sustainability

Article Acceptance and Potential of Renewable Energy Sources Based on Biomass in Rural Areas of

Alexander Titov 1, György Kövér 2, Katalin Tóth 2,Géza Gelencsér 3 and Bernadett Horváthné Kovács 1,*

1 Institute of Sustainable Development and Farming, Kaposvar Campus, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, Hungary; [email protected] 2 Institute of Economics, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, Hungary; [email protected] (G.K.); [email protected] (K.T.) 3 Vox Vallis Development Association (Koppany Valley Naturpark), 7285 Törökkoppány, Hungary; [email protected] * Correspondence: [email protected]

Abstract: The main focus of the paper is the investigation of the social potential of local renewable energy utilization in a rural peripheral region in Hungary. Public acceptance of biomass-based renewable energy sources can be crucial for rural communities in realization of their sustainable development strategy. The research area was Koppany Valley Natur Park 2000, a microregion of 10 settlements located in the South Transdanubian region. This microregion is characterized by poor and depressive socioeconomic and demographical conditions, despite its significant natural resources. The microregion’s complex development strategy includes the utilization of local resources of renewable energy. Local population survey (n = 310) was conducted (in May 2018) on local biomass   potential, knowledge, and attitudes of the local stakeholders in the microregion. Multinomial logistic regression model estimates the acceptance of population, explanatory variables are categorical Citation: Titov, A.; Kövér, G.; Tóth, demographical (personal) factors and specific factors (based on answers of respondents). Trust K.; Gelencsér, G.; Kovács, B.H. Acceptance and Potential of in local authorities, knowledge on biomass in general and on specific technologies, as well as the Renewable Energy Sources Based on education level of rural inhabitants are significant factors in supporting biomass plant establishment. Biomass in Rural Areas of Hungary. Further, the group and characteristics of acceptance groups that the local development strategy may Sustainability 2021, 13, 2294. https:// consider were defined. doi.org/10.3390/su13042294 Keywords: renewable energy; biomass; social acceptance; rural development; strategy Academic Editors: Luisa Sturiale, Matelda Reho and Tiziano Tempesta

Received: 11 January 2021 1. Introduction Accepted: 11 February 2021 Through national energy and climate plans, EU member states commit to meeting Published: 20 February 2021 a voluntary share of renewable energy by 2030, and, between 2020 and 2030, to follow a national trajectory [1]. Promotion of renewable energy sources (RES) and energy efficiency Publisher’s Note: MDPI stays neutral (EE) incentives are among the main strategic components for the fulfilment of the emission with regard to jurisdictional claims in reduction targets [2]. published maps and institutional affil- iations. Regional alternative energy [3] sources of renewable energy production is available locally, thus may contribute to lessening the rural dependence on the national grid [4]. Authors also concluded that public acceptance is one of the main factors influencing local utilization of renewable energy. Low awareness and lack of acceptance [5] are prohibiting bioeconomy transitions. The role of local inhabitants in cocreating knowledge, innovation, Copyright: © 2021 by the authors. and technology dedicated to the implementation of renewable energy projects is increasing. Licensee MDPI, Basel, Switzerland. Differentiation among respondent groups could potentially include people with indifferent This article is an open access article opinion to the public debate regarding renewable energy, thus can be a means to [6] change distributed under the terms and conditions of the Creative Commons people’s attitude. Sharing findings on local perceptions is a fast and efficient way to provide Attribution (CC BY) license (https:// general public feedback to policymakers regarding their decisions [7]. Interaction with creativecommons.org/licenses/by/ various stakeholders is required not only to solve local problems of social acceptance, but 4.0/). also to find new innovative solutions for the sustainable deployment of RES [8].

Sustainability 2021, 13, 2294. https://doi.org/10.3390/su13042294 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2294 2 of 18

The underlying factors of attitudes related to acceptance are studied broadly in RES context; among them knowledge [9], trust in stakeholders [10,11] or organizations [12], education [13], and information [14]. Moreover, participation in energy related community activities ensures more positive attitudes to renewable energy in comparison to where there is no participation. Nonmembers of the community energy initiatives tend to be more indifferent and uncertain but not more opposed to renewable energy technologies [15]. Considering the planning and implementation of RES projects in general, cooperation of experts, public, and other stakeholders is important [8,16–18], also is making them involved in the decision-making process [18,19]. Through a survey of inhabitants on public acceptance of biomass-based RES the general knowledge, innovative attitude, acceptance and willingness of application, as well as the estimation of benefits of RES had been ex- plored. Adequate knowledge on RES, lack of information sources knowledge on various bioenergy-related technologies in general and on specific technologies (biogas, biodiesel, combustion of biomass exceeded) were found as weaknesses [20]. Environmental protec- tion aspects proved to be the most relevant benefits concerning the use of RES. Willingness to collect biomass is essential in a sense of cooperative activities at the local community level without operation and maintenance of biogas power stations is hard to fulfil [21]. The potential for the production and use of biomass-based energy sources has already been considered [22] as the most exploited bioenergy source in Hungary; the amount of solid biomass needed is already available from forestry and agriculture. Biomass potential had been also investigated in 17 settlements of a Northern Hungarian micro-region (Hernad Valley) [23]. Rural development can lean on local biomass, although the common residue from the maintenance of orchards, vineyards, etc., is often overlooked by the energy sector [24]. Agricultural waste [25] and livestock waste [26] are also covered in recent models of rural economic developments as well as the regional potential of combination of agricultural residues and forestry biomass [27]. In this respect, the exploration of factors having a significant effect on the acceptance level gains importance [28]. The research being presented has a focus on defining charac- teristics of acceptance groups, including the convincible acceptance group. We suppose the uncertain population group is to realize the social potential of the Koppany Valley regarding biomass-based RES acceptance. The present study focuses on local biomass as local resource and public acceptance and potential of biomass in a specific rural community. Two main constituents were considered for deep research at a local level: 1. Public acceptance of renewable energy sources based on biomass in rural communities; 2. Assessment of the social potential regarding biomass in local communities.

2. Materials and Methods 2.1. Research Area The research area is the Koppany Valley located in in Hungary (Figure1 ). Koppany Valley Nature Park is a microregion consisting of 10 settlements along the Creek of Koppany. The lead organization is Vox Vallis Association co-operating with members of self-governments of these settlements. This area was chosen by taking into consideration already existing initiatives related to the green local society development run by Vox Vallis Development Association [29]. The plans to locally establish photovoltaic elements and biogas power plant stations are among them. The relevance of the current research is to assess the social potential of the proposed RES’s usage and to investigate awareness of the rural stakeholders on RES. The area is in one of the most underdeveloped Hungarian territories of serious economic, social, and infrastructural issues [30]. Despite this fact, there is significant potential regarding the green energy sector if considering the essential amount of local raw biomaterial production [31]. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 20

Sustainability 2021, 13, 2294 3 of 18 Despite this fact, there is significant potential regarding the green energy sector if consid- ering the essential amount of local raw biomaterial production [31].

Figure 1. Geographical locationlocation ofof KoppanyKoppany Valley Valley Naturpark Naturpark settlements settlements in in Hungary. Hungary. Source: Source own: own construction construction based based on on Open Street Map data. Open Street Map data.

TheThe potential of of biomass in the area is substantial,substantial, however, we hypothesized it is complicatedcomplicated to utilize itit duedue toto thethe social social barriers barriers such such as as lack lack of of knowledge knowledge and and low low level level of ofawareness awareness regarding regarding renewables renewables among among the localthe local stakeholders. stakeholders. This regionThis region is characterized is charac- terizedby poor by and poor depressive and depressive socioeconomic socioeconomic and demographical and demographical conditions conditions indicated indicated by low byincomes low incomes of the local of the population, local population, low educated low educated and low and skilled low humanskilled resources,human resources, barriers barriersof the local of the governments’ local governments’ decision decision makers ma [32kers], unemployment, [32], unemployment, high age high of population,age of pop- ulation,and intensive and intensive agricultural agricultural production production [31,33]. [33,31]. The managers The managers of the microregion of the microregion pursue pursuea complex, a complex, integrated integrated development development action. action A biogas. A biogas plant plant is planned is planned to be to built be built in the in thecenter center of the of the microregion microregion and and to utilize to utilize 500 500 ha ofha surroundingof surrounding cropland cropland with with alternative alterna- tivecrop crop production. production. This This land land size size is is expected expected to to satisfy satisfy the thelocal local complexcomplex demanddemand for extractableextractable protein protein for for chicken chicken feed feed and and the the 1 1KW KW biogas biogas plant plant by bythe the remains remains (fiber) (fiber) of theofthe multiannual multiannual crops. crops. With With this thiscommunity community owned owned investment investment (owners (owners will be will the be mu- the nicipalitiesmunicipalities and and the thelocal local development development NGO) NGO) the the aim aim isis to to serve serve an an integrated, integrated, complex complex developmentdevelopment that relies on the the families’ families’ human human resources resources and lands lands nearby, nearby, in which way thethe alternative cultivationcultivation cancan restore restore the the soil soil conditions conditions and and avoid avoid further further chemical chemical burden bur- denof the of Koppanythe Koppany Creek Creek [33]. [33].

2.2.2.2. Survey Survey AnAn on-site on-site visitor visitor questionnaire questionnaire survey targeting the local population was carried out inin May 20182018 inin 1010 settlementssettlements of of the the Koppany Koppany Valley Valley microregion microregion (n =(n 310). = 310). The The sample sample for forinterviews interviews was was selected selected on on a population a population quota quota base base (10% (10% of population,of population, with with no no regard regard to tothe the age, age, education, education, gender gender ratio), ratio), and and is considered is considered representative representative for the for population the population of the settlements. Snowball method was applied, when finding the next respondent. The answers of the settlements. Snowball method was applied, when finding the next respondent. The were registered by panelists and were recorded electronically later on. The panelists were answers were registered by panelists and were recorded electronically later on. The pan- junior researchers and PhD students and had been trained for the interviewing technique elists were junior researchers and PhD students and had been trained for the interviewing by a specialist. The questionnaire had been pretested and was divided into three general technique by a specialist. The questionnaire had been pretested and was divided into three blocks: personal information about respondents (background information); awareness about RES in general; acceptance and potential of biomass-based RES. Likert scale, multiple choice, and open questions were applied. Sustainability 2021, 13, 2294 4 of 18

2.3. Dataset and Variables The original dataset had 310 observations collected during the survey, out of it 303 observations were considered in the actual effective dataset after exclusion of the missing data. The current research covers part of the full dataset; the relevant parts of the survey questions consist of 13 categorical variables and one dependent categorical variable (for the detailed information of the categories see tables Tables A1 and A2 in AppendixA). The explanatory variables were divided into two subsets: personal factors and specific factors. Personal factors represent the individual characteristics of the respondent (gender, age, residence, years of living in the same area, education background, professional oc- cupation, and trust in local authorities). The specific subset of variables emphasizes the knowledge and behavioral (habitual) parameters of rural inhabitants. It characterizes the respondents’ deeper knowledge on specific terms (biomass, energy crops, and climate change). It provides additional lifestyle information about the rural personality: whether householders are involved or not in certain farming activities such as plant cultivation or animal keeping, that may count as biomass. The hypothesis was that the category variables of ‘personal’ and ‘specific characteris- tics’ (or combination of them) have a significant effect on the category outcome variable of ‘acceptance of biomass-based renewable energy’ at the local community level. We defined three groups of outcome variable: accepting (YES), not accepting (NO), or not sure to accept (MAYBE) the establishment of a biogas power plant in the rural area.

2.4. Statistical Method Multinomial logistic regression (MLR) was applied, because it provides an appropriate technique to predict the probability of category membership on a dependent variable based on multiple independent variables [34,35]. In order to understand factors beyond uncertainty, the probabilities of switching a respondent’s decision from MAYBE (as reference category) to YES or NO were predicted by 13 categorical variables. As the managers of the development strategy of the microregion mostly benefit from understanding the uncertain group, in the end we summarized the main characteristics of this (MAYBE) group and formulated some recommendations that rely on the group’s (personal and specific) features. Multicollinearity was tested through variance-inflation factors analysis (VIF). Diagnostics was done in R software, numerical diagnostics (For results output see AppendixC). The VIF calculation formula is:

1 = VIFi 2 , (1) 1 − Ri

2 2 where Ri is the R -value obtained by regressing the ith predictor on the remaining predictors. Stacked areas and lines with confidence bands effect plots were applied for visualiza- tion of the multinomial logit model results.

3. Results Altogether, 13 personal and specific variables model local population acceptance groups of biogas power plant installation. Following the brief introduction of the sample characteristics, the results are presented in two respective subsections. Prior test was applied (variance-inflation factor analyses (VIF)) to prove no concern to expect multi- collinearity of the independent variables. None of the VIF values of the variables in either subset exceeded the critical point 10 and were low (see Table A1). The multinomial logit model could be run properly.

3.1. Characteristics of the Sample The ratio of female and male respondents in the sample (n = 310) was 56%, 43%, respectively, and the majority (29% and 37%) of the respondents belonged to the age groups 31–45 years and 46–60 years. The third most frequent age group was above 60 years. This Sustainability 2021, 13, 2294 5 of 18

structure represents the age structure of the rural settlements visited in the course of the survey. The highest share of people in the sample live in the central settlement (20%) of the microregion, moderate ratios (10; 14; 18%) were only in case of three further settlements, most settlements (6 out of 10) were represented in the sample with an individual a share lower than 10%. Most people of the sample have lived in the area for more than 10 years (82%) out of this category, 62% has been settled for more than 20 years. One third of the sample obtained a vocational school degree and the other third a high school degree. Both higher education degree and primary education represent 16–16% of the sample size. More than half of the people asked are employed or self-employed, another 20% is pensioners, some of them still in school (5%) or at home with children (3%), while than unemployed or public employed ratio exceeds 10% in the sample. Almost all respondents (93%) reported their engagement with plant or crop production, while around half of them (57%) keep livestock too. AppendixB contains detailed information about the sample characteristics and the specific variables too. Based on the survey results 42% of the respondents were uncertain (MAYBE) about supporting a biogas plant (35% YES, 20% NO). (Comparably, a polish study determined [36] 56% of the respondents were supportive of biomass power plant construction.) According to the model results, we define the characteristics of each group.

3.2. Personal Profiles The results of the multinomial logistic regression of seven personal variables are demonstrated in Table1.

Table 1. Multinomial logistic regression model for the “personal factors”.

Coefficients RRR Personal Factors Dependent Variable: Dependent Variable: Std. Error z Value Pr (>IzI) No Yes No Yes age [<30] −0.560 −1.263 ** 0.571 0.283 ** 0.45410 −2.307 0.0211 * age [>60] 0.260 0.077 1.297 1.080 0.44174 0.286 0.7749 education [primary] −0.112 −0.988 ** 0.894 0.372 ** 0.38585 −1.274 0.2026 education [university degree] 0.286 −0.567 1.330 0.567 0.39466 −0.578 0.5631 gender [male] 0.425 −0.037 1.529 0.964 0.27922 0.618 0.5366 occupation [dependent] −0.538 0.011 0.584 1.011 0.41094 −0.535 0.5930 occupation [non-active, homestay] −0.907 −0.662 0.404 0.516 0.42773 −1.777 0.0756 residence [middle] −1.095 ** −0.125 0.334 ** 0.882 0.33068 −1.504 0.1327 residence [west] 0.090 −0.263 1.094 0.769 0.33842 −0.239 0.8112 trust.to.major [no] 2.804 *** −0.062 16.510 *** 0.940 0.51946 4.319 1.56 × 10−5 *** trust.to.major [yes] 0.518 2.904 *** 1.678 18.241 *** 0.30316 7.058 1.69 × 10−12 *** years.of.living [>10] 0.474 −0.038 1.606 0.963 0.39313 0.234 0.8147 Constant −1.471 ** −0.971 * 0.230 ** 0.379 * − − − Akaike Inf. Crit. 514.320 514.320 514.320 514.320 − − − Note: * p < 0.1; ** p < 0.05; *** p < 0.01; Log likelihood = −231.1598; Pseudo R2 = 0.2705480.

The results in Table1 presents the likelihood to choose ‘YES’ for acceptance of biogas plant (result variable) (compared to MAYBE) if the respondent’s answer on the personal question is YES or NO (compared to MAYBE). Detailed explanations for only significant results are supported by figures too: Figures2–5. Considering the significance level (MLR: p-value), parameters with high effect on the dependent variable were determined. Effect plots (Figures2–5) provide a graphical visualization of the significant components of Table1. According to the output of the applied multinomial logistic regression model, accep- tance is significantly influenced by the following personal variables: Age. The likelihood to choose acceptance category YES decreases by 0.28 times (the risk or odds is 72% lower) in comparison to category MAYBE, if respondent’s age group is [<30] (p < 0.05). Figure2 shows the age effect on probability of acceptance. Sustainability 2021, 13, x FOR PEER REVIEW 6 of 20

Sustainability 2021, 13, x FOR PEER REVIEW 6 of 20

question is YES or NO (compared to MAYBE). Detailed explanations for only significant results are supported by figures too: Figures 2–5. questionConsidering is YES or theNO significance (compared tolevel MAYBE) (MLR:. Detailedp-value), explanations parameters withfor only high significant effect on the resultsdependent are supported variable were by figures determined. too: Figures Effect 2–5. plots (Figures 2–5) provide a graphical visu- alizationConsidering of the significant the significance components level (MLR: of Table p-value), 1. parameters with high effect on the dependentAccording variable to thewere output determined. of the Effectapplied plots multinomial (Figures 2–5) logistic provide regression a graphical model, visu- ac- alization of the significant components of Table 1. ceptance is significantly influenced by the following personal variables: According to the output of the applied multinomial logistic regression model, ac- Age. The likelihood to choose acceptance category YES decreases by 0.28 times (the Sustainability 2021, 13, 2294 ceptance is significantly influenced by the following personal variables: 6 of 18 risk or odds is 72% lower) in comparison to category MAYBE, if respondent’s age group Age. The likelihood to choose acceptance category YES decreases by 0.28 times (the is [<30] (p < 0.05). Figure 2 shows the age effect on probability of acceptance. risk or odds is 72% lower) in comparison to category MAYBE, if respondent’s age group The group of respondents committed to YES acceptance group is characterized by is [<30] (p < 0.05). Figure 2 shows the age effect on probability of acceptance. age [30–60],The group while of of respondents respondentsNO acceptance committed committed group toby to YES age YES acceptance[>60]. acceptance The grouprespondents group is characterized is characterized in ‘convincible’ by by ageageacceptance [30–60], groupwhile while (MAYBE)NO NO acceptance acceptance is characterized group group by by age by age [>60].age [>60]. [<30]. The The respondents respondents in ‘convincible’ in ‘convincible’ acceptanceacceptance group (MAYBE) (MAYBE) is is characterized characterized by by age age [<30]. [<30].

.

Figure 2. Age effect plot (stacked areas). FigureFigure 2. Age effect effect plot plot (stacked (stacked areas). areas).

Education.Education. The TheThe likelihood likelihoodlikelihood to toto choose choosechoose acceptance acceptanceacceptance category categorycategory YES YESYES decreases decreasesdecreases by by 0.37by 0.370.37 times timestimes (the(the riskriskrisk or oror oddsodds odds isis is 63%63% 63% lower)lower) lower) inin in comparisoncomparis comparisonon to to category category MAYBE, MAYBE, if respondent’srespondent’s if respondent’s educationeduca- educa- grouption groupgroup is PRIMARY isis PRIMARY PRIMARY (p <( p( 0.05).p< <0.05). 0.05). Figure Figure Figure3 3shows shows3 shows thethe the education education effect effect effect on on onthe the theprobability probability probability ofof acceptance.acceptance. TheThe group group of ofof respondents respondentsrespondents committed committedcommitted to toto YES YESYES acceptance acceptanceacceptance group groupgroup is characterizedisis characterizedcharacterized by byby educationeducation categorycategory High High School, School, while while in in groupgr groupoup NONO NO byby by UniversityUniversity University Degree.Degree. Degree. TheThe The respondentsrespond- respond- inents the inin ‘convincible’ thethe ‘convincible’‘convincible’ acceptance acceptance acceptance group group group is characterized is ischaracterized characterized by by the bythe category the category category Primary. Primary. Primary.

Figure 3. Education effect plot (stacked areas). FigureFigure 3.3. EducationEducation effecteffect plotplot (stacked(stacked areas).areas).

Residence. The likelihood to choose acceptance category NO decreases by 0.33 times (the risk or odds is 67% lower) in comparison to category MAYBE, if respondent’s residence group is MIDDLE (p < 0.05). Figure4 shows the residence effect on the probability of acceptance. The group of respondents committed to YES acceptance group is characterized by residence place EAST (i.e., settlements on the eastern part of the microregion). The respon- dents in group NO live either in the western part (WEST) or the eastern settlements (EAST). The MIDDLE settlements’ inhabitants are less sure (more of them belong to MAYBE group). Sustainability 2021, 13, x FOR PEER REVIEW 7 of 20 Sustainability 2021, 13, x FOR PEER REVIEW 7 of 20

Residence. The likelihood to choose acceptance category NO decreases by 0.33 times Residence. The likelihood to choose acceptance category NO decreases by 0.33 times (the risk or odds is 67% lower) in comparison to category MAYBE, if respondent’s resi- (the risk or odds is 67% lower) in comparison to category MAYBE, if respondent’s resi- dence group is MIDDLE (p < 0.05). Figure 4 shows the residence effect on the probability dence group is MIDDLE (p < 0.05). Figure 4 shows the residence effect on the probability of acceptance. of acceptance. The group of respondents committed to YES acceptance group is characterized by The group of respondents committed to YES acceptance group is characterized by residence place EAST (i.e., settlements on the eastern part of the microregion). The re- residence place EAST (i.e., settlements on the eastern part of the microregion). The re- spondents in group NO live either in the western part (WEST) or the eastern settlements spondents in group NO live either in the western part (WEST) or the eastern settlements Sustainability 2021, 13, 2294 (EAST). The MIDDLE settlements’ inhabitants are less sure (more of them belong7 of 18to (EAST). The MIDDLE settlements’ inhabitants are less sure (more of them belong to MAYBE group). MAYBE group).

Figure 4. Residence effect plot. FigureFigure 4.4. ResidenceResidence effecteffect plot.plot. Trust. The likelihood to choose acceptance category YES increases by 18.24 times (the Trust.Trust. The The likelihood likelihood to to choose choose acceptance acceptance ca categorytegory YES YES increases increases by 18.24 by 18.24 times times (the risk or odds is 1724% higher) in comparison to category MAYBE, if respondent’s (therisk riskor odds or odds is 1724% is 1724% higher) higher) in incomparison comparison to to category category MAYBE, MAYBE, ifif respondent’srespondent’s trust.to.mayor variable takes group YES (p < 0.01). The likelihood to choose acceptance trust.to.mayortrust.to.mayor variablevariable takestakes groupgroup YESYES ((pp << 0.01).0.01). TheThe likelihoodlikelihood toto choosechoose acceptanceacceptance category NO increases by 16.51 times (the risk or odds is 1651% higher) in comparison to categorycategory NO increases by 16.51 16.51 times times (the (the risk risk or or odds odds is is 1651% 1651% higher) higher) in in comparison comparison to category MAYBE, if respondent’s trust.to.mayor variable takes group NO (p < 0.01). tocategory category MAYBE, MAYBE, if respondent’s if respondent’s trust. trust.to.mayorto.mayor variable variable takes takes group group NO ( NOp < 0.01). (p < 0.01). Figure 5 shows the trust effect on the probability of acceptance. FigureFigure5 5 shows shows the the trust trust effect effect on on the the probability probability of of acceptance. acceptance. The most obvious differences between the acceptance groups can be seen in case of TheThe mostmost obviousobvious differencesdifferences betweenbetween thethe acceptanceacceptance groupsgroups cancan bebe seenseen inin casecase ofof Trust. The respondents committed to YES acceptance group are characterized by YES cat- Trust.Trust. The respondents respondents committed committed to to YES YES acce acceptanceptance group group are are characterized characterized by YES by YES cat- egory of the variable trust.to.mayor. In the group of NO trust, the high probability is seen categoryegory of the of the variable variable trust.to.mayor. trust.to.mayor. In the In thegrou groupp of NO of NOtrust, trust, the high the high probability probability is seen is for the support of biogas (YES group of outcome variable). seenfor the for support the support of biogas of biogas (YES (YES group group of outcome of outcome variable). variable).

FigureFigure 5.5. TrustTrust inin mayor’smayor’s decision.decision. Figure 5. Trust in mayor’s decision. Based on the membership (Table1) in predicted acceptance groups, the personal characteristics of inhabitants can be defined according to Table2. According to the results, MAYBE group is younger, has up to 10 years of residence, basically primary educated, who are not sure about their trust in local authorities. The NO group is elderly, they live on the western part of the microregion, typically diploma holders, and have critical opinions on local authorities. Sustainability 2021, 13, 2294 8 of 18

Table 2. Personal profiles of the different acceptance groups.

Acceptance Group Personal Factors MAYBE YES NO (Convincible) gender FEMALE MALE FEMALE age * [30–60] [>60] [<30] residence * EAST WEST MIDDLE years.of.living [<10] [>10] [<10] education * HIGH SCHOOL UNIVERSITY DEGREE PRIMARY NONACTIVE, occupation ACTIVE DEPENDENT HOMESTAY trust.to.mayor * YES NO MAYBE * significance (p ≤ 0.1) The p-values are presenting the significance level of the effect of the categorical variables in the multinomial logistic regression model.

3.3. Specific Profiles The results of the multinomial logistic regression of six specific variables are demon- strated in Table3. The results in Table3 presents the likelihood to choose ‘YES’ for acceptance of biogas plant (result variable) (compared to MAYBE) if the respondent’s answer on the specific question is YES or NO (compared to MAYBE) Detailed explanations for only significant results are supported by figures too: Figures6–10. Considering the significance level (MLR: p-value), parameters with high effect on the dependent variable were determined. Effect plots (Figures6–10) provide a graphical visualization of the significant components of Table3.

Table 3. Multinomial logistic regression model for the “specific factors”.

Coefficients RRR Specific Factors Dependent Variable: Dependent Variable: Std. Error z Value Pr (>IzI) No Yes No Yes biomass.knowledge [yes] −0.177 0.554 * 0.838 1.741 * 0.28287 0.826 0.40883 climate.change.knowledge [yes] −0.940 * 0.009 0.391 * 1.009 0.53796 −1.205 0.22834 energy.crops.knowledge [yes] 0.130 1.222 *** 1.139 3.393 *** 0.28136 2.768 0.00563 ** own.animals [yes] 0.297 −0.125 1.346 0.882 0.25778 0.136 0.89217 own.plant [yes] 0.151 0.872 ** 1.163 2.393 ** 0.31974 1.623 0.10468 willingness.to.collect [yes] 0.517 1.079 *** 1.678 2.943 *** 0.28237 2.912 0.00359 ** Constant −0.587 −2.826 *** 0.556 0.059 *** − − − Akaike Inf. Crit. 600.270 600.270 600.270 600.270 − − − Note: * p < 0.1; ** p < 0.05; *** p < 0.01; Log likelihood = −286.1351; Pseudo R2 = 0.09706682.

According to the applied multinomial regression model, acceptance is significantly influenced by the following specific variables: Biomass.knowledge. The likelihood to choose acceptance category YES increases by 1.74 times (the risk or odds is 74% higher) in comparison to category MAYBE, if respondent’s biomass.knowledge is YES (p < 0.1). Figure6 shows the biomass.knowledge effect on the probability of acceptance. The probability of belonging to YES acceptance group is higher in case of reported knowledge of biomass. There is a slightly higher chance to belong to NO acceptance group if the respondent reported NO knowledge on biomass. The convincible group of respondents either support (YES) or deny (NO) biogas plant establishment (acceptance groups). Sustainability 2021, 13, x FOR PEER REVIEW 9 of 20

Sustainability 2021, 13, x FOR PEER REVIEW 9 of 20

Biomass.knowledge. The likelihood to choose acceptance category YES increases by 1.74 times (the risk or odds is 74% higher) in comparison to category MAYBE, if respond- ent’s Biomass.knowledge.biomass.knowledge is The YES likelihood (p < 0.1). Figureto choos 6 showse acceptance the biomass.knowledge category YES increases effect onby the1.74 probability times (the riskof acceptance. or odds is 74% higher) in comparison to category MAYBE, if respond- ent’sThe biomass.knowledge probability of belonging is YES (p to< 0.1).YES Figure acceptance 6 shows group the biomass.knowledgeis higher in case of reportedeffect on knowledgethe probability of biomass. of acceptance. There is a slightly higher chance to belong to NO acceptance group if the Therespondent probability reported of belonging NO knowledge to YES acceptanceon biomass. group The convincible is higher in group case ofof respond-reported knowledge of biomass. There is a slightly higher chance to belong to NO acceptance group Sustainability 2021, 13, 2294 ents either support (YES) or deny (NO) biogas plant establishment (acceptance9 of 18groups). if the respondent reported NO knowledge on biomass. The convincible group of respond- ents either support (YES) or deny (NO) biogas plant establishment (acceptance groups).

Figure 6. Biomass knowledge effect plot (lines with confidence bands).

FigureFigure 6.Climate.change.knowledge. 6. BiomassBiomass knowledge knowledge effecteffect plot plot (linesThe (lines withlikelihood with confidence confidence to bands).choose bands). acceptance category NO de- creasesClimate.change.knowledge. by 0.39 times (the risk or The odds likelihood is 61% tolower) choose in acceptance comparison category to category NO de- MAYBE, Climate.change.knowledge. The likelihood to choose acceptance category NO de- ifcreases respondent’s by 0.39 times climate.change.knowledge (the risk or odds is 61% lower) is in YES comparison (p < 0.1). to category Figure MAYBE, 7 shows if re- the bio- mass.knowledgespondent’screases by climate.change.knowledge 0.39 times effect (the on therisk probability or odds is YES is (p 61%of< 0.1).acceptance. lower) Figure in7 shows comparison the biomass.knowledge to category MAYBE, effectif respondent’sIn on case the probabilityof respondents climate.change.knowledge of acceptance. reporting NO knowl is YESedge (onp

Figure 7.7.Climate Climate change change knowledge knowledge effect effect plot plot (lines (lines with confidencewith confidence bands). bands).

Energy.crops.knowledge. The likelihood to choose acceptance category YES increases Figure 7. Climate change knowledge effect plot (lines with confidence bands). by 3.39Energy.crops.knowledge. times (the risk or odds is 239% The higher) likelihood in comparison to choose to category acceptance MAYBE, category if respon- YES in- creasesdent’s energy.crops.knowledge by 3.39 times (the risk is YESor odds (p < 0.01).is 239% Figure higher)8 shows in thecomparison energy.crops.knowledge to category MAYBE, effectEnergy.crops.knowledge. on the probability of acceptance. The likelihood to choose acceptance category YES in- creasesA similar by 3.39 pattern times for (the the risk knowledge or odds of is energy 239% higher) crops is seenin comparison to knowledge to ofcategory biomass. MAYBE, Yes acceptance group membership has higher probability if the respondent knows energy crops and lower if not. In the group of convincible people, NO knowledge on energy crops has a higher probability. Sustainability 2021, 13, x FOR PEER REVIEW 10 of 20

Sustainability 2021, 13, x FOR PEER REVIEW 10 of 20

if respondent’s energy.crops.knowledge is YES (p < 0.01). Figure 8 shows the en- ergy.crops.knowledge effect on the probability of acceptance. if respondent’sA similar pattern energy.crops.knowledge for the knowledge of energyis YES crops (p < is 0.01).seen to Figure knowledge 8 shows of biomass. the en- Yesergy.crops.knowledge acceptance group membership effect on the has probability higher probability of acceptance. if the respondent knows energy Sustainability 2021, 13, 2294 10 of 18 cropsA and similar lower pattern if not. Infor the the group knowledge of convincible of energy people, crops NO is seen knowledge to knowledge on energy of biomass. crops hasYes aacceptance higher probability. group membership has higher probability if the respondent knows energy crops and lower if not. In the group of convincible people, NO knowledge on energy crops has a higher probability.

Figure 8. 8.Energy Energy crops crops knowledge knowledge effect effect plot (linesplot (lines with confidence with confidence bands). bands).

Own.plant. The likelihood to choose acceptance category YES increases by 2.39 times (the Own.plant. The likelihood to choose acceptance category YES increases by 2.39 times riskFigure or odds 8. Energy is 139% crops higher) knowledge in comparison effect toplot category (lines MAYBE,with confidence if respondent’s bands). own.plant is YES(the (prisk< 0.05). or odds Figure 9is shows 139% the higher) own.plant in effectcomparison on the probability to category of acceptance. MAYBE, if respondent’s own.plantIn acceptance is YES group (p < 0.05). YES, theFigure probability 9 shows is higherthe own.plant if respondent effect has on crops the orprobability plants, of ac- Own.plant. The likelihood to choose acceptance category YES increases by 2.39 times ceptance.and opposite tendency is seen in the group of convincible people (MAYBE acceptance group). (the Willingness.to.collect.Inrisk acceptance or odds groupis 139% The YES, likelihoodhigher) the probability in to choosecomparison is acceptance higher to ifcategory category respondent YES MAYBE, increases has crops if byrespondent’s or plants, and2.94own.plant timesopposite (the is riskYEStendency or (p odds < 0.05). is is seen 194% Figure in higher) the 9 showsgroup in comparison theof convincible own.plant to category effectpeople MAYBE, on (MAYBE the if respon- probability acceptance of ac- group).dent’sceptance. willingness.to.collect is YES (p < 0.01). Figure 10 shows the willingness.to.collect effectIn on acceptance the probability group of acceptance. YES, the probability is higher if respondent has crops or plants, and Inopposite acceptance tendency group YES, is theseen probability in the group is higher of if respondent’sconvincible reported people willingness(MAYBE acceptance to collect biomass, and the opposite tendency is seen in the group of convincible people (MAYBEgroup). acceptance group).

Figure 9. Own plant effect plot (lines with confidence bands).

Willingness.to.collect. The likelihood to choose acceptance category YES increases by 2.94Figure times 9. 9.Own Own (the plant plant risk effect oreffect plotodds plot (lines is 194%(lines with confidence withhigher) confidence in bands). comparison bands). to category MAYBE, if respond- ent’s willingness.to.collect is YES (p < 0.01). Figure 10 shows the willingness.to.collect ef- fect onWillingness.to.collect. the probability of acceptance. The likelihood to choose acceptance category YES increases by 2.94 timesIn acceptance (the risk group or odds YES, is 194% the probability higher) in comparisonis higher if respondent’s to category MAYBE, reported if willing- respond- nessent’s to willingness.to.collect collect biomass, and isthe YES opposite (p < 0.01). tendency Figure is seen10 shows in the the group willingness.to.collect of convincible peo- ef- plefect (MAYBE on the probability acceptance of group). acceptance. In acceptance group YES, the probability is higher if respondent’s reported willing- ness to collect biomass, and the opposite tendency is seen in the group of convincible peo- ple (MAYBE acceptance group). SustainabilitySustainability 20212021, 13, 13, x, 2294FOR PEER REVIEW 11 of 18 11 of 20

Figure 10. 10.Willingness Willingness to collectto collect effect effect plot (linesplot (lines with confidence with confidence bands). bands).

Based on the membership (Table3) in predicted acceptance groups, the specific Based on the membership (Table 3) in predicted acceptance groups, the specific char- characteristics of inhabitants can be defined according to Table4. acteristics of inhabitants can be defined according to Table 4. Table 4. Specific profiles of the different acceptance groups. Table 4. Specific profiles of the different acceptance groups. Acceptance Group Specific Factors Acceptance Group Specific Factors YES NO MAYBE (Convincible) own.plant * YESYES NONO MAYBE NO (Convincible) own.animalown.plant * NO YES YES NO NO NO biomass.knowledgeown.animal * YESNO NOYES NO NO willingness.to.collect * YES YES NO energy.crops.knowledgebiomass.knowledge * * YES YES NO NO NO NO climate.change.knowledgewillingness.to.collect * * YES YES NO YES YES NO * significance (p ≤ 0.1) The p-values are presenting the significance level of the effect of categorical variables in the multinomialenergy.crops.knowledge logistic regression model. * YES NO NO climate.change.knowledge * YES NO YES * significanceAccording (p to≤ 0.1) the results,The p-values MAYBE are group presenting is aware the of significance climate change level threats,of the effect but they of categorical dovariables not have in the an activemultinomial knowledge logistic on, regression nor take part model. in, biomass production. The absolute supportive YES group is inhabitants with a good understanding of biomass and climate changeAccording challenges to and the think results, of themselves MAYBE of group active is participants aware of inclimate the biobased change economy. threats, but they do not have an active knowledge on, nor take part in, biomass production. The absolute 4. Discussion supportive YES group is inhabitants with a good understanding of biomass and climate changeThe challenges highest effect and was think indicated of themselves in case of the of trust.to.mayoractive participants variable. in Thethe likelihoodbiobased economy. to accept a power plant increases by 18 times if a respondent is willing to support the local mayor’s decision (Table1). Although it has not been yet analyzed (neither in current research4. Discussion nor by other authors according to our knowledge), the reason behind it can be explainedThe byhighest the loyalty effect of was rural indicated population in to case the localof the authorities trust.to.mayor on the one variable. hand and The by likelihood ato reluctance accept a power to take self-responsibilityplant increases by for 18 the times decision if a makingrespondent on the is other willing hand. to support the local mayor’sWe found decision that knowledge(Table 1). ofAlthough biomass andit has knowledge not been on yet energy analyzed cropscorrelate (neither with in current re- the supportive personality features towards biogas plant establishment. However, the search nor by other authors according to our knowledge), the reason behind it can be ex- evidence of such knowledge is moderate [20]. It was also found earlier that environmental protectionplained by aspects the loyalty proved of to rural be the population most relevant to the in local relation authorities to the use on of the RES one which hand and by correspondsa reluctance with to take our findingsself-responsibility on the significance for the of decision climate change making issues. on the other hand. AlthoughWe found an that increase knowledge in educational of biomass level may and not knowledge necessarily on increase energy acceptance crops correlate of with energythe supportive applications personality [11], regarding features the sociodemographic towards biogas variables,plant establishment. in Liebe and Dobers’s However, the ev- researchidence of [37 such], the knowledge trend was found is moderate that higher [20]. educated It was people also demonstratefound earlier higher that accep- environmental tanceprotection towards aspects renewable proved energy to thatbe the supports most ourrelevant own findings. in relation There to arethe three use differentof RES which cor- types of individuals in the population from the Collective Action point of view: “free riders”, unconditionalresponds with cooperators, our findings and conditional on the sign cooperatorsificance[38 of– 40climate]. “Free change riders” areissues. people who Although an increase in educational level may not necessarily increase acceptance of energy applications [11], regarding the sociodemographic variables, in Liebe and Dobers’s research [37], the trend was found that higher educated people demonstrate higher ac- ceptance towards renewable energy that supports our own findings. There are three dif- ferent types of individuals in the population from the Collective Action point of view: “free riders”, unconditional cooperators, and conditional cooperators [38–40]. “Free riders” are Sustainability 2021, 13, 2294 12 of 18

contribute nothing, do not accept renewable energy power plants in their vicinity, biogas power plant in a rural area in our context (acceptance group NO), but benefit from the provision of the goods, consume electricity from biogas, and receive lower emissions, for instance. Unconditional cooperation implies the cooperation of individuals making decisions regarding public goods independently of third parties’ expectations and actions (biogas power plant acceptance group YES). In counter to that, conditional cooperation means the contribution of individuals to a specific public good, in our case biogas power plant considered as a public good, is merely occurring if they are convinced that others are also doing so (convincible acceptance group MAYBE). In the context of renewable energy, conditional cooperation means that individuals only support new sites if they believe others are doing the same. Conditional cooperation leads to less acceptance of renewable energy. In the context of the target population group (convincible acceptance group MAYBE in our case) negative preconceptions and skepticism are among the most challenging factors of such education process [8]. According to the results of the regression model of Liebe and Dobers [37], climate change concern has a positive effect on the acceptance of wind power plants and solar fields but not on the acceptance of biogas plants, in contradiction to our own results, where climate.change.knowledge has a statistically significant positive influence on the acceptance. This discrepancy might be caused due to the inequality of the examined expressions of climate change concern and climate.change.knowledge, although these are similar by their terms. A similar survey methodology was applied in Poland in peripheral regions [4]. The authors also concluded public acceptance as a major factor influencing local utilization of renewable energy. In accordance with the quantitative methodology we applied, they admitted that investigation of the relationship between the specific factors and attitudes is a necessity to facilitate the educating process about RE.

5. Conclusions Awareness of, and partaking in, bioeconomy and trust in local authorities proved to be significant factors when considering a biomass-based complex development strategy and its support by local people. Although or findings are limited to specific conditions of the given rural microregion, many of them were supported by similar research from both in Hungary and Europe. We wanted to discover specific factors determining the social acceptance of the analyzed microregion’s complex development strategy especially its concerns on renewable energy utilization. We defined three ‘acceptance’ groups by having applied a multinomial regression model. It can be assumed that the inner structure of the microregion analyzed has a definite impact on how people feel about their engagement in the local area; but more importantly, it was recovered that the younger generation is the key population group that can be convinced about bioeconomy benefits. A positive result was to see they are already aware of climate change issues. Obviously limited by their conditions of living, but local people think themselves active participants of climate change fight and understand potentials of biomass as they reported their household residuals as bioresources. Local decision makers have the solid base of trust which is not without understanding the options bioeconomy offers for rural communities. Formal and nonformal education and inclusion of the young generation is certainly a way for strengthening this community and implementing their complex, biobased development strategy. Limitations of the survey was the spectrum of the socioeconomic characteristics we included; therefore, further questions to be asked related to our model are whether an extended factor list (family size and income, housing circumstances of household, monthly utility bills, etc.) would improve the model. Given the chosen—specific—area we targeted for the research, also the findings cannot be generalized (e.g., at country level) but the methodology can be applied in further similar cases. Sustainability 2021, 13, 2294 13 of 18

Further policy implications of the proposed methodology could be socioenvironmental systems (SES) modeling [41–44] to address complex and strategic issues of energy and climate change in a function of rural development at the local level. In rural development policy and planning, especially to reveal community preferences, AHP can have a crucial role [45]. The modeling process of SES contains wide discussion platforming among the stakeholders, possible scenarios and opportunities determination, which give place to priority based decision making. Thus, thorough policy and strategy formulation to accelerate the mechanism of public awareness and education to environmental challenges can be achieved.

Author Contributions: Conceptualization, A.T.; Methodology, G.K.; Supervision, G.G.; writing— original draft, B.H.K.; writing—review and editing, K.T. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by European Union and the European Social Fund EFOP-3.6.2-16- 2017-00018 “Let’s produce together with nature—Agroforestry as a new breakthrough opportunity”; Article publishing charge was covered EFOP-3.6.1-16-2016-00007 “Intelligent specialization pro- gramme at Kaposvar University” research projects to provide opportunities for the data collection and analysis support. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns. Acknowledgments: The authors are thankful to mayors of the settlements and the management and colleagues of the Vox Vallis Association. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

Table A1. Independent variables subset 1 “personal factors”.

Variable Description of the Variable Groups of Categorical Variable gender Respondent’s gender MALE; FEMALE age Respondent’s age [<30]; [30–60]; [>60] Respondent’s place of residence 10 settlements of the Koppany Valley were divided into three groups according to their geographical location. Western part including residence respondents living in , Kisbárapáti and ; Eastern part WEST; EAST; MIDDLE including respondents living in Törökkoppány, Koppányszántó and ; Middle part including respondents living in , Somogydöröcske, Kara, and Miklósi years.of.living The number of years of living at the local residence [<10]; [>10] Respondent’s level of education “Primary” (finished primary school at least), “high school” (obtained PRIMARY; HIGH SCHOOL; education high school diploma), “university degree” (obtained higher education UNIVERSITY DEGREE diploma) Respondent’s occupation type “Active” (including employed, self-employed, private producer respondents), ACTIVE; NONACTIVE, occupation “nonactive, homestay” (including retired, full-time mother HOMESTAY; DEPENDENT respondents) and “dependent” (including unemployed, student, public worker status respondents) Respondent’s willingness to support local mayor’s decision to install trust.to.mayor YES; NO; MAYBE biogas power plant at the local residence place Sustainability 2021, 13, 2294 14 of 18

Table A2. Independent variables subset 2 “specific factors”.

Variable Description of the Variable Groups of Categorical Variable Existence of respondent’s own or rented land with plant own.plant origin on it (at least one of these: orchard, vineyard, YES; NO forest, vegetable beds, grassland, cropland) Existence of respondent’s own or rented livestock own.animal animals (at least one of these: cattle, pig, poultry, sheep, YES; NO horse, rabbit) Stated knowledge of respondent about the biomass.knowledge YES; NO term “biomass” Stated respondent’s willingness to collect plant residues willingness.to.collect at the local residence place in order to feed proposed YES; NO biogas power plant Stated knowledge of respondent about the term energy.crops.knowledge YES; NO “energy crops” Stated knowledge of respondent about the term climate.change.knowledge YES; NO “climate change”

Appendix B. Categories of Explanatory Variables and Distribution of Respondents

Category Q 8. Have You Heard about Climate Change Before? % (n) 0 5% (17) 1 93% (287) n.a. 2% (6) Total 100% (310)

Category Q 18. Do You Know What Biomass Is? % (n) 0 38% (117) 1 60% (184) n.a. 2% (8) Total 100% (309)

Q 26. Would You Like to Have a Biogas Plant Installed in Your Category Microregion? % (n) n.a. 4% (11) 0—NO 19% (58) 1—YES 34% (106) 2—MAYBE 43% (133) Total 100% (308)

Category Q17. Would You Pay for Green Energy? (Agreement: 1 Least–5 Most) % (n) 1 15% (42) 2 15% (48) 3 35% (110) 4 15% (48) 5 13% (40) n.a. 7% (22) Total 100% (310) mean 2.98 median 3 mode 3 st. dev. 1.22 Sustainability 2021, 13, 2294 15 of 18

Category Q 29. Do You Know the Term Energy Crops? % (n) 0—NO 44% (136) 1—YES 54% (168) n.a. 2% (6) Total 100% (310)

Q 32. If the Mayor of Your Village Decides to Build a Biogas Plant, Category Would it Support Your Decision? % (n) 0—NO 9% (29) 1—YES 41% (127) 2—MAYBE 42% (129) n.a. 8% (25) Total 100% (310)

Category Gender % (n) 1—male 43% (134) 2—female 56% (173) n.a. 1% (3) Total 100% (310)

Category Age % (n) <18 1% (4) 19–30 11% (34) 31–45 29% (89) 46–60 37% (114) >60 21% (64) n.a. 1% (5) Total 100% (310)

Category Settlement % (n) Törökkoppány 20% (63) Fiad 8% (24) Kisbárapáti 14% (42) Bonnya 5% (16) Somogyacsa 9% (27) Somogydöröcske 7% (23) Szorosad 5% (16) Kara 1% (3) Miklósi 10% (32) Koppányszántó 18% (55) n.a. 3% (9) Total 100% (310)

Category Living Years % (n) 1–5 6% (19) 6–10 9% (28) 11–20 20% (63) >20 62% (191) n.a. 3% (9) Total 100% (310) Sustainability 2021, 13, 2294 16 of 18

Category Education % (n) None 2% (6) Primary 16% (51) Vocational 33% (101) High school 32% (99) Higher education / diploma 16% (49) n.a. 1 (4) Total 100% (310)

Category Occupation % (n) Pupil/student 5% (16) Employed 49% (153) Unemployed/Public worker 11% (34) Pensioner 21% (65) Full time mother (equivalent) 3% (8) Self-employed 9% (28) n.a. 2% (5) Total 100% (309)

Category Producing Crops/Plants % (n) Keeps Animals % (n) No 7% (23) 43% (133) Yes 93% (287) 57% (177) Total 100% (310) 100% (310)

Appendix C. Multicollinearity Test for Explanatory Variables

Table A3. Variance-inflation factors for the independent variables subset 1 “personal factors”.

Personal Factors VIF age 2.109581 education 1.290215 gender 1.040686 occupation 2.058305 residence 1.129471 trust.to.mayor 1.164983 years.of.living 1.124966

Table A4. Variance-inflation factors for the independent variables’ subset 2 “specific factors”.

Specific Factors VIF biomass.knowledge 1.300715 climate.change.knowledge 1.070524 energy.crops.knowledge 1.333261 own.animal * 1.106201 own.plant 1.088000 willingness.to.collect 1.050231 biomass.knowledge 1.300715 Note: * p < 0.1. Sustainability 2021, 13, 2294 17 of 18

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