jrnls.org logo
Submit

BROWSE ALL JOURNALS

SEE ALL 19 JOURNALS
Journal of Scientometric ResearchNOW IN
Article

Emerging Research Trends in Energy Footprint: A Bibliometric Analysis Using VOSViewer

Koay Ying-Yin1, Wong Zi Wen Vivien1★, Chen Fanyu1, Tan Yan-Ling2, Yiew Thian-Hee1★ Corresponding author

  1. 1Department of Economics, Teh Hong Piow Faculty of Business and Finance, Universiti Tunku Abdul Rahman, MALAYSIA.
  2. 2Faculty of Business and Management, Universiti Teknologi MARA Cawangan Johor Kampus Segamat, MALAYSIA.

CORRESPONDENCE

Wong Zi Wen Vivien

Zi Wen Vivien Wong Department of Economics, Faculty of Business and Finance, Universiti Tunku Abdul Rahman, MALAYSIA.

vivienwong@utar.edu.my

Received: 28-04-2025; Revised: 16-06-2025; Accepted: 05-08-2025.

Volume 14, Issue 3 · pp. 750–760 · PUBLISHED May-Aug 2025 · DOI: 10.5530/jscires.20251505

View on JSR original site ↗

ABSTRACT

Although the empirical research in energy footprint have been ballooned in size, bibliometric research of energy of footprint remains limited. Using VOSviewer, we wish to contribute a bibliometric assessment of emerging research trends on energy footprint from 2001 to 2023. Publication characteristics, author keywords, authors collaboration, institutions, and countries were all examined. This analysis also recorded a total of 817 papers from the Web of Science Core Collection database. Study on energy footprint gives significant evidence on the possible linkages between energy and other threatening processes; and assists policy makers to prioritize the feasible departing idea for reducing energy footprint based on the research themes and evolutionary dynamics, hence produces some useful inferences that may be important policy implications. The finding of the bibliometric analysis, containing both the network analysis and the descriptive analysis, as well as focusing on the author keyword, reveal that science, followed by computer science dominate the number of studies in terms of author keyword, keyword cluster and author keyword co-occurrence network. Based on the findings, energy footprint research is expected to increase intensely in the near future. The USA accumulated the most documents and citations over the observed period. Journal of Cleaner Production was the most productive journal while Nature was the most cited journal in energy footprint literature. Owen A had the most publications in this subject area. Machine learning, China, water-energy-food nexus, water footprint, artificial intelligence, internet of things and edge computing have been the topic of previous research. Lastly, the outcomes of this study suggest that future studies of energy footprint could find themselves more innovative by venturing into some aspects of social science such as economic growth and institutional quality, so that the literature on energy footprint could be more diverse and comprehensive.

KEYWORDS

0% READ

FULL TEXT

INTRODUCTION

Energy footprint refers to the sum of all lands used to give non-food and non-feed energy like cultivated lands for energy and fuel crops, land for forest wood fuel as well as land used for hydropower generation.[1] In essence, it is a measure of lands needed to absorb carbon dioxide (CO2) emissions, mainly resulting from human activities such as fossil fuel combustion. These emissions, had in turn, led to a dramatic rise in Greenhouse Gas (GHG) concentration since the pre-industrial era.[2, 3] With average temperatures rising by about 1.2℃, as a result, the Earth has been warming at an alarming rate over the past few centuries.[2] This has increased the risk of climate change for both natural and human systems, including sea level rise, species extinction, extreme weather, food and water shortages, etc. In the same vein, projections of rising global energy consumption, which is primarily met by fossil fuels, conflict with the Paris Agreement's (2015) goal of reducing Greenhouse Gas (GHG) emissions.[4] This necessitates changing environmental policies, particularly those relating to energy. Reducing energy use and GHG emissions without compromising economic growth is possible by increasing energy efficiency. Numerous nations like China, France, U.S., among others, focus on energy efficiency in their Nationally Determined Contributions (NDCs) to the Paris Agreement, and the Sustainable Development Goals of the UN place a strong emphasis on it.[4]

However, the goal of energy efficiency and hence reduction in GHG emissions, faces enormous challenges and treats. First of all, energy embedded in final production and consumption is not addressed by energy policies, which largely concentrate on the energy usage of production activities inside the territory (see, for example, Nieto et al., Iyer et al.,).[5, 6] The energy used for a country's territorial production may differ significantly from the energy needed for final production and consumption especially in a globalised world where international trade is characterised by vertical specialisation and global supply chains (e.g., Koopman et al., Johnson and Noguera).[7, 8] Energy strategies that focus on local manufacturing fall short of reducing a country's overall energy footprint because they neglect to take into account the energy included in imported intermediates and finished commodities. Furthermore, in view of the goal to reach net zero by 2050, the world's energy intensity has decreased at a rate that is significantly slower than previously predicted.[9] Moreover, fossil fuels continue to dominate the energy structure, which will be responsible for 80% of all global GHG emissions by 2050.[10] The issue is exacerbated by the fact that the pursuance of renewable energy like hydropower and solar power, in response to reduce over-reliance on fossil fuels, has been facing challenges in the form of limited access to modern electricity (13% of the world’s population).[10]

After all, the issues relating to energy footprint lead to bibliometric research questions of (1) What is the annual publication trend of energy footprint?; (2) Which country contributed the most to energy footprint; (3) Which is the most influential journal in the research area of energy footprint?. While there had been case studies on carbon footprint;[11, 12] while on water footprint[13] as well as bibliometric analysis on carbon footprint[14–16] water footprint;[13, 17, 18] greenhouse gas emission[19–21] focused on the environmental aspects, bibliometric research on energy footprint in particular is still limited, as far as this study is concern. The latter group of research has just been (started to be) conducted recently by Zeng et al.,[22] on household as well as by Li et al.,[14] on higher education. For example, Li et al.,[14] found that the USA and the University of California System were the most productive country and institution respectively, during the period of Year 2010-2019; and keywords identified through cluster analysis include life-cycle assessment, environmental performance and carbon management, greenhouse gas emissions, design, system, and sustainability. On the other hand, Zeng et al.,[22] revealed that carbon emission should become the focus of future research. Taken together, the recent trend of bibliometric research on energy footprint implies the increasing importance of bibliometric analysis particularly on the area of energy footprint.

Accordingly, this paper aims to bibliometrically assess the global research trends on energy footprint by investigating the existing as well as future publication characteristics, most productive source titles and publishers, most cited papers, keyword clusters etc., from 2001 to 2022. It is unique, as compared to the literature for three reasons. Firstly, prior studies have already unearthed the link between environmental footprint, biodiversity loss, ecosystem service, human well-being and ecological footprint (see: Tan et al., Wang et al., Xie et al., Yang and Meng).[23–26] This paper, however, focuses on bibliometric analysis of energy footprint. The latter is at utmost importance to tackle, for a reduced ecological footprint, compared to the former group of studies. Given the energy efficiency, it raises resource rents of countries as a sustainable energy demand drives the restore of resources, particularly the mineral resources, and mitigate negative externalities from the resource market.[27] On the other hand, tackling energy poverty and inequalities is inevitable for household well-being, reducing energy demand and therefore, reducing ecological footprint.[28] In the absence of policies aiming for sufficient energy usage, we will not be able to mitigate the effects of our lifestyles. Taken together, studying energy footprint is particularly vital to the environmental quality[29] as energy used largely leads to environmental degradation.[30] Thirdly, this research evaluates the publishing trends of energy footprint by (1) using the latest dataset (till 2022); (2) based on an extensive 817 papers record documented from 2001 to 2022; and (3) based on a renowned source of data – Web of Science Core Collection database.

In a nutshell, this study provides important insights by focusing on energy footprint, as the main term to scrutinize publishing patterns in the discipline. The study utilizes the network analysis and the descriptive analysis to identify hotspots in the field and explores the existing co-authorship networks. This comprehensive bibliometric study is beneficial for scholars in identifying research hotspots as well as its research gap and influential journals. The study will assist young researchers and postgraduate students to develop their co-authorship networks to improve their quality of research in the field of energy footprint. Additionally, the study could also help policy makers prioritize the feasible departing idea for reducing energy footprint based on the research themes and evolutionary dynamics, hence produces some useful inferences that may be important policy implications. According to Mihajlovski et al.,[31] energy footprint is an indicator of sustainability, the universal and ultimate goal of the United Nations (U.N).

METHODOLOGY

Materials

In line with Li et al.,[14] the data in this study was collected from a comprehensive bibliographic database namely Web of Science core collection (WoSCC) that provides access of high impact published articles. Based on the Web of Science core collection (WoSCC), this research manages to obtain the earliest publication with the keyword “energy footprint” in 2001. Thus, this research will collect all the publications from the Web of Science core collection (WoSCC). The data was collected on 20 December 2022. The literature search was based on topic (TS) that includes title, abstract, author keywords and Keywords Plus. The search string was TS=("energy footprint*" OR "emergy footprint*”). Emergy refers to the total energy available to produce a goods or service like biomass, money and information flow in which all values are expressed in term of the similar unit of solar energy joules.[32] No limiters were used to refine the literature searching such as document type, publication years, languages and others. Lastly, there were 817 papers record documented from 2001 and 2022.

Methods

The free software programme VOSviewer (version 1.6.18) was created by Van Eck and Waltman,[33] and it is widely used for bibliometric analysis to create networks for scientific journals, research centres, nations, and keywords.

The two components of the bibliometric analysis are the network analysis and the descriptive analysis. The study's core facts, including publications and citations for research fields, authors, journals, nations, and institutions, are outlined in the descriptive analysis. Examples of network analysis include co-citation, co-authorship, and keyword co-occurrence.

Van Eck and Waltman[33] define a citation connection as the relationship between two items in which one item cites the other. A co-citation link indicates the similarity of two sources that are both quoted in the same text. A co-authorship relationship is the similarity of things based on the quantity of documents co-authored. The thing of interest could be a term or possibly an author, a nation, a document, an organisation, or a source.

In particular, author co-citation refers to a situation in which two writers are frequently mentioned together by other academics because they share the same theoretical perspective.[34] When two researchers are connected together, it means a third author has referenced both of them together.[35] Extensive use of co-authorship analysis is made to comprehend and evaluate patterns of scientific collaboration.[36] A co-authorship network must be established in order to properly acknowledge the contributions of two or more writers or organisations, according to Newman.[37] Authors, nations, or institutions that have co-authored a paper together might be the nodes in a co-authorship network.[37]

Keywords from sources like Author Keywords and words in abstracts, offer a thorough summary of research trends in a certain field.[38] One of the categories of keywords available in the Web of Science Core Collection is Author Keywords-the keywords provided by the original authors.[39] An article's content and specifics are suitably consulted by the author's keyword analysis.[40]

RESULTS AND DISCUSSION

Publication characteristics

Figure 1 shown that there are 817 papers in total were published around the world with the trends in publication from 2001 to 2023. The first publication was in 2001 with earned only 1 citation and there is consistently to have additional one articles for next five year. It is clear shown that over the 2000s, the number of articles and citations grew steadily. However, started from year 2016 the both citation rate and publication speeded up. Based on the data collected as of 20 December 2022, there were 104 publications and 3146 citations, which is the year when both numbers reached their pinnacle in 2021. Overall, during the study period, there was a steady growth in both publications and citations. This indicates a rise in interest in disciplines concerned with the energy footprint. The future seems to hold significant promise for publications and citations, according to current patterns. Despite an increase in publication on energy footprint, however there is lack of awareness on the vital role-play by energy footprint as developed nations are growing more and more dependent on emerging nations for the supply of commodities and services. The topic of whether energy is more traded as energy incorporated in internationally traded items is raised by the fact that not only are energy markets but also supply chains in general are becoming more globalised.[41, 42] Energy security has evolved into a more complicated policy concern for nations in particular due to the relationship between energy needs and global trade. This is due to the fact that energy security is stated not only in terms of the direct trade of coal, oil, and gas, but also in terms of how dependent global supply chains are on the availability of home energy.[42]

Figure 1: Number of publication and citations, 2001-2023.

Figure 2 summarise the 10 most productive journals. The leading journal was Journal of Cleaner production (34 articles), followed by Applied Energy (16 articles) and Journal of Industrial Ecology (14 articles). Besides, from the total of 817 papers, majority of the articles were published under Elsevier (253 documents), followed by IEEE (182 documents) and Springer Nature (63 articles).

Figure 2a: The 10 most productive source titles and publishers.
Figure 2b: The 10 most productive source titles and publishers.

Meanwhile, according to Table 1 The Top 10 Most Cited Paper. The highest cited paper is refereeing to “Porous materials with optimal adsorption thermodynamics and kinetics for CO2separation” with the total citation of 1682, average per year hit 168.2 from published in Nature. The studies focused on the energy expenditures involved in separating and purifying industrial products including gases, fine chemicals, and fresh water,[43] porous metal-organic frameworks for gas storage and separation. The second highest cited paper is referring to “STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts” (1144 citation) which published under Ecological Economic in the year of 2003. The study examines the driving forces of environmental impact by using three different tools namely IPAT identity, ImPACT identity and STIRPAT model[44] “Global water crisis and future food security in an era of climate change” (651 citation) is the third most cited paper. The paper focus on analysis of the overall role of climate change, water scarcity, energy crisis, as well as credit crisis and population growth in redefining global food security.[45] Among these journals, Applied Energy had the highest impact score at 11.20, in the Year 2022. Journal of Cleaner Production and Ecological Economics with the impact score of 10.96 and 5.99 respectively. In summary, the top 10 most cited articles on the topic of energy footprint with 1682 citations, from 2001 to 2023, are as shown in Figure 1.

Table 1: The 10 most cited papers.
RankTitleAuthorsSource TitlePublication YearTotal CitationsAverage per Year
1Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separationNugent, Patrick; Belmabkhout, Youssef; Burd, Stephen D.; Cairns, Amy J.; Luebke, Ryan; Forrest, Katherine; Pham, Tony; Ma, Shengqian; Space, Brian; Wojtas, Lukasz; Eddaoudi, Mohamed; Zaworotko, Michael J.Nature20131682168.2
2STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impactsYork, R; Rosa, EA; Dietz, TEcological Economics2003114457.2
3Global water crisis and future food security in an era of climate changeHanjra, Munir A.; Qureshi, M. EjazFood Policy201065150.08
4Separation technologies for the recovery and dehydration of alcohols from fermentation brothsVane, Leland M.Biofuels Bioproducts & Biorefining-Biofpr200841727.8
5Sustainable machining: selection of optimum turning conditions based on minimum energy considerationsRajemi, M. F.; Mativenga, P. T.; Aramcharoen, A.Journal Of Cleaner Production201028722.08
6Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United StatesOnat, Nuri Cihat; Kucukvar, Murat; Tatari, OmerApplied Energy201520625.75
7Footprints of water and energy inputs in food production - Global perspectivesKhan, Shahbaz; Hanjra, Munir A.Food Policy200919413.86
8Scalpel: Customizing DNN Pruning to the Underlying Hardware ParallelismYu, Jiecao; Lukefahr, Andrew; Palframan, David; Dasika, Ganesh; Das, Reetuparna; Mahlke, Scott44th Annual International Symposium on Computer Architecture (ISCA 2017)201718931.5
9Energy and water autarky of wastewater treatment and power generation systemsGude, Veera GnaneswarRenewable & Sustainable Energy Reviews201518623.25
10A structural decomposition analysis of global energy footprintsLan, Jun; Malik, Arunima; Lenzen, Manfred; McBain, Darian; Kanemoto, KeiichiroApplied Energy201618326.14

Co-word analysis

Keyword clusters

In the bibliometric analysis, author-generated keywords play a significant role in providing the insights into the recent research hotspot. Nevertheless, research always employ keyword strategically, which may lead to a deviance from the study’s objective to capture the public attention. This occurrence can illustrate by Naidoo et al.,[46] whose research focus on “food nexus” keyword but dip into the study on job and wealth creation, improved livelihoods and well-being, and regional integration. The objective of the bibliometric study is to identify the most popular keywords in the energy footprint domain during the study period. In order to enhance the understanding of this research, the minimum number of occurrences of a keyword was set at six in all sample articles. Figure 3 shows the network visualization maps of the keywords co-occurrence. It portrays the Author Keywords co-occurrence network visualization map, which consists of a minimum of 6 occurrences, for a total of 40 keywords. The node size displays the number of times a keyword appears in the energy footprint documents. The bigger the node in size indicates the more frequently a keyword is found in the energy footprint records. The distance between two nodes represents the strength between the two keywords and their topic similarity, while the same coloured nodes display a comparable topic.[47]

Figure 3: Author keyword co-occurrence network. Notes: Minimum number of occurrences of a keyword is 6 and 40 author keywords and 7 clusters meet the threshold. The largest set of connected keywords consists of 38 authors keywords. Node= author keyword; size of node= occurrence.

The Author Keywords can be classified into seven clusters; each cluster displays a subfield of energy footprint from 2001 to 2022. Cluster 1 denotes the red cluster, which is the biggest of the seven clusters and contains of 10 items including energy efficiency, energy consumption, renewable energy, internet of things, power consumption, machine learning, edge computing, cloud computing, IOT, and machining. All these clearly response to the domain of energy efficacy. The green cluster (cluster 2) and ultramarine cluster (cluster 3) have seven items and five items, respectively. Keywords for the green cluster are energy footprint, carbon footprint, life cycle assessment, wastewater treatment, anaerobic digestion, activated sludge, and LCA, which are related to energy footprints. On the other hands, the keywords for the ultramarine cluster include energy, China, footprint, energy management, and efficiency are primarily connected to energy footprint. The gold cluster (cluster 4, 5 items) includes the keywords of sustainable development, ecological footprint, input-output analysis, embodied energy, and energy intensity while the purple cluster (cluster 5, 5 items) refers to industrial ecology with the keywords of industrial ecology, water-energy- food nexus, energy use, agriculture, and Life Cycle Assessment (LCA). Next, the sky-blue cluster with four keywords (sustainability, water-energy nexus, artificial intelligence, and desalination) is mainly associated with the sustainability mechanism. Lastly, the last cluster with orange colour shows the water footprint that comes with the keywords of water footprint and climate change.

The evolution of keywords

Figure 4 shown the network mapping of author generated keyword, by providing an insight between the interaction on the main domain-energy footprint within the field. The overlay links the keywords to the publication year which can help to trace the evolution of a specific topic over the studied period.[35] The note size given in the figure shown the volume of research in the specific area, the largest the note represent a higher number of publications. Based on Figure 4, the largest note is referring to the ‘energy footprint’ occurs 114 times across all sampled articles. Additionally, the visualisation demonstrates four colours to composite the four primary clusters, there are referring to green, blue, light green and purple. Table 2 shown the details of these composition of the four primary clusters. Although all keywords are highly associated with energy footprint, ach cluster represent a unique avenue of researcher’s interest. The green cluster primarily delves into the sustainability aspect of carbon footprint through energy footprint. Energy footprint had integrated in various carbon footprint context, which includes carbon feedstock CO2,[48] health care carbon footprint,[49] sustainability of food processing system[50] and economic performance.[51] The blue cluster shown the integration between the energy efficiency and energy footprint via technology adoption such as cloud computing[52] enable to provide service quality guarantee with lower energy consumption and carbon footprint. Besides, the research also includes the influence of energy footprint on input output analysis.[53, 54] While the light green cluster focus more on the research area of waste water treatment,[55] water energy nexus,[56] and the integration of water energy food nexus and carbon footprint[57, 58] in developing the on-farm water, energy, food and carbon footprint (WEFE) nexus index for farm performance. The purple cluster emphasises the relationship between industrial ecology and power consumption[59, 60] on the dynamic effect of industrialisation and urbanisation on energy footprint.

Figure 4: Author keywords co-occurrence time line. Notes: Minimum number of occurrences of a keyword is 6 and 40 author keywords and 7 clusters meet the threshold. The largest set of connected keywords consists of 38 authors keywords. Node= author keyword; size of node= occurrence.
Table 2: The Top Four Main Clusters of Keywords Co-occurrence.
ClusterRepresented ColorItem
1GreenCarbon footprint, sustainability, energy consumption, embodied energy.
2BlueEnergy efficiency, activated sludge, input-output analysis, energy management, desalination, cloud computing.
3Light GreenWastewater treatment, water energy nexus, water energy food nexus.
4PurpleSustainable development, energy use, industrial ecology, life cycle assessment, power consumption.

Collaboration analysis

Authors collaboration

Figure 5 illustrates the co-authorship network visualization maps. The minimum number documents of an author is 2 while the minimum number of citations of an author is 2. There are 6 clusters meet the threshold. The largest set of connected documents consists of 29 documents. Owen A had the largest node size which indicates that the author had the most documents in the subject area. Besides, the author also had the most connecting linkages from other authors. In other words, the author collaborated the most with other authors, namely Baltruszewicz, M, Brand- Correa L I, and Oswald Y. Bueno G and Lenzen M came in second and third of the authors who has the most documents in the subject area.

Figure 5: Authors co-authorship network. Notes: Minimum number documents of an author is 2 and minimum number of citations of an author is 2. 294 documents and 6 clusters meet the threshold. The largest set of connected documents consists of 29 documents. Node=author, size of the node=document.

Organizations collaboration

Figure 6 shows the organizations co-authorship network maps. The minimum number documents of an organization is 4 while the minimum number of citations of an organization is 2 documents. There are 9 clusters meet the threshold and they accumulated to 42 documents. The size of the node shows the number of documents in the subject area. The Columbia University had the most documents in the study of energy footprint then it was followed by the University of Leeds.

Figure 6: Organizations co-authorship network. Notes: Minimum number documents of an organization is 4 and minimum number of citations of an organization is 2. 76 documents and 9 clusters meet the threshold. The largest set of connected documents consists of 42 documents. Node=organization, size of the node=document.

Countries collaboration

Figure 7 shows the co-authorship network visualization of countries with at least 4 papers and 2 citations. The largest set of connected countries consists of 11 clusters which amounted to 44 countries. The largest cluster (red cluster) had 9 countries, namely Australia, Bangladesh, Indonesia, Malaysia, Pakistan, Peoples Republic of China, South Korea, Thailand, and Turkey. This is followed by the light green cluster (5 countries) which includes Austria, Norway, Romania, Sweden, and Switzerland. The third cluster (dark blue cluster, five countries) includes Belgium, Canada, Ireland, New Zealand, and South Africa. Nevertheless, the USA accumulated the most documents and citations over the observed period.

Figure 7: Countries co-authorship network. Notes: Minimum number documents of a country is 4 and minimum number of citations of a country is 2. 45 documents and 6 clusters meet the threshold. The largest set of connected documents consists of 44 documents. Node=country, size of the node=document.

Co-citation analysis

Authors co-citation

Figure 8 illustrates the authors co-citation network map. The size of node signifies the co-citation frequency as stated in the references. The larger the node, the greater the co-citation frequency. The node colour denotes the clustering by authors. Each author had at least 16 citations. There are 84 authors and 6 clusters met the threshold. Lenzen M, Hoekstra AY, and Wackernagel M obtained the most co-citations. In other words, they have been the most regularly co-cited by other authors.

Figure 8: Authors co-citation network. Notes: Minimum number of citations of an author is 16. 84 authors and 6 clusters meet the threshold. The largest set of connected authors consists of 83 authors. Node=cited author, size of node=citation.

CONCLUSION

Energy footprint led to a dramatic rise in Greenhouse Gas (GHG) concentration since the pre-industrial era. It has been recognized as one of the major environmental issues. This bibliometric assessment offers a valuable picture on energy footprint from numerous aspects. Most of the environmental related bibliometric analysis focus on carbon footprint,[14–16] water footprint,[13, 17, 18] greenhouse gas emission.[19–21] However, the bibliometric analysis on energy footprint is still limited.

Through the bibliometric analysis involving VOS Viewer Software and data collected from Web of Science database on energy footprint, this study delved into the most productive sources titles, publishers, papers, author-co-author, co-citation, keywords, countries. Notably, energy footprint served as most frequently occurred keyword and the highest Total Link Strength (TLS), recorded 114 occurrence and 124 TLS, in Year 2017. This subject area (energy footprint) is Anne Owen’s actively researched field. The author not only served as the most productive researcher (with 6487 citations) in the energy footprint area of research but she is also the most connectable/connected (for as high as 29 documents connected with other authors namely Baltruszewicz, M., Brand-Correa L. I., and Oswald, Y.) authors in the subject area, followed by Bueno, G. (second place) and Lenzen M. (third place). The consistency between the top research area-energy footprint and the most influential author reinforces the significance of these scholarly contributions in framing the discourse around energy footprint in the environmental sustainability. It is found that science, followed by computer science dominate the number of studies in terms of author keyword, keyword cluster and author keyword co-occurrence network. This implies that in general, scientific researchers are still confining their scientific research to science and computer science, given the fact that social science is also crucial to scientific research. However, higher institutional quality or governance might play a role in monitoring and restricting these footprints as the industrial players need to comply with the strongly enforced and upheld environmental standard and regulation.

As such future studies of energy footprint could find themselves more innovative by venturing into these aspects of social science, so that the literature on energy footprint could be more diverse and comprehensive. A number of other potential limitations have been found. They generally include time constraints, database and key words. The sole dependence on Web of Science as the scientific database provides rooms for improvement. It is recommended to incorporate other databases like Scopus and CNKI in order to reach a more comprehensive overview of how energy footprint serves as a hot topic of research in various aspects including co-word analysis, co-author analysis, co-citation analysis and others alike. With the support of multiple databases, researchers could ensure higher number of related literatures, thereby increasing the reliability and validity of the bibliometric analysis. Last but not least, the dataset collected as of 20 December 2022 may not capture papers published eventually because of the continuous update of database. It is advisable for future researchers to aware of the dynamic nature of databases and periodically search databases for most recent publications, which in turn allowing the study’s relevance to the latest development in the subject matter.

Taken together, the study’s findings highlighted the importance of the energy footprint in providing implications to policy makers and hence contributing towards achievement of SDG7 (Affordable & Clean Energy) and SDG 13 (Climate Change), despite a number of recommendations for future research.

REFERENCES

As published

Showing references and in-text citations exactly as published.

  1. 1.Global Footprint Network. Global Footprint Network.GOOGLE SCHOLAR
  2. 2.Change, PC. Global warming of 1.5°C. World Meteorological Organization. 2018.GOOGLE SCHOLAR
  3. 3.Shindell, D; Smith, CJ.. Climate and air-quality benefits of a realistic phase-out of fossil fuels. Nature. 2019;573(7774):408–11. https://doi.org/10.1038/s41586-019-1554-zDOIGOOGLE SCHOLAR
  4. 4.Fernández-Amador, O; Francois, JF; Oberdabernig, DA; Tomberger, P.. Energy footprints and the international trade network: A new dataset. Is the European Union doing it better?. Ecol Econ. 2023;204:107635. https://doi.org/10.1016/j.ecolecon.2022.107635DOIGOOGLE SCHOLAR
  5. 5.Nieto, J; Carpintero, Ó; Miguel, LJ.. Less than 2°C? An Economic-Environmental Evaluation of the Paris Agreement. Ecol Econ. 2018;146:69–84. https://doi.org/10.1016/j.ecolecon.2017.10.007DOIGOOGLE SCHOLAR
  6. 6.Iyer, G; Ledna, C; Clarke, L et al. Measuring progress from nationally determined contributions to mid-century strategies. Nat Clim Change. 2017;7(12):871–4.GOOGLE SCHOLAR
  7. 7.Koopman, R; Wang, Z; Wei, SJ.. Tracing value-added and double counting in gross exports. Am Econ Rev. 2014;104(2):459–94. https://doi.org/10.1257/aer.104.2.459DOIGOOGLE SCHOLAR
  8. 8.Johnson, RC; Noguera, G.. Accounting for intermediates: production sharing and trade in value added. J Int Econ. 2012;86(2):224–36. https://doi.org/10.1016/j.jinteco.2011.10.003DOIGOOGLE SCHOLAR
  9. 9.International Energy Agency (IEA).. World energy outlook 2019. Paris: IEA. 2019.GOOGLE SCHOLAR
  10. 10.United Nations.. Ensure access to affordable, reliable, sustainable and modern energy for all. SDGS. New York: United Nations. 2019.GOOGLE SCHOLAR
  11. 11.Zheng, N; Li, S; Wang, Y; Huang, Y; Bartoccid, P; Fantozzid, F et al. Research on low-carbon campus based on ecological footprint evaluation and machine learning: A case study in China. J Clean Prod. 2021;323:129181. https://doi.org/10.1016/j.jclepro.2021.129181DOIGOOGLE SCHOLAR
  12. 12.Liu, H; Wang, X; Yang, J; Zhou, X; Liu, Y.. The ecological footprint evaluation of low carbon campuses based on life cycle assessment: A case study of Tianjin, China. J Clean Prod. 2017;144:266–78. https://doi.org/10.1016/j.jclepro.2017.01.017DOIGOOGLE SCHOLAR
  13. 13.Zhang, Y; Huang, K; Yu, Y; Yang, B.. Mapping of water footprint research: A bibliometric analysis during 2006-2015. J Clean Prod. 2017;149:70–9. https://doi.org/10.1016/j.jclepro.2017.02.067DOIGOOGLE SCHOLAR
  14. 14.Li, Z; Chen, Z; Yang, N; Wei, K; Ling, Z; Liu, Q et al. Trends in research on the carbon footprint of higher education: A bibliometric analysis (2010-2019). J Clean Prod. 2021;289:125642. https://doi.org/10.1016/j.jclepro.2020.125642DOIGOOGLE SCHOLAR
  15. 15.Cao, Y; Qi, F; Cui, H; Yuan, M.. Knowledge domain and emerging trends of carbon footprint in the field of climate change and energy use: a bibliometric analysis. Environ Sci Pollut Res Int. 2023;30(13):35853–70. https://doi.org/10.1007/s11356-022-24756-1DOIGOOGLE SCHOLAR
  16. 16.Yue, T; Liu, H; Long, R; Chen, H; Gan, X; Liu, J.. Research trends and hotspots related to global carbon footprint based on bibliometric analysis: 2007-2018. Environ Sci Pollut Res Int. 2020;27(15):17671–91. https://doi.org/10.1007/s11356-020-08158-9DOIGOOGLE SCHOLAR
  17. 17.Xiao, J; Wei, J; Wu, M; Cao, X.. Bibliometric and visual analysis of crop water footprint: A widely used agricultural water resources evaluation method. Water. 2022;14(18):2866. https://doi.org/10.3390/w14182866DOIGOOGLE SCHOLAR
  18. 18.Ansorge, L; Stejskalová, L; Vološinová, D.. Bibliometric analysis of water footprint research in countries of former Yugoslavia. Acta Hydrotechnica. 2021;34(61):93–102. https://doi.org/10.15292/acta.hydro.2021.07DOIGOOGLE SCHOLAR
  19. 19.Chen, G; Bai, J; Bi, C; Wang, Y; Cui, B.. Global greenhouse gas emissions from aquaculture: a bibliometric analysis. Agric Ecosyst Environ. 2023;348:108405. https://doi.org/10.1016/j.agee.2023.108405DOIGOOGLE SCHOLAR
  20. 20.Yuan, BZ; Sun, J.. Research trend of rice and greenhouse gases based on Web of Science: a bibliometric analysis. All Earth. 2023;35(1):16–30. https://doi.org/10.1080/27669645.2022.2164412DOIGOOGLE SCHOLAR
  21. 21.Yang, W; Zhou, H; Si, F; Liu, C; Wang, W; Sun, Y et al. Bibliometric analysis of greenhouse gas research on a global scale from 2000 to 2014. Curr Sci. 2018;114(8):1624–31. https://doi.org/10.18520/cs/v114/i08/1624-1631DOIGOOGLE SCHOLAR
  22. 22.Zeng, J; Qu, J; Ma, H; Gou, X.. Characteristics and Trends of household carbon emissions research from 1993 to 2019: A bibliometric analysis and its implications. J Clean Prod. 2021;295:126468. https://doi.org/10.1016/j.jclepro.2021.126468DOIGOOGLE SCHOLAR
  23. 23.Tan, YL; Yiew, TH; Habibullah, MS et al. Research trends in biodiversity loss: a bibliometric analysis. Environ Sci Pollut Res. 2022:;1–17.GOOGLE SCHOLAR
  24. 24.Wang, B; Zhang, Q; Cui, F.. Scientific research on ecosystem services and human well-being: A bibliometric analysis. Ecol Indic. 2021;125:107449. https://doi.org/10.1016/j.ecolind.2021.107449DOIGOOGLE SCHOLAR
  25. 25.Xie, Y; Li, X; Hu, X; Hu, X.. The landscape of academic articles in environmental footprint family research: A bibliometric analysis during 1996-2018. Ecol Indic. 2020;118:106733. https://doi.org/10.1016/j.ecolind.2020.106733DOIGOOGLE SCHOLAR
  26. 26.Yang, Y; Meng, G.. A bibliometric analysis of comparative research on the evolution of international and Chinese ecological footprint research hotspots and frontiers since 2000. Ecol Indic. 2019;102:650–65. https://doi.org/10.1016/j.ecolind.2019.03.031DOIGOOGLE SCHOLAR
  27. 27.Nassani, AA; Aldakhil, AM; Zaman, K.. Ecological footprints jeopardy for mineral resource extraction: efficient use of energy, financial development and insurance services to conserve natural resources. Resour Policy. 2021;74:102271. https://doi.org/10.1016/j.resourpol.2021.102271DOIGOOGLE SCHOLAR
  28. 28.Baltruszewicz, M; Steinberger, JK; Paavola, J; Ivanova, D; Brand-Correa, LI; Owen, A.. Social outcomes of energy use in the United Kingdom: household energy footprints and their links to well-being. Ecol Econ. 2023;205:107686. https://doi.org/10.1016/j.ecolecon.2022.107686DOIGOOGLE SCHOLAR
  29. 29.Danish, R; Ulucak, R; Khan, SU.. Determinants of the ecological footprint: role of renewable energy, natural resources, and urbanization. Sustain Cities Soc. 2020;54:101996. https://doi.org/10.1016/j.scs.2019.101996DOIGOOGLE SCHOLAR
  30. 30.Shujah-Ur-Rahman, S; Chen, S; Saud, S; Saleem, N; Bari, MW.. Nexus between financial development, energy consumption, income level, and ecological footprint in CEE countries: do human capital and biocapacity matter?. Environ Sci Pollut Res Int. 2019;26(31):31856–72. https://doi.org/10.1007/s11356-019-06343-zDOIGOOGLE SCHOLAR
  31. 31.Mihajlovski, B; Fetaji, B; Abazi, L; Fetaji, M.. Analyses of Ecological and Energy Footprint as indicators of Energy Management in the transition to sustainability using social networks. In: 43rd International Convention on Information, Communication and Electronic Technology (MIPRO);. 2020;1801–6. https://doi.org/10.23919/MIPRO48935.2020.9245301DOIGOOGLE SCHOLAR
  32. 32.Nadalini, AC; Kalid, RA; Torres, EA.. Emergy as a tool to evaluate ecosystem services: A systematic review of the literature. Sustainability. 2021;13(13):7102. https://doi.org/10.3390/su13137102DOIGOOGLE SCHOLAR
  33. 33.Van Eck, NJ; Waltman, L.. VOSviewer manual: manual for VOSviewer version 1.6. 15. Leiden: Centre for Science and Technology Studies (CWTS) of Leiden University. 2020.GOOGLE SCHOLAR
  34. 34.White, HD; McCain, KW.. Visualizing a discipline: an author co‐citation analysis of information science, 1972-1995. J Am Soc Inf Sci. 1998;49(4):327–55.GOOGLE SCHOLAR
  35. 35.Udomsap, AD; Hallinger, P. A bibliometric review of research on sustainable construction, 1994–2018. Journal of Cleaner Production. 2020;254:120073. https://doi.org/10.1016/j.jclepro.2020.120073DOIGOOGLE SCHOLAR
  36. 36.Sampaio, RB; Fonseca, MV; Zicker, F.. Co-authorship network analysis in health research: method and potential use. Health Res Policy Syst. 2016;14(1):1–10.GOOGLE SCHOLAR
  37. 37.Newman, ME.. The structure and function of complex networks. SIAM Rev. 2003;45(2):167–256. https://doi.org/10.1137/S003614450342480DOIGOOGLE SCHOLAR
  38. 38.Fu, HZ; Ho, YS; Sui, YM; Li, ZS.. A bibliometric analysis of solid waste research during the period 1993-2008. Waste Manag. 2010;30(12):2410–7. https://doi.org/10.1016/j.wasman.2010.06.008DOIGOOGLE SCHOLAR
  39. 39.Zhang, J; Yu, Q; Zheng, F; Long, C; Lu, Z; Duan, Z.. Comparing Keywords Plus of WOS and author keywords: a case study of patient adherence research. J Assoc Inf Sci Technol. 2016;67(4):967–72. https://doi.org/10.1002/asi.23437DOIGOOGLE SCHOLAR
  40. 40.Comerio, N; Strozzi, F.. Tourism and its economic impact: a literature review using bibliometric tools. Tour Econ. 2019;25(1):109–31. https://doi.org/10.1177/1354816618793762DOIGOOGLE SCHOLAR
  41. 41.Lan, J; Malik, A; Lenzen, M; McBain, D; Kanemoto, K.. A structural decomposition analysis of global energy footprints. Appl Energy. 2016;163:436–51. https://doi.org/10.1016/j.apenergy.2015.10.178DOIGOOGLE SCHOLAR
  42. 42.Tian, X; Chen, B; Geng, Y; Zhong, S; Gao, C; Wilson, J et al. Energy footprint pathways of China. Energy. 2019;180:330–40. https://doi.org/10.1016/j.energy.2019.05.103DOIGOOGLE SCHOLAR
  43. 43.Nugent, P; Belmabkhout, Y; Burd, SD; Cairns, AJ; Luebke, R; Forrest, K et al. Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation. Nature. 2013;495(7439):80–4. https://doi.org/10.1038/nature11893DOIGOOGLE SCHOLAR
  44. 44.York, R; Rosa, EA; Dietz, T.. STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ. 2003;46(3):351–65. https://doi.org/10.1016/S0921-8009(03DOIGOOGLE SCHOLAR
  45. 45.Hanjra, MA; Qureshi, ME.. Global water crisis and future food security in an era of climate change. Food Policy. 2010;35(5):365–77. https://doi.org/10.1016/j.foodpol.2010.05.006DOIGOOGLE SCHOLAR
  46. 46.Naidoo, D; Nhamo, L; Mpandeli, S; Sobratee, N; Senzanje, A; Liphadzi, S et al. Operationalising the water-energy-food nexus through the theory of change. Renew Sustain Energy Rev. 2021;149:111416. https://doi.org/10.1016/j.rser.2021.111416DOIGOOGLE SCHOLAR
  47. 47.Guo, YM; Huang, ZL; Guo, J; Li, H; Guo, XR; Nkeli, MJ.. Bibliometric analysis on smart cities research. Sustainability. 2019;11(13):1–18. https://doi.org/10.3390/su11133606DOIGOOGLE SCHOLAR
  48. 48.Müller, LJ; Kätelhön, A; Bringezu, S et al. The carbon footprint of the carbon feedstock CO2: global energy demand in the context of low carbon economies. J Clean Prod. 2022;387:131496.GOOGLE SCHOLAR
  49. 49.Pichler, PP; Jaccard, IS; Weisz, U; Weisz, H.. International comparison of health care carbon footprints. Environ Res Lett. 2019;14(6):64004. https://doi.org/10.1088/1748-9326/ab19e1DOIGOOGLE SCHOLAR
  50. 50.Shabir, I; Dash, KK; Dar, AH; Pandey, VK; Fayaz, U; Srivastava, S et al. Carbon footprints evaluation for sustainable food processing system development: A comprehensive review. Future Foods. 2023;7:100215. https://doi.org/10.1016/j.fufo.2023.100215DOIGOOGLE SCHOLAR
  51. 51.Osabohien, RA; Zogbassé, S; Jaaffar AH,, Idowu OO; Al-Faryan, MA.. Renewable energy, carbon footprints, natural resources depletion and economic growth in Africa. Int J Energy Sect Manag. 2025;19(3):667–90. https://doi.org/10.1108/IJESM-07-2024-0030DOIGOOGLE SCHOLAR
  52. 52.Buyya, R; Ilager, S; Arroba, P.. Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads. Softw Pract Exp. 2024;54(1):24–38. https://doi.org/10.1002/spe.3248DOIGOOGLE SCHOLAR
  53. 53.San Miguel, G; Bañales, BM; Ruiz, D; Álvarez, S; Pérez, J; Arredondo, MT.. Carbon footprint and employment generation produced by ICT networks for Internet deployment: a multi-regional input-output analysis. Sci Total Environ. 2024;914:169776. https://doi.org/10.1016/j.scitotenv.2023.169776DOIGOOGLE SCHOLAR
  54. 54.Osei-Owusu, AK; Read, QD; Thomsen, M.. Potential energy and environmental footprint savings from reducing food loss and waste in Europe: A scenario-based multiregional input-output analysis. Environ Sci Technol. 2023;57(43):16296–308. https://doi.org/10.1021/acs.est.3c00158DOIGOOGLE SCHOLAR
  55. 55.Abyar, H; Nowrouzi, M.. A comprehensive framework for eco-environmental impact evaluation of wastewater treatment plants: integrating carbon footprint, energy footprint, toxicity, and economic assessments. J Environ Manage. 2023;348:119255. https://doi.org/10.1016/j.jenvman.2023.119255DOIGOOGLE SCHOLAR
  56. 56.Liu, X; Vu, D; Perera, SC; Wang, G; Xiong, R.. Nexus between water-energy-carbon footprint network: multiregional input-output and coupling coordination degree analysis. J Clean Prod. 2023;430:139639. https://doi.org/10.1016/j.jclepro.2023.139639DOIGOOGLE SCHOLAR
  57. 57.Omar, ME; Nangia, V.. On-farm water energy food carbon-footprint nexus index for quantitative assessment of integrated resources management for wheat farming in Egypt. Water Energy Nexus. 2023;6:122–30. https://doi.org/10.1016/j.wen.2023.09.002DOIGOOGLE SCHOLAR
  58. 58.Yue, Q; Guo, P.. Managing agricultural water-energy-food-environment nexus considering water footprint and carbon footprint under uncertainty. Agric Water Manag. 2021;252:106899. https://doi.org/10.1016/j.agwat.2021.106899DOIGOOGLE SCHOLAR
  59. 59.Kurniawan, R; Nugroho, NA; Fudholi, A; Purwanto, A; Sumargo, B; Gio, PU et al. The ecological footprint of industrial value added and energy consumption in Indonesia. Int J Energy Sect Manag. 2024;18(5):1127–53. https://doi.org/10.1108/IJESM-05-2023-0006DOIGOOGLE SCHOLAR
  60. 60.Khan, Y; Khan, MA; Zafar, S.. Dynamic linkages among energy consumption, urbanization and ecological footprint: empirical evidence from NARDL approach. Manag Environ Qual Int J. 2023;34(6):1534–54. https://doi.org/10.1108/MEQ-10-2022-0278DOIGOOGLE SCHOLAR

Cite this article

SELECT FORMAT

Ying-Yin, K., Vivien, W. Z. W., Fanyu, C., Yan-Ling, T., & Thian-Hee, Y. (2025). Emerging Research Trends in Energy Footprint: A Bibliometric Analysis Using VOSViewer. Journal of Scientometric Research, 14(3), 750–760. https://doi.org/10.5530/jscires.20251505