Circular bioeconomy and the regions: Developing a two-step multi-criteria assessment (MCA) framework to evaluate regional bioeconomy potential in 8 selected European countries

Circular bioeconomy and the regions: Developing a two-step multi-criteria assessment (MCA) framework to evaluate regional bioeconomy potential in 8 selected European countries Panos Kalimeris * , Georgios Maroulis , Panagiotis Koronaios , Antonios Rovolis Institute of Urban Environment and Human Resources, Department of Economic and Regional Development, Panteion University Athens, Greece ARTICLE INFO Keywords: Bioeconomy indicators Regional bioeconomy Multi-criteria assessment (MCA) Traffic light assessment (TLA) TOPSIS methodology Sustainable development Regional development ABSTRACT A transition to a sustainable, cleaner, and circular bioeconomy is widely recognized as a crucial pathway to achieving the ambitious goals of the European Green Deal and the “Fit for 55 ″ package. This transition fosters sustainable development, decreases reliance on fossil resources and, thus, greenhouse gases emissions (GHG’s), while it generates new economic opportunities in terms of investment, employment, and value added across various production sectors. More specifically, a regionalised approach towards enabling bioeconomy is regarded as a promising bottom-up strategy, since several European regions have already implemented relevant bioeconomy strategies. In this context, the present study aims to explore the regional potential for transitioning to a bioeconomy model within the European Union (EU). The study’s objectives are summarised into, (a) reviewing the relevant literature in order to construct an overall bioeconomy indicators pool; (b) providing a two-step Multi-Criteria Assessment (MCA) framework to identify regional bioeconomy potential for selected European countries, using only those indicators from the initial pool fulfilling certain criteria, such as regional data availability; and (c) highlighting the best-performing regions ranked according to selected groups of bioeconomy indicators that could serve as potential model regions for others. The proposed MCA framework is applied to rank regions within seven (7) selected EU countries, namely, Belgium, Czech Republic, France, Germany, Greece, Spain, Sweden, and one (1) non-EU country, Iceland. The MCA could serve as a decision-analysis tool providing valuable and practical insights for regional stakeholders and policy makers, allowing them to establish regiontailored policies to unlock their unique bioeconomy potential. 1. Introduction The concept of bioeconomy was introduced at both the academic and policy arenas quite recently. Nonetheless, a detailed literature review of the historical evolution of the term bioeconomy reveals various early academic contributions dating back to the beginning of the 20th century (Vivien et al., 2019; Gould et al., 2023). In a nutshell, the bioeconomy concept consists of all these natural flows entering into economy, using renewable/biological resources from land and sea – such as crops, forests, fisheries, animals and micro-organisms – (and their residuals, during various stages of the production process), to produce food, feed, environmental friendly materials, and new products that could substitute other more artificial commodities, such as fertilizers, plastics, textiles, and energy. The term gained popularity at the policy arena in mid-2000 ′ s, as the Organisation for Economic Operation and Development (OECD) published a policy agenda with various bioeconomy development scenarios towards 2030 (OECD, 2009), while, a year after, the European Commission (EC) developed the concept of Knowledge-Based Bio-Economy (KBBE), also known as the “Cologne paper” (Kircher et al., 2022; The Knowledge Based Bio-Economy (KBBE) in Europe: Achievements and Challenges, 2010). In order to cover different segments of the KBBE, the EC established the European Technology Platforms (ETPs) funding tool, the first type of public-private partnerships for conducting research at the European level. The discussions between the ETPs and a series of open meetings with various stakeholders 1 were captured into an important White Paper publication * Corresponding author. E-mail addresses: [email protected], [email protected] (P. Kalimeris), [email protected] (G. Maroulis), [email protected] (P. Koronaios), [email protected] (A. Rovolis). 1 The stakeholders that represented in all the ETPs were industry and academia, farmers, forest owners, consumers, and civil society organisations, indicatively. Contents lists available at ScienceDirect Cleaner and Circular Bioeconomy journal homepage: www.elsevier.com/locate/clcb https://doi.org/10.1016/j.clcb.2025.100195 Received 8 March 2025; Received in revised form 10 November 2025; Accepted 12 November 2025 Cleaner and Circular Bioeconomy 12 (2025) 100195 Available online 13 November 2025 2772-8013/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/bync-nd/4.0/ ). on bioeconomy (A White Paper, 2010). A few years later, the EC published the very first strategy “Innovating for sustainable growth: A Bioeconomy for Europe” (A White Paper, 2010). During the same period, the Obama administration in the USA introduced the term with the technical report “Bioeconomy White Paper” (The White House, 2012). Since then, almost 50 countries have developed national bioeconomy strategies or introduced policies that are steering towards a sustainable and circular bioeconomy (OECD, 2018). In line with these developments, the EC is currently preparing the updated revision of the former European bioeconomy strategy, which dates back in 2018. From a global perspective, the engagement towards facilitating the bioeconomy’s potentials has been very dynamic since 2022, according to the World Bioeconomy Forum (2023). Among the largest economies of the world, China and the USA have recently increased commitment and engagement towards this direction, as both have introduced strategies in bioeconomy. In terms of economic values, it is estimated that the global value of the bioeconomy is approximately 4 trillion US$ and, according to some projections, it is expected to rise further to 30 trillion US$, in the next decades (World Bioeconomy Forum, 2023). Focusing on the European level, a sustainable economy, functioning as an integral component of a circular economy — thus, circular, and sustainable bioeconomy in our case —, is an essential prerequisite for achieving the goals of the European Green Deal and the “Fit for 55 ″ package’s ambitions towards climate neutral by 2050 (Sardianou et al., 2024). Moreover, focusing on regional bioeconomy potentials at the EU level seems a more efficient bottom-up approach (European Commission, 2018). However, it needs to be mentioned that implementing a regional bioeconomy comes with various challenges and shortcomings. The contribution of the present study is twofold: on the one hand it aspires to provide an extensive four-pillars literature review, focusing explicitly on gathering bioeconomy indicators, and on the other hand, to further classify the resulting bioeconomy indicators dataset through an initial two-steps Multi-Criteria Assessment (hereinafter, MCA) which ranks the regions of the eight selected countries, namely: Belgium, Czech Republic, France, Germany, Greece, Iceland, Spain, and Sweden, according to their bioeconomy performance. The present study is structured as follows: Firstly, an extensive literature review was conducted, to reveal current state of knowledge on bioeconomy indicators, based on four pillars: 1) academic-scientific literature (See also Fig. 1), 2) international organizations’ reports and non-EU countries’ strategies on bioeconomy, 3) EU reports, including relevant research bodies, and EU member states’ strategies on bioeconomy, and 4) previous and ongoing projects on bioeconomy indicators and monitoring, screened for adaptable approaches for the proposed MCA (See also Fig. 2). The most utilised bioeconomy indicators were grouped into three distinct categories, namely: economy, society, and the environment. The process revealed 897 bioeconomy indicators in total. To the best of our knowledge, this is the first time in history of the relevant literature that such an extensive bioeconomy indicators dataset is ever constructed (See also Fig. 3). Secondly, the first part of the MCA was based on the Traffic Light Assessment (TLA) method, used to classify the initial database of bioeconomy indicators into four distinct groups: “green” (accepted), “orange” (potential/currently rejected), “red” (rejected), and “grey” (other/ currently rejected) indicators and, thus, to select the final set of available and quantifiable indicators, exclusively for the “Nomenclature of Territorial Units for Statistics” (hereinafter, NUTS) II level, which is the statistical classification used by the Eurostat for defining the regional level. The final “green” indicators group is comprised of 41 bioeconomy indicators (See Fig. 4). Thirdly, the second part of the MCA, focused on constructing a regional dataset for estimating the “green” indicators group for all regions of the 8 selected countries. To reveal the best performing regions, we have implemented the “Technique for Order of Preference by Similarity to Ideal Solution” (hereinafter, TOPSIS), a well-known multi-criteria decision analysis methodology, which is commonly used for ranking variables (regions, in our case) for multiple criteria (bioeconomy indicators, in our case). The ranking process used two scenarios, namely the base scenario, where assumed that all criteria (indicators) have equal weights (thus, equal importance), and a weight’s scenario, where the Shannon entropy weighing technique was applied for estimating specific weights per criteria (thus, importance differs among indicators). Furthermore, the ranking of the above-mentioned scenarios was performed for different combinations of groups of indicators. This disaggregated approach is essential as it allows useful information to be revealed for different assumptions. 2. Literature review on bioeconomy indicators As a new multidisciplinary scientific field, bioeconomy is characterized by an increasing interest among the members of scientific community, as evidenced by the growing number of scientific papers addressing the terms "bioeconomy" or "bio-based economy" (Gould et al., 2023; Staffas et al., 2013). However, the definition of bioeconomy has a long history of evolution over time, incorporating new visions, principles, and goals to align with the needs of various stakeholders. The term "bioeconomics" was first introduced by Russian biologist F.I. Baranoff in the 1920s to describe the optimal level of fish exploitation. By the 1950s, it’s meaning expanded to cover renewable resources more broadly, focusing on their sustainable use to avoid extinction (Vivien et al., 2019; Gould et al., 2023). In the 1970s, bioeconomy re-emerged by Nicholas Georgescu-Roegen as an economic framework which is functioning under physical restrictions imposed by nature (Georgescu-Roegen, 2013, 1977). With the unprecedented technological advancements of the 20th century, new aspects were emerged leading to the creation of three basic visions influencing the way that policy makers perceive bioeconomy: the so-called “bioecology” vision, based on the perspective of Georgescu-Roegen; the “bio-technology” approach, which seeks to use biotechnology to drive economic growth, without placing sustainability at the forefront (Bugge et al., 2016); and the “bio-resource” approach, focusing on replacing non-renewable resources with the renewable ones, in order to address both economic growth and sustainability challenges (Birner, 2018). To address the challenges of revealing the most utilised quantifiable bioeconomy metrics, the present study is primarily based on four (4) main pillars for conducting an extensive literature review focusing explicitly on the bioeconomy indicators (See also Fig. 3): 1. Academic/Scientific Literature: e.g. published studies in peer reviewed scientific journals and relevant textbooks. This first pillar aspires to capture essential theory and practice, methodological contributions, data availability and, most importantly, proposed and utilised bioeconomy indicators at both the national and, especially, regional levels (See also Fig. 1). 2. Reports, working papers and studies by international organizations, institutions (e.g. World Bank, OECD, FAO, UNEP, etc.) and potential national guidelines and strategies from non-EU countries which may include useful quantifiable metrics. The second pillar aspires to incorporate in the present analysis the most important contributions of the international institutions and organisations on bioeconomy indicators, in both theoretical (criteria, methodologies) and practical (bioeconomy indicators, databases) aspects. 3. Guidelines, reports, working papers and frameworks published by the EU and relevant organizations (e.g. JRC, EU, and EP publications, other official EU research entities) and potential national guidelines and strategies with quantifiable metrics from the EU countries. The third pillar of literature review focuses explicitly on the European level. 4. Relevant EU funded projects and grands on bioeconomy, over the last decade. (e.g. Horizon 2020, Horizon Europe, FP7, LIFE+, Interreg, etc.). This final pillar of literature review aspires to reveal potential databases, indicators, methodologies, guidelines, and other P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 2 Fig. 1. a). Constructing the co-occurrence network of all keywords provided by all initial 397 studies; b). the reduced co-occurrence network based only on selected keywords focusing on data, datasets, indicators, methodologies, and other quantifiable metrics. (Networks constructed with VOSviewer tool, version 1.6.20, provided by the Leiden University, available here: https://www.vosviewer.com/). P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 3 contributions to the bioeconomy conception, by investigating current and past relevant EU funded programs. Furthermore, FAO and EU/EP reports, to achieve sustainable bioeconomy at the country and/or at the macro-regional level, suggest the division of the selected bioeconomy indicators into three broad groups: Economic bioeconomy indicators; Social bioeconomy indicators; and Environmental bioeconomy indicators. For the purposes of our analysis, we adopt this categorisation throughout the construction of our bulk dataset with indicators. 2.1. The academic/scientific approach The first pillar of the literature review focused explicitly on scientific-academic publications. A search of the term “bioeconomy” with the use of the Google Scholar tool, in September of 2024, returned circa 144,000 results. Apart from Google Scholar, the scientific database Scopus (Elsevier) was utilised to substantially shortening the results of the review process. More specifically, by using the Scopus advanced search tool, focusing explicitly on the period 2010–2024, and by using specific keywords filtering (“bioeconomy” AND “indicators” AND “quantitative” AND “metrics”), the research returned 397 documents. Due to the lack of access, we further excluded 73 documents (mainly books and book chapters). From the remaining 324 documents we further excluded 96 review papers with no substantial quantitative contribution in terms of metrics and indicators. A further bibliometric analysis by using the VOS viewer tool (version 1.6.20), through constructing co-occurrence networks for keywords extracted by the remaining documents, gradually revealed those studies with more potential to provide quantifiable metrics and indicators, for the needs of the present analysis (e.g. keywords, such as: indicators, sustainability indicators, database, data mining; as well as methodologies, such as: multi-criteria assessment, footprint, forecasting, and cost benefit analysis, may be denoting studies using and/or proposing quantifiable indicators and other bioeconomy metrics). For the gradual process of shortening keywords to reveal the potentially desirable outcome, see also Figs. 1a & 1b Further one-by-one checking of the remaining 228 documents and more detailed research had shortened these numbers into circa 100 studies with potential practical and useful information for our analysis, namely, quantifiable metrics and methodologies, indicators, data, and so on. These studies can be categorised into two main groups: Fig. 2. The four pillars of the literature review on bioeconomy. Constructing the overall bioeconomy indicators’ dataset. Fig. 3. Classifying the data towards constructing the final dataset – the first part of the MCA. Fig. 4. Quantifying the results of the traffic light assessment. P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 4 •Bioeconomy theory, definitions, and strategies (mainly theoretical contributions, including metrics): (e.g. McCormick and Kautto, 2013; Pfau at al., 2014; De Besi and McCormick, 2015; Bugge et al., 2016; El-Chichakli et al., 2016; Sillanp¨ a¨ a and Ncibi, 2017; Meyer, 2017; Imbert et al., 2017; D’Amato et al., 2017; Dietz et al., 2018; Bracco et al., 2018; Heimann, 2019; Patermann and Aguilar, 2021; Frisvold et al., 2021; Holden, 2022; Kircher et al., 2022; Lang, 2022; Gardossi et al., 2023; Gould et al., 2023; Proestou et al., 2024). •Bioeconomy measuring methods and indicators (mainly methodological and empirical quantifiable contributions): (e.g. D’Adamo et al., 2022; Fuentes-Saguar et al., 2017; Sillanp¨ a¨ a and Ncibi, 2017; Siebert et al., 2018; Bracco et al., 2018; Golden et al., 2018; Ronzon and M’Barek, 2018; Jander et al., 2020; Ronzon et al., 2020; D’Adamo et al., 2020a, 2020b; US Board of Science et al., 2020; Lakner et al., 2021; Patermann and Aguilar, 2021; Czy˙ zewski et al., 2021; Kardung et al., 2021; Marcone, 2021; Welfle and Roeder, 2022; Ronzon et al., 2022; Bohvalovs et al., 2022; Gursel et al., 2023; Fern´ andez Ocamica et al., 2024; Mesa et al., 2024). The benefit of reviewing these studies was dual: On the one hand, potential operational groups of bioeconomy indicators were revealed and included into the final bulk dataset constructed to serve the needs and purposes of the present study, while, on the other hand, important theoretical and methodological conclusions were drawn for future research. 2.2. The international approach Similarly to the first pillar of the review process, Google Scholar was used, together with more targeted searching within the official webpages of various international organizations, such as the World Bank (World Bank, 2022; 2024), Organisation for Economic Co-operation and Development (OECD, 2009; 2018; Philp and Winickoff, 2019), United Nations’ (UN) Food and Agriculture Organisation (FAO) (Bracco et al., 2019; Bogdanski et al., 2021; Gomez San Juan et al., 2022), and International Advisory Council on Global Bioeconomy (IACGB, 2020; Dietz et al., 2020). Furthermore, a special search was conducted for collecting relevant bioeconomy metrics of various non-EU countries, focusing especially on the USA (U.S. Department of Agriculture, 2018; President’s Council of Advisors on Science and Technology, 2022; The White House, 2023; Frisvold et al., 2021), Canada (Biotechnology Industry Organization, 2022; Montr´ eal Process Working Group, 2013; 2014), South Africa (Government of South Africa, 2013), Argentina (Centre for Development Research, 2014; Ernst, 2023), Japan (The Cabinet Office, 2020), Malaysia (Wan Hasnul Nadzrin, 2023), United Kingdom (BBNet & BBNet & NNFCC, 2020), Australia (Bio¨ okonomie, 2017; Australian Renewable Energy Agency, 2021), New Zealand (Ministry of Business, Innovation and Employment, 2024), China (Sahu et al., 2024), Brazil (Fundaç˜ ao Getulio Vargas, 2023; Lima and Pinto, 2022), and Russian Federation (U.S. Department of Agriculture, 2012). 2.3. The european approach The third pillar of the literature review was dedicated explicitly to the EU level. Similar extensive research was conducted to reveal EU’s fundamental publications and reports on bioeconomy indicators, while specific relevant material was searched in EU’s specific research bodies, such as the European Commission’s (EC) Knowledge Centre for Bioeconomy portal (Available at: https://knowledge4policy.ec.europa. eu/bioeconomy_en), and Composite Indicators & Scoreboards Explorer (Available at: https://composite-indicators.jrc.ec.europa.eu/explorer/ explorer/indices-and-scoreboards), (See also relevant strategy reports, such as: European Commission, 2022a; 2022b; 2023), European Forest Institute (2016), European Environment Agency (See bioeconomy publications portal at: https://www.eea.europa.eu/publications/ci rcular-economy-and-bioeconomy), and the Joint Research Centre (JRC) (Giuntoli et al., 2020; European Commission - Joint Research Centre, 2020; Lasarte Lopez et al., 2023; Kilsedar et al., 2023; Mubareka et al., 2023; Giuntoli et al., 2023; Patani et al., 2024). Finally, special attention was given to the utilised bioeconomy indicators in the relevant strategies of the EU member countries: Germany (Food and Agriculture Organization (FAO), 2019; Federal Ministry of Food and Agriculture - BMEL, 2020; Federal Ministry of Education and Research - BMBF, 2020), the Netherlands (Statistics Netherlands - CBS, 2022), Austria (Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology – BMK, 2019; Austrian Ministry for Climate Action, 2017; Republic of Austria, 2019), Italy (Italian Government, 2019), Finland (Finnish Bioeconomy Strategy, 2014), Spain (Ministry of Agriculture, Fisheries, and Food – MAPA, 2015; Government of Catalonia, 2021; Basque Government, 2021), Latvia (Latvian Ministry of Agriculture, 2018), France (French Ministry of Agriculture and Food, 2018), Ireland (Government of Ireland, 2018), Belgium (Interreg North-West Europe, 2018; Walloon Region Government, 2022), Czech Republic (See Bio Hub, n.d., BIOEAST HUB CZ, available at: https://www.bio-hub.cz/en/bio-hub-en/en), Sweden (Swedish Knowledge Foundation – KSLA, 2021; Fossil-Free Sweden, 2021; Formas, 2019; Nordic Council of Ministers, 2021), and Poland (Warsaw University of Life Sciences, 2023; Kozyra et al., 2023). 2.4. The EU-funded projects approach Finally, the fourth pillar of the literature review in search for relevant indicators focused explicitly on various previous and ongoing EU-funded projects on bioeconomy. Towards this aim, many useful old and new materials were revealed and reviewed, from various projects and programmes, such as Horizon 2020, Horizon Europe, Interreg, LIFE+, FP7, to name indicatively but a few. Additionally, various toolkits, datasets, forums and other initiatives were taken into consideration. Some indicative examples of the 4th pillar of the review process are presented in the Supplementary materials. 3. The first part of the multi-criteria assessment (MCA) 3.1. The methodological framework in a nutshell The four-pillars extensive literature review, presented in the previous sections, provided the essential bulk quantifiable indicators search for the next steps of our analysis. These next steps are briefly summarised as follows: 1. Specify the borders of the examined system. In our case, the border is set exclusively at the European regional level, thus in terms of Eurostat, the NUTS II level. We use the most up to date Eurostat’s classification, valid from January of 2024, which lists 244 regions at the EU - NUTS II level (Eurostat, 2024). In our case, eight (8) countries, namely: Belgium, Germany, Greece, France, Czech Republic, Spain, Sweden and Iceland (non-EU member), provide a good representative case study of the European regional level, in terms of geospatial, population and economy’s size levels. 2. Translate the output of the four-pillars literature review into a useful dataset covering the vast majority of the available bioeconomy indicators. To do so, we have utilised the so-called traffic light decision-making assessment (TLA), to classify and manage the derived bioeconomy indicators. This step was the first part of the overall MCA methodology employed. A more detailed representation of the first part of MCA is provided in a following section (See also the Supplementary materials for more details). 3. Estimate the final group of the selected bioeconomy indicators with available data for the 125 regions (NUTS-II) of the 8 countries and perform the second part of the employed MCA, namely, to provide a robust and practical ranking methodology which could reveal some P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 5 “champion” regions, in terms of bioeconomy potential. This could offer essential information and good practices required to build the profile of a model region, as a prototype of promoting bioeconomy. For the purposes of regional ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied. A more detailed representation of the second part of MCA is provided in a following section (See also the Supplementary materials for more details). 3.2. The criteria for selecting the final set of bioeconomy indicators Selecting criteria is often challenging because it may be difficult to understand which criteria to include and/or to exclude. According to various academic studies in peer-reviewed scientific journals (see the 1st pillar of the literature review), FAO and OECD (see the 2nd pillar of the literature review) reports, as well as essays and reports of the European Union (EU) and the European Parliament (EP) (see the 3rd pillar of the literature review), it is essential to first define specific criteria for the selection of appropriate bioeconomy indicators. In this context, we adopt the following seven (7) important criteria, that the final bioeconomy indicators included in our dataset must fulfil: 1. Ought to be meaningful and clearly defined. 2. Ought to be well-established and already used in other policy frameworks. 3. Their calculation should be based on timely and frequently (at least yearly) collected data. 4. Their geographical coverage ought to be regional (explicitly in terms of NUTS II EUregional level, in our case). 5. Ought to be comparable across countries and/or products and/or sectors. 6. Ought to be comparable over time. 7. The data should be only openly available (publicly accessible) and clearly documented (transparent). Criteria 4, 5 & 7 played the most decisive role during the selection process. 3.3. The traffic light decision making assessment – the first part of the MCA The traffic light decision making methodology is a widely used tool in MCA for classifying data according to their applicability (or not) in a broad variety of purposes. The method usually evaluates three specific outcomes (just like a traffic light has three colours, thus, three potential outcomes). However, due to the high complexity of our case, we introduced a four potential outcomes traffic light assessment. Each bioeconomy indicator was individually validated whether it compromises with the assessments’ fundamental criteria, presented in Section 3.2, or not. The final classification of this, unique per indicator, process has resulted into four potential outcomes, each expressed by a unique colour (For more details, see also Picture 1, in Supplementary materials): 1. Green: The bioeconomy indicator meets all the adopted criteria, especially the data availability for all EU regions (NUTS II), hence is approved for inclusion to the final dataset. 2. Orange: The bioeconomy indicator meets some of the adopted criteria, hence is rejected. More specifically, in most of this category’s cases, there were data availability only for some specific EU regions but not for all EU regions. However, the indicators included in this category could potentially be utilised in other more targeted analysis where the total regional coverage is not a prerequisite. 2 3. Red: The bioeconomy indicator meets some of the adopted criteria and there are data available only at the national EU level, in most cases, hence is rejected. However, the indicators of this category could be used for potential bioeconomy analysis at the national EU level. 4. Grey: This is a special category included in our analysis for classifying those indicators that do not fulfil most of the adopted criteria, do not have data availability for most regional and/or national level, yet their data could be useful for other purposes (e.g. variables in modelling scenarios, geospatial analysis, mapping, more abstract descriptive analysis, specific microeconomic approaches, etc.). A final deep dive processing of the classified variables was anticipated for ensuring that all the approved (green category) bioeconomy indicators do comply with the data availability criterion for all EU regions. After the conclusion of this final checking process, the final dataset comprised by the final groups of available (green) bioeconomy indicators was constructed and implemented in estimating the regional bioeconomy indicators for the regions of the 8 selected European countries. (See a brief depiction of the process in the Fig. 3). 3.4. The final dataset of regional bioeconomy indicators – explaining the data The first part of MCA revealed in total 897 bioeconomy indicators, the total pool constructed by the bulk indicators search, out of which only a final 5 % (41 “green” bioeconomy indicators) meet all the adopted criteria and, thus, are available for estimating the EU regional bioeconomy status of the 8 selected countries, and further implement the second part of the MCA, namely the final ranking and the comparison of all regions. The “orange” category included 219 indicators (24 %), the “red” category 241 (27 %) and, finally, the “grey” category 396 (44 %). (See Fig. 4). The final dataset of the 41 selected (green) regional bioeconomy indicators is provided in the Supplementary materials (See Annex VI, Tables S14–S16). Apart from the “green” bioeconomy indicators, we have included in our analysis two more qualitive indicators. In regional analysis, it is expected that a dedicated national bioeconomy strategy is the first step that could potentially have positive effect in promoting regional bioeconomy. Moreover, if this national bioeconomy strategy is followed also by a dedicated regional bioeconomy strategy, per region of a country, then there is a good probability to expect a more increased bioeconomy potentials, dynamics, and commitment at both regional (bottom-up) and national level (up-bottom). All data for constructing these on-off qualitative bioeconomy indicators on strategy availability 2 Some indicative examples of the excluded “orange” indicators are: “Investment in the bioeconomy sectors”, including financial funds for tangible investments (such as machinery), immaterial assets (e.g. software or patents), or financial investments (e.g. bonds or equity investments), “percentage % contribution of bioeconomy sectors to regional GDP”, and “Intellectual property rights (IPRs) applications in bioeconomy subsectors” (e.g. patents, trademarks, designs, etc.) (Economy dimension); “Average income of employees in the bioeconomy sectors” and “Employment in total bioeconomy sectors as % of total employment in the region” (Social dimension). Tables with orange, red, and grey indicators may be provided by the authors upon reasonable request. P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 6 were derived by a recent report of the EC (European Commission 2010; Haarich et al., 2022). (More details are available in the Supplementary materials e.g. see paragraph 4 & Picture 2). 4. Analysis and results. the second part of the MCA 4.1. The technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision making (MCDM) The ability to perform a robust ranking analysis of numerous different quantitative and some qualitative variables is the primary prerequisite for choosing an appropriate method for multiple criteria decision-making (MCDM). For the purposes of the second part of the present MCA, we have selected the widely used TOPSIS methodology, a very common tool utilised for solving similar problems of decision making based on many different criteria and variables (Chakraborty, 2022; Madanchian and Taherdoost, 2023). In a nutshell, TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution, thus, to be closer to the best regional performance in our case, and the longest geometric distance from the negative ideal solution, thus, to be as far as possible by the worst regional performance. TOPSIS compares all the available alternatives, by giving normalising scores for each criterion (indicator in our case) and calculates the geometric distance between each alternative and the ideal/worst alternative, resulting in the best score in each variable (region in our case). 4.2. The two scenarios and the different groups of indicators In the present study, four groups of indicators (Economy, Society, Environment +two qualitative indicators) have been constructed during the first part of the MCA, so the indexing is performed separately within each group of indicators, apart from the qualitative group which is included only to the “Total bioeconomy” group (The reasoning behind this choice is further discussed into the Supplementary materials). The process of the followed ranking methodology could be briefly summarised in the following two scenarios: 1. The base scenario. The first scenario utilises an equal weight ranking for all indicators by using TOPSIS. This is the case where all criteria-indicators are equally contributing to the final ranking of all regions. The results of the base scenario are compared with the results of the second scenario. 2. The weights’ ranking scenario. The second scenario is the modified TOPSIS ranking, based on the estimated weights, resulting by dataset itself, by using the Shannon entropy weight method. Shannon entropy was first introduced in the field of informatics and communication, and it is widely adopted to measure the quantity of useful information that is provided by the dataset itself (Shannon, 2001). The Shannon entropy method has been commonly applied to determine objective weights for modifying TOPSIS ranking results and, thus, reduces decision biases and adds substantial objectiveness to the final decision assessment (Wu et al., 2018). The results of the modified weighted TOPSIS are expected to be more robust and substantial by the results of the base scenario (equal weights TOPSIS), although similarities in both scenarios are occasionally observed. The ranking process is performed for the next group of bioeconomy indicators: •Economy group: Ranking is performed into three sub-groups, namely: 1. Pure economy indicators ranking 2. Bioeconomy & innovation indicators ranking 3. Sectoral bioeconomy indicators ranking (All sectors aggregated) •Society group: Group ranking is performed as it is. •Environment: Group ranking is performed as it is. •Bioeconomy per sector: A more targeted ranking per sector of bioeconomy (8 sectors examined separately) •Total Bioeconomy: All groups of indicators are unified in one group, and the ranking is performed together for all. This ranking includes the two qualitative indicators (See also Supplementary materials, paragraph 4 & Picture 2). 4.3. Comparing and analysing the scenarios through the different groups of indicators 4.3.1. The economy group For the “Economy” group of bioeconomy indicators a special ranking is performed, based on three sub-categories of the initial group. This disaggregation is essential for revealing certain characteristics that may be omitted and obscured in an overall group ranking process. Towards this objective, we initially perform the “Pure economy” subset of the “Economy” group of indicators, which includes all the “Economy” group indicators (see Supplementary materials, Annex VI, Table 14), apart from the sectoral bioeconomy indicators (namely: value added of bioeconomy, sectoral bioeconomy employment, and sectoral bioeconomy turnover estimates) and the Regional Competitiveness Index (RCI) with all its sub-indicators. Table S1 (See Annex I in Supplementary materials) and Fig. S1 (See Annex V in Supplementary materials) compare the base scenario and the weight’s modified TOPSIS scenario. Note that only the top twenty (20) performing regions are presented in the Supplementary materials, for all examined cases. Accordingly, Fig. 5 depicts a choropleth map based solely on the second scenario of regional ranking (modified with Shannon entropy weight TOPSIS), for the “Pure economy” subset. In terms of pure economic indicators, the Belgian region “BE21-Prov. Antwerpen” is placed in the first place, followed by the Greek region “EL52-Central Macedonia”, the French region “FR10-Ile-de-France”, the Czech region “CZ06-Jihovýchod”. Although the indicators utilised in the present analysis is the result of a specific and limiting process, it is probable that the Antwerp region is placed to the first position due to several specific characteristics, such as its strategic location and its port being the second largest in Europe, its strong and diversified economy largely driven by logistics, chemical industry, a robust service sector and a significant number of small and medium-sized enterprises (SMEs). Second, the “Bioeconomy & Innovation” subset of the “Economy” group of indicators is examined. This subset of indicators includes only the sectoral bioeconomy indicators (estimates of sectoral value added, sectoral employment, and sectoral turnover, for the 8 examined sectors) and the EU Regional Competitiveness Index (RCI) with all its subindicators, trying to capture the contribution of the regional bioeconomy sectors in tandem with the innovation effect, as it is captured by the RCI’s group of sub-indicators (see Supplementary materials, Annex VI, Table S14). Table S2 (Annex I in Supplementary materials) and Fig. S2 (Annex V in Supplementary materials) compare the base scenario and the weight’s modified TOPSIS scenario. The weights ranking scenario, presented in Fig. 6, places first the Spanish “ES11-Galicia” region, an area moving systematically towards being a promising innovation hub and a start-ups pole at the EU level. Second is ranked the “FR10-Ile-de-France” region. Alternatively called the “Paris region”, as Ile de France is the most populous region in France and gathers most of the France’s elite industries, this is a well excepted outcome. However, the raking placed third place to “DEB2-Trier”, a small region in Germany, leaving fourth “FRH0-Bretagne” and fifth “ES51-Catalu˜ na”. This requires further examination, as the lack of spatial data in our analysis might cause, in some cases, imbalances in favour of smaller regions. In any case, Trier’s existing research capacity, proximity to agricultural areas and biomass production, and focus on P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 7 sustainability initiatives and the principles of circular economy could be leveraged to develop innovative bio-based solutions and, thus, potentially develop strong bioeconomy performance. Third, the “Sectoral bioeconomy” subset of the “Economy” group of indicators is ranked. This subset of indicators includes only the total aggregated sectoral bioeconomy indicators (value added, sectoral employment, and sectoral turnover, for the 8 examined sectors), trying to capture the pure contribution of the regional bioeconomy sectors. Table S3 (Annex I, Supplementary materials) and the relevant Fig. S3 (Annex V, Supplementary materials) compare the two ranking scenarios. The dominance of “FR10-Ile-de-France” region, for both ranking scenarios, is evident. More specifically, focusing explicitly to the second scenario, Fig. 7 depicts “ES11-Galicia” region as the second strongest performer, while, concerning the third place of Iceland, this may be the effect of the impact of the “Fishery sector” to the overall sectoral result. The German “DEB2-Trier” and the Spanish “ES51-Catalu˜ na” regions are ranked next, respectively. 4.3.2. The society group The “Society” group of indicators (See Annex VI, Table S15, Supplementary materials) is ranked as a whole, for both scenarios and the results are presented in Table S4 (Annex II, Supplementary materials) and in Fig. S4 (Annex V, Supplementary materials). Fig. 8 provides the modified TOPSIS ranking result of the total “Society” group of indicators through a choropleth map. The “FR10-Ile-deFrance” is the top performing region according to the selected societal indicators. Spanish regions are ranked next, namely, “ES61-Andalucía”, “ES51-Catalu˜ na”, and “ES30-Comunidad de Madrid”, respectively, followed by the French “FRK2-Rhˆ one-Alpes” region. 4.3.3. The environment group Accordingly, the “Environment” group (See Annex VI, Table S16, Supplementary materials) of indicators is ranked as a unified group, for both scenarios and the results are presented in Table S5 (Annex III, Supplementary materials) and in Fig. S5 (Annex V, Supplementary materials). Fig. 9 depicts the second ranking scenario for the “Environment” group of indicators, where the Spanish regions “ES42-Castilla-La Mancha”, “ES24-Arag´ on”, “ES61-Andalucía”, and “ES41-Castilla y Le´ on”, are occupying the four top positions, respectively, followed by the Swedish “SE33-¨ Ovre Norrland” region. 4.3.4. The bioeconomy per sector group It is of paramount importance to perform a targeted disaggregation to the “Economy” group of bioeconomy indicators, to reveal the bioeconomy potentials at the specific sectoral level, for those sectors that remain essential for the concept of bioeconomy. To do so, we explicitly perform a ranking per sector, of each of the eight (8) bioeconomy sectors included in our dataset. The per sector ranking is by far more revealing. Results per bioeconomy sector, for the second (weight’s ranking) scenario are presented below (see Fig. 10 1–8 ) (For more data, see Tables S6–S13, Annex IV, and Figures S6–S13, Annex V, in the Supplementary materials). For the Agricultural sector (A01), the “ES61-Andalucía” region in Spain, is the strongest performer (Fig. 10.1). Evidently, Andalucia is a leading region, not only in Spain but in the entire Europe, for Fig. 5. Choropleth map of the sub-group of “Pure economy” indicators ranking, for all regions of the 8 countries examined. (The second, weight’s ranking, scenario). The map created with the use of the Datawrapper online tool, available here: https://www.datawrapper.de/. P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 8 agricultural production, exporting fruits, vegetables, while the region is the world’s top producer of olive oil. Concerning the Forestry sector (A02), both ranking scenarios provide similar ranking outcomes with mainly Swedish regions with rich forest ecosystems being predominant (Fig. 10.2) which is a well-expected ranking result. Accordingly, the Fishing & aquaculture sector (A03) clearly ranks “ES11-Galicia” region in Spain and “IS00-Iceland” (This is the entire country of Iceland, there is no regional specification), as the two champion areas. This is a well explained result, since Vigo in Galicia, Spain, owns the largest fishing port of Europe, while some of the largest fishing companies in the world are headquartered there. Accordingly, Iceland is a nation with a historic, world leading fishery heritage (Fig. 10.3). “FR10-Ile-de-France” is the best performer in both rankings for most of the sectoral subsets, such as the Food, beverage, and tobacco sector (C10-C12) (Fig. 10.4), the Bio-based textiles sector (C13-C15) (Fig. 10.5), the Paper sector (C17) (Fig. 10.7), and the Bio-based chemicals, pharmaceuticals, plastics, and rubber sector (C20-C22) (Fig. 10.8), while “DEA4-Detmold” in Germany, is the best performing region in the Wood products and furniture sector (C16-C31) (Fig. 10.6). 4.3.5. The total bioeconomy group Finally, an effort to estimate the total ranking of all groups of the bioeconomy indicators (economy, society, environment, and qualitative indicators), unified as one dataset, was performed. (For more details, see Supplementary materials). The base scenario of the total bioeconomy dataset provided “FR10-Ile-de-France” as the best performing region (See Fig. 11). 5. Discussion and conclusions Designing effective guidelines and policies for promoting regional bioeconomy is a critical step for the European Union to achieve the demanding targets of the European Green Deal and the “Fit for 55 ″ package. Towards this end, it is anticipated that revealing potential best performing regions in bioeconomy will bring forward new knowledge and a better understanding of the elements and the attributes that could increase bioeconomy potentials at the EU regional and, consequently, national level. The present study should be conceived as an initial effort to design, establish, and empirically evaluate a step-by-step regional MCA blueprint for the European countries. Towards this aim, the very first step was to conduct an extensive literature review focusing explicitly into revealing the most widely used metrics and indicators for measuring bioeconomy potentials. The review was based on a four-pillars approach to extract potentially useful quantifiable metrics from the academia, the international perspective (organisations and countries), and the European perspective (EU organisations, countries, and relevant projects). The outcome of this detailed review process was the construction of a bulk bioeconomy indicators dataset. This initial pool of indicators was further classified and categorised according to certain criteria, through the traffic light assessment (TLA) methodology. Only the indicators fulfilling the required criteria were finally utilised to perform the next step of the MCA (Available in Annex VI, Tables S14–S16, Supplementary materials). The results of the present study could be conceived as a first Fig. 6. Choropleth map of the sub-group of “Bioeconomy & innovation” indicators ranking, for all regions of the 8 countries examined. (The second, weight’s ranking, scenario). The map created with the use of the Datawrapper online tool, available here: https://www.datawrapper.de/. P. Kalimeris et al. Cleaner and Circular Bioeconomy 12 (2025) 100195 9