2.1. Identification of the Regionally Available Biomass Potentials for Bioenergy
The following approach was taken to identify the biomass to be integrated into potential regionally embedded alternative bioenergy chains. The regional focus was set on a region in the vicinity of an operating biogas plant in the northwest of Germany (Figure 1
). This rural area has less fertile sandy to silty soils or peaty soils. A total of 58% of the area is used for agriculture. Prominent is a grassland-dominated agriculture (pasture as well as hay and grass silage production) for dairy farming. Horticultural activities, mainly tree nursing, are of minor relevance. The soil and climate allow for crop production over the entire vegetation period [14
The gap between the biomass potentials and the biomass currently used for bioenergy in the region was assessed in geographic information systems (GIS) analyses and by interviews with regional actors. Accordingly, the use of biomass in the case study region depends on five aspects in particular: (i) the supply chain of biomass is normally defined by operators of bioenergy plants and their access to local farmers’ biomass; (ii) a feedstock of a biogas plant is normally made up by the prevailing biomass or manure in the region; (iii) the competition for arable land to grow energy crops, including alternative biomass like grass, is high [30
]; (iv) very few farmers currently align with local landscape management associations to acquire alternative biomass [14
]; and (v) the availability of primary produced biomass is limited, in contrast to a high availability of manure.
This current situation marks a transition of regional bioenergy driven by changing European and German legal settings. The competition for biomass is very high in this region and was even intensified due to the incentives of the German Renewable Energy Sources Act (EEG). By giving high incentives for feeding-in electricity at the beginning of the EEG’s history, subsequently the maximum amount of maize used in biogas fermenters was gradually reduced. The latest version of the EEG from 2017 restricts the use of maize to up to 44% of the total mass of substrate [23
]. As the market for biogas plants was nearly saturated by the time of this last EEG revision, there are only very few biogas plants operating under this revised EEG. Hence, most of the biogas plants are allowed to use up to 60% maize, and therefore, this number was taken for one of the scenarios later in this chapter. Therefore, biogas plant operators in the case study region will be forced to reduce the amount of maize as biogas substrate in their plants when their EEG subsidies are running out. However, because of the high gas yield of maize [22
], many biogas plant operators still grow maize in these regions. More recently, RED II as part of the EU’s “Clean energy for all Europeans package” came into force. This directive asks for a broader view. With respect to bioenergy, it requires consideration of the socioeconomic conditions of bioenergy chains. It is worth mentioning that a concurrent directive on the Governance of the Energy Union and Climate Action (EU 2018/1999) discusses aspects of bringing together local authorities, civil society, businesses, investors and other stakeholders to establish a multi-level energy dialogue. This context was considered in this study to highlight the benefits of new stakeholder alliances to make the regional bioenergy supply chain more sustainable and to conform to the new EU regulations.
2.2. Analysis of the Socio-Ecological Context of Bioenergy Supply Chains for the Assessment—Definition of Criteria
Any optimization of bioenergy supply chains has to take regional conditions into account [14
]. This includes considering socio-cultural, economic conditions and legal regulations that provide the framework for the generation and use of bioenergy. It also relates to the involvement of actors from different stakeholder groups [31
]. In this assessment, socio-ecological data were integrated and applied to sustainability parameters in a multi-criteria decision analysis (MCDA) using the PROMETHEE methodology to outrank optimization scenarios. As the focus was set on socio-ecological data, most environmental indicators such as CH4
O emission occurring during the whole bioenergy supply chain were not included in this assessment and were reduced to CO2
emissions during transportation and production of biomass. However, the topic of emissions has been discussed in many other references [35
]. Resulting from the authors’ experience in biomass-related projects, criteria for the evaluation of biogas in a regional context were developed. The traditional supply-chain operations reference model (SCOR) was modified with the involvement of project partners to meet the challenges of the production of bioenergy, resulting in the identification of the processes: material supply, logistics, production, and usage [39
]. The distribution of converted energy and its accompanying emissions was not part of this study and is excluded here. Each process was specified by the definition of targets. Since different stakeholders participated in this process the best possible targets for the region were formulated and were therefore relative to this specific region. Subsequently all located targets were expressed within suitable criteria [40
]. The criteria were elaborated together with the plant operator.
identifies criteria alongside targets for processes in the regional production of bioenergy, as assessed by our local partners. Therefore, these targets fit into the area of northwestern Germany and may not be transferable to other regions, countries, and continents. Targets for the optimization of different steps of bioenergy chains were defined with the regional stakeholders. A set of criteria were attributed to the targets. Quantitative or semiquantitative data in the indicated unit were compiled for each criterion from literature, official statistical data, and regional bioenergy operators. Criteria values were then calculated for each alternative action based on: (i) data from operators directly, (ii) data and indicators from literature (e.g., the Federal Statistical Office of Germany [42
], the Bavarian State Research Institute for Agriculture (LfL) [43
], the Association for Technology and Structures in Agriculture (KTBL) [44
]), (iii) software tools (e.g., cost efficiency calculator KTBL [45
], feed-in tariff calculator German Biomass Research Centre (DBFZ) [46
], basic data from DungInfo [47
] for the calculation of humus balance), and (iv) for criteria with insufficient data the values were introduced based on expert knowledge.
The weighting of the individual criteria was applied in the outranking of options in the participatory assessment (MCDA–PROMETHEE). In addition to criteria values the PROMETHEE methodology requires information on the weighting of criteria. Therefore, the assessment followed an approach to give equal importance (0.25) to each target: material supply, logistics, production, and usage. In weighting each criterion it was assumed that all elements had the same importance for the decision-maker [48
]. For each criterion the average weighting is presented in Figure 2
. For all criteria a Gaussian preference function was applied, since it was proved to be stable [49
]. For this function the parameter σ needed to be determined. The input data were normalized from 0 (worst value) to 1 (best value), therefore, σ of 0.3 was applied as in [50
]. Calculations of some criteria were repeated, e.g., the calculation for working hours was taken from a cost efficiency calculator instead of a calculation based on performance indicators for fixed and variable working hours. Furthermore, the methane yield of the plant was taken as reference rather than the total mass of substrate.
In the case study, Target 1, Material Supply, aims at using substrates that do not compete with food or animal feed production. It tries to minimize the material costs and promote ecosystem-based farming. Energy crop cultivation will be socially accepted and at the same time bioenergy will provide secure employment. These sub-targets are expressed through different criteria. The Use of material not directly competing with food production describes the use of substrates that are not primarily used for generating biogas. First, the amount of energy crops used in an area has direct influence on other local agricultural activities. Less energy crop cultivation means less pressure on agricultural food production. Second, this will help keeping permanent grassland in the region, which has an overall positive effect on the region’s ecology. Third, using agricultural and other organic waste products as a substrate allows to close the loop of the bioenergy supply chain. Material Costs describes the price for the individual substrates such as corn silage or liquid manure, excluding transportation costs. Material costs were calculated based on information from an operator survey. Corn silage was available in the region for 30 EUR/t. Liquid cow manure was partly available from own cattle and additionally bought from farmers in the region for 4.5 EUR/t. Alternative substrates such as plant material from landscape conservation and biowaste were available at no costs; however, the owner of the plant had to provide transport for the substrate himself. The same is applicable to Soil quality (change in humus balance), reflecting that the cultivation of land induces a change in humus balance. Maize, for example, has a negative impact on the humus balance; in contrast, grassland has a positive effect. Key figures on the change of humus balance for the cultivation of different substrates are given in [47
]. The Fuel consumption agriculture criterion specifies the fuel consumption at harvest time. It is based on the use of a field chopper consuming 65 L diesel and a tractor consuming 14 L diesel to harvest 2 ha/h. This criterion does not include fuel-consuming processes during other agricultural processes such as plowing, sowing, and fertilizer application due to the lack of data. Share of maize plants in area describes the amount of cultivable land in a region that is used for energy crops. The share of farmland for cultivation of corn/maize is reduced by the usage of alternative substrates and increased by the usage of corn silage as a substrate. The current state equals the current share of maize plants in the region, according to the statistical data [51
]. Working hours agriculture assesses the working hours invested in cultivating biomass substrates. In this case study working hours in agriculture only occurred for the cultivation of corn, and 7.6 h/ha for corn silage was assumed based on the literature [52
Target 2, Logistics, aims at reducing transport costs and the regional environmental impact but it also tries to increase secure employment in the sector. Therefore, Transport costs describes the costs for shipping substrates from the farm area to biogas plants. Transport costs were calculated with a linear model, giving specific transport costs depending on the transport distance [53
]. For all substrates, distances were taken either from an operator survey or calculated based on GIS results from previous studies [14
]. Up to 50% of the current usage of corn silage was available at a very short distance of less than 1 km. However, additional corn silage needed to be bought from distances up to 15 km. Plant material from landscape conservation was only available decentralized, and therefore transport distances of up to 20 km were taken into account. The Avg. transport distance reflects the average distance of getting the substrate from its source to the biogas plant. The criterion CO2
balance transport determines the greenhouse gas (GHG) emissions occurring during transport of substrates and digestates. Working hours transport describes transporting substrates or digestate, thereby also reflecting the time for loading and unloading. Similar to the CO2
balance transport, work hours transport are directly influenced by the transport distances of the substrate. Greenhouse gas emissions for the return trip of the transport vehicle were taken into account.
Target 3, Production, aims at minimizing the operational costs, achieving a high material efficiency, thereby reducing the environmental impacts. Cost per production unit, i.e., the electricity generation costs, are made up of the variable cost, fixed costs, indirect costs, and the actual supply of electricity to the grid. The CO2
balance production criterion reflects the output of CO2
emissions at different stages of biogas production such as cultivating plants, the actual biogas generation, and digestate utilization. The CO2
balance production takes into account greenhouse emissions for the (i) provision of substrates in the form of soil cultivation, fertilizers, pesticides, and direct emissions (N2
O emission from soil) as well as the (ii) biogas production in the form of the provision of the biogas plant, operating material, leakage, and processing of biogas in a cogeneration unit. Working hours plant relates to “loading” the plant with solid or liquid substrates and tasks such as control, sample taking, documentation, or maintenance and repair. Finally, the Working hours plant mainly depends on the ratio of liquid and solid substrate, since solid substrate requires substantially more time for treatment and feeding of the plant [54
Target 4, Usage, aims at maximizing the income. Total income p.a. describes the annual income/loss before taxes. It was calculated based on a cost efficiency calculator from the Association for Technology and Structures in Agriculture [45
]. Since the annual mass was taken as reference in this case study the gas yield varied across the scenarios directly influencing the income. The gas yield assumed for alternative substrates (e.g., plant material from conservation areas 102 m3
/t) was substantially smaller than the gas yield of corn silage (197.6 m3
2.4. Substrate Compositions
The different types and masses of substrates resulted in similar annual methane yields in the Ihausen region for the three defined scenarios (Table 1
). The numbers were either generated by GIS analysis and derived from a former analysis published in [14
], or were provided by the plant operator itself or provided by the municipality. Figure 3
a,b show the material flows of each Scenario.
The current state substrate composition comprises corn silage, rye silage (whole crop), corn-cob mix, liquid cow manure, and grass from grasslands.
The max 60% corn silage substrate composition represents Scenario B with the legally highest possible corn silage amount, according to EEG 2012. As mentioned before, the increased usage of corn silage is expected to enable the biogas plant owner to increase the annual income. Next to corn silage, liquid cow manure is used and available at no extra cost to the biogas plant owner in this region due to the high volume of dairy farming in the region. The use of liquid manure reduces the solid matter percentage and guarantees a stable fermentation. As a result, Scenario B shows the lowest annual mass of substrate, followed by Scenarios C and D, and finally, Scenario A with the highest annual mass.
Scenario C, Alternative substrates, represents potentially available substrates and again not all of them are usable in a biogas plant due to legal restrictions or other uses of the substrate. The substrate composition includes plant material from roadside (central reservation and verges) and buffer strips, local residents’ and organic household waste. Furthermore, separated cow dung is used. In order to reduce transport costs regular cow manure is refined in a compaction process, separating the solid phase of the substrate. According to the owner of the biogas plant this allows to reduce 85% of the mass, while retaining 60% of the gas yield of the substrate.
Grass from grassland is abundantly available to the biogas plant owner as long as he collects and transports the substrate himself. The sole usage of grass from grasslands is considered in a variant of the alternative substrate composition as Scenario D, 100% Grass from grassland.
2.5. Multi-Criteria Decision Analysis (MCDA) According to the Socio-Ecological Context and Data with a Preference Analysis of the Identified Options According to PROMETHEE Outranking Method
The Preference Ranking Organization Method for Enriched Evaluation (PROMETHEE) was applied in this study to assess the best suitable solution within the regional bioenergy production. It is a methodology within the multi-criteria decision analysis (MCDA) family [55
]. Based on an outranking approach it allows practitioners and researchers to rank a finite set of alternative actions among criteria and is increasingly used in a wide range of applications [56
]. Furthermore, MCDA can be utilized to evaluate problems in the context of sustainability, since it is regarded as a flexible method with the possibility of facilitating the dialogue between stakeholders, analysts, and scientists [57
PROMETHEE is based on pairwise comparisons of alternatives in order to identify dominance of one alternative over another [58
]. The outranking method PROMETHEE is applicable in cases of incomplete and contradictory information. In contrast to other MCDA methods, complete compensation of criteria is possible to a limited extent within outranking methods [50
]. In the real world, decision-making problems are uncertain to some extent [59
]. This research is applied to a real world case, a biogas plant in Ihausen, Germany. Therefore, PROMETHEE was chosen as the most suitable method. Also, this MCDA procedure has been proven to be efficient and widely applied in the research field of bioenergy [60
]. PROMETHEE I involves the calculation of negative net flow
and positive outranking flows
for each scenario. The net flow describes how much a scenario was preferred over all other scenarios. PROMETHEE II provides a complete ranking of the alternatives by calculating the net flow
. The higher the net flow, the better the alternative [55
]. In this study PROMETHEE II was applied. The net flow for each scenario was calculated and is presented in the Results section. To perform the analysis, the Visual PROMETHEE 1.4 Academic Edition software (Marechal, Brussels, Belgium) was used [63