The hotspots of life cycle assessment for bioenergy: A review by social network analysis

Highlights

• Certain characteristics of LCA for biomass were visualized.

• Provide a review on the evolution of LCA for bioenergy.

• The implication of LCA for bioethanol has showed a descending trend.

• Biogas could be the direction of LCA for bioenergy.

Abstract

The purpose of this paper is to provide an up-to-date bibliometric view about the current life cycle assessment (LCA) for bioenergy. The social network analysis (SNA) method was applied to study total 2367 publications in this field. The results showed the high frequency keywords related with the “LCA” for bioenergy included three categories: (1) Bioenergy production, such as “Biodiesel”, “Bioethanol”, “Biogas” and “Biorefinery”; (2) Environmental problems, such as “Greenhouse gas” (GHG), “Environmental impact”, “Climate change”; (3) Environmental target: “Sustainability”. This means that LCA methods have been widely used in assessing the environmental impact from various types of bioenergy production process. Specially, the “GHG” attracted more attention in this research area. According to the temporal trend of the high frequency keywords, “bioethanol” is the most significant hotspot keyword of implication LCA. However, it has become colder since 2011. The environmental performance of “biogas” and “land use” began to receive attention since 2015.The evolutionary co-words network showed that the boundary of hotspots became overlapped. We also found four clusters were identified from keywords networks, i.e. the biggest cluster Cluster (I) (central cluster node linkage was “Bioethanol-GHG”), followed by Cluster (II) (central cluster node linkage was “Biodiesel-Algae”), Cluster (III) (central cluster node linkage was “Biorefinery-Sustainability”) and Cluster (IV) (central cluster node linkage was “Biogas-Anaerobic digestion”). This cluster analysis also showed that the implication of LCA for the relationship between “bioethanol” and “GHG” is the most important hotspot research field. Although “biogas” is the smallest cluster now, it could be the next important hotspot of implication LCA for bioenergy. This study provides an effective approach to obtain a general knowledge of the LCA for bioenergy and supports a deeper understanding of research directions in the future.

Introduction

Climate change mitigation requires a shift from fossil energy resources to renewables, and bioenergy is considered one of the major potential resources (Bentsen and Møller, 2017). From global perspectives, a third of oil reserves, half of gas reserves and over 80% of current coal reserves should be reduced from 2010 to 2050 in order to meet the target of keeping global warming below 2 °C (Mcglade and Ekins, 2015). What's more, fossil-fuel power generation is a major contributor to worldwide carbon emissions, making up more than 24% of total GHG emissions (Odeh and Cockerill, 2008). Given the unsustainable nature of fossil fuels, in recent years, the importance of the production and use of biomass to generate power, heat, and fuels is increasing on a global scale (Dressler et al., 2012). Renewable bioenergy is viewed as one of the ways to alleviate the current global warming crisis. Moreover, the recent IPCC and Global Energy Assessment reported on and provided the more stringent mitigation scenarios heavily rely on a large scale deployment of bioenergy with CO₂ capture and storage called BECCS technology (Creutzig et al., 2015).

At the present stage, the deployment of bioenergy provides great potential to mitigate climate change, but it also poses considerable risks. The production of bioenergy requires fossil fuel input and causes environmental impacts. There is uncertainty about the impacts of the growth of bioenergy crops on ecosystem services (Dagmar and Pete, 2017). Without a complete accounting of net GHG fluxes, development and evaluating mitigation strategies are not possible. Life cycle assessment (LCA) is an analytical tool widely used today in evaluating the advantages and disadvantages of bioenergy. Further, LCA can help to set environmental and climate performance criteria and standards for bioenergy and biofuels (Bright et al., 2012). Fig. 1 is the LCA technology framework. It follows the IOS14001 system standard. LCA is widely used in evaluating various products or projects related to environment. For example, Zuo and Zhao (2014) and Zuo et al. (2017) analyzed green building from a life-cycle perspective; Wang and Teah (2017) conducted a life cycle analysis about a small-scale horizontal axis wind turbines; Qi et al. (2017) carried out a case study on the life cycle assessment of recycling industrial mercury-containing waste.

There are lots of literatures investigating on the bioenergy, too. Mao et al., 2015a, Mao et al., 2015b characterizes the body of knowledge on biomass energy from 1998 to 2013 by employing bibliometric techniques based on the Science Citation Index (SCI)databases; Valdez-Vazquez et al (2017) proposed a sustainability evaluation framework for bioenergy production systems; Zhao et al., 2011, Zhao et al., 2016 established a “Five Forces” model as the analytical framework to investigate the competitiveness of the biomass power industry. Bentsen and Møller (2017) studied solar energy conserved in biomass. Most researches focused on the specific topic of raw materials, environmental impacts, production technologies, and economic benefits, respectively. However, few studies have reported on the overall hotspots and development trends of the research of LCA for bioenergy by bibliometrics.

Bibliometric methods are now firmly established as scientific specialties and are an integral part of research evaluation methodology especially within the scientific and applied fields (Ellegaard and Wallin, 2015). Through statistical analysis of a large number of data, the research hotspots and key points can be obtained. Bibliometric shows the current research characteristics in the form of knowledge map. The social network analysis (SNA) is an excellent bibliometric method. It can be applied to study sets of nodes (keywords) and links (co-word relationships). The analysis of co-word network can better show visual representation of a citation network, helping readers identify significant movements in research fronts and emerging research fields (Choi et al., 2011).

The objective of this study is to present the comprehensive publication status and hotspots about LCA for bioenergy by the SNA method. These results will not only provide a better understanding of global hotspots in the specific research related to the LCA for bioenergy, but may also provide useful information to broaden research area of bioenergy.