5.1 Introduction

The global pursuit of a more sustainable development has brought the bioeconomy concept to the forefront of policy discussions, research, and industrial innovation (M’barek & Wesseler, 2023). The bioeconomy offers a transformative pathway that utilizes biological resources to drive economic growth, environmental sustainability, and social well-being. This chapter explores the evolving landscape of the bioeconomy, particularly focusing on its intersection with agriculture and the circular economy, drawing insights from the BioMonitor project.

The bioeconomy encompasses a diverse range of sectors and systems that rely on renewable biological resources, including agriculture, forestry, fisheries, as well as all kinds of industries processing biomass (Kardung et al., 2021). One crucial goal is to replace fossil-based inputs with bio-based alternatives to reduce greenhouse gas emissions while fostering innovation and economic resilience. The European Commission defines the bioeconomy as incorporating all economic and industrial sectors that produce food, feed, bio-based products, energy, and services from biological resources (European Commission, 2018). This broad definition highlights the bioeconomy’s potential to contribute to multiple Sustainable Development Goals (SDGs), including food security, climate action, and sustainable consumption.

However, the bioeconomy’s interpretation and implementation can differ across countries and institutions, influenced by local priorities, resource availability, and policy frameworks (M’barek & Wesseler, 2023). The European Union has promoted the bioeconomy through initiatives like the updated Bioeconomy Strategy and the Horizon 2020 BioMonitor project. The latter has developed new methodologies and indicators to measure the bioeconomy’s size, growth, and sustainability impacts, addressing critical gaps in data and knowledge.

A central theme in this chapter is the integration of the bioeconomy with the circular economy, a concept that emphasizes continual material use, resource efficiency, and waste minimization. The idea of a circular bioeconomy merges these two paradigms, promoting the sustainable use and reuse of biological resources. This synergy is essential for achieving long-term sustainability goals, aligning economic development with ecological boundaries (Kardung et al., 2021).

This chapter also revisits historical economic theories, such as those proposed by the Physiocrats and von Thünen, to contextualize the principles of the circular economy within a broader economic framework. It underscores the relevance of price systems, spatial logistics, and resource flows in shaping sustainable economic systems. Furthermore, this chapter presents empirical findings and methodological innovations from the BioMonitor project that enhance our understanding of the bioeconomy’s contributions to sustainable development. It explores advanced modeling tools such as input–output analysis, material flow models, and hybrid frameworks, which provide valuable insights into the bioeconomy’s economic, environmental, and social dimensions. Finally, this chapter concludes with future opportunities and challenges that await the EU bioeconomy.

5.2 Bioeconomy and Sustainable Development

Over the past decades, significant advances in life science and biotechnology and a shift in public attitudes have led policymakers to increasingly prioritize the bioeconomy as a key strategy for addressing global challenges such as food security, healthcare, resource constraints, environmental preservation, and climate change (Wesseler & von Braun, 2017; Zilberman et al., 2018). To promote the development of bioeconomy, the US released a National Bioeconomy Blueprint in 2012 (The White House, 2012). Similarly, the European Union (European Commission, 2018; European Commission & Directorate-General for Research and Innovation, 2022) and several member states have endorsed bioeconomy research and policy strategies since 2010. Numerous countries, such as China, India, Brazil, and South Africa, also have implemented bioeconomy policies and strategies to promote sustainability within their economies (Dietz et al., 2018; Wesseler & von Braun, 2017).

The concept of bioeconomy varies in interpretation and scope among countries and institutions. According to the European Commission (EC), the bioeconomy covers “all sectors and systems that rely on biological resources” and all economic and industrial sectors that “produce food, feed, bio-based products, energy, and services” (European Commission, 2018). The USDA defined the term “bio-based product” as “a commercial or industrial product (other than food or feed) that is composed in whole or in a significant part of biological products or renewable domestic agricultural materials (including plant, animal, and marine material) or forestry material” (U.S. Department of Agriculture, 2008). The definitions of bioeconomy across countries or institutions share many similarities. Agriculture, forestry, fisheries, food, pulp and paper production, as well as parts of the chemical, biotechnological, and energy industries, are all considered part of the bioeconomy (Wesseler & von Braun, 2017).

The bioeconomy is viewed as a key driver of sustainable development by sustainably utilizing biological resources, thereby enhancing social, economic, and environmental well-being (Calicioglu & Bogdanski, 2021; Ferraz & Pyka, 2023). The EU bioeconomy strategy’s action plan aligns with 53 targets across 12 of the 17 Sustainable Development Goals (SDGs) (Ronzon & Sanjuán, 2020). The bioeconomy can have substantial impacts, which are effectively reflected in the broad range of SDGs (Heimann, 2019). Monitoring and evaluating the bioeconomy can help track progress on SDGs, especially in areas like economic growth (e.g., SDG 8), food security (e.g., SDG 2), and sustainable consumption (e.g., SDG 12) (Calicioglu & Bogdanski, 2021).

The bioeconomy differs from related concepts concerning sustainable development (D’Amato & Korhonen, 2021; Kardung et al., 2021), such as the “biobased economy,” “green economy,” and “circular economy.” These terms partially intersect and share common sustainability and resource efficiency goals. The green economy is often considered an overarching concept aimed at improving human well-being and social equity, as well as reducing environmental risks and ecological scarcities. The bioeconomy is generally viewed as a subset of the green economy, focusing on economic growth and technological development through the sustainable use of biological resources. The bio-based economy involves converting biological resources into products and materials, including both innovative bio-based products and traditional ones like textiles and wood products. Complementary to the bioeconomy, the circular economy emphasizes recycling, reduction, and sustainable resource use to minimize waste. The synergies between the bioeconomy and circular economy concepts are significant. The term “circular bioeconomy,” introduced by the EC and others, highlights the integration of both concepts and emphasizes the importance of applying circular principles to the bioeconomy (Kardung et al., 2021).

The conceptual framework discussed by Kardung et al. (2021) illustrates the dynamic process of the bioeconomy, highlighting the interconnectedness of various factors and actors involved. In the framework, driving forces, (re-) use of resources, policies, strategies, and legislation, as well as societal objectives, are integrated through the supply and use of biomass. Supply and demand are crucial driving forces in shaping the framework. Resource availabilities such as land, water, and labor impact the biomass market. Additionally, wastes/by-products are crucial in realizing a sustainable and circular bioeconomy. Policies, strategies, and legislation serve as instruments of governments to affect the bioeconomy. Within the biomass market, various supplies and uses of biomass are determined by the drivers and resource availability mentioned earlier. The interaction among drivers, policies, and resources influences the bioeconomy’s demand and supply, ultimately contributing to its role in achieving sustainable objectives.

The concept of a circular economy aims to minimize waste and make the most of resources, but it faces several challenges and could benefit from learning from existing economic concepts. In the eighteenth century, François Quesnay and the Physiocrats emphasized the importance of agriculture as the source of wealth. The Physiocrats’ focus on natural cycles and the flow of resources can be seen as an early precursor to circular economy principles. However, their model was limited to agricultural productivity and did not account for industrial and technological advancements. Johann Heinrich von Thünen developed a model to explain the spatial organization of agriculture around a central market. His model considered transportation costs and land use, highlighting the importance of location in economic activities. This systems approach can be applied to the circular economy by considering the logistics and spatial distribution of recycling and resource recovery facilities. However, von Thünen’s model primarily addressed agricultural land use and may not fully capture the complexities of modern industrial economies. The price system, driven by supply and demand, is crucial in resource allocation. Prices signal scarcity and value, influencing production and consumption decisions. In a circular economy, the price system can be leveraged to promote sustainable practices by internalizing environmental costs and providing economic incentives for recycling and reuse.

5.3 Measuring the Contribution of Bioeconomy to Sustainable Development

Measuring the contribution of the bioeconomy to economic growth and sustainable development is challenging due to its dynamic nature, which is affected by technological advancements, industrial progress, policy shifts, and other factors. Various approaches can be employed to measure the bioeconomy, each providing valuable insights and results.

5.3.1 Bioeconomy Value Added and Turnover

Measuring the contribution of the bioeconomy to economic growth requires understanding its share within the overall economy. One widely used approach involves using the system of national accounts, while input–output tables and statistical surveys are also commonly employed (Efken et al., 2016; Heijman, 2016). Based on MAGNET, a global forward-looking ex ante simulation model, the BioMonitor project of the European Union’s Horizon 2020 (BioMonitor, n.d.), bridged the information gap in bioeconomy research by restructuring its existing data and modeling framework and projected the EU bioeconomy production trends. The project introduced new biochemical activities (biochemicals, biopharmaceuticals, bioplastics) to create a new virtual trade and “footprints” module with a state-of-the-art baseline.

Using 2020 as the base year, significant increases are anticipated in 2030 and 2050 across all relevant sectors. The total bioeconomy production is estimated to be €3294.576 million in 2020, €3647.336 million in 2030, and €4233.644 million in 2050. The largest subsector is Food & Food Service, with estimated values of €1576.375 million in 2020, €1786.446 million in 2030, and €2172.289 million in 2050. The biofuel-solid sector shows the most significant change, nearly doubling by 2050 compared to 2020.

BioMonitor also analyzed the scenarios where fossil fuel prices rise by 30% (30% case) and 50% (50% case) above the baseline in 2050, and both positive and negative impacts are observed. In the baseline scenario, the real GDP is projected to reach 159.76 in 2050, with the base year set at 2020 (=100). However, in the 30% case, the real GDP is expected to decrease by 0.47, and in the 50% case, by 0.74. On the positive side, total emissions for 2050 are projected to be 2628.32 MtCO2e. Nevertheless, emissions are forecasted to decrease in both the 30% and 50% cases, with reductions of 227.13 and 348.12 MtCO2e, respectively.

Input–Output (IO) models map the inter-industry relationships within an economy by quantifying the inputs required by each industry to produce its outputs. An IO model depicts how the output of one industry serves as an input to another, forming a complex network of interdependencies across the pre-defined sectors/industries. To measure the bioeconomy size within an economy, various IO measurement methods have been introduced (Ronzon et al., 2024):

  • The “output-based” approach quantifies the size of the bioeconomy in terms of socio-economic indicators, focusing on the bioeconomy nature of outputs, which is determined by the bio-based share of outputs (Ronzon et al., 2022). It also considers the bioeconomy relevance of intangible outputs; Lazorcakova et al. (2022) also includes environmental indicators in bioeconomy IO measurement.

  • The “input-based” approach measures the bioeconomy value added generated by an industry in proportion to the bio-based inputs from primary industries (agriculture, forestry, fishery, aquaculture, and veterinary services) (Heijman, 2016).

  • The “weighted Input-Output” takes into account the parameters quantified by the output-based and the input-based approaches (Kuosmanen et al., 2020),

  • The “upstream-downstream” approach measures the bioeconomy value added within the industries by using IO flows between industries to calculate downstream and upstream value added by the bioeconomy (Cingiz et al., 2021, 2023).

  • The Hypothetical extraction method quantifies how much the total economy would shrink if a particular sector is removed from the IO table (Miller & Blair, 2009).

These models provide measurement tools to analyze bioeconomy growth and size, and bridge the data gaps. An advantage of the IO methodology is that it builds on data collected in standardized statistics and is easily replicable, enabling temporal and spatial comparison of the bioeconomy and its development. Furthermore, by integrating environmental indicators into IO measurement models, investment decision-making mechanisms can be identified given the economic reversible and irreversible cost of environmental impacts of each industry’s activity (Kardung, 2023).

Through the environmentally extended Input–Output (EE-IO) model, it is possible to track the flow of resources and pollutants. EE-IO assesses the upstream environmental impacts generated by downstream economic consumption and accounts for the environmental impacts in traded goods (Kitzes, 2013).

It is essential to go beyond analyzing the growth and size of the bioeconomy when measuring its progress. Tracking the dynamic processes that drive its development requires various indicators, which can be challenging for policymakers to consider altogether. Kardung and Drabik (2021) have created a theoretical framework to address this issue. This framework can accommodate any number of well-defined quantitative indicators, making it easier for decision-makers to assess the overall development of the circular bioeconomy. It achieves this by normalizing indicators with different units and dimensions and exploring patterns using Markov transition matrices. This approach helps in understanding indicators covering a bioeconomy’s economic, environmental, and social aspects. Furthermore, the framework enables the assessment of the overall development of the bioeconomy, the dimensions in which it is progressing or regressing the most, and the dynamic changes in the relative positions of individual indicators.

These models enable stakeholders such as policymakers and researchers to assess different industries’ environmental impacts, develop sustainability indicators and investment decision-making mechanisms, and further develop strategies for promoting sustainable development and economic growth. Integrating economic and environmental data through IO modeling frameworks offers a valuable tool for advancing sustainable bioeconomy growth.

5.3.2 Material Flow Model

Modeling results can be linked with material flow models and vice versa. A material flow model is a tool that helps analyze how materials move through different processes within a system. The flow model results in a material flow monitor (MFM) and provides information on resource efficiency by integrating data on inputs, processing, outputs, transportation, and inventories. It also identifies areas for efficiency improvements and assesses environmental impacts. Additionally, it evaluates recycling strategies and waste management and helps plan logistics for material distribution.

The Netherlands serves as an illustrative example of an application of the MFM (Lucas et al., 2022). In 2018, the Dutch economy utilized nearly 450 Mt. of material resources, comprising primary resources, recycled materials, and those within materials, components, and products. Approximately a quarter originated from domestic sources such as natural gas, gravel, and agricultural products, while the remaining three-quarters were sourced from abroad, including fossil fuels, metals, and materials within products. Nearly half of these material resources were exported as finished or semi-finished products, like fodder converted into meat or metals processed into machine parts. Additionally, the Netherlands imported a significant volume of material resources, which were largely re-exported to other countries without substantial processing.

The Bio Flow Monitor (BFM) is a material flow model based on MFM and is used to identify indicators relevant to bioeconomy and circular economy policy (Delahaye et al., 2023). The MFM collects data on the whole economy from a database that includes integrated statistics, recording in the case of the Netherlands supply and use tables for 400 products and 130 sectors, including biomass. However, the MFM is unsuitable for estimating the bioeconomy, so the BFM was developed. Figure 5.1 provides an overview of the methodology for estimating the BFM. The monitor records biotic and abiotic supply and use tables, providing data on biomass usage by bio-based industries and filling data gaps regarding the use of biomass. Figure 5.2 illustrates biomass flows in the Dutch economy.

Fig. 5.1
Flow chart illustrating the process of calculating biobased shares. It starts with data from Eurostat and National Statistics, detailing physical units per PRODCOM/CN code. The flow includes steps like imputing missing values, converting physical units to kilos, and calculating biobased shares. Additional processes involve conversion from wet to dry mass and from PRODCOM/CN to MFM codes, leading to the final output of MFM biotic and abiotic shares. Various organizations like NOVA and JRC are involved in specific steps.

Overview of methodology to estimate the Bio Flow Monitor. Note: Solid lines represent transitions from one stage of the process to the next. Dotted lines indicate help files that are used to make these transitions

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Fig. 5.2
Flow chart illustrating the flow of materials in the NL economy. It shows inputs from import (42) and domestic extraction (8) leading to the NL economy (59). Outputs include export (24) and losses (26). A recycling loop (8) is also depicted. The chart uses color-coded paths to represent different material categories: residuals, fodder, agro commodities, basic food, chemistry, materials, processed food, pharma, meat, and losses.

Biomass in and out flows of the Dutch economy (kilotons dry matter), 2018

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The BFM fills data gaps concerning bio-based industries’ use of biomass and provides data on kilo biotic and abiotic production per economic sector. A key result is that in terms of weight, only 19% of the Dutch production could be considered part of the bioeconomy (biotic production) in 2018. Some biomass is obtained from domestic extraction and waste flows, while other biomass is obtained from trade with other countries. The BFM allows for calculating indicators that relate domestically extracted biomass to imported biomass, addressing the dependency on foreign countries. Figure 5.2 shows biomass flows in and out of the Dutch economy, revealing that the Netherlands is very dependent on biomass imports.

Within the BioMonitor project, the concepts of a MFM and a BFM applied in the Netherlands were replicated for Italy, Spain, Slovakia, and Latvia to reveal biomass flows and the most important biomass types and sources. Contrary to the Dutch results, domestic agricultural and forest biomass plays the most important role in the Slovak and Latvian economies. In these countries, the share of domestic agriculture in the total economy also decreases in terms of socio-economic indicators. Compiling a country’s MFM or BFM from currently available public statistics is a challenge; it is only possible in close cooperation with national statistical offices (and other institutions) gathering detailed data.

The BFM also serves to assess the principle of cascading, which aims to derive the highest value from biomass through various processing stages. It helps identify the volume of dry matter per stage of the processing (cascading) pyramid, contributing to understanding the bio-based economy and biomass usage. Figure 5.3 shows the weight pyramid, indicating that agro commodities have the largest share with food processing, while pharma and chemistry-related bio-based products have the smallest share.

Fig. 5.3
Horizontal bar chart displaying various categories and their corresponding values. Categories include Pharma, Meat, Processed food, Materials, Chemical, Basic food, Agro commodities, Fodder, and Residual flows. Agro commodities have the highest value, followed by Materials and Basic food. The x-axis represents numerical values ranging from 0 to 35,000.

Cascading of biomass (in mln. kilo), The Netherlands, 2018

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In the future, other relevant indicators addressing societal problems could be derived from the BFM:

  • Resource management: supply and use of biomass by sector.

  • Climate change: greenhouse gas emissions per sector.

  • Circular economy: use of primary versus secondary biomass flows.

  • Substitution: use of biotic versus abiotic materials.

  • Economic importance: value added/employment in biotic sectors relative to abiotic sectors.

  • Losses: input biomass resource/output biomass product (or biomass waste output).

  • Resource efficiency: value added per unit biomass use.

The BFM will also be integrated with carbon accounts. Carbon accounts are part of the SEEA-EA framework. International guidelines for setting up these accounts are led by the United Nations (UN). The Netherlands’ carbon stock account is comprehensive in that all-important carbon reservoirs are included. There is a link between policy on circular economy (material use) and climate change (GHG emissions), as well as ecosystem services (sequestration) and natural resource dependency (fossil fuel extraction). Hotspots of carbon flows indicate the most efficient policy measures.

The impact of circular economy strategies on climate change mitigation depends on factors such as the amount of fossil-based carbon extracted from the environment, the quantity of carbon recycled or stored within the economy, the level of carbon emissions released into the environment, and the extent to which bio-based carbon substitutes for fossil-based carbon. These strategies can be applied to various sectors or product groups within the economy.

Our evaluation of the Netherlands’ bio-based economy revealed that it is still relatively small within the total economy. While some bio-based indicators have already been incorporated into Dutch circular economy policy, further development is needed. The BFM’s integration with carbon accounts presents exciting potential for future policy applications.

5.3.3 Bio-Based Material Availability

Using material flow models is particularly interesting for modeling bio-based materials in the chemical and construction sectors. It is essential to measure bio-based material production to assess the potential of the bioeconomy. BioMAT (Bio-based MATerials) is a new consistent framework that aims to provide projections for the markets of bio-based materials, starting with bio-based chemicals, and for the respective feedstock needs for EU member states and EU27, up to 2030 and 2050. BioMAT operates as a module of the AGMEMOD (AGriculture MEmber state MODelling), focusing on the chemical industry (Sector code: C20) with full coverage of all bio-based products. It starts with data from PRODCOM codes and offers a stylized representation of value chains. The illustrative example groups the entire output of C20 into 16 product application categories, including two intermediates (Fig. 5.4). Additionally, BioMAT differentiates between ten types of bio-based feedstocks and links them to agricultural crop production in the EU via linkage to AGMEMOD (Sturm et al., 2023; van Leeuwen et al., 2022). The production of bio-based chemicals in the EU amounted to 43 million tons in 2018, accounting for 14 percent of the total output volume of the organic chemical industry. BioMAT breaks down how these materials were used as shown in Fig. 5.5. Biofuels are the biggest category (42%), followed by agrochemicals (21%), surfactants (12%), and cosmetics and personal care products (6%). Other uses make up smaller portions.

Fig. 5.4
Flow chart illustrating the conversion of raw biomass into bio-based product categories. It starts with primary feedstocks like wheat, maize, potatoes, sugar beets, rapeseed, soybean, sunflower seed, and other biological feedstocks. These are processed into starch, sugar, and industrial oil. The chart shows pathways leading to platform chemicals, both sugar-based and oil-based. These chemicals are further categorized into applications such as solvents, polymers, paints and coatings, surfactants, cosmetics, adhesives, lubricants, fibers, biofuels, pharmaceuticals, food and feed, construction, agrochemicals, and others. The chart highlights connections to the paper and pharmaceutical industries.

Bio-based value chains covered in BioMAT. Source: Sturm et al. (2023). Note: Dashed lines are used for flows that are not the main focus of the BioMAT model (C20 “Chemical Industry”) and/or are modeled in less detail: (1) “paper industry” and “pharmaceutical industry” do not belong to C20 (chemical industry), which is the main focus of BioMAT, and are considered as aggregates. (2) “other biological raw materials” are not covered in full detail in the AGMEMOD model, which provides information on the availability/supply (quantities, prices, etc.) of these feedstocks from the respective markets for raw materials

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Fig. 5.5
Flow chart illustrating the conversion of bio-based feedstocks into bio-based chemicals. Green arrows represent various feedstocks like plant oil, ligno from forestry, ligno from agriculture, sugar, starch, animal biomass, aquatic biomass, and others, with their respective quantities in kilotons. These feedstocks undergo a conversion process, resulting in intermediate platform chemicals (4791 kt) and various bio-based chemicals. Blue arrows indicate the production of chemicals such as solvents, polymers for plastics, paints and coatings, surfactants, cosmetics, lubricants, adhesives, man-made fibers, biofuels, pharmaceuticals, food and feed, construction materials, agrochemicals, and others, each with specified quantities.

Use of biological resources for production of bio-based chemicals in the EU in 2018. Source: Sturm et al. (2023)

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5.3.4 The Socio-Economic Impacts of Wastewater Sludge Valorization: The Case of Biofertilizers in Italy

A case study on tracing uncertain sustainability footprints from sewage sludge with application in agriculture used a hybrid IO assessment in the Northern Italian context. The biorefinery under study collects 165,000 tons per year of sludge from 45 wastewater treatment plants serving 226 municipalities. The sludge is further treated as a resource rather than waste and conditioned by sulfuric acid, lime, and gypsum to produce 160,000 tons of biofertilizers annually. Sewage sludge is a by-product of municipal or industrial wastewater treatment plants. Italy produces 3.1 million tons of sludge (Tassinari et al., 2023) per year and 56.3 percent is landfilled. The case study aimed to assess organic fertilizers’ sustainability footprints from sewage sludge production.

The case study concluded that there are confidentiality issues. Because of the type of information provided to the company, it was easier to collect the data, which was nevertheless collected at a level of aggregation that ensured the confidentiality of all relevant intellectual property. Thanks to the hybrid IO analysis, it was possible to overcome the data aggregation problem in useful tools such as supply and use tables. These tables, however, still lack details for bio-based productions.

Using a multiregional IO database has made it much easier to contextualize to a broader geographic level than local or national, quantifying the externalities of economic activity in foreign countries. A Monte Carlo analysis improved the generalizability and validity of the results, addressing inherent limitations of more common life cycle thinking approaches.

Many indicators for measuring the bioeconomy fit the hybrid IO framework, allowing a comprehensive approach for systematic impact quantification. However, not all indicators are readily measurable from basic monetary IO tables. Most indicators can only be handled using multiregional IO coupled with non-economic, physical data or physical IO tables. A final recommendation concerns the employment indicator. It is important to note that available data are reported by gender and by low-, medium-, and high-skill level groups. This information can be used as other useful sub-indicators to address a more thorough social assessment of the bioeconomy.

5.3.5 Bioeconomy and EU Regional Development

The transition to a bioeconomy is expected to create new job opportunities, although some sectors, like coal mining and fossil fuel extraction, may experience job losses. Biorefineries, which convert biomass into value-added products, are a crucial component in this transition, potentially promoting economic growth and employment in rural regions. Biorefineries produce biofuels, biochemicals, bioenergy, and other biomaterials, replacing fossil-based products. They can create new value chains and attract innovative technologies, thus fostering regional economic growth and employment. However, the introduction of biorefineries might also reduce employment in sectors involved in fossil-based production. The overall impact on local employment remains ambiguous.

Biofuels stand out as some of the most prominent and economically significant outputs of biorefineries. These include biodiesel, bioethanol, and advanced biofuels, which are essential for reducing greenhouse gas emissions in the transport sector. Within the European Union, biofuels accounted for the largest share, approximately 42 percent, of bio-based chemical production in 2018, according to the BioMAT model. Production of biofuels is expected to increase from approximately 14,000 million metric tons in 2018 to 18,000 million metric tons in 2030. Biochemicals, bioplastics, and biopolymers are also high-value products from biorefineries. Intermediate chemicals production is expected to increase from approximately 5000 miot in 2018 to 8000 miot in 2030, while surfactants and agrochemicals are expected to increase by 1000 miot or less (van Leeuwen et al., 2023).

Zhu et al. (2023) have used data from the EU Joint Research Centres (JRC) and the EU H2020 BioMonitor project, covering various types of biomass processing facilities across Europe over the last decade. A linear regression model was employed to assess the determinants of employment and their impact over time at the regional level, comparing regions with and without biorefineries.

Their findings indicate that biorefineries create direct employment at the facility and indirect jobs in farming and service industries. For example, a biorefinery in Hungary created 172 direct jobs and over 5000 indirect jobs. Regions with biorefineries exhibit higher employment growth compared to those without. The study found a positive correlation between the number of biorefineries and regional employment, even after accounting for other factors like population, income, education, and infrastructure. The employment impact of biorefineries is higher around the time of establishment and decreases over time due to the initial labor needs for construction and early operation phases.

By 2022, there were 2655 biorefineries in the EU, with Germany, France, and Sweden having the highest numbers. The Netherlands, Belgium, and Germany showed the highest density of biorefineries per km2. The bioeconomy sector employed 2.56 million workers and generated significant economic value.

The study recommends improving data on biorefinery capacity, production, and status (e.g., operational, halted) to refine future analysis. Policymakers should consider the dynamic nature of biorefinery employment impacts and support regions transitioning from fossil-based to bio-based economies. Further research with more detailed econometric tools and data on small biorefineries could provide deeper insights into the regional impacts of biorefineries.

In conclusion, biorefineries have the potential to significantly contribute to regional employment and economic growth in the EU. However, their impact varies over time and across regions, necessitating continuous data collection and analysis to inform policy decisions and support the bioeconomy transition.

5.4 Future Opportunities and Challenges

The bioeconomy is being transformed by innovative technologies that enhance biomass production and enable new processing methods. Enhancing biomass production and processing is crucial to minimize concerns about the use of biomass for non-food purposes and, therefore, to promote the bio-based economy with a focus on bio-based materials and energy products. At present, this nexus persists, with the result that the production of bulk bio-based products, especially biofuels, is repeatedly questioned, while the production of bio-based products with specific properties (e.g., bio-based fine chemicals) is rather spared from such criticisms. Innovative technologies like synthetic biology are used to engineer microorganisms for the sustainable production of biofuels and bioplastics (Agarwal et al., 2022). Precision agriculture employs GPS and drones to enhance farming efficiency, while advanced biomaterials are replacing traditional plastics, reducing reliance on fossil fuels. Bioremediation utilizes microorganisms to clean up pollutants, and cellular agriculture grows meat in labs, offering a sustainable alternative. Urban fishermen are adopting aquaponics for fish cultivation. Insect burgers provide a protein-rich and eco-friendly food option. Biological carbon capture harnesses algae to convert CO2 into valuable products, while microbial electrochemical technologies generate energy from organic waste, benefiting the environment and the economy. Industrial hemp production provides opportunities for net-zero carbon building materials (Ahmed et al., 2022). These technologies are not only helping to protect the environment but also creating new economic opportunities in the bioeconomy.

Key research questions, such as achieving Net-Zero solutions, require cooperation among scientists, engineers, and policymakers. These questions involve exploring the most promising technological innovations across different sectors while addressing issues related to intellectual property rights to encourage widespread dissemination. Numerous emerging technologies for the circular bioeconomy rely on advancements in biological sciences, including CRISPR-Cas. Implementing these technologies is subject to stringent regulations, and notably, their use in plant breeding has become challenging within the existing regulatory framework. Therefore, it is also momentous to revise regulatory barriers to employ these innovations. It is essential to develop monitoring tools at both national and international levels, which involves identifying key indicators and enhancing data analytics for precise progress tracking while also establishing transparent governance mechanisms. Harmonizing national and international policies requires resolving gaps and overlaps, designing multi-level governance frameworks, and leveraging international organizations to promote coherence. This entails reconciling divergent interests, standardizing policy instruments, and overcoming political and institutional barriers through capacity-building and diplomatic negotiations.

To ensure the bioeconomy sector not only survives but thrives through innovation, it is crucial to focus on the next generation of professionals (Carrez & Rupp, 2023). This requires aligning educational programs at all levels with the bioeconomy skill and training needs. We can effectively identify and address skill and knowledge gaps by monitoring and assessing higher education. It is also essential to track students’ enrolment and graduation rates in bio-based and bioeconomy courses, as well as their subsequent career paths in the industry. Collaborating with higher education institutions to qualitatively evaluate and update curricula will help ensure that course content meets industry requirements, making necessary adjustments along the way. In the BioMonitor project, four early-stage researchers were hired to generate human capital for future exploration of the project results.

Indicators simplify the intricate complexity of describing the bioeconomy. They also represent a compromise regarding what can be quantified, the type of data collected, and its quality. The implication is that indicators and related methodology should be considered value-neutral. A link to specific policy objectives should only be made if they explicitly reference some indicators and associated methodology. For example, it would not be prudent to say that an increase in all the indicators that mark the transition toward bioeconomy means that all possible aspects of the transition have been accomplished.

The design of modeling toolboxes cannot be completely separated from other issues, such as data availability. Throughout the design process, it is important to factor in data availability since, in numerous instances, public databases do not contain data on bio-based textiles, plastics, chemicals, pharmaceuticals, or other products. Additionally, the available data series are often brief and incomplete due to confidentiality issues, missing product codes, or the relatively short time that some bio-based products have been on the market.

Developing effective and reliable methods for gathering data to monitor and assess the bioeconomy is challenging, but there has been significant progress in this area. Cingiz et al. (2021) created a model incorporating upstream and downstream connections using IO tables. Kuosmanen et al. (2020) have developed a hybrid modeling approach to reconcile various quantitative techniques for estimating value added and employment in the EU and its Member States. Regarding data collection through case studies, there is a need to establish standardized research protocols that promote transparency and reproducibility in bioeconomy case studies (Tassinari et al., 2021).

The combination of simulation models for the EU bioeconomy can capture several cross-cutting issues of a bioeconomy transition, considering competition and interactions between economic sectors. The results of modeling toolbox simulations can provide policymakers with the critical knowledge currently missing for further progress toward a circular and sustainable bioeconomy. Bioeconomy models have been extended to include additional bio-based sectors, which addresses the need to recognize water as a key factor of production and the emerging importance of the developing bio-industrial sectors. Moreover, the model has been enhanced in terms of model indicators for the bioeconomy. A new multiregional partial equilibrium for innovative-based processed product markets addresses the extent to which bio-based products can substitute their fossil-based carbon content with bio-based carbon content.

Developing a strong and effective framework for creating statistics and modeling tools for the bioeconomy is a huge undertaking. The bioeconomy is a broad strategy that addresses various economic, social, and environmental challenges at the same time. Defining the policy scope for the bioeconomy and designing related baselines and scenarios is not a simple task. It is important to consider the trade-offs between policies. The links between policies have become stronger, making assessing their objectives and elements more complicated.