Modelling biodiesel production within a regional context – A comparison with RED Benchmark

Highlights

Spatial variation in GHG emissions assessed for regional biodiesel production showed.

Mitigation potential dependent on production location within the region.

Regional variability cannot be captured with a simple regional average value.

Assessing biomass/conversion plant configurations needed for mitigation strategies.

Abstract

Biodiesel is an important bridging biofuel for reducing greenhouse gases (GHG). In 2015, Germany introduced a new GHG based quota scheme for biofuels. However, the use of default GHG values for rapeseed cultivation could provide inaccurate for specific regions and locations. Therefore, the aim of this paper was to use RELCA (a REgional Life Cycle inventory Approach) to assess the regional and spatial variation of GHG emissions associated with biodiesel production in Central Germany and to compare these results with the default values of the Renewable Energy Directive (RED), as well as to identify potential mitigation options for biodiesel production. The RELCA simulations indicated GHG emissions of 31.9–39.83 CO2eq./MJ, with emission magnitude changing between biodiesel configurations due to their locations within the CG region. In comparison with typical RED values for biodiesel, the CG simulations showed 13–31% greater mitigation potential. The results also indicated that the configuration of biomass and conversion plant needs to be assessed to develop the most appropriate mitigation strategies. Current GHG mitigation strategies are limited to the energy sector, allowing leakages within the agricultural sector. Therefore, for more spatially targeting GHG accounting to be implemented, sustainability certification should be expanded to other biomass markets.

Graphical abstract

Keywords

  • Biodiesel;
  • LCA;
  • Spatial;
  • Regional;
  • RED;
  • N2O

1. Introduction

Biodiesel, in terms of production capacities and economical relevance, is one of the most important bridging biofuels being promoted to wean society off fossil dependent mobility [1]; [2] ;  [3]. It is also particularly important for Germany, with the second highest installed capacity of biodiesel production (approx. 4.4 Mio. t/a), in Europe [3]. The majority of which is derived from the conversion of rapeseed i.e. rapeseed methyl ester (RME) [4]. Such biodiesel facilities are based on mature, relatively simple technologies, unlike advanced biofuels, which are still at various stages of development and with relatively higher investment costs. Thus, it is foreseen that biodiesel is likely to play an important role in the transportation sector at least until 2030 [5] ;  [6]. For this reason the environmental sustainability of such biodiesel still needs to be ensured, particularly in terms of greenhouse gas (GHG) mitigation.

Indeed, one of the primary goals for using biodiesel instead of fossil diesel is the reduction of GHG emissions. Going one step further to ensure reductions, Germany in 2015, introduced a new quota system for biofuels, changing from energy and mass related quotas, as stipulated under the Renewable Energy Directive (RED) [7], towards a new GHG based quota scheme. Under this scheme biofuels must now satisfy increasing requirements for GHG reduction over the entire chain, from the field through to arrival at the biofuel production plant [8]. As a result, the competition between different biofuel technologies and feedstocks is now based on the GHG-mitigation potential as the main criteria for the success of a biofuel producer [8].

Under RED [7] a biodiesel producer can estimate the GHG-mitigation potential of their biofuel across the major steps in the production chain; cultivation, conversion, transport by using: 1) the typical or default values outlined in Annex V of the directive (a form of European average); 2) a combination of actual values with default values (e.g. own plant data with default values for rapeseed production) or; 3) real empirical data collected across the whole supply chain [7]. In general for cultivation of rapeseed, the use of default values are preferred, as this reduces the amount of bureaucracy required to determine the GHG balances of the rapeseed produced [9]. However, rather than using default values outlined in the RED, (29 g CO2eq./MJ), it was recommended instead for German biodiesel producers to use emission values estimated for the different federal states (i.e. NUTS2)1[10] ;  [9]. In other words a farmer producing rapeseed in a particular Federal state would have an associated GHG value for their rapeseed (23–25 g CO2 eq./MJ RME), as long as such cultivation complied with the good farming practices outlined by the Common Agricultural Policy (CAP) [11] ;  [10].

With cultivation of rapeseed accounting to between 50 and 90% of the total GHG balance for biodiesel production, the use of the RED default values for ease of implementation, may provide inaccurate results for specific regions and locations [5]. Indeed one such study for the Veneto region, in Italy, estimated values much higher than those reported in RED for sunflower and rapeseed [12]. The reason for such discrepancies relates to two major aspects. The first is yield, which is dependent on specific geographical (e.g. soil, climate) and regional (e.g. management) conditions [5]. The second aspect relates to soil emissions, which Hennecke et al. [13] also identified as a blind spot within the RED accounting system, as there was no obligation to include more spatially detailed accounting which could capture the interaction between management practices and geographical conditions affecting such soil emissions, as well as the soil emissions themselves.

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