Technological Innovation and the Importance of Economies of Agglomeration and Modular Design Final...

8/6/2019 Technological Innovation and the Importance of Economies of Agglomeration and Modular Design Final Draft

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Technological Innovation and the Importance of

Economies of Agglomeration and Modular DesignStimulating Distributed Open Innovation Networks within the Central

Appalachian Region

2010

J. Eric Mathis

The JOBS Project

1/1/2011


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Abstract: Within the Central Appalachian region, rural communities are challenged bytheir low population density, less advanced technology activities, and lower innovativecapacity. Stimulating technological innovation requires that the rural infrastructureincludes roads, waterways, power grids, institutional structures and networks that aretied to a central nucleus, acting as an incubator for new ideas that draw on collective

resources. Collaborations between formal organizations (nonprofit organizations,research and development (R&D) firms, renewable energy (RE) companies) andinformal groups (energy generation employees, community stakeholders) can combineknowledge to guide development in the RE sector, promoting openness and diminishingthe limitations of regional isolation. With this collaborative model, a primary driver oftechnological innovation is connecting the lived experiences of employees to the R&Dprocesses of technology development. The employees firsthand knowledge of the REproduction processes (e.g., operation and maintenance) can provide valuable insight forimproving those technologies. In turn, this structure stimulates solutions-based thinking

by connecting the experiences or tacit knowledge of the RE employees with thedevelopers and manufacturers. An open forum for employee and communitystakeholder feedback can help to push forward thinking within the economicallydisadvantaged and mountainous regions found in the coal dependent counties ofCentral Appalachia. The employees can become a central part to these rural innovationclusters throughout the life cycle of a particular RE project. These informal networks of

experienced employees can effectively stimulate technological innovation, in turndrawing industry into the Central Appalachian region.

Keywords: Economies of Scale; Economies of Agglomeration; Modular Design;

Endogenous Growth Model; Neo-Economic Growth Model;Sustainable Development;Technological Evolution; Tacit Knowledge; Open Innovation


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1 Introduction

Adam Smith identified the division of labor and specialization as the two key means toachieve larger financial returns on production. Through specialization, employees wouldnot only be able to focus on specific tasks, but would, with time, improve the skills

necessary to perform tasks. Tasks performed better and faster lead to increasedproduction levels. While Adam Smith describes a model for increased efficiencythrough economies of scale, he does not account for the efficiencies present indistributed open innovation networks and their ability to stimulate technologicalinnovation, specifically the efficiencies associated with economies of agglomeration.

This paper is explicitly about the economic benefits of technological innovation; ourprimary concern is to highlight the benefits of innovation in relation to entrepreneurshipand the strength of institutions (i.e., an endogenous growth model) as opposed to thereverse, stimulating market flows on the level of price (i.e., neo-classical growth model),with technological innovation acting as a secondary condition for maintaining a thriving

market. Limitations of the neo-classical model include its failure to take account ofentrepreneurship (which may be catalyst behind economic growth) and the strength ofinstitutions (which facilitate economic growth). In addition, it does not explain how orwhy technological progress occurs. These limitations have led to the development ofendogenous growth theory, which develops technological progress and/or knowledgeaccumulation internally. Unlike previous classical models of economic development, theendogenous growth model does not see technology as a given, but as a product ofeconomic activity; additionally, endogenous growth theory holds that growth is due tothe increasing returns characterized by knowledge and technology as opposed to thediminishing returns characterized by physical capital (Cortright, 2001).

We wish to expand upon the endogenous model by situating its knowledge/technologynexus within a model of sustainable development that we believe accounts for thelimited nature of this nexus. In this framework, knowledge would be viewed much liketechnology; technological development exists in the endogenous growth model as aprimary driver of economic growth, and its novel development emerges from within anetwork of related industry actors. Therefore, we also view knowledge development asa primary driver of economic growth, and similar to technological advances, we viewknowledge development as being made possible by a supporting network of previouslyspecified tacit as well as codified knowledges. This nexus is based on the assumptionthat knowledge and technology can be infinitely shared and reused, that is, we canaccumulate knowledge and technology without limit, and therefore they are not subjectto the law of diminishing returns. Simply put, Joseph Cortright explains, One special

aspect of knowledge makes it critical to growth. Knowledge is subject to increasingreturns because it is a non-rival good (Cortright, 2001). This is simply not the case ifone accounts for the material transactions or informal processes that are involved whileproducing a particular type of knowledge and/or technology. For example, the materialsneeded to transport a research scientist to her/his job on a day-to-day basis can beseen as one of many possible transaction costs. By utilizing sustainable development,


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we believe that one can begin to supplement the limited nature of theknowledge/technology nexus - when situated solely within the endogenous growthmodel - by contexualizing its production within a reflexive development model whichaccounts for the inherent limitations found in any material system. For example, byadopting an open innovation model, regional R&D firms can reduce various transaction

costs that are typically associated with the traditional closed innovation model. We willdiscuss this in further detail in section 3.

This theory, along with an emphasis on sustainable development, will help us negotiatedevelopment within Central Appalachia by synthesizing a resource-based economy witha knowledge-based economy. This approach underscores the point that the economicprocesses that create and diffuse new knowledge are critical to shaping the growth ofurban and rural communities and individual firms. In this light, it is important to considerthat we should be reassessing the importance of institutions as providers of aframework for growth (Cortright, 2001). One way we can reconceptualize institutions inthis manner is to view them as actors to minimize unwarranted technological lock-in orpath dependence. Technological lock-ins occurs because of technical interrelatedness,economies of scale, and the quasi-irreversibility of innovation and development (David,1985). As such, lock-in occurs both in merited and unmerited situations; in the case oflock-in of particular, inefficient technologies and arrangements it is not necessary formarket forces to automatically correct these inefficient outcomes. Additionally, whilelock-ins typically refers to the routine adherence to one particular physical piece oftechnology, the same routine adherence can be seen on a larger economic level. Someeconomic theorists see economic actors, such as business firms and managers, ascreatures of routine they follow certain successful beliefs and behaviors and onlychange when their routines fail to work (Cortright, 2001). Therefore an alternativemethod of correcting inefficiencies due to lock-ins and industrial routine is needed; ifinstitutions were utilized to decrease the occurrence of these inefficiencies, there would

be room for additional innovation and, in turn, sustainable economic development. Theopen innovation model is essential for achieving sustainable innovation.

Generally, economists are understood as experts in conceptualizing flows of capital andquantifying these flows in terms of price. This paper poses an alternative and equallyvalid model, one that considers price as well as the positive feedbacks of openinnovation in relation to a particular technologys ability to remain competitive in themarket. Within this model, businesses are understood as being competitive according totheir ability to remain flexible and creative in order to meet market demands, while alsoremaining competitive on the level of price as well as encouraging sustainableeconomic growth.

It is the assumption of the development models presented in this paper, and promotedby the JOBS Project, that Central Appalachian communities who actively promote therural area as one rich in natural, cultural, and human assets will typically fosterincreased investment into their region. These same strategies which engage localresidents and RE employees in economic development help to increaseentrepreneurship, improve education, and provide rural communities with a unique


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means of capitalizing on advanced RE technologies, better enabling their ability to takepart in Americas energy transition. Moreover, RE technologies have the added abilityto bolster many related industrial sectors thus providing an even larger,macroeconomic platform of broad-based technological innovation leading to economicgrowth; a recent publication sponsored by the Ford Foundation stated that:

Green products, in particular, are proving to be highly convergent, as theybecome the defining characteristic of firms, for example in architecture,processed foods, building materials, construction, design, and consumerelectronics companies. Food processing converges with energy in areassuch as biomass, bio-fuels, and ethanol; pulp and paper converge withbiochemistry, bio-refining, and biomass power generation; waste recyclingconverges with energy, oil, cement, plasterboard, biotechnology, andaquaculture all in industrial symbiosis clusters (Regional TechnicalStrategies, Inc., 2009).

The following sections are an overview of the development model presented in thispaper as well as instituted by the JOBS Project throughout the Central Appalachianregion. The structure of this paper is as follows: Section 1 focuses on the historical rootsof innovation which apply the theory of technological evolution to various historicalperiods where knowledge emerged within a complex network of social interactions.Section 2 is a market analysis of both economies of scale and agglomeration and theirprospective relationship to either discouraging or encouraging technological innovation.Section 3 attempts to locate sources of open innovation by analyzing network affects.This section ends with a conceptual framework of distributed open innovation networkswhich situates localized integrationwithin both a modular design and an agglomeratedmanufacturing setting. Section 4 proposes an emergent endogenous growth model toenliven economic diversity within the Central Appalachian region. Lastly, section 5presents a case study which contextualizes the previous sections into a real worldmodel, a 400-700kW pyrolysis facility in Southern West Virginia. This project ispresently in the predevelopment stages.


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2 Historical Roots of Innovation

Historically, economies of scale were carried upward and onward on the shoulders ofsmall firms and the enormous creative powers of the market, of the lower story ofexchange... This lowest level, not being paralyzed by the size of its plant or

organization, is the one readiest to adapt; it is the seed bed of inspiration, improvisationand even innovation, although its most brilliant discoveries sooner or later fall into thehands of the holders of capital. It was not the capitalists who brought about the firstcotton revolution; all the new ideas came from enterprising small businesses, FernandBraudel, a French historian and a founder of the Annales School, goes on to ask, arethings so very different today? One of the leading representatives of French capital saidto me the other day: It is never the inventors who make a fortune; they have to hand itover to someone else (Braudel, 1979).

The history of innovation can be seen in the same light, that is, most creativephenomena, whether technological or scientific in nature, are typically produced by

clusters of small institutions or producers of a certain good by reducing the barriers ofcommunication which are most often found in larger institutions. Generally, the historyof innovation privileges the innovative "hero" without accounting for the collective natureof why and how the particular innovation emerged. The purpose of this section is toprovide a broader historical perspective of how and why innovation emerges and someof the primary mechanisms of which inhibit its emergence in order to develop ahistorical framework for understanding open innovation and its importance for economicgrowth.

This broad analysis of technological innovation is informed by George Basallas workentitled The Evolution of Technologywho cites Samuel Butler (1863) as one of the firstto begin analyzing the emergence of technology as an evolutionary process wheremachines developed in a fashion remarkably similar to the evolution of living beings(Basalla, 1988: 15). In order to support his thesis of novelty where the creativephenomenon is productive as opposed to imitative Basalla utilizes Herbert Spencersassertion that the entire history of innovation is fundamentally connected to a continuumfrom simple to complex, that is, a movement from the homogeneous to theheterogeneous. From Butlers evolutionary analogy and Spencers continuum, Basallaconstructs an expansion of the revolutionary model predicated upon discontinuoushistorical breaks or paradigm shifts for understanding technological change. Basallapresents an alternative model of cumulative change where major inventions resultedfrom the cumulative synthesis of a series of minor ones. From Basallas evolutionarymodel of technological change, this paper will emphasize the continual breaking downof technology into constituent parts and then reassembling these parts in a new andcreative way. In most cases these novel combinations will have the emergentphenomenon of utility, that is, to provide an answer to a particular problem thatperson(s) or institutions are trying to resolve.


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Using Braudels example of the cotton revolution, Eli Whitneys cotton gin did notspontaneously emerge within a technological vacuum. To the contrary, it emerged in acomplex network of social interactions, novel objects and specific environmentalconditions where preexisting technologies, such as the Indian gin or charka, provided amethod of approaching the problem that Whitney was trying to resolve. The charka

provided a means for cleaning long staple cotton but did not provide a solution forcleaning short staple cotton. Within this complex network, Whitney was provided severalapproaches to cleaning long staple cotton and then adapted those methods to theenvironmental conditions presented by short staple cotton. In this model, the inventor(s)is provided a complex body of novel artifacts which are then assembled in order toresolve a particular problem presented by the surrounding environment (Basalla, 1988).

To further elucidate this complex network, Basalla breaks down continuityas themechanism that produces diversityand from this large body of novelobjects theinventor(s), through a process of selection, conceptually redistributes these novelobjects in order to recombine them in hopes of meeting fundamental human needs

(Basalla, 1988: 25). For example, in the case of Francis Bacons production of scientificthought, the continuity was found outside the universities in the mechanical arts wherediversity flourished and had in them some breath of life, which were continuallygrowing (Bacon, 1960). During this time, Descartes also attributed crafts knowledge asthe fertile grounds of diversity from which selection occurs. He suggested a survey ofthose arts of less importance; that is, those which are easiest and simplest, and thoseabove all in which order most prevails. Such are the arts of the craftsmen who weavewebs and tapestry, or of women who embroider or use in the same work threads withinfinite modification of texture (Descartes, 1996).

This complex network of innovation can be further expanded as developing arelationship with the environmental conditions that the inventor or perhaps moreappropriately, group of inventors experience in their everyday lives. For example,according to historian Edgar Zilsel, experimentalism did not arise from Galileosdefiance of Aristotelian science or Francis Bacons championing of inductive logic. Zilselstates that the experimental method did not and could not have descended from themetaphysical ideas of the natural philosophers." According to Zilsel, modern sciencearose in early modern Europe through the interaction of artisans and elite intellectualswithin their environments. Moreover, this network of communication with environmentalconditions provided the raw material for scientific experimentation where the artisans,the mariners, shipbuilders, carpenters, foundry men, and miners were the real pioneersof empirical observation, experimentation, and casual research (Zilsel, 1942: 12 -15).

This emphasis upon the complex network of artisan knowledge was expanded byParacelsus, often considered the Martin Luther of medicinal practices and a rival ofFrancis Bacons model of utilizing craft knowledge without crediting the source of whichthe knowledge emerged. Bacon responded to Paracelsuss model of emphasizing anunmediated experience with nature by drawing comparisons between his adoration ofthe artisanal understandings of the material world with that of the radical reformers


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who attacked established authority in the name of social justice and equality. Baconwent on to say that from these types of attacks, it inspired German peasants to rise upagainst rural priests and landlords in the great Peasants Revolt of 1525 (Jacob, 1998:27).

Similar revolts - or more appropriately, evolutionary changes - occurred in recent historyand this reorganization of complexity gave rise to what is quite possibly one of the mostimportant inventions of the modern era, the digital computer. The digital computer was aby-product of the Cold War and hence they were designed and utilized solely by themilitary and later adopted by large, centralized firms for highly specialized tasks. In the1970s, these sluggish, large and profoundly inefficient machines began interacting withthe complex network of self taught amateur electronics hobbyists and eventually gaverise to the Altair 8800. This device, along with its interactions with the complex networkof newly emerging electronic hobbyists, uncovered the profound inefficiencies whichwere found in the computers that were produced by IBM, Wang, UNIVAC, and ControlData Corporation.

Among some of these hobbyists were Bill Gates, Paul Allen, and Monte Davidoff whobegan developing the coded programs that the Altair needed to function. From theseprograms they developed the first personal computer which sparked the technologicalexplosion in the 1980s. Moreover, the historian of computing, Steve Lohr, claimed thatprogrammers are the artisans, craftsmen, bricklayers, and architects of the InformationAge (Lohr, 2001: 7). These complex networks of programmers later gave rise to analternative to Bill Gates brainchild, Microsoft. Among some of these agents ofcomplexity were Ted Nelson, Richard Stallman and Bob Albrecht who believed that thewellspring of technological innovation is and has always been a commitment to an opensource approach which ensures that ideas propagate fast to new products. W. BrianArthur, a contemporary evolutionary economist, views systems of order, closedness,and equilibrium as ways of organizing technologies and that economies are giving wayto open-endedness, indeterminancy, and the emergence of perpetual novelty (Arthur,2009: 211). Additionally, in regards to the necessity of an open source approach toeconomic development, Cortright says that, the non-rival quality of ideas is the attributethat drives economic growth. We can all share and reuse ideas at zero, or nearly zerocost. As we accumulate more and more ideas, knowledge about how the world works,and how to extract greater use out of the finite set of resources with which the world isendowed, we enable the economy to develop further (Cortright, 2001: 6). As such,knowledge acts as a catalyst for economic growth via technological innovation. In thisvein, increased technological innovation, and therefore increased economic growth canbe expected when knowledge is allowed to freely traverse between individuals andinstitutions. However, Cortright also clarifies that under the current neo-classical modelof economic development, patents, trademarks, and copyright law allow individuals tohave certain rights to exclude others from the benefits of the ideas they have created.Keeping ideas secret trade secrets, confidential business information also allowstheir owner to exclude others from their benefits (Cortright, 2001: 5). Therefore, in orderto promote the most widespread exchange of knowledge it is necessary to reevaluate


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the method of intellectual property right ownership and development; thesereevaluations can come in the form of creating industrial relationships based on freeexchange of open source technology. Other methods for dealing with the problem ofhard-line intellectual property ownership models include reformatting the current modelof inclusion and ownership of patents; the subject of patent structure and inclusion of

participants is discussed in more detail in Section 4.Not only does an open source approach foster innovation within various processes ofprogram development, it can also prompt technological change through modular designand perhaps more importantly by situating these design approaches within a network ofmanufacturing clusters. That is, an agglomeration of small entrepreneurs can stimulateinnovation as well as redistribute the overall R&D processes; thus, increasing theoccurrence of innovation spillovers. Using the same focal point of the open sourcedesign above, the Altair 8800 incorporated a number of open slots that allowed foradditional memory and other devices to be added if the consumer so desired. Thisopen design was later adopted by the Apple II and then radically upgraded by IBM.

IBM, in a bold move, adopted an assembler role and externalized all the PCcomponents within individual competing markets (e.g., processor, hard drive, key board,mouse, etc.). This caused a positive feedback on the level of technological change aswell as the institutional make-up of the computer industry.

Perhaps the best motive forsupporting modular design is foundin the effects that it has on theinstitutional structure of a particularindustry. A 2000 study, performedby two Harvard scholars, found aninstitutional tendency towardsheterogeneity within the computerindustry and correlated this to theindustrys decision to adoptmodularization as an industrystandard. Figure 1 (right) elucidatesthis institutional tendency of movingfrom a highly homogenous industry,with IBM acting as the dominant firmin 1969 (where 71% of the marketvalue of the computer industry wastied up in IBM stock) to aheterogeneous industry by 1996where no firm accounted for morethan 15% of the total value of theindustry (Baldwin & Clark, 2002).

Figure 1 Market Value of the Computer Industry by Sector, 1950-1996 in

Constant US Dollars


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By combining these histories of continuity from the simple to the complex or amovement from the homogeneous to the heterogeneous we can begin to see theimportance of agglomeration and modular design for prompting technological changeand the expansion of the RE industry within Central Appalachia. Additionally, in regardsto the development of the RE industry, we see that new growth theory implies,

however, that we continue to increase living standards for centuries to come by steadilyimproving our knowledge of how to produce more and better goods and services withever-smaller amounts of physical resources (Cortright, 2001: 6). The following sectionwill compare various attributes of two growth models in hopes of steering federalpolicies and funding in the direction of demonstrative economic benefits without fallinginto the pitfalls of archaic growth models that have done little more than subsidize anoncompetitive environment as opposed to stimulating one.


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3 Market Analysis of Scale vs. Agglomeration

In order to contextualize the previous section we will look at the particular economicattributes of economies of scale as well as agglomeration. This section seeks to providea snapshot of the two developmental approaches by juxtaposing both models in relation

to technological innovation. By comparing the economies of scale model representingthe neo-classical growth model to the economies of agglomeration modelrepresenting an endogenous growth theoryor new growth theory we can betterunderstand how each model either stimulates or discourages innovation. The model ofeconomies of scale emphasizes stimulating growth through increasing productivity,which is essentially a homogenous model. Alternatively, the endogenous growth modelrepresents a heterogeneous model holding that policy measures can have an impact onthe long-run growth rate of an economy. For example, federal and state subsidies forR&D or education may increase the growth rate by increasing the incentive to innovate.The primary issue in this section is to address why specific development theories differin their ability to generate, imitate or apply new variety, and to identify the economic and

institutional structures through which the Central Appalachian region can retain andeven expand its competitive position in national and international markets.

Based on neo-classical economic theoriesthe contemporary understanding ofinnovation assumes that it emerges from theeconomic concept of an economy of scale(ES); as such, this will be the first term that isdefined. An ES occurs when an increasednumber of units a good or a service canbe produced on a larger scale, yet with, onaverage, lessening input costs. Alternatively,this means that as production increases for aparticular firm the overall costs of per-unitproduction decreases. This occurs on alllevels of the firm by internalizing transactioncost, as well as the means of producing aparticular good or service. For ManuelDeLanda, a contemporary emergent theorist, this signifies a tipping point where theonce heterogeneous processes of producing goods comes under the control of ahomogeneous, routinizing firm that constrains technological innovation rather thanstimulating it. According to the ES model, innovation can only occur due to a centralizedR&D atmosphere realized within the economy of scale mainly taking the form ofownership of codified knowledge in the form of patents. However, there clearly aremany viable alternative models for understanding innovation and technologicalprogress, and as such it is inherently limiting to only view innovation through the scopeof an ES. Additionally, there are other viable alternative models for new forms ofknowledge ownership ranging from distributed patent structures to complete opensourcing of all innovative breakthroughs. Moreover, much of the competition for industry


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shares takes place during the convergence toward oligopoly. Once this convergence isrealized, shares stabilize and technological innovation ceases (Lippman & Rumelt,1982; Nelson & Winter, 1982; Klepper, 1996).

In economic terms, an ES refers to a situation in which the average cost of producing an

additional unit of output (marginal cost) of a product decreases as the volume of output(scaleof production) increases (see above diagram).1 It can also be defined as a situationwhen an equal percentage increase in all inputs results in a greater percentageincrease in output. This particular model assumes that innovation comes fromcentralized R&D laboratories, that is, marginal costs are captured in the R&D stages byway of increasing volume via increase in demand for a particular technology. Thisassumes the centralized R&D model as the norm whereas an economy ofagglomeration would reduce the marginal cost that is captured in the R&D processes oftechnological innovation we will expand this further below. In general terms, marginalcost at each level of production includes any additional costs required to produce the

next unit, here being the transaction cost of R&D. For example, if producing additionalcomponents for a particular RE technology requires extra investment into R&D, themarginal cost of those extra components includes the transaction cost of R&D. Further,the marginal cost invested in the input of R&D does not result in an increase ofinnovation in output, especially when one considers the alternative, an economy ofagglomeration.

Geographical economists use the concept of aneconomy of agglomeration(EA) to describe thebenefits that producers obtain when locatingclose to other producers in a similar industry(see diagram).2 This concept is related toeconomies of scale and network effects, in thatthe more related companies that are clusteredtogether, the lower the cost of production (e.g.,firms have competing, multiple suppliers andgreater specialization and division of laborresult) and the greater the market that the firmcan sell into by competing at the level of price.

Additionally, geographic clustering of related industries also leads to a dramaticincrease in knowledge spillovers due, again, to the non-rival and nature of knowledge(Cortright, 2001). In this lens, industrial clusters can be seen as interdependent firmsrepresenting various sectors that usually find themselves in a multi-county, county, orcommunity-defined region (Regional Technical Strategies, Inc., 2009). As such,

1The increase in output from Q to Q2 causes a decrease in the average cost of each unit from C to C1.

2Three activities (P,Q and R) having their respective locational constraints can benefit from agglomeration

economies if they locate at A. The additional transport costs that may derive will be more than compensated by

the cheaper functional linkages between the activities.


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clustering will result in economic growth and development simply because ofgeographic knowledge spillover; however, even more economic growth throughinnovation could be seen if open source approaches were incorporated within thegeographic cluster. It is also important to note that the prevalence of tacit knowledgefound within an employee is much different from codified knowledge that can be

exchanged without regard to geographic location; for this reason geographic clusteringis still an important means of knowledge spillover creation even in the face ofincreasingly sophisticated communication systems, like the internet. Cortright coylyexplains the importance of noting the difference between tacit and codified knowledgeby saying, Acknowledging the economic importance of tacit knowledge requires littlemore than admitting that it requires more than a good accent and a copy of LaRousseGastronomiqueto operate a successful French restaurant (Cortright, 2001). As such,the need for openly exchanged information and tacit knowledge within a framework ofgeographically localized industrial clusters would lead to technological innovation andeconomic growth and development. Additionally, localized geographic clustering has theadded advantage of being largely affected by regional variations in environment; these

ecological niches are important to reinforcing the processes of trial and experimentationthat drive economic growth (Maskell & Malmberg, 1999). However, geography andmarket incentives will not be enough to bolster industrial clustering alone. Institutionsand initiatives will be needed in order to boost these industrial clusters either throughspecialization or association; specialization refers to the use of public or privatesector resources, while association refers to the influence of relationships and increasedinteractions between firms (Regional Technical Strategies, Inc., 2009). Additionally, thisreport states that:

Specialization affects business and technical assistance, research anddevelopment, market assistance and information, and often mostimportantly education and training, shaping it to the particular needs ofthe companies in the cluster. Association encourages and facilitatesbusiness networks and cluster-specific business associations bysupporting facilitators and collaborative projects (Regional TechnicalStrategies, Inc., 2009).

It is important to recognize that modern industrial clusters found in rural areas typicallyare borne out of company evolution, a pre-developed set of skills in the region, or as theresult of a natural resource (Regional Technical Strategies, Inc., 2009). As such, theseclusters are much more likely to emerge from within an economy of agglomeration thanfrom an economy of scale. Quite simply, a top-down implementation approach that isfound within economies of scale gives little to no room for novel conditions or relationalnetworks to emerge. Moreover, the evolutionary nature of clusters implies that clusterstrategies implemented comprehensively and from the top down are more likely tocontribute to adverse lock-in effects than promote growth (Regional TechnicalStrategies, Inc., 2009). As such it is extremely important to not force unrelated industryon a particular area, but instead utilize the naturally occurring resources, or assets, ofan area to their fullest potential. Along these lines, instead of recommending specific


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industry types, institutions can aid in the formation of industrial clusters by removingbarriers from otherwise impenetrable markets.

Even when multiple competing producers in the same sector cluster, there may beinnovation advantages because the clustering attracts more R&D firms, suppliers and

customers than a single producer could alone. Moreover, a 2005 study found that an EAhad a positive effect upon the occurrence of technological innovation and perhaps moreimportantly on the emergence of small entrepreneurs (Acs & Varga, 2005). This type ofinfrastructure consists of sources of knowledge: networks of firms that provideexpertise and technical knowledge; concentrations of R&D that enhance opportunitiesfor innovation by providing knowledge of new scientific discoveries and applications;and business services with expertise in product positioning and the intricacies of newproduct commercialization (Feldman & Florida, 1994: 210).

When considering these two economic models, the general assumption under aneconomy of scale model is that marginal cost can decrease as the volume of output

increases. This occurs for several reasons. One is that larger production volumes allowfixed coststo be spread over more units of output. Fixed costs can be understood ascosts that do not change regardless of the amount of use, or at least change relativelylittle as a function of use; that is, there are costs that must be incurred even if productionwere to drop to zero. Specifically for the RE industry, fixed costs could include factories,warehouses, and machinery. Alternatively, in regards to newly emerging technologiesfound in the RE industry, such as biomass pyrolysis that is presently being developedby the Mid-Atlantic Technology, Research and Innovation Center (MATRIC), the costsof machinery are more variable than fixed. This is because of the rapid development ofnewly emerging technology, which in turn requires a continual change in machinery inorder to keep up with increasingly efficient technological processes that meet specificdemands found in a particular market and/or environment. When applied to an EA,innovation in technology can be distributed over a large area thus enabling the producerto absorb these changes in efficiency more rapidly while remaining reflexive, thusenabling the technology to rapidly adapt in order to meet specific requirements of aparticular market. Lastly, there are current costs associated with any RE technology,and some of them will likely vary, at least in part, according to the level of output, suchas maintenance. However, these costs often tend to be relatively small according to thecosts of the main production of RE technologies themselves.

Variable costs, in contrast to fixed costs, change directly as a function of use as in theuse value of a particular RE technology in relation to its optimal output of energy and itsenvironment. Examples of variable costs are feedstock and labor for producingelectricity, diesel fuel for hauling biomass feedstock, or skilled labor for creating new REtechnologies (e.g., R&D). Large economies of scale are most likely to be found inindustries characterized by large fixed costs and consequently are not entirely reliantupon sustaining a competitive advantage in relation to technological innovation and/ormarket acceptability. Large fixed costs, and hence large economies of scale, areprevalent in capital-intensive industries such as large wind farms, coal fired power


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plants, petroleum refining, and photovoltaic manufacturing; very large levels ofproduction are required to bring unit costs down to the lowest possible levels or so it isassumed. To attain such levels of output, it is necessary to have massive investment inproduction facilities, sometimes measured in the billions of dollars. This contemporarymodel of industry fails to consider technological innovation as a key component in

generating revenue for said industry by instead creating short-term, unsustainableeconomic growth by lowering the initial investment that is commonly associated witheconomies of scale. Technological innovation occurs by encouraging the growth ofsmall businesses and localized entrepreneurship (which are typically internalized andmanaged by large firms found in the ES model) as well as providing the producers withthe ability to absorb innovative changes more rapidly by externalizing the means ofmanufacturing the component parts of a particular RE technology thus creating apositive feedback loop which again encourages the growth of small businesses andlocalized entrepreneurs. This in turn stimulates more specialization, and promotes morecompetition on the level of technological innovation. In his classic work on innovationand capitalism, Joseph Schumpeter argues powerfully that economic growth requires

technological innovation the generation of higher quality products at lower unit coststhan had previously been obtainable (Schumpeter, 1954). If the EA model, whichencourages the emergence of small businesses and local entrepreneurship, ispromoted within Central Appalachia, we can assume that its adoption will position thisregion as a leader in RE innovation and development.

Besides the distinction between economies of scale and economies of agglomeration,the model of network economicspresented in this paper takes advantage of recentdiscoveries in nonlinear science, theories of self-organization, emergence theories and,more importantly, evolutionary economics. Much like Basallas evolutionary theory oftechnology, evolutionary economics can be used to describe: (1) an increase in noveltywithin localized collective learning clusters, (2) selectionand adaptation within regionsthat are confronted with an economic environment of increasing variation, and (3) thespatial formation of newly emerging technologies as an evolutionary process, in whichthe spatial connotation of increasing returns may result in a spatial lock-inthussustaining the continuityof innovation (Basalla, 1988). Basically, these theories may beused to explain the emergence of technological innovation as more than the sum of itsparts and, in particular, innovation as an emergent system. Technology markets aresuch synergistic wholes because they emerge as a result of the unintendedconsequences of many independent decision-makers interacting. As such, seeminglysmall decisions by individual actors compound upon each other and lay the frameworkfor larger innovations. This holds true for all innovations - and therefore economicgrowth - as well; innovation and technological breakthrough does not just spontaneouslyoccur from within a technological vacuum. What is far more likely is that revolutionarilynovel technological breakthroughs are simply novel combinations of preexistingtechnologies. For example, the first automobiles certainly represent a technologicalbreakthrough in total, but as a technological system they are simply a novel combinationof subsystems - in this case wheels, axles, motors, etc.


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While considering these nonlinear economic theories one begins to realize that thetypical understanding of economies of agglomeration, which are dependent upon citiesand urban centers in order to exist, can be utilized in a way that suits the JOBSProjects rural model of development for Central Appalachia. For example, partialrelocationinto a rural setting would not erode the dynamic interplay found in the original

urban setting but in some cases would reinforce as well as reproduce the dynamiceffects found in the original complex network (Lamboy, 1986; Dosi, 1984). An additionalbenefit of partial relocation is that rural areas can look towards nearby urban areas foradditional sources of capital, research, skilled workers, or specialized supplies(Regional Technical Strategies, Inc., 2009). In regards to areas like Central Appalachia

which have historically witnessed economic development through the employ ofnatural resources, land, and low-cost labor most rural clusters were based oncommodities or value-added production from extractive industries (Regional TechnicalStrategies, Inc., 2009). However, this historical clustering can be seen as an advantagebecause those rural areas fortunate enough to have exceptional natural amenities mayalso have developed clusters around tourism or transportation, and the few that are

home to research universities may have developed some form of technology cluster(Regional Technical Strategies, Inc., 2009). In this case, these technology clustersformed around multiple engineering programs at various colleges and universitiesaround Central Appalachia are fertile ground for the development of further noveltechnologies in related engineering sectors; these types of industrial and academiccrossovers serve to further innovative breakthroughs as well as provide continuallyevolving workforce training.

In relation to the RE industry, one aspect of EAs and self-organizing networks have incommon is that their innovative properties emerge spontaneously out of the interactionsamong a variety of elements: component parts and maintenance employees, RE facilitystakeholders, technology producers, and utility companies and customers. Even morebroadly, the growth of the RE sector is directly related to growing concern over globalclimate change and sustainability. As such, this provides an especially excitingemergent opportunity to create a new market demand and branding formula forproducts that are green." Additionally, a set of new clusters may arise, based onalternative forms of energy such as biofuels, wind and solar, recycling, or restoration."Excitingly, the fastest growing opportunities, however, are in clusters representingrenewable energy, energy efficiency, and environmental clean-up opportunities."(Regional Technical Strategies, Inc., 2009) As can be seen, there is already a growingmarket demand for implementation of RE technology; by incorporating models of opensource development and utilizing the tendency for knowledge spillovers within the areasassociated with implementation and production of RE technology, innovation andeconomic growth can be expected as an emergent phenomenon.

In order to understand the processes that lead to emergent innovative networks, i.e.synergistic wholes, we need to create new ways of understanding the economic realityin Central Appalachia. In particular, instead of beginning at the top, on the level of scale(and moving down by dissecting industry into its constituent parts), or from the bottom


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(at the level of the community) we need a hybrid model between economies of scaleand economies of agglomeration or, what we refer to as, distributed open innovationnetworks. For example, instead of creating a typical model of the market, or innovativenetworks, by using a small set of economic functions that capture the behavior of an ESin relation to R&D output (i.e., technological innovation), we need to create institutional

environments which will allow a population of component part and maintenanceemployees, facility stakeholders and technology sellers, and utility companies andcustomers to interact and allow technological innovation to emerge spontaneously bymaintaining the benefits of both the top-down and bottom-up models (Cohen &Levinthal, 1989). In this way, the bottom-up strategy compensates for the weakness ofthe top-down strategies typically utilized by development organizations within the coalregions of Central Appalachia. The top-down strategy fails to consider the topography ofthe region that limits the size of a particular industrial or commercial facility incidentallysustaining a general acceptance of surface mining as a means for addressing thisbarrier by creating developable land. In contrast, a strong emphasis on the bottom-upapproach increases the likelihood of imitation and therefore reduces the overall returns

of technological innovation. Higher expropriability leads to homogeneity whichsuppresses share losses and thereby pressure to create new knowledge (Knott, 2003:702-703). This hybrid model of both the top-down and the bottom-up strategy isessentially an emergent system simply by attributing causation to both while maintainingthe social properties of creativity or innovation as irreducible within these causalrelations (Sawyer, 2005). This assumes that a distributed open innovation networkrequires a continuous influx of knowledge production, in this case, individual and groupknowledge stocks. By providing a means for local stakeholder and employeeparticipation within Central Appalachia, we can begin to build a vibrant and tangiblemodel of economic development for both the urban and rural settings, in turn bringingCentral Appalachia to the forefront of RE technological innovation and development.


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4 Sources of Innovation

There are several sources of innovation. In the dominant linear model of innovation, thecreative source is private firmsor highly centralized R&D laboratories. This is where anagent (person or company) innovates in order to sell a given product; innovation is

motivated primarily by the accumulation of capital. Once these institutions emerge, andin the case of private firm innovation, an ES emerges. Douglas North presents us withan alternative model in his book Institutions, Institutional Change and EconomicPerformance. North formulates the basic behavioral postulates that depart from theconventional neoclassical economic story of the market by focusing on the importanceof formal and informal institutions in generating human behavior, in this case theinformal institutions that could potentially promote innovation (e.g., employeeinteractions with a particular technology). Within this model, large companies are notonly in the business of producing a profit, they also formalize various processes (R&D)in order to routinize them in relation to their pursuit of profit, hence the behavior that isgenerated is an acceptance of technological innovation as being produced from

centralized R&D spillovers. However, according to traditional models the traditionalsolution to dealing with spillovers, granting strong property rights for the fruits of aninvention, may also have negative consequences (Cortright, 2001: 7). As such, thispaper is primarily concerned with developing a process for formalizing the informalnetworks of employees in order to stimulate technological innovation while retaining theemployees active role in the production of technological change. It is also of paramountimportance to not limit the production of knowledge to a specific group of firmemployees (i.e. a specific, isolated R&D department); for example, case studies of theautomobile industry have shown the extreme importance of worker led teams forcontinuous innovation and quality improvement (Cortright, 2001: 28). This active rolewill assure the participation of RE employees in the patent process by securing afinancial share in the technology that is produced while maintaining a competitive edgewithin the specific RE industry via technological innovation. This inclusion into thepatent process would effectually lead to a larger investment in knowledge by theparticular industry in which the patent is realized; this structure would also address thelack of incentives for entrepreneurs to distribute or invest in more knowledge creation.

Technological innovation and economic growth are both highly present in industrialclusters; this is due to ease of knowledge spillover between firms in both related andunrelated sectors. However, there are additional factors that must be considered whenanalyzing the innovative capacity of industrial clusters. According to a Ford Foundationreport:

Which people and businesses gain and which lose in the economydepends to a large extent on connections, relationships, and trust. Thesefactors affect the exchange of knowledge about innovations, markets,and job opportunities and they affect collaboration. The real strength ofclusters lies in the tacit knowledge that resides within the employees of


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companies in the cluster and its dispersion across companies andinstitutions (Regional Technical Strategies, Inc., 2009).

As such, geographic locations that are ripe for close-knit industrial clusters are ones thatalready have a strong pre-existing cultural identity and sense of community. Both of

these conditions are present in Central Appalachia and as such, it represents a uniquearea for focused industrial clustering within the newly emerging markets of REtechnologies. MORE??

Another source of innovation, only now becoming widely recognized, is end-userinnovation. This is where an agent (person or company) innovates for their own(personal or in-house) use because existing products do not meet their particular needs.Eric von Hippel, in Sources of Innovation, has identified end-user innovation as, by far,one of the most important aspects for understanding the emergence of innovation. Morerecent theories of innovation move beyond the simple dualism of the private firmandend-usermodels although both are still accounted for. Recent studies show that

innovation does not just happen within the industrial supply-side, or as a result of thearticulation of user demand, but through a complex set of processes that links manydifferent players together not only developers and users, but a wide variety ofintermediary organizations such as consultancies, SDO's, RE developers,entrepreneurs (e.g., community RE LLCs) and in the case of this research, informalemployee and community stakeholder networks.

Actor Network Theory (ANT) suggests that much of the successful innovation occurs atthe boundaries of organizations and industries where the problems and needs of usersare not mutually exclusive; in this case the problems which are confronted by REemployees and the needs of the RE facility owners and stakeholders can be linked withthe inherent potential of technologies in a creative process that challenges both. As analternative to the dominant linear model of innovation, ANT provides a theory ofinnovation translationwhich offers an approach to explaining innovation that does notrely on any supposedly innate nature of the innovation itself, or specific characteristicsof the change agents [RE employees] or society [local owners and stakeholders], butrather on a process of network formation in which all actors seek to persuade others tobecome their allies in promoting the acceptance of their own view of the way theproblem can best be solved (Tatnall & Gilding, 1999). With this model, the key totechnological innovation is the creation of powerful collaborative partnerships to form adistributed open innovation network, and when an innovation fails to be taken up thiscan be considered to reflect on the inability of those involved to construct the necessarynetwork of alliances amongst the other actors (McMaster & Vidgen, 1997). Getting aparticular technological innovation accepted calls for collaborative strategies aimed atthe enrollment of others in order to ensure useful technological change and acceptance.

Another source of innovation and an essential part of creating an innovation network isin how the technology itself is designed. In systems engineering, modular design or"modularity in design" is an approach that subdivides a system into smaller parts


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(modules) that can be independently created and then used in different systems to drivemultiple functionalities. It has been argued that new products are the outcome of aprocess that is based on the principle of novelty by combination (Georgescu-Roegan,1970; Arthur, 2009). Benefits of modularity include reduction in cost due to lesscustomization, a reduction in learning time and flexibility in design, as well as

augmentations that add innovative solutions by merely plugging in a new module, andexclusion of unpractical designs. Examples of modular systems are cars, computers,high-rise buildings, RE technology and perhaps most importantly, smart grids. Earlierexamples include looms, railroad signaling systems, telephone exchanges, pipe organsand electric power distribution systems. Computers use modularity to overcomechanging consumer demands and to make the manufacturing process more adaptive tochange. Modular design is an attempt to combine the advantages of standardizationand compatibility (i.e., high volume normally equals low manufacturing costs) with thoseof customization.

When situating modular design within an urban EA and its partial relocationwithin a

rural setting, we can begin to construct a comprehensive understanding of the particulardevelopment model that is presented in this paper. Speaking generally in regards to RE,the production of components typically found within the ES model is internalized by wayof vertical integration. This is usually a repercussion of the firms approach for reducingvarious transaction costs associated with externalizing the production of the componentparts. For example, there may be four component parts involved in producing aparticular RE technology (fig. 2). Typically, if the profit margin is large enough, the firmthat is producing the particular RE technology will remain static and the emergence ofnew components will not occur. However, if the design of the RE technology iscompatible with other technologies, by adopting certain industries standards, then theoriginal firm is forced to cooperate in order to sell their particular product on the market.This competitive atmosphere increases a particular technologys ability to adapt to arapidly changing market within both the demand and supply side throughcomponent integration, expansion of knowledge stocks, R&D spillovers, and perhapsmost importantly, an increase in returns for a specific RE industry. Simply, the positivefeedbacks of encouraging compatibility through industry standards stimulatetechnological change and innovation.

Figure 2 Components of RE Technology

The production of new RE technologies would then come to resemble fig.3 if and only ifthe larger firm decides that internalizing the production of components 6 and 7 isbeneficial.


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Figure 3 Components of Improved RE TechnologyIf this is not the case and the larger firm decides not to internalize the production ofcomponents 6 and 7 (much like IBMs choice not to internalize production of allcomputer components in the 1980s), then firm 2 and 3 are then created from theknowledge spillovers found within the newly emerging EA (fig. 4).

Figure 4 Firms Involved in Improved RE Technology

The emerging EA would then create alternative components that were once found in thelarger firm such as component 4 (fig. 5). This could occur for a variety of reasons, suchas the RE customer deciding to purchase a technology with the traditional componentsof 1, 2, 3,and 5, while finding that particular attributes of component 4, which isproduced by a competing firm, fits within their particular interests (fig. 5). This couldoccur if the alternative component 4 is better suited to a particular need found in thecustomers region here customers being the RE employees as well as local ownersand stakeholders.

Figure 5 Production of RE Technology with Changing Component 4

The above demand side attributes of technological innovation, when situated within asupply side, encourage knowledge spillovers and the establishment of distributedinnovation networks between producers. The first type of network is a centralized one inwhich suppliers are tied to lead supply firms as in the typical Japanese R&D firm (fig. 6);these firms integrated their R&D labs with factory floor employees in order to close theknowledge gaps that are found in the typical U.S. high technology firms. The U.S.structure of disintegration or spatial separation stifled the competitive advantages foundin the Japanese model. The centralized firms found in the Japanese model pioneerednew modes of integrationthat enabled them to generate a continuous flow of newproducts (i.e., total quality management, keiretsu, etc.). While recognizing thecompetitive advantages of the integrative approach these centralized firms did notaccount for the positive feedbacks found within modular design, specificallycompatibility.


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Figure 6 Centralized Network

Although this research notes the importance of the integrative model utilized byJapanese firms, it is our intention to expand these integrative effects into a distributedopen innovation network (fig. 7) or EA in order to increase as opposed to stifletechnological innovation.

Figure 7 Distributed Network


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For example, W1, W2, and W3 represent the localized knowledge stocks local REowners and stakeholders and RE employees at a particular RE facility with threedifferent types of processes that are suited for independent variables found within thespecific area that the facilities are operating (e.g., biomass feedstock variability, windresource, economic constraints, ecological conditions, etc.). These local knowledge

stocks are connected to R&D facilities (D1, D2), both public and private, as well as acentralized information trader (E1). A1, A2, A3, C1, C2, and C3 are the manufactures ofcomponent A and C which are found in a standardized RE system as well as future REsystems that are suitable to all facilities. Based on the collective nature ofstandardization and its relation to modular design, subassembly B, which is a product oftechnological innovation, needs to be compatible only with component C and notdirectly with other components. The continual splitting of components (technologicalinnovation) and a sustained emergence of new component manufacturers and locally-owned RE facilities can be said to be a result of the relation between W and therespective public (D1) or private (D2) R&D firm. This relationship fosters knowledgespillovers and in turn cultivates a functioning distributed open innovative network. Taken

together, all the component manufacturers (A,B,C), the localized knowledge stocks (W),the public and private R&D firms (D) and the centralized information trader (E) make upa distributed open innovation network. In contrast to centralized innovation networkswhere one dominant firm establishes the standards of compatibility, distributed openinnovation networks jointly determine standards by establishing a precedent fornegotiations between component manufacturers, R&D departments and firms, andlocalized knowledge stocks. No single actor in this network has control. Additionally, anyactor who tries to dictate standards risks being isolated if other network actors decidenot follow (Langlois & Robertson, 1991).

When situating the above distributed open innovation network within a cluster ofmanufacturers or a rural/urban economy of agglomeration, as in the case of the JOBSProject, the development of skill and know-how and the easy communication of ideasand experience allow distributed open innovation networks to develop and to growstrong. By enhancing the formation of distributed open innovation networks and theirstrength, rural/urban agglomeration can affect the following within Central Appalachia:

Accelerate the rate at which new technologies are developed in CentralAppalachia

Accelerate the rate at which the knowledge of new RE technologies enters intoand is diffused throughout Central Appalachia communities.

Accelerate the rate at which new technologies are incorporated into the productsof manufacturers.

Accelerate the rate at which these new or renewed products are adopted by thepotential customers. Accelerate the rate at which Central Appalachia can mitigate the negative

economic effects of Americas transition from a carbon intensive to a carbonneutral economy.


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5 Practical Applications in Central Appalachia

Stimulating distributed open innovation networks must employ interdisciplinary teamsbecause our focus on emergence requires a simultaneous consideration of analysis:individuals, their communication networks, and the group or industry that these

interactions are situated within. Moreover, every introduction of a new RE technologyinto the market tends to be characterized by a high degree of uncertainty (Abernathy &Utterback, 1978). We recognize that all new RE technologies are likely to be introducedin several variants, each with its own specific design, that is, they are not yetstandardized. New products are typically un-standardized because of the need forcontinual adaptation and improvement of their designs to suit customers needs, andthrough improvements of product characteristics due to experimentation with alternativeinputs (Buckley & Casson, 1976). The JOBS Project can never be sure whichtechnological design will dominate over time or when a dominant design will beestablished in the market, since market needs are ill-defined and can be stated onlywith broad uncertainty. It is our hope that this proposed strategy of development for

Central Appalachia will contribute to absolving many of these barriers in the followingways: Create a new approach to standardization which emphasizes technological

change and modular design as opposed to a single firm standardizing atechnological design which can be replicated within the RE market.

Create production assurances (i.e., new technologies are able to produce apredictable amount of energy and/or fuel) which will ensure investment in thenew technology.

Increase technological adaptation and improvement by stimulating randomcollisions ofknowledge by creating and maintaining distributed open innovationnetworks via social and virtual (web based) interactions.

Ensure a better understanding of market acceptance of new technology throughemployee/engineer collaboration via O&M database and active participation intechnological development.

Callon (1987) proposes that entities, like the JOBS Project, become strong bystrategically creating collaborative partnerships or a mass of silent others in order togive us greater strength and credibility. A distributed open innovation network maybecome durable partly due to the durability of the bonds that hold it together, but alsobecause it is itself composed of a number of durable and simplified networks. Thissolidity then results from a structure where each point is at the intersection of twonetworks: one that it simplifies and another that simplifies it (Callon 1987: 97).

However, care is needed with the term network here, as it is used in a special way todescribe shifting alliances of actors and collaborative partnerships and not some fixedthing; thus, the need for an information traderto track and organize these shifts. TheJOBS Project, which is comprised of complex networks, is often converted intoinscriptions or devices (Callon, 1986) such as, but not limited to, briefing papers,business models, strategy reports, academic papers, virtual models, sustainabilityindexes and web based integration tools. The following are examples of simplified


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networks which will contribute to the overall creation of a durable distributed innovationnetwork within Central Appalachia:

Central Appalachian Renewable Energy (CARE) advisory board: This will actas the central advisory nucleus for maintaining the social networks necessary tosustain a durable distributed open innovation network.

Local Energy Action Plan (LEAP) steering committees: These committeeswill provide localized knowledge for assessing all three indices (i.e., cultural,economic and ecological index) for maintaining the sustainable nature of ourdevelopment model.

Human Sustainability Index (HSI): Technological innovation will be intimatelyintegrated into the RE technologies surrounding environments via localknowledge stocks who will account for how specific changes in technology willaffect the areas in which the facilities will be operating. The HSI will play anessential role in accounting for how technological innovation is related to itssocial, economic and ecological environments by accounting for the technologieslifecycle. (Please see The JOBS Projects 2010 Strategy Reportfor further

explanations of the HSI.) R&D departments and firms: As an important link for supporting the model that

is presented in this paper, university R&D departments within Central Appalachiawill function as the central innovation nucleus for stimulating technologicalinnovation within established as well as emerging RE industries.

RE facility employees: This will be one of the most important aspects of thedistributed open innovation network as these employees will be the material linkto every day O&M practices as well as provide technological tweaks for aspecific RE technology. By identifying interested parties who wish to furtherdevelop their RE skills, this innovation network will provide educational pathwaysfor expanding RE use and development in Central Appalachia and possibly the

world. Modular design: This design approach will supply valuable information for R&D

as well as stimulate RE innovation. The basic premises for the design approachis to stimulate as opposed to inhibit the creation of new entrepreneurs andcomponent part manufactures and developers.

Locally-owned LLCs and entrepreneurs: These entities will provide invaluableinformation for various aspects of a particular RE facility such as: marginal costs,business models, transaction costs, local revenues generated, ecologicalfootprint and much more.

Manufacturers of component parts: These manufacturers will provide insightinto the generative nature of the distributed innovation network.

Information trader: The JOBS Project and/or MATRIC will be the primaryentities which will organize the information links into a management database.This may include the creation of a closed information management web toolwhich will capture information, organize data, and establish information linksaccording to the evolving interests of stimulating technological change.

Community and Technical Colleges: MORE??


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Once a distributed open innovation network is formed, however, that is not the end ofthe story as these networks are always unreliable and can become unstable. The entryof new actors, desertion of existing actors or changes in alliances can cause the virtualnetwork - advisory board and O&M database - to be subjected to substantial shifts andtheir contents reconsidered. Distributed open innovation networks rely on the

maintenance of its simplifications for its continued existence. These simplifications areunder constant challenge and if they break down the network will collapse, perhaps tore-form in a different configuration as a different and possibly less participatory network(e.g., tendency to monopolize). Recognition of institutional interests, both innovative andprofit driven, and integrating the innovation network within these nested interests is oneexample of maintaining the overall stability of the network (Callon, 1986).

With regard to modular design, within an object-oriented RE environment, eachcomponent of the facility can be considered as an object with its own properties,methods and actions. (Parsons & Wand, 1997) In common with the encapsulation ofobjects in object-oriented RE environments the actors, or "heterogeneous entities",

encountered in ANT, have attributes and methods and may themselves be composed ofother objects or actors (Bijker & Hughes, 1987). So, when looked into carefully, an actoritself consists of a network of interactions and associations. In the same way, aparticular network may be simplified to look like a single point actor (Law, 1992). Ourproject team, both Tech Connect and MATRIC as well as many other associatedpartners, will continually reassess the data on a quarterly base in order to ensure thesuccessful implementation of modular design within target RE technologies.

The JOBS Project seeks to punctualise (Law, 1992) a stable distributed openinnovation network and so consider it in the form of a single administrative entity, that is,the information trader (e.g., The JOBS Project and/or MATRIC). Whenever possible it isuseful to simplify, to an administrative entity, a network that acts as an element to makeit easier to deal with. An actor within the distributed open innovation network then ...can be compared to a black-box [informal network] that contains a network of black-boxes that depend on one another both for their proper functioning and for the properfunctioning of the network (Callon, 1987: 95).

Some considerations should be taken into account during the development andintegration phases of the distributed open innovation network, in particular, the effects ofeconomies of scale and agglomeration. If there are economies of scale associated withthe development of new technologies, a more than proportionate number of innovationsin general may be developed in urban and rural areas. Actually, we should expectcumulative effects here in both areas. A large supply of innovations spurs productdevelopment in the urban sector while stimulating RE implementation within the ruralareas. A high volume of RE R&D, on the other hand, spurs innovative activities amongthe suppliers of component parts in the urban areas. And on the demand side a similarprocess is working. An agglomeration with many qualified and demanding customers inthe rural areas spurs product development at the same time as a rich supply of productdevelopers spurs the adoption of new RE technologies among the customers. Hence,


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we will take into account two cumulative processes that stimulate RE development inagglomerations and in turn we should expect technological innovations and also thecreation of new RE technologies and the renewal of old technologies by means ofinnovation adoption to appear in manufacturing clusters, both in time and space (Porter,1990; DeBresson, 1989).


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6 Case Study May want to change??

In the case of our proposed pyrolysis project we are developing an emergent R&Dprogram, based on clustering effects, which will stimulate technological innovation,industry growth in both the production as well as the manufacturing sectors, accelerated

acceptance of the distributed energy production model, and many other outliers whichcannot be accounted for at this time due to the pioneer status of the JOBS Project.

The pyrolysis facility is an integrated energy system that can be modified depending onthe needs of the project. The hallmark of all well-designed pyrolysis systems isincreased efficiency of fuel use. By using waste heat recovery technology to capture asignificant proportion of heat created as a byproduct in electricity generation, thissystem will typically achieve total system efficiencies of 38 to 41 percent for producingelectricity and thermal energy. These efficiency gains will improve the economics of theoverall project, and also produce other environmental benefits.

This pyrolysis system will be a sequential generation of mechanical and thermal energyin a single, integrated system. The system will consist of a number of individualcomponentsprime mover (heat engine), generator, heat recovery, and electricalinterconnection configured into an integrated whole. The prime mover of this systemwill be a steam turbine which has little to no sensitivity to fuel moisture. This primemover will ideally burn secondary timber residues to produce shaft power. Additionaltechnologies may be used in configuring a complete CHP system, including boilers,absorption chillers, desiccants, and engine-driven chillers.

The steam turbine is a thermodynamic device that converts the energy in high-pressure,high-temperature steam into shaft power that in turn can be used to turn a generatorand produce electric power. Unlike gas turbine and reciprocating engine pyrolysissystems where heat is a byproduct of power generation, steam turbine pyrolysissystems normally generate electricity as a byproduct of heat (steam) generation. Asteam turbine requires a separate heat source and does not directly convert fuel toelectric energy. The energy is transferred from the boiler to the turbine through high-pressure steam, which in turn powers the turbine and generator.

In the thermodynamic cycle illustrated in Figure 9, called the Rankine cycle, liquid wateris converted to high-pressure steam in the boiler and fed into the steam turbine. Thesteam causes the turbine blades to rotate, creating power that is turned into electricitywith a generator. A condenser and pump are used to collect the steam exiting theturbine, feeding it into the boiler and completing the cycle. This system will utilize acondensing steam turbine for power-only applications that expands the pressurizedsteam to low pressure at which point a steam/liquid water mixture is exhausted to acondenser at vacuum conditions which is in turn circulated back into a closed-loop heatrecovery system.


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The pyrolysis facility will require technicians to maintain and repair systems, equipmentand associated controls. Those interested in pursuing this specialized trade will typicallyobtain certification from technical schools, trade schools or community colleges. Someoptions include the Boiler Operator or Boiler Technician certificate. Boiler technicianprograms often contain an apprenticeship component. This apprenticeship will occur at

the facilities the JOBS Project is presently developing throughout the state through ourSmart-Schools program. Upon completion of the boiler technician certificate, studentsusually qualify to sit for licensure examinations. We are presently developingeducational pathways from these positions which stimulate interests within the everydayinteractions of the employee and their specified tasks and linking these interactions withhighly specialized technological R&D processes. If the employee becomes interested inexpanding her/his developed skills in R&D, she/he can then apply for a specialized fieldof study of her/his choice at West Virginia University's forestry program or an associatededucational institution. West Virginia University offers an Associate of Applied Science(AAS) degree in Boiler Engineering, Feedstock management or Energy Technology.

We are presently exploring the feasibility of these educational pathways by developing aspecialized O&M database for capturing real world interactions and/or "suggestedtweaks" by the employees within various biomass processes (harvesting to energygeneration) into a virtual clustering map (KMZ format in Google Earth) which willsimulate rural economies of agglomeration by spatially locating the information capturedby the O&M database (Tantall & Gilding, 1999). This will allow us to bridge the gapsbetween the RE employees, West Virginia University's Industries of the Future BiomassProgram, MATRIC, project managers, and technology manufacturers. By bridging thesegaps we can begin to stimulate collaboration with one common goal, creating adistributed innovation network. In effect, these virtual clusters will have the same effectthat economies of agglomeration have within the urban setting. This clustering effectessentially stimulates growth in manufacturing and R&D for a particular sector, in thiscase biomass (Saxenian, 1994; ARC, 2006).

The Smart-Tech and Smart-Growth coordinators of the JOBS Projects collaborativeteam will play an essential role in creating the O&M database and virtual clusteringmaps. The O&M database will not only play an important role in stimulatingtechnological innovation but it will also aid in developing new approaches tosustainability via Human Sustainability Index (HSI). By collaboratively creating thisdatabase, innovation will be intimately integrated into the technologies surroundingenvironments by accounting for how specific changes in technology will affect the areasin which they will be operating. The virtual clustering maps will play an essential role inaccounting for how technological innovation is related to its social, economic andecological environments by accounting for the technologies lifecycle. Specifically, theselife cycles capture the available feed stocks, local skill sets, transportation infrastructure,etc.

The Smart-Market coordinators will develop the locally-owned model for this facility aswell as developing employee ownership models (e.g., stock shares, profit shares, board


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participation etc.) in order to maintain a high level of employee retention and communitysupport of these projects. In turn, all these interactions will inform the Smart-Solutionscoordinators in their efforts to influence policy, organize community events, and furtherdevelop the local steering committees and advisory board.


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Bibliography

Abernathy & Utterback (1978). Patterns of Industrial Innovation. Technology Review, 40-47.

cs, Z. & Varga, A. (2005).Entrepreneurship, Agglomeration and Technological Change."Small

Business Economics,24(3), 323-334.Alanne, K. & Saari, A. (2004). Distributed Energy Generation and Sustainable Development.

Renewable Energy & Sustainable Reviews.

Appalachian Region Commission (2006). Sources of Regional Growth in Non-Metro Appalachia.Retrieved fromhttp://rtsinc.org/publications/pdf/appalachia.pdf

Bacon, F. (1960). The New OrganonFulton H. Anderson (Ed.). New York, NY: MacMillan.

Baldwin, C. & Clark, K. (2002). The Option Value of Modularity in Design. Cambridge, MA:

Harvard Business School. Published by Authors.

Basalla, G. (1988). The Evolution of Technology. Cambridge, MA: University of CambridgePress.

Bijker, W.E., Hughes, T.P., & Pinch, T.J. (Eds.). (1987). The Social Construction ofTechnological Systems: New Directions in the Sociology and History of Technology.Cambridge, MA: MIT Press.

Boschma, R. & Lamboy, J. (1999). Evolutionary Economics and Economic Geography. Journalof Evolutionary Economics, 9, 411-429.

Braudel, F. (1979). The Perspective of the World: Civilization and Capitalism 15th-18thCentury,Volume 3. Cambridge, MA: Harper & Row Press.

Buckley, P.J. % Casson, M.C. (1976). The Future of the Multinational Enterprise. London:Homes & Meier.

Callon, M. (1987). Society in the Making: The Study of Technology as a Tool for SociologicalAnalysis. The Social Construction of Technological SystemsBijker, W. E., Hughes, T. P.and Pinch, T. P. (Eds.). Cambridge, MA: The MIT Press.

Callon, M. (1986). The Sociology of an Actor-Network: The Case of the Electric Vehicle.Mapping the Dynamics of Science and TechnologyCallon, M., Law, J. & Rip, A. (Eds.)London: Macmillan Press.

Cohen, W.M. & Levinthal, D.A. 1989.Innovation and Learning: The Two Faces of R&D.Economic Journal, Royal Economic Society99(397), 569-96.
http://ideas.repec.org/a/kap/sbusec/v24y2005i3p323-334.htmlhttp://ideas.repec.org/a/kap/sbusec/v24y2005i3p323-334.htmlhttp://ideas.repec.org/a/kap/sbusec/v24y2005i3p323-334.htmlhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://rtsinc.org/publications/pdf/appalachia.pdfhttp://rtsinc.org/publications/pdf/appalachia.pdfhttp://rtsinc.org/publications/pdf/appalachia.pdfhttp://ideas.repec.org/a/ecj/econjl/v99y1989i397p569-96.htmlhttp://ideas.repec.org/a/ecj/econjl/v99y1989i397p569-96.htmlhttp://ideas.repec.org/a/ecj/econjl/v99y1989i397p569-96.htmlhttp://ideas.repec.org/s/ecj/econjl.htmlhttp://ideas.repec.org/s/ecj/econjl.htmlhttp://ideas.repec.org/s/ecj/econjl.htmlhttp://ideas.repec.org/a/ecj/econjl/v99y1989i397p569-96.htmlhttp://rtsinc.org/publications/pdf/appalachia.pdfhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://ideas.repec.org/s/kap/sbusec.htmlhttp://ideas.repec.org/a/kap/sbusec/v24y2005i3p323-334.html


32/34

32 | P a g e

Conner, C. (2005).A Peoples History of Science: Miners, Midwives, and Low Mechanicks. NewYork, NY: Nation Books.

Cortright, J. (2001). New Growth Theory, Technology and Learning: A Practitioners Guide.

Retrieved from http://www.eda.gov/PDF/1G3LR_7_cortright.pdf

Cowart, R, (2001). Efficiency Reliability: The Critical Role of Demand-Side Resources in PowerSystems and Markets. Prepared for the National Association of Regulatory UtilityCommissioners.

David, P.A. (1985). Clio and the economics of QWERTY. AEA Papers and Proceedings,75(2),332.

Debresson C. (1989). Breeding Innovation Clusters: A source of Dynamic Development. WorldDevelopment, 17(1),1-16.

DeLanda, M. (2006). A New Philosophy of Society: Assemblage Theory and Social Complexity.New York, NY: Continuum Press.

Demirbas, A. (2002). An Overview of Biomass Pyrolysis. Energy Sources, 24, 474-482.

Descartes, R. (1996). Oeuvres de DescartesAdams, C. & Tannery, P. (Eds.). Paris, France: J.Vrin.

Dosi, G. (1984). Technical Change and Industrial Transformation. London, England: MacMillan.

Electric Power Research Institute, (2009). AEP Smart Grid Demonstration Project: Virtual Power

Plant Simulators (VPPS). Retrieved fromhttp://www.smartgrid.epri.com/pdfs/1020226AEPSmartGridProjectOverview.pdf

Farrell, J. & Morris, D. (2009). Energy Self-Reliant States. New Rules Project. Retrieved fromhttp://www.newrules.org/sites/newrules.org/files/ESRS.pdf

Feldman, M.P. & Florida, R. (1994).The Geographic Sources of Innovation: TechnologicalInfrastructure and Product Innovation in the United States.Annals of the Association ofAmerican Geographers, 84, 210-229.

Georgescu-Roegen, N. (1970).The Economics of Production. The American Economic Review,60(2), 1-9.

Harman, P.M. (1982). Energy, Force, and Matter: The Conceptual Development of Nineteenth-Century Physics. Cambridge, MA: University

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