RELCA: a REgional Life Cycle inventory for Assessing bioenergy systems within a region

Abstract

Background: The last decade has seen major development and adoption of bioenergy, particularly in Germany. This has resulted in a scattering of decentralised bioenergy plants across the landscape, due to their dependency on spatially diffuse biomass resources. Regional conditions (e.g., soils, climate, management) influence the environmental burdens resulting from biomass production and thus, also effect the environmental performance of bioenergy production. Therefore, more regionally focused life cycle approaches are required for assessing these bioenergy systems. The aim of this paper is to outline such an approach. "RELCA", is a regional life cycle inventory for assessing the regional and spatial variation in the environmental performance of bioenergy production within a region. Methods: Five modelling steps are combined to form the RELCA approach in order to determine: (1) regional crop allocation, (2) regional biomass management, (3) representative bioenergy plant models, (4) bioenergy plant catchments, and (5) indirect upstream emissions (non-regional) associated with regional bioenergy production. The challenges and options for each of these five modelling steps are outlined. Additionally, a simple example is provided using greenhouse gases emissions (GHG) to show how RELCA can be used to identify the potential regional distribution of environmental burdens associated with the production of a bioenergy product (e.g. biodiesel) within a region. Results: An approach for combining regionally distributed inventory for biomass production with regionally distributed inventory for bioenergy technologies, through the use of catchment delineation was developed. This enabled the introduction of greater regional details within the life cycle approach. As a first "proof of concept," GHG emissions were estimated for a simple example, illustrating how RELCA can identify the potential regional distribution of environmental burdens (direct and indirect) associated with producing a bioenergy product. Conclusions: RELCA (v1.0) is a powerful scoping approach, which is the first to investigate the regional and spatial variation in the environmental performance of bioenergy production within a region through the use of catchment delineation. RELCA (v1.0) is not without its limitations. Despite these, it still provides a good starting point for further discussion, improvements, and modelling developments for assessing the regional and spatial environmental implications of bioenergy production (e.g., such as impacts to soil, water, and biodiversity) for a within regional context .

Stakeholders’ Interests and Perceptions of Bioeconomy Monitoring Using a Sustainable Development Goal Framework

“…An interesting aspect from the results shown in Figure 2 regarding the different stakeholder groups, besides the expected similarities (SDGs 15,16,17,11,2,3) and significant differences (1, 7,8,5,9,6), is that the strongest divergence by far emerges in the perception of ending poverty (1). No clear evidence can be found for a general convergence of relevance between specific stakeholder groups over all 17 SDGs, as well as for all 169 subgoals.…”

“…Many of the SDGs that experts have defined as important (see Figure 1) are also highly relevant for most our stakeholders, such as ending hunger (SDG 2), water and sanitation (6), energy (7), sustainable growth and infrastructure (8 and 9), sustainable cities (11), sustainable consumption (12), climate change (13), and terrestrial and oceanic ecology (14 and 15). However, stakeholders in general see less relevance of health issues (SDG 3) but consider ending poverty (1), global partnerships (17), and education (4) as far more important. We assume that the reasons for this are the popular, general scientific, political, and societal discourse dimensions of BE (fuel vs. food, green cities and green growth) in case of similarities.…”

“…Instead, many name vague interrelationships between economic, ecological, and social issues and mainly reflect an economic perspective on topics such as biotechnology, eco-efficiency, competitiveness, innovation, reaching or retaining a leading world position, growth in economic output, and (re-)establishing a next-generation industry at large [1, 5,10,11,15,16]. Within the EU's BE strategy, the substitution of fossil carbon by renewable materials plays a big role in key industrial sectors such as chemicals and pharmaceuticals [11], which illustrates the potential in addition to the still very important energy production [17] and primary sectors in rural regions [18,19]. Many strategies highlight the contribution of BE to the circular economy and lately also to the improvement of food quality, alternative food resources, and high-tech sectors [4].…”

Resources, Collaborators, and Neighbors: The Three-Pronged Challenge in the Implementation of Bioeconomy Regions

“…Coupling these material flow models with land use models is a promising way to bring the regional and spatial impacts to light. One example of this type of modelling is RELCA (Regional Life Cycle inventory Assessment), which couples technologies in the regional landscape with the relevant available biomass and hence provides a means of estimating regional land use and the environmental burdens associated with alternative bio-based systems [105,106]. Additionally, at an international level, the combination of material flows and land use will be particularly important for capturing the "spill-over effect" due to trans-national trade and use, as the increase of biomass use may result in undesired impacts elsewhere.…”

Operationalising territorial life cycle inventory through the development of territorial emission factor for European agricultural land use

“…soil type and climate) largely influence the quantity of certain emissions (Nitschelm et al, 2016) In such a case, GIS can be directly used to extract spatial information on crop location. In the cases when the exact spatially differentiated data for biomass production is not available, GIS combined with crop allocation models and regional cropping statistics has been used to solve the issue of crop allocation in space (O'Keeffe et al, 2016). In sum, agricultural tLCA is necessary to link with GIS, indicating what is growing where (agricultural land-use map) since the association is critical to increase the accuracy of the results.…”