modelling biodiesel production within a regional context a comparison with red benchmark

Accepted Manuscript Modelling biodiesel production within a regional context – A comparison with RED Benchmark S O'Keeffe, S Majer, C Drache, U Franko, D Thrän PII: S0960-1481(17)30110-6 DOI: 10.1016/j.renene.2017.02.024 Reference: RENE 8531 To appear in: Renewable Energy Received Date: 12 May 2016 Revised Date: 30 January 2017 Accepted Date: 10 February 2017 Please cite this article as: O'Keeffe S, Majer S, Drache C, Franko U, Thrän D, Modelling biodiesel production within a regional context – A comparison with RED Benchmark, Renewable Energy (2017), doi: 10.1016/j.renene.2017.02.024 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain ACCEPTED MANUSCRIPT M AN U Regional Hotspots TE D Spatially and regionally resolved results for regional  biodiesel  catchments EP Implementation of a regionally distributed  modelling approach  GHG Emission profiles of  Biodiesel catchments AC C RELCA (REgional life cylce inventory assessment Approach) Indirect regional Emissions SC Direct regional Emissions RI PT Modelling biodiesel production within a regional context- a comparison with RED Benchmark Comparison with RED typical value for biodiesel production  and assessment of mitigation options ACCEPTED MANUSCRIPT Modelling biodiesel production within a regional context- a comparison with RED Benchmark * Leipzig, Germany Leipzig, Germany Germany S O’Keeffe1, S Majer2, C Drache1,3, U Franko4, D Thrän1,2 RI PT Helmholtz Centre for Environmental Research (UFZ), Department of Bioenergy Permoserstraße 15, 04318 Deutsches Biomasseforschungszentrum (DBFZ), Bioenergy Systems Department Torgauer Straße 116, 04347 Deutsches Biomasseforschungszentrum (DBFZ), Biorefinery Department Torgauer Straße 116, 04347 Leipzig, 10 11 06120 Halle/Saale, Germany SC Helmholtz Centre for Environmental Research (UFZ), Department of Soil Physics, Theodor-Lieser-Straße 4, 13 M AN U 12 *Email corresponding author: sinead.o-keeffe@ufz.de 14 Abstract 16 Biodiesel is an important bridging biofuel for reducing greenhouse gases (GHG) In 2015 Germany, introduced a 17 new GHG based quota scheme for biofuels However, the use of default GHG values for rapeseed cultivation could 18 provide inaccurate for specific regions and locations Therefore, the aim of this paper was to use RELCA (a 19 REgional Life Cycle inventory Approach) to assess the regional and spatial variation of GHG emissions associated 20 with biodiesel production in Central Germany and to identify potential mitigation options for biodiesel production, 21 as well as to compare these results with the default values of the Renewable Energy Directive (RED) The RELCA 22 simulations indicated GHG emissions of 31.9-39.83 CO2eq /MJ, with emission magnitude changing between 23 biodiesel configurations due to their locations within the CG region In comparison with typical RED values for 24 biodiesel, the CG simulations showed 13-31% greater mitigation potential The results also indicated that the 25 configuration of biomass and conversion plant needs to be assessed to develop the most appropriate mitigation 26 strategies Current GHG mitigation strategies are limited to the energy sector, allowing leakages within the 27 agricultural sector Therefore, for more spatially targeting GHG accounting to be implemented, sustainability 28 certification should be expanded to other markets for biomass 29 Key words: Biodiesel, LCA, spatial, regional, RED, N2O 30 Introduction 31 Biodiesel, in terms of production capacities and economical relevance, is one of the most important bridging 32 biofuels being promoted to wean society off fossil dependent mobility [1-3] It is also particularly important for 33 Germany, with the second highest installed capacity of biodiesel production (approx 4.4 Mio t/a), in Europe [3] 34 The majority of which is derived from the conversion of rapeseed i.e rapeseed methyl ester (RME) [4] Such AC C EP TE D 15 ACCEPTED MANUSCRIPT 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 10 over the entire chain, from the field through to arrival at the biofuel production plant [8] As a result, the competition 11 between different biofuel technologies and feedstocks is now based on the GHG-mitigation potential as the main 12 criteria for the success of a biofuel producer [8] SC RI PT 13 Under RED [7] a biodiesel producer can estimate the GHG-mitigation potential of their biofuel across the major 15 steps in the production chain; cultivation, conversion, transport by using: 1) the typical or default values outlined in 16 Annex V of the directive (a form of European average); 2) a combination of actual values with default values (e.g 17 own plant data with default values for rapeseed production) or; 3) real empirical data collected across the whole 18 supply chain [7] In general for cultivation of rapeseed, the use of default values are preferred, as this reduces the 19 amount of bureaucracy required to determine the GHG balances of the rapeseed produced [9] However, rather than 20 using default values outlined in the RED, (29 g CO2eq./MJ), it was recommended instead for German biodiesel 21 producers to use emission values estimated for the different Federal states (i.e NUTS2) [10,9] In other words a 22 famer producing rapeseed in a particular Federal state would have an associated GHG value for their rapeseed (23- 23 25 g CO2 eq./MJ RME), as long as such cultivation complied with the good farming practices outlined by the 24 Common Agricultural Policy (CAP) [11,10] TE D M AN U 14 EP 25 With cultivation of rapeseed accounting to between 50-90% of the total GHG balance for biodiesel production, the 27 use of RED default values for ease of implementation, may provide inaccurate results for specific regions and 28 locations [5] Indeed one such study for the Veneto region, in Italy, estimated values much higher than those 29 reported in RED for sunflower and rapeseed [12].The reason for such discrepancies relates to two major aspects 30 The first is yield, which is dependent on specific geographical (e.g soil, climate) and regional (e.g management) 31 conditions [5] The second relates to soil emissions, which Hennecke et al [13] also identified as a blind spot within 32 the RED accounting system, as there was no obligation to include more spatially detailed accounting which could 33 capture the interaction between management practices and geographical conditions affecting such soil emissions, as 34 well as the soil emissions themselves AC C 26 NUTS - Nomenclature of territorial units for statistics used by the EU (http://ec.europa.eu/eurostat/web/nuts/overview) ACCEPTED MANUSCRIPT Life cycle thinking is generally employed in combination with RED calculations to estimate the GHG-mitigation potential of biofuels [5] Here too, Malỗa, Freire [14] also identified in their review, that for many life cycle assessments of biodiesel production, the spatial details to determine the major emissions from cultivated soils e.g., nitrous oxide ( N2O) and carbon dioxide (CO2), were still missing Recently, regionally contextualised life cycle concepts were promoted to include greater spatial details in the life cylce assessments of regional bioenergy production [15] A “within regional” context or scope was identified helping to determine the regional distribution of emissions associated with bioenergy production within a regional foreground For this context the “RELCA” modelling approach was developed The goal of which is to develop a regionally distributed life cycle inventory to 10 assess the potential regional and spatial variation in the environmental performance of bioenergy production within a 11 region [16] Therefore, the aim of this paper is to: 1) determine the regional distribution of direct and indirect GHG 12 emissions associated with the production of biodiesel in the Central Germany region; 2) to explore how regionally 13 detailed life cycle approaches, such as RELCA, can be used to identify potential options for reduction and 14 improvement of the GHG emissions associated with the regional biodiesel production and 3) to compare such 15 detailed assessment results with the default values outlined in RED, in order to highlight the need for including 16 greater spatial details within the associated calculation methods 17 Material and methods 18 2.1 RELCA modelling approach 19 A “within regional” life cycle scope was implemented [15], using the RELCA modelling approach [16] to develop a 20 “regionally distributed foreground” inventory (Figure 1) RELCA determines the regional distribution of GHG 21 emissions from the foreground activities, as well as GHG emissions from non- regional activities (indirect burdens) 22 The latter refers to the associated activities producing these flows and are assumed to be outside of the region, along 23 with their associated environmental burdens (i.e released anywhere else but the region of focus and are therefore not 24 considered with a spatial orientation) RELCA combines conventional geographical modelling with conventional 25 life cycle software through the use of catchment delineation to assess the potential environmental implications of 26 bioenergy configurations (i.e bioenergy plants and their biomass catchments) SC M AN U TE D EP AC C 27 RI PT 28 The regional scope is defined as one scale lower than a country and denotes the foreground activities relating to the 29 bioenergy systems being assessed [15] The regional conversion system, transesterification, investigated in this 30 paper refers to the combination of rapeseed with different biodiesel technologies (scales) used in the region to 31 produce a biodiesel product The RELCA approach applied was retrospective and complied with the ISO LCA 32 standards [17], as well as GHG accounting method of the IPCC [18] and RED Keeping in line with the RED 33 calculation method (supplementary material A1), an attributional life cycle accounting approach was implemented 34 and all GHG emissions were allocated based on energetic content 35 (Insert Figure 1) ACCEPTED MANUSCRIPT 2.2 Geographical description of study region The regional foreground is set to the eastern German region of Central Germany (CG), which consists of three federal states, or “Bundesländer”; Saxony, Saxony-Anhalt and Thüringen [19] For the CG region, there is a north south divide with regard to climate, with northern areas having on (50 year) average relatively higher mean annual temperatures (9-10°C) and lower mean annual rainfall (450-600mm), compared with the more mountainous regions of the South’s mean annual temperatures (6-7°C) and mean annual rainfall (600-1000 mm)[20,21] The BÜK 1000 [22] is an official map of German soils, with a total of 72 soil types In CG region approximately 44 soil types can be found The “Ackerzahl” values (Az), or agricultural production value ranges mostly from 31-60, with some areas in the north central part of the region having a value as high as 90 [23] RI PT 2.3 Implementation of RELCA 11 2.3.1 Crop allocation modelling (CRAMod) 12 Approximately 3.5 million hectares are devoted to agricultural production in the CG region, of which over 400,000 13 hectares (ha) of arable land was devoted to rapeseed production in 2010 (approx 16% of arable land) [19,24] For 14 the base year (2010), rapeseed yields in the CG region ranged from 2.31 t/ to 4.41 t/ha (fresh matter) with an 15 average yield of 3.92 t/ ha, slightly higher than the 10 year average of 3.75 t /ha (2004-2013) [25] The CRAMod 16 approach outlined in [16,26] was implemented here The output geodataset provided the potential regional 17 distribution of rapeseed cultivated for the base year For each 25 hectare rapeseed grid cell (500 x 500m²), important 18 regional geographical variables (e.g climate, soil types, agricultural suitability) were also provided, in order to 19 model management and emissions associated with the regional cultivation of the rapeseed crop This is outlined in 20 the next section (BioMod) Additionally, the output from this step provides the regional biomass availability 21 required for the CAMod step (section 2.3.4) M AN U TE D (Insert Figure 2) EP 22 SC 10 2.3.2 Biomass inventory modelling (BioMod)-Direct regional flows 24 As part of the BioMod step the regional management practices were determined, as well as the direct emissions 25 associated with producing rapeseed within the CG region It was assumed the rapeseed was sown in August and 26 harvested the following July and was also assumed to be in a rotation common to the region; Wheat-Rapeseed- 27 Wheat [27] All flows relating to biomass cultivation until the point of harvesting were considered and all 28 calculations steps carried out for biomass cultivation were estimated for each rapeseed grid cell using MATLAB 29 2012b (The Math Works, Inc., Natick, Massachusetts, United States) For more detailed information on how 30 this was carried out please refer to [16] 31 AC C 23 (Insert Table 1) 32 ACCEPTED MANUSCRIPT 2.3.2.1 Management input flows To estimate the amount of nitrogen fertiliser (N) applied per grid cell the “N-Basis-Sollwert” method (required N rate) was used (eq & 2) Best farming practices were assumed for rapeseed production in the region [37-39], with the recommended N rate dependent on yield [37] (supplementary material, A2) Nrateୖୣୡ = 0.0286 × ‫ ݈݀݁݅ݕ‬− 1.7143 (1) Nୟ୮୮୪୧ୣୢ = Nrateୖୣୡ − N୫୧୬ ± Addୟୢ୨ (2) RI PT The average mineralized nitrogen in the soil (ܰ௠௜௡ ), estimated for the six “Böden Klima” (soil-climate) regions [40] found within CG were derived from various regional reports and datasets [37,41-43,39,27] Additionally, if the Akazahl value of a grid cell was less than 40, then the nitrogen fertiliser rate required (NrateRec) was adjusted (Addadj) by subtracting 10 kg N /ha The other rapeseed management assumptions are outlined in Table M AN U (Insert Table 2) SC 2.3.2.3 Field operation input flows – diesel demand 11 The tractability of the soil was also used to estimate potential diesel consumption and hence emissions from field 12 operations on a per grid cell basis This was done using the online KTBL tool [28] as it provides fuel consumption 13 assumptions for three different soil types, which were assumed to be similar to those outlined by [45]; light (25% clay) Only the major field operations were considered (Table 2) TE D 10 15 2.3.2.4 Nitrogen sourced emissions to air 17 Nitrous oxide emissions were estimated for each grid cell according to the German national guidelines outlined by 18 [46] This required estimating emissions using a Tier approach, shown in equation 32 The direct field emissions of 19 nitrous oxide (N2O) induced due to fertiliser application was estimated using the emission factor derived by Brocks 20 et al [44] (Table 3) AC C EP 16 21 (Insert Table 3) ܰଶ ܱே = ∑[ቀ‫ܧ‬ேೌ೛೛೗೔೐೏ × ‫ܨܧ‬ଵ஻௥௢௖௞ ቁ + (‫ܧ‬ேೝ೐ೞ೔೏ೠ೐ೞ × ‫ܨܧ‬ଵ ூ௉஼஼ ) + ൫‫ܧ‬ேಿಹయ ி௘௥௧ × ‫ܨܧ‬ேுଷ ൯ + ൫‫ܧ‬ே_ேை௙௘௥௧ × ‫ܨܧ‬ேை ൯ + (‫_୒ܧ‬୫୧୬ × ‫ܨܧ‬ଵ ூ௉஼஼ )] (3) 22 ‫ܧ‬ே_௥௘௦௜ௗ௨௘௦ = ෍(‫݌݋ݎܥ‬஽ெ × ‫ܿܽݎܨ‬ோ௘௡௘௪ × ܴ‫( × ܩܣܰ × ܩܣ‬1 − ‫ܿܽݎܨ‬ோ௘௠௢௩ ) + (ܰ‫( )ܩܴ × ܩܤ‬4) 23 24 The emissions resulting from rapeseed residues left on the field (ENresidues) were estimated using eq (adapted from IPCC equation 11.6 [47] The parameter assumptions are outlined in Table 25 Modified IPCC equation for N2O emissions, using only those parameters relevant for the assumed conditions modelled in this study ACCEPTED MANUSCRIPT (Insert Table 4) The nitrous oxide emissions resulting from ammonia volatilisation (IPCC class of indirect N2O emissions) were estimated using the emission factors outlined in Table Nitric oxide emissions NO were estimated per grid cell using the EF of 0.012 kg NO_N /kg N applied outlined by Stehfest, Bouwman [48] and according to [46] RI PT (Insert Table 5) 2.3.2.5 Emissions relating to soil organic carbon No land transformations regarding the conversion of grassland into arable land were identified from the available 10 land use statistics for the CG region (Supplementary material A4) Therefore, no CO2 emissions were assumed to be 11 released due to land use changes However, changes in carbon fluxes from cropland remaining cropland were 12 accounted for [18] Changes in soil C were considered using the approach outlined by Petersen et al [49], to 13 account for the potential effect of soil carbon changes for one year on the atmospheric CO2, independent of the 14 initial soil carbon level It was estimated that approximately 10% of the C added to the soil will be sequestered in a 15 100-year perspective and this factor was implemented here as in previous studies [50,51] Similar to Mogensen et al 16 [51] we assumed a reference crop of ‘wheat grown without manure input and with no straw removed’ for our 17 regional biodiesel scenarios The contribution to soil C from each crop was based on the carbon flux estimation 18 methods used in the Candy Carbon Balance model (CCB) [52,53] (Table and equations 5-10) Assuming a steady 19 state, equation 10, was used to calculate the effect of cultivating both wheat (݀‫ܥ‬ௌை஼_௪௛௧ ) and rapeseed (݀‫ܥ‬ௌை஼ೝ೛ೞ ) on 20 soil carbon The difference between both (dCSOC_y) is then used as an indicator of SOC change for the annual time 21 step occurring per grid cell, i.e carbon is either lost or sequestered The carbon balance (dCSOC_y) was then 22 multiplied by the Peterson [49] factor 24 Straw: (Insert Table 6) AC C 23 EP TE D M AN U SC ܵ‫ݓܽݎݐ‬஽ெ = ‫݌݋ݎܥ‬ிெ × ܴ‫( ܩܣ‬5) 25 ‫ܥ‬௥௘௣_௦௧௥ = ܵ‫ݓܽݎݐ‬஽ெ × ‫ܥ‬௦௧௥௔௪ × ܱܵ‫ܯ‬௥௘௣_௕௣ (6) 26 Roots: ܰ௜௡௣௨௧ = (‫݌݋ݎܥ‬ிெ × ܰ௖௢௘௙௙ଵ × ܰ௙௔௖௧ ) + ‫݌݋ݎܥ‬ே்ை் (7) ‫ܥ‬ிைெ = ‫ܰܥ‬௥௔௧௜௢ × ܰ௜௡௣௨௧ (8) ‫ܥ‬௥௘௣_௥௢௢௧ = ܱܵ‫ܯ‬௥௘௣_௥௦ × ‫ܥ‬ிைெ (9) 27 28 Total: ACCEPTED MANUSCRIPT ݀ ‫ܥ‬ௌை஼_௖௥௢௣ ~ ‫ܥ‬௥௘௣ = ‫ܥ‬௥௘௣_௦௧௥ + ‫ܥ‬௥௘௣_௥௢௢௧ (10) dCୗ୓େ_୷ = dCୗ୓େ_୵୦୲ − dCୗ୓େ౨౦౩ (11) 2.2.2.6 Emissions from field operations Emissions associated with field operations provided in Ecoinvent v2.0 were provided on a per kg diesel basis and were converted to kg emission per worked for all field operations (Table 2) The emissions were then estimated per and multiplied by 25 for each grid cell (i.e 25ha) of rapeseed (Table A2) SC RI PT 2.3.3Conversion Plant modelling (CPMod Step) Technology inventory 2.3.3.1 Operational assumptions M AN U The operational base year for biodiesel production was assumed to be from the point of harvest in autumn 2010, 11 through to autumn 2011 During this time period there were approximately 10 biodiesel producing plants in the CG 12 region3, producing less than one million tonnes of biodiesel, with oilseed rape as the main oilseed feedstock 13 [54,55]4 The production capacity for all biodiesel plants was assumed to be approximately 52% of the installed 14 capacity for all plants [56,57] The locations for each biodiesel plant within the region were determined and their 15 coordinates generated using Google maps [16] The biodiesel plants were seen to have some degree of spatial 16 clustering, with many of the small scale plants (installed capacity 150,000 t biodiesel/year) located predominantly 18 towards the northern parts of the region (DBFZ) TE D 10 19 EP (Insert Table 7) 20 2.3.3.2 Model plant concepts 22 The development of the plant concepts was carried out in collaboration with the DBFZ [61] Through investigating 23 the biodiesel production in the region (literature, internet, contact with plant operators), it was determined that most 24 biodiesel plants were attached to an oil mill (i.e no transport of oil between oil mill and transesterification plant) 25 Three biodiesel plant concepts were identified, differentiated by the different oil mill extraction technologies 26 employed (i.e cold press, hot press and hexane extraction) Therefore, the oil output, as well as the energy and AC C 21 For the purpose of this study we assume the operational year starts from the harvested rapeseed in 2010 until the following years harvest Therefore companies that operated through this year were used in this study For reasons of data sensitivity we provide a map for rapeseed production only ACCEPTED MANUSCRIPT auxiliary inputs differ between the three plant concepts The mass and energy flows as well as important modelling parameters are outlined in Table For the production of biodiesel, rapeseed oil is mixed with a catalyst (mostly potassium hydroxide, with sodium methylate used in large scale plants) and methanol in order to obtain a 98% methyl ester (biodiesel) yield After this transesterification reaction the biodiesel and glycerine are separated and the biodiesel undergoes several purification processes (e.g to remove excess methanol) and a drying step resulting in biodiesel which complies with the standard DIN EN 14214 The remaining glycerol rich stream (50%) which also contains a mixture of methanol, soaps and catalyst under goes further process steps, depending on the scale of operation It was assumed that for the smaller and medium plants the glycerine stream is purified to a concentration of approx 80% (technical grade glycerol), 10 whereas for the larger plant it was processed to pharmaceutical grade glycerine (>99%), as this was considered to be 11 more economically viable for the larger plant concepts The rapeseed demand vector for each model biodiesel plant 12 was then determined by; 1) their installed capacities; 2) the assumed operational capacities (52%) and; 3) the 13 estimated conversion efficiency from rapeseed to biodiesel (Table 7) M AN U 14 SC RI PT 2.3.4 Catchment allocation modelling (CAMod) 16 The purpose of the CAMod step is to combine the regionally distributed bioenergy technology inventory with the 17 regionally distributed biomass inventory as in Figure This is done by assigning the biomass to the associated 18 conversion plants using the demand function determined in the CPMod step (i.e tonnes of bioenergy crop required 19 for annual production capacities) In this way the spatial configuration of the biodiesel plants were estimated 20 2.3.4.1 Oil mills 21 The production of biodiesel (RME) was not the only demand for rapeseed within the region Therefore, the potential 22 demand coming from oil mills producing for another market (i.e food), was also estimated from regional reports 23 [62-64] and web searches It was assumed that for the base year around 14 oil mills were in operation, with the 24 CAMod simulations estimating approximately 25% of the regional rapeseeds being diverted to these plants, which 25 was similar to the German average reported [65] 26 2.3.4.2 Rapeseed transport 27 In order to estimate the foreground emissions associated with transporting the rapeseed from field to biodiesel plant 28 the lorry emissions outlined in Ecoinvent 2.0 [66] (CH: operation, lorry 20-28t, full, fleet average [Street]) 29 (supplementary material, A6) were used The harvested rapeseeds were transferred directly at field edge to the 30 transporting lorry It was assumed that the transport was carried out by a logistic company, with the driving route for 31 the lorry calculated for one direction only, as it was also assumed the lorry had an unrelated job in the vicinity 32 before collection of the rapeseed Therefore, only the transport of the rapeseed to the plant was estimated as part of 33 the catchment modelling AC C EP TE D 15 ACCEPTED MANUSCRIPT EF Calcium ammonium nitrate 0.0008+0.0001.ts1 Anhydrous ammonium 0.0127+0.0012.ts Urea 0.1067+0.0035.ts Ammonium sulphate3 0.0107+0.0006.ts Ammonium nitrate3 0.0080+0.0001.ts EP TE D M AN U Spring temperatures (ts) for the months March, April, May, which were found to be in the range of 4.6-8.5 ºC Assumed to be similar to Urea ammonium nitrate The statistics referred to an N mixture which was assumed to be 50:50 Ammonium sulphate: Ammonium nitrate (supplementary material, A3) AC C SC Fertiliser type RI PT Table Mineral fertilisers NH3 emissions factors as a function of spring temperature (ºC) taken from [46] used to estimate ENNH3Fert for each grid cell ACCEPTED MANUSCRIPT Table Carbon modelling parameters and assumptions from CCB model [52,53] Rapeseed 10 6.6 0.07 0.0984 Nfact3 2.7 4.47 RAG4 0.8 1.6 StrawDM5 0.86 0.86 Cstraw 0.44 0.44 SOMrep_bp7 0.5 CNratio8 50 SOMrep_rs9 0.55 0.46 30 M AN U Ncoeff1 SC CropNTot RI PT Wheat 0.4 AC C EP TE D CropNTot= N amount independent from yield (i e N amount in crop) Ncoeff1 = linear coefficient describing the relation between Nresidues and Ncrop Nfact= factor relating the N amount in main product +by-product to the natural yield (main product); is used to calculate the total N in main and by-product in order to get the nitrogen amount in crop and root residues RAG=Ratio between grain yield and amount of by-product (straw) StrawDM = DM of by-product Cstraw= C concentration in DM SOMrep_by = synthesis coefficient describing the efficiency for the replacement of SOM C from byproduct C CNratio = C to N ratio in crop and root residues SOMrep_rs = synthesis coefficient describing the efficiency for the replacement of SOM C from the C of crop and root residues 10 Other acronyms from equations 5-11; StrawDM=straw dry matter; CropFM=crop fresh matter; Crep_str=carbon supplied to soil by straw; CFOM= organic matter; Crep_root=carbon replaced by root; ACCEPTED MANUSCRIPT Infrastructure RI PT Table Activity parameters for conversion plants in the regional foreground All flows unless otherwise stated are tonnes per tonne biodiesel produced, values have been rounded up to the nearest decimal place Small Medium Large Oil mill Cold press Hot press Biodiesel Plant Batch Continuous 2.97 2.50 0.994 0.839 Thermal energy input (GJ/ t) - 1.904 3.142 Sodium hydroxide - 0.003 0.003 Phosphoric acid - 0.002 0.002 Electricity input (GJ/t) Hexane Presscake (output) 1.96 Biodiesel plants Rapseed oil 1.01 Electricity input (GJ/t)2 0.088 1.030 Methanol 0.13 Potassium hydroxide 0.01 Sulphuric acid Hydrochloric acid Sodium methyloxide Biodiesel Glycerol4,5 FFA6 Continuous 2.43 0.896 0.0025 1.5 1.43 1 0.068 0.072 0.619 0.619 0.11 0.10 0.01 - 0.003 0.001 0.01 0.017 0.01 - 0.01 - - 0.016 1 0.13 0.12 0.093 0.02 0.01 0.02 0.02 0.01 AC C Fertiliser EP Sodium hydroxide TE D Thermal energy input (GJ/t) M AN U Rapeseed1 SC Oil mills Hexane extraction Rapeseeds were delivered to the oil mill, assumed to have approx 9% moisture content and 42% oil content Electricity mix for Germany 2010 was taken from [58] Thermal energy refers to German natural gas mix, taken from [59] Output in italics refers to by-products Energetic allocation was carried out using the following lower heating values (LHV): Press cake was taken to be 18.7 MJ/kg, crude press oil 36MJ/kg and Biodiesel 37.2 MJ/kg Biodiesel [60,7] For the small and medium plants, due to investment costs of upgrading it was assumed that they processed the glycerol by product to approx 80% glycerol, however for the larger scale the glycerol produced was pharma glycerol 99% purity These by-products were not considered in the allocation FFA = Free fatty acids can be sold to chemical companies for further processing Fertiliser-derived from the potassium auxiliaries - has the possibility of being used locally Table Comparison across the different biodiesel catchments RI PT ACCEPTED MANUSCRIPT GHG Cultivation2 GHG Cultivation3 Emission Intensity4 Energetic Output 15 Energetic Output 26 LAi7 Yields8 m 23.9 44.84 2660 59.2 22.2 0.0169 3978 s 24.8 52.62 2788 52.5 18.8 0.0191 4226 l 25.5 44.33 2870 61.2 21.3 l 25.7 43.97 2985 63.2 21.2 m 26.8 50.29 2760 s 27.4 66.17 2853 s 27.6 58.55 2740 s 29.4 62.34 2773 s 32.3 68.43 2720 CG av 27.1 54.62 2794 SC Catchment scale1 3996 0.0158 4126 M AN U 0.0164 19.9 0.0182 3690 43.0 15.1 0.0233 3440 46.8 17.1 0.0214 3736 44.2 15.9 0.0226 3550 39.7 14.6 0.0252 3172 51.6 18.5 0.0199 3768 TE D 54.9 EP s=small scale; m=medium scale; l=large scale; CG av.=average for Central Germany Total GHG emissions from cultivation (CO2 eq / MJ) –energetically allocated For comparison total GHG emissions from cultivation (CO2 eq / MJ) –non-allocated results Emission Intensity of catchment (Kg CO2eq / supplying) Gross energy output per supplied (upstream energetic input have not been taken into account here) (GJ /ha) Gross energetic output per associated GHG emission of cultivation (MJ/ kg CO2 eq.) The amount of land area input (LAi) required per GJ of energy produced by the associated biodiesel plant in the configuration (ha /GJ) 8.Yields tFM/ (91% DM) AC C For ease of understanding, only plants are shown , the plant which processes mostly imported rapeseed was excluded Emission Intensity/energetic output and as well as LAi have not been calculated with energetically allocated results ACCEPTED MANUSCRIPT Table Summary statistics for profiled “Hot” and “cold” spots GHG emission analysis within the CG region Results are mixture of frequency analysis and means, unless stated results are from a frequency analysis Base Cold spots Hot spots 56.91 14.32 20.93 14.58 51.84 11.31 1.34 43.59 13.88 47.74 12.23 20.72 19.20 0.11 66.41 14.61 2.93 12.40 76.71 6.09 1.88 RI PT Geographical parameters Ackerzahl value Clay (%) Soil category (% grid cells)2 Soil category (% grid cells)2 Soil category (% grid cells)2 Soil category (% grid cells)2 Soil category (% grid cells)2 Climate parameters (mean)3 AC C EP TE D M AN U SC Mean average daily temperature (ºC) 9.03 8.86 9.24 Total annual percipitation (mm /a) 921 943 874 Spring temperature (ºC) 8.02 7.91 8.18 No Frost days (days) 87 88 86 Agronomic variables Mean rapeseed yield (t Fm /ha) 3.94 3.75 4.26 Mean wheat yield (t Fm /ha ) 7.09 6.51 7.84 Management parameters Mean N fertiliser applied (kg /ha) 160 158 169 Mean Diesel consumption (kg /ha ) 58.43 56.93 59.22 Emission factors Brocks EF low (% grid cells) 30.67% 90.48% 1.35% Brocks EF medium (% grid cells) 69.24% 9.52% 98.05% Brocks EF high(% grid cells) 0.09% 0.60% Estimated Emissions(mean) Soil N2O ( kg CO2 eq /ha ) 1314 1010 1547 CO2 loss from soil ( kg CO2 /ha ) 121 102 139 Field operations GHGs ( kg CO2 eq /ha ) 182 177 185 GHG fertiliser production ( kg CO2 eq /ha) 1003 990 1048 Total Direct GHG emissions (CO2 eq./ ha) 2852 2505 3156 No of grid cells included in analysis: Base, n=8797; Cold spots, n=1839; Hot spots, n=1331 Soil categories from [22] were identified as follows: 1) soils 8-17: Soils in Broad River values;2) soils 18-34: soils in undulating lowlands and hilly; 3) soils 35-48: soils in Loess areas; 4) soils 49-67: Mountain and hill soils from soil rocks their weather products and reposited material; 5) Alpine soils; 6) Anthrosols settlements and surface water Climate parameters provided by the [20] Emission factors (see Table 3) Emissions provided here were the largest contributors other smaller contributors are not included, hence why variables don’t sum to totals ACCEPTED MANUSCRIPT Step1 Crop allocation  modelling Spatially distributed geographical data Step3 Conversion plant  modelling Regionally distributed Conversion technology inventory M AN U Step4 Catchment allocation  modelling Catchment delineated inventory AC C EP TE D Regional Foreground Step5 Non- regional modelling SC Regionally distributed Biomass inventory RI PT Step2 Biomass inventory modelling AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT M AN U SC RI PT   TE D a  AC C EP Base S10 S20 b  AC C EP TE D M AN U SC RI PT ACCEPTED MANUSCRIPT ACCEPTED MANUSCRIPT Highlights EP TE D M AN U SC RI PT Spatial variation in GHG emissions assessed for regional biodiesel production showed; o Mitigation potential dependent on production location within the region o Regional variability cannot be captured with a simple regional average value o Assessing biomass/conversion plant configurations needed for mitigation strategies AC C •