Opportunities and Threats of Farm Biogas Diffusion at the Regional Level in Italy

Opportunities and Threats of Farm Biogas Diffusion at the

Regional Level in Italy

by

Oriana Gava

Ph. D. Thesis

Agriculture, Food and Environment

Department of Agriculture, Food and Environment

University of Pisa

U

P

Opportunities and Threats of Farm Biogas Diffusion at the

Regional Level in Italy

by

Oriana Gava

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy in

Agriculture, Food and Environment

Candidate: ___________________

Supervisor(s)

Prof. Name Surname ___________________

Prof. Name Surname ___________________

Accepted by the Ph.D School

The Coordinator

Declaration

This thesis is a presentation of my original research work. Wherever contributions of others are involved, every effort is made to indicate this clearly with due reference to the literature and acknowledgement of collaborative research and discussions.

This thesis contains no material that has been submitted previously, in whole or in part, for the award of any other academic degree or diploma.

Questa tesi è il risultato di un mio lavoro di ricerca originale. L’eventuale contributo di altri a questa tesi è stato adeguatamente indicato attraverso le citazioni bibliografiche e il riconoscimento degli studi condotti in collaborazione.

Questa tesi non contiene materiale che è stato presentato in precedenza, in tutto o in parte, per il conferimento di qualsiasi altro titolo o diploma accademico.

Signature/firma Date/data

Preface

This thesis uses materials co-authored by Elena Favilli, Fabio Bartolini and Gianluca Brunori are used in Chapter 2 and Chapter 5, by Luciana Angelini, Fabio Bartolini and Gianluca Brunori in Chapter 3 and Chapter 5, by Fabio Bartolini and Gianluca Brunori in Chapter 4 and Chapter 5.

Abstract

The provision of renewable energy by agriculture – so called agroenergy – is a key element of the Europe 2020 Strategy and has sparked the public and research debates on the bio-based economy. Hot topics involve direct and indirect land use change, as well as the ability of agroenergy to foster or hinder food and energy security. Worldwide research has dealt with those and other issues associated with the sustainability of the diffusion of agroenergy generation systems, but the subject is still open. The agricultural sectors’ contribution to the provision of energy is central issue in Horizon 2020 strategies and has shaped the public and research debates on the future of bio economy. The common agricultural policy (CAP) has been one of the main drivers of farmers’ behaviour changes and represents the main agricultural policy instrument to address viability of rural areas and maintaining the profitability of the agricultural sector. In addition, the European Commission backs the agricultural knowledge and innovation system (AKIS), being the key to successful implementation of a bioeconomy in the EU. AKIS-specific measures are also included in the rural development policy of the CAP 2014-2020. Then, studying the AKIS is important for policy planning in the EU. Chapter 2 analyses the AKIS behind the adoption of farm biogas in an area of central Italy, thereby describing adopters’ and business typologies. The methodology relies on social network analysis of primary data, collected via questionnaire to plant adopters, and focuses on the estimation of three network attributes: cohesion, knowledge co-creation, and brokerage. We highlight three business models: i.e. multifunctional farm, entrepreneurial farm, and Energy Service Company. The latter is the most widespread. Self-education, upstream industry, agronomists, farmer/biogas unions, university, public-funded projects, and public research centres are AKIS’ stakeholders, which provide information and know-how. Upstream industry is the most influential node, the one that can help knowledge diffusion across adopters, regardless of their background. Self-accessible resources are the main providers of information at the adoption-decision stage. The networks are centralized on self-education tools, while upstream industry and the Research Centre on Animal Productions is the broker. Policy intervention aimed at improving AKIS in the biogas sector should involve the upstream industry in decision making, while considering the duality self-accessible information vs. physical advisors. To contribute to the ongoing policy debate towards CAP reform, Chapter 3 provides an empirical model to simulate the impact of alternative CAP mechanism on the provision of renewable energy. By applying a

on provision of second-generation of bio fuel crops that represent a relevant option for the Tuscany farmers. Results show that CAP reform positive impacts on the supply of energy crops mainly due to the introduction of greening payments, which allows an enlarging of crops diversification. Model results stress also the income stabilisation effects of energy production introduction at farm level, due to reduction of farm exposure to the market prices fluctuations. Chapter 4 contributes to the ongoing debate on the sustainability of agroenergy. We propose an empirical model to simulate the diffusion of farm biogas installations and estimate a set of indicators covering the economic, environmental, and social dimensions of sustainability at the regional level. Model results show that agroenergy production can help farmers stabilise their income and keep viable rural areas, despite some trade-offs among socio-economic and environmental indicators. Major drawbacks are environmental risks associated with farming intensification.

Keywords: AKIS; agroenergy; bioeconomic; biogas adoption; Common Agricultural Policy;

energy production; farm household model; impact assessment; knowledge; mathematical programming; Mediterranean; real option valuation; short rotation forestry; SNA.

Acknowledgments

First and foremost, I offer my sincerest gratitude to my tutor, prof. Gianluca Brunori, who has supported me throughout my thesis with his patience and knowledge. I offer my sincerest gratitude to my co-tutor, prof. Fabio Bartolini: I attribute the level of my Doctoral degree to his encouragement and effort and without him this thesis, too, would not have been written. I thank my opponent, dr. Roberta Moruzzo, for keeping an eye on my ongoing work.

I offer my gratitude to the former and current Coordinators of the Doctoral School, who have provided the support and assistance I have needed to produce and complete my thesis. Of course, I thank the Department of Agriculture, Food and Environment for hosting me during those three years, and my colleagues at the agricultural economics group for their friendship.

I ow a debt of gratitude to dr. Jack Peerlings, who offered me his advice and support during my visit at Wageningen University. I also thank dr. Petya Slavova and dr. Dona Pickard, who hosted me at Sofia University and provided guidance to my research.

My thesis has benefited from valuable comments by prof. Davide Viaggi and prof. Cristina Salvioni. I thank them both.

Last but not least, I thank prof. Peter Midmore and dr. Dominique Barjolle for reviewing the final draft of my thesis, and prof. Maria Andreoli, prof. Cristina Salvioni, and prof. Žaklina Stojanović for agreeing to be part of the defence committee.

Table of Contents

List of Tables ... x

List of Figures ... xii

List of Annexes ... xiii

Chapter 1 ... 1

General Introduction ... 1

1.1 Statement of the problem ... 1

1.2 Purpose of the study ... 3

1.3 Thesis Outline ... 4

Chapter 2 ... 8

Exploring the Agricultural Knowledge and Innovation System that helped biogas adoption in Tuscany ... 8

2.1 Introduction ... 8

2.2 Literature review ... 10

2.2.1 AKIS and the systems of innovation approach ... 10

2.2.2 Social network analysis of AKIS ... 11

2.3 Methodology, data, and case study ... 15

2.3.1 Social network analysis ... 15

2.3.2 Network indexes ... 16

2.3.3 Data ... 18

2.3.4 Case study ... 20

2.4 Results and discussion ... 20

2.4.1 Biogas business models in Italy ... 21

2.4.2 Adopters’ typology and business models ... 22

2.4.3 AKIS: network and brokers ... 25

2.4.4 Information and know-how ... 30

Chapter 3 ... 44

Agricultural Policy Impacts on the Decision to Invest in Farm Biogas Plants. An Application Real Option Valuation in Tuscany ... 44

3.1 Introduction ... 44

3.2 Literature review ... 45

3.3 Methodology, data, and scenarios ... 47

3.3.1 Real option valuation ... 47

3.3.2 Farm household model ... 49

3.3.3 Simulation of the investment in agroenergy ... 50

3.3.4 Data ... 51

3.3.5 Scenario analysis ... 52

3.4 Results and discussion ... 53

3.4.1 Scenario simulation ... 53

3.4.2 Discussion ... 61

3.5 Conclusions ... 63

Chapter 4 ... 70

Regional Level Assessment of the impacts of Farm Biogas in a District of Tuscany ... 70

4.1 Introduction ... 70

4.2 Literature review ... 72

4.2.1 Issues associated with the diffusion of farm biogas ... 72

4.2.2 Theory ... 73

4.3 Methodology, data, and scenarios ... 74

4.2.3 Simulation of farmers’ behaviour ... 74

4.2.4 Data ... 76

4.2.5 Impact estimation ... 77

Chapter 5 ... 95

General Discussion ... 95

5.1 Introduction ... 95

5.2 Research design: methods, issues, and limitations ... 97

5.2.1 Objective 1 ... 97

5.2.2 Objective 2 ... 98

5.2.3 Objective 3 ... 99

5.3 Case study selection ... 100

5.4 Summary of main results ... 100

5.4.1 Patterns of knowledge retrieval ... 101

5.4.2 Policy impact on adoption-decision ... 102

5.4.3 Impacts of innovation-diffusion ... 103

5.5 Conclusions ... 105

5.5.1 Future work ... 108

5.5.2 Overall message of the thesis ... 108

List of Tables

Table 1. Matrix representation of two-mode relational data. Source: Author’s own elaboration. . 16 Table 2. Summary of respondent and farm characteristics. Source: Authors’ own elaboration. ... 23 Table 3. Coded survey outputs. Authors’ own elaboration. ... 26 Table 4. Degree centrality of AKIS’ stakeholders; average network density and core/periphery density; core/periphery nodes. Source: Authors’ own elaboration. ... 32 Table 5. Definitions of equation variables. Source: Author's own elaboration... 50 Table 6. Characteristics and frequencies of clusters. Source: Author’s own elaboration. ... 51 Table 7. Net present value (NPV), overall utilised agricultural area (UAA), biogas plants, sum of basic payment (BP) and greening payment (GP) and utilized agricultural area allocated to short rotation forestry (SRF) per cluster. Source: Author’s own elaboration. ... 55 Table 8. Share of UAA allocated to SRF, following the introduction of agri-environment climate payments (0, 100, 200, or 300 €/ha). Source: Author’s own elaboration. ... 58 Table 9. Number of installations per cluster with 0%, 25%, 50%, or 75% investment cost co-funded under the Rural Development Programme. Source: Author's own elaboration. ... 60 Table 10. Definitions of equation variables. Source: Author's own elaboration. ... 75 Table 11. Results of the simulation under scenario S0 (biogas technology does not exist). Source: Author's own elaboration. ... 78 Table 12. Simulation’s results under scenario S1 (incentive = € 0.28/kWh): percent change with respect to S0; introduction of energy cropping; size of adopted biogas. Unchanged results are omitted. Source: Author's own elaboration. ... 80 Table 13. Simulation’s results under scenario S2 (incentive = € 0.35/kWh): percent change with respect to S0; introduction of energy cropping; size of adopted biogas. Unchanged results are omitted. Source: Author's own elaboration. ... 82 Table 14. Simulation’s results under scenario S3 (incentive = € 0.42/kWh): percent change with respect to S0; introduction of energy cropping; size of adopted biogas. Unchanged results are omitted. Source: Author's own elaboration. ... 82 Table 15. Regional-level estimates (province of Pisa, Italy, a NUTS3 region): values and percent change with respect to S0 and S1 of sustainability indicators, introduction of energy cropping, and electricity generation. Source: Author's own elaboration. ... 84

Table 17. Simulation parameters and scenarios. Source: Author's own elaboration. ... 102 Table 18. Overview of the five simulated plants. We consider the rotation corn-triticale, i.e. the standard energy crop rotation in the region under study. Source: Author's own elaboration. ... 104 Table 19. Economic, social, and environmental impacts on the case study area. Source: Author’s own elaboration. ... 104

List of Figures

Figure 1. Two-mode aggregated graph of adopters (black circles) by AKIS’ stakeholders (white squares) matrix. Source: Authors’ own elaboration. ... 29 Figure 2. Two-mode information (A) and know-how (B) networks. Source: Authors’ own elaboration. ... 31 Figure 3. Two-mode graph of adopters (black circles: farmers; black diamonds: ESCos) by AKIS’ stakeholders (white squares) matrix. Graph layout is based on a spring embedding algorithm, where more central nodes are those with the highest degree. Source: Authors’ own elaboration. ... 32

List of Annexes

Annex 1. Major simulation parameters. Source: Bartolini et al. (2015). ... 68 Annex 2. Policy parameters for each scenario; CAP-abolishment scenario is not included as payments are missing. Source: Bartolini et al. (2015). ... 68 Annex 3. Descriptive statistics of used parameters. Farmers are given direct payments in the context of EU’s Common Agricultural policy 2014-2020. Source: Author's own elaboration. ... 93 Annex 4. Overview of the five simulated plants. We consider the rotation corn-triticale, i.e. the standard energy crop rotation in the region under study. Source: Author's own elaboration. . 94

Chapter 1

General Introduction

1.1 Statement of the problem

Biogas is a renewable gaseous fuel, generated via biochemical processing of biomass, with major applications to the combined generation of electricity and heat, in so-called cogenerating engines. Biomass includes biodegradable waste, residues and by-products from forestry, fishery and aquaculture, crop and livestock farming, food processing, urban green, and specifically grown crops (“Renewables Directive” 2009/28/EC). On-farm biogas-to-energy plants process waste and by-products from the agri-food sector and energy crops. The by-product of biomass processing, i.e. the digestate, is used as soil conditioner and low-grade fertiliser, being a fibre-rich fluid that retains over 90% biomass nutrients (OECD, 2010).

Since the 1990s, farm biogas has started spreading across European regions where intensive dairy and pig farming were the main agricultural systems, as a tool for getting rid of excess livestock manure and slurry, thereby complying with the Nitrates Directive and recovering energy and fertilising substrates from waste. However, biogas adoption on farm has turned to be significant all over Europe, and regardless of the farming system, since the following decade. In fact, the EU-wide promotion of a bio-based economy, under the 2020 energy strategy, resulted in member states’ support to agroenergy producers, which boosted the profitability of biomass-to-electricity plants (Scarlat et al., 2015; Bangalore, 2016). The 2020 energy strategy builds on a set of binding Union-wide targets to reduce the dependence on imported fossil fuels, while boosting new energy technologies (Renewable Energy Directive 2009/28/EC). By 2020, 20% of the overall energy supply for primary consumption need shift from fossil to renewable sources. Each member state has to comply with a baseline renewable energy share of 5.5% national energy consumption, with the remaining increase being calculated upon per capita gross domestic product. Italy is committed with 17% energy from renewable sources. Among the various renewable energy sources, the European Commission recommends turning to biogas, given its greenhouse gas saving potential compared to fossil fuels.

Between 2008 and 2012, the number of biogas plants across Italian farms had grown from 158 to 994 operating plants (Fabbri et al., 2013). To some extent, three interdependent crises at the energy, environmental and agricultural level might have contributed to that quick diffusion of

change (FAO, 2008), political and social instability in fossil fuel-producer countries and the emergence of state-owned energy champions had driven the global increase of fossil fuels prices until 2008 (Umbach, 2010), with growing production costs at the farm level and increased and fluctuating market prices of agricultural commodities (van der Ploeg, 2008). For example, in Italy the fall of commodity price (2008) harmed arable more than livestock systems, with an increase in both the share of land set aside and the share of farm exits (Esposti, 2009). Then, biogas adoption turned to be a differentiation strategy for arable farmers – at least, for those with capital availability. Besides, Rural Development Programs of the Common Agricultural Policy have allowed for 40% coverage of plant building expenses, since the 6th framework program (CAP 2007-2013). However, the literature stresses the importance of policy changes in the national renewable energy framework as drivers of biogas adoption, given the economic reliance on state incentives of biogas installations (Torquati et al., 2014). In Italy, major policy changes are two. Firstly, the Italian government has acknowledged agroenergy production among farming specializations (2006), in case at least 51% biomass is self-supplied. Secondly, feed-in tariffs for electricity produced from biomass have granted farmers with a 15-year-flat price of €0.28 per each kWh electricity plugged into the national grid (Italian Government regulation DM 18/12/2008). That scheme has applied to plants below 1MWh rated power that outsource biomass (at most 49%) within 70 km from plant site.

Like in other EU countries, energy service companies or ESCos (Hannon et al., 2013) have been key to biogas diffusion in Italy, by sponsoring farm-based installations (Cannemi et al., 2014). This process has presumably driven business model isomorphism (Carrosio, 2013), at least in northern Italy, where around 90% operating biogas plants are located (Fabbri, 2013). This different plant concentration is associated to pedo-climatic features that underlie the prevalence of local agricultural systems. Intensive livestock husbandry is widespread just northern Italy, while arable farming prevails in central and southern Italy. Given plant distribution, most evaluations of the Italian biogas sector deal with northern regions. Then, there is a need for research over central and southern Italy. Among the central and southern regions, biogas has significantly spread just in Tuscany, with three operating plants in 2010 and 29 in 2014 (Fabbri, 2013; ARPAT, 2015). Analysing the determinants of biogas adoption across Tuscan farms would provide a basis towards the evaluation of the biogas sector in central and southern Italy.

Competing claims about the sustainability of energy production from agricultural sources are still feeding the debate around the diffusion of agroenergy, particularly within the academy

sustainability assessments (see, e.g., Paine al. 1996; Hoogwijk et al. 2003; Hazell and Pachauri, 2006), but the sustainability of biogas-to-energy systems is still debated (Kirkels et al., 2012). Also, little is known about the underlying determinants of biogas adoption, in geographical areas where farming systems are mainly arable and biogas has spread much more recently than in livestock intensive regions. Then, there is a need to investigate the decision-making process of potential adopters of biogas-to-energy technology in arable areas, such as Tuscany. Waste from intensive livestock farming is enough for feeding the plant, with reduced need for biomass outsourcing; agricultural residues from arable farming, instead, does not provide enough biomass, then arable farms have to cultivate dedicated energy crops to be used as plant feed, thus raising obvious sustainability concerns with respect to the potential land and water use change from food to biomass production. Though being self-sufficient in terms of biomass production is feasible, biogas operators would need to outsource some biomass, depending on uncontrollable factors (e.g., climate conditions). Hence, it is worth understanding farmers’ preferences towards either plant installation or introduction of energy cropping on farm, while highlighting the extent to which the CAP could contribute to those preferences. Research is needed for highlighting the outcomes of biogas diffusion, to understand the effectiveness of 2020 targets (REN21, 2014; JRC, 2014), given the general institutional agreement towards the mainstreaming of a bioeconomy (Sorda et al., 2010; Staffas et al., 2013). In addition, downscaling sustainability assessment of that diffusion to the regional level would help understand the current economic, environmental, and social issues that biogas may solve or complicate, locally. A thorough analysis of the current state of the biogas sector, including knowledge dynamics and sustainability is essential for addressing the conflicting objectives of different decision makers and for promoting a robust decision-making process in the context of sustainable development.

1.2 Purpose of the study

To date, agricultural economists have mainly approached the process of agroenergy adoption on farm through econometric or mathematical programming models. The former aim at explaining the underlying determinants of farmers’ investment choices, moving from revealed or stated preferences. The latter simulate the choices of profit-maximising farmers under different policy or market conditions. Beside rational behaviour, both methodologies assume that farmers can access perfect information, thus missing to investigate knowledge flow and the

literature acknowledging the embeddedness of economic activities and recognizing that stakeholder positions within the network influence adopter ability to access information, knowledge, resources and technology (Inkpen and Tsang, 2005); those Authors evaluate the extent to which network composition and structure may influence the diffusion of innovation in different economic activities using network analysis tools.

Against that background, the general objective of the thesis is studying the patterns of knowledge retrieval by biogas adopters and attempting to improve the existing knowledge with evidence about determinants and impacts of biogas adoption/diffusion, taking Tuscany as a case study. This would pinpoint opportunities and threats of biogas diffusion across farming systems (and geographical areas) that are not intrinsically suitable for it. To pursue those objectives, my research has involved exploratory analysis and ex post assessments. The overarching purpose of this thesis is helping decision makers to improve and target the existing regulations about farm biogas, particularly when it comes to installations in Mediterranean areas that miss intensive livestock farming. Then this thesis could be a scientific contribution to evidence-based policy in the bioeconomy.

To provide different perspectives and end with manageable research questions, I have broken down the general objective of the thesis into three specific objectives, each being addressed individually in a dedicated chapter. The three specific objectives are as follows:

Objective 1 – Patterns of knowledge retrieval. Describing the biogas sector, depicting the agricultural knowledge and innovation system behind biogas adoption across Tuscany, and analysing the extent to which the system of relationships within the AKIS contributed to adoption and daily plant operations.

Objective 2 – Policy impact on adoption-decision. Identifying the propensity of farmers of a sub-region of Tuscany to enter the agroenergy market via biogas adoption or introduction of energy cropping, within the current CAP (2014-2020); highlighting the extent to which policy change may encourage, hinder, or delay investment decisions.

Objective 3 – Impacts of innovation-diffusion. Evaluating the impact of the current biogas sector on economy, society, and the environment, focusing on a sub-region of Tuscany.

1.3 Thesis Outline

This thesis consists of five chapters, including this introduction and a concluding chapter (Chapter 5), dedicated to the discussion of overall thesis’ findings, their policy implications,

core of the thesis and address the three objectives above via multiple methodologies; particularly:

Chapter 2 is an exploratory study that aims at describing the biogas sector and the associated agricultural knowledge and innovation system in Tuscany, thereby highlighting the functions carried out by that system. The methodology involves social network analysis of primary data under the innovation system framework. Social network analysis allows measure the extent to which the relational structure of the system under study facilitates or hinders the flow of information among system elements.

Chapter 3 applies a real option approach to official data from the last Italian Agricultural Census (2010) with the purpose of assessing the impact on farmers’ investments in agroenergy of some policy measures introduced by the CAP 2014-2020; particularly, the paper considers farmers’ propensity to enter the agroenergy market by analysing two alternative options, i.e. installing a biogas plant or allocating a share of their utilised agricultural area to energy cropping (short rotation forestry). Real option valuation highlights the ability of the policy framework under study to promote timely investments or to delay them.

Chapter 4 estimates the impact of biogas diffusion on economy, society, and the environment at the regional level, taking the Italian province of Pisa (NUTS 3) as a case study. The paper simulates the behaviour of farmers facing the decision of adopting or not a biogas plant on farm, by solving a profit maximisation problem via mathematical programming. The observations are representative farm types identified by cluster analysis to data from the last Italian Census of Agriculture (2010). This integrated impact assessment provides an overview of the most significant opportunities and threats of biogas diffusion – and of pursuing EU’s 2020 targets – at the regional level.

References

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Bartolini, F., Angelini, L. G., Brunori, G., & Gava, O. (2015). Impacts of the CAP 2014–2020 on the Agroenergy Sector in Tuscany, Italy. Energies, 8(2), 1058-1079.

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Chapter 2

Exploring the Agricultural Knowledge and Innovation System

that helped biogas adoption in Tuscany

2.1 Introduction

Innovation in agriculture is a systemic event (Fagerberg 2005; EU SCAR, 2012), as it results from social and economic processes of regional rural development (Ward and Brown, 2009), as well as from interactions among innovation adopters and between innovation adopters and knowledge organizations outside the farm (Smits et al., 2010). The structure of knowledge organization networks can influence innovation processes (Radosevic, 2010), given their relatively higher importance compared to traditional production factors (land, labour, capital) (Marshall, 1920; Hermans et al., 2002). Structure refers to the type of individuals and their mutual relationships. Institutional set-ups where those organizations operate and interact are known as Agricultural Knowledge and Innovation Systems, or AKIS (Röling and Engel, 1991; Edquist, 2005; World Bank, 2006). AKIS are sets of individuals and individual-individual relations that deliver knowledge-intensive services (R&D, education, extension) to the agricultural sector (Den Hertog, 2000). Those services can drive innovation demand, orient innovation supply, support research and demonstration programs, inform about funding opportunities, and, eventually, improve the economic and/or environmental performance of farms. Studying the AKIS is important for policy planning in the EU (Moreddu and Poppe, 2013; Knierim et al., 2015), particularly in the context of the bioeconomy. The European Commission backs regional AKIS, being the key to successful implementation of a bioeconomy in the EU (European Commission, 2012; EU SCAR, 2013). In Italy, the national Bioeconomy Action Plan (Italian Government, 2016) promotes AKIS’ re-organization at the regional level1_, following the recommendations of the Italian Ministry of Agriculture (MIPAAF, 2013). AKIS-specific measures are also included in the rural development policy of the Common Agricultural Policy 2014-2020 (e.g., public-private partnerships in agricultural research and agribusiness), which in Italy is implemented at the regional level. Among the different sectors of the

bioeconomy, agroenergy is probably the one that relies more on inter-industry relations and knowledge networks (Golembiewski et al., 2015). Then, agroenergy is expected to be a major beneficiary of AKIS’ re-organization. At time of writing, the academic literature about the current organization of agroenergy’s AKIS in Italy is poor. Two papers are worth mentioning: (i) Pantaleo et al. (2014), who evaluate the extent to which Energy Service Companies (ESCos) may help the diffusion of biogas-to-electricity plants in Italy; (ii) Manos et al. (2014), who analyze the role of public-private partnerships in the sustainable diffusion of agroenergy in Italy and Greece. Neither paper, though, adopts the perspective of agroenergy adopters. Acknowledging the suggestion of Spielman et al (2009), here we adopt that perspective, for analyzing the AKIS that has allowed the establishment of an agroenergy sector in Tuscany, a region of central Italy. As for agroenergy, we selected biogas, being the most widespread agroenergy in Italy (GSE, 2015). Biogas plants process waste from the agri-food sector and/or energy crops via bacterial fermentation, and recover energy for selling to the national Energy Authority, which pays them a fixed price (feed-in tariff scheme) of €0.28/kWh, at most2_. Among the twenty administrative regions of Italy, we chose Tuscany for two main reasons: (i) contrary to most regions of northern Italy, the biogas sector in Tuscany is still in its infancy (Fabbri et al., 2011; Fabbri et al., 2013) – early adopters (Rogers, 2003); compared to the other regions of central and southern Italy, the biogas sector in Tuscany is more developed (Fabbri et al., 2013).

Against that background, this article would add to the agricultural economics literature on the AKIS by seeking to answer the following three research questions (RQs):

RQ1: “Who are biogas adopters in Tuscany and what are biogas business models?”

RQ2: “What does the Agricultural Knowledge and Innovation System of in Tuscany look like?”.

RQ3: “To what extent different stakeholders of the Agricultural Knowledge and Innovation System helped biogas adoption in Tuscany through the diffusion of relevant knowledge?” The RQs are relevant for at least four reasons. Firstly, knowledge flow within the network is critical for farmers’ decision towards biogas adoption (Lundvall, 2007). Secondly, the AKIS of biogas might differ from that of food/feed/fiber, being likely to include elements from both agricultural and energy sectors. In addition, biogas adopters would need different knowledge sources, compared to the adopters of more traditional agricultural innovation (Varis and

Littunen, 2010). Thirdly, traditional AKIS’ stakeholders (see Pascucci and DeMagistris, 2011) might not be as relevant for biogas producers as they are for more traditional farmers. Lastly, research, viz. Carrosio (2013), has classified biogas business models in northern Italy only. That classification is not directly applicable to regions of central Italy, like Tuscany, given the significant north-south differences in terms of development stage of the biogas sector and drivers of biogas adoption. We are not aware of published studies depicting the biogas sector in central-southern Italy.

Operationally, we adopt a systemic perspective and provide a social network analysis (SNA) of primary data. We use SNA because it has proved to be a useful, but underused, tool for studying knowledge flows within the AKIS (Spielman et al., 2011). We also review the literature about biogas in Italy for trying and matching biogas business models in Tuscany with existing classifications.

The paper is structured towards five paragraphs, including this introduction. Next paragraph delivers a review of the relevant literature that informed the analytical choice of the paper. Paragraph 3 focuses on research design. Paragraph 4 presents and discusses the outputs of data processing. In the conclusions, we pinpoint key research findings, deliver policy recommendations, and suggests improvements and directions for further research.

2.2 Literature review

2.2.1 AKIS and the systems of innovation approach

Many Authors applied the systems of innovation approach (Lundvall, 1985) to the study of innovation in agriculture, by considering the set of stakeholders and stakeholder-stakeholder relations that jointly participate to the generation, adoption, and diffusion of a given innovation in a geographical area (Markard and Truffer, 2008). Stakeholders are firms and public or private organizations, which can collaborate towards innovation. The AKIS has a larger scope than the innovation systems tout court, because it includes knowledge flows. AKIS’ stakeholders can control the downstream flow of knowledge from sources to potential adopters of innovation, and the upstream flow of tacit knowledge from early adopters to the industry or research centers. Under a systemic (network) perspective, those flows are stakeholder-stakeholder relations (ties).

The major application of the systems of innovation approach is policy evaluation; the objective is providing recommendations to policy-makers, to support innovation at the micro-level

(Spielman et al, 2009). Some Authors pinpoint the factors that may influence the innovation performance of farms, including potential adopters’ behavior, the coexistence of a given innovation with established technologies, production, or management systems, stakeholder overlaps, and additional drivers of lock-in situations (Klerkx and Leeuwis, 2008). That kind of approach is particularly effective when the innovation under study is radical, such as, e.g., the shift to renewable energy systems (Markard et al., 2009; McLellan et al., 2016). Other Authors focus on stakeholders’ functions within the system under study, to highlight the extent to which those functions can promote or hinder the process of generation, adoption, and diffusion of a given innovation (Jacobsson and Johnson, 2000). Stakeholders’ functions are as follows: (i) entrepreneurial activities and experimentation; (ii) knowledge development and diffusion through networks, (iii) guidance for the search; (iv) market formation; (v) mobilization of resources; (vi) creation of legitimacy; (vii) development of positive externalities (Hekkert et al., 2007; Bergek et al., 2008). Under this framework, assessing AKIS’ performance involves consider the extent to which AKIS’ elements (stakeholders and relations) contribute to the correct implementation of their joint functions (Bergek et al., 2008). The evaluation may involve the whole set or a subset of functions. Concerning AKIS’ ability to circulate knowledge, two functions are relevant, i.e. (ii) knowledge development and diffusion through networks and (vii) development of positive externalities, where externalities are knowledge spillovers (Marshall, 1920). When the AKIS of a specific innovation is the unit of analysis, spillovers are outside the study boundaries and “knowledge development and diffusion through networks” is the only observable function.

2.2.2 Social network analysis of AKIS

This paragraph reviews the most recent literature about SNA applications to the study of knowledge exchanges within the systems of innovation in agriculture. The academic literature about SNA of knowledge networks is wide (see Phelps et al., 2012); we concentrate on SNA applications to the study of AKIS. SNA is a flexible and useful methodology for mapping the AKIS and assessing its ability to carry out its function(s) (Spielman et al., 2009; Klerkx et al., 2010). SNA is just one of the available analytical tools for AKIS’ evaluation (Klerkx et al., 2012). Researchers may decide upon adopting other methodologies that would turn to into more informative results, depending on various issues, viz. type and availability of data, geographical scope of the enquiry, and specific research question (e.g., Hunt et al., 2014; Hermans et al.,

2015; Läpple et al., 2015, 2016). Reviewing those approaches is beyond the scope of this paper; the reader can refer to Klerkx et al. (2012) for an overview.

Spielman and Colleagues (2011) apply SNA under the innovation systems approach, for understanding the way how new forms of knowledge circulation and stakeholder diversity within the AKIS impact on the patterns of innovation of Ethiopian smallholders. The paper tests SNA as tool for investigating the AKIS. Findings highlight that public organizations were at the core of the innovation system, while market and civil society played a more peripheral role. However, a private credit and savings company could effectively drive innovation, by delivering a training program about loan opportunities and effective use of loans on farm. SNA turned to be useful for examining the interplay between farmers and AKIS’ stakeholders, as well as for delivering policy recommendations. Matouš et al. (2013) also focus on Ethiopian farmers and on their relationship with extension services and extension workers. The paper addresses farmers’ receptiveness to knowledge about conservation agriculture and highlight network-related factors affecting knowledge acquisition. The methodology integrates ego-network analysis within a broader econometric estimation. Being in contact with extension workers was not significant in terms of innovation adoption; neither network density was significant. Instead, informal knowledge exchange via relations with knowledgeable peers was significant. They suggest policy makers to correct the flaws of agricultural extension, to improve service delivery to farmers interested in extension, and to prevent strong peer-peer relations. Isaac (2012) evaluates the extent to which the structure of agricultural information networks cocoa producers in Ghana. – particularly, the relations between producers and agrarian-based organizations – can help to innovate agricultural practices for achieving environmental sustainability and production security. The flow of agrarian information between producers and organizations (i.e. the Ministry of Agriculture, agrarian NGOs, people involved the local informal diffusion of informal knowledge and support) was bi-directional. Training programs for farmers helped that flow and promoted long lasting relationships between farmers and training providers. Network efficiency in conveying relevant information to producers was higher when producers were directly linked to organizations. The Author recommends to policy makers to stabilize the links between producers and AKIS’ stakeholders. Cadger et al. (2016) also set their study in Ghana. They describe the positions of farmers affiliated or not with agriculture development organizations within the knowledge network, and analyze the correlation of farmers’ attributes with network indexes. Farmers affiliated with development projects benefited from higher knowledge diversity than not affiliated ones, with increased

willingness to innovate and ability to control and circulate knowledge among affiliated farmers. In addition, affiliated farmers grew a larger variety of crops. The providers of extension services turned to be knowledge hubs. Aguilar-Gallegos et al. (2015) evaluate the extent to which various agricultural information networks, made of farmers and extension workers, help innovate the agricultural production system and drive economic impacts on farm in the Mexican palm oil sector. The study considers the number of connections a farmer establishes while seeking knowledge, the number of times a farmer is mentioned by other farmers as a source of information, and the number of farmers that recognize extension workers as important elements in the information network. Findings show that farmers who could establish many connections had bigger and more heterogeneous information networks. Two farmers’ features were associated with higher levels of innovation adoption, i.e. being a source of information for other farmers and having connections to extension workers. Higher rates of innovation adoption were linked to higher oil palm yields and higher farmers’ incomes; then, the type of information network may allow farmers reach a certain level of adoption. The study suggests that the program under review was a good strategy to boost innovation adoption. Reed and Hikey (2016) deals with the network of relationships among individual partners of two producers’ cooperatives in Senegal. The aim of the paper is defining network performance for each cooperative, that is network’s ability to allow knowledge diffusion about agricultural innovations. Poor network performance was associated with missing information about innovation intermediation and low levels of knowledge sharing. Good network performance was associated with the presence of many brokers. Knowledge transmission within each network was vertical, with innovation brokers generally holding leadership positions. The Authors suggest policy makers to implement participatory approaches for boosting horizontal (farmer-farmer) ties within the knowledge network. Results highlighted that the lack of information could constrain agricultural innovation among famers within each agricultural cooperative. The Authors suggest to decision makers within each cooperative to develop communication strategies for creating contacts between peripheral farmers and external sources of knowledge. Moving to developed countries, Hermans et al. (2013) focuses on Dutch cooperatives, though from a different perspective. The Authors investigate the affiliation networks created by the participation to programs for mitigating the environmental impact of agriculture. Program leaders are two environmental cooperatives; project partners are AKIS’s stakeholders, i.e. individuals working for public or private organizations involved in agricultural research and extension services. The Authors assess partners’ rate of participation

to projects (knowledge co-creation) and meetings (knowledge upscaling), organizations’ ability to affiliate participants, as well as innovation brokerage (Moore and Westley, 2011). University researchers were the main creators of knowledge, their work being well regarded and influential among project partners. Board members of the environmental cooperatives carried out knowledge upscaling at many dissemination events over time; farmers could also boost upscaling and political translation of knowledge, given their direct interest in the new practice. Innovation brokers mainly owned to a farmers’ union. The Authors recommend to organizers of innovation networks to promote innovation diffusion beyond the immediate participants, for impacting on other levels of the innovation system. Organizers should try and incorporate knowledge co-creation, knowledge upscaling, and innovation brokerage within each network, and make sure that project partners can play multiple roles in the innovation network. Lubell and Colleagues (2014) present the results of multi-year research across winegrape growers in California (USA). The paper aims at providing empirical evidence to support the idea that agricultural extension needs renovation. The Authors call for the synergic integration of social, technical, and experiential learning pathways, via the promotion of boundary-spanning partnerships, i.e. organizations made of groups of people with different backgrounds (e.g., farming, research, agrarian NGO, industry) that co-produce knowledge meant to boost innovation. The analysis concentrates on individuals’ centrality within communication relationships among farmers and between farmers and AKIS’ stakeholders. The Authors differentiate between farmers that carry out some extension activities and farmers tout court; the latter are boundary spanning individuals and hold the greatest knowledge diffusion potential across the networks. The Authors suggest local policy makers to promote boundary spanning partnerships that include the most popular extension organization in the network and recommend to provide extension professionals with incentives to learn innovative strategies of intervention.

We conclude this literature review with a real-life application of SNA (Lamb et al., 2016) and an ethnographic study that embeds network concepts (Klerkx and Proctor, 2013). Lamb et al. (2016) bring together the findings of a multi-year co-innovation project for development and adaptation of conservation agriculture production systems in Kenya and Uganda, to test SNA as a tool for improving and legitimating participatory research. Market stakeholders affiliated to the pharmaceutical industry (“agrovets”) turned to have an important role in spreading knowledge. Farmers recognized to be rather isolated and extension workers proposed to use the relatively good reputation of agrovets to facilitate the communication of knowledge about

innovation to farmers. Visualizing the outputs of SNA during feedback workshops helped discussing about the roles of extension services and agrovets. Farmers’ proved to be supportive of conservation agriculture. After project’s end, extension workers applied SNA, with the help of farmers, to identify areas towards which addressing the promotion of conservation agriculture. Klerkx and Proctor (2013) acknowledge that recent research has expressed concerns around fragmentation and disconnection withi land management advisory systems. The Authors rise interest into the way how advisory professionals build in their knowledge base and evaluate whether fragmentation and disconnection are issues that researcher should keep on investigating. Advisors under study are applied ecologists, land agents/surveyors, and large animal vets in the UK. Drawing on Smedlund (2008), the Authors evaluate advisory networks in terms of their centralization, distribution, and decentralization patterns. Centralized networks included people mainly from the same organization, who operated within close-knit communities of practice to keep themselves up to date. Problem solving was barely addressed. Very few individuals could create bridges with the outside. Knowledge sources were the web, professional magazines, books, and education stakeholders. Distributed networks included advisors, who generally worked for the same organization. Retrieved knowledge included technical information, problem-solving, best-practice, and mentoring, and relied on informal exchange with colleagues. Individuals within the network collaborated for delivering training and organizing conferences, thereby creating connections with the outside: inter-professional ties broadened advisors’ knowledge base. Decentralized networks involved the exchange of formal and/or informal knowledge among advisors and scientific experts, via face to face (e.g., at conferences) or remote (e.g. via the web) interactions. Retrieved knowledge concerned problem solving and development of new skills. This type of networks helped scientific knowledge enter advisory services, but could not help innovate the service itself. Eventually, advisors turned to be willing to improve and widen their knowledge base; however, they need innovation brokers for creating connections outside their own organizations. In Authors’ view, inter-professional and cross-sector collaboration may contrast this weakness.

2.3 Methodology, data, and case study

2.3.1 Social network analysis

Social network analysis is a widespread tool for investigating systems of interconnected individuals. Many academic books guide researchers throughout network metrics and their

empirical applications; Scott (1991) and Wasserman and Faust (1994) are probably the most popular introductory handbooks, though more specific text are widely available, depending on researcher’s interest.

SNA is the process of manipulating data about individuals (nodes) and their reciprocal relations (ties, or edges when relations’ direction matters), to visualize them as graphs and extract new information from them. Such data are known as relational data and the system of network elements, i.e. nodes and ties, is known as network’s structure. SNA describes each node or the whole network, in terms of their ability to circulate and/or control the flow of information, based on the calculation of a series of indexes that consider node location, interconnections, and grouping patterns.

After data collection, relational data are stored in matrix format. Rows and columns display nodes, and elements relations: matrix elements equal 1 in case an edge exists between two nodes and 0 otherwise. Rows and columns may display the same set of nodes, as in one-mode networks, or two different sets of nodes, as in two-mode networks. Contrary to one-mode networks, edges in two mode networks connect mode one to mode two only; edges represent the relation that maps actors of mode one to actors of mode two (Borgatti and Halgin, 2011). Here, edges display the relation of adopters’ knowledge (information and know-how) retrieval from AKIS’ stakeholders. Table 1 exemplifies the approach to matrix building.

Table 1. Matrix representation of two-mode relational data. Source: Author’s own elaboration.

Mode 2 →

AKIS'stakeholder1 AKIS'stakeholder2 AKIS'stakeholder3

Mode 1 BiogasAdopter1 0 1 1

↓ BiogasAdopter2 1 1 0 BiogasAdopter3 1 0 0

The table is explanatory purposes only and does not display data from this papers’ research.

2.3.2 Network indexes

Cohesion. We calculate two complementary measures: density and centralization (Freeman, 1979; Scott, 1991). Density is the proportion of pairs of nodes that have ties out of the maximum possible number ties in the two-mode network (Borgatti and Everett, 1997). Density is inversely correlated with the ease of knowledge exchange. High density may lead to trusty relationships (Bodin and Crona, 2009), which can hinder the entrance of new knowledge within the network.

Given the generally weak relationships among adopters, efficient downstream flow of knowledge from AKIS’ stakeholders and self-accessible sources to adopters is crucial within policy-driven-innovation networks (van der Valk et al, 2011; Isaac, 2012). Centralization measures the extent to which edges are evenly distributed among nodes, viz. the extent to which density is organized around particular focal points (Scott, 1991), which turn to be the most influential ones, i.e. the ones to whom most individuals owing to the other set of nodes turn to for seeking knowledge (Scott, 1991). Centralized networks are for routine problem solving and updating. Distributed networks are for more complicated problem solving and informal exchange of tacit knowledge. Decentralized networks are for complex problem solving and for knowledge exchange with the outside, as well as for developing service innovation. We highlight network areas with different cohesion, by fitting a core/periphery model to our data. The core/periphery algorithm calculates a “coreness” value for each node based on density and degree centrality, and defines node-node distance based on coreness similarity (Comrey, 1962; Borgatti and Everett, 1999).

Knowledge co-creation. We use two interdependent indexes: degree centrality (Freeman, 1979) and adopters’ affiliation rate to AKIS’ stakeholders (Hermans et al., 2013). Degree centrality is the ratio between the number of edges a node is involved in and the maximum possible number of edges in the network. In a two-mode network, degree centrality is calculated for each set of nodes. Here, we focus on AKIS’ stakeholders. Degree centrality allows evaluate network dynamism (Freeman, 1979). In innovation systems, adopters collaborate and co-create knowledge with a variable set of AKIS’ stakeholders. This dynamism helps innovation, particularly when different types of network individuals have different backgrounds (Van de Ven, 1999), as for example in projects with multiple partners (Hermans et al., 2013). The index for knowledge co-creation tout court is adopters’ affiliation rate to AKIS’ stakeholders, i.e. the degree centrality of AKIS’ stakeholders divided by the number of adopters. The lower the index, the greater the opportunities for AKIS’ stakeholders to learn, develop new ideas, and convey those ideas to upstream industry and policy makers (Moore and Westley, 2011; Hermans et al., 2013).

Brokerage. We use a qualitative criterion, i.e. boundary-spanning relations (Lubell et al., 2014), and an index, i.e. betweenness centrality (Freeman, 1979). Following Lubell et al. (2014), we identify those nodes with boundary-spanning relations among AKIS’ stakeholders. Those nodes are the ones that play multiple roles, supply multiple types of knowledge, and have reciprocal exchange of knowledge with innovation adopters. Betweenness centrality measures the extent

to which a node allows shortest paths among network nodes. Here, we calculate the index for AKIS’ stakeholders. Betweenness centrality is a key concept in the study of knowledge flow (Freeman, 1979). Nodes with high betweenness can control the flow of knowledge through the network; those nodes are gatekeepers, as they bridge providers and seekers of knowledge (Barzilai‐Nahon, 2008). Here, we use betweenness centrality for ranking AKIS’ stakeholders, based on their gatekeeping potential.

2.3.3 Data

The tools for primary data collection are face to face semi-structured interviews and questionnaires. The level of analysis is the farm, notably biogas-hosting farms in Tuscany. The unit of analysis is the person in charge of plant operations management on farm. We distinguish two main purposes for seeking knowledge, i.e. gathering (i) technical and economic details and funding opportunities, i.e. information, and (ii) solving issues associated to plant and farm management, i.e. know-how (Kogut and Zander, 1992). We went through three steps: (i) identification of the study population; (ii) network questionnaire; (iii) coding and completing the network with perceived relations (Burt, 2010).

Step 1. The study object is biogas adopters’ network of knowledge retrieval from AKIS’ stakeholders, including self-accessible sources. There are two sets of individuals, i.e. seekers (biogas adopters) and suppliers (AKIS’ stakeholders and self-education) of knowledge. The study population has two vertical and horizontal boundaries. (Burt, 2010). Biogas adopters in Tuscany define the vertical boundary. The regional authority for environmental protection of Tuscany (ARPAT) helped us contacting all biogas adopters in Tuscany (technical features and geographical location of plants are available online (ARPAT, 2015)). AKIS’ stakeholders and self-accessed knowledge mentioned by plant adopters define the horizontal boundary of the population. We drafted a preliminary list of AKIS’ stakeholders via interviews with experts, which we refined after questionnaire administration to adopters. Expert interviews followed a draft with two sections. Section one addressed the dynamics of biogas diffusion in Tuscany. Section two was meant for pinpointing the institutions, enterprises, and private practitioners that have had a role in the process of knowledge diffusion across potential biogas adopters (name generator). We interviewed eight experts, which we identified via a snowball procedure starting from two research agronomists from a public university in Tuscany, with expertise on energy cropping. The remainder interviewees were as follows:

• the person in charge of biogas plants’ census at ARPAT;

• a freelance agronomist providing consultancy to biogas adopters in Tuscany;

• a researcher with over twenty-year expertise in the field of agroenergy, working for the Research Centre on Animal Production (CRPA), i.e. the major public research and extension organization that deals with farm biogas in Italy;

• two officers of the Regional Government of Tuscany with over ten-year expertise in rural policy planning in Tuscany;

• an energy engineer who manages biogas plants’ in behalf of an ESCo.

The combination of the eight name generators originated the roster to be used within the network survey to farmers.

Step 2. For collecting relational data, we administered a network questionnaire to the persons in charge of investment decisions on farm. Provided that knowledge is generally a combination of information (coded knowledge, such as technical details) and know-how (tacit knowledge, such as management practicalities) (Kogut and Zander, 1992), we investigated relations such as information and know-how sourcing from AKIS’ stakeholders. The questionnaire included three sections. Section one addressed respondent and farm details, with questions ranging from education, to land use change on farm and labor supply due to plant establishment. Section two had (closed-end) network questions, notably flexible roster-recall tables (Wasserman and Faust, 1994), which respondents could integrate with missing elements. Questions were of the following two types: (i) “To whom did you turn when you needed to decide on biogas adoption?”; (ii) “Why did you turn to that specific actor? [(A) for gathering technical and economic information about the plant (e.g., plant sizing, land requirements, return on investment, funding opportunities; (B) for learning how to manage the plant or the farm, including problem solving]”. Section three included open-ended questions about knowledge exchange relations with other biogas adopters, and type of relationship with listed AKIS’ stakeholders. Respondents could name missing stakeholders. Eventually, we ended up with an improved list of AKIS’ stakeholders that included self-education through books, the web, and attendance to fairs and conferences.

Step 3. We coded the list of institutions, enterprises, and private practitioners to the categories of AKIS’s stakeholders in Italy (Materia, 2012; Caggiano, 2014); we also coded the list of respondents to two homogeneous categories, based on the outcomes of section one of the questionnaire.

The collection of network data from plant adopters occurred across spring and summer 2015. We collected valid questionnaires for 13 out of the 29 operating farm biogas plants in Tuscany (ARPAT, 2015).

2.3.4 Case study

In Italy, there are around 1000 operating farm biogas plants, 90% of which are in the north and mostly associated to intensive dairy and pig farming (Fabbri, 2013). In those farming systems, getting rid of excess livestock waste is a priority, for preventing environmental damage and complying with the Nitrates Directive (91/676/EEC). Instead, farming systems in central and southern Italy are mainly arable, thereby missing excess polluting waste to be used as biogas input. Therefore, plant adoption needs structural change on farm, to rearrange productive activities. This has somewhat prevented biogas from spreading, despite the availability of profitable feed-in tariffs since 2009 (DM 18-12-2008). Pedo-climatic and topographic differences between northern and central-southern Italy might be additional reasons for the uneven distribution of biogas plants across the country.

To date, Tuscany is the only region of central-southern Italy where the introduction of the feed-in tariff scheme has driven relevant adoption of farm biogas. The number of operatfeed-ing plants had increased from four to 29 between 2011 and 2014 (Fabbri et al., 2011; ARPAT, 2015). Research has shown that adopting biogas in some sub-regions of Tuscany could improve farmers’ income, push the supply of rural labor, and help meeting 2020 energy targets (Bartolini et al., 2015; Bartolini et al., forthcoming). However, biogas diffusion in Tuscany has fed a debate around potential plants’ sustainability and the impacts on land and water use across the region.

2.4 Results and discussion

This paragraph depicts the current business models of farm biogas in Italy, based on academic literature review (sub-paragraph 4.1.), and overviews the biogas sector and related business models in Tuscany, based on both network questionnaires and literature review (sub-paragraph 4.2.). Then, we move to the outputs of SNA. Firstly (sub-paragraph 4.3.), we depict the AKIS, focusing on stakeholders’ role within the network (brokerage). Secondly (sub-paragraph 4.4.), we address the types of knowledge that biogas adopters are interested in, and on the patterns of

knowledge retrieval. Lastly (sub-paragraph 4.5.), assess network’s cohesion and potential for knowledge co-creation.

The software for elaborations is UCINET® (Borgatti et al., 2002). Index values are standardized from 0 to 1. All network layouts are based on a spring embedding algorithm, which defines nodes relative position based on the average shortest path between node pairs and degree: the higher the degree the more central the node.

2.4.1 Biogas business models in Italy

Carrosio (2013) is the most relevant of the few academic papers that have analyzed the organizational patterns of the Italian biogas sector, to date. The Author classifies existing biogas business models under four categories: multifunctional farms, entrepreneurial farms, entrepreneurial bioenergy farms, and community bioenergy farms. Multifunctional farms adopt biogas for reaching the maximum possible vertical integration and autonomy from the market; they supply energy resources, while maintaining food/feed production systems. Plant size is set coherently with farm’s potential in terms of biomass supply; then, plants are usually small (around 100 KWh rated power). Entrepreneurial farms pursue the objective of continuous scale enlargement (van der Ploeg, 2008), they adopt biogas to increase profit margins via supply diversification and ecological upgrade. Farms are large (500-1000 ha), at least for the Italian context, and livestock-based. Biogas allows increase the number of livestock units, while complying with the Nitrates Directive. Plant is sized to maximize the returns from electricity sale and does not depend on farm's productive capacity3_{. In Author’s view, this is the major} business model in Italy. Entrepreneurial bioenergy farms have completely shifted from food/feed production to energy cropping. Plant size is decided under a profit maximization logic. Some of those farms have established of joint enterprises with industrial organizations that take over the investment and arrange contracts with plant-host farmers; the latter just deal with biomass production and processing within the plant. Those industrial organizations (Energy Service Companies or ESCos) are enterprises that operate diverse renewable energy plants. Like other EU countries (cf. Hannon et al., 2013), ESCos have quickly widespread in the Italian biogas sector, following the release of the feed-in tariff scheme. Recently, Pantaleo

3 _{Under the Italian regulation, plant rated power cannot exceed 999 kWh to be accounted as farming activities and}

and Colleagues (2014) have published a detailed overview of ESCos’ development and current operating models worldwide. The last, and more innovative, business model category is the community bioenergy farm. The plant is integrated within the local community, which, in turn, supplies biomass and benefits from excess heat production via a private distribution grid. Plant size depends on the supply capacity of the area under community management. In Italy, this model has got popular in some areas of the north (Wirth, 2014).

2.4.2 Adopters’ typology and business models