1. Introduction
In order to promote the German energy transition (Energiewende), the Germany Renewable Energy Sources Act (EEG) went into effect as one pillar of the climate protection policies in 2000 [
1]. This policy has the primary purpose of encouraging the generation of different renewable energy types [
2]. One segment of the EEG policy focuses on the promotion of bioenergy production. Bioenergy has been widely considered as a significant contributor to global renewable energy production [
3]. It has a competitive advantage as its production does not strongly depend on fluctuating resources such as wind and solar [
4]. The core measure of EEG to promote biogas production is a nationwide unified remuneration scheme that provides the plant operators a guaranteed price for the generated electricity for 20 years [
2,
5,
6]. This financial support has efficiently motivated the farmers to adopt the biogas plants Germany-wide in the last two decades. Between 2000 and 2017, the number of biogas combined heat and power plants increased from 850 to 9331 in Germany, with the cumulative installed power capacity rising from 50 to 4800 MW [
7]. According to the statistics of 2017, around 95% of all the operating biogas plants in Germany were running on manure and energy crops. The proportions of animal excrement and energy crops were around 50% and 49% of the total substrate input, respectively [
8]. The cultivation area of silage maize for biogas production increased substantially from less than 200,000 to around 900,000 ha during the period from 2007 to 2018 [
9].
Researchers have agreed that EEG is an effective renewable energy policy, especially in promoting German bioenergy production [
10,
11,
12]. The nationwide high biogas production adoption rate (BPAR) made Germany the leading country in biogas production development (BPD) with a biogas production of 329 PJ and a share of 50% of total biogas production in the European Union (EU) in 2015 [
13,
14,
15]. However, the opinions on the German state-level BPD were different. For instance, DBFZ [
16] reported that Bavaria, Lower Saxony, and Baden-Württemberg were the leading states in biogas production. 3N-Kompetenzzentrum [
17] argued that Lower Saxony occupied the top position in bioenergy production. Diekmann et al. [
18] conducted various assessments to evaluate the BPD in each German state and reported mixed results. Agentur Für Erneuerbare Energy [
19] praised Thuringia as the most successful state for producing green electricity using bioenergy. Daniel-Gromke et al. [
20] also showed that Bavaria, Lower Saxony, and Baden-Württemberg together provided more than half of the number of biogas plants in Germany. In contrast, Vergara and Lakes [
4] argued that EEG strongly promoted biogas production in Brandenburg, where the number of biogas plants has increased substantially.
The studies mentioned above mainly deployed the number of biogas plants and the total plant output as gauges for the state-level BPD [
17]. However, these measures might not be appropriate in such cross-state comparisons. For instance, more biogas plants can be built in the states with a greater administrative area, e.g., Lower Saxony and Bavaria. Moreover, other exogenous effects that also have influences on the BPD were not controlled for. For example, a higher total plant installed capacity can be expected in the state where the resources for biogas production are rich. Taking land potentials as an example, the comparison among the federal states showed that by far the most considerable land potentials for renewable feedstock cultivation are located in Lower Saxony and Bavaria, where both the total number of plants and the total output are the highest [
21]. Therefore, to understand the German state-level BPD under the energy transition, an unbiased indicator of development and the control of exogenous effects are needed in the comparison.
The main goal of this study was to identify the necessities of regional-based studies for German BPD analysis by answering the following research questions: (1) Is there state-level imbalanced BPD under the energy transition in Germany? (2) Are the environmental impacts of BPD distinguished between different levels of BPD? To study these two research questions, we adopted both empirical and spatial analyses. As the proxy of the state BPD, we used the BPAR in our study. By employing the multivariate linear regression with a dummy variable model (MLRDV), we accounted for the exogenous effects in the comparison among states. These effects were feedstock richness, production cost, and financial availability. On the basis of the empirical results, we identified the highest- and the lowest-BPAR states for further spatial analysis. The spatial analysis procedure was designed following the difference in difference (DiD) analysis approach [
22]. By comparing the agricultural structural changes, biogas production densities, and the landscape changes of the selected states, we could clearly understand BPD’s impact on the environment, while accounting for other effects. If there are state-level imbalanced BPD and if a subsequent difference in the severity of environmental problems caused by BPD is detected, there would be a necessity to have more regional-based studies to study the BPD and the corresponding environmental problems in Germany.
Figure 1 provides an overview of the research framework for this study.
4. Discussion
After controlling for the exogenous environmental and economic effects, the empirical results indicated that the BPARs varied enormously among the studied German federal states. Since the EEG promotion program was unified at the national level, the variations in state-level BPAR could be due to differences in state-level promotion programs, such as Energie und Klimaschutz in Saxony, Energie in Saxony-Anhalt, and Bioenergiewettbewerb in Baden-Württemberg or farmers’ personality traits and risk cognitions [
68]. In this section, we focus on discussing the farmers’ personality traits and investment risk cognitions and their biogas production adoption behaviors.
For many farmers in Germany, operating biogas plants was an alternative investment to diversify their income sources [
5,
43,
54]. Under the same investment conditions and returns provided by EEG, it was clear that farmers from different states anticipated this investment differently. As reported in previous studies, there existed a correlation of attitudes toward behavior [
69,
70]. Therefore, the variations in attitudes toward bioenergy production investment led to farmers’ heterogeneous behaviors. Since, in the regression model, the exogenous effects that objectively influence the biogas production adoption were controlled for, the variations in behaviors resulted from the differences in farmers’ endogenous factors that influenced the investment decision subjectively. Studied systematically in behavior finance, these factors were the investors’ personality traits and risk cognitions, such as time preference, risk preference, and perception [
69,
71,
72,
73].
As found by Liu [
74], more risk-averse farmers needed significantly more time to adopt a new form of agricultural biotechnology. Moreover, in Germany, a large number of biogas plants were operated in private hands [
15]. Local farmers needed credit to facilitate the construction of biogas plants [
75]. As discussed in Brown et al. [
76], more risk-averse households were less tolerant of fluctuations in their financial circumstances and, therefore, were less prone to take debt. Daly et al. [
77] also argued a positive linear relationship between the probability of applying loans and the risk attitude. Therefore, despite the availability of low-interest biogas plant construction loans, risk-averse farmers might still resist holding debt to build biogas plants [
78]. Furthermore, as demonstrated by Wang et al. [
73], the responses to the “wait-or-not” question (
$3400 this month or
$3800 next month) were highly heterogeneous among a large segment of the population in the world. Even inside Germany, there was a difference in time preference between the residents of former East and West Germany [
79]. These findings indicated that people’s activities varied in terms of their orientation toward the present or toward the future. Due to the loan repayments, the farmers’ annual income might be even lower than before when operating biogas plant in the first few years. After paying back all the debts, much higher profits could be obtained by the operators. Thus, risk-neutral but less patient farmers might refuse to adopt biogas plants. However, to have a more conclusive argument, a further regional study is needed.
To raise the BPAR, the EEG’s remuneration is to be increased to attract the farmers who are comparably reluctant to build biogas plants. However, since the EEG subsidy scheme is nationwide unified, the increase in remuneration might lead to overreaction in states where farmers are willing to operate biogas plants for even lower subsidy. In the current study, we already observed significant state-level differences in adopting biogas production. A further increase in the subsidy to promote biogas adoption in states such as Bavaria and Baden-Württemberg might lead to overreactions in, for instance, Saxony-Anhalt and Mecklenburg-Vorpommern.
Compared to Bavaria, Saxony-Anhalt with a much higher BPAR was more vulnerable to biogas production-related agricultural and environmental problems. For instance, we observed stronger agricultural production structure change reflecting in maizification in Saxony-Anhalt than in Bavaria. Since we could not distinguish the purposes of the cultivated silage maize between the feed for livestock and the substrate for biogas production, the expansion of the total silage maize cultivated area could also be induced by the development of livestock farming.
Figure 6 presents the livestock unit change (German: Großvieheinheit) on hand in Germany between 1999 and 2016. Generally, Germany showed a decline in livestock on hand during this period, with approximately 88% of counties in Germany having a negative growth rate. Compared to Bavaria, where only 3% of counties had an increase in livestock unit on hand, 43% of counties from Saxony-Anhalt showed expansion of livestock farming from 1999 to 2016. The decrease rates in livestock unit on hand of the counties in Saxony-Anhalt were normally no less than −25%, whereas, in Bavaria, these rates were down to −50%. In summary, both states experienced a decline in livestock farming; while, in Bavaria, the livestock units on hand declined by more than 18%, Saxony-Anhalt had a relatively flat downward trend with the decrease rate being less than 8% during the period 1999 to 2016. As shown in
Figure 7, the nationwide cultivation area of silage maize used for livestock farming also decreased from 2007 to 2015, while the cultivation area of silage maize for biogas production strongly increased in these 9 years [
80]. Therefore, we could draw a preliminary conclusion that the observed substantial maize expansion in Saxony-Anhalt was mainly due to the rapid development of biogas production.
The increased land use for maize production crowded out the local food and cultural crop cultivation [
46,
47,
62,
63]. Moreover, the environmental impacts of biogas plants were attributed mainly to energy crop production from the life-cycle perspective. The results of a regional life cycle assessment (RELCA) model showed that the feedstock cultivation contributed about 52–67% of the total GHG emissions for biogas production in Central Germany [
81,
82]. Compared to a fossil-fuel-based system for electricity and heat supply, it appeared that specific eutrophication and acidification potentials for biogas from maize were significantly higher [
15].
The biogas production density analysis indicated that the biogas production was more concentrated in urban areas than in other land-use types in Saxony-Anhalt. According to Daniel-Gromke et al. [
20], about 92.60% of the total input for biogas production in Germany was energy crops and animal excrement. In Saxony-Anhalt, only 15 biogas plants were biowaste digestion plants until 2016 [
83]. Therefore, only a small proportion of plants located in urban areas ran on household and industrial wastes. The operation of other urban-located plants relied on energy crops and manure, which needed to be transported from arable land. We further observed that most of the biogas plants in Bavaria were small-scale, while the plants’ installed capacity in Saxony-Anhalt was generally large. This might be because about 49% of Saxony-Anhalt farms were larger than 100 ha, while only 4% of farms in Bavaria reached this scale. Around 50% of the farms in Bavaria were less than 20 ha [
84]. The high demand for transported feedstock in Saxony-Anhalt raised the cautions of the potential iLUC risks and GHG emissions. Additionally, agricultural biogas plants in the urban area might also influence the city and town dwelling life quality. As argued by Paterson et al. [
85], many residents resist the biogas production under the motto “not in my backyard” because biogas plants are smelly and plant operation brings a risk of explosion.
The land change analysis results suggested high fragmentation of arable land and pastures in both states from 2000 to 2018. One of the driving factors was the intensified animal farming and energy crop cultivation during the EEG period. This was consistent with Csikos et al. [
44], who detected changes in landscape patterns, reduced crop diversity, and the homogenization of arable land and pastures, after introducing biogas plants. However, compared to Bavaria, Saxony-Anhalt with more rapid biogas production development showed lower landscape heterogeneity and higher vulnerability to fragmentation. Regarding the forest, Campbell and Doswald [
86] and Hartmann [
87] reported a negative relationship between biogas production development and species biodiversity due to loss of habitat. In the current study, although there was no severe fragmentation detected in both states, the forest in Saxony-Anhalt showed a relatively strong increase in the number of patches and a decrease in the mean patch size. This observation also indicated a potential threat of biogas production to species habitat and regional biodiversity.
5. Conclusions and Outlook
This study contributed to the current literature of biogas production development under Germany’s energy transition by comparing the German state-level biogas production development during the studied period from 2000 to 2015, after accounting for the exogenous effects. We identified that there were uneven developments of biogas production among the federal states in Germany. Moreover, unlike most other studies that claimed Bavaria and Lower Saxony were the leading states in biogas production development, we found that the per capita biogas plant installed capacity in the former East Germany states was significantly higher. Apart from other reasons such as scales of farms, development of livestock farming, and state-level support, this could be due to the diversities of farmers’ personality traits and risk cognitions, which led to different attitudes toward the biogas production investment. Furthermore, in the spatial analysis, we identified that the biogas production-related environmental problems in the states with higher per capita biogas plant installed capacity were more severe. For instance, stronger maize expansion was observed in the region with higher BPAR. Additionally, we observed that more biogas plants in the state with higher BPAR were located in nonarable areas, which could result in iLUC and higher GHG emissions during feedstock transportation. Furthermore, we also found that fragmentation of arable land and pastures accompanied the biogas development. Higher-BPAR regions were more vulnerable to habitat and regional biodiversity losses. Therefore, to increase the BPAR in the states where the farmers responded to the EEG weakly, an increase in the nationwide unified subsidy might lead to an overreaction of those strong response states. The overreactions could lead to severe agricultural and environmental problems identified in this research.
The presented study also had limitations due to data availability. Firstly, we could not obtain the data about the feedstock types of each biogas plant. Moreover, as mentioned in the discussion, in the regional crop statistic record, the cultivated silage maize’s usage was not differentiated between biogas production feedstock and livestock fodder. We surveyed the published literature and found that the Integrated Administration and Control System (IACS) could be a very crucial data source for similar research. This information system could provide spatially and temporally precise information on agricultural land use, and it classifies the total silage maize area into different groups according to their utilization [
4].
The current study results emphasized the necessity of future regional-based studies to support more sustainable bioenergy management under the German energy transition. For instance, to sustainably develop the national level biogas production and avoid overreaction of some states, future studies should focus on the states where farmers are less willing to adopt biogas production. Therefore, a more regional-based behavior finance study supported by local survey data could help to understand the farmers’ concerns in adopting biogas plants and could provide the solution to increasing the BPAR of these states or regions. Furthermore, to study the impacts caused by biogas production development, more regional-based studies are required to cope with the regional heterogeneities in the future. Some well-developed approaches can be found in the literature. For instance, to evaluate the environmental burdens associated with the bioenergy production value chain, regional-based LCA is more applicable. One of the regional-based LCA models is the RELCA model [
82], which can capture site-specific characteristics and enable a reliable and accurate environmental impact assessment. In terms of the social impact of bioenergy production, the regional specific contextualized social life cycle assessment model (RESPONSA) [
88,
89] could be applied.