Social Acceptance for the Sustainability of the Agri-Biomethane Supply Chain: A PLS-SEM Analysis

Abstract

The transition from fossil fuels to renewable energy sources (RES) offers numerous benefits that can enhance the well-being of local communities. However, it is imperative to ensure that the public perceives RES projects as fully sustainable, a goal that requires a comprehensive evaluation of their social acceptance. Among the various RES options, the development of the biomethane supply chain has gained particular importance in Europe, especially considering the Russia-Ukraine conflict and the 2022 “REPowerEU” plan. In this context, the present study conducts a preliminary analysis of the social acceptance of agri-biomethane, focusing primarily on the key variable of “trust” in public administrators and energy investors to ensure its comprehensive sustainability. To this end, a survey was administered to a sample of 290 stakeholders residing in the Apulia region (Southern Italy). The data were analysed using PLS-SEM analysis to ensure robust, evidence-based findings. The results highlight the critical importance of building trust within local communities to foster social acceptance of agri-biomethane production. This can be achieved by strengthening perceived benefits, enhancing public knowledge, and promoting social participation. These insights may support policymakers in making informed decisions about community well-being and the energy transition, particularly in overcoming potential social barriers to the development of this supply chain.

1. Introduction

The environment, and its protection, is the main motivation that drives citizens to support and to oppose renewable energy sources (RES). The lack of information about RES projects, along with resulting changes in the territories where they are realized, and the inadequate engagement of the local community involved, often affect their social acceptance (SA) and the overall sustainability [1]. Indeed, citizens perceive such projects as impositions by energy investor or public authorities, since they are often excluded by the decision-making process. This exclusion can lead to a growing distrust toward towards institutions or energy entrepreneurs. In fact, citizen opposition to the construction of RES plants can significantly influence the actions of the local administrators who are responsible for granting authorizations, leading to delays or the abandonment of such projects [1]. Thus, public authorities play an important role, as they are responsible not only for meeting international targets for the development of RES, such as those promoted by the UN Agenda 2030 on sustainable development, but also for protecting the local population from misinformation regarding potential impacts on health, the environment, and landscape heritage [2]. As result, one effective strategy to prevent and/or address such conflicts is to invest in building knowledge and trust. Citizens wish to be informed both during the planning (e.g., plant location) and the operation phase (e.g., transparency in monitoring and controls) [3]. This flow of information should promote participation in these phases, thereby increasing trust in public administrators and energy investors [4,5]. Additionally, economic and environmental benefits, provided as compensation measures for local communities, can also play an important role, encouraging greater social acceptance of RES plants and supporting their full sustainability.

What has been stated above is particularly relevant for the implementation of biogas and/or biomethane plants, which are likely less common worldwide than wind and photovoltaic farms but are experiencing growth. For example, according to an analysis conducted in Italy by Assolombarda [3], in some Italian regions where new biomethane projects are proposed, opposition movements frequently emerge. These involve local committees, environmental organizations, political parties, and public administrations, indicating that the primary barriers to the expansion of such plants are predominantly social in nature. Furthermore, as detailed in Section 2 (Section 2.1), there are only a few academic studies addressing the social acceptance of biogas at both national and regional levels, and none focused on biomethane primarily derived from residual agricultural and livestock feedstock, i.e., agri-biomethane [6], with the sole exception of one study examining biomethane produced from municipal urban waste [7].

In light of the aforementioned points, the objective of this study is to fill the gap in the academic literature by conducting a survey among stakeholders residing in the Apulia region (NUTS 2) in Southern Italy, aimed at evaluating their social acceptance of the agri-biomethane supply chain. Specifically, the authors focus on trust in public administrators and energy investors, which is recognized as a key variable influencing social acceptance. To this end, the authors created a theoretical model to investigate the mechanisms and extent to which trust influences social acceptance of an agri-biomethane supply chain (Section 3). The survey results were then analyzed using the statistical methodology PLS-SEM to provide scientific support for the findings (Section 4).

The novelty and originality of this study lie in several key aspects. First, the investigation focuses on biomethane of agricultural origin—an emerging energy supply chain that has only recently been incentivized by the European Commission in response to the Russian-Ukrainian conflict and the “RE-PowerEU” plan of 2022, and the National Recovery and Resilience Plan (PNRR, Mission 2). Second, the case study is geographically limited to a single Italian region (Apulia), which, to the best of our knowledge, has not been previously analysed in this context. Third, the study emphasizes the evaluation of trust over social acceptance, recognizing it as a key construct influencing the latter, as highlighted by Häußermann et al. [5]. Fourth, the survey was administered to a diverse sample of respondents, differentiated by gender, age, and role within the biomethane production supply chain. Finally, the data were analysed using PLS-SEM, an advanced form of Structural Equation Modelling (SEM), a statistical technique rarely applied in this field and previously used in its base version only in a study on electricity and heat production from solid biomass [1].

The expected findings aim to provide policymakers with evidence-based insights to support future decision-making related to community well-being and the energy transition, with particular emphasis on overcoming potential social barriers to the development of the agricultural biomethane supply chain.

2. Literature Background

2.1. Social Acceptance

According to Wüstenhagen et al. [8], social acceptance comprises three distinct dimensions: socio-political, community, and market acceptance. Socio-political acceptance refers to public opinion and the level of support expressed by relevant stakeholders—including citizens, institutional actors, and policymakers—toward technologies and policies perceived as appropriate. A lack of robust socio-political consensus during the project implementation phase of energy facilities may result in project failure [9,10]. Community acceptance, on the other hand, pertains to local-level support, particularly from residents and territorial administrations. In democratic contexts, active citizen involvement is crucial, especially in decision-making processes related to the siting of renewable energy infrastructure. Nonetheless, several factors—such as perceived risk, environmental impacts, or effects on quality of life—can undermine acceptance, potentially triggering opposition from local communities and giving rise to the “Not In My Back Yard” (NIMBY) phenomenon [11]. Resistance may occur at different stages of a project and evolve over time, shaped by psychological, social, and economic factors [12]. Market acceptance constitutes the third dimension and is shaped by economic and market dynamics, operating under the law of supply and demand—where supply comes from energy providers and demand from end users. Often, the benefits of new technologies are not immediately perceived by users, making investment in information campaigns and awareness-raising initiatives to enhance understanding of the economic and functional value of these innovations important [13].

2.2. Social Acceptance of Biogas and Biomethane

Over the last two decades, the number of publications on social acceptance of RES has increased. According to Prosperi et al. [1], most of these studies focus on the factors that influence citizens’ attitudes toward the installation of new plants and, consequently, their acceptance of RES, such as innovative infrastructure and technologies [14]. In addition to these influencing factors, socio-technical barriers—including institutions, organizations, and culture—also play an important role. These barriers are often associated with the limited involvement of key stakeholders and the low level of acceptance among residents living near the plant site [1]. Despite the growing interest in RES, research specifically addressing the social acceptance of biogas plants remains limited compared to other renewable energy technologies. In the academic literature, only one study focuses on the social acceptance of biomethane derived from the organic fraction of municipal solid waste [7]. Through a survey, this research also investigated the economic impact of this RES to evaluate its sustainability to improve municipal solid waste management in Rome. Except for this case, several studies have reported relatively high levels of public acceptance for biogas plants in countries such as Switzerland, Sweden, Germany, France, and Italy. In contrast, biogas development has faced substantial local opposition in the United Kingdom and Denmark [15]. Numerous studies have explored the factors influencing the acceptance of biogas projects, highlighting the crucial role of engaging external stakeholders, particularly the local community. Involving stakeholders early in the process and promoting awareness and understanding of biogas technology contribute significantly to project acceptance [16]. Furthermore, financial incentives and endorsement from key decision-makers are widely recognized as essential drivers of these initiatives. The introduction of biogas technology in a given area can also influence other operators, as local implementation and networking may encourage broader community adoption. Therefore, the success of biogas projects often depends on the active participation of the local population and relevant stakeholders [17,18].

Other studies, in particular, focus also on the social acceptance associated with the socio-economic opportunities of biogas [19,20,21,22], while others examine its production technologies [23], both at national and regional levels.

Other studies compare the social acceptance of biogas with that of other RES [24,25,26]. Some provide systematic literature reviews of the social acceptance of both biogas and other various RES [16,27,28]. In terms of methodology, most studies primarily rely on surveys as the main data collection tool, frequently administered via online platforms. For example, Bourdin et al. [20] employed the Contingent Valuation Method to assess public preferences. Lisiak-Zielińska et al. [15] combined survey research with visual assessment using the JAR-WAK landscape evaluation method. Mertins et al. [18] conducted semi-structured interviews with biogas plant operators and complemented their findings with a systematic literature review. Various statistical techniques have been used to analyse the data, including Principal Component Analysis and Redundancy Analysis [22], ANOVA [19] and non-parametric tests such as the Kruskal–Wallis and Dunn tests [7,15].

Finally, about the assessment of trust as a key factor influencing social acceptance, only a few studies have been published on this topic, such as [4,5], and even these do not specifically focus on biogas and/or biomethane (see details in Section 3.2).

3. Materials and Methods

3.1. Case Study: The Apulia Region

The case study selected for this research is the Apulia region (NUTS level 2), a territory in southern Italy with a considerable area of more than 19,000 km². The territory is administratively divided into six provinces (NUTS level 3): Bari, B.A.T, Brindisi, Foggia, Lecce and Taranto [29]. The region comprises 257 municipalities and has a population of almost 4 million inhabitants (Figure 1).

Geographically, the Apulia region is highly heterogeneous along its entire length. It is bordered by the Adriatic Sea to the east and the Ionian Sea to the southwest. The northern part features mountainous areas (i.e., Subappennino Dauno), alternating with hilly terrain along the central ridge. However, the territory is mainly flat, covering 53%, of the region and encompassing the second largest plain in Italy, known as “Tavoliere delle Puglie” [30]. Agriculture is the main economic activity, with a Utilised Agricultural Area (UAA) exceeding 2 million hectares. The most widespread crops are cereals, followed by vegetables and fruit trees. Moreover, there are estimated to be over are over 6000 livestock farms [31]. Therefore, the entire agri-food supply chain provides a large amoung of residual vegetable and animal biomass, which could be used for energy production by thermochimical, biological, and physical-chemical tecnologies.

In 2023, the main types of electricity production from RES were: (i) wind (52.6%); (ii) photovoltaic (34.2%); and (iii) biomass (13.1%). Biomass is also utilized for thermal energy production, followed by electricity generation, with a smaller share dedicated to biofuels for transportation [32]. According to a previous study by Labianca et al. [6], there is an unexpressed potential of the use of residual biomass for the production of biogas and biomethane. Consequently, strengthening this supply chain could represent a significant opportunity for economic growth of the region. The prospective conversion of the 52 existing biogas plants [33] to biomethane, as well as the development of new ones, could foster a new paradigm in the Apulian energy market, generating added value for the territory.

3.2. Theoretical Model

To assess trust, considered the main variable influencing social acceptance, the authors propose a theoretical model, which is a simplified adaptation of those mainly used by [4,5] (Figure 2).

As shown in the model, various social acceptance-related variables (latent constructs) are incorporated. These include exogenous variables such as experience, knowledge, and socio-demographic factors, and endogenous variables such as trust. In detail, experience is linked to knowledge, as it can significantly influence the latter [4]. However, knowledge is often asymmetrical, uncertain and incomplete, as it results from information that is not always acquired correctly [1]. Socio-demographic factors (i.e., gender and education) are also important, as they shape individual perception of green technologies [34]. At the centre of the model is trust, a multidimensional construct [35] grounded in interpersonal relationships and collaboration dynamics [36]. In this theoretical model, trust plays a crucial role for the socio-political acceptance of new RES, as stressed by [4,5]. In this context, trust acts as the target variable, with the hypothesis that higher levels of trust—particularly when associated with compensations tools—lead to more favourable stakeholder attitude toward the development of an agri-biomethane energy supply chain. According to [4], trust also involves a justice dimension, which consists of two components: (i) procedural justice, identified with the social participation variable (i.e., commitment of people or community in the decisional making acting); and (ii) distributive justice, identified with the compensation tools variable, in terms of economic and environmental benefits for local communities and each individual. These benefits can serve additional predictors in addressing environmental, economic, and energy-related challenges, ultimately influencing social acceptance [37,38,39].

3.3. Data Collection

To investigate the variables influencing the social acceptance of agri-biomethane, a statistical survey was conducted using an anonymous questionnaire administered to randomly selected participants. This methodology is widely used in social science research on RES, as it allows for the development of surveys tailored to the research objectives. Based on the literature [40], the main stages of questionnaire design were identified [40].

The research team conducted a pilot study on a heterogeneous sample of 20 individuals to assess the clarity and comprehensibility of the questionnaire. Finally, 289 out of 298 questionnaires received were included in the statistical analysis. Eight responses were excluded because the respondents did not reside in Apulia region.

The data collection process followed these steps.

First, the target respondents were identified through a random sampling approach, while allowing participants to specify their role within the agri-biomethane supply chain. This strategy ensured a statistically representative general public sample comprising local authorities, stakeholders, and citizens [8]. The only common socio-demographic criterion required was current or past residence in the Apulia region for the past five years.

Second, the survey questions (items) were formulated to represent the variables associated with the categories outlined in the theoretical model (Figure 2). This selection of these items was informed by findings from the relevant literature.

Once the target population and variables had been defined, the third step involved determining the procedures for survey distribution. A method suitable for large-scale analysis and opinion mining [41] was selected to ensure widespread and easily accessible distribution of survey via digital tools (i.e., Google Forms).

After defining the target subjects, questions, and distribution methods, the survey was administered as follows: (a) the link to the questionnaire was shared via the internet and social media platforms (i.e., Instagram, Facebook, WhatsApp); (b) QR codes linked to the survey were displayed in various public locations.

The decision not to provide preliminary information prior to the survey was made deliberately to avoid biasing respondents based on prior knowledge and experiences. Data were collected using a structured, anonymous questionnaire divided into eight sections, according to the theoretical model as follows: (i) Socio-demographic characteristics (sections no. 1, 2, 3, 4); (ii) Experience (section no. 5); (iii) Knowledge (section no. 5); (iv) Trust (section no. 5); (v) Compensation tools (section no. 8); (vi) Social participation (sections no. 5); (vii) Benefits (sections no. 5, 6, 7); (viii) Responses were mandatory for all sections. Sections (1), (2), and (3) featured dichotomous or multiple-choice questions with a single response allowed, while the remaining sections included questions with responses based on a six-point Likert scale (1 = strongly agree; 6 = strongly disagree). Each section aimed to identify latent constructs (that is variables) not directly observable through single questions but detectable through a set of associated items (questions).

Table 1. Mean and Standard Deviation (SD) of Latent Constructs.

Latent Construct and Measurement Model	Indicator Code	Item	Mean	SD
Compensation Tools Do you agree with the current environmental and economic compensation tools?	C_T_1	Tax breaks	3.78	1.44
	C_T_2	Investments for community well-being	3.90	1.49
	C_T_3	Land restoration	3.91	1.45
	C_T_4	Economic refunds	3.76	1.41
	C_T_5	Investments in public facilities	3.83	1.50
Social Participation	S_P_1	Do you believe that citizens should have greater access to project documents?	3.94	1.43
	S_P_2	Do you believe that citizens should actively participate in the preliminary processes leading to the construction of agri-biomethane plants?	3.94	1.41
Trust	T_1	Do you believe that agri-biomethane producers ensure the safety and health of citizens?	3.73	1.35
	T_2	Do you believe that the authorities, when granting permits for the construction and operation of agri-biomethane plants, are able to ensure the safety and health of citizens?	3.70	1.37

shows the latent constructs (variables) along with the mean and standard deviation of each corresponding item (question), identified by an indicator code and included in the various sections of the survey. Specifically, for each latent construct, indicator codes were identified to represent the key aspects of the three core variables: Compensation tools, Social participation, and Trust. Starting with latent construct, five indicators were identified, as follows: (i) Tax breaks; (ii) Investments for community well-being; (iii) Land restoration; (iv) Economic refunds; (v) Investments in public facilities.

First, Tax breaks and economic refunds can be considered two distinct policy tools. The former consists of a reduction or exemption from taxes owed by producers or consumers, while the latter refers to direct payments made by the government or an authorized body to producers, usually linked to production volumes and regulatory compliance. For instance, a tax break could take the form of a reduced tax rate on biomethane production, whereas an economic refund might be a payment issued by the Gestore dei Servizi Energetici (GSE) to a biomethane plant that meets specific production and environmental requirements [42].

Second, Investments for community well-being concern funding for local renewable energy infrastructure, creating green jobs, supporting farmers with feedstock and waste management, and reducing reliance on fossil fuels.

Regarding Land restoration, this refers to: the use of digestate, derived from the biomethane production, as a biofertilizer to reduce soil pollution; the reduction in greenhouse gas emissions due to renewable energy production; etc.

The last indicator, related to Investments in public biomethane facilities, expresses the possibility for private investors to allocate part of their revenues or royalties to public facilities related to biomethane plants and to the local community, such as the construction of roads or the improvement of public infrastructure and spaces dedicated to citizens.

The Social participation indicator is constructed on two questions, both drawn from academic literature. The first highlights the importance for citizens to monitor the application of correct procedures before and after the plant is built. On the contrary, the second refers to citizens involvement as decision-makers, thus playing an active role in the agri-biomethane supply chain.

Finally, the Trust indicators are mainly based on the ability of authorities and operators to ensure the plant safety and to prevent eventual health issue for citizens. The mean values range from a minimum of 3.70 to a maximum of 3.94, indicating an overall positive agreement among the sample of respondents regarding the issues presented. It is also noted that the standard deviation (SD) values range from a minimum of 1.35 to a maximum of 1.50, highlighting a consistent level of variability across the indicators.

3.4. PLS-SEM and Measurement Models

To analyse the data collected in the survey, the PLS-SEM (Partial Least Squares Structural Equation Modelling) method was used. This method is particularly suitable for exploratory studies for several reasons. First, it allows the use of latent variable models even with small sample sizes [43]. Second, PLS-SEM has several advantages: it maximizes the variance explained by the dependent variable and, unlike simple SEM, it does not rely on the assumption of data distribution. In this study, all constructs are reflective in nature, meaning that the latent construct is assumed to determine its indicators (Table 1).

The indicators are considered manifestations of the latent construct and are expected to show high levels of correlation with each other. In other words, the indicators represent the same underlying characteristic. Therefore, it is important to assess convergent and discriminant validity.

Convergent validity ensures that indicators are highly correlated with each other. It is assessed using the average variance extracted (AVE) [44], which states that a construct is valid if its AVE value is greater than 0.5. Discriminant validity ensures that constructs are clearly distinct from one other. This is assessed using the heterotrait-monotrait ratio (HTMT), where values below 0.85 indicate that the constructs measure different and non-overlapping aspects [44].

Finally, the internal consistency of the reflective constructs was assessed using Cronbach’s α [45] and composite reliability (CR) [46]. Both measures indicate good consistency when their values are above 0.7.

Furthermore, the PLS-SEM model initially included all socio-demographic variables. However, not all were found to be statistically significant. In the final model, only selected demographic dummy variables (based on gender and status) were included. Moreover, an exogenous variable labelled Knowledge was included, derived from two dichotomous questions: “Do you know how agri-biomethane is produced?” and “Do you know what agri-biomethane is?”. A further dummy variable was based on the question: “Do you think the agri-biomethane plant could have a positive impact?”.

PLS-SEM was employed to construct a structural model that examines the relations among the latent constructs hypothesized in the conceptual model shown in Figure 2.

Bootstrapping (with 5000 resamples) was used within PLS-SEM to estimate the standardized path coefficients and their respective significance values (p < 0.05 considered significant). The resulting model was assessed for both explanatory power and predictive ability. For the exploratory power, the R² index was used, which indicates the proportion of variance in the dependent variable explained by the model. An R² greater than 0.19 is considered acceptable. For the predictive ability, the model’s predictive ability was assessed using the PLS predict algorithm. It was applied to calculate the Q² value for each component of the dependent variable (FP). Good predictive performance is indicated by Q² values greater than zero [46]. All steps of the analysis are summarized in

Table 2. Evaluation Criteria and Thresholds for Reflective and Structural PLS-SEM Models.

Measurement Model	Criteria	Assessment	Threshold
Reflective Model	Convergent Validity	Cronbach’s α	Cronbach’s α > 0.7
		Composite Reliability (CR)	CR > 0.6
		Average Variance Extracted (AVE)	AVE > 0.5
	Discriminant Validity	Heterotrait-Monotrait Ratio (HTMT)	HTMT < 0.85 or <0.90
Structural Model		Significance and Relevance of Path Coefficients	p-value < 0.05
		Explanatory Power of the Model R2	R < 0.19 is unacceptable
		Predictive Relevance (PLSpredict) Q2	Q2 > 0

. The analyses were performed using SMART-PLS 4.0.9.8 software, following the methodological guidelines of [46].

4. Results

4.1. Descriptive Analysis

Descriptive analysis was used to evaluate the socio-demographic characteristics of the sample (

Table 3. Socio-demographic characteristics of the sample (n = 289).

Characteristics	Items	ni	%
Gender	Female	150	51.91%
	Male	139	48.10%
Age	18–29	90	31.14%
	30–39	71	24.57%
	40–49	57	19.72%
	50–59	49	16.96%
	60–69	16	5.54%
	over 70	6	2.08%
Educational Level	Master’s Degree/PhD	40	13.84%
	Bachelor’s Degree	170	58.82%
	High-School Diploma	73	25.26%
	Middle-School Diploma	6	2.07%
Status	Citizen	201	69.55%
	Sector Expert	14	4.84%
	Agricultural Entrepreneur	24	8.30%
	Investor	4	1.38%
	Institutional	11	3.81%
	Student	35	12.11%
Province of residence	Bari	28	9.69%
	Barletta-Andria-Trani	18	6.23%
	Brindisi	15	5.19%
	Foggia	192	66.44%
	Lecce	17	5.88%
	Taranto	19	6.57%
Perception of impact of agri-biomethane (Benefit)	Negative	28	9.69%
	Positive	261	90.31%

). Among the 289 valid answers, 150 respondents (51.91%) identified as female and 139 (48.10%) identified as male. Of the six possible age groups, ranging from 18 to over 70 years, the largest number of responses (90 individuals, accounting for 31.14% of participants) came from the 18–29 age bracket, followed in descending order by the next two age groups. Regarding educational attainment, the majority of respondents—170 (58.82%)—held either a bachelor’s or master’s degree. A total of 192 responses (66.4%) were received from the province of Foggia (NUTS 3 level), followed by 28 responses (9.7%) from the province of Bari. Among participants, 174 stated that they were unaware of any agri-biomethane plants in their municipality. However, among those who were aware of such facilities, approximately 26.2% reported living more than 5 km away from them. Respondents were also asked two questions: “Do you know how agri-biomethane is produced?” and “Do you know what agri-biomethane is?”. Only 38.41% of the sample answered yes to both questions. An important result comes from the perception of the impact of agri-biomethane, i.e., 261 respondents (90.31%) perceived positive benefits associated with it.

4.2. PLS-SEM Analysis

The results of the PLS-SEM analysis were evaluated through several key steps, as described in Section 3.

Table 4. Reflective Measurement Model Results.

Construct	Item Code	Outer Loadings	AVE	Cronbach’s α	CR
Compensation tools	C_T_1	0.958 ***	0.856	0.958	0.960
	C_T_2	0.903 ***
	C_T_3	0.905 ***
	C_T_4	0.928 ***
	C_T_5	0.931 ***
Knowledge	K_1	0.761 ***	0.672	0.785	0.633
	K_2	0.882 ***
Social participation	S_P_1	0.936 ***	0.850	0.848	0.843
	S_P_2	0.907 ***
Trust	T_1	0.927 ***	0.860	0.840	0.838
	T_2	0.928 ***

Note: *** p < 0.001.

and

Table 5. Discriminant Validity Assessment Between Constructs Using HTMT.

Construct Pair	HTMT
Compensation tools <-> Benefits	0.239
Gender = Female <-> Benefits	0.129
Gender = Female <-> Compensation tools	0.143
Knowledge <-> Benefits	0.256
Knowledge <-> Compensation tools	0.137
Knowledge <-> Gender = Female	0.240
Social participation <-> Benefits	0.109
Social participation <-> Compensation tools	0.505
Social participation <-> Gender = Female	0.065
Social participation <-> Knowledge	0.140
Status = Student <-> Benefits	0.048
Status = Student <-> Compensation tools	0.084
Status = Student <-> Gender = Female	0.047
Status = Student <-> Knowledge	0.087
Status = Student <-> Social participation	0.126
Trust <-> Benefits	0.299
Trust <-> Compensation tools	0.621
Trust <-> Gender = Female	0.212
Trust <-> Knowledge	0.388
Trust <-> Social participation	0.551
Trust <-> Status = Student	0.084

present the results related to the convergent and discriminant validity of the reflective measurement model. Figure 3 illustrates the relations among the constructs, and Table 5 reports the statistics related to the model’s predictive validity.

Regarding the reflective constructs, as shown in Table 5, the indicators for Compensation tools, Knowledge, Social participation, and Trust meet the criteria for convergent validity, as all present outer loadings above the threshold of 0.70 and are statistically significant (p < 0.001). This confirms that the indicators adequately reflect their corresponding latent constructs. Furthermore, in terms of convergent validity measured by AVE, all constructs exceed the minimum threshold of 0.50, with AVE values ranging between 0.672 and 0.879. This indicates that most of the variance of the indicators is explained by the latent construct they are associated with. Furthermore, CR values and Cronbach’s α are consistently above the minimum threshold, demonstrating excellent internal consistency among the indicators and, consequently, strong reliability. The only exception is the Knowledge construct, for which the CR value is borderline (CR = 0.633). However, it still exceeds the minimum threshold acceptable for explorative studies (CR > 0.60) [46]. In summary, the items were found to be well-formulated, internally consistent, and adequately representative of the construct they were intended to measure.

Discriminant validity was assessed using the HTMT ratio, calculated for each pair of reflective constructs (Table 4). Findings confirm that the constructs included in this study capture distinct aspects of the phenomenon under investigation. All HTMT values are below the commonly accepted threshold of 0.90, indicating that the constructs are uncorrelated and exhibit excellent discriminant validity. These findings suggest that the items successfully capture different dimensions of social acceptability of agri-biomethane, as intended by the authors, with no overlap among or redundancy derived from the constructs.

The structural model shown in Figure 3 was developed using the bootstrapping procedure within the PLS-SEM framework. The paths among constructs are almost all highly significant (p < 0.001). Compensation tools have a positive and statistically significant effect on Trust (β = 0.377, p < 0.001). This means that for every one-unit increase in the perceived effectiveness of Compensation tools, the level of Trust increases by an average of 0.377 units. In other words, the more individuals believe in the effectiveness of compensatory measures, the greater their trust in the system. Similarly, respondents who demonstrate a higher propensity for social participation also tend to show higher levels of Trust (β = 0.280, p < 0.001). This indicates that a one-unit increase in the participation construct is associated with a 0.280-unit increase in Trust, suggesting that the willingness to engage in collective initiatives contributes to building greater Trust in agri-biomethane systems.

The knowledge construct is based on two dichotomous elements: the respondent’s familiarity with the concept of agri-biomethane and their experience visiting an agri-biomethane plant.

Figure 3 shows that greater awareness (Knowledge) can lead to greater Trust. Specifically, the path coefficient between Knowledge and Trust is positive and significant (β = 0.469, p < 0.001). This indicates that respondents who move from a lower to a higher level on the Knowledge scale show, on average, a 0.469-unit increase in Trust.

Furthermore, individuals who perceive the impact of agri-biomethane systems as positive (Benefit) demonstrate higher levels of Trust (β = 0.388, p = 0.008). This means that a one-unit increase in perceived Benefits corresponds to a 0.388-unit in Trust. Regarding socio-demographic variables, Student status has a positive and significant impact on Social participation (β = 0.354, p = 0.008). This result highlights a greater predisposition among younger and more educated respondents to engage in participatory practises.

Another variable considered was gender, with p-values slightly above the significance threshold (p > 0.05) but borderline. By gender, women showed lower acceptance of Compensation tools than men (β = −0.224, p = 0.055), and also lower levels of Trust (β = −0.166, p = 0.065). Although not statistically significant, these coefficients suggest that being female is associated with a 0.224-unit decrease in the acceptance of Compensation tools and a 0.166-unit decrease in Trust. These results indicate potential gender-related differences. The goodness and adequacy of the structural model, with respect to the dependent variable, is indicated by the coefficient of determination (R²) of 0.440, meaning that 44.0% of the variance in Trust is explained by the model. This highlights the model’s good explanatory power.

Finally, the model’s predictive validity was assessed using the PLSpredict algorithm. As demonstrated by the Q² predict values consistently above zero for the two Trust indicators (

Table 6. Predictive Validity of the Model (Q² predict Values).

Trust	Q2 Predict
T_1	0.543
T_2	0.524

), the model provides accurate and reliable forecasts. This not only provides further confirmation of the model’s validity but also suggests its potential use for predictive purposes—namely, to assess public trust in the agri-biomethane supply chain based solely on the other variables included in the model.

5. Discussion

The main objective of this research was to conduct a preliminary analysis of the social acceptability of the agri-biomethane supply chain, by examining the variables that characterize it. Specifically, the study focused on the role of trust in public administrators and energy investors as a target variable, using an innovative statistical methodology (i.e., PLS -SEM), which have not previously been in academic research on biomethane and on demographic-territorial standpoint. As a result, it fills a gap in the providing a more precise and detailed analysis of the current state of the art of this supply chain.

As highlighted in the literature review, this topic remains a novel and less explored area in the academic research context. In fact, currently only one study was dedicated entirely to the SA of biomethane [7], while the rest of the literature primarily focus on biogas, and only secondarily on biomethane as an upgrading [15,16,17,18,19,20,21,22,23].

As regards the abovementioned similar study [7], the present research introduces some different elements. In details, while it focused on the sustainable social dimension of biomethane, especially on aspects such as waste management and its related benefits, the present study takes a different approach. First, it concentrates primarily on agricultural feedstock by-products and not on the municipal organic waste fraction. Second, it broadens the geographical scope, providing a more diverse and representative overview (Apulia Region), against the other research that focuses on exclusively to the municipality of Rome. Finally, while [7] considers as the central element in the evaluation of social acceptability, this study emphasizes the centrality of trust in assessing social acceptability in the agri-biomethane context.

From a methodological perspective, numerous studies on the social acceptability of biogas have focused on the importance of various variables, such as awareness [15,19], knowledge [15,17,20,23], participation [18,21,23], and trust [16], as well as environmental factors, including waste management [17] and landscape impact [15,22], and economic variables, such as individual income, incentives, and wiliness to pay [20,22].

As already stressed in the previous sections, the present study adopts a similar approach by incorporating diverse social, economic, and environmental variables. However, it distinguishes itself by focusing exclusively on Trust as the primary and target dependent variable, analysing its interrelations with the other constructs. This choice aligns with Mancini and Raggi [17], who, in their literature review on the social acceptability of biogas, emphasized the role of trust.

This is also supported by the statistical findings, which demonstrate that Trust is a primary and predictive driver for social acceptance of agri-biomethane (Figure 3). Specifically, elevated levels of social acceptability are explained by the strong relation between Knowledge and Trust (β = 0.469, p < 0.001). As highlighted in the literature [23], the findings demonstrate that higher levels of Knowledge about biogas production activities are positively associated with greater acceptability. This relationship is not only statistically significant but also stronger than those observed for the other variables. Unlike previous studies in which Knowledge is typically considered as a target element [24], this work treats it as a predictor.

Moreover, unlike any of the aforementioned studies, this research includes Compensation tools as a variable capable of mitigating negative perceptions related to agri-biomethane production. This variable was found to have a positive and statistically significant impact on Trust (β = 0.377, p < 0.001).

In terms of Social participation, priori studies treated this construct differently. For example, in [19], it was conceptualized within a cooperative perspective, aimed at generating synergy effects among farmers engaged in biogas production. In [24], it was treated as an independent variable closely linked to Knowledge of biogas. In both cases, higher levels of Social participation were associated with increased social acceptability among specific actor groups (i.e., farmers, stakeholders, and institutions). On the contrary, the present research engages a broader stakeholder base across the agri-biomethane supply chain. Findings highlighted that Social participation also maintaining a strong and statistically significant relation with Trust (β = 0.280, p < 0.001).

As concern, on the contrary, the socio-demographic variables, the factor Education is incorporated into the model through the Student status (i.e., role of respondent in the supply chain), which influences Trust via Social participation (see Figure 3). In practice, the status of student corresponds to a higher level of education, mostly university, and age range of 18–29 years. The Age factor, although well distributed and representative of the sample, was not statistically significant. Finally, Proximity was excluded from the model because almost 75% of the sample was unaware of the presence of agri-biomethane plants in their residential area, making this factor unlikely to influence Trust.

Another aspect considered in this study is related to the enlargement of the sample for the survey: indeed, it was administered to a diverse population, varying in age and status in the agri-biomethane supply chain, rather than focusing solely on specific categories typically emphasized in prior studies [19]. This approach reflects greater attention to local communities, even if most respondents were residents of the province of Foggia, with participants distributed throughout the Apulia region. This concentration can be explained because this area has the highest density of RES, particularly biogas plants, which likely increases local awareness and knowledge of the topic. Therefore, these residents were more willing to engage with the survey, strengthening the value of responses.

Unlike most existing studies [7,15,16,17,18,19,20,21,22,23], this research did not include either a preliminary session or basic definitions before administering the questionnaire; the choice was deliberate, aiming to reduce potential bias. Really, given the residents’ familiarity with RES-related issues in the Apulia region, knowledge limitations were expected to be minimal: indeed, more than 60% of respondents demonstrated familiarity with the topic.

Finally, the geographical scope of the investigation extends to the entire of Apulia region in Southern Italy, whereas previous studies limited to a single province within same region [7,23].

6. Conclusions

The research conducted a survey on a sample of 289 stakeholders residing in the Apulia region (Southern Italy) to assess their social acceptance for the complete sustainability of the agri-biomethane supply chain, with a particular focus on the key variable Trust in public administrators and energy investors. Building on a theoretical model proposed by the authors, the study employed the PLS-SEM methodology to provide scientific support for the findings. The results confirm that the relations among the identified variables are, in almost all cases, highly significant. Specifically, Knowledge has the strongest positive and statistically significant impact on Trust, followed by Compensation tools and, lastly, by Social participation. In other words, the more individuals believe in the effectiveness of compensatory tools—namely economic and environmental benefits—or the greater their knowledge of technological processes and administrative procedures, the higher their level of trust. This study fills a gap in the academic literature, as no studies to date have specifically examined agri-biomethane and the evaluation of Trust using PLS-SEM methodology as statistical approach.

However, there are some limitations regarding this study. First, as an exploratory study, the questionnaire did include other potentially relevant constructs, such as income, party or environmental association affiliation, number of family members, which could provide deeper insights into the factors underlying social acceptance of agri-biomethane projects. Second, since the sample consisted of respondents residing in Apulia region, findings cannot be generalized to other geographical areas.

Therefore, future research should consider: (i) the inclusion in the questionnaire of additional socio-demographic variables to better capture social acceptability dynamics; (ii) the replication of the study in other regions, to enable comparative analyses across diverse territorial and socio-political settings.

Nevertheless, the current findings may already offer valuable insights to support policymakers in making informed decisions regarding community well-being and the energy transition. This knowledge can help to foster public acceptance of agri-biomethane facilities through, for instance, public engagement initiatives aimed to support energy planning in the decision-making process. Furthermore, information campaigns could be tailored to target specific age groups that are generally less engaged or sensitive to these issues. Ensuring free open access to document for both citizen and association could contribute to developing a more transparent control system. Finally, structured dialogues between policymakers and communities can foster mutually beneficial relationships, while addressing potential social barriers to the development of the agri-biomethane supply chain.