Measuring the Bioeconomy Economically: Exploring the Connections between Concepts, Methods, Data, Indicators and Their Limitations

Abstract

Despite its relevance, measuring the contributions of the bioeconomy to national economies remains an arduous task that faces limitations. Part of the difficulty is associated with the lack of a clear and widely accepted concept of the bioeconomy and moves on to the connections between methods, data and indicators. The present study aims to define the concepts of bioeconomy and to explore the connections between concepts, methods, data, and indicators when measuring the bioeconomy economically and the limitations involved in this process. The bioeconomy concepts were defined based on a literature review and a content analysis of 84 documents selected through snowballing procedures to find articles measuring “how big is the bioeconomy?” The content of the 84 documents was uploaded to the Quantitative Data Analysis (QDA Miner) software and coded according to the bioeconomy concept, the methods or models used, the data sources accessed, the indicators calculated, and the limitations reported by the authors. The results of the occurrence and co-occurrence of the codes were extracted and analyzed statistically, indicating the following: the measurement of the bioeconomy (i) needs to recognize and pursue the proposed concept of a holistic bioeconomy; (ii) rarely considered aspects of a holistic bioeconomy (3.5%); (iii) is primarily based on the concept of biomass-based bioeconomy (BmBB) (94%); (iv) the association with the concept of biosphere (BsBB) appeared in 26% of the studies; (v) the biotech-based bioeconomy (BtBB) was the least frequent (1.2%); (vi) there is a diversity of methods and models, but the most common are those traditionally used to measure macroeconomic activities, especially input-output models; (vii) depending on the prevailing methods, the data comes from various official statistical databases, such as national accounts and economic activity classification systems; (viii) the most frequently used indicators are value added, employment, and Greenhouse Gases (GHG) emissions; (ix) there are various limitations related to the concept, methods and models, data, indicators, and others, like incomplete, missing, or lack of data, aggregated data, outdated data or databases, uncertainty of the estimated values, the subjectivity in the bio-shares weighting procedures, and other limitations inherent to methods and models. We conclude that current efforts only partially measure the contributions of the bioeconomy, and efforts should be encouraged toward a full assessment, starting by recognizing that the measurement of a holistic bioeconomy should be pursued.

1. Introduction

In the last two decades, the bioeconomy has emerged as the promise of a new paradigm toward developing a sustainable economic system [1]. This expectation is based on the renewability of biological resources and the circulation of chemical elements. Thus, the bioeconomic paradigm is superior to the economic system based on finite resources whose stocks are finite and non-renewable [2]. As new political and economic stimuli have been prospected, new ventures are taking place in the field of bioeconomy, leading various countries to adopt specific programs, strategies, or guidelines to stimulate the bioeconomy.

Although the European Commission (EC) started discussions and the implementation of EU Framework Programmes in biotechnology and life science in the last century (1982), institutionally, the bioeconomy concept first appeared in a proposition by Christian Patermann grounded in knowledge-based bioeconomy (KBBE) as a vision for the European bioeconomy in 2030 [3]. The concept was proposed in 2007 in the Cologne paper “En Route to the Knowledge-Based Bio-Economy (KBBE)”. The purpose was to aggregate competitiveness and sustainability into circular bio-based solutions while addressing climate neutrality, environmental performance, safety, and social acceptance by respecting planetary boundaries [4]. The Organization for Economic Co-operation and Development (OECD) followed this approach, proposing an official agenda for promoting bioeconomy in 2009 [5].

In 2012, the European Union launched its strategy for developing a bioeconomy that connected the economy to nature for sustainable growth [6,7]. Since then, national governments worldwide have made efforts to promote bioeconomy activities and boost national economies officially. These include the Netherlands, Sweden, the United States, and Russia in 2012, Malaysia and South Africa in 2013, Germany and Finland in 2014, and France in 2016 [8]. Brazil launched its national bioeconomy program in 2019 [9], while China included the bioeconomy in its 14th five-year plan for 2021–2025 [10]. Currently, dozens of countries have specific or related bioeconomy strategies [11,12].

However, one aspect that differentiates national strategies is the essence of the bioeconomy concept considered when outlining the actions to be stimulated. In the European Union (EU), for example, the concept was updated in 2018 to add sectors and systems that rely on biological resources in addition to those primary sectors that produce renewable biological resources [13,14]. Although it is recognized that there is no widely accepted concept of the bioeconomy [15], in the United States, it can be said that former President Barack Obama’s National Bioeconomy Blueprint is based on three main and correlated axes: knowledge, technology, and innovation [16]. From the North American perspective, the bioeconomy is the economic activity driven by research and innovation in life sciences and biotechnology, enabled by technological advances in engineering, computing, and information sciences [15,17]. Genetic engineering, molecular biology, bioinformatics, and synthetic biology are some driving technological platforms. The Bioeconomy Brazil Program focuses on extracting renewable resources and exploring biodiversity as a strategy for the sustainable development of smallholder and indigenous communities [9]. Recently, the Brazilian strategy for bioeconomy has been revised based on a wider concept that includes innovations and technologies to add value and generate work and income sustainably and environmentally friendly [18].

In scholarly circles, several authors have differentiated bioeconomy approaches by considering the resources, processes, and effects that activities trigger in the economic and biophysical systems. Bugge et al. [19] classified the bioeconomy into the following: (i) bioresources, indicating products composed wholly or in large part of biomass; (ii) biotechnology—products or processes enabled by innovation in the life sciences; (iii) bioecology based on sustainable processes and sustainably sourced products. Vivien et al. [20] categorized the bioeconomy into the following: (i) Type I—an ecological economy that is compatible with the biosphere constraints; (ii) Type II—a science-based economy driven by industrial biotechnology; (iii) Type III—a biomass-based economy. Befort [21] categorized the visions of the bioeconomy based on socio-technical regimes, proposing the following: (i) a Biotech-bioeconomy, which is technology-driven, spreading biotechnology into health, food, industry, etc., and industrialization of the living; (ii) a Biomass-bioeconomy, whose mission-driven towards the substitution of oil for biomass. Other variants of classifications, typologies, and narratives are explored in the literature, as in [22,23]. However, the classifications cited are sufficient for the following: (i) to show evidence that a widely accepted concept of bioeconomy is lacking; (ii) link the national concepts to the visions of the bioeconomy in the literature, being EU close to bioresources or Type III, US to biotechnology or Type II, and Brazil to bioecology or Type I; (iii) reflect on what are the implications for the measurement of the bioeconomy.

Despite these conceptual differences, stakeholders’ interests exist, and there are efforts to show the economic importance of bioeconomy and to monitor indicators to attract investment in bioeconomy-related activities. Although awareness differs between stakeholders, the economic dimension is still relevant [24,25]. In this regard, Ronzon et al. [26] estimated that the bioeconomy contributed 4.7% of the EU-27’s Gross Domestic Product (GDP) in 2017. In the US, the contribution of the bio-based bioeconomy to the national GDP in 2016 was 5.1% and could reach 7.4% [15]. Specifically, the biotechnology-based bioeconomy reached 2.0% of the US GDP in 2012, up from 0.004% in 1980. This is an annual growth rate of over 10% [27]. In Brazil, the bioeconomy value chain accounted for 19.6% of GDP in 2019 [28], which is a higher share than the 13.8% of added value found by [29].

Consequently, one question that arises is as follows: How are concepts, methods, data, and indicators connected in the process of measuring bioeconomy economically, and what are the limitations involved? A relevant study in this regard is the review by [30], which lists the methods, variables, and data used to measure the sustainability of the bioeconomy, including the economic dimension. However, it does not define concepts nor explore the connections between methods, data, and indicators and their limitations. The present study aims to advance in detailing all these connections to measuring “how big is the bioeconomy?” Therefore, the present study aims to define the concepts of bioeconomy and to explore the connections between concepts, methods, data, and indicators when measuring the bioeconomy economically and the limitations involved in this process.

2. Materials and Methods

The first step in reaching this study’s goal is to define the concepts of bioeconomy. Once the bioeconomy concepts have been defined, the next step is to carry out a content analysis of carefully selected documents. This section describes the methodological procedures used to complete these research steps. The following steps to achieve these goals are explained in detail.

2.1. First Step: Defining the Concepts of Bioeconomy

As mentioned earlier, there is no widely accepted definition for the bioeconomy that incorporates all its idiosyncrasies. However, defining the concept of bioeconomy is crucial for exploring its connections with methods, data, and indicators. A literature review was conducted on scientific articles that reported conceptual analyses, visions, narratives, or approaches to the bioeconomy. The concepts were then organized based on the similarity of their fundamental elements. Three concepts were defined as partial, as they adopted a restricted vision of the bioeconomy. Based on the characteristics and limitations of these partial concepts, the concept of a holistic bioeconomy was proposed that combines many characteristics and elements of partial concepts. This new concept has advantages for measuring the true value of the bioeconomy.

2.2. Second Step: Articles Selection and Content Analysis

This section describes the procedures adopted for searching and selecting articles, building the database, defining the codes, coding the content of articles, and extracting the results.

2.2.1. Searching for Articles

Content analysis requires selecting documents of interest. In this respect, the scope of documents to be retrieved was restricted to measuring bioeconomy economically. The methodological strategy chosen to find documents (scientific articles, technical reports, government reports) fitting to the desired scope follows the snowballing procedures.

Snowballing is a well-established and widely used technique for exploring related documents by identifying articles relevant to your topic of interest from key and cited-by articles [31]. In some cases, snowballing proved to be a more efficient method for retrieving articles of interest than the databases themselves [32], depending on the database [33]. Synthetically, according to Greenhalgh and Peacock [31], snowballing refers to using the reference list of a paper or the citations of a paper to identify additional ones. You have to start with a few articles currently in or around your topic of interest. These articles are referred to as the “start set”. Once the start set is defined, it is time to conduct backward and forward snowballing.

The snowballing procedures followed the steps described by [34,35,36]. According to Jalali and Wohlin [37], systematic literature studies can be conducted differently regarding the first step. Based on the specificity of this study, we opted to start with the reference lists of a starting set of papers, called the “start set”. The start set of papers was identified in two previous studies by [38,39], which focus on the economic measurement of the bioeconomy. By merging both lists and excluding duplicated papers, a list of 10 papers remains as the start set, as listed in

Table 1. List of papers serving as the start set for snowballing procedures.

Document Order and Information	Reference
Nowicki P, Banse M, Bolck C, Bos H, Scott E. 2008. Biobased economy. State-of-the-art assessment. Rep., Agric. Econ. Res. Inst., The Hague, The Netherlands.	[40]
2. Vandermeulen V, Prins W, Nolte S, Van Huylenbroeck G. 2011. How to measure the size of a bio-based economy: evidence from Flanders. Biomass Bioenerg. 35: 4368–4375.	[41]
3. Efken J, Banse M, Rothe A, Dieter M, Dirksmeyer W, et al. 2012. Volkswirtschaftliche Bedeutung der biobasierten Wirtschaft in Deutschland. Work. Pap., 07/2012, Johann Heinrich v. Thünen-Inst., Braunschweig, Germany. (*)	[42]
4. Rosegrant MW, Ringler C, Zhu T, Tokgoz S, Bhandary P. 2013. Water and food in the bioeconomy: challenges and opportunities for development. Agric. Econ. 44(s1):139–150.	[43]
5. Golden JS, Handfield RB, Daystar J, McConnell TE. 2015. An economic impact analysis of the U.S. biobased products industry: a report to the Congress of the United States of America. Ind. Biotech. 11(4):201–209.	[44]
6. Ronzon T, Santini F, M’Barek R. 2015. The bioeconomy in the European Union in numbers. Facts and figures on biomass, turnover and employment. Rep., Eur. Comm., Joint Res. Cent., Inst. Prospect. Tech. Stud., Sevilla, Spain.	[45]
7. Heijman W. 2016. How big is the bio-business? Notes on measuring the size of the Dutch bio-economy. NJAS 77:5–8.	[46]
8. Carlson R. 2016. Estimating the biotech sector’s contribution to the US economy. Nat. Biotechnol. 34(3):247–255.	[27]
9. Ronzon, T.; Piotrowski, S.; Tamosiunas, S.; Dammer, L.; Carus, M.; M’barek, R. 2020. Developments of Economic Growth and Employment in Bioeconomy Sectors across the EU. Sustainability, 12, 4507.	[26]
10. Kuosmanen, T.; Kuosmanen, N.; El Meligi, A.; Ronzon, T.; Gurria Albusac, P.; Iost, S.; M’Barek, R. 2020. How Big is the Bioeconomy? Reflections from an economic perspective; Publications Office of the European Union: Luxembourg.	[47]

Note: Documents are ordered by publication date. (*) Efken et al. 2012 [42] was included in the start set papers but excluded from content analysis because it is in German. Source: papers selected from [38,39].

.

We began the backward and forward snowballing procedures from the start set papers. Backward snowballing means using the reference list of a starting set paper to identify new documents to include in the literature review, while forward snowballing refers to identifying new papers based on those papers citing the paper being examined [34]. A series of iterations were conducted during backward and forward snowballing. The following procedures were carried out to accomplish the first iteration. Firstly, we took each of the papers from the start set and thoroughly examined the list of references, searching for potentially additional papers to be included in the review database. The question “How big is the bioeconomy?” [47] illustrates the guiding question we sought in the papers. Secondly, we went to the Scopus database, searched for each paper, and accessed the papers citing them. For documents such as reports not indexed in Scopus, we looked for their citation by filing the query within “References” with the document title. In both the backward and forward snowballing process, the title of the cited or citing papers was evaluated, and according to the adherence to the goal of the present study, the papers were pre-selected to compose the review database. Then, the pre-selected papers were revised according to the criteria described in Section 2.2.2—Selecting for articles. The second iteration started from the selected documents, and backward (cited) and forward (citing) snowballing was carried out. This process was repeated in five iterations until no additional papers were selected, as shown in Figure 1. The search for the articles was carried out from August to the end of October 2023.

2.2.2. Selecting for Articles

The criteria to include an article in the database are as follows: (i) the word “bioeconomy” or “bio-economy” should appear in the title, abstract, keywords, or at least in the introductory contextualization; (ii) the study should measure some economic aspects of a sector or a bioeconomy system within local, regional, national, or multiple countries’ economies; (iii) the study should describe a method to measure the bioeconomy share in the economic system. Therefore, studies based on reviews, analyzing national strategies or policies on bioeconomy, describing the bioeconomy based purely on official statistical data or conceptual frameworks, or assessing only socio-environmental aspects of bioeconomy sustainability were excluded from the analysis.

At the end of the snowballing process, 84 documents, including articles and technical or scientific reports, were selected to have their content analyzed (Table S1 in Supplementary Materials).

2.2.3. Building the Database

The first step to analyzing the documents’ content was the construction of an appropriate database. To make the process more reliable, we used the software, Qualitative Data Analysis (QDA Miner), v. 5, provided by Provalis Research (https://provalisresearch.com/products/qualitative-data-analysis-software/, accessed on 25 September 2024). The textual content of each document was uploaded to the QDA Miner environment, and its year of publication and first author name were assigned as related variables. The associated variables are interesting for comparing results over time and by authors. We chose to compare authors and studies in the present study and did not conduct a temporal analysis. At the end of this process, the text content of the documents was electronically stored and coded in the QDA Miner environment using the software’s functions.

2.2.4. Defining the Codes

We did not use automated text mining processes in this study. Instead, the content of the documents was personally coded by the authors. The textual content of the documents can be coded using a list of predefined codes or an appropriately designed code structure according to the purpose of this study. Although a list of possible and useful codes could be extracted from the literature in studies like [30,38,39,48,49,50], we decided to construct a particular code structure.

Considering the purpose of this study, we defined a fundamental root-code structure based on concepts, methods, data, indicators, and limitations. The studies’ geographical scope (country, region, etc.) was also considered a root code. Only the codes under the root code “bioeconomy concepts” were predefined based on Section 2.1 and 4.1. of this article. Thus, only the codes BmBB (biomass-based bioeconomy), BtBB (biotechnology-based bioeconomy), and BsBB (biosphere-based bioeconomy) could be used in the coding process. The codes for the other root codes were defined as the content was analyzed and coded. Thus, new codes were identified and added to the code structure. As the list of codes expanded, the codes began to repeat themselves, and the occurrence of additional codes ceased. The final code structure can be found in Table S2 in Supplementary Materials.

2.2.5. Coding the Articles’ Content

With the textual database and code structure available in the QDA Miner user interface, the next step was to code the content of the documents. The coding process involves linking a given code to a fraction of the corresponding text. For example, if, during the content analysis, a passage of text that characterized the use of the biomass-based bioeconomy concept was identified, this text fragment was selected, and the code “BmBB” was linked to it. The same procedure was applied to the other codes relating to methods, data, indicators, and limitations. Therefore, at the end of the coding process, the content of the 84 documents was coded entirely considering bioeconomy concepts, measurement methods, data used, indicators explored, and any limitations identified. Finally, a complete review of duplicate codes in the same document was completed, and corrections were made where necessary.

2.2.6. Extracting the Results

Using the QDA Miner’s statistical resources made it possible to generate some figures directly, and the occurrence and co-occurrence of codes were downloaded so that other figures could be generated in other software.

3. Results

3.1. The Concepts of Bioeconomy: From Partial to Holistic Bioeconomy

In this section, we propose the concept of a holistic bioeconomy based on three partial conceptual approaches discussed in the literature and the recognition that these concepts are not mutually exclusive, in agreement with [17]. We argue that the holistic bioeconomy should be considered and pursued in measuring the bioeconomy.

3.1.1. Biomass-Based Bioeconomy (BmBB)

In line with the work of Bugge et al. [19], Vivien et al. [20], Befort [21], and Giampietro [51], this approach seeks to measure the bioeconomy from a concept that bases the bioeconomy on the primary input, biomass, or bioresources [17]. Biomass is defined as all organic matter produced by photosynthesis convergence of solar energy [52] or primary or secondary organic matter (waste) derived from plants, animals, or microorganisms. It can be used as a renewable input to produce energy, food, fiber, or material [53]. Biorefineries are the technological platforms for transitioning from fossil to renewable materials and energy [20,53,54]. Measuring the BmBB, therefore, starts by identifying the primary sectors that produce biomass, which could suggest a narrow view of the bioeconomy. Studies, such as that by [55], have identified agriculture, livestock, and forestry as the primary sectors of the BmBB. However, methodological approaches may differ regarding the method, data and indicators used to measure the BmBB. The fact is that even though biomass is essentially produced in three or four economic sectors, it flows between almost all sectors of the economic system, being consumed or transformed with added value (Figure 2). Thus, measuring the BmBB provides information on physical or monetary flows that express how large the BmBB is in terms of an economy’s GDP, for example. In national bioeconomy strategies, the BmBB concept is well represented by the European Bioeconomy Strategy [13]. The BmBB is close to an economic system where bioresources and bioproducts are supplied and demanded through the value chains and market structures [56].

3.1.2. Biotechnology-Based Bioeconomy (BtBB)

Although some researchers around the world have already nominated biotechnology use for different purposes, we defined the BtBB as the second approach to measuring the bioeconomy. Alternatively, the term “biotechonomy” can be applied to define the scope of this approach [57] or knowledge-based bioeconomy (KBBE), as originally proposed by Christian Patermann [3]. Conceptually, this approach aligns with the view of bioeconomy as synonymous with biotechnology suggested by [17,19,58] and the Type II bioeconomy identified by [20] and reinforced by [21]. The BtBB is based on biotechnologies or bioengineered solutions developed from biological assets (biodiversity) and used to solve society’s problems on different fronts: human health and medicine (Red), agriculture (Green), marine (Blue), industry (White), food and nutrition (Yellow), deserts and arid regions (Brown), bioterrorism and biocrime (Black), intellectual properties, patents and publications (Violet), nanobiotechnology and bioinformatics (Gold), and environmental protection (Grey) (see Table 1 in [58]). The BtBB has gained strength from advances in genomics, synthetic biology, genetic engineering, and other modern biotechnology platforms [4,59]. Therefore, while the BmBB is based on raw materials, the BtBB is based on knowledge, innovation, and processes for adding value to biodiversity, including biomass. Measuring the BtBB involves identifying the relevant activities and sectors related to biotechnology, their value, and the value of their application for society and the biosphere. Resources, processes, and products are integrated into the dynamics of the economic system, increasing the added value and contribution of the BtBB in national economies. The intermediate part of Figure 2 represents this conceptual approach. Studies on BtBB measurements suggest investment in research and development (R&D) and the number of biotechnology patents as indicators [60,61]. The aims and scope of the US Bioeconomy Blueprint is an example of a national strategy more aligned with the BtBB concept [16].

3.1.3. Biosphere-Based Bioeconomy—BsBB

The third conceptual approach to the bioeconomy is referred to as the biosphere-based bioeconomy (BsBB). The term “biosphere” is used because it is the part of the Earth’s environment in which living organisms are found and interact to produce a steady-state system. Within the biosphere, there are environments and ecosystems. The environment is composed of physical (land, water, air) and biological conditions in which organisms live as well as social, cultural, economic, and political conditions. An ecosystem consists of living and non-living things interacting in an area of the biosphere, where energy and material flow are fundamental concepts [62]. So, the entire web of life and its trophic hierarchy can be seen as a natural marketplace and thus the bioeconomy itself [63]. Therefore, the biosphere is the broader term that houses the conditioning factors and life itself—the basis of the bioeconomy. While the BmBB and BtBB are closely associated with inputs and the technological means for adding value, respectively, the BsBB is the broadest and most integrative approach of all. The BsBB meets the scope of the bio-ecological or agroecological vision of the bioeconomy by [17,19,64] and the Type I Bioeconomy proposed by [20], which associates economic activity with the limits of the biosphere and the laws of non-equilibrium thermodynamics [51,65,66,67]. On the political scene, the Brazilian bioeconomy program focused on an economic system based on socio-biodiversity, innovation, and technology is close to the BsBB concept [9,18].

3.1.4. Holistic Bioeconomy

The breakdown of bioeconomy concepts occurs beyond academia. Among policymakers, the association of the bioeconomy with the concepts of BmBB and BtBB is prevalent [21]. In addition to policymakers, other civil society stakeholders have a fragmented view of the bioeconomy. Farmers and forest owners, for example, perceive the bioeconomy to be more associated with the BmBB concept. Industry and commerce members and researchers see the bioeconomy in a more balanced way between the BmBB and BtBB. The media associates the bioeconomy exclusively with the BmBB, while the BsBB concept is more mentioned among citizens and consumers [68]. This behavior may reflect the utopian perception regarding the BsBB. However, the bioeconomy needs to be sketched into the human activities that are genuinely embedded into the local ecosystem and biosphere, where we perpetually communicate with the environment, deliberately or not [63].

The partial view of the bioeconomy has limitations and practical implications, especially for formulating integrative policies and strategies that maximize the potential of the bioeconomy. This is why scientific research must search for a holistic view of the bioeconomy. Other authors recognize the need to reconcile the different bioeconomy concepts [17,21]. In this direction, the adoption of a systemic perspective is proposed [23]—a whole vision from a sector to a system [58] or a holistic system with thinking and planning to integrate the multifaceted sectors interconnected in the circular bioeconomy [69]. In addition, a holistic approach to encompass the relationships between the different concepts, in particular the BmBB-BtBB and BsBB, is advocated by [20,70,71]. The European Bioeconomy University included in its Forum 2023 in the thematic session “Identifying synergies for a holistic Bioeconomy approach” to provide an informative overview of the historical evolution and diverse concepts within the bioeconomy to overcome the complexity involving a range of visions, values, and narratives [72]. However, the outline of the holistic approach was not clearly stated.

Holism claims that evaluating and measuring the holistic bioeconomy is more important because it highlights the interactions and contributions of and between all the partial concepts. Therefore, advances toward the construction of conceptual and methodological alternatives need to be grounded in General Systems Theory (GST) [73] and Complex Adaptive Systems (CAS) [74]. They recognize, at the very least, the type, hierarchy, boundaries, components, and relationships between the elements of systems and the complexity surrounding systemic dynamics.

In general, the BmBB and BtBB approaches are referred to in the literature more as a closed system model of the economic system, open only to the economy system of other countries. However, the BsBB necessarily means considering the economic system as an open system interacting with the biophysical system. The biosphere provides the necessary substrates for maintaining and developing all biodiversity and its contributions to society [75]. Biodiversity is the basis for creating value chains in the holistic bioeconomy seeking to increase economic value with the least use of preferably renewable energy and low entropy generation. Therefore, assuming that the partial bioeconomy concepts are open systems and that there is a hierarchy between them, the BmBB and BtBB are subsystems of the BsBB system, as represented in Figure 2.

Figure 2. Generic conceptual framework of the holistic bioeconomy based on the hierarchy of partial concepts. In general terms, the central part of the figure (orange) represents the concept of BmBB, indicating the production of biomass as a fundamental input and its intermediate and final flows between the intermediate demand of the economic sectors and the final demand of the government, households, and the external market. The intermediate part (purple) represents the concept of BtBB in its different categories of development of biotechnological solutions. BtBB interfaces with the economic system to which it provides its solutions and receives the knowledge of human capital to apply to the biotic resources of the different kingdoms of nature (BsBB). The outermost part of the figure represents the concept of BsBB (green) or, more specifically, the biosphere where the biotic and abiotic resources are located. The biosphere provides the necessary substrates for maintaining and developing all biodiversity, which underpins the holistic bioeconomy. The scheme, therefore, suggests a hierarchical system structure in which BmBB and BtBB are subsystems within the BsBB system. The arrows with thicker lines illustrate interconnections between bioeconomy concepts and their main elements. The arrows with thinner lines indicate the connections between secondary elements, usually within the same concept. Source: elaborated by the authors.

Figure 2. Generic conceptual framework of the holistic bioeconomy based on the hierarchy of partial concepts. In general terms, the central part of the figure (orange) represents the concept of BmBB, indicating the production of biomass as a fundamental input and its intermediate and final flows between the intermediate demand of the economic sectors and the final demand of the government, households, and the external market. The intermediate part (purple) represents the concept of BtBB in its different categories of development of biotechnological solutions. BtBB interfaces with the economic system to which it provides its solutions and receives the knowledge of human capital to apply to the biotic resources of the different kingdoms of nature (BsBB). The outermost part of the figure represents the concept of BsBB (green) or, more specifically, the biosphere where the biotic and abiotic resources are located. The biosphere provides the necessary substrates for maintaining and developing all biodiversity, which underpins the holistic bioeconomy. The scheme, therefore, suggests a hierarchical system structure in which BmBB and BtBB are subsystems within the BsBB system. The arrows with thicker lines illustrate interconnections between bioeconomy concepts and their main elements. The arrows with thinner lines indicate the connections between secondary elements, usually within the same concept. Source: elaborated by the authors.

The BsBB perspective is relevant because it makes it possible to measure the flows from the biosphere into the economic system and vice versa. The BtBB intermediates some of these flows by taking resources from both the BmBB (e.g., knowledge and financial support) and BsBB (e.g., biodiversity and DNA) and providing solutions for the environment and society. Many BtBB contributions to the BmBB may be embodied in the BmBB outputs because of biotechnologies on crop yields, for example. At the same time, other flows are directly exchanged between the BsBB and the BmBB, like externalities (e.g., CO₂ emissions) and ecosystem services (e.g., cycling materials). Therefore, the consumption of flows and stocks and the role of funds for the absorption of waste and pollution become measurable. Sustainability aspects of the bioeconomy can be gauged [51,76], despite the criticism regarding the sustainability labels as bio-based use, CO₂-neutral, or climate-friendly as a consumption stimulus into the economic system [63].

The holistic measurement of the bioeconomy is challenging since many contributions of nature are provided by the BsBB with no charge for the BtBB and BmBB. Additionally, the exchange in nature is measured in energy units, while in the economy, it is in monetary terms. In the 1990s, Howard T. Odum proposed a challenging task of aligning ecology and economics through a new method–the emergy analysis. He aimed to assess the value of energy and materials involved in biological or economic processes, converting them into a common unit (seJ-solar emjoules). The emergy accounts for the direct and indirect solar energy required to produce the services and products of ecosystems and the national economies [77]. It allows for calculating the emergy and money ratio, which is called “emdolar”, and reveals how much emergy is needed to produce US$1 GDP. However, the complexity of this methodology and the lack of an interdisciplinary approach have limited its applications in economic science.

Despite this, the BsBB approach cannot be ignored in the methods developed for measuring national bioeconomies. The bioeconomy’s contributions to sustainability and the environment have already been highlighted in studies such as [61,78]. Vivien et al. [20] suggest that the lack of emphasis on the BsBB is due to a hijacking of Georgescu-Roegen’s term “bioeconomy” by economic activities that want to convey a sustainability appeal. Therefore, given the distorted use of the term bioeconomy, the public needs to be very critical of all attempts by political or commercial groups to use the concept of sustainability to mask their true interests [63].

On the other hand, the intransigence of the laws of thermodynamics, on which the BsBB is based, makes this approach challenging to measure. Giampietro and Funtowicz [79,80] classified this theoretical fragility as “uncomfortable knowledge” to assess the economy–biophysical interface [79,80]. Ignoring the impacts of the laws of thermodynamics cannot be a limiting factor for advances in conceptual discussions of the bioeconomy. The challenge lies in developing suitable methods to measure its dimensions and implications. Far from a disciplinary approach grounded in economics, it requires an interdisciplinary or even transdisciplinary engagement supported by post-normal science and biosemiotics [79,81,82,83,84].

In the political arena, national strategies are based on partial bioeconomy concepts. Some countries have emphasized the primary input (biomass), while others focus on processes (biotechnology) or natural resources. The concepts assumed in national policies lead to a biased and partial measurement of the contributions of the bioeconomy. National strategies for a holistic bioeconomy would have the potential to stimulate countless other development initiatives. It should be integrated into national accounting and measured as effective contributions of the bioeconomy to national economies.

Therefore, the holistic bioeconomy is an integrated and integral systemic approach to the systems pertaining to each partial concept (BmBB, BtBB and BsBB). Measuring the holistic bioeconomy must consider the hierarchy of systems and subsystems and their boundaries, components, and interactions. The central issue is that the elements of each concept that contribute to the holistic bioeconomy are expressed in different units of measurement. Direct economic measurements of the holistic bioeconomy are unfeasible because their relationships are not always measured in economic terms (monetary value). Therefore, the system represented by the holistic bioeconomy is complex and challenging to measure, but it needs to be considered, as explained by [51,70].

3.2. Authors, Documents and Countries: A Brief Characterization

This section characterizes the set of documents regarding their content similarity and shows the leading countries in the studies on measuring the economic impact of the bioeconomy. Such results are essential to understand the context from which the main results originated.

The first set of results shown in Figure 3 illustrates the clusters of documents grouped by degree of similarity.

Cosine similarity is one of the most popular similarity measures applied to text documents, such as in numerous information retrieval applications and document clustering [157]. Cosine similarity is a measure of similarity between two vectors obtained from the cosine angle multiplication value of two vectors being compared [158]. A zero value indicates total dissimilarity between the documents, while a value of one means that the documents are entirely similar. Documents with a Cosine index greater than 0.5 can be considered to have high similarity. Although Cosine similarity is widely applied in document content analysis, in the case of this study, Cosine similarity is based on the occurrence of codes. Therefore, documents with the same codes would result in a cluster with a Cosine similarity value of one.

A total of 10 main document clusters were considered based on the similarity of code occurrence. Overall, there is a high degree of similarity between the 84 documents, given that the lowest Cosine index value was higher than 0.5 for all documents. In particular, some documents showed very high similarity, with a Cosine index close to one. This is the case, for example, with the documents “18 Daystar, J. et al. 2018” [107], “19 Daystar, J. et al. 2020” [108], “27 Golden, J. S. et al., 2016” [109], and “28 Golden, J. S. et al., 2015” [44]. This group of documents consists of a series of technical reports drawn up by a team of researchers and addressed to the US Congress. These authors co-authored the four documents and each year, the report updates the data using essentially the same method, data, and indicators. Another example is documents 64 and 65 by Piotrowski et al. in 2018 and 2019 [132,133], respectively. Therefore, the high level of similarity is due to the recurrence of the same codes.

Another feature to be highlighted in the formation of clusters is the effect of co-authorship on the grouping of documents. Many of the authors who form the clusters have worked together with the authors of other documents. In this sense, the cluster formed by the documents “15 Cingiz, K. et al. 2021” [39] to “76 van de Pas, J. 2015” [146] is worth highlighting. These clusters represent the efforts of research groups developing joint studies to measure the bioeconomy using relatively similar concepts, methods, data, and indicators.

The high level of similarity between the documents may also have been affected by the geographical scope of the studies. Only 47 countries had data and indicators from their national economies investigated. However, most of the documents report bioeconomy results from countries that are members of the European Union. Germany has the highest number of records, followed by Poland, Spain, the Netherlands, and the Czech Republic (Figure 4). Some documents are specific to one country, while others report results from groups of countries, such as EU-28, EU-27, EU-15, and so on. In these cases, the country code was assigned to the document whenever country-specific results could be identified.

Only some countries outside the EU have been included in studies. Of these, the United States had the highest number of studies on its bioeconomy (5), followed by Japan (3), Norway (3), and China (2). Despite the potential of the bioeconomy, countries in Latin America, Africa, Oceania, and Asia need studies that measure their bioeconomy.

These initial results illustrate a specific pattern in efforts to measure the bioeconomy. Although the studies used various methods, data, and indicators, studies that proposed cutting-edge or significantly innovative methodologies still need to be identified.

3.3. Connections between Concepts, Methods, Data, Indicators and Limitations

Figure 5 gives an overview of the connections between bioeconomy concepts, methods, data, indicators, and limitations through cooccurrence.

At this point, a result worth highlighting is that most studies (94%) measured the bioeconomy associated exclusively with the biomass-based bioeconomy (BmBB). This is likely due to the concentration of studies in EU member countries, where the official concept of the bioeconomy is more aligned with the bio-based economy. In 22 documents, economic and environmental aspects were measured together, and these documents were coded with both concepts (BmBB + BsBB). Three documents measured aspects of the three bioeconomy concepts (BmBB + BsBB + BtBB).

As elaborated in Section 3.1, we recognize the existence of at least three conceptual visions of the bioeconomy—BmBB, BtBB, and BsBB—and we consider the holistic bioeconomy results from the integration of the three concepts. However, only a few studies recognized the three partial concepts, like [95], but are limited in measuring the holistic bioeconomy.

The BsBB concept was considered by the authors of approximately one-quarter of the studies but not explicitly grounded in ecological economics. Essentially, the studies are restricted to combining the measurement of specific indicators of environmental impacts or resource use combined with socioeconomic performance, such as [147,148]. Although biotechnology has been advocated as an inclusive and sustainable technological platform for the bioeconomy [159], few studies have been dedicated to measuring the economic impacts of the BtBB. Carlson’s [27] was the only study that dealt exclusively with the BtBB, using data compilation to estimate the contribution of biotechnology to the North American economy. Possibly, the lack of structured data that adequately informs the contributions of the BtBB is a limitation. Given this, the few studies considering the BtBB have mainly explored the turnover of biotech industries or aspects related to patent registration. Besides this, human resources training and R&D investments have been used to evaluate knowledge capital, generally associated with KBBE [4].

Figure 6, Figure 7 and Figure 8 explore more clearly the connections between bioeconomy concepts and methods, data, and indicators, respectively. A miscellaneous 72 methods were coded in the documents analyzed. Figure 6 shows those methods with a frequency of two or more occurrences. The methods that occurred only once were grouped under “Other Methods”. This is the case, for example, with Causality Analysis, Case Studies and Cointegration Tests, among others.

Macroeconomic studies exploring economic systems’ structures and structural changes often resort to input-output models (IOMs). While IOMs measure the interconnections between economic sectors, social accounting matrices (SAMs) are expansions of IOMs that add other agents such as government and households [160]. Together, IOMs and SAMs make it possible to calculate various dimensions of economic activity, such as intermediate and final demand, technical multipliers, and backward and forward linkages, among others.

The studies by [26,39,46,55,98,100,135,136,137], for example, used input-output models (IOMs). Variations on IOMs, such as the Multiregional Input-Output Model (MRIO) and Environmentally-Extended Input-Output Model (EEIO) or Environmentally-Extended Multiregional Input-Output Model (EEMRIO), were used by [147] to aggregate data and measure environmental aspects associated with the bioeconomy. Complementarily, ref. [155] combined Principal Component Analysis (PCA), Autoregressive Distributed Lag (ARDL), and IOM to identify the key sectors and the short- and long-term effects of the bioeconomy on Japan’s GDP.

Measuring the bioeconomy using traditional macroeconomic methods can be justified by using the concept of the bioeconomy restricted to the BmBB, where biomass is the main input and primary production sectors are the key sectors. This sectoral structure makes it possible to use data from national input-output tables and measure the participation of the bioeconomy like any other economic activity. In this sense, the measurement of the BmBB differs little from what is usually measured as agribusiness [161].

Similarly, Figure 7 shows the connections between the concepts and the data or data sources used to measure the bioeconomy. Likewise, the data presented in the figure occurred twice or more. Data that only occurred once are grouped under “Other data sources”.

The unavailability of suitable data has been an obstacle to adequately measuring the bioeconomy. Part of this difficulty is related to the conventional structure of the organization of national accounts. There is no classification of specific sectors and the bioeconomy and, as stated in [38], the bioeconomy crosses sectors and cannot be treated as a traditional sector of the economy. Consequently, from the perspective of conventional economic analysis, efforts have been aimed at identifying sectors and activities related to the bioeconomy.

National classification systems for economic sectors and activities have been used alone or in combination to delimit the scope of data representative of the bioeconomy. Thus, coding structures for sectors and activities, such as the Statistical Classification of Economic Activities (NACE) [40,98,100,135,136,137], Classification of the Functions of Government (COFOG), Common International Classification of Ecosystem Services (CICES) [55], Statistical Classification of Products by Activity in the European Economic Community (PRODCOM) [147], Classification of Products (CPA) [100], in the EU, and the National Income and Product Accounts (NIPAs), North American Industry Classification System (NAICS), North American Product Classification System (NAPCS) [61,162], and Impact Analysis for Planning (IMPLAN) Industry Scheme, in the US, are examples of classification systems used in delimiting the bioeconomy.

The strategy to delimitate the bioeconomy is to estimate the relative share of biomass (bio-share) in the economy’s sectors, activities, or products. Researchers have resorted to using sectoral statistical data, such as Istituto Nazionale di Statistica (ISTAT), exploring regional transitions to the bioeconomy using a socio-economic indicator: the cases of Italy [93], PRODCOM [134], and NACE [132,133,135,136]. They also combine statistical data with the biomass content in products reported in the scientific literature [55] or the opinions of sectoral experts collected through interviews. Alternatively, Lazorcakova et al. [100] estimated minimum, maximum, and middle bio-share based on the classification of products provided by the CPA. However, as there is no official data on the subject, determining bio-shares is just as tricky as classifying activities, leaving researchers to use imprecise estimates that compromise the accuracy of the estimated values.

Figure 8 best shows the connections between the concepts and indicators used to measure the bioeconomy. By default, only indicators with a frequency of two or more are listed, while those with a unit frequency are aggregated under “Other Indicators”.

Value added and GDP have been the recurrent measures of the bioeconomy in the literature [26,39,46,55,95,137,155,162]. Other studies have considered the turnover of industries related to the bioeconomy [135]. The socioeconomic contributions of the bioeconomy have also been investigated, mainly through the labor market and jobs [26,55,135,136,137,162]. The environmental impacts of the bioeconomy have been assessed in terms of GHG emissions [95,100] or natural resources like water and land [148]. These indicators have been calculated and reported in absolute values, such as millions of $ number of employees, and tons of CO₂ of fossil origin, often summed up with biogenic ones, or, in relative terms, such as value added/GDP, turnover/GDP, percentage of jobs/total jobs, and growth rate of these variables over time [26,39,46,55,100,137]. In general, the indicators used to express the contribution of the bioeconomy to national economies are those usually used to measure other economic activities, considering the methods predominantly used.

When analyzing the content of the documents, an effort was made to identify limitations reported by the authors associated with measuring the bioeconomy. In this process, 71 unique limitations were identified and grouped by those related to the bioeconomy concept, data, method, indicators, and others, as shown in Figure 9.

Data limitations were the most frequently reported, accounting for 36.6% of all unique limitations. In this sense, incomplete data, missing or lacking data, aggregated data, outdated data or databases, unavailable data, scarce or limited data, and data quality were the most frequent limitations. As stated by Meyer [163], while statistical data for the primary production of biomass in agriculture, forestry, and fishery are relatively well established, the economic shares of downstream stages in biomass-based value chains are not readily available.

The second position regarding limitations is those associated with methods or models. A total of 25 unique limitations (35.2%) were found in this category. The most frequently reported method limitations were the uncertainty of the estimated values, the linearity assumptions of the models, difficulties in combining models, the subjectivity of some weighting procedures, the absence of supply restrictions in the models, and the assumption of fixed prices [164]. In the same vein, the limitations of the data are closely related to the methods and models used. The limited availability of timely input-output tables is pointed out as a critical limitation of IOM data [165].

The limitations of methods or models and data were the most relevant and accounted for more than 70% of the unique limitations. Among the remaining limitations, the category of other limitations and bioeconomy concepts account for 23.9% of the unique limitations. In this sense, the main limitations related to the concept of the bioeconomy concern the lack of specific codes in classification systems to measure biotechnology activities or sectors correctly. The existence of different definitions for the term bioeconomy, the criteria used to delimit the scope of the bioeconomy, and the limited definitions of the bioeconomy also were identified. Complementarily, other frequent limitations are associated with the criteria or lack of precision in bio-share estimates, the geographical scope of studies restricted to the borders of a country or region, and limitations related to the measurement of biotechnology (BtBB).

The category of limitations related to indicators was the least relevant, with only three unique limitations. These limitations are important to note. They signal concern about the environmental aspects of the BsBB. They also recognize the limitations of the indicator results due to the criteria used for weighting the participation of biological resources in economic activities. Additionally, they highlight the need to develop specific indicators to assess the BsBB.

4. Discussion

4.1. Challenges to Measure the Holistic Bioeconomy Economically

Jander and Grundmann [88] state that monitoring bioeconomy transitions and their effects can be considered a Herculean task, as they cannot be easily captured using current economic statistics. This study’s findings reinforce and detail the difficulties and limitations associated with this endeavor. However, it is necessary to move forward, recognizing and confronting the challenges. Of course, many challenges could be listed. Below, we highlight some that are relevant.

The first and perhaps most important question is as follows: Which bioeconomy will be measured? In this sense, recognizing the existence of different conceptual approaches and moving towards measuring the holistic bioeconomy is fundamental. It is necessary to consider the complexity surrounding the etymology of the word “bioeconomy”. Efforts to measure the BmBB are valid and commendable but insufficient. Attempts to measure the BsBB use a conceptual perspective restricted to environmental impacts but need to develop an economic approach to these impacts. The few studies that have considered the BtBB concept have come up against inaccurate and inadequate data.

The restricted conceptual scope of bioeconomy measurements may be associated with the structure of national accounts, which implies a deliberate choice between predefined sectors. The proposal of a specific satellite account has been suggested as an alternative to qualifying the measurements of the bioeconomy. Vargas et al. [166] proposed satellite accounts for the bioeconomy of Latin American countries, finding that the bioeconomy represents, on average, 17.2% of the production of the countries analyzed. The creation of a National Bioeconomy Account (CNBio) for Brazil was proposed by [167], while [61] proposed a satellite account structure for the North American bioeconomy. Satellite accounts can be useful for measuring the bioeconomy, even when observing the different concepts of bioeconomy as undertaken by [61,167]. However, their structure can still be limited to the sectors and activities with data available in the national account structures.

The first challenge inevitably leads to a second: How do you economically measure something with no market and economic parameters? This challenge is strongly associated with the BsBB concept. In particular, there is a market for various abiotic natural resources, such as oil, land, and minerals. So, the market parameters (supply, demand, price) are known, and such resources can be incorporated into the usual models without major difficulties. The same is not true of diverse biotic resources. It performs ecosystem services or serves as the basis for biotechnology applications and the production of bioengineered solutions, such as multiple microorganisms living in the soil, air, and water [168].

Carbon dioxide is an example of an abiotic resource facing challenges in the current economic scenario. Due to the growth of global economies based on nonrenewable energy, anthropogenic activity fosters greenhouse gas emissions, including CO₂, CH₄, and N₂O [169]. As a result, the world’s climate change discussions from the Intergovernmental Panel on Climate Change (IPCC) are crucial. To promote the sustainable development of nations, a carbon market can reduce the expenses associated with achieving global emissions targets [170]. However, due to the lack of global market regulation, incorporating carbon accounting into bioeconomic measurement is challenging. Additionally, it is difficult to embed the value of green biotechnologies related to carbon capture and sequestration in bioeconomic measurement. Although only biotic resources fall within the scope of the bioeconomy, abiotic resources are essential as life substrates.

Despite the challenges of measuring aspects associated with the BsBB, the valuation of ecosystem services has been a rapidly developing frontier in recent decades. Although the bioeconomy has not yet been strongly linked to the ecosystem services concept, both deal with sustainability [64]. Perillo et al. [171] state that the ecosystem services approach has become the dominant criterion for studying human and natural relationships. Valuing ecosystem services means finding values, including monetary values, for the benefits of ecosystem services [172]. Therefore, this approach exemplifies alternative approaches to connect the BsBB to BmBB concepts of bioeconomics. The valuation of specific ecosystem services can be carried out using appropriate methods.

Provisioning services include the classes of nutrition (terrestrial plants and foodstuffs, freshwater and animal foodstuffs, marine plants and animal foodstuffs, drinking water), materials (biotic and abiotic materials), and energy (renewable fuels). These ecosystem services can be economically valued using various methods, such as market-based (market prices, substitute goods, net prices, etc.), cost-based (avoided cost, conversion cost, damage cost, mitigation cost, opportunity cost, etc.), production-based (bioeconomic modeling, factor income, stated preference, choice modeling, etc.), or revealed preference (hedonic price, avoidance behavior, travel cost method, etc.) [172,173,174]. Vermaat et al. [175] concluded that land use changes from bioeconomy strategies may impact the provisioning, regulating, and cultural ecosystem services differently. An application of the economic valuation of ecosystem services in the Amazon estimated the value of habitat provision for species, carbon sequestration, water regulation, recreation, and ecotourism for local populations at US$ 410 ha⁻¹yr⁻¹ [176]. The available and recommended methods can also be used to value other regulating, supporting, and cultural classes of ecosystem services.

The challenge in making progress in measuring the contributions of the BsBB lies in obtaining data and developing methods and indicators that capture the contributions of the biosphere (energy) and value these flows (monetary). In this respect, the conventional input-output analysis could be used to measure the metabolic pattern involved in the social-ecological system as a metabolic system [70]. Additionally, using emergy accounting developed by [77] may be a suitable methodological alternative. The results provided by the National Environmental Accounting Database [177] reflect the relationship between emergy and currency for the economies of various countries. Among the indicators produced, the sej/GDP ($) ratio is relevant. According to the most current results available, the sej/GDP ($) ratio for the Chinese economy was in the order of 3.33 × 10¹², while for Germany, Brazil, and the United States, it was 4.80 × 10¹², 2.81 × 10¹², and 1.86 × 10¹². It denotes different intensities of participation of the biosphere (energy) in the wealth produced by these countries [178]. Further development of these analyses includes the integration of emergy accounting and IOMs. This allows for a detailed analysis of the economic activity and the dynamics between sectors and resources, as exemplified by the studies by [179,180,181]. However, the unavailability of up-to-date data and the lack of standardization of emergy assessments for global analyses are still challenges to overcome.

The United Nations’ initiative for developing a System of Environmental-Economic Accounting (SEEA) is promising in data production. The SEEA “is a framework that integrates economic and environmental data to provide a more comprehensive and multipurpose view of the interrelationships between the economy and the environment and the stocks and changes in stocks of environmental assets, as they bring benefits to humanity” [182]. The SEEA system of accounts is integrated with the System of National Accounts-SNA and is currently being developed and implemented in 90 countries [183]. Therefore, the possibilities for broadening the conceptual scope of the bioeconomy are widening with the development of databases and methods that can connect concepts such as the BsBB and BmBB, for example.

The second challenge gives rise to a third one, which lies at the interface between the BsBB and BtBB: How do we establish the proportionality of the contribution between biological organisms (bio-share) and knowledge (knowledge-share) in the BtBB? Many biotechnological solutions result from applying accumulated human knowledge and physical resources to biological organisms to produce a desired result: food, plant, or medicine. The book “The Genesis Machine: Our Quest to Rewrite Life in the Age of Synthetic Biology” by [184] and the microbial cell factory approach [185] illustrate this relationship well. Would the new route to insulin production be possible only with the knowledge used to map the genetic code? Could Escherichia coli produce insulin voluntarily? The answer to both questions is “no”. Synthetic insulin production requires the knowledge to map the Escherichia coli genome and genetically modify the bacteria to express insulin production through synthetic biology. The economic value and, therefore, the market price of insulin is known, but the bio-share and knowledge-share are unknown. There are many other examples in this direction. The proposition of a system dynamics model for the so-called “biotechonomy” was proposed by [186], but much work remains to be undertaken.

A fourth challenge is the inclusion of other sectors and economic activities beyond the primary ones. Most studies have measured the bioeconomy based on a restricted set of primary sectors, especially agriculture, livestock, fishing, and forestry. This section assumes that biomass is the bioeconomy’s primary input. Although biomass is relevant, other equally valuable biological inputs are produced and used in many sectors. In this sense, sectors such as the biochemical industry, pharmaceuticals, human and animal health, marine, nanobiotechnologies, and environmental protection are rarely mentioned. However, they should be considered when measuring the holistic bioeconomy.

Lastly, the most daunting challenge is integrating efforts to measure the holistic bioeconomy. The limitations of the methods, data, and indicators used in the restricted measurement of the bioeconomy indicate the difficulty in developing methods, producing data and information, and generating indicators capable of measuring the holistic bioeconomy. The challenge increases when it comes to measuring the bioeconomy economically. Input-output models or even satellite accounts or environmental-economic accounts require a time-consuming and costly process of collecting and recording data. In addition, holistic bioeconomy requires data from the biosphere to be considered. Environmental data can have specific time and place attributes, increasing the difficulty in obtaining reliable and current data. So, how can this be overcome?

The modern world has been marked by increasing digitalization and is moving towards a Society 5.0, marked by a close connection between physical and cyber spaces [187]. Digital integration requires resources that convert facts from the physical world into digital data and then into information. Thus, mechanisms that collect and store those data, integrate and analyze them in an automated way, and communicate relevant information to other devices or stakeholders make up the cyber-structure of the bioeconomy. In this sense, advances in the development of electronic devices, sensors, scanners, drones, a global positioning system (GPS), satellites, wearable devices, digital data storage, digital twins, the internet of things, big data, data analytics, quantum computing, digital platforms, deep and machine learning, artificial intelligence, metaverse, and blockchain are examples of possibilities for advancing recording and measurement of the holistic bioeconomy through digitalization.

4.2. Limitations of This Study

The results reported in the current study were influenced by the scope defined in the purpose and the methodological choices. Consequently, the limitations inherent in the process may have interfered in some way with the outcomes. The main limitations are highlighted below.

Firstly, this study focused on identifying research that measured the economic contribution to the bioeconomy. This delimitation left out other measurements related to the bioeconomy, such as circularity and sustainability in its economic, environmental, and social dimensions. The literature review by [30] presents methods and indicators related to the sustainability of the bioeconomy, including the economic dimension. However, our results are more detailed and include limitations.

The snowballing strategy for identifying and selecting documents for content analysis may have excluded other documents not cited in the literature consulted due to variables such as language or journal indexing base. Examples of this are the works by [188] for the Argentine bioeconomy and by [28,29,189] for the Brazilian bioeconomy. Possibly, other similar studies around the world could have been retrieved, expanding the number of countries measuring their bioeconomy in Figure 4. Therefore, choosing other search strategies could have broadened the spectrum of documents. Although using alternative search strategies could have broadened the range of documents, the additional articles included in the analysis also employed the concepts, methods, data, and indicators outlined in the results of the current study.

No predefined structure was used to code the concepts, methods, data, indicators, and limitations. Instead, the possibility of adding codes as they were identified in the documents was kept open. In addition, we tried to adopt a nomenclature that was as faithful as possible to that used by the authors. These choices may have resulted in a long list of codes; some could have been aggregated, resulting in different values. This is the case, for example, with the codes “IOM”, “MRIO”, and “EEIO”, which, alternatively, could have been merged into the code “IOM”.

The researcher’s subjectivity can bias qualitative studies. For this reason, it would be advisable for two or more researchers to code the documents. The QDA Miner software has resources for coding by a team of researchers and statistically comparing the coding results. However, due to restrictions on the number of licenses for the software and the researchers’ access to the licensed equipment, coding was only carried out by one senior researcher.

5. Conclusions

This study aimed to define the bioeconomy and explore the links between concepts, methods, data, and indicators, along with the limitations of measuring it economically. Based on the findings, we conclude that efforts to measure the economic contributions of the bioeconomy in terms of national economies, although important, are essentially restricted to the concept of biomass-based bioeconomy. Thus, the estimates on how big the bioeconomy is are only partially assessed. The economic contributions of the bioeconomy have mostly been measured by economists, whose theoretical and econometric backgrounds are grounded in the disciplinary field of economics.

To fully explore the contributions of the bioeconomy, the concept of a holistic bioeconomy should be considered. In addition, the adaptation of conventional methods, data, and indicators from macroeconomic analysis imposes several limitations that shorten the real contribution of the bioeconomy. Several challenges stand in the way of developing new methods, obtaining suitable data, and creating indicators. Although some progress is being made, it is necessary to step up efforts in this direction, starting by recognizing that the bioeconomy is made up of a set of complex and hierarchical systems. The contributions of economists are fundamental to the process, but we need to be open to a transdisciplinary dialog with researchers from other areas of knowledge. Advances in digitalization technologies and processes can contribute to a more accurate measurement of the bioeconomy’s contributions.

This study reinforces the diversity of bioeconomy concepts and introduces the holistic bioeconomy as a broader, interdisciplinary approach to measurement. In addition, the findings detail the connections between the methods, data, and indicators used to measure each bioeconomy concept. In doing so, numerous limitations are identified, providing a research agenda so that the measurement of the bioeconomy can be increasingly better evaluated.