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Innovation: The European Journal of Social Science Research

Volume 29, 2016 - Issue 3: Energy, society, and culture – transforming the order of energy
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Articles

The impacts of energy from biomass on the perceived quality of life of the rural population in Brandenburg, Germany

Sandra Venghaus Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, GermanyCorrespondences.venghaus@fz-juelich.de
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Jens Hoffmann Potsdam Institute for Climate Impact Research, Telegrafenberg A31, 14473 Potsdam, GermanyView further author information
Pages 337-372
Received 04 Nov 2015
Accepted 18 May 2016
Published online: 18 Jul 2016

Articles

The impacts of energy from biomass on the perceived quality of life of the rural population in Brandenburg, Germany

Abstract

On a global scale, the share of energy produced from biomass in total energy production has significantly increased in recent years. At the same time, the ecological and social risks and benefits related to the increasing production of bioenergy are not yet fully understood. From a social perspective, bioenergy has often been promoted as a promising development strategy for rural development. Little research, however, has explicitly addressed questions as to whether the current developments in bioenergy production actually lead to noticeable changes in rural living conditions. In this article we thus present the results of an exploratory investigation of the perceived impacts of bioenergy production on the quality of life of the rural population in the federal state of Brandenburg, Germany. For this purpose, a survey instrument was developed based on the theoretical concept of quality of life adapted specifically for the case of bioenergy. The analysis revealed that the perceptions of impacts on general living conditions in the region are considerably more pronounced than perceptions of impacts on the personal living environment. Statistically significant linkages between the impacts of bioenergy and life satisfaction could be identified primarily for nature and ecology-related effects.

1. Introduction

Significant political efforts in recent years have helped to increase the share of renewable energies in gross energy consumption in Germany from 6.2% in 2000 to the current level of 27.4% (BMWi 2015). This trend was further accelerated by the German government’s decision to phase out nuclear energy production by 2022 (Knopf et al. 2014), heralding the “Energiewende” (the energy transition) in the course of which the federal government aims to increase renewable energies’ share of electricity generation to at least 40–45% by 2025 and to 55–60% by 2035 (EEG 2014).

This decision, as shall be further discussed, has had a significant influence on the land-use patterns and crop rotations on agricultural lands in Germany. As previous research has shown, political decisions related to energy security and climate change have induced major land-use changes affecting large areas of land (e.g. Searchinger et al. 2008). Bioenergy is expected to constitute an important pillar in future energy supply especially due to its potential to provide base load energy in systems with a high share of fluctuating renewable energy (Szarka et al. 2013). Furthermore, the use of energy from biomass is technologically well established: depending on the requirements, it can serve to produce electricity, heat or fuel (e.g. Azar, Johansson, and Mattsson 2013; Klein et al. 2014). Set off by extensive governmental subsidies and the creation of investment incentives for private investors, as well as the favorable technical conditions of agricultural production, a veritable bioenergy boom has developed in Germany (e.g. Kaup and Selbmann 2013; Schlegel and Kaphengst 2007).

This bioenergy boom has been accompanied by a strongly polarized, controversial debate on a number of different levels (e.g. Kaphengst, Wunder, and Timeus 2012; Plevin et al. 2010; Searchinger et al. 2008; WBGU 2008; Wright and Reid 2011; Zichy et al. 2014). The political agenda is influenced by a variety of interests, including reduced dependency on oil and gas imports and the minimization of damaging greenhouse gas emissions (e.g. Schaper and Theuvsen 2008; WBGU 2008). In this context, bioenergy has often been praised for its potential to improve the security of future energy supplies, to help to mitigate climate change, or to foster rural development (e.g. Bain and Selfa 2013; Hazell and Pachauri 2006; Rossi and Hinrichs 2011; WBGU 2008; Zichy et al. 2014).

In Germany, especially the promise of bioenergy to become an economic amplifier for rural development has often been put forward in the debates around bioenergy. The increased use of biomass for energy generation has been viewed as an opportunity to revitalize the agricultural and forestry sectors and to create local employment opportunities in rural areas (e.g. Kaphengst, Wunder, and Timeus 2012; MLUL 2010; WBGU 2008). Many critics, however, question these positive impacts, contending that state subsidies attract large investors, thereby driving up land rent prices; this, they claim, could squeeze out small-scale agricultural operations (e.g. Brendel 2011; Habermann and Breustedt 2011; Cotula, Dyer, and Vermeulen 2008). Furthermore, it is argued that the added value generated by large biogas companies does not necessarily remain in the region, but may instead primarily benefit large agro-industrial companies (e.g. Bain 2011; WBGU 2008).

While mere statistics (e.g. the number of jobs created in the bioenergy sector) confirm some positive (macro-)economic effects (e.g. IEA Bioenergy 2013; MLUL 2010), little research has explicitly addressed the question, whether these economic trends in bioenergy production actually lead to noticeable improvements in the living conditions in rural areas.1 To analyze this question, the federal state of Brandenburg – located in the north-east of Germany surrounding the metropolitan area of Berlin – was chosen as a suitable research area. In spite of rather poor soil conditions and unfavorable climate conditions, over half of Brandenburg’s area is currently used for agriculture; in 2007, energy crops were cultivated on roughly 18% of the agricultural area (MLUL 2010). At the same time, with an average density of 87 inhabitants/km2, Brandenburg is rather sparsely populated. Between 2002 and 2011, the overall population in Brandenburg declined on average by 0.5% per year (Statistical Office for Berlin-Brandenburg 2014). According to current projections, the population in Brandenburg is expected to further decline by over 10% until 2030. While this number includes the moderate expected decline in the metropolitan areas surrounding Berlin, the population in more rural peripheries may decline by as much as 18% during this period (Statistical Office for Berlin-Brandenburg 2012). Facing these developments, the current biomass strategy of the federal state of Brandenburg considers the production and use of biomass as one important pillar for stabilizing and re-strengthening rural areas in Brandenburg (MLUL 2010). Given the debate about the (potential) social merits of bioenergy production in Brandenburg, underlying objective of the presented research was to investigate whether the increasing production of bioenergy in Brandenburg has had a noticeable influence on the perceived quality of life in Brandenburg.

For this purpose, a survey instrument was developed based on the theoretical concept of quality of life adapted specifically for the case of bioenergy. The remainder of this article is structured as follows. The paper continues with a brief description of the research background including the current state of bioenergy production in Germany and its relevance in the case study area, the federal state of Brandenburg, Germany. In the following Section 3, the research method will be outlined before we discuss the results of the empirical analysis in Section 4. Finally, the paper concludes with a discussion of the policy and research implications in Section 5 and general conclusions in Section 6.

2. Background

2.1 Energy from biomass

Because of strong political efforts, the share of energy in Germany produced from renewable energies has significantly increased in recent years. During the 10 years from 2003 to 2013, the share of renewable energies in total energy production has more than tripled (BMWi 2014). In 2013, 25% of gross electricity consumption in Germany was provided by renewable energies. Energy from biomass has played a decisive role in this development: of the energy from renewable sources, about 31% was produced from biomass (BMWi 2014).

Until recently, energy crop cultivation in Germany was dominated by oilseed canola. However, since the amendment of the 1994 Renewable Energies Law (“Erneuerbare-Energien-Gesetz”), the cultivation of maize and cereals for use in biogas plants has risen sharply. According to the Federal Research Institute for Agriculture (“Bundesforschungsanstalt für Landwirtschaft”), maize is used in 90% of newly built biogas plants (Kaltschmitt, Hartmann, and Hofbauer 2009).

At the beginning of the debate, proponents in Germany emphasized the assertion that, in the context of finite and increasingly costly conventional energy sources, energy from biomass is a forward-thinking method of energy conversion (Zschache, von Cramon-Taubadel, and Theuvsen 2009). From an economic perspective, energy from biomass was said to create an additional source of income for the German agricultural sector (WBGU 2008). From a political point of view, the use of biomass for energy generation leads to a diversification of energy sources and thereby reduces dependency on fossil fuels. Proponents claim that this also means greater independence from oil and gas-producing states – most of which have authoritarian leaderships – and thereby represents a broader contribution to the promotion of democracy and freedom (Zichy et al. 2014).

Due to increased state funding and the associated growth of bioenergy use in Germany, the topic has garnered ever more public attention and, increasingly, doubts and criticism have emerged (Zichy et al. 2014; Zschache, von Cramon-Taubadel, and Theuvsen 2009). The criticism relates primarily to the questions of competition with food production and nature conservation, as well as the impacts on climate and environment, which are difficult to quantify.

In Germany, the debate is dominated by ecological and environmental problems as well as social and cultural issues (Zichy et al. 2014). The fear is that, if the current level of subsidization is maintained or further intensified, the increased cultivation of energy crops will lead to competition between different forms of land use: for example, food and animal feed production, organic farming, nature conservation areas and the cultivation of energy crops. Because the cultivation of crops for energetic use has already changed and will likely continue to change the structure of land use, primarily negative consequences for biodiversity from monoculture cultivation of, for example, maize or canola, as well as soil and water protection, are to be expected (e.g. Dornburg et al. 2010; Meyer-Marquart, Feldwisch, and Lendvaczky 2006).

One of the arguments often used in support of bioenergy is that the technology drives economic development. The cultivation and use of biomass are said to boost employment rates and economic growth in rural areas (e.g. Bain and Selfa 2013; Hazell and Pachauri 2006; Rossi and Hinrichs 2011; Selfa et al., 2011; WBGU 2008; Zichy et al. 2014). The already weakened agricultural sector, it is claimed, is bolstered by new income-generation opportunities and jobs, and the area can also expect to benefit as a business location on the basis that regional value creation increases its attractiveness for other branches of industry. Energy from biomass is seen as a lucrative line of business which opens up new possibilities for farmers (Zschache, von Cramon-Taubadel, and Theuvsen 2009). Some view these arguments with skepticism. State subsidies have made investment in land to produce energy from biomass a financially attractive endeavor, one which attracts many large investors (Brendel 2011), while the numerous small-scale farmers are excluded or find themselves confronted with negative effects (e.g. Cotula, Dyer, and Vermeulen 2008; Zschache, von Cramon-Taubadel, and Theuvsen 2009). Critics claim that this drives up land rent prices (e.g. Brendel 2011; Habermann and Breustedt 2011; Kaphengst, Wunder, and Timeus 2012), with the result that especially traditional farmers face significant pressure. When land rent agreements expire, many cannot afford the increased rents and lose their contracts to biogas plant operators who, given guaranteed payments for bioenergy, have access to greater monetary funds (Brendel 2011).

In order to analyze the extent to which the potential effects of increased energy crop cultivation, as discussed, have direct (and perceptible) social impacts on life in rural areas, the concept of quality of life was used to develop an analytical framework.

2.2 The case study region – Brandenburg, Germany

The federal state of Brandenburg is located in the north-east of Germany surrounding the metropolitan area of Berlin and encompasses an area of about 29,500 km2 (Figure 1).

Figure 1. The federal state of Brandenburg in Germany.

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Brandenburg is characterized by a moderate continental climate with milder winters in the west and drier summers in the east. Precipitation declines from west to east and does not exceed an average accumulated precipitation of 604 mm/a, characterizing Brandenburg as one of the driest regions of Germany (Hüttl et al. 2011). The ecosystems of Brandenburg are protected through 453 nature reserves, 116 landscape conservation areas, national parks and biosphere reserves (LUA 2009). Although one-third of the area is already part of conservation areas, the biodiversity of Brandenburg is under severe threat due to intensive land use with rising application of pesticides and fertilizers, water pollution and eutrophication, land-use changes and drainage (LUA 2009).

The total agricultural area in Brandenburg encompasses 1319 thousand ha (MIL 2012). In spite of poor soil fertility (34% of the agricultural land in Brandenburg is classified as poor soil quality) and increasing drought throughout the region (Hagedorn 2011), around half of the state’s area is used for agriculture. The main crop in Brandenburg is rye (grown on roughly half of the cropland) (MIL 2012). The map in Table 1 shows the distribution of different land-use types in Brandenburg.

Table 1. Land use in the federal state of Brandenburg

In addition to the unfavorable climate and soil conditions, the intensive cultivation of energy crops in Brandenburg is partly anchored in Germany history. With the German unification in 1990 agriculture in the former German Democratic Republic faced a situation of rapid comprehensive restructuring. The extensification programme of the EU was especially attractive to farms in Brandenburg characterized by their low-yield soils, so that in 1991 and 1992 organic farming spread quickly (Nölting & Boeckmann, 2005). Partly due to these historical preconditions and Brandenburg’s specific agro-ecological characteristics, the state’s successful energy political programme has made the state of Brandenburg a leading region for dynamic and ecological developments within Europe.

With respect to its demographic characteristics, Brandenburg – as discussed in the introduction – is very scarcely populated and is expected to continue to face significant depopulation with a decline of up to 18% by 2030 especially in its rural areas (Statistical Office for Berlin-Brandenburg 2012, 2014). Almost 40% of Brandenburg’s total population lives in the regions directly adjacent to Berlin (EU 2007). Furthermore, with a per capita GDP of 23.751 €, Brandenburg’s per capita economic performance corresponds to only 71.2% of the German average (Statista 2014). Within its rural development plan, the state of Brandenburg has thus defined the following three strategic policy objectives (EU 2007):

  • Strengthening the creation of value added and improving competitiveness of agricultural production to secure jobs and developing rural areas into a knowledge-based economic area.

  • Securing and improving the natural potential; support for the development of a strategy to reduce the climate change risk; securing agricultural production in all parts of the region in order to preserve the specific cultural landscapes.

  • Support for the creation of employment outside agriculture; stabilization of population development by improving quality of life in rural areas.

According to the state’s “Biomass Strategy” (MLUL 2010), the production and energetic use of biomass already contribute to the sustainable stabilization of rural areas by securing and creating jobs. Bioenergy is thus considered an important pillar in the states' rural development plan. Addressing especially the third of the above objectives, the focus of this research was thus set on investigating, whether the current developments in bioenergy production have actually contributed to a perceived improvement in quality of life in the rural areas of Brandenburg.

3. Method

3.1 Quality of life

The term “quality of life” originated in economic welfare research. Economist Arthur Cecil Pigou used the term at the beginning of the twentieth century in his book, The Economics of Welfare. He considered quality of life to be a dependent variable of social action and a non-economic measure of welfare (Pigou 1932). The measurement of social progress was limited to bolstering and improving individuals’ standards of living through material prosperity. Over the following decades, it was believed that societal problems could only be solved by industrially driven economic growth. At the beginning of the seventies, however, this perspective was increasingly called into question, and the social and ecological costs of growth were increasingly scrutinized. With The Limits to Growth, the 1972 study by the Club of Rome, a first serious attempt was made to highlight the constraints on growth. The findings of happiness research further strengthened public skepticism regarding the role of economic growth in classical welfare theory (e.g. Anderson, Mikulic, and Sandor 2010; Birnbacher 1998; Stiglitz, Sen, and Fitoussi 2009; Veenhoven 2012). The quality of life construct was increasingly associated with social indicator research (e.g. Diener and Suh 1997; Hagerty et al. 2001).

Revealed by the diverse range of applications, “quality of life” is a vague concept and difficult to define (Galloway et al. 2005). Previous definitions include:

The WHO defines Quality of Life as individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns. It is a broad ranging concept affected in a complex way by the person’s physical health, psychological state, level of independence, social relationships, personal beliefs and their relationship to salient features of their environment. (WHO 1997)

Quality of life is a broader concept than economic production and living standards. It includes the full range of factors that influences what we value in living, reaching beyond its material side. (Stiglitz, Sen, and Fitoussi 2009)

In a social science context, quality of life is predominantly understood as a multidimensional construct consisting of various subjective and objective components (e.g. Felce and Perry 1995; Liu 1976; Michalos 2014; Noll 2002; Rapley 2003; Veenhoven 2012). Given this encompassing character, the concept seems well suited for analyzing the social effects of bioenergy production, which may impact people’s lives in a number of very different ways (Zichy et al. 2014). For decades, the quality of life concept has been used to capture societal conditions (Diener and Suh 1997). Due to its subjective frame of reference, quality of life has the characteristics of a barometer of public opinion (e.g. Binstock and George 2006). The cognitive evaluation of living conditions elucidates a person’s individual positioning regarding their living situation. This subjectivity opens up the possibility of capturing people’s personal perceptions, opinions and attitudes on a particular topic (Binstock and George 2006).

In order to operationalize the subjective dimension of quality of life, this article uses the measure of life satisfaction and, in particular, domain-specific life satisfaction, because it allows important dimensions of life to be evaluated separately (e.g. Pavot and Diener 2008). Weidekamp-Maicher (2008) divides life satisfaction into “global” and “domain-specific” satisfaction. Global life satisfaction is usually measured by asking how satisfied people are with their lives overall, for example, on a scale from 0 to 10; domain-specific life satisfaction reflects the individual evaluation of selected dimensions of life (Weidekamp-Maicher 2008). It is concerned with those dimensions which relate to particularly important areas of life. In order to measure domain-specific life satisfaction, different areas of life are divided into dimensions, which are then assigned sub-indicators (Fahrenberg et al. 2000).

3.2 Development of the survey instrument based on the quality of life concept

In order to identify the dimensions relevant for the bioenergy-specific survey instrument, different articles and studies on the topic of quality of life were considered. The focus was on studies and articles which proposed specific quality of life dimensions (e.g. Empacher and Wehling 1999; Hagerty et al. 2001; Stiglitz, Sen, and Fitoussi 2009; WHO 1997). From the literature review, those domain-specific quality of life dimensions most frequently discussed include: (a) health, (b) material well-being, (c) social ties and relationships, (d) work and profession, (e) housing, (f) security and (g) environmental conditions as shall be further discussed below.

It is important to note that the aim of this study is to investigate the perceived – rather than objective – impacts of bioenergy production on the quality of life in rural Brandenburg. Because a person’s general perception of his/her overall level of quality of life will likely also impact their perceived influence from bioenergy production, this aspect plays an important role in the analysis. Therefore, even though some of the introduced dimensions do not appear directly related to the production of bioenergy, they may nonetheless influence the subjectively perceived effects of bioenergy production and have thus been included in the survey design. In order to account for these effects, the survey instrument consists of two perspectives. In a first section, the respondents were asked to state in how far they feel the production of bioenergy has had an influence on a total of eleven aspects of quality of life (and whether these effects are predominantly positive or negative). To account for the fact that people naturally differ in their predispositions and personal preferences and may thus assess the impact of bioenergy on quality of life very differently, the subsequent survey section addressed the respondents’ personal assessment of their own level of quality of life in the different dimensions. In the following sections both the 11 aspects of quality of life that may be influenced by bioenergy production as well as the different, generally relevant domain-specific dimensions of quality of life shall be introduced.

From a socio-ethical perspective Zichy et al. (2014, 57) have identified three aspects of relevance for the rural population in the discourses around energy from biomass:

  1. A regional interest exists in the preservation of our natural habitats as the fundament of our livelihoods. This implies to a nature-ethical perspective on the topic.

  2. A regional interest exists in the economic well-being within the region. The cultivation of biomass for energy purposes generally conforms to this objective, as it provides jobs in the agricultural production and the processing industry as well as in associated industries. Positive effects occur especially. if the dominant share of added value remains in the region.

  3. A regional interest exists in avoiding any form of reduction in quality of life, for example, through noise, smell, architecture perceived as inappropriate, or damage to the culturally established or familiar landscape. This interest may be impacted by the production of bioenergy negatively (e.g. non-domestic or invariant plant species, potentially unpleasant smell, e.g. through manure or digestates) as well as positively (e.g. by the aesthetics of fields, a principal maintenance of the cultural landscape).

In order to translate these considerations into the survey instrument to investigate the perceived impacts of bioenergy on the quality of life in the case study region, the following 11 aspects (Table 2) were derived on the basis of Zichy et al. (2014) and the literature on domain-specific quality of life as will be discussed in the subsequent sections.

Table 2. Perceivable indicators.

The indicators “appearance of the landscape”, “biodiversity” and “air quality” originate from the “environmental conditions” dimension. The “personal job security” and “employment market” indicators are drawn from the “security” dimension. The indicators “recreational opportunities” and “attractiveness of the region” emerge from the argument that energy from biomass generates value creation, thereby increasing the attractiveness of the region – including with regard to the recreational opportunities on offer. The indicator “economic development in the region” is intended to test whether people in the region are actually integrated into bioenergy’s value creation chain, or whether the population tends more toward the argument that it is mainly large, external investors who profit from bioenergy, and that the value creation does not reach people in the region. The indicators “personal quality of life” and “quality of life in the region” are intended to capture a more general sense of the topic. To identify whether the respective effects are perceived positively or negatively, for each effect a second question inquires an estimation of the level of (un-)satisfaction with the perceived effects (see also Appendix 4).

3.2.1. Domain-specific dimensions of quality of life

With respect to domain-specific quality of life, the different dimensions considered in the survey design shall briefly be introduced.

3.2.1.1. Health

In many cases, health is seen as the most important quality of life dimension, because bad health has a negative impact on all other dimensions. In this article, health is considered from the individual, subjective perspective. The subjective perspective emphasizes the perception of one’s own body as well as the subjective welfare of the individual. The evaluation of these variables is orientated around individual theories of illness which use cognitively influenced evaluations to elucidate one’s personal state of health (Erhart, Wille, and Ravens-Sieberer 2009). Drawing on the established state of health questionnaire (SF-36) (Turner-Bowker et al. 2008; Ware, Snow, and Kosinski 1993), this article operationalizes state of health by means of a general question regarding the subjective state of health (Appendix: v50).

3.2.1.2. Material well-being

An analysis of various studies concerned with quality of life dimensions found that, in almost all cases, one sub-section is concerned with capturing the material side of life (Cummins 1996). Proponents of bioenergy highlight its capacity to act as an economic driver at a regional level and the attendant improvement in living conditions which results from the inflow of money (Zschache, von Cramon-Taubadel, and Theuvsen 2009). Weidekamp-Maicher (2008) describes the relevant characteristics of material well-being as being, in essence, income, standard of living, wealth and consumption. Drawing on Weidekamp-Maicher’s model, four indicators were identified for the dimension of “material well-being”:

  • income satisfaction;

  • personal property;

  • standard of living and

  • opportunities available to one’s family as a result of one’s financial situation (Appendix: v51–v54).

3.2.1.3. Social ties and relationships

People with many social contacts in general evaluate their lives more positively (e.g. Maderthaner 1995; Stiglitz, Sen, and Fitoussi 2009). In order to operationalize the dimension “social ties and relationships”, this article captures satisfaction using the following indicators:

  • overall contact with other people;

  • engagement in society;

  • social activities (e.g. clubs, church etc.) and

  • circle of friends and acquaintances (Appendix: v55–v58).

3.2.1.4. Work and profession

People spend a considerable part of their lives working. With regard to the predicted impacts of energy from biomass on regional employment, it is argued that the technology and state funding revitalize the agricultural sector by opening up new income-generating opportunities and that, at the same time, new jobs are also created in other sectors (Zichy et al., 2014; Zschache, von Cramon-Taubadel, and Theuvsen 2009). In order to measure the impacts of energy from biomass on the regional population’s quality of life, it therefore seems necessary to include the work and profession dimension in the indicator system. With the help of the “questionnaire on life satisfaction” (Fahrenberg et al. 2000), four indicators of the “work and profession” dimension were identified:

  • satisfaction with one’s position at work;

  • personal success in one’s profession;

  • opportunities for promotion at one’s place of work and

  • variety offered by one’s profession (Appendix 1: v59–v62)..

3.2.1.5. Housing

The Institute for Social Research and Analysis (SORA) has developed a model in which overall satisfaction with housing is subdivided into “specific satisfaction with housing” and “satisfaction with living environment” (2005). Given the assumption that energy from biomass invigorates the labor market and thereby generates value creation in the region, the “housing” dimension is interesting for two reasons: firstly, an improvement or worsening in one’s personal, material well-being could alter one’s housing situation and secondly, potential economic growth could have an impact on the space available for housing in the bioenergy regions (SORA 2005). Drawing on SORA’s model, this article identifies the following indicators for the “housing” dimension:

  • satisfaction with the size of one’s home;

  • the condition of one’s home;

  • one’s housing expenses (e.g. rent) and

  • the location of one’s home (Appendix 1: v63–v66).

3.2.1.6. Security

In their indicator system, Hagerty et al. (2001) select “personal safety” as an important dimension in capturing quality of life. The Canadian Council on Social Development has developed its own “Personal Insecurity Index” (CCSD 2003). Given that Germany is, with regard to the physical and health dimension, a relatively secure country, this article concentrates on the economic dimension of security. Economic security is an important indicator in capturing the impacts of energy from biomass on the regional population’s quality of life. The predicted invigoration of the labor market could have considerable impacts on the “security” area of life. An improvement in, and reinforcing of, employment opportunities could improve an individual’s assessment of their personal security. However, energy from biomass could, equally, be a factor in increasing economic insecurity. The squeezing out of farmers by large investors and the much-discussed land rent increases could threaten livelihoods. Given the subjective focus of this article, the “perception index” of the Personal Security Index seems well-suited to identifying the indicators for subjective economic security:

  • satisfaction with expected (financial) old-age provision;

  • economic livelihood;

  • future earning opportunities;

  • professional future;

  • regional opportunities for further training and

  • educational standards of children in the region (Appendix 1: v67–v72).

The indicators “regional opportunities for further training” and “educational standards of children” are intended to bring the future into consideration, because this plays an important role in the security dimension (Stiglitz, Sen, and Fitoussi 2009). Given the assumption that energy from biomass generates value creation in the region, thereby boosting economic growth (Zichy et al. 2014), this could lead to an improvement in the region’s educational provision.

3.2.1.7. Environmental conditions

This dimension underlies the discussion around the environmental impacts of energy from biomass. The cultivation of energy crops is said to lead to monocultures and to encourage the use of genetic engineering. It is claimed that monocultures have a detrimental effect on regional biodiversity (Zichy et al. 2014). Many also criticize the way in which the increased cultivation of energy crops (e.g. maize) changes the landscape (Brendel 2011). In addition, there is a debate about “indirect land use change” (e.g. Searchinger et al. 2008) and the increase in CO2 emissions caused by importing energy crops from overseas (Zichy et al. 2014). Negative impacts on anthropogenic climate change are, therefore, also an expected consequence of the generation of energy from biomass (Witzke 2007).

In Germany, the dangers of, for example, weather-related disasters, epidemics or access to drinking water pose less serious threats. For this reason, the indicators for the “environmental conditions” dimension are orientated around nature’s value as a recreational resource, as well as its aesthetic value:

  • satisfaction with recreational opportunities in the area;

  • appearance of the landscape in the area;

  • air quality in the area and

  • degree of noise pollution in the area (Appendix 1: v76–v79).

Furthermore, personal preferences play a decisive role in the evaluation of the subjective quality of life (Birnbacher 1998; Weidekamp-Maicher 2008). Capturing the lifestyle variables “recreational activities” and “environmental awareness” is intended to create a link between participants’ preferences and the perceived impacts of bioenergy. Figure 2 provides an overview of the survey concept.

Figure 2. Overview of the survey concept.

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4. Results

4.1 Empirical analysis

The survey was carried out during the months of September and October 2011 using the software eQuestionnaire™ v.2009. The survey’s target group was the population of the state of Brandenburg. In order to test the questionnaire, a pre-test was carried out with a total of 10 participants. The randomly chosen subjects faced the survey under real test circumstances. Following the survey, the respondents were asked to comment on the structure of the survey, possible challenges related to comprehension, as well as its length. Survey completion, on average, took about 10 minutes and no problems with the survey design were identified. The pretests were not included in the data analysis.

In order to generate the necessary returns, the link to the survey was distributed via press release of the Potsdam Institute for Climate Impact Research (PIK) to regional newspapers as well as further Public Relations contacts. Additionally, the link was published in various regional forums2 in Brandenburg and was sent to different regional organizations3 with the kind request to further distribute the link to the questionnaire. In order to reach also people who do not use the internet, face-to-face surveys were conducted randomly using printed versions of the questionnaire in the city center of the town of Schwedt/Oder. Schwedt/Oder, in the Uckermark region, is located in an area of extensive bioenergy production, where the population might notice more direct impacts. In order to prevent effects from misinterpretations of the term “energy from biomass”, the introductory page of the survey was also used to delineate the topic-relevant terminology, according to which “energy from biomass” includes both the cultivation of crops for energetic use (e.g. maize, wheat, canola etc.) as well as the production of electricity in biogas facilities and biofuels (e.g. bioethanol, biodiesel etc.) as automotive fuels.4

In total, 20 completed questionnaires from Schwedt/Oder were included in the data analysis together with 90 completed online surveys, representing the total sample of 110 respondents. Target group of the survey was the population of Brandenburg. The total population of Brandenburg in 2014 was roughly 2.5 m, 49.2% of which were male, 50.8% female. Because, however, the majority of surveys was filled out online, it must be expected that a bias exists toward respondents with an Internet affinity. Accordingly, as compared to the target population, the sample is characterized by an overrepresentation of the age groups 20–60 years (Table 3). Figure 3 provides an overview of selected sample characteristics.

Figure 3. Selected sample characteristics.

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Table 3. Age structures of the sample and the target population.

In order to also consider to what degree the respondents were likely exposed to direct effects from the production of bioenergy, they were further asked to state the distance between their home town and the nearest biogas production facility.5 In total, 27% of the sample responded with “less than 10 km” and 25% estimated the distance to be roughly 10–50 km, so that about 50% of the sample lives within 50 km from a biogas production facility. This is of relevance because biogas production facilities generally draw their inputs from farmers within a radius of 50–80 km.

4.1.1 The perceived impacts of bioenergy on quality of life

Based on the introduced survey design, the perceived impacts of bioenergy on quality of life in rural Brandenburg has been interrogated on the basis of 11 items (Table 2). In a first step, a reliability analysis was thus carried out for the eleven items considered. The corresponding Cronbach’s α of 0.853 suggests a high internal consistency and reliability of the scale. The Cronbach’s α could not be further improved by a reduction in items.

In order to reduce the variable set and allow for differentiated interpretations where necessary, an exploratory factor analysis was conducted. With a Kaiser–Meyer–Olkin value of 0.845, the sampling adequacy can be considered as very good.6 The analysis resulted in a two-factor solution with the factors labeled as “personal living environment” and “living conditions in the region”.

The first factor was named “personal living environment” because the items particularly relate to the participants' personal living conditions and personal opportunities in the region (Table 4). Items v36, v38, v42 and v44, in particular, cannot be evaluated without a personal assessment. The Cronbach’s α value of these person-specific impacts is 0.863 and can, therefore, be considered high. The value could not be improved by a reduction in items.

Table 4. Factor ‘personal living environment’.

The table shows that the impacts of bioenergy on the more general domains were perceived more strongly than those on the rather specific domains recreational opportunities or job security as the mean values of items v36 and v40 show.

The second extracted factor was labeled as “living conditions in the region” (Table 5). Although with a Cronbach’s α of 0.651,7 the internal consistency of the scale is slightly below the generally acceptable level of 0.7, from a rational, interpretive perspective the differentiation between those items that directly relate to the personal living environment and those that refer to more general living conditions in the region seems reasonable. This argument can be further supported by the interesting fact that there is a noticeable difference between the two factors’ mean values. This bipartition of impact factors also correlates to the general assumptions of socio-economic impact assessment in the context of bioenergy production that supposes the distinction between direct impacts (i.e. the “direct consequences of a proposed project’s location, construction or operation on the socio-economic environment” (e.g. increased employment opportunities or increased income levels)) and indirect impacts (i.e. the “secondary consequences of direct impacts” (e.g. increased business opportunities or altered consumption patterns)) (e.g. Vis, Dörnbrack, and Haye 2014).

Table 5. Factor ‘living conditions in the region’.

In general, the impacts on the personal living environment seem to be less strongly perceived (M = 2.32, SD = 0.94) than the impacts on the more general living conditions in the region (M = 3.10, SD = 0.77). While the survey data does not directly provide an explanation for this difference, a potential hypothesis is that this might be an indicator of the strongly polarized media discourse, in which the impacts of energy from biomass are contentiously discussed at a number of different levels and may thus distort the perception of direct impacts on the individual perception (see e.g. Wright and Reid 2011).

This hypothesis becomes more plausible when analyzing the differences between those respondents who do or do not feel well informed about the topic of “bioenergy”: effects from the production of bioenergy are more strongly perceived the better informed the respondents feel.8 The analysis revealed that respondents who feel generally well informed tend to perceive considerably stronger effects on aspects within the personal living environment (M = 2.76, SD = 1.06) than those who do not feel well-informed (M = 1.94, SD = 0.83). This difference is statistically significant (t(66) = −3.342, p < .01). With respect to the general living conditions in the region, similar differences were found: respondents, who feel well-informed, perceive stronger effects (M = 3.30, SD = 0.73) than those who do not feel well-informed (M = 2.93, SD = 0.74). This difference is also statistically significant (t(66) = −2.021, p < .05).

In total, only 53.1% of the total variance can be explained by the two factors. The low value of the total variance can partly be explained by the fact that, after consideration of the box plot diagrams, a number of “outliers” can be identified which relatively strongly increase the spread. Because the values in question relate to purely personal perceptions, participants with extreme opinions can increase the variance.

4.1.2 Findings on satisfaction with impacts

The impacts of bioenergy on biodiversity received the most negative evaluation (M = 2.96, SD = 1.37).9 When asked how satisfied with the impacts of bioenergy on the biodiversity of their region, 66% of participants answered that they were “very dissatisfied” to “mostly dissatisfied”. Only 11.6% of participants were “mostly satisfied” to “satisfied” with the impacts. Nineteen percent of participants answered the question with “neither satisfied nor dissatisfied”. A similar attitude was evident in subjects’ satisfaction regarding changes in the appearance of the region’s landscape10 (M = 3.71, SD = 1.49). 42.6% judged the impacts to be mostly negative. 28.8% responded that they were satisfied, but 28.7% responded with “neither satisfied nor dissatisfied”. In the context of the set of questions, these two items primarily address the ecological impacts of bioenergy. In the public discourse on biomass in Germany, these impacts are the subject of particularly heated discussion. It is therefore to be expected that a majority of people would deem there to have been changes in landscape and biodiversity, despite the fact that many do not directly experience these impacts. Especially when evaluating the impacts of energy crop cultivation on biodiversity, detailed knowledge of regional biodiversity is necessary. Given the assumption that an ordinary citizen would not possess such specialized knowledge, the expectation is that a majority of the sample has been polarized by the media discourse.

Regarding satisfaction with the impacts on recreational opportunities (M = 3.92, SD = 1.34), what is striking is that almost half of participants appear to be undecided (45.8% answered the question with “neither satisfied nor dissatisfied”). Furthermore, the limited number of respondents to the question (48 out of 110) suggests a high level of uncertainty about a possible link between bioenergy and a region’s recreational opportunities. 29.8% of participants were dissatisfied with impacts on recreational opportunities; 27.2% were satisfied.

Impacts on the regional labor market11 (M = 4.33, SD = 1.15) tended to be viewed positively (40% responded that they were “mostly satisfied” to “very satisfied”); however, in this case, too, a large proportion of participants was undecided (45.6%). Just 14.4% were dissatisfied with the impacts. There were similar tendencies regarding economic development in the region (M = 4.44, SD = 1.11). Of the 97 participants that responded to the question, 47.5% were satisfied, 35.1% were undecided regarding economic development, while 17.5% had a negative attitude. Impacts on air quality (M = 4.50, SD = 1.375) also received relatively positive evaluations. While 29.5% responded with “neither satisfied nor dissatisfied”, 50% of participants expressed a degree of satisfaction regarding the impacts of bioenergy on the air quality of their region.

Regarding satisfaction with impacts on job security (M = 4.42, SD = 1.16) and material well-being (M = 4.00, SD = 1.32), the low respondent numbers are striking (job security = 48, material well-being = 54). One explanation for this is that less than half of the participants actually perceived impacts on the two areas. Over half (57.3%) felt that bioenergy has no impact on their personal security. Of the remaining 42.7%, 41.7% evaluated the impacts of bioenergy on their job security positively. 43.8% were undecided, while 14% had a negative attitude. Regarding material well-being, 49.1% felt that bioenergy has no impact on this area. In total, 31.5% were satisfied with the impacts, while 29.7% were dissatisfied. 38.9% were undecided. The low number of respondents could be due to the way in which the question was posed, since its directness did not allow for generalized assessments or standpoints to be represented. This observation suggests that questions regarding people’s satisfaction with impacts on job security and material well-being are evaluated more impartially than, for example, those regarding ecological impacts.

Questions regarding satisfaction with the impacts of bioenergy on personal quality of life (M = 4.07, SD = 1.26) and the overall quality of life of the region (M = 4.05, SD = 1.18) tended to be answered in similar ways. In total, 31% of respondents were satisfied with the impacts on their own quality of life.12 45.1% were undecided and 23.9% were dissatisfied. Participants evaluated their satisfaction with overall quality of life slightly more positively.13 While 38.4% were undecided, 34.9% were satisfied. 26.7% were dissatisfied with the impacts. The slightly more positive evaluation of overall quality of life could be linked to the fact that more generalized questions tended to garner more positive responses than more specific questions.

Finally, participants were asked about their satisfaction with impacts on the attractiveness of the region14 (M = 4.04, SD = 1.29). A large proportion of participants (43.3%) was undecided about their level of satisfaction. 25.6% were dissatisfied with impacts. A total of 31.1% were satisfied with impacts.

In conclusion (Table 6), it can be observed that questions regarding the level of satisfaction with ecological impacts (impacts on the appearance of the landscape and on biodiversity) received the most negative responses. Questions regarding bioenergy’s role as an economic driver (impacts on the labor market and on economic development) received the most positive responses.

Table 6. Summary of the different levels of satisfaction.

A general observation is that a large number of respondents is undecided regarding their level of satisfaction with the impacts of bioenergy. Along with the possibility that participants simply do not perceive any impacts, this uncertainty could be due to factors such as inadequate background knowledge or polarization by the media.

4.1.3 Links between bioenergy and quality of life

In the course of this study, it was demonstrated that a majority of respondents state they perceive an impact of bioenergy on certain domains of their quality of life. In order to analyze their level of satisfaction with these impacts, the frequency distribution of individual items was examined. On the Likert scale, a frequently even distribution implied a wide range of levels of satisfaction with the impacts of bioenergy on particular areas of life. In addition, participants’ life satisfaction was captured. Here, a strikingly positive evaluation of life satisfaction dimensions was evident.

In order to allow assertions to be made regarding the link between energy from biomass and the life satisfaction of the population of Brandenburg, the quality of life dimensions were systematically examined for links to the impacts of bioenergy.

First, a comparison was made between different groups within the constructs. For this purpose, several t-tests were carried out.15 It emerged that a positive or negative level of satisfaction with the impacts of bioenergy often resulted in a contrasting assessment of the environmental conditions dimension.

Participants who were less satisfied with the impacts of bioenergy on air quality (M = 4.91, SD = 0.93) also stated they were less satisfied with the environmental conditions in which they live than participants who stated they were more satisfied with the impact of bioenergy on air quality (M = 5.79, SD = 0.69). This difference is statistically significant (t(76) = −4.31, p < .001). Furthermore, a difference in impacts on personal quality of life and the environmental conditions dimension can be identified. Participants who stated they were less satisfied with the impacts of bioenergy on their personal quality of life stated a more negative opinion of the environmental conditions in which they live (M = 4.59, SD = 1.06) than participants who stated a positive view of the impacts of bioenergy on their personal quality of life (M = 5.55, SD = 0.67). This difference is statistically significant (t(69) = −4.435, p < .001). A similar relationship can be observed in the case of satisfaction with impacts on quality of life in the region. Those participants who were more satisfied with the impacts claimed a considerably more positive perception of the environmental conditions in which they live (M = 5.59, SD = 0.69) than those subjects who stated they were dissatisfied with the impacts on quality of life in the region (M = 4.89, SD = 1.05). This difference, too, is statistically significant (t(84) = −3.594, p < .01). A further difference regarding the evaluation of the environmental conditions dimension was identified in connection with the impacts of bioenergy on the attractiveness of the region. Those participants who were more dissatisfied with the impacts evaluated their environmental conditions more negatively (M = 5.09, SD = 0.97) than those who were satisfied with the impacts (M = 5.55, SD = 0.76). This difference is statistically significant (t(88) = −2.330, p < 0.05). There is also a difference in evaluations of economic development in the region. Participants who evaluated the impacts of bioenergy on economic development positively were more satisfied with the environmental conditions in which they live (M = 5.55, SD = 0.75) than those respondents who evaluated the impacts negatively (M = 4.99, SD = 0.82). This value is statistically significant (t(95) = −2.764, p < .01).

Overall, after consideration of the environmental conditions dimension, it can be noted that the differences identified are of a similar nature. Those participants who stated they were dissatisfied with an impact also evaluated their level of satisfaction with their environmental conditions more negatively.

In addition to the tests carried out on the differences between the various groups, the data were also examined for linkages. This revealed a moderate correlation between satisfaction with air quality and the environmental conditions dimension (r(66) = 0.443, p < .01). The more satisfied participants were with the environmental conditions in which they live, the more satisfied they also were with the air quality in their region. One explanation for such a linkage could be that people who are largely satisfied with the environmental conditions in their area tend to have a less critical view of mechanisms which could potentially harm the environment. In this case, it is evident that people’s dissatisfaction with the environmental conditions of the region in which they live is directly linked to the air quality of their region.

A linkage was also identified between the environmental conditions dimension and the impacts of bioenergy on material well-being. The moderate, significant correlation (r(62) = 0.432, p < .01) suggests that participants are more satisfied with their environmental conditions if they are also more satisfied with the impacts of bioenergy on their material well-being. Here, too, the effect can be observed that people who are more satisfied with the environmental conditions of their region evaluate the impacts of bioenergy more positively. Those who adopt a more critical stance on the environmental conditions of their region also perceive the impacts of bioenergy on their material well-being in more negative terms. One explanation for this could be that people who are more sensitized to environmental issues have an altogether more critical view of bioenergy.

In order to further investigate the connections between bioenergy and quality of life, subjects’ preferences, in the form of the variables environmental awareness16 and recreational activities, were examined for links with bioenergy. The examination of recreational activities did not produce any significant results.

The examination of environmental awareness found relevant differences in the evaluation of bioenergy’s ecological impacts. Participants who stated a high level of environmental awareness tended to state a higher level of dissatisfaction with the impacts of bioenergy on the appearance of the landscape (M = 3.34, SD = 1.67) than those participants whose environmental awareness was less pronounced (M = 4.04, SD = 1.22). This difference is statistically significant (t(99) = 2.406, p < .05). Furthermore, participants who had a pronounced sense of environmental awareness were more dissatisfied with the impact of bioenergy on biodiversity (M = 2.52, SD = 1.43) than participants whose environmental awareness was less pronounced (M = 3.34, SD = 1.19). This difference is statistically significant (t(92) = 3.015, p < .01). The examination of environmental awareness clearly shows that participants with a high level of environmental awareness were less satisfied with the ecological impacts of bioenergy than participants with a lower level of awareness.

When considering environmental awareness, it is important to bear in mind that people with a high level of awareness place particular value on things such as “appearance of the landscape” and “biodiversity”. As a result, naturally these participants evaluate the impacts of biodiversity more negatively, if biodiversity is evaluated primarily based on the very visible increase in monoculture production of canola or maize.

5. Discussion

5.1 Policy implications

In order to discuss the study’s practical implications in more specific detail, those impacts of bioenergy relevant to the perceived quality of life will be discussed both with regard to their impact on the personal living environment and with respect to their more general impact on living conditions in the region.

5.1.1 Perceived influence on the personal living environment

Asking for an estimate of bioenergy’s perceived impact on personal quality of life was intended to facilitate an overall assessment of the specific impacts identified and, where necessary, to take into account personal assumptions which were not documented but which influenced participants’ evaluations. A majority of 53.6% of participants evaluated the perceived impact of bioenergy on their personal quality of life either as limited or non-existent. This tendency was confirmed by the statistical differences discussed above. Concrete differences in people’s evaluation of quality of life were evident only with regard to the environmental conditions dimension. It can therefore be assumed that the impacts of bioenergy are primarily perceived by those participants who have a higher level of environmental awareness. This observation was also supported by a comparison of means using random samples. Here, it emerged that those participants who tended to be dissatisfied with bioenergy’s impacts on quality of life gave a more negative evaluation of the environmental conditions in which they live.

In the context of an overall consideration of personal quality of life and its relation to the impacts of bioenergy, it is evident that a majority of residents does not perceive any concrete impacts. The group which does perceive impacts and is dissatisfied with them perceives them in such a way that it results in a more negative evaluation of the environmental conditions dimension of quality of life.17 On the basis of this observation, political and economic actors would be well advised to establish a dialogue with citizens and initiatives that demonstrate an ecological motivation or whose goals are the protection or conservation of the landscape and the safeguarding of biodiversity.

5.1.2 Quality of life in the region

Participants’ perception of the impacts of bioenergy on quality of life in the region was similar to their perception of the impacts on personal quality of life, but was perceived slightly stronger. Furthermore, more participants responded they were satisfied with the impacts. One explanation for this could be the observation that more generalized questions about the impacts of bioenergy result in stronger responses than those with a clear personal connection. Given that the questions were formulated in similar ways, both variables can be interpreted analogously. For this reason, no detailed exploration of the results is presented here.

In summary, having considered the various impacts of bioenergy with regard to the quality of life dimensions identified, it can be stated that the population does perceive impacts on specific domains of quality of life. It was also shown that, in some cases, the impacts have an influence on people’s perceived quality of life. Regarding the specific impacts of bioenergy, a worsening in the environmental conditions dimension was observed. In the case of the other dimensions, no statistically significant correlations were identified.

It can be concluded that a section of the population does notice social impacts from bioenergy, namely in the form of reduced satisfaction with environmental conditions. Further social impacts could not be statistically proven in this study.

5.2 Research implications

In the course of this article, a number of topic areas were identified in which further research is required. As discussed above, subjective evaluation is not sufficient to create a comprehensive depiction of quality of life. This article captured the subjective quality of life dimension using the concept of life satisfaction; however, in order to produce a comprehensive picture of bioenergy’s impacts on quality of life, the objective quality of life dimension should also be included in an evaluation and be set into relation to the perceived impacts. This research could draw on existing primary and secondary data on quality of life in Brandenburg (SOEP, Federal Statistical Office). Moreover, in order to ascertain the strength of potential impacts, the various quality of life dimensions should be weighted according to their significance for those surveyed. To this end, a cluster analysis based on the survey’s demographic and specific lifestyle variables (e.g. age, sex, occupation, educational background etc.) may provide for a reasonable segmentation of the population in order to generate a more comprehensive and demerged understanding of those aspects which influence the assessment of effects from the production of bioenergy on the perceived quality of life.

6. Conclusions

Objective of this article was to further the debate on the social impacts of bioenergy. In order to capture potential social impacts on the rural population, a survey instrument was developed based on the concept of quality of life adapted for the case of bioenergy.

In order to test the concept’s internal consistency and validity, a reliability analysis and a factor analysis were carried out and detailed in the empirical section of this article. The concept is structured around two factors: “impacts on the personal living environment” and “impacts on the living conditions in the region”. The survey revealed that the perceptions of the latter, more general impacts on living conditions in the region are considerably more pronounced than perceptions of impacts on the personal living environment. One potential reason that was, however, not tested within the study, may be that the population is polarized by the current media discourse and subconsciously adopts or forms an opinion on those impacts which receive particular media attention. This would mean that direct impacts on the personal sphere of life are perceived less strongly than impacts on those topics which are the subjects of particularly contentious media debate – for example, changes in the appearance of the landscape or damage to biodiversity. This argument is further supported by the fact that impacts on the appearance of the landscape and on biodiversity received the most negative evaluations. This is despite the fact that it is questionable whether a negative impact, for example, on biodiversity, has a concrete impact on quality of life or life satisfaction. In its entirety, biodiversity is not something which is easily observable, and thus it tends to be more apparent to those who are well informed. As one very specific facet of biodiversity, however, the growing dominance of energy crops (i.e. in Germany especially maize and oilseed canola) on farmlands is very visible. Further research is thus necessary to determine, whether or to what degree the strongly perceived, negative impact on biodiversity is, in fact, a result of the perceived “monocultural” landscape, or whether these numbers also indicate that many respondents were, to an extent, echoing the media discourse.

Statistically significant linkages between the impacts of bioenergy and life satisfaction could be identified for the “environmental conditions” dimension. The more dissatisfied participants were with particular impacts of bioenergy (e.g. the region’s attractiveness or air quality), the more dissatisfied they were with the environmental conditions in which they live. Although there may be different reasons for this correlation, this observation may suggest that it is largely people with a stronger environmental consciousness who evaluate bioenergy’s impacts in negative terms. Here, too, one could ask whether these people are echoing the media discourse, or whether they actually notice direct impacts on their life satisfaction.

Overall, it is striking that statistically significant correlations could be found only between the “environmental conditions” dimension and the impacts of bioenergy. Based on this observation, we can conclude that the population does not view bioenergy as a potential threat to their life satisfaction – nor, however, as a potential boon. One possible explanation for this is that the impacts are partly indirect in nature and, as a result, are not perceived by the population. In order to form a comprehensive picture of bioenergy’s impacts on the population’s quality of life, it would therefore be necessary to take objective factors into consideration.

Viewed on a subjective level, the social impacts of energy from biomass can be considered to be limited. People do perceive impacts; however, they tend to have relatively little influence on their life satisfaction. In order to make more comprehensive assertions about the link between social development and bioenergy, the overall economic impact would have to be taken into consideration alongside the objective factors.

Additional information

Funding

This work was supported by the German Federal Ministry for Education and Research (BMBF) under [grant number 01UU0901A].

Notes

1. Exceptions include the edited book of Rutz and Janssen (2014), as well as a series of publications of a special section on the Socioeconomic Dimensions of US Bioenergy as published in Biomass and Bioenergy, Vol. 35, No. 4, April 2011. With a focus on global effects, the project Global-Bio-Pact (http://www.globalbiopact.eu/) provides valuable input.

2. Forums included, for example, the forum of the Schwedt/Oder community, online available at: http://410592.forumromanum.com/member/forum/forum.php?&USER=user_410592&onsearch=1.

3. Organizations included, for example, the NABU Brandenburg, Landfrauenverbund Brandenburg, ADFC Brandenburg etc.

4. Different research approaches often require a very specific understanding and thus definition of the terms used. Because, however, the objective of this study is to capture the perceived rather than the actual effects, the terminology in this study was deliberately chosen to encompass mainly broader keywords such as “biomass”, “bioenergy” or “biogas”.

5. There are currently almost 9000 biogas production facilities in Germany (www.statista.com). Because biofuels (i.e. in Germany primarily bioethanol and biodiesel) are generally produced in few, large-scale production facilities, it was specifically asked for biogas production facilities in this question.

6. Bartlett’s test of sphericity exhibited a p-value of .000, thereby further confirming this approach.

7. A reduction in items could not further improve the Cronbach’s α.

8. It can be assumed that those respondents who feel well-informed about the topic are also more familiar with the media discourse.

9. The question was answered by 94 of 110 participants in the study. The other 16 participants stated that they did not perceive any impacts of bioenergy on the biodiversity of their region. They were therefore not asked about their satisfaction with the impacts of bioenergy on the biodiversity of their region.

10. One hundred and one of 110 participants responded to the question.

11. Ninety of 110 participants responded to the question.

12. Seventy-one of 110 participants responded to the question.

13. Eighty-six of 110 participants responded to the question.

14. Ninety of 110 participants responded to the question.

15. It is frequently argued that nonparametric tests are more appropriate for smaller data sets. The choice of the appropriate test method, however, does not primarily depend on sample size, but should instead be based on the specific characteristics of the data set. Accordingly, nonparametric tests are especially well suited in the case of non-normal distributions or for analyzing ordinal data (e.g. Altman et al. 2000). Because, however, the data of this study is normally distributed, t-tests were chosen as the appropriate test method despite of the comparatively small, but sufficiently large sample size (e.g. Fritz, Morris, and Richler 2012; de Winter 2013).

16. Because the items in the scale of environmental awareness were adopted from other, already validated studies on the topic, and due to the limited scope of this article, no reliability analysis or factor analysis was carried out. The items identified for environmental awareness were added together to form a Likert scale.

17. It should be noted here that there is a statistical difference between the groups; however, the more negative evaluation of the quality of life dimension is not necessarily related to the impacts of bioenergy. Other factors may also play a role here.

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Appendix 1. Structure of the survey instrument

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Appendix 2. Demographic sample characteristics

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Appendix 3. Levels of satisfaction

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Appendix 4. The survey instrumenta

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