The role of pilot and demonstration plants in technology development and innovation policy

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The role of pilot and demonstration plants in technology development and innovation policy Hans Hellsmark a,∗ , Johan Frishammar b , Patrik Söderholm c , Håkan Ylinenpää b a b c

Chalmers University of Technology, Environmental Systems Analysis, SE-412 96 Gothenburg, Sweden Luleå University of Technology, Entrepreneurship and Innovation, SE-971 87 Luleå, Sweden Luleå University of Technology, Economics Unit, SE-971 87 Luleå, Sweden

a r t i c l e

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Article history: Received 27 May 2014 Received in revised form 22 April 2016 Accepted 14 May 2016 Available online xxx Keywords: Pilot and demonstration plants Demonstration project Technological development Innovation policy

a b s t r a c t Pilot- and demonstration plants (PDPs) represent bridges between generating basic knowledge and technological breakthroughs on the one hand, and industrial applications and commercial adoption on the other. This paper reports on a longitudinal study of how two technological fields that received significant public funding evolved—biochemical conversion of biomass and thermal conversion of black liquor. In doing so, this study makes two contributions. First, it provides a framework for analyzing the roles of various types of PDPs in developing new technology. The framework highlights the learning processes taking place at and around these plants and how they contribute to reducing different types of risks. It also elaborates on the importance of actor networks and institutional preconditions, and how both network performance and institutions can be influenced through various strategies. Second, the article contributes with new insights into the challenges of innovation policy in a PDP context. A policy mix is often required because policy cannot be considered meaningfully at a single level of government and will therefore be influenced heavily by limited foresight and politics (both nationally and locally). Therefore, policy must address both the need for parallel and iterative public funding of R&D and different types of plants, as well as attempts to directly influence collaborative processes in actor networks. © 2016 Elsevier B.V. All rights reserved.

1. Introduction This paper focuses on how to understand the roles of different types of pilot- and demonstration plants (PDPs) in developing technology. This issue is of particular importance for developing less emission-intensive technologies. Notably, to avoid irreversible changes to the global climate, substantial reductions in greenhouse gas emissions are necessary. In the European Union (EU), a longterm target of reducing carbon dioxide emissions to about 80% to 95% below 1990 levels by 2050 has been adopted (European Commission, 2011). To reach such targets, significant changes in current production systems and technologies are necessary, and new non-fossil-fuel-based technological systems must be developed and diffused within short timeframes (Johnsson, 2011). This also calls for policy instruments that promote research and development (R&D) and learning processes, as well as private and public

∗ Corresponding author. E-mail address: [email protected] (H. Hellsmark).

efforts through which new technology can be developed, verified, and brought to the market. To this end, a significant share of public R&D budgets has been directed toward investments in different types of PDPs. For example, within the EU, specific programs (e.g., NER-300) focus solely on funding this type of infrastructure. Major efforts have also been taken within the Horizon 2020 program to provide funding to technology demonstration activities and so-called flagship projects. Similar programs exist in Japan, the USA, and China (e.g., Hendry et al., 2010; Zhou et al., 2015), as well as other countries. Pilot- and demonstration plants are key elements in helping develop new technologies with the potential to address climate challenge (e.g., Bossink, 2015; Frishammar et al., 2014). These plants often balance between verifying technologies on one hand and creating a first commercial market on the other. Thus, the development activities taking place in and around PDPs address not only pure technical challenges, but also reduce the organizational-, market-, and institutional risks and uncertainties that key stakeholders face in advancing new technologies (Hendry et al., 2010; Karlström and Sandén, 2004).

http://dx.doi.org/10.1016/j.respol.2016.05.005 0048-7333/© 2016 Elsevier B.V. All rights reserved.

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Although investments in PDPs are central for advancing new knowledge, surprisingly little academic research has been conducted that analyzes the role of such plants. This research deficit is further underscored by the fact that PDP investments are often associated with major socio-technical challenges involving significant public support (Frishammar et al., 2014; Klitkou et al., 2013). Pilot- and demonstration plants are mentioned in the engineering and natural science literature as arenas for scientific experiments and in the management literature when elaborating product and process development (Lager et al., 2013). Still, neither of these literatures focuses specifically on PDPs. Somewhat more attention has been devoted to PDPs in the literature on the dynamics of technological change, especially in studies with a strong focus on sustainability transitions (Geels and Raven, 2006; Hellsmark, 2010; Hendry et al., 2010; Lefevre, 1984; Smith and Raven, 2012). Even in this strand of research, though, relatively few scholars make PDPs the focal point in their analysis. Prior research highlights that PDPs assist in advancing toward dominant designs and industrial scale production, but also in developing the broader socio-technical system. The latter implies aligning emerging technology with prevailing institutions (e.g., legal rules, standards, codes of conduct). Nevertheless, although it has been emphasized that PDPs may take on different roles—for example, some are primarily experimental, whereas others demonstrate the value of a process (Brown and Hendry, 2009; Karlström and Sandén, 2004)—the extant literature provides limited insights into PDPs through the different phases of technology development. This limitation also relates to the implications for how to design and implement public policy measures to support PDP activities (Frishammar et al., 2014). Prior experience shows that most technologies developed in laboratories fail to make it to the market; that developing new knowledge fields may take several decades; and that many firms that engage in such development risk suffocating in the so-called “valley of death,” referring to the lack of funding and paying customers that occurs before the technology is mature (Moore, 1998). Moreover, existing definitions of PDPs often indicate little about their actual roles in developing technology and innovation policy. For example, the International Energy Agency (IEA) defines a pilot plant as a facility that operates discontinuously, partially demonstrates the feasibility of a technology, and is not embedded in the entire value chain. A demonstration plant is defined as a plant that can be operated continuously over an extended period of time; it also demonstrates the entire production process and is embedded in a value chain.1 These definitions, however, say little about what the actor networks at and around the plants accomplish and how learning processes can be strengthened. Consequently, the need exists to further conceptualize the role of PDPs in developing technological fields, as well as better understanding innovation policy in the PDP context. To do so, we take our point of departure in the innovation and sustainability transitions literature (e.g., Bergek et al., 2008; Geels, 2002; Hekkert et al., 2007; Kemp et al., 1998). As such, we acknowledge that technology development is a complex and far from linear process, involving significant iterations between learning and R&D (Kline and Rosenberg, 1986). The main purpose of the present study is to develop a framework that can be used to analyze the role of different types of PDPs in developing technological fields toward commercialization. We focus on the development of technologies that have received significant public funding and support. Although prior literature provides the conceptual foundation and several key analytical dimensions,

1 See, for instance, the IEA Bioenergy Task 39 (http://demoplants.bioenergy2020. eu/).

empirical insights are based on a longitudinal analysis of the development of biorefinery technologies in Sweden over a 30-year time period. The main contribution of the present paper is a generic framework for analyzing the role of PDPs in technology development. This framework improves our understanding of the links between basic R&D and commercialization, which is essential in all technological development processes and not the least about the role of innovation policy in these processes. We introduce a typology of different PDPs and highlight the learning processes taking place at and around these PDPs. We also examine how innovation policy may contribute to reducing different types of risks. Moreover, the framework addresses the importance of well-functioning actor networks and considers differences in institutional conditions. It also elaborates how network processes and institutions can be influenced, in part through public policy interventions. The framework is developed primarily with a focus on process technology development and builds on experiences from renewable energy generation in general and biorefinery technologies in particular. The purpose of biorefinery technologies is to enable the coproduction of heat, electricity, chemicals, and transportation fuels from cellulose-based feedstocks such as forest or agricultural residues (Sandén, 2012). This paper is structured as follows. In Section 2, we provide the theoretical background and a literature review of prior research on PDPs. We use this literature to identify key analytical dimensions for guiding the empirical investigation. Section 3 explicates methods, and section 4 examines the historical development of biorefineries in Sweden. Section 5 presents the analytical framework, explicates the key points, and discusses key challenges for different types of PDPs. In Section 6, we illustrate the usefulness of our framework for analyzing innovation policy in a PDP context. Finally, Section 7 concludes the paper and suggests directions for future research.

2. Theoretical background and key analytical dimensions Despite the central role of PDPs in developing new technology (Rosenberg and Steinmueller, 2013), previous studies lack an analytical framework in which this role can be investigated formally (Frishammar et al., 2014; Klitkou et al., 2013). Against this background, we draw on prior research to identify and discuss key analytical dimensions of such a framework, which may allow a systematic discussion of the contributions PDPs make to technology development.

2.1. A brief overview of existing PDP literature Existing research on PDPs may be categorized into three literature streams (Frishammar et al., 2014). First, in natural science and engineering studies addressing PDPs, there is a strong focus on the pure technical and chemical challenges involved in up-scaling and verifying new technology (Dinca and Badea, 2013; Pettinau et al., 2015). Research in this domain is empirical and focuses heavily on reporting results from PDP experiments (e.g., Amutio et al., 2012; Clark et al., 2004). Second, the technology- and innovation management literature also elaborates on PDPs, although they tend to be treated as side issues when studying learning, validation, and verification of product or process innovation (Lager et al., 2013). Although this literature goes beyond pure technical challenges, the analyzes typically center on single firms or groups of collaborating firms (e.g., Pisano, 1994; Yli-Kauhaluoma, 2013). Consequently, the institutional settings in which PDPs are embedded tend to be treated implicitly and the interplay between PDPs and the innovation systems is therefore downplayed.

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Third, the most all-encompassing treatment of PDPs to date is provided by innovation and transition studies, in which the role of PDPs is recognized to go beyond reducing technical uncertainties in firm-level learning. Here, PDPs are key to developing the entire socio-technical system, thus also contributing to establishing new markets, aligning institutions (i.e., legal rules, standards, norms etc.), and public attitudes. These wide system boundaries also imply that specific attention is devoted to the role of innovation policy (e.g., Hellsmark and Jacobsson, 2012; Hendry et al., 2010). Prior empirical research on PDPs within the innovation and transition literatures has a strong focus on sustainable technology development and transitions of the energy system (Bossink, 2015; Klitkou et al., 2013; Zhou et al., 2015). This work confirms the notion that PDPs may take different roles as a technological field progresses (Kemp et al., 1998; Macey and Brown, 1990). Pilotand demonstration plants may thus contribute to various learning processes; they reduce uncertainties and permit up-scaling but also assist in aligning new technology to institutional and organizational structures (e.g., Brown and Hendry, 2009; Harborne et al., 2007). This development is nonlinear, iterative, and uncertain; PDPs must therefore be flexible so they can alter and iterate between technology verification and technology diffusion (Lefevre, 1984). The research has also identified important factors influencing the success (or failure) of PDPs (e.g., Brown et al., 1993). Various scholars have proposed different typologies of PDPs (Hendry et al., 2010; Karlström and Sandén, 2004; Macey and Brown, 1990). This literature, however, does not problematize the evolvement of actor networks, learning processes, and institutions over time, as well as how these can be related to the challenges of developing new technology. For example, an important challenge is that the actor network expands as a technology moves closer to the market, implying increasingly complex relationships among industrial partners and between industry and government (Harborne et al., 2007; Lefevre, 1984). This poses challenges in terms of building communities of users and firms and where social and political considerations may play important roles (Geels et al., 2008; Hendry et al., 2010).

2.2. Theoretical points of departure Although early research on PDPs from a systems perspective was empirically driven, research in the area of sustainability transitions over the past 15–20 years has provided a lens for meaningful theoretical analysis (e.g., Markard et al., 2012). In particular, PDPs have been highlighted—but nevertheless not extensively elaborated—in research on Strategic Niche Management (SNM) and Technological Innovation Systems (TIS). Strategic niche management facilitates regime shifts by creating, supporting, and protecting emerging technologies in specialised niches (Smith and Raven, 2012). Core assumptions are technological lock-in and strong path dependence, both of which favour incremental development of existing technological regimes rather than progression of new ones (Raven, 2005; Schot and Geels, 2008). The transition to a new technological regime, therefore, requires protective spaces or niches in which technology can be shielded (i.e., protected from mainstream selection pressures) and nurtured. In the present paper, we primarily focus on nurturing, that is, on the processes that support developing technology and growing the niche, although we acknowledge shielding in protecting PDP activities from market pressures. A niche is a breeding place for evolving novel technological solutions, regulatory structures, and user practices, as well as developing complementary technologies and infrastructure (Kemp et al., 1998). This is particularly important for radical innovations, includ-

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ing many new technologies in the sustainability domain (Schot and Geels, 2008; Smith and Raven, 2012). Niches emerge in the form of experimentation projects and PDPs, which constitute “the main space for nurturing path-breaking innovations” (Smith and Raven, 2012; p. 1027). The SNM literature notes that facilitating learning in actor networks is critical to the nurturing process not only to improve the design of technology, but also for questioning conceptions of users and regulations (Hoogma et al., 2002; Schot and Geels, 2008). Finally, the SNM literature also emphasizes that socio-technical regimes (e.g., rules, knowledge base, skills) and niches interact with socio-technical landscapes, which constitute the structural trends and major events that normally are exogenous to developing the socio-technical regime (e.g., Geels, 2002; Markard et al., 2012). The TIS literature also has a strong emphasis on nurturing new technology. It delineates the boundaries around a given technological field (Carlsson and Stankiewicz, 1991) and defines a TIS as “. . .a set of networks of actors and institutions that jointly interact in a specific technological field and contribute to the generation, diffusion and utilization of variants of new technology,” (Markard and Truffer, 2008; p. 611). A TIS comprises three structural elements: actors, networks, and institutions (Bergek et al., 2008) and complements SNM literature by highlighting the processes that encourage learning and reducing uncertainty in different actor networks. Actors develop technology that allocate resources to the TIS. The resulting actor networks involve formal and informal contacts among technology suppliers, end users, NGOs, universities, institutes, public authorities, and so on, which set out to develop a common knowledge base, make the technology ready for the market given existing institutional and economic barriers, but align existing institutions to the requirements of the TIS (Van de Ven, 1993, 2005). The institutions (i.e., laws, norms and beliefs) set basic rules, define what is considered desirable and legitimate, and form expectations about the future of a given technological field. The formation of a TIS is usually a lengthy and highly uncertain process in which institutions, actors, and technologies coevolve (Nelson, 1994). Key stakeholders frequently underestimate the length and difficulties of this so-called formative phase, which may last for decades (Breshanan et al., 2001; Grubler, 1998). It is therefore critical to understand the various types of uncertainties and risks at play. These risks may cause various types of system weaknesses that hamper developing the TIS (Wieczorek and Hekkert, 2012). Thus, in the formative phase, the TIS is inherently weak. Progress in R&D may be slow, and new markets may fail to materialize. This may deter important actors from contributing. To reduce uncertainties and risks, actors critically depend on experiments with different technological solutions. Pilot- and demonstration plants play a critical role in making such experimentation possible. Technological innovation systems were not designed as a framework for studying PDPs. However, recent TIS applications have highlighted PDPs. Pilot- and demonstration plants are believed to be of critical importance to advance innovation processes and systems (Haase et al., 2013; Hellsmark, 2010), and the lack of successful demonstration projects may prohibit developing a TIS (Harborne and Hendry, 2012). However, this literature has not fully uncovered the interplay among actors, networks, and institutions over time and the role of PDPs as a technological field progresses. Nor have they in detail elaborated how different types of PDPs can be used to address various challenges that arise. Despite the emphasis on actors, networks, institutions, and learning in the TIS literature, it has been criticized for privileging the structure of the system and downplaying agency and the roles the actors may play (e.g., Flanaganan et al., 2011). When adopting a network approach to innovation processes, it is therefore useful to draw on ideas elaborated in policy network

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Table 1 Five key analytical dimensions for understanding the role of PDPs in technology development. Risk reduction

Learning processes

Actors and Agency

Network performance and management

Institutional pre-conditions

New sustainable technology is a highly uncertain process, involving technical, market-related, organizational, and institutional risks when moving from laboratory to full-scale production. Learning is essential for reducing risk, including feedback loops, cumulative causation and different “motors” for the interaction among functions. Learning involves generating tacit knowledge about technical challenges but also about market preferences, institutional constraints, and the physical infrastructure. Pilot- and demonstration plant (PDP) activities are typically embedded in actor network structures consisting of industrial firms, university researchers, and other private and public actors, which together shape the context for technological development by assuming different roles. The actors are viewed as structural elements on the one hand, but their capacity to take action and assume a critical role in the development process is equally important (i.e., agency). Different network characteristics may influence how challenges are addressed, as well as how to govern the evolution of the networks. Because cooperation and learning do not emerge spontaneously, network management, that is, actions taken to advance the interactions in a PDP network, becomes critical. The efforts to manage networking processes take place within an institutional context of rules and codes of conduct. These will emerge at multiple layers of government, and they therefore also encompass sub-national rules. Compared to incumbent technologies, new technological systems often operate in less-developed institutional and organizational settings. It is therefore imperative that technology not only align with existing institutions but also that institutions align with new technology.

theory and public network management theory as well (e.g., Bache and Flinders, 2004; Hooghe and Marks, 2003; Kickert et al., 1997; Pierre and Peters, 2005). Arguably, this permits more in-depth analyzes of not only the necessary processes but also the performance of successful actor networks. It also allows analyzing how to govern the evolution of these networks, that is, so-called network management. Specifically, Klijn (2005) distinguished between strategies aimed at influencing the network by changing the underlying rules, that is, institutional design and strategies aimed at influencing the collaborative process within the institutional framework, that is, process design. We believe this categorization is useful for disentangling different ways to influence the actor networks surrounding development activities taking place at PDPs (e.g., their level of heterogeneity, integration, and stability). 2.3. Key analytical dimensions for analyzing the role of PDPs Neither SNM nor TIS research constitutes theories or frameworks for analyzing the role of PDPs for technological development. Combined with insights from policy network theory, however, they provide conceptual foundations for constructing such a framework by delineating key analytical dimensions. Below, we identify five such dimensions, which are summarized in Table 1. The first two dimensions, risk reduction and learning processes, represent the two most significant outcomes of PDP activities, whereas the remaining three, actors and agency, network performance and management, and institutional preconditions address some of the most critical preconditions for these outcomes to materialize. Risk reduction is important because developing new, sustainable technology is a highly uncertain process (Hendry et al., 2010; Brown and Hendry, 2009). In previous TIS studies, four categories of risks have been identified: technical, market-related, organizational, and institutional (Bergek et al., 2008; Hekkert et al., 2007; Hellsmark, 2010). Technical risks relate to different types of design choices and future development of complementary technologies, products, and resources along a (imagined) value chain. Reducing the initially

Bergek et al., (2008), Hekkert et al., (2007), Hellsmark, (2010), Jacobsson and Bergek, (2004), Schmookler, (1966), Schot and Geels, (2008). Kamp et al., (2004), Hendry and Harborne, (2011), Raven (2005), Sagar and van der Zwaan, (2006), Schot and Geels, (2008), Suurs, (2009).

Flanagan et al., (2011), Jacobsson and Bergek, (2004), Hellsmark and Jacobsson, (2009), Schot and Geels, (2008), Kemp et al., (1998)

Boschma, (2005), Koopenjan and Klijn, (2004), Klijn, (2005), Flanagan et al., (2011).

Coenen et al., (2012), Ostrom and Crawford (2005), Flanagan et al., (2011).

poor price-performance ratio of new technology typically requires several “secondary” innovations (Schmookler, 1966). Moreover, the process of up-scaling is associated with considerable risks as new product or process properties are added and existing ones are lost. Market-related risks involve whether or not a sufficient market demand for the new technology (and its subsequent applications) will emerge, and if it does, how long that it will take for demand to emerge. In the case of sustainable technologies, these risks may be associated with both the economic prospects for competing incumbent technologies, that is, the fossil fuel price risk, and with uncertainties inherent in launching a new product, that is, the product market risk.2 Furthermore, organizational risks center on realizing a future value chain, including what actors will be involved, their agency, and roles. Finally, institutional risks refer to whether and how legal rules, beliefs, and standards will be adapted to support (or counteract) the emerging technology (Jacobsson and Bergek, 2004). The SNM literature also highlights risks and the role of experimentation for reducing risks, but from the angle of niche development. This development is contingent on diversity that enhances learning and network development. Still, too much diversity may hamper development by generating uncertainty and risk (Schot and Geels, 2008). Risk thus prevents full commitment and may fragment resource investments and the emergence of stable rules. However, the SNM literature tends to treat all experiments, including those in different types of PDPs, in similar manners. It thus lacks a clearer assessment of how different types of PDPs may promote risk reduction as a new technological field develops. Both the TIS and the SNM research highlight that technological development is rooted in different learning processes, which are necessary for reducing risk. In addition, feedback loops, cumulative causation, and different drivers for interaction among functions

2 For example, in the bioenergy field, synthetic fuels seldom conform to the specifications of conventional gasoline and diesel; therefore, their use may require a tailor-made infrastructure, including new types of engines and filling stations.

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has been brought forward in TIS studies (Suurs, 2009). The SNM literature treats learning processes as equally important. These are required for new technologies to bridge the valley of death between basic R&D and commercialization. Learning involves technical challenges but also market preferences, institutional constraints, and infrastructure (Schot and Geels, 2008). Although PDP projects seem to be organized around a “technology push” logic to facilitate learning, they lack the systematic assessment of what kind of learning takes place in PDPs and how learning processes can be promoted further. This suggests that it is essential to acknowledge the importance of different modes of learning in the context of PDP technology development (see also Hendry and Harborne, 2011). Here, we distinguish between two categories of learning processes. First, given that PDP activities tend to be closely linked to both basic R&D and technological diffusion, Kamp et al. (2004) distinguished among: (a) learning-by-searching (R&D to acquire basic knowledge about processes etc.); (b) learning-by-doing (tacit knowledge acquired during manufacturing); (c) learning-by-using (know-how emanating as feedback from user experiences); and (d) learning-by-interacting (know-how generated through the interaction between various types of actors). Second, the SNM literature distinguishes between first- and second-order learning. The former involves learning about how to improve the design of technology, whereas the latter implies that conceptions of users, technology, and government regulations are not only tested but also questioned (Hoogma, 2000). Second-order learning is therefore critical for radical regime shifts (Raven, 2005). These categorizations, however, do not imply that learning is a linear process with clearly demarcated phases; indeed, important feedback and feed-forward loops are at play (Sagar and van der Zwaan, 2006). Public policy may play a critical role in encouraging learning and reducing risk. Recognizing that knowledge generated through the learning at PDPs has public-good characteristics, firms will often not appropriate all benefits from their investments. For this reason, they are likely to underinvest in learning unless support is provided (e.g., through R&D subsidiaries and technology deployment schemes). In addition, few decentralized actors are willing to assume the initial investment risks, and venture capitalists will often not be able fill this funding void (e.g., Mazzucato, 2014). This is particularly the case in knowledge-based sectors, where capital intensity and technological complexity are high, such as in developing new sustainable technologies. It is essential to distinguish between actors and agency, that is, between actors as structural components on the one hand (e.g., Bergek et al., 2008) and their capacity to take action and assume a role in the development process on the other (e.g., Flanaganan et al., 2011). Pilot- and demonstration plant activities are typically embedded in actor network structures that consist of industrial firms, university researchers, and other private and public actors, which together shape the context for technological development by assuming different roles. Some actors are prime movers that move first into a new technology, thus raising awareness and increasing legitimacy (Jacobsson and Bergek, 2004; Jacobsson, 2008). Prime movers could also be a constellation of actors (e.g., Hellsmark and Jacobsson, 2009; Jacobsson and Johnson, 2000). The SNM literature highlights the need for outsiders who provide out-of-the-box thinking (Schot and Geels, 2008); indeed, a dominance of established firms can lead to excessively defensive behaviour. Pilot- and demonstration plants should be purposefully used to create alliances among actors along future value chains that have the capacity to develop new technology as well as different organizational solutions that influence the political landscape of the technology. Such alliances are, of course, not built instantaneously; instead, they are created in path-dependent learning cycles, through decades of experimental development in combina-

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tion with different types of economic incentives (Hellsmark, 2010; Karlström and Sandén, 2004; Kemp et al., 1998). Although successful actor networks are key to developing new technological regimes, the challenges involved in governing these networks must be equally recognized. For this reason, it is important to include the dimension of network performance and management, that is, how different network characteristics may influence how challenges are addressed and how the evolution of the networks is governed. The policy network literature suggests that actor diversity is key; networks composed of a heterogeneous set of actors may often be more successful in securing necessary resources (e.g., competence and public support) and sustaining the network’s innovative capacity (e.g., Boschma, 2005). However, although successful PDP activities require the involvement of multiple actor types and roles (e.g., end-users to facilitate learning; lobbyists to argue for institutional change), conflicts of interest often arise. These include, for example, risk sharing, management control, and information dispersion (e.g., spreading new knowledge versus securing immaterial rights) (Lefevre, 1984), or targeting a particular application versus discovering market opportunities (Brown and Hendry, 2009). Because cooperation and learning do not emerge spontaneously, network management becomes critical (Koopenjan and Klijn, 2004). The existing innovation and transition literatures emphasize the importance of institutional alignment (what policy network theory would refer to as institutional design). It is equally important, however, to consider different process design strategies. In a PDP context these could involve: (a) activating actors and resources, including efforts to exclude certain actors or resources from the process; (b) establishing goal-achieving strategies that encompass attempts to facilitate goal congruency; and (c) guiding interactions, including how leading actors influence how the development process is organized (Klijn, 2005). Network management may be difficult, however, because seldom is there a clear-cut network manager, public–private partnerships, or other governance form available. In a similar vein, the SNM literature has discussed the need for governance through “endogenous steering,” that is, multiple actor groups establish niches through collective enactment rather than government orchestration (Schot and Geels, 2008). Although the national government (or an authority assigned by the government) often may play a role in these networks, public policy in general cannot be understood at a single level of governance (Flanaganan et al., 2011). Our fifth and final analytical dimension acknowledges the ways in which PDP actor networks, and the efforts to manage these networking processes, exist within an institutional context of rules and codes of conduct. These emerge at multiple layers of government and therefore encompass sub-national rules. For example, Coenen et al. (2012) emphasized the need to analyze institutional embedding within a geographical context. These institutional preconditions often favor innovations that fit into existing processes. In contrast, new technological systems operate in less stable institutional settings. For this reason, it is imperative that technology not only become aligned with existing institutions, but also that institutions align with emerging technology. In addition to process design strategies, it is therefore useful to discuss strategies aimed at influencing the actor network by changing the underlying rules (i.e., institutional design) (Klijn, 2005). Following the typology Ostrom and Crawford (2005) suggested, in a PDP context the so-called scope rules and pay-off rules may be of particular importance. The former defines the scope of potential outcomes (in turn influencing the actor networks’ search for solutions), whereas the latter associates certain actions with benefits and costs, thus affecting the incentives actors have to participate in technology development. Research and development subsidies and policy support schemes clearly fall into the pay-off rule cate-

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gory. Due to the strong position of incumbent technology, existing rules must also be questioned (e.g., competition law), making niche support difficult for national governments. Based on complexity, it may indeed be difficult for policy makers to navigate among the various roles PDPs play and understand their specific challenges and opportunities (e.g., iterating between R&D and learning). In fact, (Lefevre (1984, p. 488) noted, “Premature demonstration is not caused by naive optimism. Prematurity often becomes evident only as the demonstration project unfolds.” Another critical policy challenge is the fact that most developed countries are characterized by dispersed power, not merely upward and downward from the national level, but also outward to quasistate and non-state actors (e.g., Hooghe and Marks 2003; Pierre and Peters 2005). Policy stems not only from a political administrative hierarchy, but is also formulated and implemented within multi-actor networks beyond formal hierarchies. As such, there is no single level of network management, again highlighting the need to consider the types, roles, and agency of actors initiating and pursuing PDP activities (see also Flanaganan et al., 2011). In sum, this array of challenges further underscores the need for a framework to analyze the role of different types of PDPs in technology development. 3. Case study design and method We employ a longitudinal case study to construct an analytical framework and derive insights concerning the role of PDPs in developing technology and subsequently in formulating innovation policy. The case study permits a context-rich analysis (e.g., Flyvbjerg, 2006), because it takes decades for a given technological field to progress from a first discovery to commercial installations. Such progress also requires several PDPs at various scales (Grubler, 1998; Wilson, 2012). Our empirical cases involve two complementary technological fields: (a) thermal conversion of black liquor and (b) biochemical conversion of biomass. For decades, these two fields have constituted the core of biorefineries in Sweden and both have a long, consecutive history of employing PDPs. They therefore permit indepth analyses of the role of different types of PDPs in technology development. Furthermore, while acknowledging that all cases display idiosyncrasies, we believe that these two fields provide generic lessons for other technologies. For example, biorefinery technologies can handle a multitude of feedstocks to produce advanced chemicals, materials, transportation fuels, heat, and electricity. Due to this heterogeneity, no easy, quick-fix policy solutions exist for scaling up these technologies (Hellsmark and Jacobsson, 2012). Furthermore, the lessons drawn should be useful for other complex technological fields where publicly funded PDPs play key roles. Each case is analyzed with the analytical dimensions identified in Section 2, thus focusing on actors and agency, network performance and management, learning processes, risk reduction, and institutional preconditions. Historical developments have been carefully reconstructed and divided into explicit episodes during which PDPs have been constructed. The analytical dimensions in each case were comprised into a table in which each episode is a column. Because there is no absolute linear progression, the episodes often overlap (c.f. Kline and Rosenberg, 1986). Still, rough time estimates are indicated (see Tables 2 and 3). The history of each case was reconstructed using two complementary data sources: personal interviews and workshops and secondary data. Both fields—thermal conversion of black liquor and biochemical conversion of biomass—are relatively well documented. Previous studies by Sandén and Jonasson (2005) and Ulmanen (2013) were particularly useful in the case of biochemical conversion of biomass, and Bergek (2002) and Hellsmark (2010)

were useful for understanding thermal conversion of black liquor. These earlier works combined with annual reports and other official reports facilitated comparing and crosschecking information gathered through interviews. The personal interviews targeted key individuals directly involved in relevant organizations and actor networks over the time period 1980–2013. A total of 17 individuals were interviewed (see Appendix A for a list of respondents, their experiences, and roles in each case). Each interview was semi-structured, lasted for 1.5–2.5 h, and focused on the role of PDPs for developing each field. We also addressed challenges associated with these types of plants, such as overcoming different types of risks. Each interview was recorded and notes were taken, and these notes were then rechecked against the recordings. In addition, all respondents were given the opportunity to read and react to the manuscript before submission. When citing information obtained from a respondent, we provide the respondent’s number (see Appendix) in parentheses following the relevant information. Finally, the empirical findings from the present study were validated and further developed through two stakeholder workshops in Gothenburg (January 21, 2014 and February 5, 2014). A total of 50 representatives from each of the two technological fields participated in these workshops, where they were given the opportunity to comment on and provide feedback on the emerging analytical framework.

4. The role of pilot and demonstration plants in developing biorefineries in Sweden The development of modern biorefineries for producing various types of advanced chemicals, transportation fuels, heat, and electricity from cellulosic materials has centered on two main technological fields: biochemical and thermochemical conversion of heterogeneous types of biomass (Pandey et al., 2011). In Sweden, both fields have been considered strategically important during different periods since the turn of the 19th century. In contrast to most other countries, the early expansion of the chemical industry in Sweden was not based on access to inexpensive oil. Instead, it relied on low-priced electricity and largely emerged as a side-activity to the forest industry. As early as 1909, the first cellulose-based sulfite lye, a by-product from sulfite pulp, was produced through biochemical conversion. It was used to make ethanol and a large variety of other chemicals, which were increasingly adopted in various industrial processes over time. During the Second World War, when imported chemicals were in short supply, major efforts were undertaken to further develop this “forest-based chemistry.” By the mid-1950s, 50–60 different chemicals were derived from sulfite lye (Berglund, 2010). In the late 1950s and onward, however, cheap oil and increasingly stringent environmental laws led most sulfite pulp mills in Sweden to either shut down or convert to the sulfate process. Steps were also taken to develop a domestic petrochemical industry on the west coast of Sweden. These investments were funded in part by vested interests in the forest industry, and the development drew heavily on the long experience with forest-based chemicals. Today, however, they now rely on oil and natural gas as feedstock (Berglund, 2010). As a result, the production of chemicals gradually transcended from the sulfite-based pulp mills to the emerging chemical industry cluster. Nevertheless, to maintain the acquired competencies in forestbased chemistry and biochemical conversion of biomass, limited production of sulfite ethanol was maintained at the Domsjö pulp mill in Örnsköldsvik. A network of actors centered on cellulosebased ethanol emerged here, and these came to invest in several different PDPs.

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Episodes 1&2: 1983–1997

Episode 3: 1990–2002

Episode 4: 1998∼

Episode 5: 2002∼

Episode 6: 2007/2008

Episode 7: 2012∼

Pilot- and demonstration plants (PDPs)

Commercial sulphite-based ethanol production (Örnsköldsvik)

Bench-scale pilot plant (Lund University)

Ethanol production at the PDP EtanolPiloten AB (EPAB) (Örnsköldsvik)

Developed a nation-wide infrastructure. Fleet trials and large-scale use of imported ethanol

Planned commercial-scale PDPs in northern Sweden

SP national research infrastructure, Örnsköldsvik

Actor networks

MoDo, SEKAB

SSEU, SEKAB, Lund, municipalities

SEKAB

Existing industry with little interest in expanding the actor network

Emphasis on activating new actors and resources

ECN, EPAB, SEKAB, Universities in Luleå and Umeå Coalition-building and interaction guiding to facilitate funding of PDP

SEKAB, SSEU, Ford dealership, Scania

Network performance & management

Expanded actor networks along the value chain. Goal-achieving strategies.

SEKAB was the dominating actor in the network

SP, Energy Agency, Vinnova, widened customer base SP stepped in to provide an active but neutral ownership and operation

Learning processes

Standardized production with limited learning

Basic knowledge generation through learning-by-searching

Institutional preconditions

Political interest in security-of-supply

Ethanol R&D program I (1993–1997)

Small-scale fleet trials with ethanol and established new infrastructure (e.g., vehicles) in more than 10 cities SSEU, SEKAB, Scania, Ford, municipalities Broadening the actor network along the value chain to gain legitimacy Learning-by-using, i.e., feedback from customers leading to new standards CO2 tax exemption on ethanol, regional support

Generation of private proprietary knowledge, and reference plant Ethanol R&D program II (1998–2004), and EU state aid rules (-)(-)(-)

Significant spill over effects; ethanol becomes the dominate alternative fuel in the market Pump law, EU mandate on blend of ethanol, high fossil fuel price levels

Knowledge on negative impact of ethanol reduces its legitimacy Financial crisis and legitimacy concerns

Technical risk Fossil price risk Product risk Organizational risk Institutional risk

(-) (-) (-)

(-)

Mix of publicly available and proprietary knowledge generation New funding from Swedish Energy Agency

(-)(-)(-) (+)(+)(+)

(-)(-) (-)

(-)(-)

(-)(-)(-) (-)

(-)

List of abbreviations: EtanolPiloten AB (EPAB), SP Technical Research Institute of Sweden (SP), Foundation for Swedish Ethanol Development (SSEU), European Union (EU).

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Table 2 The development of the biochemical conversion field in Sweden, 1983–2013.

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Episodes 1 1978–1986

Episode 2 (a, b): 1986–2003

Episode 3 (a, b): 1995–1999

Pilot- and demonstration plants (PDPs)

Plans for gasification PDP

CommercialBooster verification PDP, scale booster Frövi PDP, New Bern, North Carolina, USA

Verification PDP based on IGCC, Skoghall

Planned BLGAll PDP projects IGCC research cancelled plant and commercial PDP in Piteå

Actor networks

Nynäs, Chemrec, Nycomb

Kvaerner, Assi-Domän

Kvaerner, Stora

Kvaerner, AssiDomän, Nycomb

Network performance & management

Emerging, Customer-supplier interactions, dominated by the interests although limited, of Kvaerner. Plans (later abandoned) for broadening the actor network actor network through the construction of a commercial-scale plant.

Learning processes

Technical know-how (one patent)

Institutional preconditions

Oil crises and security-ofsupply concerns

Technical risk

(-)

Episode 5 (a, b): 2008–2011

Episode 6 2008–2011

Episode 7 2011

DME buses BLG verifica-tion PDP, Piteå

DME PDP, Piteå, and DME fleet trial. Planned commercial- scale DME/Methanol plant, Domsjö

Commercial scale National infrastructure for BLG DME/Methanol and related technologies plant in Domsjö cancelled

Kvaerner, AssiDomän

Chemrec, BLG program, ETC, Swedish Energy Agency, Volvo

BioDME (EU), ETC, VTT, Venture Partner Domsjö

Aditya Birla

LTU supported by the Swedish Energy Agency

Network weakened, Chemrec spun-off and near bankruptcy

Swedish Energy Agency assumes the role of a network manager, and forms the BLG Program

PDP became closely aligned to the interests of Chemrec, which used it to form alliances along value chain

Aditya Birla rejected the prospects for a first commercialscale plant

LTU assumed the role of network manager, opening up infrastructure for a wide range of users

R&D through learning-by-doing and interacting, including customer feedback and testing various applications.

Limited learning, technical risk assessed as too high

Resolving remaining technical issues

Private proprietary knowledge and reference plant

Political risk not handled; Chemrec near bankruptcy

Wider set of actors, experimenting with related technology. Less targeted PDP activities

Capacity limits at existing pulp and paper mills

Deregulation and cash flow crises in energy sector

FABEL, BLG program, Biofuel Directive CO2 tax exemption

Large-scale demo grant (the Swedish Energy Agency)

Short-term New funding schemes carbon tax relief, leading to a high product risk

(-)

Kvaerner, Weyerhaeuser, DOE, ORNL, Nycomb

(-)(-)(-)

Episode 4: 1999

Political interest FABEL funding in alternatives to nuclear

(-)

Fossil price risk

(+)(+)(+)

Institutional risk

(-)(-)(-)

(-)(-)

(-)

(-)(-)

(-)(-)

(-)(-)

(-)(-)

(+)(+)(+)

Product risk Organizational risk

(-)

(+)(+)(+) (-)

(-)

(-)(-)(-)

(-)

(-)(-)

(+)(+)(+)

List of abbreviations: EtanolPiloten AB (EPAB), SP Technical Research Institute of Sweden (SP), Foundation for Swedish Ethanol Development (SSEU), European Union (EU).

(+)(+)(+)

(+)(+)(+)

Episode 8 2012∼

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Table 3 The development of thermal conversion of black liquor in Sweden, 1978–2013.

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The early history of the field of thermochemical conversion of biomass came at a later time. The oil crises in the 1970s stimulated significant R&D activities at the major universities in Sweden, at the main national laboratory for nuclear energy research (Studsvik AB), and at the privately owned oil refinery Nynäs Petroleum. Over time, these efforts resulted in the emergence of a loosely defined network of actors, primarily focusing on producing alternative transportation fuels (e.g., methanol). In the late 1980s, this network was divided into two smaller networks. One of these networks centered on black liquor gasification (BLG) with the company Chemrec as the key actor, and the second one focused on so-called pressurized fluidized bed (FB) gasification.3 In the remainder of this section, we analyze the development of the biochemical conversion field and the thermal conversion of black liquor, emphasizing the role of different PDPs in developing each respective field.

4.1. The development of biochemical conversion of biomass Table 2 summarizes the development of biochemical conversion in Sweden from the early 1980s and onward, and outlines seven key episodes. In the early 1980s the pulp and paper company MoDo owned and operated the Domsjö mill and was the only sulfite mill-based ethanol producer in the country. The ethanol was purified and converted into downstream products through a jointventure company, SEKAB, founded in 1985 and co-owned with the chemical company Berol Kemi. Both partners developed substantial knowledge in forest-based chemistry. An important institutional precondition for this small actor network was the political emphasis on security-of-supply and related financial support from the national government, which considered the competence of this network to be of strategic importance in the event of a crises (e.g., Sandén and Jonasson, 2005); see Episode 1 in Table 2.4 Support was based on an agreement with the Agency for Civil Emergency Planning (ÖCB) and helped SEKAB manage the risks associated with a lower price on fossil fuels. The production was straightforward, however, resulting in limited new knowledge through learning processes. Over time, the ambitions of SEKAB grew beyond supplying emergency fuel (Ulmanen, 2013). In the mid-1980s, the Swedish forestry- and agriculture-based interests in ethanol production joined forces and established the Foundation for Swedish Ethanol Development (SSEU) (Respondent #8); Episode 2. Establishing the SSEU led the network to expand with new actors, including the municipality of Örnsköldsvik and the Federation of Swedish Farmers (LRF), both taking on new roles. SEKAB essentially viewed SSEU as its lobbyist (Respondent #8), which promoted the development

3 Black liquor is a by-product of the chemical pulp-making process and contains residues from the wood and the dissolved chemicals. It has a high heating value and can be used to produce heat and steam and to recycle dissolving chemicals. Following the Chernobyl accident in 1986, Swedish electric utilities focused on FB gasification but exited this development in the 1990s. A more recent network has centered on the development of so-called atmospheric, fast internal circulating fluidized bed (FICFB) technology. This network has, for example, been engaged in constructing a semi-commercial PDP (Gobigas) producing (since 2014) methane as a vehicle fuel. 4 All analytical dimensions are outlined in Table 2, except “agency,” which is further described in the main text. The authors have qualitatively assessed the various risks. When the risk level was reduced due to the construction of PDPs, this is marked with (-), (-)(-), or (-)(-)(-) in Table 2, and when risk level increased it is marked with (+), (+)(+), or (+)(+)(+) depending on the magnitude of the change in risk in both cases. Furthermore, when a crisis affected technology development, a separate column in Table 2 has been inserted and marked in yellow. For illustrative purposes, the growth and the occasional decline of an actor and technology structure of each case is illustrated with a blue circle that either increases or decreases in size compared to the previous episode. The above logic also applies to upcoming Table 3.

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of cellulosic ethanol, first as a raw material for the chemical industry and later on as a vehicle fuel (Sandén and Jonasson, 2005). In the late-80s, SSEU began to lobby for constructing a large demonstration plant to verify different technological options for cellulosic ethanol. In 1993, the government responded by launching a research program (1993–1997) with the goal of verifying and evaluating ethanol production on a large scale (Ulmanen, 2013). This program was preceded by a discussion of technological options. SSEU preferred the so-called CASH (Canadian American Swedish Hydrolysis) technology. Still, the Swedish research community argued strongly for the so-called hydrothermic and enzymatic hydrolysis processes, an area in which Sweden was believed to have a comparative advantage. For this reason, funding was provided to construct a small bench-scale pilot plant at Lund University. In contrast to what SSEU had advocated, this plant aimed at learning through basic R&D rather than verifying what others had already developed (Ulmanen, 2013; Respondent #6). Developing ethanol production progressed slower than SSEU had anticipated. To raise awareness and stimulate the market for ethanol, the foundation initiated ethanol vehicle fleet trials (Episode 3). This meant that the infrastructure and the vehicles were put into commercial operation, although on a limited scale. These trials relied on conventional ethanol production from wheat and sugar cane. Several Swedish municipalities wanting to improve local air quality eagerly participated. The fleet trials were also stimulated by the carbon tax, introduced in Sweden in 1991, from which ethanol was exempted. While SSEU and some municipalities promoted the introduction of vehicles, Scania supplied buses and trucks, whereas a local Ford dealership introduced the first flexible-fuel vehicles (FFV). SEKAB developed the fuel standards and supplied the ethanol (although the limited domestic production was supplemented by imports). By 1997, more than 300 buses and 300 FFVs had been tested in more than 10 Swedish cities, and several new filling stations were established (Ulmanen, 2013; Respondent #8). The fleet trials helped reduce both product- and organizational risk and supported a broader set of learning processes than had previous development activities. This learning involved know-how acquired in using the fuel (e.g., leading to new standards), as well as learning based on actors’ involvement, interaction, and networking. The most significant achievement of the fleet trials, however, was of a network management nature; SSEU’s efforts to build new coalitions helped mobilize broader actor support, which added new knowledge and legitimacy to the technological field. Ulmanen (2013) argued that this expressed interest was critical to paving the way for a new R&D program. In 1998, the “Program for ethanol production from forest feedstock” (Ethanol program II) was initiated (Episode 4), and the Swedish government supported it with about USD $26 million over the period 1998–2004. This second program had a strong focus on building an industrial-scale PDP to verify results generated in the first ethanol program. An important network management activity in constructing the new plant was forming the alliance EnergiCentrum Norr (ECN). The purpose of ECN was to develop the cellulosic ethanol technology and enable the construction of the plant. To ensure “neutral” ownership and maximize opportunities for public funding of the planned PDP (e.g., complying with EU state aid rules), a new publicly owned company was formed: EtanolPiloten AB (EPAB). EPAB was owned in equal parts by the holding companies of two universities in northern Sweden: Umeå University and Luleå University of Technology. SEKAB received a small minority share (Respondents #8 and #14). The universities, however, were passive owners; therefore, to manage the associated risks, EPAB and ECN signed a contract stipulating that ECN would take responsibility for construction, permits, and all practical matters, including the eventual demolition of the PDP. This legal arrange-

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ment addressed many of the organizational risks for the actors. In 1997, however, SEKAB merged with ECN and assumed most of the risks involved (Swedish Companies Registration Office, 2005; Respondents #7 and #8). This also meant that the plans for the new PDP, and its subsequent operation, became heavily influenced by SEKAB’s visions (Respondent #10). In 2001, funding to build a new PDP in Örnsköldsvik, the socalled EtanolPiloten, was secured. A total of about USD $25 million was invested with grants from the Swedish Energy Agency, EU regional structural funds, and several regional actors (Respondent #17). The regional actors, not the least of which was the municipality of Örnsköldsvik, perceived that the new technology was an important vehicle for regional development and job creation. An important goal-achieving strategy was thus to align the technology development with regional development goals. The new plant aimed to verify the technology and was inaugurated in 2005 (Respondent #8). At this time SEKAB—now named SEKAB E-technology after the merger with ECN—planned to scale technology up rapidly and was hoping to have commercial-scale PDPs ready for operation by 2008–2009 (Swedish Companies Registration Office, 2004). The R&D program on ethanol ended in 2004, but the Swedish Energy Agency granted SEKAB continued development support, which further aligned development activities at the PDP to the commercial interests of SEKAB. Despite the fact that the majority shares were formally held by two universities (through EPAB), however, it was difficult even for the universities to get access to the plant (Respondent #10). In addition, the PDP became the main focus in the four-year EU project NILE (covering the period 2005–2009), which aimed to develop new yeast strains and enzymes for bioethanol production from cellulose (Swedish Companies Registration Office, 2007a). Hence, the institutional preconditions for technology development were relatively favorable in terms of funding. As such, the technical risks associated with the PDP could be reduced significantly, and learning through industrial-scale experiments created new knowledge about operating the plant on a continuous basis. As the technology progressed further, extended fleet trials were also launched, and the ethanol came into commercial use as oil prices increased and the carbon tax level was raised. Moreover, the EU mandated an increasing blend of ethanol in ordinary gasoline, resulting in a rapidly growing demand for first-generation ethanol (Episode 5). In 2005, development was further supported at the national level when the so-called “pump law” was introduced, which required all major petrol stations to provide an alternative fuel. Because ethanol was already available at reasonable prices, a nation-wide infrastructure quickly emerged, and by 2010 about 200 000 FFVs and 619 heavy vehicles had been sold (Swedish Parliament, 2009; Ulmanen, 2013). As a result, the product and organizational risks were reduced further. To meet growing demand, SEKAB invested in producing firstgeneration ethanol and increased its imports of the fuel. The company expanded considerably, and in 2008 it had a turnover of USD $250 million and more than 100 employees (Swedish Companies Registration Office, 2003, 2008). Still, although this positive development facilitated increasing customer awareness, in clarifying institutional barriers and in strengthening the actor network, the domestic second-generation technology was not yet fully developed. With the onset of the global financial crisis in 2008, though, the institutional preconditions for PDP development activities changed. There was a sharp drop in gasoline prices, making ethanol uncompetitive, despite the presence of the carbon tax relief (Episode 6). In addition, the use of (first-generation) sugar

cane ethanol started to lose legitimacy as reports on harsh social conditions and environmental degradation began to flourish in the media. As a result, SEKAB reported financial losses and had to ask shareholders for additional financial support (Swedish Companies Registration Office, 2007b, 2009).5 These problems hampered the company’s ability to build commercial scale PDPs, and clearly illustrate the importance of the fossil market risk. In 2011, the technology had reached a level of maturity that motivated SEKAB to use only 20% to 25% of the plant’s capacity to test feedstock from potential customers. Technology was now verified and ready for up-scaling, but the institutional preconditions were still unfavorable. To secure access to the plant, SEKAB looked for an entity that could assume financial responsibility for the PDP (Respondents #4, #7, #10, and #17). Backed by new funding from (primarily) the Swedish Energy Agency and Vinnova, the SP Technical Research Institute of Sweden has gradually taken over the responsibility for the PDP since 2012. SP’s ambition has been to provide neutral ownership and management of the plant; the institute has created a flexible and permanent research infrastructure that targets a broader network of national and international actors to demonstrate and verify processes of a wide range of raw materials to produce chemicals, fuels, solid fuels, and so on (Respondents #10 and #17). The focus of the plant and the associated learning processes have thus broadened to include multiple actors, their individual objectives, and technological development processes, rather than developing second-generation ethanol and targeting the specific objectives of SEKAB.

4.2. Developing thermal conversion of black liquor Table 3 summarizes the development of thermal conversion in Sweden from 1978 to 2013. In the mid-1970s, the Swedish company Nynäs Petroleum AB owned and operated a conventional oil refinery close to Stockholm. Due to the oil crises, the company attracted government funding to extend its operations to include so-called entrained flow gasification of coal, extraheavy oils, and bitumen to produce ammonia, methanol, and other chemicals that were in short supply at the time. These plans engaged many stakeholders, but were eventually abandoned in the mid-1980s when oil prices plummeted (Jacobsson, 1994; Schein, 1990). Although the first attempt to introduce a new type of refinery technology failed, it stimulated in-depth knowledge development in entrained flow gasification. It also energized the emergence of a new actor network with the ability to develop novel ideas into new industries and applications. With his background in the pulp and paper industry, the chief engineer at Nynäs Petroleum, JanErik Kignell, identified the similarity between black liquor and the extra-heavy oils used in the entrained flow gasifier. Based on this, he filed a patent application on a process that could replace the relatively inefficient recovery boilers at pulp and paper mills. Nynäs Petroleum also formed the consulting company Nykomb Synergetics to pursue technical and economic evaluations in gasification of difficult fossil resources (Respondent #2; Episode 1). Kignell’s patent was later acquired by Chemrec, which had been working on similar ideas (Bergek, 2002; Respondent #12).

5 Due to the relatively large staff needed to operate the plant (typically eight operators and one manager), the cost of maintaining the infrastructure had also been far from non-negligible (about USD $2.5 million per year), regardless of whether the PDP was operating or not.

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By the end of the 1980s, the actor network expanded as the manufacturer of conventional recovery boilers, Kvaerner, recognized the potential of Chemrec’s technology and acquired the company (Episode 2). In collaboration with pulp and paper company AssiDomän, Kvaerner constructed an industrial-scale PDP in Frövi to verify the technology and to provide opportunities for learningby-doing processes. The focus was on a less advanced application aimed at boosting the capacity of the previously installed recovery boilers, rather than entirely replacing them (Episode 2a). After the technology was verified successfully, Kvaerner managed to sell a commercial-scale Booster unit to Weyerhaeuser in New Bern, North Carolina, USA (Episode 2b). In the early 1990s, Kvaerner, now in collaboration with the Swedish pulp and paper company Stora, continued by constructing an industrial-scale pressurized BLG pilot plant in Skoghall with the aim of replacing conventional recovery boilers and enabling electric power generation through IGCC technology (Episode 3a).6 As gasification technology was scaled up in New Bern, however, unanticipated problems emerged that proved to be challenging (Episode 2b). With funding from the US Department of Energy (DOE) and in collaboration with Oak Ridge National Laboratory (ORNL) and Nykomb Synergetics, Chemrec embarked on a lengthy process of developing and testing new materials at Weyerhaeuser’s installation. Rather than waiting for technical issues to be resolved, Kvaerner and Nykomb Synergetics went ahead and planned a commercial-scale plant at Assi-Domän’s site in the town of Piteå (Episode 3b). To manage the relatively high technical risk, the actors also decided to build a relatively small test facility close to the larger plant to conduct experiments and advance knowledge development. This combined strategy permitted learning through both R&D and operating the facility. In the mid-1990s, the Swedish governmental body FABEL decided to support the project with a grant of USD $30 million, covering 50% of total investment costs (Respondents #11, #12, and #16). The Swedish government’s interest in the project was based mainly on the political desire to identify alternatives to nuclear energy. These plans never materialized, however, primarily due to the growing financial difficulties in the mid-1990s for equipment manufacturers in the electric generation sector, including Kvaerner (Episode 4). Altered institutional preconditions were in part a result of deregulating electricity markets in Europe, which created expectations of increased competition, lower prices, and therefore lower investments. For this reason, market-related and institutional risks were reevaluated by the involved actors and perceived as very high. Kvaerner decided to sell Chemrec, which eventually became wholly owned by Nykomb Synergetics (Respondent #12). Nykomb, though, did not possess the financial resources to pursue technology development in the BLG field on industrial or commercial scales. In the early 2000s, Chemrec nearly went bankrupt, and 15 years of technological development almost came to an end. However, FABEL was overtaken by the Swedish Energy Agency, which saw the potential for using BLG in producing alternative fuels while simultaneously strengthening the competitiveness of Sweden’s pulp and paper industry (Respondent #16). This implied that a new episode for BLG development was initiated, with the Swedish Energy Agency assuming the role of a temporary network manager and building on the competence of Chemrec (Episode 5). The Agency first permitted Chemrec to keep the funding previously granted by FABEL to construct an industrial-scale PDP in Piteå to verify BLG technology in producing new fuels. It then also launched a new publicly funded R&D program—the so-called BLG

6 IGCC refers to the Integrated Gasification Combined Cycle. The first installations of this technology emerged in the mid-1990s, and it was thought to hold great promise in replacing conventional coal boilers at a much higher electric efficiency.

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program—for the construction and research associated with the new plant (Respondents #5 and #16). To manage the actor network and resources within the BLG program, a new company was formed called BLG Programmet AB. This company, and the new PDP, was formally owned by the holding companies of Luleå University of Technology and Umeå University together with Chemrec. Chemrec employed all operating staff at the plant and was granted the intellectual property rights. Through this legal arrangement, the Swedish Energy Agency ensured long-term knowledge development and technology transfer. Organizational risks for the involved actors were reduced, and a significant amount of new government funding (about USD $9 million) to construct the plant, without being subject to EU state aid legislation, were secured (Respondent #16). The new PDP began operating in 2005 (Episode 5b) and was physically hosted by the research laboratory Energy Technology Centre (ETC), located right outside the gates of Assi-Domän’s pulp mill in Piteå. ETC conducted basic and applied research in collaboration with the major universities and research institutes in Sweden and could provide skilled operators, laboratory engineers, and auxiliary equipment needed to develop the technology and set up experiments (Respondents #5 and #9). Moreover, as this PDP was being constructed, the remaining technical issues at the plant in New Bern were resolved, implying that the technical risk had been reduced substantially (Episodes 5 and 2b). Chemrec took the role as network manager, and by means of several strategies, the company ensured that funding, legitimacy, and political support were available. It mobilized venture capital from Vantage Point and Volvo Group Venture Capital and attracted EU funding (USD $35 million) to develop the existing PDP to also verify and demonstrate dimethyl ether (DME) production. The latter included setting up a small-scale fleet trial in which DME could be tested as a vehicle fuel, thus enabling improvements in the technology as a result of feedback from infrastructure and user experiences (i.e., learning-by-using; Episode 6). Volvo AB had identified DME as the most efficient vehicle fuel to replace diesel in buses and trucks, and the company had already tested the new fuel in a prototype DME bus in the late 1990s (Respondents #1 and #13; Episode 5a). Furthermore, Chemrec increased the strength of its political network and made credible the claim that DME could be developed as a new and efficient vehicle fuel. The Swedish government responded by providing funds to construct the first commercialscale PDP. These funds were deemed necessary to manage the technical risk of the first customer that took on the challenge of verifying the technology commercially (Respondent #12). The privately owned mill Domsjö in Örnsköldsvik attracted USD $62 million of these funds, and declared it wanted to be the first commercial customer of Chemrec’s technology (Episode 6). Before the provision of the grant had been scrutinized by the EU (Directorate General for Competition), an Indian company, Aditya Birla Group, acquired Domsjö (Episode 7). Although the EU Commission ruled that the PDP support did not violate state aid rules, Aditya Birla considered the technology to be too focused on DME as a transportation fuel. The Group’s interest was mainly in chemicals and producing viscous fibers (Respondent #3). Aditya Birla also concluded that the political risk associated with a long-term investment was significant, especially because the carbon tax relief on renewable fuels in Sweden was decided on an annual basis. When Aditya Birla abandoned the project, Vantage Point and Volvo Group Venture Capital decided to discontinue their financial support to Chemrec, which laid off almost all personnel to avoid bankruptcy. To secure the acquired competencies, Luleå University of Technology (LTU) stepped in and took over key personnel and the physical infrastructure on the condition that the Swedish Energy Agency would continue funding research at the PDP in Piteå (Respondents #5 and #14). Since 2012, LTU and the Agency have

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attempted to turn the facilities for verifying BLG and DME production into a national infrastructure (test center), which could serve a wider research community and set of technologies (Episode 8). 5. Pilot and demonstration plants in technology development: an analytical framework The need for an analytical framework exists to better understand the roles of publicly funded PDPs in technology development. While prior literature has recognized that PDPs may take different roles, prior typologies are relatively simple, such as noting that some PDPs “test” technologies, products, processes, and so on, whereas others “promote” market diffusion and commercialisation (e.g., Karlström and Sandén, 2004). Our case studies indicate, however, a wide range of different PDP types. In total, thirteen PDPs were realized and several additional plants were planned but then cancelled. By drawing on case analysis and related literature (e.g., Hendry et al., 2010), Table 4 presents an extended typology, identifying four main types of PDPs along with descriptions of roles for each type. It also exemplifies how the different PDPs in the Swedish biorefinery cases fit into this typology. Table 5 outlines the main analytical dimensions, and the overall contribution of this framework in relation to the different types of PDP categories that we have identified. Next, we undertake a detailed discussion by outlining how the different PDP types contrast on the five analytical dimensions. In doing so, we draw examples from our case studies, but also from related empirical work on, for example, solar PV and wind power. Although these technological fields may differ substantially (e.g., Zhou et al., 2015; Harborne et al., 2007), the framework highlights several common challenges. 5.1. Type I: high profile pilot and demonstration plants The role of high-profile PDPs is to create awareness and legitimacy for a specific application, product, process, or service. Our case study revealed only one such PDP, namely the DME bus produced by Volvo. An important objective for Volvo was to raise public awareness and signal to policy makers and other actors that DME was a future vehicle fuel and that Volvo was prepared to supply such vehicles. Another example was when US Department of Energy sponsored a competition in which teams were challenged to design, build, and operate solar-powered houses that were cost-effective, energy-efficient, and attractive (Hendry et al., 2010). Lampel (2001) noted that this strategy of show-and-tell has a long history in US companies. Actors often pursue high-profile PDPs with clear commercial objectives and seek to influence and receive feedback from endusers and policy makers. Companies investing in high-profile PDPs attempt to gain maximum exposure at minimum costs, for example, at highly visible sites or at industry exhibitions. Of all PDP types, this is the least complex and has a rather uncomplicated network management structure, because they are often funded and owned by individual actors. Moreover, there is little need for institutional alignment (in either direction),7 because investments tend to be funded through R&D and marketing budgets of large firms. Some technology learning at the firm level may result, but this knowledge, in most cases, is highly contextual and cannot easily be transferred into a production process. For example, just because Volvo constructed one DME bus does not imply that they acquired

7 This does not imply, however, that regulatory constraints are absent in these cases. For example, even one-off PDPs may have to comply with existing safety regulations as evidenced in a demonstration project involving fuel cell buses in Spain (Harborne et al., 2007).

the knowledge necessary for serial production. In fact, high-profile PDPs typically precede (by many years) PDPs designed to open commercial markets. Consequently, high-profile PDPs may not reduce risks in any real sense, although in some cases they may assist in identifying potential institutional and market constraints.

5.2. Type II: verification pilot and demonstration plants The main objective of verification PDPs is to test, evaluate, and characterize different technological options for a certain application. There are two sub-categories of such PDPs: lab-scale verification PDPs (Type IIa) and industrial-scale verification PDPs (Type IIb). The difference resides in their size and in what roles they play in developing technology. Type IIa: Lab-scale verification PDPs primarily aim to reduce technical risk by creating new scientific and practical knowledge that later can be applied at larger PDP scales or in commercial settings. Goals of such PDPs often resemble R&D and could facilitate fruitful iterations between generating basic knowledge and up-scaling efforts within larger PDPs. A particularly important role may be to eliminate some less feasible technological options, thus permitting progress toward a narrower portfolio. In biochemical conversion, the bench-scale pilot plant at Lund University, with the purpose of advancing knowledge on hydrothermic and enzymatic hydrolysis for ethanol production is a representative illustration. In addition, in Piteå, there were plans to create a small-scale PDP for the BLG-IGCC application, which would have fit this category. Prior literature shows that this type of PDP is often owned by individual organizations (Hellsmark 2010; Ulmanen 2013). The interests of these organizations thus play a central role for the objectives of PDP activities. In our cases, universitybased researchers and their interests played a dominant role for creating lab-scale verification PDPs. Researchers primarily prioritized expanding the scientific knowledge base and constructing unique research infrastructures, which create advantage over other researchers (see also Jacobsson et al., 2014). Consequently, learning processes and outcomes have a strong focus on advancing scientific and engineering knowledge. Typically, this knowledge is then diffused widely to: (a) the academic community through scientific publications; (b) through patents, licenses, and university spin-offs; or (c) society and individual firms through the educational system, public debate, expert committees, and so on (Perez Vico, 2010). For these PDPs, network management becomes quite uncomplicated, especially in cases where equipment is owned and operated by a single organization. For example, for university-owned labscale verification PDPs, there is little need to balance academic and commercial interests. Correspondingly, when private actors own, their interests differ, but the infrastructure would still closely align to their interests, with less scope for knowledge spillovers to other actors (Frishammar et al., 2014). Also, there is little interest in expanding the actor network through different process design strategies. Finally, apart from the existence of well-functioning public R&D funding schemes for public owners, there are few critical institutional constraints for constructing and operating labscale verification PDPs. Type IIb: Industrial-scale verification PDPs aim to verify new technology for a specific application through a reference plant at large but not necessarily commercial scale. This type could help create industrial capacity among technology suppliers and potential customers. These PDPs, often backed by public funding, tend to be pursued by commercial actors with the purpose of technological verification and for forming alliances with potential customers, suppliers, or universities.

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Table 4 Categories and roles of PDPs in technology development including case numbers and examples. Categories and roles

Type I: High profile PDPs. Creating awareness and legitimacy by an actor with clear commercial interests in a specific application, thereby influencing and receiving feedback from potential customers and policy on a new technology, product, process, or service. Type IIa: Lab scale verification PDPs. Testing, evaluating, and characterizing a technology for a particular application, reducing mainly technical risk for potential stakeholders by creating new scientific and practical knowledge that can be applied at a larger PDP scale or in a commercial setting.

No. of PDPs/Cancelled

Pilot and demonstration activities in Swedish biorefinery development

1/0

• DME buses for illustrating DME as a future possibility, Volvo (Table 2, Episode 5a)

1/1

• Research bench-scale PDP for advancing basic knowledge development, Lund University (Table 1, Episode 2) • BLG–IGCC research plant in Piteå cancelled (Table 2, Episode 3b & Episode 4)

5/0

• Industrial scale verification of cellulosic ethanol production in “Etanolpiloten,” Örnsköldsvik (Table 1, Episode 4) • Industrial scale verification of booster application, Frövi (Table 2, Episode 2a) • Industrial scale pilot for developing the IGCC application, Skoghall (Table 2, Episode 3a) • Industrial scale BLG verification plant, Piteå (Table 2, Episode 5b) • Industrial scale DME demonstration, Piteå (Table 2, Episode 6)

1/3–4

• Planned commercial scale plants in northern Sweden (3–4) and Poland (1) cancelled (Table 1, Episode 6) • Commercial scale booster, New Bern, North Carolina, USA (Table 2, Episode 2b) • Commercial scale BLG–IGCC, Piteå cancelled (Table 2, Episodes 3b & 4) • Commercial scale DME/Methanol plant, Domsjö cancelled (Table 2, Episodes 6 & 7)

3/0

• Small-scale fleet tries ethanol in 10–12 cities in Sweden (Table 1, Episode 3) • Ethanol becomes commercial through large-scale fleet tries and imported ethanol (Table 1, Episode 5) • DME fleet trial, Piteå (Table 2, Episode 6)

2/0

• SP BDP national research infrastructure, Örnsköldsvik (Table 1, Episode 7) • National infrastructure for BLG and related technologies, LTU (Table 2, Episode 8)

Type IIb: Industrial scale verification PDPs. Creating new knowledge, verifying technology by constructing a reference plant at a large but not necessarily optimal scale for a specific application, thereby creating an industrial capacity among technology suppliers and their potential customers.

Type IIIa: Deployment PDPs. Improving performance and reducing costs by accumulating operational experience, stimulating incremental innovation and increasing efficiency along value chains as well as getting access to users’ know-how and experiences.

Type IIIb: Market introduction of down- and up-stream auxiliary technologies. Creating and testing different value chains in practice, reducing the product and organizational risks, creating public acceptance, mobilizing further resources to the field, and addressing institutional risks by developing new standards and stimulating the introduction of new regulations, thereby paving the way for investments in Type IIIa PDPs. Type IV: Permanent test centers. Serving a wide set of industrial actors and academia to make continuous improvements, testing new technological options, thus facilitating basic and applied research. These target a wider set of different applications and purposes than the other types of plants.

Five such PDPs were constructed in the biorefinery field in Sweden. Notably, Chemrec and SEKAB were modestly sized firms, but secured public funding to construct industrial-scale verification PDPs. They involved significant parts of the Swedish university structure, forming knowledge networks to further reduce technical risks. The PDPs were also used to form industrial alliances and political networks through collaborative development projects. In addition, the plants supplied small volumes of specific products for niche markets, allowing for fleet attempts, which allowed initial value chains to form. Similar examples exist for industrial-scale verification PDPs in related technological fields. One is the EC-funded wind power program WEGA, which provided a systematic scientific evaluation of more than ten different prototypes of large wind turbines (e.g., Hau et al., 1993). According to Hendry et al. (2010), these evaluations permitted the industry “to make the leap from kW-scale to MWscale turbines, with accompanying cost reduction and weight” (p. 4509). The combination of public funding and commercial interests may make PDP activities difficult to manage. In the case of Swedish biorefinery development, commercial interests largely influenced who accessed the plants, as well as who owned the intellectual property rights from experiments. This may be particularly common in the cases where PDP activities emphasize technical challenges that require company input in their design (e.g., Hendry et al., 2010).

Compared to lab-scale verification PDPs, it becomes increasingly important to activate new actors and resources, especially to prepare for constructing commercial-scale (Type III) PDPs. Experiences from the wind power and solar PV fields indicate that manufacturers sometimes act merely as suppliers, while regional authorities and electric utilities may take the role as network manager (Hendry et al., 2010). The learning processes taking place are often characterized by a mix of general knowledge development (i.e., learningby-searching) to inform policy makers, society at large, and potential customers about technological opportunities and proprietary knowledge development.8 Therefore, these PDPs help reduce organizational, market-related, and technical risks for commercial actors. Institutional preconditions are typically more complex than for lab-scale verification PDPs, in part because this type requires more funding than private company budgets permit. Because public funding is needed, technology must be well aligned with the intentions of government funding programs. This may impose different institutional constraints such as conditions for diffusing knowledge

8 The scope of learning outcomes will in part be determined by the structure of actor networks. For example, in Japan, the semiconductor industry has been heavily involved in solar PV PDP activities, making them leading manufacturers in global markets (Shum and Watanabe, 2007).

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Table 5 A framework of the role of PDPs in technology development. Categories

Type I: High profile PDPs

Type IIa: Lab scale verification PDPs

Type IIb: Industrial scale verification and validation PDPs

Type IIIa: Deployment PDPs

Type IIIb: Market introduction of down- and up-stream auxiliary technologies

Type IV: Permanent test centers

Actors and Agency

Network performance and management

Learning processes

Risk reduction

Institutional preconditions

Often pursued and funded by individual actors or alliances that want to create awareness and legitimacy and receive feedback from potential end-users. Interests of individual organizations play a dominant role in determining the PDP’s objective. Pursued by commercial actors to form alliances, political networks and demonstrate industrial capacity. Verifying new technologies along a specific value chain.

Infrastructure and any results are typically owned, governed, and controlled by individual actors. Relatively limited network management structure.

Do not reduce technical risks in any real sense, but may reduce market and institutional risks indirectly.

Operated by single actors and closely aligned to their interests. Limited actor networks.

Feedback on the technology from end-users, policy makers, etc. Learning is often highly contextual and cannot be transferred into production process. Learning processes focus on advancing scientific and engineering knowledge, i.e., learning-by-searching.

A potentially complex ownership and network management structure. Important to activate new actors/resources, with increased scope for conflicts of interest.

A mix of general and proprietary knowledge development. Also important for learning-by-using and learning-by-interacting processes.

Important for reducing market and organizational risks, but also technical risks.

Customers and investors to technology suppliers that can integrate technology in existing operations or building stand-alone plants.

Often operated by single firms to serve as first industrial-scale plants. Complex actor networks, with need for process design strategies.

Private proprietary knowledge for reducing cost of operations, but with significant spillover effects. Feedback from end-users and policy.

Important for reducing market risks and organizational risks, but also technical risks.

All actors needed to develop and test value chains, i.e., technology suppliers, customers, infrastructure providers, and government actors.

Organized and managed by a wide network of actors, each owning specific parts of the infrastructure. Process design strategies often necessary.

Standards, infrastructure, manufacturing processes, as well as feedback from customers and society.

Reducing the product and organizational risks as value chains are formed, tested, and evaluated.

A single actor serves the interest of many actors with often potentially conflicting interests.

Active and neutral ownership, which can balance commercial and academic interests. IPR management is important.

A mix of general and proprietary knowledge development.

Mainly reduces the technical risks involved in making the development of new technological options possible.

Nothing obvious. PDPs can be funded e.g. through R&D and marketing budgets of large firms and do not require any alteration of existing institutions at firm- or societal levels. Nothing obvious. PDPs are often funded through existing governmental research programs or internal R&D budgets. Moderate. Industrial scale PDPs are relatively costly compared to Type IIa PDPs and will therefore rely more heavily on public research funding. Increased need for institutional alignment. Significant. For radically new technologies. Play an important role for visualizing the extent and content of constraint before a large-sale deployment is possible. Moderate to significant. Small-scale value chains can be tested at relative ease at a local level, but as they are expanded, constraints are increased because they depend on a simultaneously aligning actors, technology, and institutions. None to significant depending on timing. If introduced too early, funding schemes and customer base will not be ready.

and so on. The latter, for example, has been an integral part of the EC programs funding solar PV (e.g., Gillett et al., 2001).

5.3. Type III: deployment pilot and demonstration plants These plants are closely connected to market deployment and field trials, and also come in two sub-categories: Deployment PDPs (Type IIIa) and PDPs aimed at market introduction of down- and up-stream auxiliary technologies (Type IIIb). Type IIIa: Deployment PDPs imply the construction of commercial-scale plants. These plants aim to improve performance and reduce costs by gaining operating experience, increasing efficiency along value chains and getting access to users’ know-how and experiences. In the Swedish biorefinery fields, at least five attempts were made to construct such PDPs, but only one was finally constructed, namely, the Weyerhaeuser plant in New Bern, North Carolina, USA, in the late 1990s. Brown and Hendry (2009) and Harborne et al. (2007) provided examples of deployment PDPs in the solar PV and fuel cell technology fields, respectively. For example, the German 1000 Roof program (1991–1995) was an important vehicle for developing solar PV inverters and establishing UE standards for grid connection.

Mainly reduces technical risks by expanding the scientific knowledge base.

If realized, customers and investors would pursue these types of plants to technology suppliers that can integrate them into existing operations or by building stand-alone plants. Pilot- and demonstration plants could be owned and operated by private firms and serve as reference plants for technology suppliers, providing them with access to customer feedback. This also implies the emergence of a more complex actor network with more opportunities for conflicts of interest. As such, an even greater focus must be put on network management. Our case studies, as well as experiences from solar PV and wind power (Hendry et al., 2009) propose that regional authorities and actors may play a particularly important role. They own land and premises, control the planning process, and may create niche markets through procuring or establishing regional feed-in tariffs or lobbying for nation-wide feed-in tariffs. For this reason, an important network management strategy may be that of achieving goals, for example, by aligning regional development goals with those of technological development. Naturally, this makes the performance of the PDP actor networks highly sensitive to regional contexts. In terms of learning processes and outcomes, it should be noted that whereas verification PDPs (Type II) play important roles in developing individual firms’ learning technology and products, deployment PDPs help build demonstration and value chains (Harborne and Hendry, 2009; Hendry et al., 2010). Learning-by-

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using and learning-by-interacting are thus key learning processes. For deployment PDPs, learning outcomes could involve proprietary knowledge used to reduce costs of operation and increase stability, but with potentially significant spillover effects that could attract new actors to the technology. Therefore, deployment PDPs are important for reducing technical, market-related, and organizational risks. Finally, as illustrated by the many failed attempts to construct deployment PDPs in the Swedish biorefinery cases, institutional risks are significant, because the first commercially operating plant will produce large volumes but at high capital costs. Hence, if institutional and market risks are not sufficiently addressed, there will be limited or no investment. For radically new technologies, these problems are further reinforced. Type IIIb: PDPs aiming at market introduction of down- and upstream auxiliary technologies are similar to Type IIIa in that they focus on market deployment. However, this subcategory recognizes that many technological fields consist of a nested hierarchy of technologies, including auxiliary technologies located up- and down-stream of the core conversion technology. These can be pursued more or less independently from Type IIIa and thus play a different (and complementary) role in technology development. Three such PDPs were pursued and manifested, that is, fleet trials where the infrastructures for ethanol and DME was tested (and in the case of ethanol commercially up-scaled). The conditions for such trials, however, were quite different across cases. In biochemical conversion, large volumes of first generation ethanol were readily available and formed the basis of both small-scale and large-scale trials, thus making actors less dependent on verification and deployment PDPs to supply products to these markets. In the case of DME, in contrast, the actors relied on small volumes produced by an industrial-scale verification plant. Type IIIb PDPs can also be found in other technological fields, such as fuel cell bus demonstrations in Japan, Europe, and the USA (Harborne et al., 2007). These types of PDPs involve broadening actor networks, such as lobby organizations (e.g., SSEU, BAFF), infrastructure providers (e.g., Preem, Total), vehicle suppliers (e.g., Scania, Volvo), and municipalities and end-users, providing these with clear roles in the innovation system to form the value chain. Collaboration among these actors thus occurs at both the technology development and commercialization stages. Learning outcomes typically center on developing standards, infrastructure, and feedback from customers and society. For example, Harborne et al. (2007) concluded that fuel-cell bus PDPs have implied learning for diverse ranges of stakeholders. This enhanced not only the performance and lowered costs of vehicles, but highlighted infrastructure issues and critical regulatory constraints. Because Type IIIb PDPs are instrumental for forming value chains, they reduce product and organizational risks as value chains are formed and tested. Institutional risks are also reduced when new standards and regulations emerge. An important challenge, however, has been that many fleet trials primarily have been technical experiments. Although they promote learning processes and highlight institutional constraints, they are seldom followed by changed policy measures, that is, tax incentives, planning frameworks, and so on. In part, this is evident from our biorefinery cases but also from the study of fuel-cell bus demonstrations in Harborne et al. (2007). 5.4. Type IV: permanent test centers The role of permanent test centers (Type IV) is to provide a learning facility that serves a wide set of industrial actors and researchers to make continuous improvements and test new technological options, thereby facilitating both applied and basic research. Typ-

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ically, these test centers target a wider set of applications than the aforementioned PDPs. They normally lack the visibility of field tests but may provide cost-effective testing throughout the whole technology development process (Hendry et al., 2010). In our two case studies, attempts were made to create this type of infrastructure. These occurred when the main commercial actors wanted to pull out of industrial-scale verification PDP investments. In the BLG case, a university took over the infrastructure, and in biochemical conversion, a research institute stepped in. In an ideal situation, this type of infrastructure would perhaps be created following a high industrial demand for particular types of knowledge, alongside the realization of deployment PDPs and establishing new markets. Permanent test centers of this kind exist in many other technological fields. A well-known example is the Danish Wind Turbine Test Station, established in 1978, which, together with the introduction of a feed-in tariff scheme in the early 1980s, positioned Denmark as a key knowledge and innovation hub in wind power technologies (Garud and Karnoe, 2003). In the case of permanent test centers, active and neutral ownership is preferred. This may balance diverse commercial and academic interests in developing knowledge. Managing intellectual property rights is often pivotal for managing the learning processes taking place at these plants. Because this type of infrastructure typically is constructed after verification PDPs, and parallel to or after deployment PDPs, their main role is to reduce technical risks but also allow development of new variants and technological options. Test centers have also been important to address wider system issues, for example, when new technologies such as solar PVs are connected to the grid. In the Swedish biorefinery cases, we observed that institutional constraints were significant, because there were no mass markets for the products that deployment PDPs produced. This resulted in the current test center PDPs suffering from few customers and with little room to closely align basic R&D and demonstration activities. The experiences from the solar PV and wind power fields show that parallel and iterative funding of R&D and PDPs have been critical for reducing costs—in solar PV through material development and in wind power by up-scaling turbine size (Hendry et al., 2010).

6. Innovation policy challenges and implications in a PDP context The analytical framework serves as a tool for future research to better understand the role of PDPs in long-term development of new technologies. In addition, it provides a point of departure for discussing the challenges of identifying and designing appropriate innovation policy in the PDP context. Innovation policy encompasses the multitude of actions public authorities undertake that influence innovation processes, whether intentionally or inadvertently (Carlsson, 2015). Selecting policy instruments should build on the causes of problems in the innovation system, and their design should be related closely to key activities in the system (Borrás and Edquist, 2013). These authors divided activities into four categories: providing (a) knowledge inputs (e.g., basic R&D, competence building); (b) demand-side activities (e.g., forming new markets); (c) constituents (e.g., creating and changing organizations or institutions and networking); and (d) support services for innovating firms (e.g., incubation activities, funding, consultancy services) (Borrás and Edquist, 2013). This categorization is a useful starting point for discussing the innovation policy challenges of PDPs. Our PDP typology and analytical dimensions, however, also provide additional insights. In this section, we highlight two issues that deserve specific policy attention. First, it is imperative to analyze providing knowledge, demand-side activities, and support services together. Basic R&D,

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intermediate PDPs activities, and test centers often overlap, with the boundaries among them often blurred. Our two cases illustrated the importance of parallel and iterative funding of both R&D and PDPs and in related domains, that is, solar PV and wind power development (e.g., Hendry et al., 2010). Moreover, in addition to iterative R&D–PDP processes, it is essential to deal with the overlapping roles of different PDPs and how these assist in addressing risks of various kinds. Second, previous literature has proposed that public policy concerns different levels of government and can be created within informal, multi-actor networks beyond formal hierarchies (e.g., Pierre and Peters, 2005; Flanaganan et al., 2011). Borrás and Edquist (2013) acknowledged this by emphasizing the need for networking. Our framework adds to this literature; indeed, it provides insights from policy network research by highlighting the roles of various process design and institutional design strategies to influence performance of actor networks surrounding PDPs. Our case studies, along with examples from other fields, have illustrated the relevance of these strategies for innovation in PDPs.

Fig. 1). Relying solely on public subsidies to R&D and investments in a selection of PDPs will likely lead to a start–stop approach and will not promote overall commercialization (see also Hendry et al., 2010). The consequences of not following up R&D and PDP support programs with specific deployment policies is evident when comparing the modest solar PV development in US with that of Germany (Brown and Hendry, 2009). It is thus important to create clear overlaps and iterations not only among different policy initiatives but also between different types of PDPs to foster technology development. The governance of failures becomes a matter of providing possibilities for going back and forth between different PDPs where various designs, actor networks, and value chains are tested and continuously evaluated. When this is achieved successfully, the actor networks can be reconfigured and mobilized for setting up new experiments, allowing learning from past experiences. For public policy, the main challenge then becomes providing the right incentives at the right time so that such a process can be facilitated. Also for policy makers, however, this will be a process of learning and trial-and-error.

6.1. Achieving systematic progress from technology to market

6.2. Network management by means of institutional design and process design

The experimentation and learning at PDPs—combined with basic R&D—are essential for reducing risks, while bringing new products and processes to large-scale markets. Our analytical framework adds a contingency dimension to innovation policy challenges by providing novel insights into what different types of PDPs can (and cannot) accomplish, for example, with regard to which risks can be reduced or which learning processes can be facilitated. It therefore helps match policy actions with intended PDP outcomes more effectively. Clearly, PDPs have the potential to provide a protective space for new technologies, but also help induce a process in which selection pressure is gradually increased (Kemp et al., 1998; Smith and Raven, 2012). In our PDP typology, selection pressure increases as deployment PDPs are pursued and technology eventually becomes better capable of competing with incumbent alternatives. Fig. 1 illustrates the progress of a given technological field by using the so-called S-curve, and delineates how the four main types of PDPs play different roles for progressing technology. Verification PDPs are essential early on in the technology development process, but need to be complemented gradually with deployment PDPs as technology matures. As the deployment phase approaches, that is, the period overlapping the formative and growth phases, investments in deployment plants need be pursued in parallel with permanent research infrastructure (Type IV). This does not necessarily imply, however, that investments in high-profile and verification PDPs should cease. Providing such incentives may very well continue in parallel. Our case studies illustrate that each respective actor network was able to reduce technical risks, product risks, and organizational risks through various types of PDPs. The fossil fuel market risk and prevailing institutional preconditions, however, did not work in favor of constructing deployment PDPs. Policy investment and R&D support addressed technical risks, whereas market- and institutional risks were largely ignored despite the fact that they are significantly more important when moving from verification to deployment PDPs. This suggests that different policy instruments are needed for various types of PDPs. For all categories, public R&D and investment support are important, but as technology matures, these need be complemented with purposefully designed market creation incentives, so-called deployment policies. These could, for example, come in the form of fixed or mark-up feed-in tariffs (e.g., Kåberger, 2013) that directly address the market risk before more general policy instruments (e.g., carbon taxes, etc.) come into play (see

Active network management is important because public policy cannot be understood at a single level of government (Flanaganan et al., 2011); it is largely shaped within multilevel networks with interactions taking place at the local level and with local authorities. Some scholars have even suggested that such networks are necessary to address complex challenges in the energy and environment field (e.g., Bulkeley, 2005). This holds some key challenges for innovation policy in a PDP context. For example, promoting overlaps and iterations among different support schemes and PDPs requires proficient coordination among various branches of government. Appropriate policy instruments for the various PDPs reside not only within different governmental bodies but also at different levels (e.g., regional, national, European). In addition, these may operate under different institutional logics and with partly conflicting goals (Flanaganan et al., 2011). Adding to this complexity, government actors and authorities at different levels often take different active roles in the networks at and around the PDPs. Our case studies indicate that local authorities may often take an active part in forming and influencing performance of the networks (see also Brown and Hendry (2009) and Harborne and Hendry (2009) for similar examples in wind power and solar PV). Their actions to advance the interactions in a network may be based on institutional or process design strategies. Institutional design strategies, of course, are used at different levels of government, where R&D and PDP investment support and deployment policies constitute important pay-off rules. Such rules affect perceived costs and benefits of engaging in development activities. However, institutional design may also materialize in the form of choice rules, thus defining what is permitted and required at several levels of government. For example, the funding of PDPs must comply with other legislation, most notably competition law (EC state aid rules). In our cases, the Swedish Energy Agency took an important network management role in setting up the arrangements (e.g., EPAB) needed to comply with EC law. Moreover, local authorities often act as gatekeepers in that they own land and control the planning processes, grant building permits, impose local taxes and fees, and so on. Furthermore, because PDP activities do not unfold in a simple linear fashion, but rather through repeated cycles of development, it becomes important to identify and pursue strategies that aim to continuously influence collaborative processes within actor networks. For private actors, it often becomes important to achieve

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Fig. 1. PDPs and the Development of a Technological Field, the Role of Innovation Policy.

goal congruency among the involved actors. Actors often attempt to purposefully align technology development to their interests and have communicated different goals and visions. This often leads to activating other actors with complementary resources, including public authorities at the local level. However, extended actor networks also imply a greater risk for conflicts of interest and a further need for goal-achieving strategies, such as linking technology development to regional development goals. Public authorities may also take lead roles in influencing the structure of the network, for example, by activating actors. Our cases show that in biorefinery development, the Swedish Energy Agency has done this through fostering knowledge development and learning, but also by enabling other actors (e.g., Chemrec, SEKAB) to assume the network management role. However, these strategies were not always entirely successful. For example, recent industrial-scale verification PDPs were constructed without much customer involvement, and because very few deployment PDPs has been constructed, customer involvement has decreased in the development process. This reinforces the need for public policy to allow for overlaps between different types of PDPs and to address this challenge when forming process design strategies. Such overlaps permit private actors to become network managers, small firms (e.g., specialised equipment manufacturers) to grow when technology progresses, established firms to learn about their potential and their potential customers, and capital goods suppliers and other actors along a possible value chain to invest resources. If this cannot be achieved through a combination of financial incentives and other means, experience indicates that venture capitalists will not be willing to fill the funding void (Mazzucato, 2014). This is particularly the case in knowledge-based sectors where capital intensity and technological complexity are high. In the Swedish biorefinery case, this occurred when venture capitalists quickly pulled out from Chemrec when a market did not materialize as expected. In sum, an important policy challenge in terms of process design is to set goals, resolve goal conflicts, and stimulate (activate) a mix of public and private actors to take active roles in the network. However, as other actors assume the role of network managers it becomes important for public authorities to act as “policy coordi-

nators,” that is, to help achieve coordination among different policy actors (e.g., ministries, national versus local authorities). If this fails, there is a risk that PDPs do not “find a secure place in the armory of government innovation strategy” (Brown and Hendry, 2009; p. 2560).

7. Conclusion and avenues for future research Fostering a technological field by constructing and operating PDPs takes time. It depends heavily on the availability of various types of PDPs to verify, diffuse, and continuously strengthen the knowledge base. Failure and continuous learning are essential. This is especially true for development of sustainable technologies for which public policy and public funding play important roles. The present study contributes a framework for analyzing the role of PDPs in progressing technology development, based on experiences from Swedish biorefinery technology. This framework provides new insights into what different types of PDPs can (and cannot) accomplish. The framework should be useful for analyzing the challenges involved in bringing new technologies to the market, and for better understanding the role of public policy in promoting such development. Still, more research is needed on the role of PDPs in technology development. For example, in the empirical analysis, we primarily address technology development from a national (i.e., Swedish) perspective, and thus (in part) downplay the role of international collaborations and spillovers. An important question, therefore, concerns when to develop a domestic knowledge base and support further knowledge-building through different types of PDPs and when to leverage other countries’ experiences and absorb international knowledge. Moreover, our analysis emphasizes the importance of combining policy instruments that support basic knowledge generation (i.e., R&D support) on the one hand and deployment and learning-by-doing and learning-byusing processes on the other. We have not addressed, however, how potential deployment policies should be designed or implemented in practice to support technological development. Furthermore, the framework recognizes the importance of explicitly considering network performance and management

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strategies. This issue has remained fairly undeveloped in existing innovation system research. Previous literature often neglects the wider processes of building communities of users, manufacturing firms, and so on, while at the same time recognizing the specific (often regional and local) contexts in which such highly social and political processes take place. Acknowledgments We gratefully acknowledge financial support for the present study from the Swedish Research Council Formas, the Swedish Energy Agency, the Bio4Energy research environment, Vinnova (the “Forest Chemistry” program), f3, and Chalmers Energy Initiative (CEI). We have also received useful comments on an earlier draft of the paper during two workshops with representatives of the forest industry, the chemical industry, and other researchers in the field. Comments from Editor Ed Steinmueller and anonymous Research Policy referees were immensely useful and highly appreciated. Any remaining errors reside with the authors.

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Appendix A. List of Respondents #

Respondent

Interviewed for case:

Role and background of the respondent

1 2 3

Danielsson, Peter Ekbom, Tomas Elg Christoffersson, Kristina

#2: Thermochemical #2: Thermochemical #2: Thermochemical

4 5

Engström, Clas Gebart, Rikard

#1: Biochemical #2: Thermochemical

6 7

Jönsson, Leif Lindgren, Thore

#1: Biochemical #1: Biochemical

8

Lindstedt, Jan

#1: Biochemical

9 10 11 12 13 14

Nordin, Anders Norström, Markus Persson, Tore Rudberg, Jonas Röj, Anders Sterte, Johan

#2: Thermochemical #1: Biochemical #2: Thermochemical #2: Thermochemical #2: Thermochemical #2: Thermochemical

15 16 17

Svensson, Stefan Tegnér, Lars Wännström, Sune

#1: Biochemical #2: Thermochemical #1: Biochemical

Former director of Environmental Management at Volvo Buses AB Senior Consultant at Nykomb Synergetics Head of innovation and new product development at Domsjö Fabriker Aditya Birla, CEO of Dominova CEO of SP Processum in Örnsköldsvik Professor at Luleå University of Technology, former manager of ETC and coordinator of the BLG program Professor of Chemistry at Umeå University CEO of E-technology (former ECN), and head of the biorefinery business area at SEKAB Senior Advisor at SEKAB, VD LINDAB, and former VP Marketing and Communications of SEKAB E-Technology. Professor at Umeå University and the former managing director at ETC Business Area Manager Energy at SP Technical Research Institute of Sweden Former CEO and Vice President Biorefinery Development, Smurfit Kappa Former CEO of Chemrec Volvo Technology, expert on new fuels Principal of Luleå University of Technology and former principal of Linnaeus University, part-owner of BLG Programmet AB and EPAB CEO of MoRe Research Former director of FABEL and technical director of the Swedish Energy Agency Senior research advisor at SP Technical Research Institute, and former head of research at SEKAB

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