**1. Introduction**

Greenhouse gas emissions have been an environmental issue of great concern in the past two decades. According to the Intergovernmental Panel on Climate Change (IPCC) report, without action, the global temperature will rise by more than 1.5 ◦C in the future, while in the past 10,000 years, the climate change was only 1 ◦C [1]. The emergence of the carbon footprint concept facilitates the measurement of greenhouse gas emissions. The term "carbon footprint" evolved from the ecological footprint proposed by Wackernagel in 1996 [2]. It is a concept formed by the integration of ecological footprint and carbon emissions. With the deepening of the global division of labour and the complexity of the commodity supply chain, the assessment of the environmental impact of modern economic

activities has become more complicated. The unique advantages of life cycle assessment (LCA) as a "cradle-to-grave" measurement method are taken seriously by researchers. The concept of the carbon footprint involves the carbon emissions throughout all the stages of a product's life, which is similar to the core idea of the LCA approach. The combination of the two can be an effective tool for measuring the environmental impact of economic activities. When discussing the relationship between carbon footprint and LCA, carbon footprint can be regarded as either a carbon emission measurement method or a result calculated by this method. When it is regarded as a carbon emission measurement method, the relationship between carbon footprint and LCA can be regarded as a subordinate relationship between measurement methods. It is the application of LCA with a single impact category (climate change). When it is considered as a result of calculation, it can be measured by carbon inventory methods (this method uses the corresponding emission factors to calculate the emissions of various greenhouse gases based on the guidelines for national greenhouse gas inventories compiled by IPCC) [3], input–output method (IOA is a "from top to bottom" analysis method, which reflects the relationship between initial input, intermediate input, total input and intermediate output, final output and total output. It transforms the economic relationship between production sectors or regions into the physical relationship of greenhouse gas emissions, which clearly reflects the exchange process of emissions and distributes them to various sectors or regions, thus making the direct and indirect carbon emission relationship clear) [4] and the LCA method. The relationship between carbon footprint and LCA is the relationship between measurement results and methods. In this paper, the concept of "carbon footprint" is regarded as the measurement result, and the related literature of "carbon footprint" micro-measurement is concerned, that is, carbon footprint research from the perspective of LCA.

Some progress has been made by researchers in this field. The related literature has reviewed this topic from various perspectives such as the expansion of the carbon footprint concept and the application of LCA in earlier literature, city carbon emission [5], diet carbon footprint [6] and the re-examination of LCA [7] in recent literature. Few studies have adopted a knowledge map to carry out a visual analysis of the intellectual structure of the related fields, but a knowledge map is beneficial for showing where knowledge can be found within a group or organization, and how to find those with the most expertise. Specifically, a knowledge map is an emerging bibliometric tool, which can provide a visual knowledge graph of the existing literature and a serialized knowledge spectrum [8]. CiteSpace, a relatively widely used bibliometric tool based on network theory, is a Java application which provides a variety of analysis methods such as co-citation analysis, keyword co-occurrence analysis and collaboration network analysis. Therefore, we utilized this software to visualize the existing literature on carbon footprint research in a LCA perspective, and explored the intellectual structure of this field and its possible development trend by focusing on the scope, core literature, collaboration network and research front.

The rest of this paper is organized in four sections. Section 2 describes the data collection process and illustrates the result of basic analysis. Section 3 establishes the disciplines and terms network, co-citation network and collaboration network to show intellectual structure of the focus topic. Section 4 gives a document co-citation network and keyword co-occurrence network based on the results of Section 3 to analyse the emerging trends of focus topic. In the last section, the key findings are summarized and discussed to implicate further study on carbon footprint in a LCA perspective.

### **2. Data Collection and Basic Analysis**

The literature analysed in this paper is from the core collection of Web of Science, including SCI-EXPANDED and SSCI. In the process of retrieval, we searched the "Article" category published in English by retrieving the keyword "carbon footprint", "carbon footprints" or "carbon footprinting", and a total of 6180 papers were identified with their published time ranging from 2003 to 2019. Then we narrowed the results by retrieving the above keywords separately together with "life cycle assessment"

or "LCA", and 1563 papers were identified with their published time ranging from 2006 to 2019 (retrieval on 9 June 2019). The year-by-year distribution of these papers is shown in Figure 1.

**Figure 1.** The number of published papers on carbon footprint and life cycle assessment (2006–2018).

Figure 1 shows the number of papers published in corresponding years. The sample literature had an explosive growth in 2008, after which it entered a stage of rapid development. With regard to the relative number, the increase of papers in 2008 compared to a past year reached 500%, and the growth rate (compared to a past year) exceeded 100% (183.33% and 123.53% respectively) in both 2009 and 2010 as well, after which the growth slowed down. In terms of the absolute number, papers had a double-digit growth per year from 2009; the top five countries/regions were the USA (340), People's Republic of China (PRC) (177), Italy (159), Spain (148) and England (137) (counting first and co-authors); the top five journals were *Journal of Cleaner Production* (375), *International Journal of Life Cycle Assessment* (125), *Science of the Total Environment* (66), *Sustainability* (64) and *Journal of Industrial Ecology* (52); the top five authors were VAZQUEZ-ROWE I (18), KUCUKVAR M (18), TATARI O (13), FEIJOO G (13) and HERTWICH EG (13).

### **3. The Intellectual Structure of the Focus Topic (Carbon Footprint Research in a LCA Perspective)**

This paper focuses on the analysis of TYPE II papers through the bibliometric method. The scientometric software, CiteSpace, was utilized to describe the intelligent structure of the focus topic. Firstly, we constructed a subject categories co-occurrence network to identify the fields and research contents involved in the focus topic. Secondly, a co-citation network was established to analyse the features of clusters. Finally, we built a collaboration network to analyse the partnerships between the source regions, institutions and authors.

### *3.1. The Disciplines and Terms Network Based on the Focus Topic*

### 3.1.1. Noun Term Co-Occurrence Network

Noun term co-occurrence analysis refers to the extraction of noun terms from the titles, keywords and abstracts of the sample papers and the construction of a noun term network for literature analysis in accordance with the criterion that two noun terms are adopted in the same document. When two noun terms appear in the same paper, it is considered that there is a non-negligible relationship between the two terms [9]. The noun term network can display hot topics and topic distribution of a discipline.

We used Citespace to construct the noun term network. Citespace uses three technology means to simplify the process of calculation during the process of network generation. Firstly, Citespace uses the idea of divide-and-conquer algorithms to separate the whole co-citation network into individual networks which are called time slices at the first stage, and integrate them at the second stage after some treatments. In the construction of noun term co-occurrence network, the whole network was divided into 14 individual networks from 2006 to 2019. Secondly, Citespace provides two main criteria to filter data in individual network—citation threshold and the rank of being cited. In the construction of the noun term co-occurrence network, this paper used citation threshold. It contains three specific criteria—citation quantity, together with co-citation frequency and co-citation coefficient (c, cc and ccv) and the selected articles were above the threshold on these indicators. The threshold was set as (2,3,15), (3,3,20), (3,3,20) at three levels in chronological order to form the noun term co-occurrence network, and the rest was determined by linear interpolation. This paper used the criterion of the rank of being cited in later sections as well. Thirdly, Citespace provides minimum spanning tree mode and pathfinder mode to prune the network to highlight the key points. In noun term co-occurrence network, this paper used the minimum spanning tree mode [10].

Next, we extracted noun terms from the sample papers and merged those with the same meaning in the network (singular and plural forms, abbreviation and full name, etc.) to construct a noun term network as shown in the upper right corner of Figure 2. The connections in the network illustrate which words often appear together in the same paper, such as the connections between LCA and terms like climate change and global warm as shown in Figure 2. Table 1 reveals the 12 words with the highest frequency in the network, demonstrating that environmental impact was the most concerned topic in the field, which may be explained by considering that LCA was used to evaluate the environmental impact of carbon footprint. These high-frequency noun terms first appeared between 2008 and 2011 during which the field experienced an explosive development, which is consistent with the previous analysis. For more detailed analysis, we set the thresholding (c, cc, ccv) parameter as (10,10,15), (10,10,20) and (10,10,20) to extract the noun terms in the relatively more important co-cited literature and drew a network diagram, as shown in Figure 3. The font size in Figure 3 represents the frequency of occurrence of noun terms and the node size the centrality of noun terms, which means that when the font size is bigger, the noun term appears more frequently and the square of the yellow box is lager, the centrality of noun term is higher. The centrality measures the number of links between focus noun term and other noun terms, which shows the power of the focus noun term in network, and nodes with higher centrality are called hub nodes. For example, environmental impact not only appeared frequently, but also had high centrality in the network, which implied that environmental impact was a term related to many issues of the focus topic. It can also be understood that the focus topic was about the environmental impact. In addition, greenhouse gas emissions, climate change, global warming and supply chain were also important noun terms.

**Figure 2.** Life cycle assessment (LCA) in the whole noun term co-occurrence network.


**Table 1.** Top 12 highest frequency noun terms.

**Figure 3.** Streamlined noun term co-occurrence network.

### 3.1.2. Subject Categories Co-Occurrence Network

The text file provided by Web of Science contains a Subject Categories (SC) field which represents the name of the discipline to which the included documents belong, and each paper is assigned to one or more such names. This information can be used to analyse which subjects are covered by the literature on carbon footprint assessment in a LCA perspective.

Under the same parameter setting as that of the noun term co-occurrence network, a subject categories network as shown in Figure 4 was constructed. The size of node represents the occurrence number, which means that when the node radius is larger the subject category contains more sample papers, and the several biggest nodes are called landmark nodes. A node with a purple ring is called a pivot node which owns higher value of betweenness centrality. Betweenness centrality is an index to measure the sum of probability of the focus to be on an arbitrary shortest path in a network, which shows the importance of the focus node as a bridge role. As we can see from Figure 4, Environmental Science and Ecology is the most common subject category to which the sample literature belongs, followed by Engineering, Green and Sustainable Science and Technology and Energy and Fuels. Food Science and Technology is a subject category with high centrality. That is to say, the sample papers often contain interdisciplinary contents of these disciplines and other disciplines. The thickness and colour of lines which link two nodes make sense as well. The thicker the line, the deeper the relationship between the two categories, and the darker the line, the earlier the relationship between the two categories is established. Figure 4 illustrates that Environmental Sciences and Ecology has a deep and long-time relationship between Business and Economics.

**Figure 4.** Disciplines shown as a minimum spanning tree network of subject categories.

### *3.2. The Co-Citation Network based on the Focus Topic*

### 3.2.1. Document Co-Citation Network

Co-citation refers to the relationship between two documents cited by the same later paper. The co-citation relationships between a series of documents form a document co-citation network. Such a network is a useful tool for document analysis, which can provide clues for understanding literature development and is more reliable than simple analysis of citation relationships [11]. We put two years per slice, selected the top 50 levels of the most cited references from each slice and used pathfinder to prune (set as base settings), and then got document co-citation network with clusters shown in Figure 5. As shown in Figure 5, Finnveden [12], Vries [13], Weidema et al. [14], Finkbeiner [15] and Roy [16] have higher centrality. Finnveden [12], Vries [13] and Roy [16] discussed the LCA approach. Finkbeiner [15] deepened the field's understanding of "carbon footprint". Weidema et al. [14] analysed the relationship between "carbon footprint" and "LCA". These documents have been cited together with many different documents and have become the basic literature in the field.

**Figure 5.** Document co-citation network with clusters.

The top 10 most frequently cited documents related to the focus topic are shown in Table 2. The most frequently cited one is International Organization for Standardization (ISO) [17], which provides the international standard for the LCA approach. Since then, LCA has been widely used in the measurement of carbon footprint and all later sample documents have cited ISO [17]. The far-reaching impact of ISO [17] on the subsequent literature can be seen more clearly in Figure 6. The sample documents from 2008 to 2011 were cited relatively frequently, and eight of the top 10 most frequently cited were published during this period. This shows that the literature had experienced an explosive growth in quantity during this period, providing a solid foundation for future research in this field.


**Table 2.** Top 10 references based on cited frequency.

**Figure 6.** Time-zones of document co-citation.

References with top cited frequency after ISO [17] can be simply divided into three categories: the research focusing on carbon footprint, LCA and their relationship. The first category includes Hertwich et al. [18], Flysjo et al. [19], Finkbeiner [15] and Galli [20] et al. Hertwich et al. [18] discussed the quantification of greenhouse gas emissions associated with the final consumption of goods and services in 73 countries of 14 regions from a global trade perspective. The analysis indicated that food accounts for 20% of greenhouse gas emissions, the operation and maintenance of residential areas 19% and transportation 17%. Food and services are more important in developing countries, while transportation and food products are growing rapidly with increasing income and are dominant in developed countries. Flysjo et al. [19] adopted the LCA approach to analyse the carbon footprint of dairy production by two different farm systems in New Zealand and Sweden. Finkbeiner [15] explored the significance of discussing the concept of carbon footprint, pointing out that it is not without limitations (even pollution treatment will produce carbon emissions), and then summarized the opportunities and challenges in the relevant research. Galli et al. [20] extended the concept of carbon footprint to ecological, carbon and water footprint, and analysed the similarities and differences between the three, thus defining the concept of "footprint family" and developing an integrated Footprint approach to assess the environmental impact of human behaviours; the second category includes Finnveden et al. [12], Vries et al. [13], Roy et al. [16], etc. Finnveden et al. [12] and Vries et al. [13] are highly cited documents after ISO [17]. Both review the LCA approach and Finnveden et al. [12] is more comprehensive, while Vries et al. [13] only reviews the literature on livestock products. Roy et al. [16] also reviewed the LCA approach, focusing on the topic of food products; the third category includes Weidema et al. [14], Rotz et al. [21], etc. Weidema et al. [14] discussed whether the emergence of the carbon footprint concept would facilitate the development of the LCA approach. He believed that the concept of carbon footprint made the measurement easier and simpler to master than the traditional LCA approach. Therefore, it is very suitable for governments to show consumers the environmental impact of their consumption, which explains that the concept of carbon footprint promotes the application of the LCA approach. Rotz et al. [21] introduced the Dairy Greenhouse Gas model and discussed the specific application of the partial life cycle assessment in the carbon footprint of dairy production systems.

As discussed above, the basic documents in the field has contributed mainly to literature review and definition. Next, we performed cluster analysis on the sample references and the major clusters are shown in Table 3. The second column is the cluster size and the third column the Silhouette index, which is used to measure the cluster quality and ranges from 0 to 1. The closer the index is to 1, the better the cluster quality. The Silhouette scores of all the clusters in Table 3 are above 0.7, indicating that the cluster quality was good. The fourth to sixth columns are cluster labels extracted from abstracts via the LSI, LLR, and MI methods respectively [22]. It can be seen that the most concerned topics in the sample literature were the carbon footprint assessment related to beef production, dairy industry and fishery (cluster#0, cluster#1, cluster#2), as well as urban systems and grain system (cluster#7, cluster#8), based on which other factors in the environment (cluster#4, cluster#5) were discussed in the references. In addition, the literature related to the carbon footprint measurement (cluster#3) and the carbon footprint warning mechanism (cluster#6) also formed clusters.



### 3.2.2. Author Co-Citation Network

The document co-citation lays a foundation for the analysis of the author co-citation and the journal co-citation relationships. We put one year per slice, selected the top 50 levels of the most cited references from each slice with no pruning, and established the author co-citation network. The network contained 269 authors, among who there were 1116 co-citation relationships. As can be seen from Figure A1 of Appendix A and Table 4, group authors also provided important cited literature for the field. As analysed above, the document published by ISO that provided the LCA standard is a

reference a researcher much read if they study this field, and ISO has become the most important group author. In addition, IPCC has also contributed to the research in this field. For instance, it published "The IPCC Guidelines for National Greenhouse Gas Inventories". Food and Agriculture Organization of the United Nations (FAO), European Commission (EC), British Standards Institution (BSI) are other important group authors. Ecoinvent provides important data resources for the research in this field.


**Table 4.** Top 6 group authors based on cited frequency.

The cited frequency of some group authors was high and that of personal authors on average was not low. The citation count of each of the top 10 cited individuals was more than 100. As shown in Table 5, only C. Cederberg, E.G. Hertwich and I. Vazquez-rowe were among the top 20 personal authors in terms of the number of published papers and citations. The rest of the highly cited authors were widely recognized in the field for one or two high-quality papers. For example, B.P. Weidema, R. Frischknecht and S. Suh became highly cited authors because of the co-authored paper, "Life Cycle Assessment: Part 1: Framework, Goal and Scope Definition, Inventory Analysis, and Applications", which was published in *Environment International*. In addition, the key papers of the top five highly cited authors focus on methods, including those by B.P. Weidema and R. Frischknecht, and T. Wiedmann's "A Review of Recent Multi-Region Input–Output Models Used for Consumption-Based Emission and Resource Accounting", J.B. Guinee's "Handbook on Life Cycle Assessment Operational Guide to the ISO Standards", and M. Lenzen's "System Boundary Selection in Life-Cycle Inventories Using Hybrid Approaches".


**Table 5.** Top 20 personal authors based on cited frequency.

### 3.2.3. Journal Co-Citation Network

We set the same parameters as those of the author co-citation network to build a journal co-citation network, in which there were 169 journals and 635 connections. As shown in Figure A2 of Appendix A and Table 6, the most cited journal was *Journal of Cleaner Production* (1135 citations), which also had a high degree of centrality (0.14). *Environmental Science and Technology* and *Science of the Total Environment* were also with high citations and centrality. The second most cited was the *International Journal of Life Cycle Assessment* with 1037 citations, but its centrality in the network was not high (0.09). That is to say, its co-citations with other journals was relatively low. From the basic analysis, *Journal of Cleaner Production*, *International Journal of Life Cycle Assessment*, *Science of the Total Environment* and *Journal of*

*Industrial Ecology* were not only the journals with the most publications, but also the most cited ones. In addition, they were the major journals in which papers on the focused topic were published. Table 6 also illustrates the impact factor of each journal in 2018. According to Table 6, journals with higher impact factor were usually those with high citations and centrality in the focus topic. Only *Renewable and Sustainable Energy Reviews* in Table 6 did not comply with this rule, which may be due to the deviation caused by the time factor.


**Table 6.** Top 10 journals based on cited frequency.
