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When considering no man is an island—assessing bioenergy systems in a regional and LCA context: a review

  • REGIONAL TOPICS FROM EUROPE
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Abstract

Purpose

With many environmental burdens associated with bioenergy production occurring at the regional level, there is a need to produce more regional and spatially representative life cycle assessment of bioenergy systems. On the other hand, such assessments also need to account for the global and cumulative impacts along the full bioenergy life cycle in order to support effective regional policy measures and decision making. Therefore, the challenge is to find a balance. In other words, how should we define the regional context for bioenergy system assessments in order to complement life cycle thinking? The aim of this review is to answer this question by providing an overview of important considerations when assessing bioenergy systems in a regional and LCA context and how these two contexts intersect. It also aims to help guide and orientate LCA practitioners interested in including more regional aspects in their bioenergy studies. Until now, such a review which explores the integration of regional and life cycle contexts in relation to bioenergy systems and their products has not been done.

Methods

As a first step, we define what we mean by the term region. We then look at the potential burdens relating to bioenergy systems and their relationship with the regional context. In a next step, we explore life cycle thinking and the intersection between the regional and LCA contexts by providing some examples from the literature. We then discuss the benefits and limitations of such regionally contextualized life cycle approaches in relation to bioenergy production systems and indeed other alternative biomass uses.

Results and discussion

Three regional contexts were identified to help orientate life cycle thinking aiming to assess the regional and nonregional environmental implications of bioenergy production. These contexts were as follows: “within regional,” “regional and ROW,” and “regionalized.” The added value of implementing a regionally contextualized life cycle approach is the ability, therefore, to include greater regional and spatial details in the assessments of bioenergy production systems, without losing the links to the diversity of global supply chains. Thus, providing greater geographical and regional insight into how such potential burdens can be reduced or shifted burdens avoided or how associated regional production activities could be optimized to mitigate such burdens.

Conclusions

The use of different regional contexts as proposed in this paper is not only useful to orientate life cycle thinking in relation to bioenergy systems but also for the assessment of alternative novel bio-based systems.

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Notes

  1. We refer to burdens here as environmental interventions, e.g., emissions and resource use.

  2. “No man is an island” is a poem written by John Donne in which he describes the connectivity of life, especially man to his fellow man and to the natural environment; therefore, this line helps to summarize the complexity and connectivity of the global supply chains, which is what makes conducting more regional and spatial LCAs so challenging, i.e., that although we focus on a region, there are so many other (global) connections that must also be accounted for.

  3. For this paper, we assume a territory and a region to be the same.

  4. The area supplying biomass to the conversion plant is referred to as the catchment area. Many factors influence the size of the catchment area; yields, biomass availability, the operational capacity of the bioenergy plant and its efficiency. This sets the boundary for the inclusion of biomass produced for the specified conversion plant and correspondingly, all associated life cycle burdens, as well as their spatial distribution (Bernesson et al. 2004; Dunnett et al. 2008; Walla and Schneeberger 2008)

  5. The classification of which determines the type of LCI structure which needs to be implemented (consequential or attributional), based on the extent to which a decision may influence or have consequences beyond the foreground system, e.g., through different market mechanisms.

  6. The following equations are sourced from Heijungs and Suh (2002): g = BA −1 f: LCI algebra where the product of the technology matrix A (assumed to be square) and the environmental interventions matrix B, results in the solution vector g of environmental burdens associated with meeting a functional unit (f) or demand function, e.g., MJ of biofuel. The results of the inventory or the g vector are burdens aggregated across the whole supply chain, regardless of where they were produced or released (Hauschild 2006), e.g., g N2O per MJ biofuel.

    h = Qg = QBA −1 f: The LCIA is the second step in which each environmental burden in the LCI (i.e., all the rows in g) are multiplied with characterization factors (Q) weighting their contribution toward a particular environmental impact (h). Characterization factors (CFs) are usually derived using different physically based models which reflect the general mechanics of emission sources, dispersion, deposition, and potential risk of environmental impacts (Bellekom et al. 2009).

  7. Although the authors do mention the approach is also applicable to options for introduction of spatial modeling

  8. They used Ecoinvent either directly (Mutel and Hellweg 2009), as in the case of electricity production in Europe or indirectly, or by linking it to other regionally differentiated data sets, as was the case with the American study (Mutel et al. 2011). They also established their own open source software (http://brightwaylca.org/).

  9. It must be noted here that both studies were conducted for Japan, for which regional I/O tables are available, therefore enabling the assessment of interacting regions. For other regions, this might be a more arduous task.

  10. However, in the study of Loiseau et al. (2014), they determined that the microdecision was the most applicable to their study conditions.

  11. For further insight, please refer to (Bare 2010; Bare et al. 2000; Bare et al. 1999; Bare and Gloria 2006; Bare and Gloria 2008; Bellekom et al. 2009; Finnveden and Nilsson 2005; Hauschild 2006; Hauschild et al. 2008; Heijungs 2012; Heijungs et al. 2003; Jolliet et al. 2003; Mutel and Hellweg 2009; Pennington et al. 2004)

  12. They determined it was difficult to distinguish between local and regional impacts, as in general the cumulative effects of local impacts can sum to the regional level.

  13. See (Potting and Hauschild 2005).

  14. There are many different definitions in the literature regarding different levels of geographical and spatial inclusion within LCIA approaches and the level to which characterization factors have been differentiated. For the purpose of this paper, we use the definitions of Heijungs (2012) where regionally differentiated LCIA distinguishes between different “regionally defined data sets” (e.g., different EU countries or USA and EU), whereas spatially differentiated LCIA distinguishes between different types of landscapes, e.g., urban and rural.

  15. For more information on uncertainties in LCA, please refer to (Cherubini and Strømman 2011; Heijungs and Huijbregts 2004; Huijbregts 1998; Huijbregts et al. 2001), input–output uncertainties (Wiedmann 2009; Williams et al. 2009), spatial (Crosetto et al. 2000; Fisher 1999; Mutel et al. 2011; Phillips and Marks 1996).

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Acknowledgments

This work was made possible by funding from the Helmholtz Association of German Research Centres within the project funding “Biomass and Bioenergy Systems.” We would also like to thank the reviewers, whose contribution has helped to improve the quality of the manuscript.

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Authors and Affiliations

  1. Department of Bioenergy, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318, Leipzig, Germany

    Sinéad O’Keeffe, Alberto Bezama & Daniela Thrän

  2. Department of Bioenergy Systems, Deutsches Biomasseforschungszentrum (DBFZ), Torgauer Straße 116, 04347, Leipzig, Germany

    Stefan Majer & Daniela Thrän

Authors
  1. Sinéad O’Keeffe
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  2. Stefan Majer
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  3. Alberto Bezama
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  4. Daniela Thrän
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Correspondence to Sinéad O’Keeffe.

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O’Keeffe, S., Majer, S., Bezama, A. et al. When considering no man is an island—assessing bioenergy systems in a regional and LCA context: a review. Int J Life Cycle Assess 21, 885–902 (2016). https://doi.org/10.1007/s11367-016-1057-1

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