Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain

Venus, Terese E.; Beale, Caroline; Villalba, Roberto Article — Published Version Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain Circular Economy and Sustainability Provided in Cooperation with: Springer Nature Suggested Citation: Venus, Terese E.; Beale, Caroline; Villalba, Roberto (2024) : Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain, Circular Economy and Sustainability, ISSN 2730-5988, Springer International Publishing, Cham, Vol. 4, Iss. 3, pp. 1751-1772, https://doi.org/10.1007/s43615-024-00357-7 This Version is available at: https://hdl.handle.net/10419/316972 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ / Published online: 29 February 2024 Vol.:(0123456789) https://doi.org/10.1007/s43615-024-00357-7 1 3 ORIGINAL PAPER Innovation andNetworks intheBioeconomy: ACase Study fromtheGerman Coffee Value Chain TereseE.Venus1 · CarolineBeale2· RobertoVillalba2 Received: 9 August 2023 / Accepted: 19 February 2024 © The Author(s) 2024 Abstract The transition to acircular bioeconomy requires innovation across many sectors, but social dynamics within a sector’s network may affect innovation potential. We investigate how network dynamics relate to the perceptions and adoption of bioeconomy innovation using a case study from the food processing sector. Our case study of the German coffee value chain represents a technologically advanced sector with a strong sustainability focus and potential for residue valorization, which is an important dimension of a sustainable circular bioeconomy. We identify three distinct views (pioneers, traditional and limited users) related to residue valorization, map linkages between actors using social network analysis, and highlight barriers to innovation. We collected data through an online survey and semistructured interviews with key actors in the coffee roasting sector. Within the social network analysis, we find that public waste managers are closely linked to the most influential actors, state actors such as the customs and tax offices can quickly interact with others in the network and promote the spread of information (highest closeness centrality) and specific roasters play an important role as intermediaries for efficient communication (highest betweenness centrality). Finally, we identify four main barriers including the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. To support a sustainable bioeconomy, we recommend that policy makers revise the primary regulatory frameworks for waste (e.g., German Recycling Act) to clarify how to classify food residues, their disposal structures and broaden their use streams. Keywords Bioeconomy· Coffee· Value chain· Germany· Social network analysis· Residue valorization Circular Economy and Sustainability (2024) 4:1751–1772 * Terese E. Venus terese.v[email protected] 1 Bioeconomy Economics, University ofPassau, Dr.-Hans-Kapfinger-Straße 30, 94032Passau, Germany 2 Agricultural Production andResource Economics, Technical University ofMunich, Alte Akademie 14, 85354Freising, Germany Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Introduction The emergence of a sustainable bioeconomy depends on sufficient availability of biological resources, which do not compete with food or feed production [1]. To limit competition with food and feed production, an important aspect of the sustainable bioeconomy is a cross-sectoral use of residues, especially those stemming from the food industry [2, 3]. This follows the biomass cascading principle, which refers to the integrated optimization of biomass usage for raw materials and energy [4] and is a key component of the transition to a sustainable, circular bioeconomy [3]. This means that biomass should be used multiple times (i.e., for material uses) before it is ultimately converted to energy [4, 5]. Given the growing demand for biomass, residual biomass presents many opportunities for valorization but the profitable exploitation of residues (waste) is a complex problem [6]. Residue valorization involves a variety of actors including the regulators, waste managers and the private firms producing waste. This extends to the broader category of circular business models, in which the traditional customer–supplier relationships are replaced by multi-actor relationships [7]. In the context of the bioeconomy, it has been found that underlying power dynamics in social network structures can affect innovation potential. Previous studies have focused on the role of different actors in supporting the transition to the bioeconomy. The studies focused on different aspects of the transition with focus on networks around the wood-based bioeconomy [8, 9], bioenergy [10–12] and bioplastic [13]. Alternatively, a few studies have focused on the role of a specific actor in the transition, such as primary producers [14] or large companies through their mergers & acquisitions activities [15]. One case study investigated the network supporting the feedstock of cassava [16]. A case study from the Thessaly region in Greece, where agriculture is the predominant economic activity, explored the main challenges to the circular waste bioeconomy. To exploit the potential of agricultural and agro-industrial residues, they argue that organizational, societal and knowledge actions are key and that stakeholders within the network must synergize and collaborate [17]. Although the majority of studies from social sciences and economics literature focused on holistic and broader aspects of the bioeconomy (e.g. wood-based bioeconomy, biofuels, etc.), the technical bioeconomy literature often focused on valorization of specific feedstocks [18]. Although there is a strong technical focus on the use of agricultural and food industry residues, we found few papers which investigate barriers to their usage and the potential of key actors in promoting the adoption of these innovations. To better align with the technical expertise developed for the use of different biomass residues, we focus on a case study from the German food processing industry. The German coffee value chain offers the ideal case study due to the cascading potential of coffee residues and the industry’s status as a leader in agricultural commodity eco-certification [19, 20]. Thus, we focus on bioeconomy initiatives in the downstream coffee value chain and the role of coffee roasters as key actors, who dominate the downstream activities (e.g., roasting; packaging) of a highly vertically integrated industry. Specifically, we investigate perceptions of using coffee residues, analyze how actors such as roasters can support bioeconomy initiatives by mapping their position within the network and identify policy barriers to the development of the bioeconomy in the German coffee roasting industry. 1752 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Case Study: theGerman Coffee Value Chain The global coffee value chain can be organized into upstream, downstream and consumption segments (Fig. 1). In our case study, we focus on the German coffee value chain. This includes the downstream actors: importers, traders, roasters, various government agencies (customs, taxes) and roasters. The global coffee industry’s downstream activities usually attract the highest profits and margins along the value chain, including coffee trading, roasting, branding and packaging. Globally, Germany has become an economic powerhouse in the coffee industry. Many German companies roast beans, sell them domestically or re-export them. Coffee processing produces ten million tons of agricultural residues, which often end up in a waste incineration plant or are left unused [20]. Coffee residues may be used in applications ranging from energy production to nutritional supplements to fertilizers. The high content of phenolic compounds in coffee residues makes them particularly promising for cascading. Although the potential application of high-nutrient coffee residues has been studied [20], and the concept of the bioeconomy has garnered attention on the international stage, it is unclear which barriers stand in the way of residue usage as part of circular bioeconomy initiatives. Every step of green coffee production generates residues including the husk through the dry-method; pulp through the wet-method; defective and premature coffee beans through harvesting and roasting; coffee silverskins through roasting; and spent coffee grounds coffee through brewing [20]. The residues’ physical and chemical properties, consistency and perishability largely depend on how the green coffee was produced (i.e., dryor wet-method). Roasting involves three major steps: the bean drying stage, the browning stage (the Maillard reaction) and the roasting stage (exothermic reaction/ beans crack). At large-scale coffee companies, an additional step using water follows these stages to cool the beans. The coffee silverskin is a thin layer which encloses the two coffee beans and detaches during the roasting process [21]. They are estimated to amount to about 76 million kg/year and is one of the main coffee residues available in Germany [22]. Although research on its applicability is still in its infancy, coffee silverskins are considered a comparatively stable product due to their lower moisture content [23]. Additionally, silverskins are rich in protein, dietary fiber and soluble fiber [23] and their antioxidant properties could be used for antioxidant beverages, flakes, bread, biscuits, and other snacks for weight control [24]. Fig. 1 Overview of the Coffee Value Chain 1753 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 This means the silverskins could have the biggest economic potential and health benefits. However, as its antioxidant activity is correlated with high amounts of phenolic compounds and caffeine, it can cause health issues. Thus, detoxification is essential for a safe and sustainable use [25]. Moreover, depending on the processing method of the green beans, there are different technical distinctions between dry and wet silverskins. Wet silverskins are difficult to convert into an economically viable product because it can be timeconsuming and costly to dry them as the most common means of drying them are through freeze-drying and spray drying [26]. Other residues from coffee production include defective and premature beans, spent coffee grounds and coffee oil. As they could decrease the final product’s quality, defective and premature beans are removed before the roasting process. Although there are only few studies on their potential application fields, it was found that chlorogenic acid and caffeine can be extracted from defective/premature beans [27]. During the production process, ground coffee is treated in percolators through which hot water flows to extract the soluble components. In addition to large industrial operations, spent coffee grounds are produced in large quantities in catering establishments such as coffee houses, hotels or cafeterias. However, there is no study on the amount of spent coffee grounds generated in those facilities, and private households are probably the largest source of coffee grounds. Based on a study that assessed the oil content of spent coffee grounds generated during domestic coffee brewing, it is estimated to be up to 20% of content [28]. The coffee oil can be processed into biodiesel. Although spent coffee grounds are suitable for compost in crop production, it is not suitable as animal feed due to missing acid components [29]. From a legal standpoint, the framework for biogenic waste, residual materials and byproducts is defined by European, federal and state directives, regulations and laws. According to the EU Waste Framework Directive (Directive 2008/98/EC on waste), all agricultural residual materials or by-products accumulated in the EU are to be regulated as residuals, co-products or by-products rather than waste. This is because these are generated alongside the cultivated biomass [1]. Implemented into German law under the Kreislaufwirtschaftsgesetz” (German Recycling Act), it states that if residual materials, coproduct or by-product is reused, marketed or used to produce other products, it must be classified as a by-product (§ 4 KrWG). However, the KrWG does not provide clear guidance on how to classify a by-product. Rather than the state, the federal states are ultimately responsible for making the final classification of agricultural by-products accumulated in their jurisdiction. This is because federal law is supplemented by the waste legislation of the respective federal states, which have waste laws with supplementary provisions and administrative regulations in place. In practical terms, this means that the federal states can legally classify by-products if the federal government has not done so. Moreover, federal state authorities and subordinate bodies carry out control tasks to ensure that the overall use does not lead to harmful effects on humans and/or the environment while the federal state’s tax and customs authorities are responsible for the correct taxation of the by-products when they are resold. In terms of taxes, German coffee is also subject to the German coffee tax. This tax is collected by the federal states’ customs authorities, but the tax revenue is due to the federal government. For roasted coffee, it is €2.19 per kilogram and for soluble coffee, €4.78 per kilogram [30]. Companies can reclaim the coffee tax if the coffee or coffee-containing goods were originally taxed but justify a refund. The development of the bioeconomy related to coffee production and processing falls within the larger issue of governance of sustainability in the coffee value chain. Sustainability (environmental, social and economic) has been of increasing concern for coffee 1754 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 consumers worldwide with a strong focus on the upstream value chain. In a meta-analysis of eco-labelling of coffee including organic, Country of Origin and fair trade labels, it was found that organic labels generate the highest willingness to pay due to their perceived health and environmental benefits [19]. In particular, the German coffee landscape has experienced several recent changes. In a cross-country comparison of willingness to pay for Fair Trade coffee, Germans were found to pay for Fair Trade coffee, but only up to a critical level [31]. Further, in more recent comparisons of sustainability labels for coffee, it was found that consumers feel environmental sustainability and biodiversity is important, but tend to avoid choosing “Wild Grown” coffee labels compared to Fair Trade or Organic Coffee [32]. Moreover, Germany’s biggest coffee corporations, such as Tchibo, Melitta, and Darboven have become members of certification organizations (e.g., Rainforest Alliance/UTZ) allowing them to sell specific coffee blends labelled as “certified coffee”. Despite increasing awareness of sustainably sourced coffee amongst coffee corporations and consumers, coffee residues use has found little to no attention in state environmental strategies. Methods To understand how underlying dynamics within a network may affect innovation potential, we use a mixed-method approach, which combines a survey and interviews. The survey was used to assess the intensity of each firm’s current bioeconomy practices and their perceptions of future initatives. The interviews were divided into two sections: social network questions (i.e. relational questions, which were used to map flows of communication and knowledge) and open questions about the challenges faced by the firm in adopting bioeconomy initatives. Conceptual Framework Social networks play an important role in learning and the diffusion of technology, opinions and behaviors [33]. Particularly given the innovations needed for the transition to the bioeconomy and adoption of bioeconomy initiatives (e.g. new business models), individual actors may affect the behavior of their peers. For example, in various fields, such as agricultural economics, there is a long tradition of studying the effects of peers on technology adoption [34]. Beyond studying the effects of peers, it is important to understand the position of each actor within the network to understand power relationships [33]. In our case, the network itself is situated within a global value chain. From a governance perspective, there are three main approaches to understand chains, networks and their functions in global production: global commodity chains, global value chains and global production networks [35]. We use the concept of the value chain as it is best suited to evaluate power relationships [36] within the coffee value chain and their role in governing and facilitating sustainability innovations (i.e. transition to the bioeconomy). In the value chain concept, the conceptualization of power is strongly centered on the linkages between lead actors link and other members of the chain. To understand these linkages, we apply network theory, which goes beyond the depiction of actors linked through a set of ties and rather evaluates why actors occupy a specific position within a network and the effect this has on the network as a whole [37]. 1755 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Data Collection The target population was employees from coffee roasting companies located in Germany. According to the German Coffee Association, they have over 360 members including various organizations and traders as well as large roasters and 150 specialty roasters [38]. Assuming that most roasters are members of the association, we were able to identify 83 professionals (e.g. sustainability managers, managing directors, scientists, engineers), which we invited for participation. Thus, we estimate that our sample represents around 4% of the industry. This included sustainability managers, managing directors, scientists, or engineers. Respondents were invited to participate by email, phone and at coffee and gastronomy trade fairs as well as recruited via the snowball process. In total, 18 respondents from 15 German coffee companies participated. Two companies had two respondents each, who responded as a team. Of the companies, three were micro-sized (up to 9 employees), seven were small/medium-sized (up to 249 employees) and five were large (more than 250 employees). In addition to interviews with coffee companies, two expert interviews with a recycling company and the German coffee association were conducted to corroborate the findings. One roaster (coffee11) did not participate in the survey. Figure2 shows the structure of our data collection and anaylsis. Our study comprised of (i) an online survey and (ii) semi-structured interviews with 15 German coffee roasting companies and two experts. Our online survey focused on the technical measures used in the context of coffee residue valorization and perceptions of the firm’s potential. Thus, in "Case Study: the German Coffee Value Chain" section, we explain the terminology and processing of coffee and its residues, which helped us design the survey instruments. We sent the online survey in advance to the respondents and used their responses in the survey to guide our development of the interviews. Both the translated survey and interview guide can be found in the Supplementary Material. After respondents filled out the online survey, we scheduled follow-up interviews online or in person. The interviews were organized in two parts. In the first part, we asked open questions about the firm’s challenges in implementing bioeconomy initiatives (e.g. economic feasibility, use of waste and residues as a resource, resource efficiency, preservation of product value, waste management, integrated bio-biorefineries and general sustainability issues). In the second part, we asked relational questions about the network’s structure. Specifically, we asked: “How often are you in contact with actor X during the past year (April 2021-April 2022)?”. This included various mediums of communication: in-person, phone calls and emails. If they were in contact, respondents were asked to select one of four categories, which we coded with the following values (width: 1–4): “rarely Fig. 2 Overview of data collection and analysis 1756 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 in contact”, “in contact”, “often in contact” and “very often in contact”. Interlinkages between actors who did not have any contact (0 – not in contact) were not included. Data Analysis Survey The results from the quantitative survey were used to descriptively assess the residues used at each firm, identify perceptions towards residue usage and form the basis of the qualitative interviews. From the categories of residue types (silverskins, defective beans, coffee grounds and other), we created a count of the number of sources used. We summarized the share of each residue type used and its stream. For perceptions, we included 25 Likert-scale questions about the perception of residue usage. These were organized into 7 categories: knowledge, potential business area, current business area, profitability, laws and regulatory environment, connections with other actors and administrative capacity. Similar to the analysis in the Q-methodology, we use principal component analysis (PCA) to identify interrelationships between respondents, rather than the variables as in regular PCA [39]. We use the qmethod package in R to analyze the data and specify an unforced distribution. This allows us to identify unique perspectives (i.e. group individuals with similar opinions) among the different coffee firms. PCA is standard multivariate data reduction technique that can be used to reduce a complex dataset to a lower dimension [40]. In our analysis, we were aimed to understand commonalities in attitudes about residue valorization between different firms. We organized our data as followed: the perceptions about residue usage (each cell with a score based on the degree that each respondent agreed/disagreed with each statement) are treated as rows and respondents are treated as columns. In the first step, a correlation matrix is built and PCA reduces the correlation matrix into components, which are ordered based on the total variability explained [39]. For this reason, the first components explain the greatest share of variability. In the second step, several of the components are selected and rotated to obtain a clearer structure of the data. The rotation of the components is mathematically optimized (varimax). The researcher may select how many components to select based on several criteria. Based on a Scree plot, eigenvalues, total explained variance, number of respondents significantly associated with each component and qualitative interpretation [41, 42]; we extract three components. We evaluate and describe each perspective by exploring a plot of the z-scores, which represent the typical opinion on each statement for a member of a given perspective. The z-scores represent the position of each statement in the sorting grid. The scoring was performed on a Likert scale, which is represented by z-scores ranging from -2 (strongly do not agree) to 2 (agree strongly). In the Appendix, Fig.5 compares the three perspectives and the z-score shows a representative score for each opinion statement. Social Network Analysis To complement this analysis, we performed a social network analysis to depict the governance structures of the German coffee value chain. We analyzed the social network using the igraph package in R [43]. This allowed us to evaluate actors’ specific positions within a network including the centrality; in-betweenness and eigenvector metrics as well as the connectively of 1757 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 the whole network (i.e., graph density.) The graph density represents the ratio of the number of edges and the number of possible edges [44]. By definition, a network is a graph on a set N of nodes with a finite number of members n [33]. Nodes may also be refered to as agents. The graph is a pair ( N , g ) where g is an nxn adjacency matrix on the set of nodes. Within this, gij indicates the relationship between i and j . The relationship (or link, edges or ties) between nodes can be present, absent or weighted. The graph can be directed or undirected, which depends on assumptions about the relationship. If the relationship requires mutual consent (e.g. contract), the network is often represented as an undirected graph. We focus on flows of knowledge and measure the frequency of contact for each actor (e.g., on average, a coffee roaster is in contact with a coffee importer four times a month). We focused on contact within the past year (April 2021-April 2022). For example, we were able to determine whether being in contact with a coffee importer twice a month is below or above the average. As not all respondents were able to give precise numerical answers (e.g., sometimes, often, etc.), the following responses were coded as 2.5 (rarely in contact) to 100 (very often in contact). Moreover, interlinkages between actors who were not in contact (0 – not in contact) – were not included. We present descriptive measures for the full social network (including the individual IDs of each coffee roaster). This allows us to understand the betweenness, degree, closeness and eigenvector calculated for each node in the graph. The betweenness centrality is a measure of the shortest number of paths passing through each node, which allows us to measure the actor’s role as a bridge. The shortest path betweenness of a given vertex v can be calculated following: where gijk is the number of deodesics from i to k through j [45–47]. The degree centrality is a measure of the number of edges connected with each node (actor), which can be understood as a measure of popularity or importance in the network. Based on the adjacency matrix, A=(aij) , degree centrality can be formulated as [48]: Closeness centrality is a measure of how close a given node is to other nodes. Thus, actors with higher closeness centrality have shorter average distances to other actors. Thus, the lower the score, the more central the actor. The closeness centrality refers to the inverse sum of distanaces to all other vertices in the graph. It can be formulated as [49]: Eigenvalue centrality is a measure of the influence of an actor in the network, which also considering the influence of its neighbors. If an actor has a higher eigenvalue centrality, it is connected to other influential actors. It can be formulated as a non-zero vector c=(ci)i∈N such that, for some scalar λ>0 , we will have [50]: C B(v)=∑ i,j∶i≠j,i≠v,j≠v givj gij 𝜎 D(x)= n ∑ i=1 a ix 1 ∑i≠v d vi λ ci= ∑j≠i gjicjforalli∈ N 1758 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 At the same time, respondents believed that the state did not appropriately foster bioeconomy initiatives. For example, several participants mentioned the unsustainable practices incentivized by the coffee tax refund for burnt coffee batches. In the case of burned batches, companies can reclaim the coffee tax if the coffee or coffee-containing goods were originally taxed. Respondents described the situation in which, in order to receive a refund for the previously paid coffee taxes, the state (in the role of the customs authority) may contaminate burnt batches to render them unsellable (ID8, ID11, ID9). This was viewed critically as using these residues for biogas generation would not qualify for a tax refund. Discussion In our exploratory study about bioeconomy initiatives in the German coffee sector, we focus on three key topics: perceptions of using coffee residues, the role of different actors in supporting the transition and barriers to the development of the bioeconomy in the German coffee roasting industry. The four barriers relate to state regulations, contact with adjacent industries and sustainability attitudes. One barrier to the adoption of bioeconomy initiatives in the coffee value chain is related to the the structure of the coffee social network. According to the roasters, the state Small/medium SpecialityRoasters PrivateWasteManagement CoffeeProducers Importers Consumers Customs Certification PublicWasteManagement Tax Research Foodretailers RegionalFarmers Otherbusinesses CoffeeAssociation Gastronomy Logistics Charities Hotels Studentinitatives Micro BigTen FacebookGroup Nurseries Pharma,cosmetic,agribusinesses LargeWorldCoffeeOrganization Packaging Internationalorganizations Fig. 4 German coffee roasting network (simplified to show size of roasting firms) 1765 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 actors occupy a central role within the bioeconomy network and there are limited means for other actors to exert their influence or initiate bioeconomy projects. Additionally, non-coffee firm actors (e.g. pharmaceutical, cosmetic industries) are viewed as influential for the adoption of bioeconomy initiatives, but are weakly linked to coffee roasters, who would be the main suppliers of residual biomass. Thus, it is critical that non-firm actors (specifically state actors) lead efforts as they determine the economic and political conditions for bioeconomy initiatives. This is closely related to the second barrier, which is related to regulations for residues at state, federal and European levels. Although the German government has made efforts to foster bioeconomy by classifying most agricultural waste as residues or by-products (rather than as waste) in the Recycling Management Act (de: KreislaufwirtschaftsgesetzKrWG), our findings show that the legal specification of coffee by-products is unclear. In our study, most participants viewed the residues from coffee processing as waste and more importantly, disposed of them as organic waste or residual waste. Only a minority of respondents sold residues to waste disposal companies as waste products to generate biogas. Another example of challenges related to residue usage for energy generation is related to the bureaucratic implications of resale. Specifically, we find that there are different taxation regulations of each federal state. This must be simplified as only large firms had thee administrative requirements to manage residue resale. This aligns with previous findings that found that a lack of EU authorization for by-products use for cascading represents the biggest obstacle for pharmaceutical and agricultural use [56]. Thus, bioeconomy initiatives in the coffee value chain require political initiatives to include coffee residues on the food contact list and the development of a comprehensive, federal regulatory framework that supports streams of material use (in addition to existing energy generation). As this could potentially be the case for other value chains in Germany, additional research is required. Further, if there is evidence that legislation is also unclear for the use of residues from other value chains, policymakers would be more willing to adjust the regulatory framework. The third barrier is related to the economies of scale and the logistics of gathering, transporting and using residues. The resale of residues requires significant administrative capacities, which means that only large coffee companies would be capable of coordinating residue resale. Further, residues (e.g. silverskins) are perishable and can only be stored for a limited time. Thus, they require an efficient infrastructure and storage system to prevent mold and contamination. However, it is likely that only larger companies would have sufficient quantities of residues to render these systems profitable. Finally, the fourth barrier is related to differing viewpoints and visions about the circular economy and sustainability in the coffee sector. There are two aspects here: (i) critical views of circular nature of residues for energy generation and (ii) the focus on the upstream value chain in certification. In the public discourse, there have been critical views as to whether residue usage for energy generation should be considered as part of the circular economy. For example, the environmental NGO Greenpeace has declared that only material use of residues is part of the circular economy. This strongly contrasts with the only existing bioeconomy imitative currently used in the coffee sector: energy generation. In a broader sense, the issue of the bioeconomy is closely related but not synonymous to discussions of sustainability in the coffee value chain. For example, while many respondents focused on social and ecological sustainability in producer countries within the upstream value chain (e.g. fair trade, organic labels), discussions of sustainability in the downstream chain are focused primarily on packaging, but do not represent a core concern. Thus, the downstream activities in the value chain must also be incorporated into sustainability discussion within the coffee chain. 1766 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Conclusions As the underlying power dynamics in social network structures can affect innovation potential, we focus on a case study from the German coffee value chain to understand the cascading potential of coffee residues based on the perceptions of key actors and the structure of the social network. Specifically, we investigate perceptions of the economic feasibility of using coffee residues, analyze how actors such as roasters can support bioeconomy initiatives in coffee value chain and identify policy barriers to the development of the bioeconomy in the German coffee roasting industry. We find that in terms of residue collection, silverskin is the most collected residue, followed by defective beans and spent coffee grounds. Awareness of residue amounts does not appear to be a barrier for usage as the majority of participants are aware of the actual accumulated generated amount of different residues. In terms of current streams of residue usage, 40% of participants from all companies regardless of size dispose of their silverskins as waste. The residues (i.e. silverskin) are primarily treated as organic waste, which means it is either disposed of by waste facilities or given away for free (e.g. to farmers for biogas). Additionally, we identify three perspectives related to residue valorization: (i) residue pioneers, (ii) exclusive roast utilizers and (iii) conventional roasters. These represent different attitudes toward residue usage among coffee roasters. While residue pioneers demonstrate willingness to explore other channels of residue usage, exclusive roaster utilizers only focus on exploring potential business opportunities related to coffee grounds, beans, and silverskin in the energy sector. Finally, conventional roasters are not yet pursuing opportunities associated with residues from coffee roasting due to lack of knowledge or administration/logistical capacities. In our social network analysis, we find that public waste managers have the highest degree and eigenvector centrality, specific coffee roasters have the lowest closeness centrality (closest to other actors) and specific roasters have the highest betweenness centrality (i.e. intermediaries). To support bioeconomy initiatives in the coffee value chain, policymakers must address the four main barriers related to the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. Specifically, there are inconsistencies between German federal states in terms of waste regulations, which has made it difficult to use residues (e.g., coffee silverskins) for material usage. Instead, coffee residues are used primarily for energy use. Further, the resale of residues requires significant administrative capacities, which means that only large coffee companies are capable of coordinating residue resale. As we focused on residues from downstream activities in the coffee value chain, our study could be complemented by studies on the upstream side of the value chain (e.g., green coffee beans; role of producers and traders) as well as the demand-side of residues (e.g. buyers of biomass, adjacent industries). From a practical perspective, future research should investigate the specific requirements within German and European waste regulations to understand other legal frameworks for handling coffee and other food instury residues in the context of the EU Waste Directive. From the technical perspective, further research is needed to develop specific technologies to prevent mold and contamination for residues from the food industry at different scales. From the theoretical perspective, research is needed on additional policy and regulatory tools for fostering the adoption of innovations in the transition to a sustainable bioeconomy. 1767 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Appendix −2 −1 0 123 z− s core s statements 2 16 1 5 3 15 7 21 22 18 23 17 13 8 19 20 14 6 25 4 9 11 24 12 10 Factor 1 Factor 2 Factor 3 Fig. 5 Overview of z-scores of the three perspectives. Note: the z-scores represent score for each of the three factors (perspectives). For example, the figure shows that for statement 10, perspective 3 highly disagrees whereas perspectives 1 and 2 highly agree. The shape is filled in if it is significantly different from the other groups 1768 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Table 4 List of statements about residue usage perceptions. Note: These statements were pre-defined by the researcher and the respondents rated them on a Likert scale according to how much they agreed/disagree with each statement (scale of 1–5). The number of the statement corresponds to the statements represented in Fig.5 No Category Statement 1 Knowledge Silverskin: Our company knows how many (i.e., in kg) by-products are produced during the roasting process 2 Knowledge Beans: Our company knows how many (i.e., in kg) by-products are produced during the roasting process 3 Knowledge Grounds: Our company knows how many (i.e., in kg) by-products are produced during the roasting process 4 Knowledge Other: Our company knows how many (i.e., in kg) by-products are produced during the roasting process 5 Potential Business Silverskin: Coffee by-products represent a potential business area for our company/coffee roaster 6 Potential Business Beans: Coffee by-products represent a potential business area for our company/coffee roaster 7 Potential Business Grounds: Coffee by-products represent a potential business area for our company/coffee roaster 8 Potential Business Other: Coffee by-products represent a potential business area for our company/coffee roaster 9 Current Business Silverskin: Coffee by-products are already a business for our company/coffee roaster 10 Current Business Grounds: Coffee by-products are already a business for our company/coffee roaster 11 Current Business Beans: Coffee by-products are already a business for our company/coffee roaster 12 Current Business Other: Coffee by-products are already a business for our company/coffee roaster 13 Need information Silverskin: Our company would like to know whether it would be profitable to sell our by-products 14 Need information Beans: Our company would like to know whether it would be profitable to sell our by-products 15 Need information Grounds: Our company would like to know whether it would be profitable to sell our by-products 16 Need information Other: Our company would like to know whether it would be profitable to sell our by-products 17 Laws It would make sense for the state to standardize the resale of coffee by-products by law, as it would make it easier 18 Laws For our company, economic support from the state/EU (e.g. subsidies) would be an important impetus for the resale of coffee byproducts 19 Other actors usage Our company is aware of recyclers that recycle coffee by-products 20 Other actors usage It is lucrative for our company that coffee by-products are to be used to generate energy (e.g. biogas) 21 Other actors usage It is lucrative for our company that coffee by-products are or should be used in the pharmaceutical, cosmetics, food and agricultural industries, among others 22 Administration Our company has the administrative capacity (e.g. staff) to handle the resale of by-products 23 Administration Our company has sufficient facilities and capacity to store coffee by-products 24 Administration Our company has the logistical means to transport the coffee by-products to a recycling facility 25 Administration Our company has the financial means to store and ship the coffee by-products to a recycling facility 1769 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s4361502400357-7. Funding Open Access funding enabled and organized by Projekt DEAL. Declarations Conflict of Interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References 1. Thrän D, Moesenfechtel U (2020) Das System Bioökonomie. Springer, Berlin Heidelberg, Berlin, Heidelberg 2. Karan SK, Hamelin L (2021) Crop residues may be a key feedstock to bioeconomy but how reliable are current estimation methods? resources. Conserv Recycl 164:105211. https:// doi. org/ 10. 1016/j. resco nrec. 2020. 105211 3. Stegmann P, Londo M, Junginger M (2020) The circular bioeconomy: its elements and role in european bioeconomy clusters. Resour Conserv Recycl X 6:100029. https:// doi. org/ 10. 1016/j. rcrx. 2019. 100029 4. Haberl H, Geissler S (2000) Cascade utilization of biomass: strategies for a more efficient use of a scarce resource. Ecol Eng 16:111–121. https:// doi. org/ 10. 1016/ S09258574(00) 00059-8 5. Muscat A, de Olde EM, Ripoll-Bosch R etal (2021) Principles, drivers and opportunities of a circular bioeconomy. Nat Food 2:561–566. https:// doi. org/ 10. 1038/ s4301602100340-7 6. Tuck CO, Pérez E, Horváth IT etal (2012) Valorization of biomass: deriving more value from waste. Science 337:695–699. https:// doi. org/ 10. 1126/ scien ce. 12189 30 7. Donner M, Gohier R, De Vries H (2020) A new circular business model typology for creating value from agro-waste. Sci Total Environ 716:137065. https:// doi. org/ 10. 1016/j. scito tenv. 2020. 137065 8. Giurca A, Metz T (2018) A social network analysis of Germany’s wood-based bioeconomy: social capital and shared beliefs. Environ Innov Soc Trans 26:1–14. https:// doi. org/ 10. 1016/j. eist. 2017. 09. 001 9. Korhonen J, Giurca A, Brockhaus M, Toppinen A (2018) Actors and politics in Finland’s Forest-based bioeconomy network. Sustainability 10:3785. https:// doi. org/ 10. 3390/ su101 03785 10. Falcone PM, Lopolito A, Sica E (2018) The networking dynamics of the italian biofuel industry in time of crisis: finding an effective instrument mix for fostering a sustainable energy transition. Energy Policy 112:334–348. https:// doi. org/ 10. 1016/j. enpol. 2017. 10. 036 11. Ludovico N, Dessi F, Bonaiuto M (2020) Stakeholders mapping for sustainable biofuels: an innovative procedure based on computational text analysis and social network analysis. Sustainability 12:10317. https:// doi. org/ 10. 3390/ su122 410317 12. Niang A, Torre A, Bourdin S (2022) How do local actors coordinate to implement a successful biogas project? Environ Sci Policy 136:337–347. https:// doi. org/ 10. 1016/j. envsci. 2022. 06. 019 13. Morone P, Tartiu VE, Falcone P (2015) Assessing the potential of biowaste for bioplastics production through social network analysis. J Clean Prod 90:43–54. https:// doi. org/ 10. 1016/j. jclep ro. 2014. 11. 069 14. Harrahill K, Macken-Walsh Á, O’Neill E (2023) Identifying primary producers’ positioning in the irish bioeconomy using social network analysis. Clean Circ Bioecon 5:100042. https:// doi. org/ 10. 1016/j. clcb. 2023. 100042 15. Waßenhoven A, Block C, Wustmans M, Bröring S (2021) Analyzing an emerging business ecosystem through m&a activities: the case of the bioeconomy. Bus Strat Dev 4:258–278. https:// doi. org/ 10. 1002/ bsd2. 149 1770 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 16. Poku A-G, Birner R, Gupta S (2018) Is Africa ready to develop a competitive bioeconomy? the case of the cassava value web in Ghana. J Clean Prod 200:134–147. https:// doi. org/ 10. 1016/j. jclep ro. 2018. 07. 290 17. Fytili D, Zabaniotou A (2022) Organizational, societal, knowledge and skills capacity for a low carbon energy transition in a circular waste bioeconomy (CWBE): observational evidence of the Thessaly region in Greece. Sci Total Environ 813:151870. https:// doi. org/ 10. 1016/j. scito tenv. 2021. 151870 18. Machin Ferrero LM, Wheeler J, Mele FD (2022) Life cycle assessment of the argentine lemon and its derivatives in a circular economy context. Sustain Prod Consum 29:672–684. https:// doi. org/ 10. 1016/j. spc. 2021. 11. 014 19. Abdu N, Mutuku J (2021) Willingness to pay for socially responsible products: a meta−analysis of coffee ecolabelling. Heliyon 7:e07043. https:// doi. org/ 10. 1016/j. heliy on. 2021. e07043 20. Hoseini M, Cocco S, Casucci C etal (2021) Coffee by-products derived resources. a review. Biomass Bioenergy 148:106009. https:// doi. org/ 10. 1016/j. biomb ioe. 2021. 106009 21. Klingel T, Kremer JI, Gottstein V etal (2020) A review of coffee by-products including leaf, flower, cherry, husk, silver skin, and spent grounds as novel foods within the European Union. Foods 9:665. https:// doi. org/ 10. 3390/ foods 90506 65 22. del Pozo C, Rego F, Yang Y etal (2021) Converting coffee silverskin to value-added products by a slow pyrolysis-based biorefinery process. Fuel Process Technol 214:106708. https:// doi. org/ 10. 1016/j. fuproc. 2020. 106708 23. Borrelli RC, Esposito F, Napolitano A etal (2004) Characterization of a new potential functional ingredient: coffee silverskin. J Agric Food Chem 52:1338–1343. https:// doi. org/ 10. 1021/ jf034 974x 24. Narita Y, Inouye K (2014) Review on utilization and composition of coffee silverskin. Food Res Int 61:16–22. https:// doi. org/ 10. 1016/j. foodr es. 2014. 01. 023 25. Martuscelli M, Esposito L, Di Mattia C etal (2021) Characterization of coffee silver skin as potential food-safe ingredient. Foods 10:1367. https:// doi. org/ 10. 3390/ foods 10061 367 26. Tan S, Kusumocahyo SP, Widiputri DI (2016) Pulverization of coffee silverskin extract as a source of antioxidant. IOP Conf Ser: Mater Sci Eng 162:012027. https:// doi. org/ 10. 1088/ 1757899X/ 162/1/ 012027 27. Alves RC, Rodrigues F, Antónia Nunes M etal (2017) State of the art in coffee processing by-products. In: Handbook of coffee processing by-products. Elsevier, pp 1–26. https:// doi. org/ 10. 1016/ B978-0128112908. 00001-3 28. Campos-Vega R, Loarca-Piña G, Vergara-Castañeda HA, Oomah BD (2015) Spent coffee grounds: a review on current research and future prospects. Trends Food Sci Technol 45:24–36. https:// doi. org/ 10. 1016/j. tifs. 2015. 04. 012 29. Leow Y, Yew PYM, Chee PL etal (2021) Recycling of spent coffee grounds for useful extracts and green composites. RSC Adv 11:2682–2692. https:// doi. org/ 10. 1039/ D0RA0 9379C 30. Bundesfinanzministerium. In: Glossareinträge - Kaffeesteuer [in German]. https:// www. bunde sfina nzmin ister ium. de/ Conte nt/ DE/ Gloss arein traege/ K/ 005_ Kaffe esteu er. html? view= rende rHelp.Accessed 24 Feb 2024 31. Basu AK, Hicks RL (2008) Label performance and the willingness to pay for fair trade coffee: a cross-national perspective. Int J Consum Stud 32:470–478. https:// doi. org/ 10. 1111/j. 14706431. 2008. 00715.x 32. Basu AK, Grote U, Hicks R, Stellmacher T (2018) Multiple certifications and consumer purchase decisions: a case study of willingness to pay for coffee in Germany. In: Parvathi P, Grote U, Waibel H (eds) Fair Trade and organic agriculture: a winning combination?, 1st edn. CAB International, UK, pp 61–75 33. Jackson MO (2011) An overview of social networks and economic applications. In: Handbook of social economics, vol 1. Elsevier, pp 511–585. https:// doi. org/ 10. 1016/ B978-044453187-2. 00012-7 34. Conley T, Christopher U (2001) Social learning through networks: the adoption of new agricultural Technologies in Ghana. Am J Agr Econ 83:668–673. https:// doi. org/ 10. 1111/ 00029092. 00188 35. Bush SR, Oosterveer P, Bailey M, Mol APJ (2015) Sustainability governance of chains and networks: a review and future outlook. J Clean Prod 107:8–19. https:// doi. org/ 10. 1016/j. jclep ro. 2014. 10. 019 36. Porter ME (1985) The competitive advantage: creating and sustaining superior performance. Free Press, New York. Republished with a new introduction, 1998 37. Borgatti SP, Halgin DS (2011) On network theory. Organ Sci 22:1168–1181. https:// doi. org/ 10. 1287/ orsc. 1100. 0641 38. (2024) Deutscher Kaffeeverband. https:// www. kaffe everb and. de/ en.Accessed 24 Feb 2024 39 Zabala A (2014) qmethod: a package to explore human perspectives using Q methodology. R J. 6:163 40. Shlens J (2014) A tutorial on principal component analysis. https:// doi. org/ 10. 48550/ ARXIV. 1404. 1100 1771 Circular Economy and Sustainability (2024) 4:1751–1772 1 3 41. Venus TE, Hinzmann M, Bakken TH et al (2020) The public’s perception of run-of-the-river hydropower across Europe. Energy Policy 140:111422. https:// doi. org/ 10. 1016/j. enpol. 2020. 111422 42. Venus TE, Strauss F, Venus TJ, Sauer J (2021) Understanding stakeholder preferences for future biogas development in Germany. Land Use Policy 109:105704. https:// doi. org/ 10. 1016/j. landu sepol. 2021. 105704 43. Csárdi G, Nepusz T, Müller K etal (2023) igraph for R: R interface of the igraph library for graph theory and network analysis 44. Wasserman S, Faust K (1994) Social Network Analysis: Methods and Applications, 1st edn. Cambridge University Press 45. Brandes U (2008) On variants of shortest-path betweenness centrality and their generic computation. Soc Netw 30:136–145. https:// doi. org/ 10. 1016/j. socnet. 2007. 11. 001 46. Butts, Carter T. betweenness: Compute the Betweenness Centrality Scores of Network Positions. https:// www. rdocu menta tion. org/ packa ges/ sna/ versi ons/2. 7-2/ topics/ betwe enness. Accessed 29 Jan 2024 47. Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1:215–239. https:// doi. org/ 10. 1016/ 03788733(78) 90021-7 48. Landherr A, Friedl B, Heidemann J (2010) A critical review of centrality measures in social networks. Bus Inf Syst Eng 2:371–385. https:// doi. org/ 10. 1007/ s125990100127-3 49. Csardi, Gabor closeness: Closeness centrality of vertices 50. Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92:1170–1182. https:// doi. org/ 10. 1086/ 228631 51. Venturini T, Jacomy M, Jensen P (2021) What do we see when we look at networks: visual network analysis, relational ambiguity, and force-directed layouts. Big Data Soc 8:205395172110184. https:// doi. org/ 10. 1177/ 20539 51721 10184 88 52. Davidson C (2009) Transcription: imperatives for qualitative research. Int J Qual Methods 8:35–52. https:// doi. org/ 10. 1177/ 16094 06909 00800 206 53. Gioia DA, Corley KG, Hamilton AL (2013) Seeking qualitative rigor in inductive research: notes on the gioia methodology. Organ Res Methods 16:15–31. https:// doi. org/ 10. 1177/ 10944 28112 452151 54. Döring N, Bortz J (2016) Forschungsmethoden und Evaluation in den Sozialund Humanwissenschaften. Springer, Berlin Heidelberg, Berlin, Heidelberg 55. Kolleck N (2013) Social network analysis in innovation research: using a mixed methods approach to analyze social innovations. Eur J Futures Res 1:25. https:// doi. org/ 10. 1007/ s403090130025-2 56. Beale C (2022) Circular Bioeconomy in the German-Brazilian coffee value chain: The role of German coffee stakeholders in promoting the bioeconomy in the German-Brazilian coffee value chain – a network analysis. Master’s Thesis, Technical University of Munich 1772