Ecological Economics 72 (2011) 18–27

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Ecological Economics

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Modeling the bio-refinery industry in rural areas: A participatory approach for policyoptions comparison

Antonio Lopolito a, Gianluca Nardone a, Maurizio Prosperi a, Roberta Sisto b,⁎, Antonio Stasi a

a Department of Production and Innovation in Mediterranean Agriculture and Food Systems (PrIME), University of Foggia, Via Napoli 25-71122 Foggia, Italyb Department of Economics, Mathematics and Statistics (DSEAGMeG), University of Foggia, Via Romolo Caggese 1-71100 Foggia, Italy

⁎ Corresponding author at: Department of DepartmenMerceological and Geographical Science (DSEAGMeGRomolo Caggese 1-71100 Foggia, Italy. Tel.: +39 0881 7

E-mail address: r.sisto@unifg.it (R. Sisto).

0921-8009/$ – see front matter © 2011 Elsevier B.V. Alldoi:10.1016/j.ecolecon.2011.09.010

a b s t r a c t

a r t i c l e i n f o

Article history:Received 9 February 2011Received in revised form 25 August 2011Accepted 12 September 2011Available online 27 October 2011

Keywords:Biorefinery industryPolicy options comparisonRural areasParticipatory approachFuzzy Cognitive Maps

The development of bio-refineries has become a relevant topic in the EU's agenda. However, the promotion ofa new industry in rural areas is typically hindered by the scarcity of human capital, lack of information, infra-structures, and competing interests. In this context, public support is unavoidable to assist promotion of thisinnovative sector. The various policy options reveal some strengths and drawbacks, posing the problem offinding the best trade-off to public decision makers. In this paper we aim at developing a methodology tosupport policy decision making within the biorefinery framework, with the purpose of determining a wayto identify the most suitable policy option given the actual uncertainty in developing the bio-refinery indus-try in rural areas. The empirical experiment, based on a simulation of the enforcement of four identified pol-icy instruments, highlights that, although subsidies and incentives to profitability of dedicated crops appearto have the greatest effects on the development of bio-refinery, the best performances are exhibited by tech-nological innovation and information options.

t of Business Economics, Legal,), University of Foggia, Via53727, +39 320 4394618.

1 For instance, in Ienergy production.

rights reserved.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

The development of bio-refineries has become a relevant topic inthe EU's agenda, since optimum utilization of energy and safeguard-ing of the environment are the two most essential goals. In fact, theintensification of biomass use for energy and goods productioncan potentially reduce the demand of non-renewable resources(e.g. fossil fuels), consequently inducing a reduction in green housegases (GHGs) emissions that are responsible for climate change. Animportant source of raw materials for the bio-refinery industry isrepresented by agricultural crop residues (Cherubini and Ulgiati,2010). Indeed, bio-refinery is perceived as an integrated system ofbio-based firms that produce a wide range of high value goods (che-micals, bio-fuels, food and feed ingredients, biomaterials, includingfibers and power) from biomass raw materials using innovativetechnologies (Bio-refinery Euroview Consortium, 2008).

The main limitation of the use of raw materials from agriculture isrelated to their typical low economic value and energy density. Infact, long distance transportation is a limiting factor in thermodynam-ic and economic terms (Mayfield et al., 2007). For this reason, it ispossible to evince that the bio-refinery industry (or at least the firstproduction lines involving agricultural residues) should be located

as close as possible to the main agricultural or forestry areas. Thisimplies that optimal efficiency in terms of energy and economic termsis achievable only when bio-refineries are settled in rural areas.1 Ac-cordingly, in the long run, the total efficiency is expected to becomparable with fossil refineries.

This may, in turn, represent a valuable opportunity for rural areas torevitalize the economy of local communities. Indeed, some scholarsemphasize that biomass production represents a new source of farmer'sincome, but that it also may stimulate the creation of some new busi-ness and job opportunities on non-agricultural sectors, and thereforefoster rural economic development (Bailey et al., 2011; Domac et al.,2005; Krajnc and Domac, 2007).

However, the promotion of a new industry in rural areas is typicallyhindered by the scarcity of human capital, in terms of availability of hightechnological skills, capabilities, and the lack of financial capital.Regarding bio-refinery in particular, lack of information, infrastructures,and competing interests are also identified as barriers (Mayfield et al.,2007). In this context, public support is unavoidable to assist pro-motion of this innovative sector (Hetemäki et al., 2010; Steenblikand Simón, 2007).

Among the different policy instruments aimed at supporting bio-refinery in rural areas, Steenblik and Simón (2007) identify variouskinds of support as infrastructure investments and direct subsidiesto private investments. In addition, Arthur D. Little Inc. (2001) alsoproposes education, training, R&D support, demonstration projects,

taly 70 km is considered the maximum distance for efficient bio-

Table 1Policy options for biorefinery development.Source: our research

Type of barriers Level of governance

State/suprastate Local

Lack of infrastructures (1) Infrastructureinvestments

(1) Infrastructureinvestments

Lack of humanresources

(2) Education (3) Training

Excessive costs oftechnology deployment

(4) R&D support;(5) direct subsidies toprivate investments

Lack of experience (6) Demonstration projects,(7) benchmarking

Incomplete information (8) Information provisionLack of coordination (9) Voluntary agreementsUnclear market for finalproducts

(10) Targets and mandates;(11) Market price support

(10)Targets and mandates;(12) environmentalregulation

19A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

benchmarking, information provision, voluntary agreements, environ-mental regulation, targets and mandates, and market price support. Aspresented in Section 2, the various policy options reveal some strengthsand drawbacks, posing the problem of finding the best trade-off topublic decision makers.

Various methodologies available in the literature (e.g. input outputanalysis, multi-criteria decision making, cost–benefit analysis, cost-effectiveness analysis) are useful to undertake economic analysis,aimed at supporting policy decision making. However, traditional ap-proaches basically refer to the assumption of complete informationand rational expectations, which seems inadequate when the analysisrefers to innovative projects such as bio-refinery, due to the lack of reli-able information on prices, technology, and stakeholders' involvement.

Nonetheless, there are a few approaches that allow dealing withthe uncertainty and the complexity arising from such projects realizedunder unfavorable conditions (i.e. rural areas). In particular, whenscientific data on previous experiences are not available, the policy-making process would benefit from models based on people'sknowledge (Ozesmi and Ozesmi, 2004). Among the various tools ableto capture stakeholders' views, themost cited in literature are full inter-views (Upham and Tomei, 2010), multi-criteria decision analysis(Elghali et al., 2007), and the Q methodology (Cuppen et al.,2010). In this paper we challenge a Fuzzy Cognitive Map (FCMs)approach to overcome the rationality issue of traditional models, andtherefore to cope with uncertainty and novelty. This analytical tool isbasedon the economics of complex systems (Beltratti et al., 1996; Epsteinand Axtell, 1996). Section 3 deals with this issue and will introduce themethodology.

This paper, in particular, aims at developing a methodology to sup-port policy decision making within the bio-refinery framework, withthepurpose of determining away to identify themost suitable policy op-tion given the actual uncertainty in developing the bio-refinery industryin rural areas.

Section 4 illustrates how this methodology works, applying it to areal case study in the South of Italy. The results of empirical studiesare presented and discussed in Section 5. The paper concludes withsome final remarks in Section 6.

2. Policy Options for Fostering the Bio-refinery Industry

Bio-refinery is a facility system that integrates several biomass con-version technologies to produce power (e.g. woody biomass-firedpower plants, co-firing of biomass with coal or with natural gas, utiliza-tion of landfill gas), fuels (such as bio-ethanol, biodiesel) and a widerange of goods (chemicals, food and feed ingredients, biomaterials, fi-bers) usingmultiple types of feedstock, including harvesting residues ex-tracts from effluents, fractions of pulping liquors, as well as agri-biomass,recycled paper, and municipal and industrial wastes (Bio-refineryEuroview Consortium, 2008; Hetemäki et al., 2010; Taylor, 2008).

The principle underlying the concept of bio-refinery is theachievement of optimum utilization of the various fractions of biomass,potentially leading to increased efficiency and enhanced sustainabilityof biofuel and bioenergy production (Mozaffarian, 2009). It is anattractive concept, capable of gathering growing interest both fromthe economic community and policy makers. The major expectedbenefits are related to energy security enhancement, climate changemitigation, increase of industrial competitiveness, development of dif-ferent organic wastes, and creation of new opportunities for agriculture(Hetemäki et al., 2010).

The location of bio-refinery in rural areas seems to be a promisingopportunity, due to the large availability of rawmaterials from agricul-tural crops andwaste and residues from agro-industry. Public interven-tion plays a crucial role in the exploitation of this potentiality sincevarious barriers need to be overcome. In particular, the operation ofbio-refinery, especially for its early stages, faces lack of infrastructures,lack of human resources, excessive costs of technology deployment,

lack of experience, incomplete information, lack of coordination, andan unclear market for final products. Public support directed to addresssuch hurdles come from different policy domains (energy, industrial,agricultural, and environmental policies) and derive from several gov-ernance levels (state/suprastate, local). Thus, various competencesand matters overlap and lead to the obvious consequence of a largenumber and a fragmented array of alternative options. To grasp anoverall picture of such a complex scenario, a classification based ontwo main dimensions, such as the type of barriers and the level ofgovernance, is proposed (Table 1). It is based on various studies,undertaken either by government agencies or academic research(Arthur D. Little Inc., 2001; Biomass Task Force, 2005; BIOPOL, 2009;Hetemäki et al., 2010; Mozaffarian, 2010; Steenblik and Simón, 2007).

Following is a brief description of the most relevant policy options.

Infrastructure investments. Direct public investments or publicsubsidies are needed in order to create, upgrade, and reinforce theinfrastructure system related with road networks, electric lines,inverters, power generation, communication systems (telephone,internet), energy supply, and energy delivery systems (e.g. heating,cooling transmission). The risk of creating unnecessary facilities,which do not comply with the actual development orientation ofthe rural area, exists for this type of policy option, especiallywhen co-ordination between national and local governance levels is lacking.Education. The creation of human capital in terms of knowledge andcompetences is a typical public function provided through theeducation system (e.g. college and university degrees). Highereducation is boosted by funding R&D activities (e.g. post doctoraldegrees, university spin-offs, and internship programs). In thiscase, coordination with industrial sectors is required in order totailor the education programs with long-term strategies, since therisk of incurring unnecessary competences, thus leading to under-employment, is highly relevant.Training. Social policies aimed at building professional skills mayplay a crucial role in addressing young workers or reconvertingunemployed workers toward new and emergent economic sectors.Typically, this measure operates over a medium term and thusmay reduce the risk of creating unnecessary skills.R&D support. This policy option is aimed at the creation ofscientific knowledge and empirical evidence, which are crucial to-ward developingnew technologies. They are typical public goodsfor which private investments are scarce (usually provided onlyby large firms) and therefore public support is needed, especially

20 A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

when the technology has been conceived for small and mediumfirms.Direct subsidies to private investments. Represent a typical incentivefor bio-refinery schemes (Gilbertson et al., 2008; Mayfield et al.,2007). These types of subsidies aim at lowering both the fixedcosts and the investor risks of new plants, improving the return oninvestment. While subsidies are crucial in stimulating bio-refineryproduction, they are also considered to have the greatest level of dis-tortion on production decisions, potentially leading to an inefficientoutcome. Steenblik and Simón (2007) cite the amplified impact ofthe oil price on the agricultural market, the increase of farm com-modity prices, and reduction of trade between efficient producercountries as examples of distortions arising from price support.

Demonstration projects. This option is suitable for gaining experi-ence from small scale experiments aimed at attracting potentialparticipants' interests (e.g. investors, credit institutes, local publicpolicy makers, suppliers of raw materials, final users) who are notyet aware of the opportunities deriving from the new business.The limited scale of the experiment may hide technical problemsarising from the implementation of large scale plants, leading toexcessive enthusiasms toward the project.

Benchmarking. Accumulating success stories helps to formstakeholders' expectations toward the new technology. A drawbackof this option is that it relies on the limited, although heterogeneous,range of examples that refer to diverse contexts, causingmisinterpre-tation of technical and economic information.Public information provision. The provision of information, bymeansof information programs and consumer education programs at localand national levels, is one of the most important drivers for thedevelopment of new technologies. Its lack can leave people with avague and potentially distorted understanding of the novelty,which may obstruct its development (Mayfield et al., 2007). Publicinformation should be tailored to different stakeholders' require-ments (e.g. technical details of cropping practices for farmers, riskevaluation for private entrepreneurs, impact on the quality of lifefor local citizens).Voluntary agreements are crucial to efficiently coordinate theactivities in order to guarantee the procurement of servicesnecessary for the development of bio-refinery. In particular, thecore actors involved in this coordination process should be thebiomass suppliers (farmers and agro-industry producers) and thebio-refinery. The aim is to reduce transaction costs and to developan efficient logistics system. In this context, the participation ofagro-mechanical service companies, specialized in collection,stocking, and delivery of raw materials, will play a relevant role.Another contribution may be credit and insurance companies'financial support.Targets and mandates. This policy option aims at creating a marketfor bio-fuel and bio-energy and to address regulatory issues. A limit-ing factor of this option is the fact that, in most cases, mandates donot distinguish among biofuels according to their feedstock or pro-duction methods, despite wide differences in environmental costsand benefits. This implies that governments could end up support-ing a fuel that is more expensive and has a higher negative environ-mental impact than its corresponding petroleum product (Steenblikand Simón, 2007).Market price support. Since they are subsidies determined on thequantity produced, their effect is to rapidly boost the supplyside. As in the case of direct subsidies, they cause market distor-tions toward excessive supply, which cannot be cleared by the

market. In addition, higher prices of final products generate equityproblems due to the transfer of wealth from consumers/tax-payers to producers.Environmental regulation. By strengthening environmental stan-dards, environmental regulation creates opportunities for greenproducts. Within this category falls also environmental tax on pol-luting goods or services. One undesirable effect of this option isthe increase of prices of related goods and services, resulting in awelfare loss.

3. Dealing with the Complexity of Bio-refinery Development

3.1. Arguments to Adopt an Approach Based on the Theory of ComplexSystems

As highlighted in the previous section, the development of bio-refinery is affected by many barriers that may be overcome by adopt-ing some policy measures with an effectiveness that depends on thespecific context, and also on their mutual coordination. This is thecause of unavoidable uncertainty, characterized by:

– unpredictability: the effect of a certain policy option is affected bythe activation and the level of other policy options and the state ofcontext variables. This leads to the nonlinear response of the policyoutcome and the rejection of the “ceteris paribus condition”;

– ignorance: lack of full and reliable information, due to the fact thatthe development of a bio-refinery in rural areas still represents aninstance of new business opportunities, and experiences are stillvery limited;

– adaptive behavior: the local community may play a relevant role,either in providing the necessary political consensus in the devel-opment of the project (see Mozaffarian, 2009), or by accepting the(although limited) negative effects exerted.

It follows that the neo-classical paradigm is unsuitable to disen-tangle complexities of the real world and, therefore, adoption of analternative approach is preferred. Recent studies have proposed acomplex theory systems approach, which paves the way to adoptionof empiricism of economic analysis. A comprehensive discussion ofthe main advantages of complex theory systems, compared to neo-classical paradigms, is provided by Ramos-Martin (2003).

This approach aims at providing tools to support decision making(Le Moigne, 1995; Ramos-Martin, 2003; Zhang, 2002). This is partic-ularly relevant in the case of large projects with rising long termeffects. In this domain, policy makers need further insights on the ef-fect of their decisions that cannot be provided by the neo-classicaltools which are based on assumptions that are rather unrealistic inthe case of new business formations in rural areas, such as theavailability of complete information, the achievement of equilibrium,the rational behavior of economic agents, and the “ceteris paribus”condition.

In contrast, the theory of complex systems emphasizes the role ofempiricism, where knowledge of the world is generated by experi-ence, rather than by reason (Ramsay, 1998). In his study, Heckman(2001) claims that, “empirical research is intrinsically an inductiveactivity, building up generalizations from data, and using data totest competing models, to evaluate policies and to forecast the effectsof new policies or modifications of existing policies”.

When social acceptance is a pre-condition for the projects' successand scientific data on previous experiences are not available for thenovel nature of the projects, the policy-making process benefitsfrom models based on people's knowledge (Ozesmi and Ozesmi,2004). In this view, stakeholders are playing increasingly significantroles in co-shaping environmental policy with government (Uphamand Tomei, 2010). The basic assumption is that members of thelocal community, who have been living and working in the same

21A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

system for a long time, provide the best source of information (localembedded knowledge) about processes and mechanisms that maycontribute to the success of the project. The literature on themost suit-able methodological tools able to capture stakeholders' views is quitevarious. In particular, Upham and Tomei (2010) suggest applying fullinterviews; Elghali et al. (2007) develop amulti-criteria decision analy-sis,while Cuppen et al. (2010) propose theQmethodology. In this paperwe propose a Fuzzy Cognitive Map (FCMs) approach to overcome therationality issue of traditional models, and therefore to cope with theuncertainty and novelty, which are the typical features of economiccomplex systems (Beltratti et al., 1996; Epstein and Axtell, 1996).

3.2. Fuzzy Cognitive Mapping

An analytical tool required to perform a study of a complex system,is based on the collection and processing of information related to theelements participating in the system and the connection structurelinking each element. Participatory approaches have been largelyadopted to investigate the perception of local communities towardthe development of large projects. Some scholars (Giupponi et Al.,2008; Thrupp et al., 1994) argue that participatory methods havemany advantages such as facilitating planning of strategies and activi-ties that better meet the needs of people, enabling local people toexpress their views and planners/policy makers to gain better under-standing of local needs and to develop interdisciplinary interactionand integrated views, thus tending toward “sustainable development”objectives, enhancing democratic processes and equity in planningand decision-making. The overall advantage relies on the reduction ofconflicts and, therefore, on higher public acceptance and effectiveness.

To our knowledge, there is very little literature, academic or profes-sional, on participatory policy's tools with respect to bio-refinery issues.

The approach considered here is FCMs, which is a participatorymethod that is rather easy to adopt because of its comprehensibilityalso by non-professionals. It has a high level of integration (necessaryfor complex issues related to rural development), it can be performedin a short time, and it provides an overall description of the system.The main advantages of FCMs, with respect to other semi-quantitativemethods or qualitative modeling methods are described in van Vlietet al. (2010).2

First of all, it is a flexible and manageable way to deal with dynamicsystems. Moreover, FCMs allow feedbacks among variables to be con-sidered, and this is very useful in providing causal relationshipsamong system elements (Coban and Secme, 2005). FCMs are simpleto build and, even if in the initial stage of mapping some relevant con-cepts are omitted, these can be quickly included to provide a completepicture of the scenario. Secondly, they dealwith qualitative information,which can be obtained by local stakeholders, thus allowing the re-searcher to overcome the lack of reliable secondary data. Thirdly, it iseasy to build on the knowledge of local people and, although it doesnot make quantitative predictions, it is suitable for investigating theresulting effects imposed by the changes in certain conditions thatmight affect the whole system (Ozesmi and Ozesmi, 2004).

Basically, FCMs can be perceived within the set of qualitativemodels describing how a given system works (Ozesmi and Ozesmi,2004). They have been introduced by Tolman (1948) as an applica-tion for psychology research and have been further developed overtime (Axelrod, 1976; Kosko, 1986; Yaman and Polat, 2009). An FCMis made by two fundamental elements, concepts or variables (thesecan represent single measurable physical quantity or complex aggre-gate and abstract ideas) and the relationships among them. Thesystem represented by the FCM is developed via a participatoryapproach in which insights about the operation of the system are

2 Actually, a large variety of tools and methods are available to tackle the complexityin economic studies (artificial neural networks, genetic algorithms, non linear equationsystems and agent based modeling).

drawn from the knowledge and the personal perception and expecta-tions of the actors who are part of the system. Once the map isobtained, its structure can be analyzed by means of graph theory,which allows for insights about the complexity of the whole systemand roles of its variables. Another analytical feature of the FCMs isthat the system outcomes can be determined through fuzzy cognitiveinference. This allows for the consideration of two research goals.First, it is possible to know where the system will go if things remainas they are (whether it converges toward a stable state or settles intoa limit cycle). Furthermore, it is also possible to ask “what-if” ques-tions and determine the state of the system when various policymixes are implemented (Kosko, 1987). In this research we proposethe following methodological steps:

- Activating the (individual) brain storming asking the main researchquestion;

- Aggregating the relevant variables and drawing of the social cogni-tive map with a participatory approach;

- Analyzing the structure of the social cognitive map;- Analyzing the outcomes of the social cognitive map and simulatingdifferent policy scenarios.

4. The Empirical Experiment

4.1. Activating (Individual) Brain Storming Asking the Main ResearchQuestion

The analysis was conducted in April 2009, in the province of Foggia(Apulia region, Italy). The relevance of the study area lies in the fact thatit is one of the largest agricultural areas in Southern Italy and has a highpotential for producing agricultural raw materials (co-products andby-products) suitable for bio-refinery processing.

In order to draw the cognitive map, 18 people were invited to de-scribe and discuss their perceptions and expectations toward thesocio-economic aspects related to the development of bio-refinery intheir own area and about the use of raw materials. According to theexisting relevant literature (Krajnc and Domac, 2007; Rösch et al.,2009; Scholes, 1998; Yiridoe et al., 2009), different groups of stake-holders were considered: farmers, private entrepreneurs, researchers,technological transfer agents, consumers, local citizens, policy makers,and institutions. In the end, 10 people accepted to collaborate in theresearch. They had different backgrounds, such as producers,consumers, academic researchers (agronomist, biologist, economist), arepresentative of the local government, environmental group, con-sumers' association, and had various levels of knowledge on the topicof the investigation. First of all, the participants were informed aboutthe purpose of the analysis providing them with only a few essentialelements. This is to help them understand the objective of thediscussion, while avoiding any biased information. Then, theparticipants were asked to respond to the main research question“What sort of effects do you expect to derive from the development ofa bio-refinery industry in this area?” Each stakeholder describedthe most relevant variables. In order to make this approachsuitable for the objective of the current study, stakeholders wereasked to identify some variables suitable to be converted intopolicy options. In fact, at the end of this phase, among a total of30 variables that were identified, described and discussed, someof them were also suitable to be treated as policy options.

4.2. Aggregating the Relevant Variables and Drawing the Social CognitiveMap with a Participatory Approach

During the discussion section, once the participants reached a suffi-cient consensus on the meaning of each concept, they were asked tocode the concepts into a concise form, in order to achieve a compromisebetween the preciseness required by the logical definition of variables

Table 2Variable list.

Senders:1: Public information (E.0.6.6); 2: Subsidies to bio-refinery (E.0.5.5); 3: Profitabilityof biomass crops (E.0.5.5); 4: Geographic dispersion of biomass (En.0.5.5); 5:Availability of biomass from spontaneous species (En.0.2.2).

Transmitters:6: Development of bio-refinery industry (E.11.19.30); 7: Technological innovation(E.2.8.10); 8: Enhanced use of regional agricultural vocation (En.6.3.9); 9:Transport costs (E.5.3.8); 10: Industrial diversification (E.4.4.8); 11: Participationin public decision making (S.2.4.6); 12: Economies of scale (E.3.3.6); 13: Land usechange (En.2.3.5); 14: Market distortions (E.4.1.5); 15: Induced industrialdevelopment (E.2.2.4); 16: Uncertainty toward the new industry (S.2.1.3).

Receivers:17: Development of residues and wastes (En.6.0.6); 18: Job opportunities (S.5.0.5);19: Biomass supply from dedicated crops (En.3.0.3); 20: Agricultural sectorprofitability (E.3.0.3); 21: Consumer goods' prices (E.2.0.2); 22: Territory'sreputation (S.2.0.2); 23: Loss in residents' well-being (S.2.0.2); 24: Dispersion ofbio-refinery plants (E.2.0.2); 25: Concentrated bio-refinery settlements (E.2.0.2);26: Loss in competitiveness for firms not involved (E.2.0.2); 27: Technologicaltransfer (E.2.0.2).

The following abbreviations mentioned after the name indicate the nature of thevariable: E = economic, S = social, En. = environmental, its in-degree; out-degreeand centrality.

22 A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

and the unavoidable vagueness of natural human language. This pro-cess led to a reduction of the original 30 to 27 variables, which are listedin Table 2. According to their logical meaning, the 27 variables havebeen classified in the three following categories: economic (16 vari-ables), social (5 variables), and environmental (6 variables).

Given that the main policy objective is the development of the bio-refinery industry3 (var. #6), the next step is to identify, according tostakeholders' perceptions, the variables most suitable to be treatedas policy drivers. During the social cognitive process, stakeholders in-dicated the following four policy options:

– option 1) subsidies for bio-refinery (var. #2), that are perceived asespecially important at the start-up stage of industrial researchand investments;

– option 2) profitability of biomass crops (var. #3), intended as allkinds of measures (e.g. subsidies and incentives to dedicatedcrops) aimed at orientating agricultural productions toward bio-mass crops;

– option 3) technological innovation (var. #7), perceived by stake-holders as an intervention directed to foster R&D activities of pub-lic institutions or private investors

– option 4) public information (var. #1), is viewed as crucial in stim-ulating the emergence of new expectations and needs (e.g. thesubstitution of traditional goods with more environmentallyfriendly options derived from bio-refinery processes) for the po-tential consumers, and interest and opportunity for the potentialinvestors.

On the other hand, stakeholders indicated five potential undesir-able policy impacts. These are medium/long term effects that are,according to the perception of stakeholders, directly or indirectlycaused by the adopted policy options. Here it is argued that thelower the impact of the policy on these 5 indicators, then the moredesirable it is. Considering this fact, the variables representing thesefive undesirable impacts are briefly reviewed in the following.

Land use change (var. #13). This is perceived as a drawback to thebio-refinery development process since it can be associated to bio-diversity erosion and decline of ecosystems. Indeed, the enlarge-ment of cultivated superficies for the production of dedicated

3 This variable has emerged as one of the most influential. This confirms the initialexpectation and is consistent with a social cognitive process built around the item ofbio-refinery.

crops can cause loss of forests and natural grass, negatively affect-ing natural habitats and ecosystems.Market distortion (var. #14). Is perceived to be related to the equi-librium of the local economic system. It means that stakeholdersare concerned about potential economic distortions derivingfrom competition limitations due to public incentives.Consumer goods price (var. #21). These fears derive from the factthat the policy directed to boost the bio-refinery industry canalso have a restrictive effect on the supply of commodities. More-over, the (partial) substitution of petroleum-based technologieswith those derived from the bio-refinery industry may cause a sig-nificant increase of production costs, leading to an overall increasein consumers' final goods prices.Loss of residents' well-being (var. #23). This variable is also re-ceived negatively affected by the enhancement of the bio-refineryindustry. This negative effect emerges when decisions concerningthe use of natural resources, or infrastructure developments areperceived to be unfair.Loss in competitiveness of firms not involved in bio-refinery (var.#26). This variable is a receiver. It can be perceived as a particularform of market distortion. It increases when the firms involved inthe bio-refinery industry are subsidized by public policy.

Finally, participants were asked to specify the causal relationshipsbetween each variable and their intensity according to Kosko (1986)into three discrete and increasing degrees: weak (strength equal to0.33), moderate (0.66), and strong (1.00). The final outcome is theFCM in graphical form, representing the starting point for the net-work analysis (Fig. 1).

4.3. Analyzing the Structure of the Social Cognitive Map

In order to analyze the structure of the social cognitive map weused the social network analysis (SNA), which is based on the graphtheory (Wasserman and Faust, 1994). The next step is to representthe map in the so-called adjacency matrix (Harary et al., 1965) formedby the intersection of the 27 concepts (Table 3). SNA offers several in-dexes to analyze this matrix (Wasserman and Faust, 1994). Wefocused on the punctual indexes (described in Table 4) which allowdistinguishing between three types of variables (Coban and Secme,2005): i) senders (also called forcing functions, or givens, or tails),which send stimulus toward other variables (positive out-degree)but do not receive it (zero in-degree); ii) transmitters, which bothreceive and transmit relations, they are the connecting fabric of thesystem; and iii) receivers (also called utility variables, or ends, orheads) that have a positive in-degree and zero out-degree. Resultsare reported in Table 2.

The map depicts a rather complex system, due to the fact that re-ceivers are much more numerous than senders, meaning that the sys-tem can generate a lot of outcomes using only a few controllingforces. Another characteristic denoting the complexity of the systemis the high proportion of transmitters, which play a relevant role inspreading the stimulus produced by senders to the whole system.The SNA insights represent the four identified policy options as thebasis for the simulations.

4.4. Analyzing the Outcomes of the Social Cognitive Map and SimulatingDifferent Policy Scenarios

The simulation is carried out using the auto-associative neuralnetwork method (Reimann, 1998). In the neural network language,the quantitative attributes of the FCM are the nodes (the variables)and the links (see Fig. 2). This method does not concern the structureof the system but rather its outcomes and dynamics. Following this

Fig. 1. Biorefinery Fuzzy Cognitive Map.

23A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

method, the value of each variable (Ci) in an iteration (t) can becomputed as:

Cti ¼ f ∑

n

j¼1Ct−1j wji þ Ct−1

i

!ð1Þ

where Cit and Ci

t−1 are the values of the node i at the end and at thebeginning of the iteration, respectively. Cjt−1 and e Cj

t are the values

Table 3The adjacency matrix of the FCM.

Variables⁎

From⁎⁎To⁎⁎⁎

6 7 8 9 10 11 12 13 14 15

1 0.672 0.67 0.673 1.00 0.674 1.005 0.676 0.67 0.67 0.677 1.00 0.678 1.009 −1.0010 0.6711 −0.6712 1.0013141516 −0.33

⁎ For the name of the variables see Table 1 (numbers correspond).⁎⁎ Empty rows omitted.⁎⁎⁎ Empty columns omitted.

of the subsequent node j at the beginning and at the end of the itera-tion, respectively. wji is the corresponding strength of the link fromnode j to node i, and f is a threshold function that transforms theresult of the multiplication in the interval [0,1] wherein the nodestake their values (Yaman and Polat, 2009). Usually the logistic func-tion that assumes the form 1/(1+e− 1x) is used. As pointed out byOzesmi and Ozesmi (2004), this non-negative transformationallows for a better understanding of activation levels of variablesand allows a qualitative comparison among them. The simulation

16 17 18 19 20 21 22 23 24 25 26 27

−0.67 0.670.33

0.67

1.00 0.33 1.00 0.67 0.67 0.671.00

0.670.67

1.000.33

0.67

Table 4Punctual indices for structural analysis.

Index Formulation Description

In-Degree (iDvi) iDvi ¼ ∑K¼1

Naki

The in-degree shows the cumulativestrength of connections (aki) entering thevariable i and coming from other variables k.N is the number of variables.

Out-Degree (iOvi).iOvi ¼ ∑

K¼1

Naik

The out-degree shows the cumulativestrength of connections (aik) exiting fromthe variable i and reaching the othervariables k. N is the number of variables.

Centrality(or total degree)

Ci= iDvi+ iOvi Centrality describes the contribution of avariable in a cognitive map by showing howa variable is connected to others and thecumulative strength of these links. Thisindex is calculated as the sum of the in-degree and out-degree indices.

24 A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

starts attaching a specific value (usually 1) as the initial state of Ci.The iteration is repeated until the system does not converge to anequilibrium point, called steady state.

Different policy scenarios can also be compared by trigging thenodes corresponding to the variables acting as possible policy drivers,in order to evaluate their influence on the system. Since nodes are re-ciprocally (directly or indirectly) interconnected, we propose a pres-sure indicator (Pi) that the system can exert on the node i (genericcomponent of the system) in a given moment, defined as follows:

Pi ¼ Cpwpi

� �þ ∑

n

j¼1Cjwji

!ð2Þ

where Cp is the value of the node affected by the policy enforcementat the end of the simulation,wpi is the weight of the link from node p tonode i, Cj is the value of the variable j at the end of the simulation andwji

has the same meaning above. This formulation allows distinguishingbetween the various effects of the policy enforcement since the firstelement enclosed within parenthesis measures its direct effect on thevariable i, while the second element in parenthesis represents theindirect effect (effect mediated by variable j) (Fig. 2).

5. Results

After drawing and codifying stakeholders' perceptions toward a bio-refinery system, we have also performed simulations on the resulting

Fig. 2. Nodes and links in neural networks.

FCM. The basis for such an analysis is themap (Fig. 1) and its numericalrepresentation in the form of adjacency matrix (Table 3).

The results of the various runs are summarized in Tables 5 and 6.Table 5 accounts for the effects of the policy options in terms of devel-opment of the bio-refinery (row 1) and in production of undesirableimpacts (rows 2–6). In more detail, the first column reports the stead-y-state values, and indicates the direction of the system without spe-cific policy intervention. This calculation gives us an idea of therelative importance of the variables in the system. As it emerges, itcan be observed that the development of a bio-refinery industryraises concerns in stakeholders, in particular in terms of market dis-tortions, which exhibits the highest value, while the effect on landuse is the lowest. Obviously, the ideal policy is able to increase thevalue of bio-refinery development, while reducing undesired impacts.However a trade-off between desired and undesired effects is a morerealistic expectation of the policy. In fact, each of the four simulatedpolicy options is effective toward the development of bio-refinery,but also produces some undesired effects. As it emerges, the greatesteffect on the bio-refinery industry is exerted by option 1 (subsidies),option 2 has a medium effect, while technological development andinformation provision appears relatively less important. A more de-tailed description of the effects of each policy option is providedlater in this section.

In order to grasp the overall (direct and indirect) effectiveness andpressure of the policy options, Eq. (2) has been applied. Changes withrespect to the steady-state values are shown in Table 6. The first row re-ports the effectiveness of each policy option in developing bio-refinery.In the second row, there is ameasure of the overall pressure (calculatedas the sum of pressure on the five undesirable impacts) that the systemexerts in terms of undesirable impacts. Both the effectiveness and theoverall impact are distinguished in direct and indirect effects. Thethird row reports a measure of the performance of the policy op-tions, perceived as the differences among the effectiveness andthe overall pressure.

Following is a detailed description of the effects of the four policyoptions.

Option 1 “subsidies for bio-refinery”. In comparison with the otheroptions, subsidies offer the greatest contribution in terms of bio-refinerydevelopment (0.0401). This is due to the fact that this policy variable isthe only one directly connected to variable #6 (Fig. 3). Although verysmall, indirect effects are also present. These, however, are caused by aloop among variables #6–#7–#8, which is a mechanism that is alsopresent in the other three policies.

According to the economic theory, the main undesired impact ofsubsidies is represented by market distortions (var. #14, whichincreases of 0.0683). As a consequence, another negative impact isrepresented by the loss of competitiveness offirms that are not involvedin the bio-refinery industry (var. #26, 0.0445). This drawback of subsi-dies is known as “Dutch disease” in the economic literature and appearswhen excessive public support in a specific sector (usually primarysector) causes decline in other sectors (usuallymanufacturing) (Cordenand Neary, 1982).

Some other negligible effects are exerted on consumer goodsprices (var. #21) and loss in residents' well-being (var. #23).

The net effect (−0.2685) reveals that this policy option is notpromising in the medium/long run, since its pressure on undesiredimpacts exceeds its effectiveness.

Option 2 “profitability enhancement of biomass crops”. This option isthe second most effective to achieve the objective (0.0105), but farless effective than option 1. However, this is the only policy posinga threat to land use change. In fact, subsidies to dedicated cropsmay induce farmers to dramatically change their cropping patternsby introducing alien species, which may not be suitable to the localenvironment.

Related economic fears (var. #14, #21, #26) are negligible sincethey are indirectly affected by this policy (Fig. 4).

Table 6Performance of policy options (differences with respect to steady state).

Absolute value Changes

Steady-state Option 1 Option 2 Option 3 Option 4

Subs. to bio-refinery Profitability of biomass crops Technologic. develop. Public information

Effectiveness (tot.)a

- direct- indirect

1.6556 0.33450.33330.0011

0.08020.00000.0802

0.06060.00000.0606

0.02920.00000.0292

Overall press. (tot.)b

- direct- indirect

2.7491 0.60300.50000.1030

0.35480.33330.0215

0.01630.00000.0163

0.00800.00000.0080

Performancec −1.0936 −0.2685 −0.2746 0.0443 0.0212

Italics indicates the subcomponents (direct and indirect effects) of both Effectiveness and Overall pressure.a It is calculated by applying Eq. (2) to the variable "Development of the bio-refinery".b It is the sum of the pressures calculated by applying Eq. (2) on the five undesirable impacts.c Performance is calculated as the difference between total effectiveness and overall pressure.

Table 5Effects of alternative policy options.

Absolute value Changes (with respect to steady-state)

Steady-state Option 1 Option 2 Option 3 Option 4

Subs. to bio-refinery Profitability of biomass crops Technologic. develop. Public information

Objective of the policy1) Development of bio-refinery 0.8396 0.0401 0.0105 0.0080 0.0039

Undesired impacts2) Land use change 0.5826 0.0000 0.0782 0.0000 0.00003) Market distortions 0.7095 0.0683 0.0014 0.0011 0.00054) Consumer goods prices 0.6364 0.0062 0.0016 0.0012 0.00065) Loss in residents well-being 0.6364 0.0062 0.0016 0.0012 0.00066) Loss in competitiveness for firms not involved 0.5995 0.0445 0.0001 0.0001 0.00004Total impact (sum of variables from 2 to 6) 0.1252 0.0830 0.0883 0.00178

25A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

On the whole, the performance of this option is the worse(−0.2746).

Option 3 “Technological innovation”. This option operates directlyon a virtuous self reinforcing loop (among var. #6–#7–#8, seeFig. 5). Therefore, although this policy objective's contribution is rather

Fig. 3. Option 1 subsidies for bio-refinery.

small (0.008), it appears promising. Actually, technological innova-tion requires long term commitment in order to exert a positivetriggering effect.

Furthermore, negative impacts of this option are negligible and,consequently, this is the most promising policy option.

Option 4 “provision of public information”. Due to the long chain ofeffects (var. #1, #11, #9, #12, #6, see Fig. 6), the stimulus provided byinformation provision is very low: the lowest in comparison with theother policies (0.0039). Its operation relies on facilitating coordinationamong economic agents (var. #11) that reduce logistic costs (var. #9),and support the establishment of economies of scale (var. #12) withan overall effect on production cost reduction. As it emerges, the unde-sired impacts of this policy are almost nil. Thus, paradoxically, this is thesecond best promising policy.

6. Final Remarks

Awareness and concern for the provision of green energy as ameansto prevent the exhaustion of fossil energy sources and to mitigateclimate change, stem from the development of bio-refinery schemesfed with biomass derived from agricultural crops. This raw material ischaracterized by low energy concentrations and, therefore, a strategyto reduce logistics costs is crucial in order to achieve economic feasibil-ity of the project. For this reason, rural areas are the most favoritecandidate sites for the implementation of bio-refineries.

However, the development of industrial projects in rural areas isobstructed by a series of obstacles, which may undermine the feasi-bility and profitability of the investment. Under these conditions,the role of different levels of public authorities (state/suprastate andlocal) is the direct intervention, by implementation of some policy

Fig. 6. Option 4 provision of public information.

Fig. 4. Option 2 profitability enhancement of biomass crops.

26 A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

instruments. Among these policy options, direct subsidies to pro-ducers are the most popular and determine immediate effects interms of capturing the interest of private investors and stimulatingentrepreneurship. However, several drawbacks are also expected,which may affect different social groups of the local community.

In order to investigate possible desired and undesired effects of pol-icy intervention, this paper has presented a comparison between fourpolicy options identified by local stakeholders as the most suitable toenhance the development of a bio-refinery in a local community locatedin the South of Italy. Themethodological approach of FCMs, proposed inthis study, is based on complex theory. In comparison with more tradi-tional economic tools, it allows for a long-term analysis by consideringthe viewpoints of the stakeholders involved in the project.

Fig. 5. Option 3 technological innovation.

The empirical experiment is based on a simulation of the enforce-ment of the four identified policy instruments: subsidies to bio-refinery,profitability enhancement of biomass crops, technological innovation,and public information provision.We have considered the effectivenessof each policy option in terms of the enhancement of bio-refinery.Moreover, the analysis has also investigated the pressure the policiesexert on undesired impacts. Finally, effectiveness and pressure exhib-ited by various policies have also been disentangled in direct and indi-rect effects.

Comparison of the output highlighted that, although subsidies andincentives to profitability of dedicated crops appear to have the great-est effects on the development of bio-refinery, the best performancesare exhibited by technological innovation and information options.This is due to the fact that technology innovation and informationhave the potential to exploit virtuous partners that are able to fosterthe bio-refinery industry with relatively low impacts. In contrast, op-tions based on subsidies and incentives to dedicated crops exert highpressure on undesired impacts, thus potentially resulting in unsus-tainable patterns.

These findings contrast against the current policy decisions that, atstate/suprastate level, are placing a significant risk on subsidies in-struments. Thus, some policy efforts should be redirected from subsi-dies and financial instruments to R&D activities and informationprograms, which are expected to succeed in the medium/long term.

This result should be considered in light of some caveats. In partic-ular, the study aims at showing the potentiality and flexibility of acognitive participative approach as FCMs to policy issues. It is basedon limited participation by experts and stakeholders at the locallevel. At present, the lessons and insights drawn from this experimentare used to support the Local Action Groups (the major rural develop-ment agencies) in planning projects that will gain a high consensusamong the local community, who are supposed to co-operate forthe success of the projects. Indeed, the main upshot of FCM is thatthe method can provide an artificial consensus between the differentparties and reveal the degree of similarity of the stakeholder groups'viewpoints. This is particularly useful for determining points that liebetween different stakeholders' perceptions and gaining insightsinto which groups are likely to need more work in order to reach anagreement. It is unlikely that any final implementation plan will ex-actly agree with all stakeholders' preferences, but FCM can assist inadding transparency to the whole process and in building trust andcredibility of the implementing body (Mouratiadou and Moran,2007). The fact that the model provides an abstract and simplified

27A. Lopolito et al. / Ecological Economics 72 (2011) 18–27

representation of the real world, depending on the subjective percep-tion of the participating stakeholders, implies that realistic and robustsupport of policy making should be based on experiment replicationin the same area with different groups.

Acknowledgments

The authors thank the anonymous reviewers for their valuablecomments on an earlier version of this article.

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