The impact of German biogas production on European and global agricultural markets, land use and the environment

Energy Policy 62 (2013) 1268–1275

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

The impact of German biogas production on European and global agricultural markets, land use and the environment Wolfgang Britz a, Ruth Delzeit b,n a b

Institute for Food and Resource Economics, University of Bonn, Nussallee 21, 53115 Bonn, Germany Kiel Institute for the World Economy, Hindenburgufer 66, 24105 Kiel, Germany

H I G H L I G H T S


Recent changes to that program decrease green maize use but increase land demands.
The program could raise EU prices for cereals by 3%.
Agricultural land use expansion outside of the EU estimated at 1 Mio ha.

art ic l e i nf o

a b s t r a c t

Article history: Received 12 June 2012 Accepted 21 June 2013 Available online 29 July 2013

As part of its climate policy, Germany promotes the production of biogas via its so-called RenewableEnergy-Act (EEG). The resulting boost in biogas output went along with a significant increase in production of green maize, the dominant feedstock. Existing studies of the EEG have analysed its impacts on German agriculture without considering market feedback. We thus expand existing quantitative analysis by also considering impacts on European and global agricultural markets, land use and the environment by combining a detailed location model for biogas plants, the Regionalised Location Information System-Maize (ReSi-M2012), with a global Partial Equilibrium model for agriculture, the Common Agricultural Policy Regional Impact (CAPRI) model. Our results indicate that the German biogas production is large enough to have sizeable impacts on global agricultural markets in prices and quantities, causing significant land use change outside of Germany. While profits in the agricultural sector increase, food consumer face higher prices, and subsidies for biogas production are passed on to electricity consumers. The German biogas program, as long as it is almost entirely based on non-waste feedstocks, is probably not a promising avenue towards a GHG-saving renewable energy production, but a rather expensive one. & 2013 Elsevier Ltd. All rights reserved.

Keywords: Biogas Land use Policy analysis

1. Introduction Based on the European Renewable Energy Road Map (European Commission, 2007) and the Renewable Energy Directive (European Commission, 2009) Member States of the European Union (EU) are obliged to establish national mandatory targets which aim to increase the share of renewable energies for primary energy consumption to 20% by 2020, and to 10% in case of transport. Regarding the transport sector, member states indicated in their National Action Plans to meet the 10% target to a large share by biodiesel (about 8.6% from 10%) and bioethanol. The impact of these biofuel targets on land use and agricultural prices have been

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analysed in various studies (Banse et al. (2008), Kretschmer and Peterson (2009), Al-Riffai et al. (2010) Britz and Hertel (2011), Laborde (2011)). With regard to the 20% target on primary energy consumption by 2020, Germany aims at renewable energy shares of 14% in the heating sector, 17% for fuels and 27% in electricity production (BMU, 2007). In the latter case, Germany's Renewable Energy Source Act (EEG) promotes electricity production from biogas along with other renewable energies such as wind and solar. The EEG guarantees producers of electricity from renewable energies feed-in tariffs (FITs) above those paid for electricity from fossil fuels in order to compensate higher production costs. The production of biogas in Germany which is in the focuses of our paper is mainly based on the fermentation of biomass, with green maize being the dominant feedstock. Due to incentives set by the EEG (see Section 2.2 for details), green maize cultivation for biogas production has expanded significantly over the past years.

W. Britz, R. Delzeit / Energy Policy 62 (2013) 1268–1275

The German Advisory Council on the Environment (SRU) criticises that development due to possible serious negative environmental effects on soil, water and biodiversity (SRU, 2007, pp. 2 and 43). The recent reform of the EEG, which came into force in 2012, aims to promote alternative feedstocks for biogas production to decrease the use of green maize. While the impact of the EEG on the German agricultural sector has been addressed by Delzeit et al. (2012a, 2012b) and Gömann et al. (2011), its influence on the EU and global agriculture has not been analysed yet. Assessing impacts of the last two versions of the ambitions German biogas policies (EEG versions 2009 and 2012) against the background of EU and global biofuel targets is therefore the objective of this paper. The paper is structured as follows: in the following section, we introduce two economic simulation models we combine for the quantitative analysis: the Regionalised Location Information System-Maize (ReSi-M2012) as well as the Common Agricultural Policy Regional Impact (CAPRI) model. In addition, we provide an overview on important policies on EU level as well as a detailed description of the EEG. Section 3 is devoted to the presentation of results on biogas production and land use in Germany, land use change in the EU and the world, as well as feedbacks on agricultural markets.

2. Modelling framework and data In order to assess how the German subsidies to biogas production impact agricultural markets, an analysis across scales is necessary. The market and land use effects in Germany of these subsidies depend on how much biogas plants of a certain size class are build and which feedstocks are used for their operation. Manure, to give an example, would not require additional land. Other feedstocks such as green maize, grass silage or other ensilaged grains differ in their yields in the same region, while yields for the very same crop differ across regions. The market impacts and land demands thus depend also where the plants are erected. We therefore employ the highly detailed location model ReSI-M2012 (see Section 2.2) to simulate the regional demands for different feedstocks. In order to translate these regional demands into effects on agricultural markets and land demands beyond Germany, a global agricultural sector model is needed which gives sufficient detail in depicting the effects of demand for biogas feedstocks at the regionals scale. We have chosen CAPRI (see Section 2.1) due to its unique combination of regional supply-side models for agricultural with a global market model for agricultural products. The simulated feedstock demands from ReSI-M2012 enter as exogenous additional demand quantities at the regional level into CAPRI. 2.1. Modelling German, EU and global agriculture: CAPRI The CAPRI model (Britz and Witzke, 2011) is a global comparative-static partial equilibrium model with a strong focus on Europe, consisting of a supply and a market module. The supply module, covering the EU, Norway, Turkey and Western Balkans, comprises independent aggregate non-linear programming models representing approximately 50 crop and animal activities of all farmers, in the version applied by us for 280 administrative units at the regional level (NUTS II1). Each programming model maximises regional agricultural income at given prices, subject to technical constraints for feeding, young animal trade, fertilisation, set-aside, a land supply curve and production quotas. Green maize 1 For a description, see: http://ec.europa.eu/eurostat/ramon/nuts/basicnuts_re gions_en.html.

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and grass, important for our analysis, are treated as non-tradable, their production is steered by their production costs and their substitution value with tradable feed such as concentrates based on cereals. For the EU, the different coupled and de-coupled subsidies of the so-called first Pillar 1 of the Common Agricultural Policy (CAP), as well as major ones from Pillar 2 (Less Favoured Area support, agri-environmental measures, Natura 2000 support) are depicted in detail. Prices for agricultural outputs in the programming models are rendered endogenous based on sequential calibration (Britz, 2008) between the supply models and a market model. The latter is a global spatial multi-commodity model covering 77 countries or country aggregates in 40 trade blocks and about 50 products. The Armington approach (Armington, 1969), assuming that the products are differentiated by origin, allows simulating bilateral trade flows and related bilateral as well as multilateral trade instruments, including tariff-rate quotas. For the current study, our baseline captures developments in exogenous variables such as policy changes, population growth, GDP growth and agricultural market development for the year 2020. It is aligned with the global Aglink-COSIMO baseline prepared by OECD and FAO and thus includes the expected effects of biofuel policies in OECD and other countries (OECD/FAO, 2011). Specifically, it integrates simulation results from the PRIMES energy model for the bio-fuel sector (Capros et al., 2010). The baseline assumes ‘status-quo‘ policy, meaning current policies remain in force while taking into account those future changes that are already agreed and scheduled in the legislation. It thus covers the CAP Mid-Term Reforms, the reforms of the sugar markets, and the CAP Health Check, i.e. further decoupling of direct payments, no set-aside obligation, and increased modulation phased in gradually by 2012; milk quotas are phased out by 2015. Our comparison point differs from that standard baseline by removing any feedstock demand in Germany for biogas production. 2.2. Modelling location decision of biogas plants: ReSI-M2012 2.2.1. German biogas production and policies The main technology used in Germany to produce biogas is socalled heat-electricity plants (BHPPs), where thermal energy emitted from an electricity producing combustion engine is used locally as a by-product. That requires a suitable heat sink such as the central heating of nearby buildings. An alternative to a BHPP located near the fermenter is to feed upgraded biogas into a natural gas pipeline to drive a BHPP close to large heat demander such as a district heating station. But this is economically efficient only for large-scale biogas plants where economies of scale offset the high costs of upgrading biogas. The EEG has its origins in the Stromeinspeisungsgesetz (SEG) which was created in 1990 (BGBl, 1990) and for the first time required electricity suppliers to pay producers of renewable energies fixed prices of the energy they generate and allowed them to pass on costs to consumers. The first version of the EEG was enacted in 2000 and subsequently revised in 2004, 2008 and 2011 (BGBI, 2000, 2004, 2008, 2011). In the revision in 2004, FITs were divided into a basic payment per kWhel (“Grundvergütung”) and additional per unit subsidies adjusted depending on input, plant size and plant technology. The so-called “NaWaRo” (renewable resources) bonus as an important of these subsidies is paid if biogas is gained from manure and/or from plants or parts of plants which are produced in agricultural, silvicultural or horticultural farms (for more details on definitions see BGBl, 2004, Section 8 (2)). Another so-called combined heat and power generation (CHP) bonus depends both on the actual amount of heat used and on the plant's electricity efficiency. It benefits larger plants as

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the efficiency as well as the share of heat used is generally lower in small plants ( o150 kWel). A technology bonus is paid if CHP is applied and biomass is transformed by thermo-chemical gasification or dry fermentation, the biogas produced is processed to natural gas level quality or electricity is gained from fuel cells, gas turbines or other applications, which are defined in BGBl, 2004, Section 8 (4). With the amendment of the EEG in 2008, the share of renewable energies for total electricity production was aimed to increase to 30% by 2020 (BGBl, 2008). Against the background of rising food prices in 2007/2008, the EEG 2009 introduced a special bonus for small scale plants (≤150 kWel) using at least 30% manure to reduce the land demand for biogas production to avoid further pressure on food prices. Further on, FITs were increased compared to former versions of the EEG to offset higher input costs related to the food price spike and to trigger a further expansion of biogas production capacities. Specifically, the CHP bonus was raised, while additionally small plants were supported by an increase of the basic tariff for the first 150 kWel and of the NaWaRo bonus for capacities up to 500 kWel. Table 1 illustrates that small-scale plants especially benefit from the EEG 2009 if they are able to claim all subsidies. According to FNR (2009), 530,000 ha have been used in 2009 for the cultivation of inputs for biogas production, accounting for approximately 5% of total agricultural land in Germany, or about 1/ 4 of what the EU used to subsidise in the past as renewable energy area EU wide. Medium term land use changes caused by the EEG 2009 are simulated in economic models by Gömann et al. (2011) and Delzeit et al. (2012a, in press). Their results suggest that the legislation might meet its target of increased electricity production from biogas, but that more land is used compared to the EEG 2004, both in sum and per produced unit of electricity (see Delzeit et al., in press). That higher land demand per unit of electricity comes unexpected as the EEG 2009 introduced higher subsidies for manure use, specifically to reduce land demands. Both studies show indeed that newly erected plants use more manure, but highlight that the low energy efficiency of small-scale plants rendered economically attractive by the amendment (see Table 1) combined with the low energy content of manure overturn the positive feedstock mix effect. A new amendment of the EEG came into force in 2012 and like the EEG 2009 it aims to “(…) facilitate a sustainable development of energy supply, particularly for the sake of protecting our climate and the environment, to reduce the costs of energy supply to the national economy, also by incorporating external long-term effects, to conserve fossil fuels and to promote the further development of technologies for the generation of electricity from renewable energy sources” (this English translation is taken from BGBl, 2008 Section 1). While the EEG 2009 aimed to achieve a 30% share of renewable energies for electricity production by 2020, this target is increased in the EEG 2012 to 35% in 2020 and up to 80% in 2050 (BGBl, 2011 §1). In order to reduce the input of

green maize, and to simplify the system of FITs, substantial changes were introduced in the amendment of the EEG 2012. “NaWaRos” are now divided into two classes with the so called substance tariff class II containing ecologically desirable substances (BGBl, 2011). Additionally, the use of maize and grains is limited in sum to maximal 60% on the mass content. As another major change, the full amount of FITs can only be claimed when at least 60% of the produced heat is used (BGBL, 2011). 2.2.2. The standard version of the location model ReSI-M ReSI-M as a regionalised location model takes the following interdependent factors into account which impact the optimal location and size of biogas plants: output prices according to legislation, the availability of raw materials and resulting transportation costs, production costs, and the possibilities to use the produced crude biogas and heat. Its standard version only considers maize and manure as feedstocks whereas the extended version used by us accounts for additional inputs to reflect the changes in the EEG 2012. The standard version was developed by Delzeit et al. (2012a) to simulate the number of biogas plants erected in regions based on independent, individual investments. It takes into account the plant's location in sub-regions and their type, characterised by size and feedstock mix. This is done by iteratively maximising the return on investments (ROI) for biogas plants in NUTS 3 regions inside each German NUTS 2 region. Given that the EEG guarantees output prices for 20 years after constructing a plant, this period is taken as the planning horizon. It is assumed that investments in plants are ranked and realised according to their net present ROI. Following the classification of FITs in the EEG 2004 and 2008, four plant sizes are distinguished: 150, 500, 1000 and 2000 kWel. Two pathways of using the produced crude biogas are considered: (1) direct use in BHPPs and (2) upgrading biogas, inducting it into pipelines and finally use it in a BHPP (compare Section 2). In the standard version, the model considers maize and manure as feedstock. Aggregated across biogas plants, total feedstock at different prices for maize (21–53 €/t) is determined for each NUTS 3 region, which by interpolation allows for regional maize demand curves to be derived. The number of plants erected nr,t of a specific type t in a NUTS 3 region r at price w is assumed to depend on plants' ROIs. The ROI is calculated from yearly operational profit πr,t, and total net present value of investment costs It divided by the length of the planning horizon T, ROI r;t ðwÞ ¼

π r;t : I t =T

ð1Þ

Yearly operational profit is the difference between revenues – output yt times price pt – and the sum of operational costs net of feedstock costs oct, and feedstock costs (see Eq. (2)). Feedstock costs are determined by the given input demand xt multiplied by the sum of average per unit transport costs tcr;t and

Table 1 Feed- in tariffs for EEG 2009. Source: BGBl, 2008.

Basic feed-in tariff NaWaRo bonus Manure bonus Bonus CHG Technology bonus Max. possible subsidy from EEG (€ cent/kWhel) on top of spot market price

≤150 kWel

≤500 kWel

≤5 MWel

5–20 MWel

11.67 7 4 3 2 27.67

9.18 7 1 3 2 22.68

8.25 4 0 3 2 17.25

7.79 0 0 3 0 10.79

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Table 2 Feed- in tariffs for EEG 2012. Source: BGBL. 2011.

Basic feed-in tariff Substance tariff class STC I Substance tariff class STC II Gas processing bonus Small manure installations n

≤75 kWel

≤150 kWel

≤500 kWel

≤750 kWel

≤5 MWel

5–20 MWel

14.3 6 8 ≤ 700 Nm3/h:3;≤1000 Nm3/h: 2; ≤1400 Nm3/h: 1 25

14.3 6 8

12.3 6 8

11 5 8/6n

11 4 8/6n

6 0 0 0

Over 500 kW and up to 5,000 kW only 6 Ct/kw h for electricity from manure (BiomasseV).

feedstock price w. π r;t ¼ yt pt oct xt ðtcr;t þ wÞ:

ð2Þ

Average per unit transport costs tcr;t are the outcome of a transport cost minimisation problem which reflects inter alia regional availability of feedstock in the regions from where the feedstock is taken. Availability of feedstock depends on regionally differing “location factors”. These are feedstock yields as well as the share of arable land on total land, the spatial distribution of this share and the amount of feedstock that is already used. This spatial distribution determines the homogeneity of a region. For a detailed description of the standard model, see Delzeit et al. (2012a).

2.2.3. Extended version of ReSI-M Based on the changes of the EEG 2012 described in Section 2.2.1, the extended model now includes five plant sizes (75, 150, 500, 1000 and 2000 kWel) and considers also ensilaged grass, sugar beets, and grains as possible inputs in different input shares and thus residue amounts. Note, that in opposite to green maize, the input prices for these additional inputs are kept constant. It is presumed that biogas producers can choose between five different input mixes: (A) 40% manure (STC II), 50% maize and 10% WPS grains (all STC I); (B) 20% manure (STC II), 60% maize and 20% WPS grains (all STC I); (C) 10% manure (STC II), 60% maize and 30% WPS grains (all STC I); (D) 40% manure (STC II), and 60% maize (STC I); and (E) 80% manure and 20% maize. Whereas option (E) is only applicable for 75 kWel-plants which might claim the “small manure installations bonus” based on share of mass content (mass percent) (see Table 2); the other options are available to all plants and introduced to analyse the profitability of the differentiation in the two STCs. Delzeit et al. (2012c) show that relevant feedstock shares of grass silage under the EEG 2012 are rather unlikely. Grass silage is hence not considered as a feedstock in the analysis. In order to reduce computing time, pre-calculation determine and exclude unprofitable biogas plant types taking into account plant size, input mix, and regional availability of gas pipelines and demand for heat for housing.

2.2.4. Data underling ReSI-M Data on production costs, including those for heat use, are taken from Urban et al. (2008) and Achilles (2005). Regional maize supply functions were derived from sensitivity analysis with the RAUMIS model in the NaRoLa Project (Gömann et al., 2011), which also contributed maize yields, while input prices biogas plant pay for sugar beet and ensilaged grains are taken from FNR (FNR, 2010, pp. 174). These input prices are assumed to include transport costs,

and there is no endogenous demand function generated in the model. Maize and manure transport costs stem from Toews and Kuhlmann (2007), and Kellner (2008). Spatial information on heterogeneity of arable land is gained from a GIS-analysis and based on data from Leip et al. (2008). For a detailed description of the model, data sources, GIS-analysis and sensitivity analyses of important input parameters we refer to Delzeit et al. (2012a).

3. Scenarios setting – The reference scenario is defined to carry forward current legislation as defined in Section 3.1. In the reference scenario, there is no feedstock demand for German biogas production in the CAPRI model. – The EEG 2009 scenario: in this scenario, the CAPRI model is adjusted to mimic the demand for feedstock stemming from the ReSI-M model. In the ReSI-M model, FITs according to the EEG 2009 legislation are adopted and feedstock of existing plants is considered. Regional maize markets are determined by intersecting simulated demand functions from ReSI-M as well as simulations of the supply functions by RAUMIS of the target year 2020. – The EEG 2012 scenario: in the EEG 2012 scenario, FITs according to the current version of the EEG 2012 and again, feedstocks demand of existing biogas plant are taken into account. Comparing impacts on agricultural markets and land use change caused by these two versions of the EEG allows drawing conclusions on the impact of biogas production with different feedstocks.

4. Results 4.1. Feedstock demand for biogas production in Germany While under the EEG 2009 scenario, biogas is mainly produced by small scale plants with low energy efficiencies, under the EEG 2012 the dominant size is 500 kWel plants using 50% mass content of green maize as the most cost efficient input and 10% of grains. The remaining 40% stem from manure which belongs to the STC II and thus ensures that the plants receive higher tariffs per kWhel. Total electricity and heat production from biogas in Germany in the target year 2020 is illustrated in Fig. 1, showing that the reform of the EEG results in an increase of electricity production of 13%. With about 14 TWh, biogas contributed 2.4% to final electricity consumption in 2010 (BMU, 2012). At constant final consumption, that share would increase to about 8% according to our simulations. Under the EEG 2009, small scale plants which do not use heat turn out as the dominated plant type. Under the EEG 2012, mainly medium sized plants are simulated which receive the full amount

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180

300

160

250

140 120 mio GJ

mio GJ

200 150 100

100 80 60

50

40 0

20

EEG 2012 Scenario Heat in GJ simulated plants

Electricity in GJ existing plants

Electricity in GJ simulated plants

Fig. 1. Electricity and heat production under EEG 2009 and EEG 2012 in mio GJ in 2020.

of the FITs. That would require that at least 60% of the produced heat is utilised which thus implies a tremendous increase in heat use compared to the EEG 2009. That might be somewhat optimistic as medium size plants produce heat amounts exceeding by far the typical heat demand on farm. The simulated heat supply would hence require far more widespread use of, e.g. small district heating systems near biogas plants. The resulting feedstock demand under the EEG 2009 scenario in 2020 consists mainly of green maize, since the most profitable plant types use 30% manure and 70% green maize based on fresh mass content. Under the EEG 2012 scenario, biogas operators are forced to use certain minimum shares of other feedstocks than green maize and manure. Results thus show that despite a higher energy output, about 54 mio t less green maize is demanded, while additionally 40 mio t of grains are inputted (see Fig. 2). For results on the regional distribution of green maize production see Delzeit et al. (2012c). Fig. 2 illustrates the share of feedstock according to their energy contribution in 2020. Under the EEG 2009, maize contributes 93% of the feedstock energy, despite a mass content of manure of 30% reflecting the low energy content of manure. Energy contribution shares under the EEG 2012 scenario are quite different: the share of green maize, still economically the preferred input, is forced down to 55%, whereas now grains delivers a sizeable energy share of 42%. The two left bars of Fig. 3 illustrate the relative increase in total land area used under the EEG 2012 compared to the EEG 2009 for the target year 2020. It is far higher than the 13% increase in electricity production. The reason is that less maize is used under the EEG 2012, which delivers as a relative protein low C4 crop per ha more energy compared to grains. Indeed, as the two right bars show, per energy unit, the land demand increases by about 30%: the feedstock mix under the EEG 2012 demands about 20,000 ha of land per produced GJ instead of 13,000 ha under the EEG 2009. From the viewpoint of regional environmental externalities such as nitrogen leaching, erosion risk or biodiversity, the shift from maize production towards grain might be seen as positive as maize is generally considered (e.g. SRU, 2007 pp. 2) as a less desirable crop, especially when cropped in larger shares, compared to grains. However, in total and per produced GJ more land is demanded, so that also land use effects in Germany and the rest of the world must be considered. In the following section we discuss the impacts on the German agricultural sector and change in land use. 4.2. Land use change in Germany The massive subsidisation of biomass based biogas production leads in both counterfactual scenarios to sizeable changes in agricultural land use in 2020. Under the EEG 2009, where

0

EEG 2009 Green maize

GPS

EEG 2012 Manure

Fig. 2. Energy production by feedstock, based on energy content in the target year 2020.

25

3500 3000

20

2500 2000

15

1500

10

1000 ha / GJ

Heat in GJ existing plants

1000 ha

EEG 2009 Scenario

1000 5

500

0

0 EEG 2009 EEG 2012 Green maize Grains

EEG 2009 EEG 2012 Green maize Grains

Fig. 3. Total land and land demand per electricity unit produced in 2020.

incentives favour green maize as the dominant feedstock, about 1.5 Mio ha of green maize are additionally cropped whereas its feed use is reduced. Only 0.3 Mio ha stem from an expansion of agricultural land use, more than 0.5 Mio ha are due to reduced cereal areas. Permanent grasslands are also descreased by about 0.2 Mio ha, where extensive grasslands are reduced by about 8% compared to only 3% for more intensive ones. In most regions, the reduction do not reach 10% which are a kind of “soft” upper limit under current EU regulations. The remaining hectares stem from various crops, the largest reduction are observed for coarse grains, pulses and rape seed with reduction around 10%. Under the EEG 2012, green maize areas increase only by about 0.65 Mio ha. As the changes in non-tradables are somewhat smaller, the area reduction is generally smaller than under the EEG 2009, reaching 70–80% of the changes observed under the EEG 2009. Grass land reduction is more than halved to only about 75,000 ha. But changes in the animal sector, as more area is needed, are larger compared to the EEG 2009. 4.3. Market feedbacks and resulting total land use change in the EU and world The land demand in Germany for biomass used for biogas production reduces Germany's exports or increases its imports of agricultural goods, especially for cereals, oilseeds and animal products from ruminants which are fed with silage maize and/or grass (silage). As all agricultural markets inside the EU are highly integrated, the changes in Germany have immediate spill-over effects into EU and from there to global markets for agricultural products. The resulting changes in EU net trade lead to prices changes in EU and global markets. EU price changes can be expected to be large in beef and dairy markets which are characterised by a relatively high border protection. For the EEG 2009, where less land is demanded for biogas production

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compared to the EEG 2012, EU market prices for cereals increase by about 2% and for oilseeds by about 1% in the target year 2020. Especially the higher cereal prices also let feed prices increase, which in return drives up prices for animal products, meat prices increase by about 0.7%. The highest price increases for animal products are simulated for beef and raw milk with about 1.5%. Price increase in world markets are naturally more muted. Cereal prices, which show the highest change, only increase by about 0.3% outside the EU, as transport margins and border protection dampen price transmission in many world regions. Equally, a larger share of the adjustments as a response to the changes in Germany takes places in the EU and reduces the effect on world markets. Nevertheless, total agricultural use outside the EU increases by 0.8 Mio ha. Under the EEG 2012, in total more land is demanded for biogas production, leading to higher pressure on markets. Consequently, cereal area expanions in the EU outside of Germany under the EEG 2012 offset the 0.3 Mio ha reduction in Germany, but the expense of other crops. However, overall land use in EU is rather inelastic as additional hectares cannot claim area based subsidies under the first pillar of the CAP which are paid out to a historically fixed area base. Accordingly, agricultural land use expansion in the EEG 2012 occurs basically only outside the EU with about 1 Mio ha. Also price changes are somewhat higher compared to the EEG 2009, such as for EU cereal prices with a 3% increase. 4.4. Economic welfare analysis The higher prices for agricultural products map into higher agricultural income. For the target year 2020, agricultural gross value added in the EU increases by 2.1 Bio € (EEG 2009) respectively 2.4 Bio € (EEG 2012) whereas consumers lose about 1.3 Bio € (EEG 2009) or 1.7 Bio € (EEG 2012) purchasing power in agricultural markets. Further losses occur in industries such as dairies which demand agricultural outputs. In total, there are slight welfare losses in EU agricultural markets in economic terms by about 0.4 Bio € (EEG 2009) and 0.2 Bio € (EEG 2012). But welfare changes occur also outside the EU. Agricultural producers worldwide benefit from higher demand; profits increase by 0.5 Bio € (EEG 2009) and 0.9 Bio € (EEG 2012). Similar to the EU, consumers outside the EU lose, about 1.7 Mio € under the EEG 2009 and 2.3 Bio € under the EEG 2012. The higher losses for consumers outside the EU stem from the fact that non-EU producers deliver now more to the EU2 . Welfare changes also occur for electricity consumers in Germany. According to a regulation on a compensation mechanism (“Ausgleichsmechanismusverordung”) (BGBI, 2011, Part 4) end users of electricity pay the so-called EEG apportionment. The apportionment is determined by the difference between FITs and the market price of the produced electricity. According to the German Bundesnetzargentur, the EEG apportionment in 2011 amounts to 3.53 € cent/kWhel (Bundesnetzargentur, 2012) at average FITs in 2010 of about 16.33 ct/kWhel (BDEW, 2011, p.39). For our analysis, that average subsidy for all types of electricity production subsidied by the EEG are of limited value as the FITs 2 Welfare changes for consumers in CAPRI are based on the equivalent variation derived from the Generalized Leontief Expenditure system used in CAPRI's global market model, evaluating the effect of changed prices compared to the baseline. Welfare changes for agricultural producer in the EU are measured based on changes in agricultural Gross Value Added, i.e. the difference between market revenues and subsidies minus intermediate input costs in the scenario compared to the baseline. For all other agents (dairies, other processors, and feed industry), profit changes due to price changes compared to the baseline are calculated from the normalized quadratic profit functions underlying the net-put functions in the global market model. For further information, refer to Britz and Witzke (2011).

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between biogas and other types of production (solar, wind etc.) differ considerably (e.g. 8.85 ct/kW h for wind and 43.57 ct/kW h for photovoltaic (BDEW (2011)). However, comparing FITs between the two versions of the EEG indicates advantageousness for electricity consumers. Our results show that subsidies under the EEG 2009 scenario sum up to about 22 cent/kWhel and are higher than those paid under the EEG 2012 scenario (about 16 cent/kWhel). The main reason is a higher energy efficiency of plants constructed under the EEG 2012 scenario compared to small scale plants erected under the EEG 2009 scenario. 4.5. Feedbacks on environmental quality in the EU Due to the inelastic reaction of EU agriculture on the extensive margin, i.e. by expanding agricultural land cover, a main effect of the higher prices triggered by the feedstock demand emanating from the German EEG are increased yields. That is only feasible by increasing intermediate input use per hectare by driving up fertiliser doses or plant protection. Equally, farmers switch to more high yielding crops; for cereals, to give an example, area reductions of coarse grains with lower yields are higher than those of wheat and grain-maize so that average cereal yields increase. The combined effect is an increase in nutrient needs of crops, which results in an EU wide increase of nitrogen retained in the harvested part of the crop by 0.6% under the EEG 2009 and by about 0.9% under the EEG 2012 in 2020. Higher feed costs decrease ruminants herds such that organic nitrogen availability is reduced by 0.4% (EEG 2009) respectively 1.1% (EEG 2012). As pulses and fodder areas comprising leguminoase are reduced, also nitrogen from biological fixation drops. As the consequence of these factors, the use of mineral nitrogen for fertilisation increases by 1.7% (EEG 2009) respectively 2.5% (EEG 2012). Gas losses of nitrogen are almost stable as mineral nitrogen shows lower gaseous losses compared to manure application which is reduced. As the combined consequence of the described change, nitrogen surpluses at soil level increase by about 0.5%. 4.6. Energy efficiency and induced land use change: are there GHG savings? Major aims of the German biogas program as indicated are the reduction of GHG emissions and fossil fuel savings. Maize as a C4 crop produces relatively high amounts of energy per ha, in the range of 210 GJ. Under typical cropping condition in Germany where green maize is fertilised to a larger extent by manure, about 20 GJ of fossil energy are used per ha (diesel, energy in seed, energy in mineral fertilisers, depreciation of energy used in machinery etc., according to a Life Cycle Analysis (LCA) integrated into CAPRI by Kränzlein, 2008). These 20 GJ produce, depending on the efficiency of the biogas plant, about 70 GJ of electric energy. We hence invest about 20 GJ of fossil energy inputs to produce 70 GJ of electricity, probably saving 100 GJ or more of fossil energy at the margin if coal or natural gas would have been used to produce the electricity before. These findings might hence suggest that the program indeed meets the target of GHG savings. This argumentation along the line of a classical LCA analysis is in our views highly misleading. Similar to the discussion about GHG emissions of first generation biofuels, it neglects the fact that the hectare now used for biogas production was collecting already solar energy before in the form of other agricultural products. This implies that either emissions stemming from land use and further changes need to be taken into account, or only the net gain of additional biomass can be credited. If we indeed only substitute, e.g. a ha of wheat by a ha of green maize, the net gain (if at all) in solar energy collected in the

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biomass minus fossil energy in intermediate inputs used is most probably rather small. Further on, the LCA above assumes that input coefficients are constant, which is also not true when largescale acreage changes occur. The relatively high energy use efficiency in silage maize compared to other crops stems also from the fact that relatively little inorganic fertiliser is used as maize is typically fertilised to a large extent with manure. But at constant manure quantity, an additional ha can maximally receive the manure of the crop it replaces. That means that the 20 GJ per ha as an average under current conditions are probably a bad estimate for additional hectares of green maize. The fossil use energy LCA in CAPRI takes such changes in a counterfactual run into account. It shows indeed that the fossil energy based GJ per ha of green maize increases by more than 10% in the simulations, to about 23 GJ per ha. The substitution effects – green maize crowding out other solar energy collecting crops – plus the change in intermediate energy consumption become visible if we look at the total energy in marketable products produced by German agriculture, after deducting deliveries for biogas use. The total agricultural energy delivery for final consumption (food, fiber, and exports) net of biogas use by agriculture drops by almost 10% in the two scenarios. That number can be easily understood by the fact that from about 18 Mio ha of agricultural land in Germany, about 10% are used in our counterfactual for biogas production. Probably only the price induced reduction in demand will really lead to larger GHG savings. The agricultural output which was before stemming from the areas now devoted for feedstock production for biogas, minus reduced demand, must be produced elsewhere. There are two fundamental ways how that is achieved. Firstly, intensification occurs, i.e. more intermediate inputs are used per ha of land, and the crop mix is shifted to more high yielding crops. The related fossil energy input per ha of usable agricultural area in Germany increases by about 1.8% in both scenarios and already contributes to the fact that only 10% of final output net of biogas measured in energy is lost. Secondly, more land will be brought into agricultural production, an effect which is quite limited in the EU due to the fact that additional hectares cannot claim the Single Farm Payment of the CAP. The acreage expansion is therefore mostly found in the Non-EU. However, we are unable to attribute CO2 emissions to that expansion as we miss a global land use model in our analysis. But the existing work on induced land change from biofuel policies and the related GHG emissions might hint at the fact that CO2 emissions from these land use changes could easily offset any savings of CO2 emissions by reduced fossil energy use in Germany. That is especially true as Germany has decided to close down all nuclear power plants in the follwing years, so that the biogas based electricity production is probably at the margin mostly replacing nuclear power with zero CO2 emissions, and not, as typically analysed in LCA, fossil based energy production. We hence have to conclude that GHG emission and fossil energy savings from the legislation at global scale are probably quite small and could even be negative.

5. Summary and conclusions Germany uses subsidies, differentiated by plant size and feedstock mix, to generate rather strong incentive for biogas production from agricultural biomass. Our analysis focuses on land use and market effects in Germany, the EU and at global scale of that legislation, combining a highly detailed location model for biogas plants in Germany ReSI-M with a global partial equilibrium model CAPRI which comprises regional programming models for Europe.

ReSI-M simulates how many and which types of biogas plants are built at regional level from which the feedstock demand for biogas production at regional level for grain maize, grass silage and grains is derived. That feedstock demand is inputted into CAPRI, which simulates consequences on agricultural markets and land use. To compare the impact of biogas legislation favouring different feedstock mixes, we analyse feedstock demand from two versions of the EEG. Our results indicate that the German program is large enough to have sizeable impacts on global agricultural markets, estimating an induced land use change outside the EU of up to 1 Mio ha and price changes for agricultural outputs in the EU of up to 3% as in case of cereals. We thus have similar findings for induced land use change and related market and environmental consequences as those found for biofuel production from agricultural feedstock which require land as input. A simple LCA which compares the agricultural fossil input use for producing the feedstock with the energy generated from electricity of produced biogas is thus highly misguiding: it neglects the fact a hectare now used to produce biogas feedstock was already used before to harvest solar energy. GHG emission saving occur almost entirely when higher prices provoked by the progam dampen final demand for agricultural products. Otherwise, gains are entirely or to a large extent offset by the effects of agricultural land use expansion and intensification of agriculture to produce the outputs originally stemming from the land now used for feedstock production. We thus conclude that the German biogas program, as long as it is almost entirely based on non-waste feedstocks, is probably not a promising avenue towards a GHGsaving renewable energy production, but a rather expensive one. References Achilles, W., 2005. Faustzahlen für die Landwirtschaft. Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL) (in German). Darmstadt, 1095 pp. Armington P., 1969. A Theory of Demand for Products Differentiated by Place of Production. IMF Staff Papers 16, 159–76. Al-Riffai, P., Dimaranan, B., Laborde, D., 2010. Global Trade and Environmental Impact Study of the EU Biofuels Mandate. Final Report for the Directorate General for Trade of the European Commission, International Food Policy Research Institute. Available from: 〈http://trade.ec.europa.eu/doclib/docs/ 2010/march/tradoc_145954.pdf〉. Banse, M., Van Meijl, H., Tabeau, A. and Woltjer, G., 2008. Will EU biofuel policies affect global agricultural markets? European Review of Agricultural Economics 35 (2), 117–141. BDEW (Bundesverband der Energie-und Wasserwirtschaft e.V), 2011. Erneuerbare Energien und das EEG: Zahlen, Fakten, Grafiken. Energie-Info, Berlin. Available from: 〈http://www.bdew.de/internet.nsf/id/3564E959A01B9E66C125796B003 CFCCE/$file/BDEW%20Energie-Info_EE%20und%20das%20EEG%20%282011%29_ 23012012.pdf〉. BMU (German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), 2007. Klimaagenda 2020: Klimapolitik der Bundesregierung nach den Beschlüssen des Europäischen Rates. Klimaschutz bedeutet Umbau der Industriegesellschaft. Bundesumweltminister Sigmar Gabriel, Regierungserklärung, 26.04.2007 (in German). Deutscher Bundestag, Berlin: BMU. Available from: 〈http://www.bmu.de/reden/bundesumweltminister_sigmar_gabriel/ doc/39239.php〉. BMU (German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), 2012. Erneuerbare Energien in Zahlen. Berlin, 32 pp. BGBl (Bundesgesetzblatt), 1990. Gesetz über die Einspeisung von Strom aus erneuerbaren Energien in das öffentliche Netz (in German). 07.12.1990, 2633– 2634. BGBl (Bundesgesetzblatt), 2000. Gesetz für den Vorrang Erneuerbarer Energien (in German). Nb. 13, 31.03.2000, 305–309. BGBl (Bundesgesetzblatt) Part 1, 2004. Gesetz zur Neuregelung des Rechts der Erneuerbaren Energien im Strombereich (in German). 21.7.2004, 1918–1930. BGBl (Bundesgesetzblatt) Teil 1, 2008. Gesetz zur Neuregelung des Rechts der Erneuerbaren Energien im Strombereich und zur Änderung damit zusammenhängender Vorschriften vom 25.10.2008 (in German). 2074–2100. BGBl (Bundesgesetzblatt) Teil I, 2011. Gesetz zur Neuregelung des Rechtsrahmens für die Förderung der Stromerzeugung aus erneuerbaren Energien, 04. 08 2011 (42), p 1634ff. Britz, W., 2008. Automated model linkages: the example of CAPRI. Agrarwirtschaft 57, 8. Britz, W., Hertel, T.W., 2011. Impacts of EU biofuels directives on global markets and EU environmental quality: an integrated PE, global CGE analysis. Agriculture, Ecosystems and Environment 142 (1–2), 102–109.

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