Understanding System Interdependencies in Sustainable Paper Production from Residue Grass biomass: Insights from Fuzzy Cognitive Mapping

Understanding System Interdependencies in Sustainable Paper Production from Residue Grass biomass: Insights from Fuzzy Cognitive Mapping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Understanding System Interdependencies in Sustainable Paper Production from Residue Grass biomass: Insights from Fuzzy Cognitive Mapping Zhengqiu Ding, Philipp Grundmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5123019/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This research investigates the pulp and paper industry's transition to sustainability by valorizing unused roadside and natural grasses for paper production. Large-scale production from residual grass poses multifaceted challenges, requiring collaboration across stakeholders, from biomass collection to manufacturing. To understand key drivers and barriers within this complex system, experts from various fields, including local farmers, researchers, policymakers, and industry executives were interviewed, leading to the development of a Fuzzy Cognitive Map (FCM). The analysis explores various scenarios to assess how socio-economic, technological, and political factors influence the transition to low-carbon practices. These scenarios highlight the effects of varying levels of technology development, economic conditions, and policy support on the transition's progress and outcomes. Results show that the system is highly sensitive to shifts in socio-economic and political conditions. Political interventions play a crucial role, especially during energy crises and increased public demand for sustainable solutions. Grass-based paper production is seen as a viable pathway, but challenges such as the economic feasibility of emerging technologies remain. We recommend targeted policies to improve the economic viability of grass-based products and optimize biomass allocation between energy and bio-based products, ensuring a more balanced and sustainable transition. Earth and environmental sciences/Environmental social sciences Earth and environmental sciences/Environmental social sciences/Energy and society Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios Earth and environmental sciences/Environmental social sciences/Sustainability pulp and paper industry sustainable production grass biomass fuzzy cognitive maps stakeholders circular bioeconomy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction The pulp and paper industry (PPI) faces a complex challenge: it must transition to a more sustainable bioeconomy while embracing green technologies for sustainable growth 1 . Achieving this objective requires strategic leveraging of existing resources and fostering the development of environmentally friendly solutions to reshape the industry 2,3 . Understanding the emerging sustainability megatrends, driving the future of the PPI is crucial 3 . Notably, this sector is one of the most carbon-intensive industries in the European Union (EU). High energy consumption is a key requirement for manufacturing processes in these industries 2,4,5 . Moreover, the depletion of forests resulting from wood extraction for wood-based bioeconomy has had significant environmental consequences 6 . This depletion increases the risks, costs, and constraints associated with conducting business and heightens competition. Nevertheless, it also presents new business opportunities to materialize alternative resources 2 . In the current global pulp consumption, wood pulp accounts for approximately 63%, while wastepaper pulp represents 34% and non-wood pulp 3% 7 . Increasing the efficient utilization of non-wood fiber resources, including grasses, holds significant potential in optimizing raw materials for papermaking processes and offers a promising solution to mitigate environmental destruction caused by deforestation 8 . Evidence has been found in the ecological footprints for the pulping from non-wood fiber compared with the harvested forestry 9 . The characteristics demonstrated by paper products made from grass pulp affirm the suitability of grass as a viable alternative to wood for papermaking 10 . Furthermore, the substantial biomass output of non-wood plants, coupled with the abundance of agricultural residues, holds the potential to yield significant quantities of non-wood fibers. These fibers could serve as substitutes for a considerable portion of imported pulp 11 . Transitioning to non-wood fiber for paper production presents a multifaceted system change with many different implications from a sustainability perspective. It is essential to consider both the direct and indirect effects of any land-use changes resulting from increased biomass demand 12 . A substantial body of literature has critiqued the sustainable use of biomass, especially concerning socio-ecological issues 13 . One potential yet underexplored resource is vegetation from roadside verges, currently underutilized for feed production. These areas could serve as beneficial feedstock, leading to positive outcomes such as enhanced biodiversity and nutrient recycling 14 . Transforming these green waste into a valuable resource could significantly advance efforts toward a more resource-efficient and circular economy 15,16 The existing literature predominantly highlights non-wood-based paper production concerning technology development and feasibility, with a focus on product properties and viable sources 7,17–20 . Despite that, studies on socioeconomic barriers remains limited 2 . Specifically, there is a lack of research on stakeholders' perspectives regarding the use of roadside and nature grass for papermaking. Understanding these social dynamics and their complex interconnections with economic, technological, political, environmental, and industrial factors is crucial for promoting the adoption of non-wood-based paper production on a broader scale. To achieve transformative change for sustainability, new approaches that recognize the complexity of local systems and incorporate the perspectives of stakeholders are essential. Some approaches study the social aspects of this industry include social life cycle assessment 21 , Delphi method 1 , and social network analysis 22 . Similarly, Fuzzy Cognitive Maps (FCMs) are effective in guiding decision-making processes grounded in human reasoning and offer a powerful approach by representing systems as a network of concepts and causal relationships, where the strength and nature of connections are expressed in fuzzy terms 23 . They facilitate the capture and communication of stakeholders' insights, enhancing decision-making 24 . The FCM approach has been applied in various studies. These include formulating effective bioeconomy policies 25–27 , assessing social acceptante 28,29 . Other studies focus on analyzing bio-energy production systems 30 , managing biomass at the farm level 31 , planning for regional bioeconomy 32 and water resource management 33 . Additionally, the implementation of technological approaches for biomass management has been explored 34 . This study significantly contributes to future discussions in the literature on opportunities for decarbonizing the paper and pulp industry 2 . It also contributes to the literature on sustainable transitions by emphasizing actor-oriented approaches that explicitly connect individual-level perspectives with system-level analysis in transition studies 35 . To the best of our knowledge, it is the first to apply FCMs to investigate stakeholders' perspectives on transitioning to circular bioeconomy to utilize roadside and natural grass for paper production. Consequently, the research questions in the paper are as follows: What are the primary barriers hindering the transition process towards utilizing roadside and nature grass for paper production, and which drivers and policy actions show the most promise in facilitating this transition? Additionally, how do socio-economic, political, and technological factors interconnect with each other in this process? The contribution lies in systematically developing and comparing various factors involved in transitioning to non-wood-based paper production. This research assist decision-makers in business, politics, and civil society by identifying barriers and pathways for the future deployment of PPI. 2 Literature review and theoretical background 2.1 Challenges in utilizing non-wood-based fibers in the Pulp and Paper Industry The PPI has undergone significant transformations in response to environmental concerns and the urgency for sustainable practices. A notable development in this context is the exploration of alternative fiber sources, particularly grass-based fibers, which present a promising avenue for reducing the industry's environmental footprint 36 . As depicted in Figure 1 (A) and (B) , non-wood fiber production in Europe constitutes less than 10% of total wood production 37 . Upon closer examination of production across European regions, Eastern Europe dominates non-wood fiber production, while Northern countries contribute less, likely due to their abundant forestry resources. Western Europe, meanwhile, retains a relatively small share of overall production. On the other hand, as indicated in Figure 1 (C) based on the data from European Central Bank 38 , the import prices for manufacturing paper, pulp, and paperboard soared from 2020 to 2022. While there was a slight decrease in 2023, future projections indicate that prices will rise again and remain at a relatively high level. In this context, seeking alternative sustainable resources for paper production becomes increasingly relevant. Despite notable progress in research and development using non-wood fiber for papermaking, critical gaps persist in the existing literature that need to be addressed. Foremost among these gaps is the absence of comprehensive studies on the variability of grass fibers sourced from different origins and its consequential effects on the quality and properties of paper products. Addressing this gap is necessary for ensuring consistent paper quality 18,19 . Although the environmental benefits of grass-based fibers are well-documented 9 , comprehensive life cycle assessments and economic analyses are still relatively scarce. A holistic understanding of grass-based paper production's environmental and economic sustainability is vital for informed decision-making 39,40 . Research endeavors focusing on the development and optimization of processing technologies for grass-based fibers, encompassing pretreatment methods and pulping techniques, are essential for overcoming existing challenges in integration into the paper industry 7,17,20 . Of particular concern are the cost implications associated with the bulky nature of raw materials, which pose logistical challenges and may impede large-scale commercial operations 41,42 . Additionally, the PPI is undergoing significant changes, particularly in the development of new business models that integrate with the local bioeconomy and interface with socio-technical systems 2 . Related to this, a critical yet underexplored area pertains to market acceptance and consumer perceptions of paper products derived from grass fibers. Understanding the factors influencing consumer choices and preferences can drive adoption and inform market strategies 43,44 . Additionally, the alignment of policies and regulations with the utilization of grass-based fibers in the paper industry remains a significant research gap. Investigating the impact of governmental policies, incentives, and regulations on the adoption and growth of grass-based paper production is crucial for creating a supportive environment for sustainable practices. Robust modeling tools are needed to understand and analyze these dynamics, capturing the nuances of these interconnections and predicting potential outcomes. 2.2 Participatory modeling for regional bioeconomy development Participatory modeling, rooted in post-normal science, recognizes the complexity, uncertainty, and value conflicts inherent in highly complex systems, advocating for collaborative approaches that actively involve diverse stakeholders to address urgent, real-world problems 45 . These problems are addressed within the studied system, through either the creation or assessment of models representing that system. When standard scientific methods fail to provide sufficient evidence, it becomes necessary to integrate local knowledge and engage in iterative participatory processes to formulate solutions that are both comprehensible and politically feasible while maintaining scientific rigor 46 . It is commonly recognized that stakeholders hold insights, perspectives, and expertise that can greatly enhance the research process in the bioeconomy 32,47 . In this context, the importance of various factors in sustainable bioeconomy development has been assessed. In the transition to a local bio-based energy production system, competitiveness, by-products, feedstock availability, and jobs were found to be the most important factors. Conversely, land availability, knowledge, existing symbiotic industry, and environmental sustainability were recognized to be the least important factors. Community acceptance and social capital consistently retained a moderate impact 32,48 . Interestingly, a contradictory finding emerges from another study examining the transition to bio-energy systems in different regions, where environmental factors were deemed most influential on decision-making processes related to low-carbon policies 49 . This highlights the complexity of transitioning to a bioeconomy, necessitating careful examination of each system. Notably, in the development of technology to valorize biomass, political factors emerge as the most influential, surpassing socio-technical factors in their impact 34 . This underscores the need for holistic and participatory analyses in navigating the complexities of transitioning to a sustainable bioeconomy. 3 Materials and Methods 3.1 Study case This study uses the case of grass fibers for paper production using grass biomass sourced from roadside areas and national parks in the Netherlands. This case is a demonstration action developed within the GO-GRASS project. In the Netherlands, grass sourced from roadside areas had historically served as cattle feed during the 1970s. However, by the 1980s, farmers' demand for such grass decreased, leading to a shift in disposal methods, with a growing portion being deposited at waste sites. Subsequently, from the late 1980s onward, composting emerged as the primary disposal method for roadside grass. Meanwhile, grass sourced from natural areas found utility in both cattle feed and stable litter on farms 50 . Over recent decades, a significant portion of these natural areas has been designated for conservation purposes. Even so, the nutritional value of grass from these lands has declined over time, attributed to reduced nutrient content and biomass removal practices aimed at nitrogen reduction. Grass clippings from road verges contribute to approximately 30% of green waste generated from public green spaces, amounting to roughly 900 kilotons per year 51 . The majority of this grass is collected and composted, while the remaining portion is subjected to either fermentation or left to decompose onsite. Although composting presents a circular solution, it yields a low economic value, causes considerable amounts of CO 2 emissions, and does not generate any revenues from the grass clippings to verge owners 51 . Consequently, both roadside and nature area managers are exploring alternative uses for grass resources 50 As illustrated in Table 1, the shift in the utilization of both roadside and natural grass resources, from cattle feed to waste disposal and the exploration of alternative uses reflects evolving agricultural practices as well as economic and ecological considerations. This transformation emphasizes the necessity for a comprehensive assessment of the economic and environmental impacts associated with the management of roadside and natural grass. Moreover, it presents an opportunity to explore new sustainable uses for these abundant grass resources in the Netherlands 52,53 . Considering the substantial volume of grass generated annually, examining the feasibility of utilizing grass biomass for the regional bioeconomy transition appears highly relevant. 3.2 Methods of fuzzy cognitive mapping Fuzzy cognitive maps are fuzzy graph structures for representing causal reasoning 23 . In recent years, FCM has emerged as a valuable method for incorporating stakeholder knowledge into models aimed at understanding the factors contributing to bioeconomy development 25,27,32,49 . By combining stakeholder insights with other evidence, these maps depict distinct knowledge systems, facilitating the establishment of shared perspectives and common reference points regarding specific aspects of bioeconomy development. The term "fuzzy" refers to the assignment of weights by stakeholders to evaluate the influences of various factors, adding depth to the analysis. Essentially, these maps offer flexible models of specific reasons for individuals or groups of individuals, providing visual representations of their cognitive structures. Cognitive mapping is mostly used to represent individual “mental models” as well as their usefulness for comparing and characterizing the aggregated beliefs and knowledge of a community 54 In the FCM model framework, each concept (C 1 , C 2 , …… , C j ) is visually represented as a node, while the connections between factors are depicted as edges or arrows linking these nodes. These arrows symbolize assumptions about causal relationships and attribute varying values to weigh the strength of each relationship, denoted as W ij , with values ranging from -1 to 1. Positive signs (W ij > 0) indicate that an increase in one node leads to an increase in the linked node, while negative signs (W ij < 0) signify that an increase in one node results in a decrease in the linked node. A weight of W ij = 0 indicates the absence of a causal edge in the graph. It's important to note that these causal weights reflect the opinions of knowledge holders, their explanatory models, and theories of change, rather than constituting predictive statistical models 55 . Figure 2 illustrates a simple example of a FCM with four concepts. By comparing the maps generated by different stakeholder groups, FCMs can reveal both the similarities and differences between alternative explanatory models and theories of change 56 . Moreover, FCMs can substantially enhance integrated assessment applications by complementing quantitative model approaches, which typically rely on one perspective rooted in available data, with qualitative insights obtained from a structured stakeholder engagement process 57 . 3.2.1 Data collection Between October and November 2022, we conducted semi-structured interviews with the participants, integrating the FCM exercise into the process. Each interview session spanned approximately 1.5-2 hours. Directly with the interviewees, each cognitive map was created. Subsequently, we transcribed the interview data gleaned from these sessions and the individual cognitive maps. Additionally, we performed triangulation with both the collected data and the resulting cognitive maps. The selection of participants for data on the FCM process in developing innovations for non-wood-based fiber paper production is a crucial stage in ensuring the accuracy and relevance of the mode. It is essential to involve individuals with a diverse range of expertise and perspectives pertinent to the field (as indicated in supplementary material Table S 3). This includes stakeholders such as experts from the paper and pulp industry, local farmers involved in grass biomass production, owners of paper manufacturing and printing companies, representatives from local government bodies, consultants specializing in local bioeconomy development, and researchers focused on technology development. 3.2.2 Ethical approval The data for this study was collected through face-to-face interviews, with no experimental procedures involved. All data collection methods adhered to the relevant regulations and guidelines, specifically REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL, concerning the protection of natural persons regarding the processing of personal data and the free movement of such data. The data collection protocols followed the European Union’s GDPR principals. To ensure participants’ privacy, all personal data was anonymized, and identifying details were coded to maintain confidentiality. Furthermore, informed consent was obtained from all participants for the publication of any identifying information included in the study. 3.2.3 Drawing the integrated maps There are three fundamental stages involved in constructing FCM maps, following the methodology outlined by 58 . The process of developing our FCMs is depicted in Figure 3 . Firstly, we identified indicators in Step 1 and Step 2. We utilized a literature review to compile a list of factors pertinent to the development of a regional bioeconomy system using residual roadside and natural grass for paper production as indicated in Supplementary material Table S4 . Through our semi-structured interviews, new concepts emerged, which we subsequently included in the final aggregated maps. To streamline complexity, concepts sharing similar meanings were clustered together (see the details in the supplementary material). Next, in Step 3, we constructed individual cognitive maps with various stakeholders. During this process, interviewees assigned weights to factors based on different weight scales provided to them (as detailed in the supplementary material). In Step 4, the objective is constructing the integrated FCM, taking into account overlapping concepts across individual maps. In this step, an extended FCM map was constructed by aggregating the different FCM maps of individuals or groups of individuals to obtain an overarching FCM map that represents the total cognition of all participants. Individual FCMs may share common concepts; they may propose two or more weights for the same interconnection. In such cases, we compute a new weight as the average of the existing weights 59 . This process helps to consolidate the various perspectives represented in the individual maps into a unified model. Finally, the analysis of the maps was conducted and interpreted using specific tools. We utilized FCMapper (https://www.fcmappers.net/joomla/index.php), a purpose-built software for FCM developed by researchers, which has also been employed in prior publications such as by Morone et al. 27 . Additionally, we used Mental Modeller (https://www.mentalmodeler.com/), a free browser-based software for FCM that has been widely utilized in academic research. These tools facilitated the analysis and interpretation of the FCMs, enabling a comprehensive understanding. 4 Results The results of the first part showed the factors affecting the development of paper production from grass fibers. The factors reflect the cognitive awareness of the stakeholders and experts participating in the interviews as well as from the literature. All categories, subcategories, and themes based on content analysis as well as the weights have been illustrated in Table 2 . 4.1 Descriptive analysis of the constructed fuzzy cognitive maps As presented in Table 2, our study identifies a comprehensive set of 26 system variables through an extensive literature review and in-depth interviews. These variables cover various aspects of the system and are broadly categorized into six main groups: economic (EC1-EC8), social (SO1-SO6), technological (TL1 & TL2), environmental (EM1 & EM2), sectoral variables (SE1-SE4), and policy and regulation (PO1-PO4). The aggregated network comprises 107 connections between these system variables. Table 2 provides a comparative analysis of the centrality levels and out- and in-degrees of the different indicators. The aggregated network exhibits a density index of 0.158284, which is lower than that of the original individual maps, which range from 0.20 to 0.40. A lower density index for the aggregated network is due to the increased number of variables included. This indicates that only 15.8% of the maximum potential connections among the 26 concepts are realized. In graph theory, the out-degree and in-degree indices delineate the collective connectivity of nodes, respectively measuring the sum of outgoing and incoming connections. These metrics are derived by aggregating the absolute values of the associated matrix's row and column sums. Centrality, visualized through node size, serves as a gauge of map complexity, elucidating the interconnections between variables and the cumulative intensity of these associations 24 The findings reveal that SE4 (Non-Wood fiber-based paper production) exhibits the highest centrality, registering at 8.11, indicating its significant interconnectedness from the stakeholders' standpoint. Stakeholders perceive the transition to non-wood fiber-based paper production as potentially beneficial for regional bioeconomy development, particularly through the valorization of residual grass biomass. This notable centrality stems primarily from its comparatively high in-degree index, signifying a considerable influence from other network elements. This observation aligns with the inherent complexity associated with transitioning to a localized bioeconomy system. Furthermore, several other variables demonstrate relatively elevated centrality values: EC2 (The competition of the feedstock), EC4 (Development of competitive business models), SO3 (Public awareness), and PO4 (Promote R & D and cooperation) recorded centrality scores of 2.13, 2.25, 2.03, and 2.31, respectively. Consequently, these variables play pivotal roles in facilitating the regional bioeconomy transition owing to their heightened sensitivity to changes. Moreover, our analysis recognizes three key system drivers: EC3 (Rising wood cellulose price), EC7 (Energy crisis), and PO1 (Regulatory impact on waste stream management) as shown in Figure 4 . Although these drivers exhibit relatively lower centrality indices, they contribute significantly to the system dynamics. Notably, economic conditions and political variables exert substantial influence on the system, reflecting stakeholders' confidence in their capacity to drive the regional bioeconomy transition by leveraging residual grass biomass for paper production and promoting local economic growth 43 . Conversely, social factors demonstrate heightened reliance on other variables, as evidenced by a greater number of incoming connections compared to outgoing connections. While centrality-based feature importance methods offer valuable insights into the significance of concepts within a network, they overlook the dynamic nature of the network itself 60 . As such, relying solely on centrality-based approaches may not provide a comprehensive understanding of concept relevance. Therefore, in the following section, we will perform a dynamic analysis of the system variables, examining the interconnectedness of different clustered categories with other variables, and conduct a what-if analysis to create both best and worst-case scenarios to observe potential system changes. 4.2 Scenario analysis of the constructed regional grass-based bioeconomy system To compare the baseline and simulated results, we obtained the steady-state outcome after at least 20 iterations without intervention, starting with initial state vectors set at 1. This steady-state outcome serves as the baseline for interpretation. Relative changes between this baseline scenario and the new steady state are also assessed to interpret the results. During the simulation process, a value of 0 indicates the absence of a given concept in the system at a particular iteration, while a value of 1 signifies the maximum presence of the concept 24 . Additionally, a value of 0.1 denotes the minimum presence of the concept. To facilitate sensitivity analysis, values at 0.75, 0.5, and 0.25 are also incorporated for comparison. Figure 5 illustrates the results of the what-if analysis. Figure 5 (I) pertains to the manipulation of all policy and regulation concepts to respective values of 1, 0.75, 0.5, 0.25, and 0.1. Similar adjustments are made for economic, social, and technological factors, enabling comparative analysis across different scenarios. The rationale underlying the classification of these system variables into categories such as policy & regulation, economic, social, and technological is explicated in Figure 5. This figure elucidates the potential impact of these factors on the overall system structure. Through a comprehensive understanding of the interaction between these factors and the system structure, valuable insights are gained into how policy, economic, social, and technological decisions can shape the dynamics of the entire system. In general, as depicted in Figure 5 (I), the political and regulatory dimensions, including PO1 (regulatory impact on waste stream management), PO2 (supportive governmental sustainable procurement projects), PO3 (financial support and funding), and PO4 (promotion of R&D and cooperation), exhibit a dynamic influence on the entire system, particularly on economic, social, and sectoral factors. The significant presence of these political concepts correlates with positive impacts observed on economic factors, such as "locally sourced feedstock availability" "competitive circular business model development" and "economic feasibility". Regarding social factors, the presence of these political dimensions is anticipated to mitigate societal concerns related to "fear and risk" and "knowledge gaps on sustainable biomass production". Additionally, technology factors such as "technology optimization and integration" stand to benefit from this presence, albeit to a lesser extent. Conversely, a lower level of presence of these concepts may negatively affect technology factors, as current technological development may require substantial governmental support, particularly financially. Furthermore, the paper & pulp industry is also expected to benefit from these political and regulatory initiatives, with positive percentage changes observed, nearing 2%. Upon a closer examination of the changes resulting from reducing the low-level presence of these policy initiatives, stronger negative impacts become apparent. This underscores the importance of maintaining a strong political presence to facilitate the transition effectively. When examining the technological dimension of the system as indicated in Figure 5 (II), the significant presence of "Technology optimization and integration" and "Technical feasibility challenges for scaling up" yields substantial influences, primarily clustered around economic factors such as "New competitive circular business development", "Economic feasibility" and "Production cost". These advancements are perceived positively by stakeholders, as they enhance economic viability and reduce barriers to scaling up sustainable technologies. Moreover, technological advancements reduce societal fear and risk associated with utilizing roadside grass as a product source, further reinforcing stakeholder confidence in sustainable practices. Additionally, technological factors influence "Product life cycle management/performance" contributing positively to carbon emission reduction and overall environmental benefit. Despite the positive contributions of technological advancements, their influence on sectoral factors, particularly in utilizing roadside and nature grass-based biomass for paper production, is limited compared to political dimensions. The highest observed change from the steady-state outcome to the simulated results, under the maximum presence of technological factors, is at 1.2%. All illustrated in Figure 5 (III), the economic clustering factors exert a significant influence on social, political, and sectoral dimensions, contingent upon the robust presence of economic conditions. Conversely, a diminished presence of these conditions may lead to adverse effects, particularly evident in indicators such as "Farmers' perception of sustainability", "Public awareness" and "Public acceptance & willingness to pay". For instance, a negative impact of up to 9% compared with the steady-state value is observed in the factor "Public awareness". Absent strong economic conditions, the transition towards a local circular bioeconomy utilizing grass biomass for paper production faces substantial hurdles. A notable decrease of 3.5% from the steady-state value is perceived with the minimum presence of economic condition variables. Stakeholders prioritize economic factors as pivotal in steering this transition, overshadowing the perceived impacts of political and technological factors. Stakeholders prioritize economic factors as pivotal in steering this transition, eclipsing the perceived impacts of political, social, and technological factors. When scrutinizing the social dimension of the system as shown in Figure 5 (IV), its effect extends to economic, political, and sectoral factors, particularly affecting indicators such as "Locally sourced feedstock availability", "Competitive circular business model development" and "Niche market formation" at the policy level. Additionally, at the policy level, the presence of factors such as "Supportive governmental sustainable procurement projects”, and “Promote R&D and cooperation” as well as at the sectoral level, "Regional innovation network" and "Non-wood fiber-based paper production" is influenced. The robust presence of social factors in the system facilitates these positive transitions. Conversely, a lack of strong social support may impede the transition process. This observation justifies the interconnectedness of social, economic, political, and sectoral dimensions within the transition towards a local circular bioeconomy. Continuing the analysis, we delve into the effects of system drivers identified by stakeholders on the entire system, considering both their combined and individual impacts. The identified drivers encompass "Rising wood price”, "Energy crisis" and "Regulatory impact on waste stream management". In general, as depicted in Figure 6 (i), these drivers predominantly influence economic, social, and sectoral categories, particularly in scenarios representing both best and worst cases. Indicators such as "Locally sourced feedstock availability", "Competition of feedstock", "Farmers' perception on sustainability", "Public awareness" and "Non-wood fiber-based paper production" exhibit notable effects. Notably, the "Competition of feedstock" indicator displays significant sensitivity to the presence of system drivers. In instances of strong drivers’ presence, intensified competition for feedstock arises, whereas low driver presence diminishes such competition. This phenomenon aligns with the rationale that rising wood prices, especially amid an energy crisis, prompt industries to seek alternative biomass sources, thereby intensifying competition for feedstock used in paper production. Examining the individual effects of each driver, demonstrated in Figure 6 (ii, iii, and iv), reveals distinct impacts on social factors. Interestingly, the rising wood price directly influences farmers' perceptions of biomass sustainability. Conversely, the energy crisis amplifies public awareness of sustainable development issues. Higher energy prices resulting from the crisis prompt consumers to reconsider sustainability, thereby positively influencing public awareness. 5 Discussion In the result section, we have identified the influential role of political support and regulatory initiatives in driving the transition towards on-wood fibers for paper production. This aligns with previous research by Ladu et al. 25 and Morone et al. 27 , suggesting that a policy mix incorporating economic and financial support for sustainable bioeconomy is likely to yield the most favorable outcomes. Dedicated policies could provide a balanced push for the bio-based economy transition towards a circular and innovative trajectory. When policies aimed at promoting sustainability are weakened or absent, the potential for negative consequences on economic, social, and technological factors becomes evident. Therefore, sustaining political engagement and regulatory support is imperative to realize the desired transition towards a sustainable bioeconomy. In addition, understanding the interplay of coordination, timing, and scale in policy mixes is essential for grasping how different instruments can accelerate sustainability transitions. The Swedish paper and pulp industry exemplifies the need for destabilizing policies, such as stringent environmental regulations, to precede innovation policies. These initial measures create an environment where novelty creation policies can effectively drive industry transformation 61 . Furthermore, the case study under examination revolves around an immature technology still in its nascent phase. Early-stage technology development can be affected by economic activity. During economic downturns, there is often less funding available for policies supporting new technology development, as public funds are limited. Stakeholders may worry that funding will be prioritized for activities with immediate economic impact rather than for long-term technological advancement. This highlights the importance of policymakers ensuring consistent support for early-stage technology development, even during economic uncertainty, to foster innovation and economic growth over time 62 . Transitioning to sustainability also requires a balanced politico-economic framework. Gawel et al. 63 argue that achieving sustainability relies on finding the right balance between regulatory frameworks and market mechanisms to implement necessary transition policies. Effective policy interventions and market regulations play a pivotal role in steering the transition towards sustainability and ensuring that technological advancements align with overarching societal objectives. This assertion is supported by research findings highlighting the significance of political factors in driving the development of technology aimed at valorizing biomass. Specifically, these political influences are identified as primary drivers for overcoming barriers, demonstrating a greater impact compared to socio-technical factors 34 . This correlation aligns with our findings. Therefore, it underscores the critical importance of implementing robust policy frameworks and regulatory mechanisms to effectively align technological advancements with sustainability goals 64 Aligned with Rajeswar 65 , our findings suggest an inclination towards overestimation of the sustainability gains associated with novel technologies, juxtaposed with an under-exploration of complex and potentially adverse impact pathways during early innovation phases. It is often argued that excessively regulated innovation systems might impede much-needed technological advancement. Nevertheless, this should not serve as a justification for neglecting thorough scrutiny in technology impact assessments 66 . The development of more sophisticated models to assess and integrate industry-based technology is required 67 . Furthermore, the results indicate that although economic incentives exhibit a high degree of connectivity with other development factors, scenario analysis suggests that they may also result in undesired outcomes, such as environmental degradation due to resource overexploitation. In essence, our research suggests that relying solely on economic incentives is unlikely to facilitate a transition towards sustainability in this system. From our simulated results, it is evident that stakeholders perceive a significant competition for utilizing grass biomass for various purposes, particularly in light of concerns surrounding the European Union's energy security following the Russian-Ukrainian conflict. The conflict has highlighted the EU's heavy reliance on imports of raw materials from these countries, emphasizing the need to develop alternative sources or renewable raw materials for material use and energy production and to reduce imports of raw materials from conflict regions 68 . The potential utilization of roadside grass for bioenergy production, as highlighted by Meyer et al. 14 and Ravi et al. 69 , presents a promising opportunity. In the face of this competition for biomass use across different sectors, policymakers should focus on establishing clear guidelines for biomass utilization. This is especially important given the institutionalization of bioeconomy strategies at the European level. These strategies provide a framework for developing cohesive approaches to biomass utilization that consider both environmental and economic factors. Additionally, the energy crisis between 2021 and 2023 has catalyzed increased social awareness among the public, underscoring the significance of nurturing and fostering robust social factors to drive this transition effectively. This emphasizes the need for comprehensive strategies that take into account the interplay between social, economic, and political dimensions. For a successful transition, it is essential to adopt both top-down and bottom-up approaches 43 . This involves engaging stakeholders at various levels of governance and society, ensuring inclusivity, and promoting collective action towards sustainable solutions. Nonetheless, this study has several limitations. Firstly, there is a lack of clarity in illustrating the global importance of the system, primarily because the analysis focuses solely on a single country case. Moreover, the scope of the study could be expanded by incorporating additional factors identified through expert knowledge. While relationships between factors are considered, they may not fully capture the complexity of the system, and updating them based on new information could enhance accuracy. To illustrate, further exploring other scenarios beyond those considered in the study could offer additional insights into the system's dynamics. This study highlights the role of policy mixes in accelerating sustainability transitions but also has several limitations. Implementing the proposed policy frameworks is complex and may vary in difficulty across different regulatory and economic environments. Additionally, the associated costs of these policy changes have not been examined, including both immediate financial burdens and long-term economic impacts. Future research should focus on quantifying these costs to provide a clearer outlook for strategic development. Understanding these financial implications is important for policymakers and industry stakeholders. Finally, this study only partially addresses the need for continuous adaptation and revision of policies in response to the dynamic nature of sustainability transitions. Studies should further consider a more iterative approach to policy development, accounting for ongoing changes and feedback mechanisms within the industry. 6 Conclusions This study significantly contributes to the discourse on decarbonizing the paper and pulp industry by applying fuzzy cognitive maps to capture and model local stakeholders' perceptions of transitioning to a local bioeconomy using roadside and natural grasses for paper production. The analysis reveals the pivotal role of political support and regulatory initiatives, with key drivers including rising wood prices, energy crises, and regulatory impacts on waste stream management. Political interventions are particularly influential at the regional level, emphasizing the need for comprehensive policy frameworks that address socio-economic, political, environmental, and sectoral barriers. Technological advancements, though beneficial for economic viability and reducing societal fears, have a lesser impact compared to political dimensions. Economic factors are prioritized by stakeholders and significantly influence public awareness and acceptance. The interconnectedness of social dimensions with other factors further highlights the complexity of the transition. Recommendations for policymakers include enhancing stakeholder engagement through inclusive top-down and bottom-up approaches, ensuring clear and adaptive guidelines for biomass utilization, and sustaining financial support for early-stage technologies. It is essential to prioritize ongoing assessment of technology impacts to inform evidence-based policy decisions effectively. Future research should focus on quantifying the broader implications of policy changes to provide a clearer strategic outlook and emphasize the novel insights gained in the context of advancing sustainability within the paper and pulp industry. Declarations Acknowledgments This study was conducted as part of the GO-GRASS project (Grass-based circular business models for rural agri-food value chains) and received funding from the European Union’s Horizon 2020 research and innovation programme under [grant agreement No. 862674]. The authors would like to express their sincere gratitude to Gosse Hiemstra at Hiemstra Bruin, along with other interview partners, for their valuable insights and contributions to this research. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions Statement Z.D. was responsible for the methodology, validation, formal analysis, investigation, data curation, and the preparation of the original draft. Z.D. also contributed to the reviewing, editing, and visualization of the manuscript. P.G. contributed to the conceptualization of the research, provided resources, and played a key role in reviewing and editing the manuscript. P.G. also supervised the project and secured funding for the research. Data availability The interview data that support the findings of this study are available from the corresponding author upon reasonable request. References Toppinen, A., Pätäri, S., Tuppura, A. & Jantunen, A. The European pulp and paper industry in transition to a bio-economy: A Delphi study. Futures 88 , 1–14 (2017). Furszyfer Del Rio, D. D. et al. Decarbonizing the pulp and paper industry: A critical and systematic review of sociotechnical developments and policy options. Renewable and Sustainable Energy Reviews 167 , 112706 (2022). Pätäri, S., Tuppura, A., Toppinen, A. & Korhonen, J. Global sustainability megaforces in shaping the future of the European pulp and paper industry towards a bioeconomy. Forest Policy and Economics 66 , 38–46 (2016). Eckert, S. Varieties of framing the circular economy and the bioeconomy: unpacking business interests in European policymaking. Journal of Environmental Policy & Planning 23 , 181–193 (2021). Gerres, T., Chaves Ávila, J. P., Llamas, P. L. & San Román, T. G. A review of cross-sector decarbonisation potentials in the European energy intensive industry. Journal of Cleaner Production 210 , 585–601 (2019). Holz, J. R. Threatened sustainability: extractivist tendencies in the forest-based bioeconomy in Finland. Sustain Sci 18 , 645–659 (2023). Liu, Z. et al. Pulping and Papermaking of Non-Wood Fibers. in Pulp and Paper Processing (IntechOpen, 2018). doi:10.5772/intechopen.79017. Daud, Z., Mohd Hatta, M. Z., Mohd Kassim, A. S., Aripin, A. M. & Awang, H. Analysis of Napier grass (Pennisetum purpureum) as a potential alternative fibre in paper industry. Materials Research Innovations 18 , S6-18-S6-20 (2014). Kissinger, M., Fix, J. & Rees, W. E. Wood and non-wood pulp production: Comparative ecological footprinting on the Canadian prairies. Ecological Economics 62 , 552–558 (2007). Obi Reddy, K., Uma Maheswari, C., Shukla, M. & Muzenda, E. Preparation, Chemical Composition, Characterization, and Properties of Napier Grass Paper Sheets. Separation Science and Technology 49 , 1527–1534 (2014). Ververis, C., Georghiou, K., Christodoulakis, N., Santas, P. & Santas, R. Fiber dimensions, lignin and cellulose content of various plant materials and their suitability for paper production. Industrial Crops and Products 19 , 245–254 (2004). Popp, J., Kovács, S., Oláh, J., Divéki, Z. & Balázs, E. Bioeconomy: Biomass and biomass-based energy supply and demand. New Biotechnology 60 , 76–84 (2021). Boyer, M., Kusche, F., Hackfort, S., Prause, L. & Engelbrecht-Bock, F. The making of sustainability: ideological strategies, the materiality of nature, and biomass use in the bioeconomy. Sustain Sci 18 , 675–688 (2023). Meyer, A. K. P., Ehimen, E. A. & Holm-Nielsen, J. B. Bioenergy production from roadside grass: A case study of the feasibility of using roadside grass for biogas production in Denmark. Resources, Conservation and Recycling 93 , 124–133 (2014). Liu, X., Xie, Y. & Sheng, H. Green waste characteristics and sustainable recycling options. Resources, Environment and Sustainability 11 , 100098 (2023). D’Adamo, I., Gastaldi, M., Imbriani, C. & Morone, P. Assessing regional performance for the Sustainable Development Goals in Italy. Sci Rep 11 , 24117 (2021). Anupam, K., Sharma, A. K., Lal, P. S. & Bist, V. Physicochemical, Morphological, and Anatomical Properties of Plant Fibers Used for Pulp and Papermaking. in Fiber Plants (eds. Ramawat, K. G. & Ahuja, M. R.) vol. 13 235–248 (Springer International Publishing, Cham, 2016). Ferdous, T., Ni, Y., Quaiyyum, M. A., Uddin, M. N. & Jahan, M. S. Non-Wood Fibers: Relationships of Fiber Properties with Pulp Properties. ACS Omega 6 , 21613–21622 (2021). Małachowska, E. et al. Influences of Fiber and Pulp Properties on Papermaking Ability of Cellulosic Pulps Produced from Alternative Fibrous Raw Materials. Journal of Natural Fibers 18 , 1751–1761 (2021). Pari, L., Baraniecki, P., Kaniewski, R. & Scarfone, A. Harvesting strategies of bast fiber crops in Europe and in China. Industrial Crops and Products 68 , 90–96 (2015). Costa, D., Quinteiro, P., Pereira, V. & Dias, A. C. Social life cycle assessment based on input-output analysis of the Portuguese pulp and paper sector. Journal of Cleaner Production 330 , 129851 (2022). Giurca, A. & Metz, T. A social network analysis of Germany’s wood-based bioeconomy: Social capital and shared beliefs. Environmental Innovation and Societal Transitions 26 , 1–14 (2018). Kosko, B. Fuzzy knowledge combination. Int. J. Intell. Syst. 1 , 293–320 (1986). Kontogianni, A. D., Papageorgiou, E. I. & Tourkolias, C. How do you perceive environmental change? Fuzzy Cognitive Mapping informing stakeholder analysis for environmental policy making and non-market valuation. Applied Soft Computing 12 , 3725–3735 (2012). Ladu, L., Imbert, E., Quitzow, R. & Morone, P. The role of the policy mix in the transition toward a circular forest bioeconomy. Forest Policy and Economics 110 , 101937 (2020). Lopolito, A., Nardone, G., Prosperi, M., Sisto, R. & Stasi, A. Modeling the bio-refinery industry in rural areas: A participatory approach for policy options comparison. Ecological Economics 72 , 18–27 (2011). Morone, P., Yilan, G. & Imbert, E. Using fuzzy cognitive maps to identify better policy strategies to valorize organic waste flows: An Italian case study. Journal of Cleaner Production 319 , 128722 (2021). Kokkinos, K., Lakioti, E., Papageorgiou, E., Moustakas, K. & Karayannis, V. Fuzzy Cognitive Map-Based Modeling of Social Acceptance to Overcome Uncertainties in Establishing Waste Biorefinery Facilities. Front. Energy Res. 6 , 112 (2018). Morone, P., Falcone, P. M. & Lopolito, A. How to promote a new and sustainable food consumption model: A fuzzy cognitive map study. Journal of Cleaner Production 208 , 563–574 (2019). Kokkinos, K., Karayannis, V. & Moustakas, K. Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes. Front. Energy Res. 8 , 622210 (2021). Assogba, G. G. C., Adam, M., Berre, D. & Descheemaeker, K. Managing biomass in semi-arid Burkina Faso: Strategies and levers for better crop and livestock production in contrasted farm systems. Agricultural Systems 201 , 103458 (2022). Penn, A. S. et al. Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region. PLoS ONE 8 , e78319 (2013). Kolahi, M., Davary, K. & Omranian Khorasani, H. Integrated approach to water resource management in Mashhad Plain, Iran: actor analysis, cognitive mapping, and roadmap development. Sci Rep 14 , 162 (2024). Reißmann, D., Thrän, D. & Bezama, A. What could be the future of hydrothermal processing wet biomass in Germany by 2030? A semi-quantitative system analysis. Biomass and Bioenergy 138 , 105588 (2020). van den Broek, K. L., Negro, S. O. & Hekkert, M. P. Mapping mental models in sustainability transitions. Environmental Innovation and Societal Transitions 51 , 100855 (2024). El-Sayed, E. S. A., El-Sakhawy, M. & El-Sakhawy, M. A.-M. Non-wood fibers as raw material for pulp and paper industry. Nordic Pulp & Paper Research Journal 35 , 215–230 (2020). FAO. FAOSTAT Statistical Database. [Forestry Production and Trade]. (2023). European Central Bank. ECB Data Portal. [Manufacture of pulp, paper and paperboard]. (2023). Lee, C. L., Chin, K. L., H’ng, P. S., Hafizuddin, M. S. & Khoo, P. S. Valorisation of Underutilized Grass Fibre (Stem) as a Potential Material for Paper Production. Polymers 14 , 5203 (2022). Sun, M., Wang, Y. & Shi, L. Environmental performance of straw-based pulp making: A life cycle perspective. Science of The Total Environment 616–617 , 753–762 (2018). Jahan, M. S., Rahman, M. M. & Ni, Y. Alternative initiatives for non-wood chemical pulping and integration with the biorefinery concept: A review. Biofuels, Bioproducts and Biorefining 15 , 100–118 (2021). Ramdhonee, A. & Jeetah, P. Production of wrapping paper from banana fibres. Journal of Environmental Chemical Engineering 5 , 4298–4306 (2017). Ding, Z., Hamann, K. T. & Grundmann, P. Enhancing circular bioeconomy in Europe: Sustainable valorization of residual grassland biomass for emerging bio-based value chains. Sustainable Production and Consumption 45 , 265–280 (2024). Orozco, R. & Grundmann, P. Readiness for Innovation of Emerging Grass-Based Businesses. Journal of Open Innovation: Technology, Market, and Complexity 8 , 180 (2022). Funtowicz, S. O. & Ravetz, J. R. Science for the post-normal age. Futures 25 , 739–755 (1993). Voinov, A. & Gaddis, E. B. Values in Participatory Modeling: Theory and Practice. in Environmental Modeling with Stakeholders (eds. Gray, S., Paolisso, M., Jordan, R. & Gray, S.) 47–63 (Springer International Publishing, Cham, 2017). doi:10.1007/978-3-319-25053-3_3. Kyriakarakos, G., Patlitzianas, K., Damasiotis, M. & Papastefanakis, D. A fuzzy cognitive maps decision support system for renewables local planning. Renewable and Sustainable Energy Reviews 39 , 209–222 (2014). Caferra, R., Colasante, A., D’Adamo, I., Morone, A. & Morone, P. Interacting locally, acting globally: trust and proximity in social networks for the development of energy communities. Sci Rep 13 , 16636 (2023). Kokkinos, K., Karayannis, V. & Moustakas, K. Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment. Science of The Total Environment 721 , 137754 (2020). Vural Gursel, I. et al. Local Supply of Lignocellulosic Biomass to Paper Industry in Gelderland : Development of Circular and Value-Added Chains . https://research.wur.nl/en/publications/b1482511-2b77-4d92-ae99-b88f84b03412 (2020) doi:10.18174/522235. Rijksdienst voor Ondernemend Nederland. Biogas Uit Gras Een Onderbenut Potentieel: Een Studie Naar Kansen Voor Grasvergisting . https://www.rvo.nl/sites/default/files/2014/04/Definitief_Een%20studie%20naar%20kansen%20voor%20grasvergisting.pdf (2014). de Jong, J. J., Bijlsma, R. J. & Spijker, J. H. Randvoorwaarden Biodiversiteit Bij Oogst van Biomassa . https://edepot.wur.nl/210074 (2012). de Vries, B. et al. Energie à La Carte : De Potentie van Biomassa Uit Het Landschap Voor Enegiewinning . https://edepot.wur.nl/45536 (2008). Gray, S. A., Zanre, E. & Gray, S. R. J. Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs. in Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms (ed. Papageorgiou, E. I.) 29–48 (Springer, Berlin, Heidelberg, 2014). doi:10.1007/978-3-642-39739-4_2. Felix, G. et al. A review on methods and software for fuzzy cognitive maps. Artif Intell Rev 52 , 1707–1737 (2019). Sarmiento, I. et al. Fuzzy cognitive mapping and soft models of indigenous knowledge on maternal health in Guerrero, Mexico. BMC Medical Research Methodology 20 , 125 (2020). Mourhir, A. Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management. Environmental Modelling & Software 135 , 104891 (2021). Barbrook-Johnson, P. & Penn, A. S. Fuzzy Cognitive Mapping. in Systems Mapping 79–95 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-031-01919-7_6. Glykas, M. Fuzzy Cognitive Maps. Advances in Theory, Methodologies, Tools and Applications . Tools and Applications. Studies in Fuzziness and Soft Computing vol. 247 (2010). Nápoles, G., Ranković, N. & Salgueiro, Y. On the interpretability of Fuzzy Cognitive Maps. Knowledge-Based Systems 281 , 111078 (2023). Scordato, L., Klitkou, A., Tartiu, V. E. & Coenen, L. Policy mixes for the sustainability transition of the pulp and paper industry in Sweden. Journal of Cleaner Production 183 , 1216–1227 (2018). Philp, J. The bioeconomy, the challenge of the century for policy makers. New Biotechnology 40 , 11–19 (2018). Gawel, E., Purkus, A., Pannicke, N. & Hagemann, N. A Governance Framework for a Sustainable Bioeconomy: Insights from the Case of the German Wood-based Bioeconomy. in Towards a Sustainable Bioeconomy: Principles, Challenges and Perspectives (eds. Leal Filho, W., Pociovălișteanu, D. M., Borges de Brito, P. R. & Borges de Lima, I.) 517–537 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-319-73028-8_26. Ding, Z. & Grundmann, P. Development of Biorefineries in the Bioeconomy: A Fuzzy-Set Qualitative Comparative Analysis among European Countries. Sustainability 14 , 90 (2022). Rajeswar, J. Deconstructing the development paradigm: a post-structural perspective. Sustainable Development 18 , 245–251 (2010). Biber-Freudenberger, L., Ergeneman, C., Förster, J. J., Dietz, T. & Börner, J. Bioeconomy futures: Expectation patterns of scientists and practitioners on the sustainability of bio-based transformation. Sustainable Development 28 , 1220–1235 (2020). Mandeep, Gupta, G. K., Liu, H. & Shukla, P. Pulp and paper industry–based pollutants, their health hazards and environmental risks. Current Opinion in Environmental Science & Health 12 , 48–56 (2019). Liao, S. The Russia–Ukraine outbreak and the value of renewable energy. Economics Letters 225 , 111045 (2023). Ravi, R. et al. Exploring the environmental consequences of roadside grass as a biogas feedstock in Northwest Europe. Journal of Environmental Management 344 , 118538 (2023). Tables Table 1 Overview of roadside and nature grass usage and estimated biomass quantities in the Netherlands Roadside grass Nature grass Reference Usage of the grass 1970s Cattle feed Cattle feed and litter in stables 1980s Demand for farmers decreased and often more deposited at waste dumps Vural Gursel et al. 50 1980 onwards Composting Recent years Less attractive to farmers, and demanding alternative use of the grass and development for bioeconomy (e.g., bioenergy, paper ) Vural Gursel et al. 50 Estimated quantity 900,ooo tons year -1 Rijksdienst voor Ondernemend Nederland 51 225,000 ton DM year -1 263,244 tons DM year -1 de Vries et al. 53 de Jong et al. 52 Table 2 Descriptive analysis from the constructed FCMs Concepts Out degree In degree Centrality Driver Ordinary EC1 Locally sourced feedstock availability 1.08 0.84 1.92 ü EC2 Competition of feedstock 1.12 1.01 2.13 ü EC3 Rising wood cellulose price 1.16 0.00 1.16 ü EC4 New competitive circular business development 0.44 1.80 2.25 ü EC5 Economic feasibility 0.95 0.43 1.38 ü EC6 Production cost 0.54 0.19 0.73 ü EC7 Energy crisis 0.75 0.00 0.75 ü EC8 Niche market formation 0.53 0.37 0.90 ü SO1 Stakeholders participation 1.12 0.17 1.28 ü SO2 Farmers' perception of sustainability 0.46 0.83 1.29 ü SO3 Public awareness 0.98 1.05 2.03 ü SO4 Public acceptance & willingness to pay 0.61 0.70 1.31 ü SO5 Fear and risk 0.46 0.69 1.14 ü SO6 Knowledge gap in sustainable biomass production 0.12 0.33 0.45 ü PO1 Regulatory impact on waste stream management 0.67 0.00 0.67 ü PO2 Supportive governmental sustainable procurement projects 0.55 0.18 0.73 ü PO3 Financial support and funding 0.68 0.23 0.91 ü PO4 Promote R & D and cooperation 1.51 0.79 2.31 ü TL1 Technology optimization and integration 0.95 0.37 1.32 ü TL2 Technical feasibility challenges for scaling up 0.84 0.09 0.93 ü EM1 Environment benefits: nature conservations 0.16 0.42 0.57 ü EM2 Product life cycle management/ performance 0.97 0.06 1.02 ü SE1 Sustainable industry practices 0.34 0.29 0.63 ü SE2 Prevalence of wood-based Paper in the printing sector 0.13 0.04 0.17 ü SE3 Regional innovation network 1.29 0.13 1.42 ü SE4 Non-wood fiber-based paper production 0.36 7.75 8.11 ü Density Nr. Factors Nr. Connections Nr. Driver Nr. Receiver Nr. Regular Connections 0.15824 26 107 3 0 107 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Reviews received at journal 08 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers invited by journal 24 Oct, 2024 Editor assigned by journal 15 Oct, 2024 Editor invited by journal 11 Oct, 2024 Submission checks completed at journal 10 Oct, 2024 First submitted to journal 20 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5123019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375703757,"identity":"56f5dcc5-daf5-4d6d-89c0-2a8d16dd01d4","order_by":0,"name":"Zhengqiu Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFCCxAYGhgoGBgMIjxlEGBCh5QyDBFjZAeK0JDAwMLaRokW+Pbnt4dd5dnXmDLwHH3/4Yy2v28C88QE+LYw9D9uNZbclS1g28CUbHGxLN9x2gK0YrzXMEolt0pLbmCUM7r8xkzjYcJhx2wEeMwl8WtjAWubUSxiAVB74c9geqMX8Bz4tPEAtkh8bDkO1sB1OBNmCTweDBM/DNmmGY8cld4L8crYtPXnbYbZivA6Tb09/JvmjppofFGIPKv5Y22473rzxA15rgICZB+JGGJeQeiBg/IGiZRSMglEwCkYBGgAAdStKVZ3MDKoAAAAASUVORK5CYII=","orcid":"","institution":"Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)","correspondingAuthor":true,"prefix":"","firstName":"Zhengqiu","middleName":"","lastName":"Ding","suffix":""},{"id":375703758,"identity":"e4989472-940f-4516-b863-98165907aa63","order_by":1,"name":"Philipp Grundmann","email":"","orcid":"","institution":"Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB)","correspondingAuthor":false,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Grundmann","suffix":""}],"badges":[],"createdAt":"2024-09-20 10:51:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5123019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5123019/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84358-4","type":"published","date":"2025-01-09T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70697015,"identity":"66359c28-ba63-448c-88ed-70c3af54e23a","added_by":"auto","created_at":"2024-12-05 17:45:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113187,"visible":true,"origin":"","legend":"\u003cp\u003ePulp production from non-wood fiber and price index for paper, pulp, and paperboard manufacturing in Europe. (A) Comparison of non-wood based pulp production volumes between the world and Europe. (B) Breakdown of non-wood based pulp production across four European regions. (C) Trend in the price index for pulp in Europe.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/0b176b4c5dc442d99d8b7a21.png"},{"id":70697022,"identity":"4045ec69-8d71-432b-8ea3-444e31a12267","added_by":"auto","created_at":"2024-12-05 17:45:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70824,"visible":true,"origin":"","legend":"\u003cp\u003eA simple example fuzzy cognitive map comprising four concepts: C1, C2, C3, and C4, along with their associated positive and negative weights\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/e770a20396dd1f4ab3e1ff9f.png"},{"id":70697016,"identity":"6dfd4962-36fa-465b-a419-13fc481d715a","added_by":"auto","created_at":"2024-12-05 17:45:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSteps of the FCM development process.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/57e2be3449abc5cf6fdf4d32.png"},{"id":70697017,"identity":"a85d5b5a-f6eb-4e2a-8c2f-8d4d9e172e36","added_by":"auto","created_at":"2024-12-05 17:45:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":380408,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated fuzzy cognitive maps. The figure shows integrated fuzzy cognitive maps with averaged weights from individual maps. Three key drivers are highlighted in red: EC 3, EC 7, and PO 1. The remaining factors, shown in blue, are considered ordinary factors.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/f2dc4a1d997be4012f09e974.png"},{"id":70697018,"identity":"229100fd-96ab-4c97-9740-02510bc0d59b","added_by":"auto","created_at":"2024-12-05 17:45:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":167344,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates simulation results depicting the best and worst-case scenarios\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(I) demonstrates the system's change resulting from policy factors (PO1, PO2, PO3, PO4). (II) indicates the system's change from technological factors (TL1, TL2). (III) displays the intervention effect of economic factors (EC1-EC8) on the entire system. (IV) illustrates the intervention effect of social factors (SO1-SO6) on the system.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/b52c2a85f9b58edc26da9aef.png"},{"id":70697629,"identity":"85c37b28-50aa-499f-9270-a6ba70fed5db","added_by":"auto","created_at":"2024-12-05 17:53:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158410,"visible":true,"origin":"","legend":"\u003cp\u003edepicts the best and worst-case scenarios regarding the identified system drivers (EC3, EC7, and PO1) i. shows the combined effect of the three drivers on the system; ii. illustrates the impact of rising wood prices on the entire system; iii. Examines the influence of policy drivers on the system; iv. analyzes the impact of the energy crisis as a driver on the system.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/83cdd0d64200ede1ee269536.png"},{"id":73694058,"identity":"c96db915-884c-4f3f-8403-69b052973d79","added_by":"auto","created_at":"2025-01-13 16:10:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1857550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/26e889fa-b9e4-4bc9-9f41-bf8526984bc2.pdf"},{"id":70697628,"identity":"2fc00dd6-8fdc-4a50-bc74-e0a002bb84cd","added_by":"auto","created_at":"2024-12-05 17:53:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":662714,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5123019/v1/f470daafc3f2ffb971db785e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding System Interdependencies in Sustainable Paper Production from Residue Grass biomass: Insights from Fuzzy Cognitive Mapping","fulltext":[{"header":"1 Introduction ","content":"\u003cp\u003eThe pulp and paper industry (PPI) faces a complex challenge: it must transition to a more sustainable bioeconomy while embracing green technologies for sustainable growth\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e. Achieving this objective requires strategic leveraging of existing resources and fostering the development of environmentally friendly solutions to reshape the industry \u0026nbsp;\u003csup\u003e2,3\u003c/sup\u003e. Understanding the emerging sustainability megatrends, driving the future of the PPI is crucial\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. Notably, this sector is one of the most carbon-intensive industries in the European Union (EU). High energy consumption is a key requirement for manufacturing processes in these industries\u0026nbsp;\u003csup\u003e2,4,5\u003c/sup\u003e. Moreover, the depletion of forests resulting from wood extraction for wood-based bioeconomy has had significant environmental consequences\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e. This depletion increases the risks, costs, and constraints associated with conducting business and heightens competition. Nevertheless, it also presents new business opportunities to materialize alternative resources\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the current global pulp consumption, wood pulp accounts for approximately 63%, while wastepaper pulp represents 34% and non-wood pulp 3%\u0026nbsp;\u003csup\u003e7\u003c/sup\u003e. Increasing the efficient utilization of non-wood fiber resources, including grasses, holds significant potential in optimizing raw materials for papermaking processes and offers a promising solution to mitigate environmental destruction caused by deforestation\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e. Evidence has been found in the ecological footprints for the pulping from non-wood fiber compared with the harvested forestry\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e. The characteristics demonstrated by paper products made from grass pulp affirm the suitability of grass as a viable alternative to wood for papermaking\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e. Furthermore, the substantial biomass output of non-wood plants, coupled with the abundance of agricultural residues, holds the potential to yield significant quantities of non-wood fibers. These fibers could serve as substitutes for a considerable portion of imported pulp\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Transitioning to non-wood fiber for paper production presents a multifaceted system change with many different implications from a sustainability perspective. It is essential to consider both the direct and indirect effects of any land-use changes resulting from increased biomass demand\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. A substantial body of literature has critiqued the sustainable use of biomass, especially concerning socio-ecological issues\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. One potential yet underexplored resource is vegetation from roadside verges, currently underutilized for feed production. These areas could serve as beneficial feedstock, leading to positive outcomes such as enhanced biodiversity and nutrient recycling\u003csup\u003e14\u003c/sup\u003e. Transforming these green waste into a valuable resource could significantly advance efforts toward a more resource-efficient and circular economy\u0026nbsp;\u003csup\u003e15,16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe existing literature predominantly highlights non-wood-based paper production concerning technology development and feasibility, with a focus on product properties and viable sources\u0026nbsp;\u003csup\u003e7,17\u0026ndash;20\u003c/sup\u003e. Despite that, \u0026nbsp;studies on socioeconomic barriers remains limited\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e. Specifically, there is a lack of research on stakeholders\u0026apos; perspectives regarding the use of roadside and nature grass for papermaking. Understanding these social dynamics and their complex interconnections with economic, technological, political, environmental, and industrial factors is crucial for promoting the adoption of non-wood-based paper production on a broader scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo achieve transformative change for sustainability, new approaches that recognize the complexity of local systems and incorporate the perspectives of stakeholders are essential. Some approaches study the social aspects of this industry include social life cycle assessment\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e, Delphi method\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e, and social network analysis\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. Similarly, Fuzzy Cognitive Maps (FCMs) are effective in guiding decision-making processes grounded in human reasoning and offer a powerful approach by representing systems as a network of concepts and causal relationships, where the strength and nature of connections are expressed in fuzzy terms\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e. They facilitate the capture and communication of stakeholders\u0026apos; insights, enhancing decision-making\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e. The FCM approach has been applied in various studies. These include formulating effective bioeconomy policies\u0026nbsp;\u003csup\u003e25\u0026ndash;27\u003c/sup\u003e, assessing social acceptante\u0026nbsp;\u003csup\u003e28,29\u003c/sup\u003e. Other studies focus on analyzing bio-energy production systems\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e, managing biomass at the farm level\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e, \u0026nbsp;planning for regional bioeconomy\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e and water resource management\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e. Additionally, the implementation of technological approaches for biomass management has been explored\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study significantly contributes to future discussions in the literature on opportunities for decarbonizing the paper and pulp industry\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e. It also contributes to the literature on sustainable transitions by emphasizing actor-oriented approaches that explicitly connect individual-level perspectives with system-level analysis in transition studies\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e. To the best of our knowledge, it is the first to apply FCMs to investigate stakeholders\u0026apos; perspectives on transitioning to circular bioeconomy to utilize roadside and natural grass for paper production. Consequently, the research questions in the paper are as follows: What are the primary barriers hindering the transition process towards utilizing roadside and nature grass for paper production, and which drivers and policy actions show the most promise in facilitating this transition? Additionally, how do socio-economic, political, and technological factors interconnect with each other in this process? The contribution lies in systematically developing and comparing various factors involved in transitioning to non-wood-based paper production. This research assist decision-makers in business, politics, and civil society by identifying barriers and pathways for the future deployment of PPI.\u003c/p\u003e"},{"header":"2 Literature review and theoretical background ","content":"\u003ch2\u003e2.1 Challenges in utilizing non-wood-based fibers in the Pulp and Paper Industry\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe PPI has undergone significant transformations in response to environmental concerns and the urgency for sustainable practices. A notable development in this context is the exploration of alternative fiber sources, particularly grass-based fibers, which present a promising avenue for reducing the industry\u0026apos;s environmental footprint \u003csup\u003e36\u003c/sup\u003e. \u0026nbsp;As depicted in Figure 1\u003cem\u003e\u0026nbsp;(A) and (B)\u003cstrong\u003e,\u003c/strong\u003e\u003c/em\u003e non-wood fiber production in Europe constitutes less than 10% of total wood production \u003csup\u003e37\u003c/sup\u003e. \u0026nbsp;Upon closer examination of production across European regions, Eastern Europe dominates non-wood fiber production, while Northern countries contribute less, likely due to their abundant forestry resources. Western Europe, meanwhile, retains a relatively small share of overall production. On the other hand, as indicated in Figure 1 \u003cem\u003e(C)\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/em\u003ebased on the data from European Central Bank \u003csup\u003e38\u003c/sup\u003e, the import prices for manufacturing paper, pulp, and paperboard soared from 2020 to 2022. While there was a slight decrease in 2023, future projections indicate that prices will rise again and remain at a relatively high level. In this context, seeking alternative sustainable resources for paper production becomes increasingly relevant.\u003c/p\u003e\n\u003cp\u003eDespite notable progress in research and development using non-wood fiber for papermaking, critical gaps persist in the existing literature that need to be addressed. Foremost among these gaps is the absence of comprehensive studies on the variability of grass fibers sourced from different origins and its consequential effects on the quality and properties of paper products. Addressing this gap is necessary for ensuring consistent paper quality\u0026nbsp;\u003csup\u003e18,19\u003c/sup\u003e. Although the environmental benefits of grass-based fibers are well-documented\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e, comprehensive life cycle assessments and economic analyses are still relatively scarce. A holistic understanding of grass-based paper production\u0026apos;s environmental and economic sustainability is vital for informed decision-making\u0026nbsp;\u003csup\u003e39,40\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch endeavors focusing on the development and optimization of processing technologies for grass-based fibers, encompassing pretreatment methods and pulping techniques, are essential for overcoming existing challenges in integration into the paper industry\u0026nbsp;\u003csup\u003e7,17,20\u003c/sup\u003e. Of particular concern are the cost implications associated with the bulky nature of raw materials, which pose logistical challenges and may impede large-scale commercial operations\u0026nbsp;\u003csup\u003e41,42\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, the PPI is undergoing significant changes, particularly in the development of new business models that integrate with the local bioeconomy and interface with socio-technical systems \u003csup\u003e2\u003c/sup\u003e. Related to this, a critical yet underexplored area pertains to market acceptance and consumer perceptions of paper products derived from grass fibers. Understanding the factors influencing consumer choices and preferences can drive adoption and inform market strategies \u003csup\u003e43,44\u003c/sup\u003e. Additionally, the alignment of policies and regulations with the utilization of grass-based fibers in the paper industry remains a significant research gap. Investigating the impact of governmental policies, incentives, and regulations on the adoption and growth of grass-based paper production is crucial for creating a supportive environment for sustainable practices. Robust modeling tools are needed to understand and analyze these dynamics, capturing the nuances of these interconnections and predicting potential outcomes.\u003c/p\u003e\n\u003ch2\u003e2.2 Participatory modeling for regional bioeconomy development\u003c/h2\u003e\n\u003cp\u003eParticipatory modeling, rooted in post-normal science, recognizes the complexity, uncertainty, and value conflicts inherent in highly complex systems, advocating for collaborative approaches that actively involve diverse stakeholders to address urgent, real-world problems \u003csup\u003e45\u003c/sup\u003e. These problems are addressed within the studied system, through either the creation or assessment of models representing that system. When standard scientific methods fail to provide sufficient evidence, it becomes necessary to integrate local knowledge and engage in iterative participatory processes to formulate solutions that are both comprehensible and politically feasible while maintaining scientific rigor \u003csup\u003e46\u003c/sup\u003e. It is commonly recognized that stakeholders hold insights, perspectives, and expertise that can greatly enhance the research process in the bioeconomy \u003csup\u003e32,47\u003c/sup\u003e. \u0026nbsp;In this context, the importance of various factors in sustainable bioeconomy development has been assessed. In the transition to a local bio-based energy production system, competitiveness, by-products, feedstock availability, and jobs were found to be the most important factors. Conversely, land availability, knowledge, existing symbiotic industry, and environmental sustainability were recognized to be the least important factors. Community acceptance and social capital consistently retained a moderate impact \u003csup\u003e32,48\u003c/sup\u003e. Interestingly, a contradictory finding emerges from another study examining the transition to bio-energy systems in different regions, where environmental factors were deemed most influential on decision-making processes related to low-carbon policies \u003csup\u003e49\u003c/sup\u003e. This highlights the complexity of transitioning to a bioeconomy, necessitating careful examination of each system. Notably, in the development of technology to valorize biomass, political factors emerge as the most influential, surpassing socio-technical factors in their impact \u003csup\u003e34\u003c/sup\u003e. This underscores the need for holistic and participatory analyses in navigating the complexities of transitioning to a sustainable bioeconomy.\u0026nbsp;\u003c/p\u003e"},{"header":"3 Materials and Methods","content":"\u003ch2\u003e3.1 Study case\u003c/h2\u003e\n\u003cp\u003eThis study uses the case of grass fibers for paper production using grass biomass sourced from roadside areas and national parks in the Netherlands. This case is a demonstration action developed within the GO-GRASS project. In the Netherlands, grass sourced from roadside areas had historically served as cattle feed during the 1970s. However, by the 1980s, farmers\u0026apos; demand for such grass decreased, leading to a shift in disposal methods, with a growing portion being deposited at waste sites. Subsequently, from the late 1980s onward, composting emerged as the primary disposal method for roadside grass. Meanwhile, grass sourced from natural areas found utility in both cattle feed and stable litter on farms \u003csup\u003e50\u003c/sup\u003e. Over recent decades, a significant portion of these natural areas has been designated for conservation purposes. Even so, the nutritional value of grass from these lands has declined over time, attributed to reduced nutrient content and biomass removal practices aimed at nitrogen reduction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrass clippings from road verges contribute to approximately 30% of green waste generated from public green spaces, amounting to roughly 900 kilotons per year\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e. The majority of this grass is collected and composted, while the remaining portion is subjected to either fermentation or left to decompose onsite. Although composting presents a circular solution, it yields a low economic value, causes considerable amounts of CO\u003csub\u003e2\u003c/sub\u003e emissions, and does not generate any revenues from the grass clippings to verge owners\u0026nbsp;\u003csup\u003e51\u003c/sup\u003e. Consequently, both roadside and nature area managers are exploring alternative uses for grass resources\u0026nbsp;\u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table 1, the shift in the utilization of both roadside and natural grass resources, from cattle feed to waste disposal and the exploration of alternative uses reflects evolving agricultural practices as well as economic and ecological considerations. This transformation emphasizes the necessity for a comprehensive assessment of the economic and environmental impacts associated with the management of roadside and natural grass. Moreover, it presents an opportunity to explore new sustainable uses for these abundant grass resources in the Netherlands \u003csup\u003e52,53\u003c/sup\u003e. Considering the substantial volume of grass generated annually, examining the feasibility of utilizing grass biomass for the regional bioeconomy transition appears highly relevant.\u003c/p\u003e\n\u003ch2\u003e3.2 Methods of fuzzy cognitive mapping\u003c/h2\u003e\n\u003cp\u003eFuzzy cognitive maps are fuzzy graph structures for representing causal reasoning \u003csup\u003e23\u003c/sup\u003e. In recent years, FCM has emerged as a valuable method for incorporating stakeholder knowledge into models aimed at understanding the factors contributing to bioeconomy development \u003csup\u003e25,27,32,49\u003c/sup\u003e. By combining stakeholder insights with other evidence, these maps depict distinct knowledge systems, facilitating the establishment of shared perspectives and common reference points regarding specific aspects of bioeconomy development. The term \u0026quot;fuzzy\u0026quot; refers to the assignment of weights by stakeholders to evaluate the influences of various factors, adding depth to the analysis. Essentially, these maps offer flexible models of specific reasons for individuals or groups of individuals, providing visual representations of their cognitive structures. Cognitive mapping is mostly used to represent individual \u0026ldquo;mental models\u0026rdquo; as well as their usefulness for comparing and characterizing the aggregated beliefs and knowledge of a community \u003csup\u003e54\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn the FCM model framework, each concept (C\u003csub\u003e1\u003c/sub\u003e, C\u003csub\u003e2\u003c/sub\u003e, \u0026hellip;\u0026hellip; , C\u003csub\u003ej\u003c/sub\u003e) is visually represented as a node, while the connections between factors are depicted as edges or arrows linking these nodes. These arrows symbolize assumptions about causal relationships and attribute varying values to weigh the strength of each relationship, denoted as W\u003csub\u003eij\u003c/sub\u003e, with values ranging from -1 to 1. Positive signs (W\u003csub\u003eij\u003c/sub\u003e \u0026gt; 0) indicate that an increase in one node leads to an increase in the linked node, while negative signs (W\u003csub\u003eij\u003c/sub\u003e \u0026lt; 0) signify that an increase in one node results in a decrease in the linked node. A weight of W\u003csub\u003eij\u003c/sub\u003e = 0 indicates the absence of a causal edge in the graph. It\u0026apos;s important to note that these causal weights reflect the opinions of knowledge holders, their explanatory models, and theories of change, rather than constituting predictive statistical models \u003csup\u003e55\u003c/sup\u003e. Figure 2\u003cem\u003e\u0026nbsp;\u003c/em\u003eillustrates a simple example of a FCM with four concepts. By comparing the maps generated by different stakeholder groups, FCMs can reveal both the similarities and differences between alternative explanatory models and theories of change \u003csup\u003e56\u003c/sup\u003e. Moreover, FCMs can substantially enhance integrated assessment applications by complementing quantitative model approaches, which typically rely on one perspective rooted in available data, with qualitative insights obtained from a structured stakeholder engagement process \u003csup\u003e57\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.2.1 Data collection\u003c/h3\u003e\n\u003cp\u003eBetween October and November 2022, we conducted semi-structured interviews with the participants, integrating the FCM exercise into the process. Each interview session spanned approximately 1.5-2 hours. Directly with the interviewees, each cognitive map was created. Subsequently, we transcribed the interview data gleaned from these sessions and the individual cognitive maps. Additionally, we performed triangulation with both the collected data and the resulting cognitive maps. The selection of participants for data on the FCM process in developing innovations for non-wood-based fiber paper production is a crucial stage in ensuring the accuracy and relevance of the mode. \u0026nbsp;It is essential to involve individuals with a diverse range of expertise and perspectives pertinent to the field (as indicated in supplementary material Table S 3). \u0026nbsp;This includes stakeholders such as experts from the paper and pulp industry, local farmers involved in grass biomass production, owners of paper manufacturing and printing companies, representatives from local government bodies, consultants specializing in local bioeconomy development, and researchers focused on technology development.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e3.2.2 Ethical approval\u003c/h3\u003e\n\u003cp\u003eThe data for this study was collected through face-to-face interviews, with no experimental procedures involved. All data collection methods adhered to the relevant regulations and guidelines, specifically REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL, concerning the protection of natural persons regarding the processing of personal data and the free movement of such data. The data collection protocols followed the European Union\u0026rsquo;s GDPR principals. To ensure participants\u0026rsquo; privacy, all personal data was anonymized, and identifying details were coded to maintain confidentiality. Furthermore, informed consent was obtained from all participants for the publication of any identifying information included in the study.\u003c/p\u003e\n\u003ch3\u003e3.2.3 Drawing the integrated maps\u003c/h3\u003e\n\u003cp\u003eThere are three fundamental stages involved in constructing FCM maps, following the methodology outlined by \u003csup\u003e58\u003c/sup\u003e. The process of developing our FCMs is depicted in Figure 3\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we identified indicators in Step 1 and Step 2. We utilized a literature review to compile a list of factors pertinent to the development of a regional bioeconomy system using residual roadside and natural grass for paper production as indicated in Supplementary material Table S4\u003cem\u003e.\u003c/em\u003e Through our semi-structured interviews, new concepts emerged, which we subsequently included in the final aggregated maps. To streamline complexity, concepts sharing similar meanings were clustered together (see the details in the supplementary material).\u003c/p\u003e\n\u003cp\u003eNext, in Step 3, we constructed individual cognitive maps with various stakeholders. During this process, interviewees assigned weights to factors based on different weight scales provided to them (as detailed in the supplementary material).\u003c/p\u003e\n\u003cp\u003eIn Step 4, the objective is constructing the integrated FCM, taking into account overlapping concepts across individual maps. In this step, an extended FCM map was constructed by aggregating the different FCM maps of individuals or groups of individuals to obtain an overarching FCM map that represents the total cognition of all participants. Individual FCMs may share common concepts; they may propose two or more weights for the same interconnection. In such cases, we compute a new weight as the average of the existing weights\u003csup\u003e59\u003c/sup\u003e. This process helps to consolidate the various perspectives represented in the individual maps into a unified model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, the analysis of the maps was conducted and interpreted using specific tools. We utilized FCMapper (https://www.fcmappers.net/joomla/index.php), \u0026nbsp;a purpose-built software for FCM developed by researchers, which has also been employed in prior publications such as by Morone et al.\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e. Additionally, we used Mental Modeller (https://www.mentalmodeler.com/), a free browser-based software for FCM that has been widely utilized in academic research. These tools facilitated the analysis and interpretation of the FCMs, enabling a comprehensive understanding.\u003c/p\u003e"},{"header":"4 Results","content":"\u003cp\u003eThe results of the first part showed the factors affecting the development of paper production from grass fibers. The factors reflect the cognitive awareness of the stakeholders and experts participating in the interviews as well as from the literature. All categories, subcategories, and themes based on content analysis as well as the weights have been illustrated in Table 2\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e4.1 Descriptive analysis of the constructed fuzzy cognitive maps\u003c/h2\u003e\n\u003cp\u003eAs presented in Table 2, our study identifies a comprehensive set of 26 system variables through an extensive literature review and in-depth interviews. These variables cover various aspects of the system and are broadly categorized into six main groups: economic (EC1-EC8), social (SO1-SO6), technological (TL1 \u0026amp; TL2), environmental (EM1 \u0026amp; EM2), sectoral variables (SE1-SE4), and policy and regulation (PO1-PO4). The aggregated network comprises 107 connections between these system variables. Table 2 provides a comparative analysis of the centrality levels and out- and in-degrees of the different indicators. The aggregated network exhibits a density index of 0.158284, which is lower than that of the original individual maps, which range from 0.20 to 0.40. A lower density index for the aggregated network is due to the increased number of variables included. This indicates that only 15.8% of the maximum potential connections among the 26 concepts are realized.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn graph theory, the out-degree and in-degree indices delineate the collective connectivity of nodes, respectively measuring the sum of outgoing and incoming connections. These metrics are derived by aggregating the absolute values of the associated matrix\u0026apos;s row and column sums. Centrality, visualized through node size, serves as a gauge of map complexity, elucidating the interconnections between variables and the cumulative intensity of these associations\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings reveal that SE4 (Non-Wood fiber-based paper production) exhibits the highest centrality, registering at 8.11, indicating its significant interconnectedness from the stakeholders\u0026apos; standpoint. Stakeholders perceive the transition to non-wood fiber-based paper production as potentially beneficial for regional bioeconomy development, particularly through the valorization of residual grass biomass. This notable centrality stems primarily from its comparatively high in-degree index, signifying a considerable influence from other network elements. This observation aligns with the inherent complexity associated with transitioning to a localized bioeconomy system. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, several other variables demonstrate relatively elevated centrality values: EC2 (The competition of the feedstock), EC4 (Development of competitive business models), SO3 (Public awareness), and PO4 (Promote R \u0026amp; D and cooperation) recorded centrality scores of 2.13, 2.25, 2.03, and 2.31, respectively. Consequently, these variables play pivotal roles in facilitating the regional bioeconomy transition owing to their heightened sensitivity to changes.\u003c/p\u003e\n\u003cp\u003eMoreover, our analysis recognizes three key system drivers: EC3 (Rising wood cellulose price), EC7 (Energy crisis), and PO1 (Regulatory impact on waste stream management) as shown in Figure 4\u003cem\u003e.\u0026nbsp;\u003c/em\u003eAlthough these drivers exhibit relatively lower centrality indices, they contribute significantly to the system dynamics. Notably, economic conditions and political variables exert substantial influence on the system, reflecting stakeholders\u0026apos; confidence in their capacity to drive the regional bioeconomy transition by leveraging residual grass biomass for paper production and promoting local economic growth\u0026nbsp;\u003csup\u003e43\u003c/sup\u003e. Conversely, social factors demonstrate heightened reliance on other variables, as evidenced by a greater number of incoming connections compared to outgoing connections. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile centrality-based feature importance methods offer valuable insights into the significance of concepts within a network, they overlook the dynamic nature of the network itself \u003csup\u003e60\u003c/sup\u003e. As such, relying solely on centrality-based approaches may not provide a comprehensive understanding of concept relevance. Therefore, in the following section, we will perform a dynamic analysis of the system variables, examining the interconnectedness of different clustered categories with other variables, and conduct a what-if analysis to create both best and worst-case scenarios to observe potential system changes.\u003c/p\u003e\n\u003ch2\u003e4.2 Scenario analysis of the constructed regional grass-based bioeconomy system\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo compare the baseline and simulated results, we obtained the steady-state outcome after at least 20 iterations without intervention, starting with initial state vectors set at 1. This steady-state outcome serves as the baseline for interpretation. Relative changes between this baseline scenario and the new steady state are also assessed to interpret the results. During the simulation process, a value of 0 indicates the absence of a given concept in the system at a particular iteration, while a value of 1 signifies the maximum presence of the concept \u003csup\u003e24\u003c/sup\u003e. Additionally, a value of 0.1 denotes the minimum presence of the concept. To facilitate sensitivity analysis, values at 0.75, 0.5, and 0.25 are also incorporated for comparison.\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates the results of the what-if analysis. \u003cem\u003e\u0026nbsp;\u003c/em\u003eFigure 5 (I) pertains to the manipulation of all policy and regulation concepts to respective values of 1, 0.75, 0.5, 0.25, and 0.1. Similar adjustments are made for economic, social, and technological factors, enabling comparative analysis across different scenarios. The rationale underlying the classification of these system variables into categories such as policy \u0026amp; regulation, economic, social, and technological is explicated in Figure 5. This figure elucidates the potential impact of these factors on the overall system structure. Through a comprehensive understanding of the interaction between these factors and the system structure, valuable insights are gained into how policy, economic, social, and technological decisions can shape the dynamics of the entire system.\u003c/p\u003e\n\u003cp\u003eIn general, as depicted in \u0026nbsp;Figure 5 (I), the political and regulatory dimensions, including PO1 (regulatory impact on waste stream management), PO2 (supportive governmental sustainable procurement projects), PO3 (financial support and funding), and PO4 (promotion of R\u0026amp;D and cooperation), exhibit a dynamic influence on the entire system, particularly on economic, social, and sectoral factors. The significant presence of these political concepts correlates with positive impacts observed on economic factors, such as \u0026quot;locally sourced feedstock availability\u0026quot; \u0026quot;competitive circular business model development\u0026quot; and \u0026quot;economic feasibility\u0026quot;. \u0026nbsp;Regarding social factors, the presence of these political dimensions is anticipated to mitigate societal concerns related to \u0026quot;fear and risk\u0026quot; and \u0026quot;knowledge gaps on sustainable biomass production\u0026quot;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, technology factors such as \u0026quot;technology optimization and integration\u0026quot; stand to benefit from this presence, albeit to a lesser extent. Conversely, a lower level of presence of these concepts may negatively affect technology factors, as current technological development may require substantial governmental support, particularly financially. Furthermore, the paper \u0026amp; pulp industry is also expected to benefit from these political and regulatory initiatives, with positive percentage changes observed, nearing 2%. Upon a closer examination of the changes resulting from reducing the low-level presence of these policy initiatives, stronger negative impacts become apparent. This underscores the importance of maintaining a strong political presence to facilitate the transition effectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen examining the technological dimension of the system as indicated in Figure 5 (II), the significant presence of \u0026quot;Technology optimization and integration\u0026quot; and \u0026quot;Technical feasibility challenges for scaling up\u0026quot; yields substantial influences, primarily clustered around economic factors such as \u0026quot;New competitive circular business development\u0026quot;, \u0026quot;Economic feasibility\u0026quot; and \u0026quot;Production cost\u0026quot;. These advancements are perceived positively by stakeholders, as they enhance economic viability and reduce barriers to scaling up sustainable technologies. Moreover, technological advancements reduce societal fear and risk associated with utilizing roadside grass as a product source, further reinforcing stakeholder confidence in sustainable practices. Additionally, technological factors influence \u0026quot;Product life cycle management/performance\u0026quot; contributing positively to carbon emission reduction and overall environmental benefit. Despite the positive contributions of technological advancements, their influence on sectoral factors, particularly in utilizing roadside and nature grass-based biomass for paper production, is limited compared to political dimensions. The highest observed change from the steady-state outcome to the simulated results, under the maximum presence of technological factors, is at 1.2%.\u003c/p\u003e\n\u003cp\u003eAll illustrated in Figure 5 (III), the economic clustering factors exert a significant influence on social, political, and sectoral dimensions, contingent upon the robust presence of economic conditions. Conversely, a diminished presence of these conditions may lead to adverse effects, particularly evident in indicators such as \u0026quot;Farmers\u0026apos; perception of sustainability\u0026quot;, \u0026quot;Public awareness\u0026quot; and \u0026quot;Public acceptance \u0026amp; willingness to pay\u0026quot;. \u0026nbsp;For instance, a negative impact of up to 9% compared with the steady-state value is observed in the factor \u0026quot;Public awareness\u0026quot;. Absent strong economic conditions, the transition towards a local circular bioeconomy utilizing grass biomass for paper production faces substantial hurdles. A notable decrease of 3.5% from the steady-state value is perceived with the minimum presence of economic condition variables. Stakeholders prioritize economic factors as pivotal in steering this transition, overshadowing the perceived impacts of political and technological factors. Stakeholders prioritize economic factors as pivotal in steering this transition, eclipsing the perceived impacts of political, social, and technological factors.\u003c/p\u003e\n\u003cp\u003eWhen scrutinizing the social dimension of the system as shown in Figure 5 (IV), its effect extends to economic, political, and sectoral factors, particularly affecting indicators such as \u0026quot;Locally sourced feedstock availability\u0026quot;, \u0026quot;Competitive circular business model development\u0026quot; and \u0026quot;Niche market formation\u0026quot; at the policy level. Additionally, at the policy level, the presence of factors such as \u0026quot;Supportive governmental sustainable procurement projects\u0026rdquo;, and \u0026ldquo;Promote R\u0026amp;D and cooperation\u0026rdquo; as well as at the sectoral level, \u0026quot;Regional innovation network\u0026quot; and \u0026quot;Non-wood fiber-based paper production\u0026quot; is influenced. The robust presence of social factors in the system facilitates these positive transitions. Conversely, a lack of strong social support may impede the transition process. This observation justifies the interconnectedness of social, economic, political, and sectoral dimensions within the transition towards a local circular bioeconomy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContinuing the analysis, we delve into the effects of system drivers identified by stakeholders on the entire system, considering both their combined and individual impacts. The identified drivers encompass \u0026quot;Rising wood price\u0026rdquo;, \u0026quot;Energy crisis\u0026quot; and \u0026quot;Regulatory impact on waste stream management\u0026quot;. In general, as depicted in Figure 6\u003cem\u003e\u0026nbsp;(i),\u003c/em\u003e these drivers predominantly influence economic, social, and sectoral categories, particularly in scenarios representing both best and worst cases. Indicators such as \u0026quot;Locally sourced feedstock availability\u0026quot;, \u0026quot;Competition of feedstock\u0026quot;, \u0026quot;Farmers\u0026apos; perception on sustainability\u0026quot;, \u0026quot;Public awareness\u0026quot; and \u0026quot;Non-wood fiber-based paper production\u0026quot; exhibit notable effects. Notably, the \u0026quot;Competition of feedstock\u0026quot; indicator displays significant sensitivity to the presence of system drivers. In instances of strong drivers\u0026rsquo; presence, intensified competition for feedstock arises, whereas low driver presence diminishes such competition. This phenomenon aligns with the rationale that rising wood prices, especially amid an energy crisis, prompt industries to seek alternative biomass sources, thereby intensifying competition for feedstock used in paper production.\u003c/p\u003e\n\u003cp\u003eExamining the individual effects of each driver, demonstrated in Figure 6 \u003cem\u003e(ii, iii, and iv),\u003c/em\u003e reveals distinct impacts on social factors. Interestingly, the rising wood price directly influences farmers\u0026apos; perceptions of biomass sustainability. Conversely, the energy crisis amplifies public awareness of sustainable development issues. Higher energy prices resulting from the crisis prompt consumers to reconsider sustainability, thereby positively influencing public awareness.\u0026nbsp;\u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eIn the result section, we have identified the influential role of political support and regulatory initiatives in driving the transition towards on-wood fibers for paper production. This aligns with previous research by Ladu et al.\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e and Morone et al.\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e, suggesting that a policy mix incorporating economic and financial support for sustainable bioeconomy is likely to yield the most favorable outcomes. Dedicated policies could provide a balanced push for the bio-based economy transition towards a circular and innovative trajectory. When policies aimed at promoting sustainability are weakened or absent, the potential for negative consequences on economic, social, and technological factors becomes evident. Therefore, sustaining political engagement and regulatory support is imperative to realize the desired transition towards a sustainable bioeconomy. In addition, understanding the interplay of coordination, timing, and scale in policy mixes is essential for grasping how different instruments can accelerate sustainability transitions. The Swedish paper and pulp industry exemplifies the need for destabilizing policies, such as stringent environmental regulations, to precede innovation policies. These initial measures create an environment where novelty creation policies can effectively drive industry transformation\u0026nbsp;\u003csup\u003e61\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, the case study under examination revolves around an immature technology still in its nascent phase. Early-stage technology development can be affected by economic activity. During economic downturns, there is often less funding available for policies supporting new technology development, as public funds are limited. Stakeholders may worry that funding will be prioritized for activities with immediate economic impact rather than for long-term technological advancement. This highlights the importance of policymakers ensuring consistent support for early-stage technology development, even during economic uncertainty, to foster innovation and economic growth over time\u0026nbsp;\u003csup\u003e62\u003c/sup\u003e. Transitioning to sustainability also requires a balanced politico-economic framework. Gawel et al.\u0026nbsp;\u003csup\u003e63\u003c/sup\u003e argue that achieving sustainability relies on finding the right balance between regulatory frameworks and market mechanisms to implement necessary transition policies. Effective policy interventions and market regulations play a pivotal role in steering the transition towards sustainability and ensuring that technological advancements align with overarching societal objectives. This assertion is supported by research findings highlighting the significance of political factors in driving the development of technology aimed at valorizing biomass. Specifically, these political influences are identified as primary drivers for overcoming barriers, demonstrating a greater impact compared to socio-technical factors\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e. This correlation aligns with our findings. Therefore, it underscores the critical importance of implementing robust policy frameworks and regulatory mechanisms to effectively align technological advancements with sustainability goals\u0026nbsp;\u003csup\u003e64\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAligned with Rajeswar\u0026nbsp;\u003csup\u003e65\u003c/sup\u003e, our findings suggest an inclination towards overestimation of the sustainability gains associated with novel technologies, juxtaposed with an under-exploration of complex and potentially adverse impact pathways during early innovation phases. It is often argued that excessively regulated innovation systems might impede much-needed technological advancement. Nevertheless, this should not serve as a justification for neglecting thorough scrutiny in technology impact assessments\u0026nbsp;\u003csup\u003e66\u003c/sup\u003e. \u0026nbsp; The development of more sophisticated models to assess and integrate industry-based technology is required\u0026nbsp;\u003csup\u003e67\u003c/sup\u003e. Furthermore, the results indicate that although economic incentives exhibit a high degree of connectivity with other development factors, scenario analysis suggests that they may also result in undesired outcomes, such as environmental degradation due to resource overexploitation. In essence, our research suggests that relying solely on economic incentives is unlikely to facilitate a transition towards sustainability in this system.\u003c/p\u003e\n\u003cp\u003eFrom our simulated results, it is evident that stakeholders perceive a significant competition for utilizing grass biomass for various purposes, particularly in light of concerns surrounding the European Union\u0026apos;s energy security following the Russian-Ukrainian conflict. The conflict has highlighted the EU\u0026apos;s heavy reliance on imports of raw materials from these countries, \u0026nbsp;emphasizing the need to develop alternative sources or renewable raw materials for material use and energy production and to reduce imports of raw materials from conflict regions\u0026nbsp;\u003csup\u003e68\u003c/sup\u003e. \u0026nbsp;The potential utilization of roadside grass for bioenergy production, as highlighted by Meyer et al.\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e and Ravi et al.\u0026nbsp;\u003csup\u003e69\u003c/sup\u003e, presents a promising opportunity. In the face of this competition for biomass use across different sectors, policymakers should focus on establishing clear guidelines for biomass utilization. This is especially important given the institutionalization of bioeconomy strategies at the European level. These strategies provide a framework for developing cohesive approaches to biomass utilization that consider both environmental and economic factors.\u003c/p\u003e\n\u003cp\u003eAdditionally, the energy crisis between 2021 and 2023 has catalyzed increased social awareness among the public, underscoring the significance of nurturing and fostering robust social factors to drive this transition effectively. This emphasizes the need for comprehensive strategies that take into account the interplay between social, economic, and political dimensions. For a successful transition, it is essential to adopt both top-down and bottom-up approaches\u0026nbsp;\u003csup\u003e43\u003c/sup\u003e. This involves engaging stakeholders at various levels of governance and society, ensuring inclusivity, and promoting collective action towards sustainable solutions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNonetheless, this study has several limitations. Firstly, there is a lack of clarity in illustrating the global importance of the system, primarily because the analysis focuses solely on a single country case. Moreover, the scope of the study could be expanded by incorporating additional factors identified through expert knowledge. While relationships between factors are considered, they may not fully capture the complexity of the system, and updating them based on new information could enhance accuracy. To illustrate, further exploring other scenarios beyond those considered in the study could offer additional insights into the system\u0026apos;s dynamics. This study highlights the role of policy mixes in accelerating sustainability transitions but also has several limitations. Implementing the proposed policy frameworks is complex and may vary in difficulty across different regulatory and economic environments. Additionally, the associated costs of these policy changes have not been examined, including both immediate financial burdens and long-term economic impacts. Future research should focus on quantifying these costs to provide a clearer outlook for strategic development. Understanding these financial implications is important for policymakers and industry stakeholders. Finally, this study only partially addresses the need for continuous adaptation and revision of policies in response to the dynamic nature of sustainability transitions. Studies should further consider a more iterative approach to policy development, accounting for ongoing changes and feedback mechanisms within the industry.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis study significantly contributes to the discourse on decarbonizing the paper and pulp industry by applying fuzzy cognitive maps to capture and model local stakeholders\u0026apos; perceptions of transitioning to a local bioeconomy using roadside and natural grasses for paper production. The analysis reveals the pivotal role of political support and regulatory initiatives, with key drivers including rising wood prices, energy crises, and regulatory impacts on waste stream management. Political interventions are particularly influential at the regional level, emphasizing the need for comprehensive policy frameworks that address socio-economic, political, environmental, and sectoral barriers. Technological advancements, though beneficial for economic viability and reducing societal fears, have a lesser impact compared to political dimensions. Economic factors are prioritized by stakeholders and significantly influence public awareness and acceptance. The interconnectedness of social dimensions with other factors further highlights the complexity of the transition. Recommendations for policymakers include enhancing stakeholder engagement through inclusive top-down and bottom-up approaches, ensuring clear and adaptive guidelines for biomass utilization, and sustaining financial support for early-stage technologies. It is essential to prioritize ongoing assessment of technology impacts to inform evidence-based policy decisions effectively. Future research should focus on quantifying the broader implications of policy changes to provide a clearer strategic outlook and emphasize the novel insights gained in the context of advancing sustainability within the paper and pulp industry.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of the GO-GRASS project (Grass-based circular business models for rural agri-food value chains) and received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under [grant agreement No. 862674]. The authors would like to express their sincere gratitude to Gosse Hiemstra at Hiemstra Bruin, along with other interview partners, for their valuable insights and contributions to this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZ.D. was responsible for the methodology, validation, formal analysis, investigation, data curation, and the preparation of the original draft. Z.D. also contributed to the reviewing, editing, and visualization of the manuscript. \u0026nbsp;P.G. contributed to the conceptualization of the research, provided resources, and played a key role in reviewing and editing the manuscript. P.G. also supervised the project and secured funding for the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interview data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eToppinen, A., P\u0026auml;t\u0026auml;ri, S., Tuppura, A. \u0026amp; Jantunen, A. The European pulp and paper industry in transition to a bio-economy: A Delphi study. \u003cem\u003eFutures\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 1\u0026ndash;14 (2017).\u003c/li\u003e\n\u003cli\u003eFurszyfer Del Rio, D. D. \u003cem\u003eet al.\u003c/em\u003e Decarbonizing the pulp and paper industry: A critical and systematic review of sociotechnical developments and policy options. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e \u003cstrong\u003e167\u003c/strong\u003e, 112706 (2022).\u003c/li\u003e\n\u003cli\u003eP\u0026auml;t\u0026auml;ri, S., Tuppura, A., Toppinen, A. \u0026amp; Korhonen, J. Global sustainability megaforces in shaping the future of the European pulp and paper industry towards a bioeconomy. \u003cem\u003eForest Policy and Economics\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 38\u0026ndash;46 (2016).\u003c/li\u003e\n\u003cli\u003eEckert, S. Varieties of framing the circular economy and the bioeconomy: unpacking business interests in European policymaking. \u003cem\u003eJournal of Environmental Policy \u0026amp; Planning\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 181\u0026ndash;193 (2021).\u003c/li\u003e\n\u003cli\u003eGerres, T., Chaves \u0026Aacute;vila, J. P., Llamas, P. L. \u0026amp; San Rom\u0026aacute;n, T. G. A review of cross-sector decarbonisation potentials in the European energy intensive industry. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e210\u003c/strong\u003e, 585\u0026ndash;601 (2019).\u003c/li\u003e\n\u003cli\u003eHolz, J. R. Threatened sustainability: extractivist tendencies in the forest-based bioeconomy in Finland. \u003cem\u003eSustain Sci\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 645\u0026ndash;659 (2023).\u003c/li\u003e\n\u003cli\u003eLiu, Z. \u003cem\u003eet al.\u003c/em\u003e Pulping and Papermaking of Non-Wood Fibers. in \u003cem\u003ePulp and Paper Processing\u003c/em\u003e (IntechOpen, 2018). doi:10.5772/intechopen.79017.\u003c/li\u003e\n\u003cli\u003eDaud, Z., Mohd Hatta, M. Z., Mohd Kassim, A. S., Aripin, A. M. \u0026amp; Awang, H. Analysis of Napier grass (Pennisetum purpureum) as a potential alternative fibre in paper industry. \u003cem\u003eMaterials Research Innovations\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, S6-18-S6-20 (2014).\u003c/li\u003e\n\u003cli\u003eKissinger, M., Fix, J. \u0026amp; Rees, W. E. Wood and non-wood pulp production: Comparative ecological footprinting on the Canadian prairies. \u003cem\u003eEcological Economics\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 552\u0026ndash;558 (2007).\u003c/li\u003e\n\u003cli\u003eObi Reddy, K., Uma Maheswari, C., Shukla, M. \u0026amp; Muzenda, E. Preparation, Chemical Composition, Characterization, and Properties of Napier Grass Paper Sheets. \u003cem\u003eSeparation Science and Technology\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 1527\u0026ndash;1534 (2014).\u003c/li\u003e\n\u003cli\u003eVerveris, C., Georghiou, K., Christodoulakis, N., Santas, P. \u0026amp; Santas, R. Fiber dimensions, lignin and cellulose content of various plant materials and their suitability for paper production. \u003cem\u003eIndustrial Crops and Products\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 245\u0026ndash;254 (2004).\u003c/li\u003e\n\u003cli\u003ePopp, J., Kov\u0026aacute;cs, S., Ol\u0026aacute;h, J., Div\u0026eacute;ki, Z. \u0026amp; Bal\u0026aacute;zs, E. Bioeconomy: Biomass and biomass-based energy supply and demand. \u003cem\u003eNew Biotechnology\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 76\u0026ndash;84 (2021).\u003c/li\u003e\n\u003cli\u003eBoyer, M., Kusche, F., Hackfort, S., Prause, L. \u0026amp; Engelbrecht-Bock, F. The making of sustainability: ideological strategies, the materiality of nature, and biomass use in the bioeconomy. \u003cem\u003eSustain Sci\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 675\u0026ndash;688 (2023).\u003c/li\u003e\n\u003cli\u003eMeyer, A. K. P., Ehimen, E. A. \u0026amp; Holm-Nielsen, J. B. Bioenergy production from roadside grass: A case study of the feasibility of using roadside grass for biogas production in Denmark. \u003cem\u003eResources, Conservation and Recycling\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 124\u0026ndash;133 (2014).\u003c/li\u003e\n\u003cli\u003eLiu, X., Xie, Y. \u0026amp; Sheng, H. Green waste characteristics and sustainable recycling options. \u003cem\u003eResources, Environment and Sustainability\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 100098 (2023).\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Adamo, I., Gastaldi, M., Imbriani, C. \u0026amp; Morone, P. Assessing regional performance for the Sustainable Development Goals in Italy. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 24117 (2021).\u003c/li\u003e\n\u003cli\u003eAnupam, K., Sharma, A. K., Lal, P. S. \u0026amp; Bist, V. Physicochemical, Morphological, and Anatomical Properties of Plant Fibers Used for Pulp and Papermaking. in \u003cem\u003eFiber Plants\u003c/em\u003e (eds. Ramawat, K. G. \u0026amp; Ahuja, M. R.) vol. 13 235\u0026ndash;248 (Springer International Publishing, Cham, 2016).\u003c/li\u003e\n\u003cli\u003eFerdous, T., Ni, Y., Quaiyyum, M. A., Uddin, M. N. \u0026amp; Jahan, M. S. Non-Wood Fibers: Relationships of Fiber Properties with Pulp Properties. \u003cem\u003eACS Omega\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 21613\u0026ndash;21622 (2021).\u003c/li\u003e\n\u003cli\u003eMałachowska, E. \u003cem\u003eet al.\u003c/em\u003e Influences of Fiber and Pulp Properties on Papermaking Ability of Cellulosic Pulps Produced from Alternative Fibrous Raw Materials. \u003cem\u003eJournal of Natural Fibers\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1751\u0026ndash;1761 (2021).\u003c/li\u003e\n\u003cli\u003ePari, L., Baraniecki, P., Kaniewski, R. \u0026amp; Scarfone, A. Harvesting strategies of bast fiber crops in Europe and in China. \u003cem\u003eIndustrial Crops and Products\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 90\u0026ndash;96 (2015).\u003c/li\u003e\n\u003cli\u003eCosta, D., Quinteiro, P., Pereira, V. \u0026amp; Dias, A. C. Social life cycle assessment based on input-output analysis of the Portuguese pulp and paper sector. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e330\u003c/strong\u003e, 129851 (2022).\u003c/li\u003e\n\u003cli\u003eGiurca, A. \u0026amp; Metz, T. A social network analysis of Germany\u0026rsquo;s wood-based bioeconomy: Social capital and shared beliefs. \u003cem\u003eEnvironmental Innovation and Societal Transitions\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 1\u0026ndash;14 (2018).\u003c/li\u003e\n\u003cli\u003eKosko, B. Fuzzy knowledge combination. \u003cem\u003eInt. J. Intell. Syst.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 293\u0026ndash;320 (1986).\u003c/li\u003e\n\u003cli\u003eKontogianni, A. D., Papageorgiou, E. I. \u0026amp; Tourkolias, C. How do you perceive environmental change? Fuzzy Cognitive Mapping informing stakeholder analysis for environmental policy making and non-market valuation. \u003cem\u003eApplied Soft Computing\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3725\u0026ndash;3735 (2012).\u003c/li\u003e\n\u003cli\u003eLadu, L., Imbert, E., Quitzow, R. \u0026amp; Morone, P. The role of the policy mix in the transition toward a circular forest bioeconomy. \u003cem\u003eForest Policy and Economics\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 101937 (2020).\u003c/li\u003e\n\u003cli\u003eLopolito, A., Nardone, G., Prosperi, M., Sisto, R. \u0026amp; Stasi, A. Modeling the bio-refinery industry in rural areas: A participatory approach for policy options comparison. \u003cem\u003eEcological Economics\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 18\u0026ndash;27 (2011).\u003c/li\u003e\n\u003cli\u003eMorone, P., Yilan, G. \u0026amp; Imbert, E. Using fuzzy cognitive maps to identify better policy strategies to valorize organic waste flows: An Italian case study. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e319\u003c/strong\u003e, 128722 (2021).\u003c/li\u003e\n\u003cli\u003eKokkinos, K., Lakioti, E., Papageorgiou, E., Moustakas, K. \u0026amp; Karayannis, V. Fuzzy Cognitive Map-Based Modeling of Social Acceptance to Overcome Uncertainties in Establishing Waste Biorefinery Facilities. \u003cem\u003eFront. Energy Res.\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 112 (2018).\u003c/li\u003e\n\u003cli\u003eMorone, P., Falcone, P. M. \u0026amp; Lopolito, A. How to promote a new and sustainable food consumption model: A fuzzy cognitive map study. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e208\u003c/strong\u003e, 563\u0026ndash;574 (2019).\u003c/li\u003e\n\u003cli\u003eKokkinos, K., Karayannis, V. \u0026amp; Moustakas, K. Optimizing Microalgal Biomass Feedstock Selection for Nanocatalytic Conversion Into Biofuel Clean Energy, Using Fuzzy Multi-Criteria Decision Making Processes. \u003cem\u003eFront. Energy Res.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 622210 (2021).\u003c/li\u003e\n\u003cli\u003eAssogba, G. G. C., Adam, M., Berre, D. \u0026amp; Descheemaeker, K. Managing biomass in semi-arid Burkina Faso: Strategies and levers for better crop and livestock production in contrasted farm systems. \u003cem\u003eAgricultural Systems\u003c/em\u003e \u003cstrong\u003e201\u003c/strong\u003e, 103458 (2022).\u003c/li\u003e\n\u003cli\u003ePenn, A. S. \u003cem\u003eet al.\u003c/em\u003e Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region. \u003cem\u003ePLoS ONE\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e78319 (2013).\u003c/li\u003e\n\u003cli\u003eKolahi, M., Davary, K. \u0026amp; Omranian Khorasani, H. Integrated approach to water resource management in Mashhad Plain, Iran: actor analysis, cognitive mapping, and roadmap development. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 162 (2024).\u003c/li\u003e\n\u003cli\u003eRei\u0026szlig;mann, D., Thr\u0026auml;n, D. \u0026amp; Bezama, A. What could be the future of hydrothermal processing wet biomass in Germany by 2030? A semi-quantitative system analysis. \u003cem\u003eBiomass and Bioenergy\u003c/em\u003e \u003cstrong\u003e138\u003c/strong\u003e, 105588 (2020).\u003c/li\u003e\n\u003cli\u003evan den Broek, K. L., Negro, S. O. \u0026amp; Hekkert, M. P. Mapping mental models in sustainability transitions. \u003cem\u003eEnvironmental Innovation and Societal Transitions\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 100855 (2024).\u003c/li\u003e\n\u003cli\u003eEl-Sayed, E. S. A., El-Sakhawy, M. \u0026amp; El-Sakhawy, M. A.-M. Non-wood fibers as raw material for pulp and paper industry. \u003cem\u003eNordic Pulp \u0026amp; Paper Research Journal\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 215\u0026ndash;230 (2020).\u003c/li\u003e\n\u003cli\u003eFAO. FAOSTAT Statistical Database. [Forestry Production and Trade]. (2023).\u003c/li\u003e\n\u003cli\u003eEuropean Central Bank. ECB Data Portal. [Manufacture of pulp, paper and paperboard]. (2023).\u003c/li\u003e\n\u003cli\u003eLee, C. L., Chin, K. L., H\u0026rsquo;ng, P. S., Hafizuddin, M. S. \u0026amp; Khoo, P. S. Valorisation of Underutilized Grass Fibre (Stem) as a Potential Material for Paper Production. \u003cem\u003ePolymers\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 5203 (2022).\u003c/li\u003e\n\u003cli\u003eSun, M., Wang, Y. \u0026amp; Shi, L. Environmental performance of straw-based pulp making: A life cycle perspective. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e616\u0026ndash;617\u003c/strong\u003e, 753\u0026ndash;762 (2018).\u003c/li\u003e\n\u003cli\u003eJahan, M. S., Rahman, M. M. \u0026amp; Ni, Y. Alternative initiatives for non-wood chemical pulping and integration with the biorefinery concept: A review. \u003cem\u003eBiofuels, Bioproducts and Biorefining\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 100\u0026ndash;118 (2021).\u003c/li\u003e\n\u003cli\u003eRamdhonee, A. \u0026amp; Jeetah, P. Production of wrapping paper from banana fibres. \u003cem\u003eJournal of Environmental Chemical Engineering\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 4298\u0026ndash;4306 (2017).\u003c/li\u003e\n\u003cli\u003eDing, Z., Hamann, K. T. \u0026amp; Grundmann, P. Enhancing circular bioeconomy in Europe: Sustainable valorization of residual grassland biomass for emerging bio-based value chains. \u003cem\u003eSustainable Production and Consumption\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 265\u0026ndash;280 (2024).\u003c/li\u003e\n\u003cli\u003eOrozco, R. \u0026amp; Grundmann, P. Readiness for Innovation of Emerging Grass-Based Businesses. \u003cem\u003eJournal of Open Innovation: Technology, Market, and Complexity\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 180 (2022).\u003c/li\u003e\n\u003cli\u003eFuntowicz, S. O. \u0026amp; Ravetz, J. R. Science for the post-normal age. \u003cem\u003eFutures\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 739\u0026ndash;755 (1993).\u003c/li\u003e\n\u003cli\u003eVoinov, A. \u0026amp; Gaddis, E. B. Values in Participatory Modeling: Theory and Practice. in \u003cem\u003eEnvironmental Modeling with Stakeholders\u003c/em\u003e (eds. Gray, S., Paolisso, M., Jordan, R. \u0026amp; Gray, S.) 47\u0026ndash;63 (Springer International Publishing, Cham, 2017). doi:10.1007/978-3-319-25053-3_3.\u003c/li\u003e\n\u003cli\u003eKyriakarakos, G., Patlitzianas, K., Damasiotis, M. \u0026amp; Papastefanakis, D. A fuzzy cognitive maps decision support system for renewables local planning. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 209\u0026ndash;222 (2014).\u003c/li\u003e\n\u003cli\u003eCaferra, R., Colasante, A., D\u0026rsquo;Adamo, I., Morone, A. \u0026amp; Morone, P. Interacting locally, acting globally: trust and proximity in social networks for the development of energy communities. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 16636 (2023).\u003c/li\u003e\n\u003cli\u003eKokkinos, K., Karayannis, V. \u0026amp; Moustakas, K. Circular bio-economy via energy transition supported by Fuzzy Cognitive Map modeling towards sustainable low-carbon environment. \u003cem\u003eScience of The Total Environment\u003c/em\u003e \u003cstrong\u003e721\u003c/strong\u003e, 137754 (2020).\u003c/li\u003e\n\u003cli\u003eVural Gursel, I. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eLocal Supply of Lignocellulosic Biomass to Paper Industry in Gelderland : Development of Circular and Value-Added Chains\u003c/em\u003e. https://research.wur.nl/en/publications/b1482511-2b77-4d92-ae99-b88f84b03412 (2020) doi:10.18174/522235.\u003c/li\u003e\n\u003cli\u003eRijksdienst voor Ondernemend Nederland. \u003cem\u003eBiogas Uit Gras Een Onderbenut Potentieel: Een Studie Naar Kansen Voor Grasvergisting\u003c/em\u003e. https://www.rvo.nl/sites/default/files/2014/04/Definitief_Een%20studie%20naar%20kansen%20voor%20grasvergisting.pdf (2014).\u003c/li\u003e\n\u003cli\u003ede Jong, J. J., Bijlsma, R. J. \u0026amp; Spijker, J. H. \u003cem\u003eRandvoorwaarden Biodiversiteit Bij Oogst van Biomassa\u003c/em\u003e. https://edepot.wur.nl/210074 (2012).\u003c/li\u003e\n\u003cli\u003ede Vries, B. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eEnergie \u0026agrave; La Carte : De Potentie van Biomassa Uit Het Landschap Voor Enegiewinning\u003c/em\u003e. https://edepot.wur.nl/45536 (2008).\u003c/li\u003e\n\u003cli\u003eGray, S. A., Zanre, E. \u0026amp; Gray, S. R. J. Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs. in \u003cem\u003eFuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms\u003c/em\u003e (ed. Papageorgiou, E. I.) 29\u0026ndash;48 (Springer, Berlin, Heidelberg, 2014). doi:10.1007/978-3-642-39739-4_2.\u003c/li\u003e\n\u003cli\u003eFelix, G. \u003cem\u003eet al.\u003c/em\u003e A review on methods and software for fuzzy cognitive maps. \u003cem\u003eArtif Intell Rev\u003c/em\u003e \u003cstrong\u003e52\u003c/strong\u003e, 1707\u0026ndash;1737 (2019).\u003c/li\u003e\n\u003cli\u003eSarmiento, I. \u003cem\u003eet al.\u003c/em\u003e Fuzzy cognitive mapping and soft models of indigenous knowledge on maternal health in Guerrero, Mexico. \u003cem\u003eBMC Medical Research Methodology\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 125 (2020).\u003c/li\u003e\n\u003cli\u003eMourhir, A. Scoping review of the potentials of fuzzy cognitive maps as a modeling approach for integrated environmental assessment and management. \u003cem\u003eEnvironmental Modelling \u0026amp; Software\u003c/em\u003e \u003cstrong\u003e135\u003c/strong\u003e, 104891 (2021).\u003c/li\u003e\n\u003cli\u003eBarbrook-Johnson, P. \u0026amp; Penn, A. S. Fuzzy Cognitive Mapping. in \u003cem\u003eSystems Mapping\u003c/em\u003e 79\u0026ndash;95 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-031-01919-7_6.\u003c/li\u003e\n\u003cli\u003eGlykas, M. \u003cem\u003eFuzzy Cognitive Maps. Advances in Theory, Methodologies, Tools and Applications\u003c/em\u003e. \u003cem\u003eTools and Applications. Studies in Fuzziness and Soft Computing\u003c/em\u003e vol. 247 (2010).\u003c/li\u003e\n\u003cli\u003eN\u0026aacute;poles, G., Ranković, N. \u0026amp; Salgueiro, Y. On the interpretability of Fuzzy Cognitive Maps. \u003cem\u003eKnowledge-Based Systems\u003c/em\u003e \u003cstrong\u003e281\u003c/strong\u003e, 111078 (2023).\u003c/li\u003e\n\u003cli\u003eScordato, L., Klitkou, A., Tartiu, V. E. \u0026amp; Coenen, L. Policy mixes for the sustainability transition of the pulp and paper industry in Sweden. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e183\u003c/strong\u003e, 1216\u0026ndash;1227 (2018).\u003c/li\u003e\n\u003cli\u003ePhilp, J. The bioeconomy, the challenge of the century for policy makers. \u003cem\u003eNew Biotechnology\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 11\u0026ndash;19 (2018).\u003c/li\u003e\n\u003cli\u003eGawel, E., Purkus, A., Pannicke, N. \u0026amp; Hagemann, N. A Governance Framework for a Sustainable Bioeconomy: Insights from the Case of the German Wood-based Bioeconomy. in \u003cem\u003eTowards a Sustainable Bioeconomy: Principles, Challenges and Perspectives\u003c/em\u003e (eds. Leal Filho, W., Pociovălișteanu, D. M., Borges de Brito, P. R. \u0026amp; Borges de Lima, I.) 517\u0026ndash;537 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-319-73028-8_26.\u003c/li\u003e\n\u003cli\u003eDing, Z. \u0026amp; Grundmann, P. Development of Biorefineries in the Bioeconomy: A Fuzzy-Set Qualitative Comparative Analysis among European Countries. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 90 (2022).\u003c/li\u003e\n\u003cli\u003eRajeswar, J. Deconstructing the development paradigm: a post-structural perspective. \u003cem\u003eSustainable Development\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 245\u0026ndash;251 (2010).\u003c/li\u003e\n\u003cli\u003eBiber-Freudenberger, L., Ergeneman, C., F\u0026ouml;rster, J. J., Dietz, T. \u0026amp; B\u0026ouml;rner, J. Bioeconomy futures: Expectation patterns of scientists and practitioners on the sustainability of bio-based transformation. \u003cem\u003eSustainable Development\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1220\u0026ndash;1235 (2020).\u003c/li\u003e\n\u003cli\u003eMandeep, Gupta, G. K., Liu, H. \u0026amp; Shukla, P. Pulp and paper industry\u0026ndash;based pollutants, their health hazards and environmental risks. \u003cem\u003eCurrent Opinion in Environmental Science \u0026amp; Health\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 48\u0026ndash;56 (2019).\u003c/li\u003e\n\u003cli\u003eLiao, S. The Russia\u0026ndash;Ukraine outbreak and the value of renewable energy. \u003cem\u003eEconomics Letters\u003c/em\u003e \u003cstrong\u003e225\u003c/strong\u003e, 111045 (2023).\u003c/li\u003e\n\u003cli\u003eRavi, R. \u003cem\u003eet al.\u003c/em\u003e Exploring the environmental consequences of roadside grass as a biogas feedstock in Northwest Europe. \u003cem\u003eJournal of Environmental Management\u003c/em\u003e \u003cstrong\u003e344\u003c/strong\u003e, 118538 (2023).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Overview of roadside and nature grass usage and estimated biomass quantities in the Netherlands\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 42px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoadside grass\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNature grass\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsage of the grass\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1970s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003eCattle feed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003eCattle feed and litter in stables\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1980s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003eDemand for farmers decreased and often more deposited at waste dumps\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eVural Gursel et al. \u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e1980 onwards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003eComposting\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRecent years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cem\u003eLess attractive to farmers, and demanding alternative use of the grass and development for \u0026nbsp;bioeconomy (e.g., bioenergy, paper )\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eVural Gursel et al. \u003csup\u003e50\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"4\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated quantity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003e900,ooo tons year\u003csup\u003e-1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRijksdienst voor Ondernemend Nederland \u003csup\u003e51\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003e225,000 ton DM year\u003csup\u003e-1\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cem\u003e263,244 tons DM year\u003csup\u003e-1\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003ede Vries et al. \u003csup\u003e53\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 204px;\"\u003e\n \u003cp\u003ede Jong et al. \u003csup\u003e52\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Descriptive analysis from the constructed FCMs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcepts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOut degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIn degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentrality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDriver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrdinary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eLocally sourced feedstock availability\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eCompetition of feedstock\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eRising wood cellulose price\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eNew competitive circular\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ebusiness development\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eEconomic feasibility\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eProduction cost\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnergy crisis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEC8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eNiche market formation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eStakeholders participation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eFarmers\u0026apos; perception of sustainability\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003ePublic awareness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003ePublic acceptance \u0026amp; willingness to pay\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eFear and risk\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSO6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eKnowledge gap in sustainable biomass production\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eRegulatory impact on waste stream management\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eSupportive governmental sustainable procurement projects\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eFinancial support and funding\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003ePO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003ePromote R \u0026amp; D and cooperation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eTL1 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eTechnology optimization and integration\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eTL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eTechnical feasibility challenges for scaling up\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnvironment benefits: nature conservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eEM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eProduct life cycle management/ performance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eSustainable industry practices\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003ePrevalence of wood-based Paper in the printing sector\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eRegional innovation network\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003eSE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u003cem\u003eNon-wood fiber-based paper production\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026uuml;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eNr. Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;Nr. Connections\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eNr. Driver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNr. Receiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNr. Regular Connections\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.15824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pulp and paper industry, sustainable production, grass biomass, fuzzy cognitive maps, stakeholders, circular bioeconomy","lastPublishedDoi":"10.21203/rs.3.rs-5123019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5123019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This research investigates the pulp and paper industry's transition to sustainability by valorizing unused roadside and natural grasses for paper production. Large-scale production from residual grass poses multifaceted challenges, requiring collaboration across stakeholders, from biomass collection to manufacturing. To understand key drivers and barriers within this complex system, experts from various fields, including local farmers, researchers, policymakers, and industry executives were interviewed, leading to the development of a Fuzzy Cognitive Map (FCM). The analysis explores various scenarios to assess how socio-economic, technological, and political factors influence the transition to low-carbon practices. These scenarios highlight the effects of varying levels of technology development, economic conditions, and policy support on the transition's progress and outcomes. Results show that the system is highly sensitive to shifts in socio-economic and political conditions. Political interventions play a crucial role, especially during energy crises and increased public demand for sustainable solutions. Grass-based paper production is seen as a viable pathway, but challenges such as the economic feasibility of emerging technologies remain. We recommend targeted policies to improve the economic viability of grass-based products and optimize biomass allocation between energy and bio-based products, ensuring a more balanced and sustainable transition.","manuscriptTitle":"Understanding System Interdependencies in Sustainable Paper Production from Residue Grass biomass: Insights from Fuzzy Cognitive Mapping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-05 17:45:50","doi":"10.21203/rs.3.rs-5123019/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T10:27:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-08T06:05:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T20:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127527324853058349928548090262396023909","date":"2024-10-31T06:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20229417940620714816959571985660834696","date":"2024-10-25T09:14:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32442649058914533309875207500205092686","date":"2024-10-25T08:39:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-24T11:25:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-15T16:39:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-11T14:00:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-10T09:56:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-20T10:49:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e110b6eb-f2f4-48d3-a482-f4a7a6dc3575","owner":[],"postedDate":"December 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39998982,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":39998983,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society"},{"id":39998984,"name":"Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios"},{"id":39998985,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2025-01-13T16:03:47+00:00","versionOfRecord":{"articleIdentity":"rs-5123019","link":"https://doi.org/10.1038/s41598-024-84358-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-09 15:57:48","publishedOnDateReadable":"January 9th, 2025"},"versionCreatedAt":"2024-12-05 17:45:50","video":"","vorDoi":"10.1038/s41598-024-84358-4","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84358-4","workflowStages":[]},"version":"v1","identity":"rs-5123019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5123019","identity":"rs-5123019","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}