academy of Sciences’, the ‘Helmholtz Association’ of Germany, the ‘National Research Council' of Italy, and the ‘Indian Institute of Technology’ with -0.5, 1.2. 1.4, and 1.6% publication surpluses/deficits, respectively.

It is notable that some institutions have a heavy presence in the population papers: the ‘National Scientific Research Center’ of France, the ‘China University of

TABLE 2.3 Institutions

Institutions

Country

No. of Sample Papers (%)

No. of Population Papers (%)

Difference

(%)

1

Dept. Energ.

USA

5

1.7

3.3

2

Dept. Agric.

USA

4

0.5

3.5

3

Indian Inst. Technol.

India

3

1.4

1.6

4

Massey Univ.

New Zealand

3

0.2

2.8

5

Natl. Renew. Ener. Lab.

USA

3

0.2

2.8

6

Univ. Ottawa

Canada

3

0.2

2.8

7

Chinese Acad. Sei.

China

2

2.5

-0.5

8

Helmholtz Assoc.

Germany

2

0.8

1.2

9

Natl. Res. Counc.

Italy

2

0.6

1.4

10

Penn. State Univ.

USA

2

0.3

1.7

II

Univ. Michigan

USA

2

0.3

1.7

12

Aston Univ.

UK

2

0.2

1.8

13

Calif. Inst. Technol.

USA

2

0.2

1.8

14

Chevron

USA

2

0.2

1.8

15

Colorado Sch. Mines

USA

2

0.2

1.8

16

INRAE

France

2

0.2

1.8

17

INRIA

France

2

0.2

1.8

18

Karlsruhe Inst. Technol.

Germany

2

0.2

1.8

19

Oregon State Univ.

USA

2

0.2

1.8

20

Dept. Interior

USA

2

0.2

1.8

21

Castilla La-Mancha Univ.

Spain

2

0.2

1.8

22

Univ. Genoa

Italy

2

0.2

1.8

23

Univ. Nebrasca Lincoln

USA

2

0.2

1.8

24

Univ. Queensland

Australia

2

0.2

1.8

Petroleum’, the "Russian Academy of Sciences’, the ‘University of Alberta’ of Canada, the ‘Council of Scientific Industrial Research' of India, the ‘Superior Council of Scientific Investigations’ of Spain, the ‘University of Calgary’ of Canada, ‘Tsinghua University’, the ‘China National Petroleum Company’, ‘Tianjin University’ of China, the ‘University of Malaya’ of Malaysia, ‘Zhejiang University’ of China, the ‘Federal University of Rio de Janeiro’ of Brazil, the ‘Islamic Azad University’ of Iran, and ‘Shanghai Jia Tong University’ of China with at least 0.5% of the population papers each.

 
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