Constructing an open access economy-wide database for bioeconomy impact assessment in the European Union member states

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=cesr20 Economic Systems Research ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/cesr20 Constructing an open access economy-wide database for bioeconomy impact assessment in the European Union member states Alfredo J. Mainar-Causapé, George Philippidis & Ana I. Sanjuán-López To cite this article: Alfredo J. Mainar-Causapé, George Philippidis & Ana I. Sanjuán-López (2021) Constructing an open access economy-wide database for bioeconomy impact assessment in the European Union member states, Economic Systems Research, 33:2, 133-156, DOI: 10.1080/09535314.2020.1785848 To link to this article: https://doi.org/10.1080/09535314.2020.1785848 © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 27 Jul 2020. Submit your article to this journal Article views: 2613 View related articles View Crossmark data ECONOMIC SYSTEMS RESEARCH 2021, VOL. 33, NO. 2, 133–156 https://doi.org/10.1080/09535314.2020.1785848 Constructing an open access economy-wide database for bioeconomy impact assessment in the European Union member states Alfredo J. Mainar-Causapé a, George Philippidis a,b,cand Ana I. Sanjuán-López c aEuropean Commission, Joint Research Centre, Seville, Spain; bAragonese Agency for Research and Development (ARAID), Centre of Agro-Food Research Technology of Aragón (CITA), AgriFood Institute of Aragón (IA2), Spain; cCentre of Agro-Food Research and Technology of Aragón (CITA), AgriFood Institute of Aragón (IA2), Spain ABSTRACT The bioeconomy encompasses the extraction, processing and transformation of renewable biological resources and waste streams, connected to activities as diverse as food, feed, energy and manufacturing. Under the auspices of the European Union’s ‘Green Deal’ strategy, this broad collective of sectors is promoted as a cornerstone for achieving sustainable growth. Progress in developing ex-ante tools of economy-wide modelling analysis to assess its performance is, however, hindered by a paucity of consistent and comprehensive data. To overcome this shortcoming, the construction steps for a new set of open access social accounting matrices (dubbed ‘BioSAMs’) is described for a detailed and comprehensive selection of traditional and contemporary bio-based accounts for each of the EU member states. To illustrate its potential, a structural analysis based on three different and complementary methods (Rasmussen-Jones, hypothetical extraction method and eigenvector) is performed to classifybio-basedsectorwealthgeneratingpropertiesandtoidentify high performance (‘key’) sectors. ARTICLE HISTORY Received 25 July 2019 KEYWORDS Bioeconomy; social accounting matrix; key sector analysis; impact assessment 1. Introduction The European Green Deal (EC, 2019a) is the flagship growth strategy that seeks to transform the economic, social and environmental landscape in the European Union, to ensure a resource responsible and sustainable model of human development. As a basis for realising this goal, the Circular Economy Action Plan (EC, 2019b)promotes(inter alia)the reuse of primary resources and waste, the decoupling of economic growth from energy usage and the ambition of climate neutrality by 2050. As part of this ambitious vision, the EU bioeconomy strategy (EC, 2012;EC,2018) formalises a traditional model of human development focussing on biomass (i.e. biologically renewable resources and biological waste) as a source of prosperity. As a building block of the ‘circular economy’ (Carus & CONTACT Alfredo J. Mainar-Causapé [email protected] European Commission, Joint Research Centre, Seville, Spain Supplemental data for this article can be accessed here. https://doi.org/10.1080/09535314.2020.1785848 © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. 134 A. J. MAINAR-CAUSAPÉ ET AL. Dammer, 2018;Georgescu-Roegen,1977), the multidimensionality of sustainable growth requires a bioeconomy that can reconcile itself with our planet’s biophysical boundaries and deliver desirable outcomes on a number of fronts (e.g. employment, growth, climate change, biodiversity, energy and food self-sufficiency). In principle, these challenges can be navigated with the help of biotechnological advancements and sensibly targeted public policies, although there remains considerable debate and associated literature on how best to achieve these goals (e.g. OECD, 2012; The Bell et al., 2018;Pfauetal.,2014;White House, 2012). Against this background, evidence-based economic insights on this broad and diverse sector form an invaluable part of the policy debate. Examples in the economic literature discuss the merits of biomass allocation choices through the lens of appropriately designed indicators, the stages of economic development within the bioeconomy or the identification of synergies and/or trade-offs in the achievement of policy goals. For example, McCormick and Kautto (2013) and Swinnen and Weersink (2013)discusstheEUand global bioeconomy, respectively, and the subsequent challenges in policy design and implementation; Fritsche and Iriarte (2014) analyse the practical implementation of different framework analyses in different EU regions based on different concepts of high-value sectors; and M’barek et al. (2014) describe the first attempts to obtain valid socio-economic indicators for the analysis of the bioeconomy in the EU. Furthermore, whilst secondary data coverage of traditional bioeconomy activities is ‘relatively’ accessible, data inventories of more-contemporary biomass sources and bio-based activities are scarce, which severely hampers the estimation of the economic importance of these evolving sectors within national economies. Of the few available examples, Ronzon et al. (2017) estimate indicators of production and employment for 16 NACE Rev.2 sectors (related to bioeconomy) for 2014; Camia et al. (2018)assessEUbiomassproductionforprimary sector bioeconomy activities, between 2004 and 2015; Spekreijse et al. (2019)focus on different biochemical indicators (production, turnover, trade and price data) in the EU for ten different chemical product categories for 2018, with an extrapolation to 2025. These studies, whilst valuable to the policy debate, only take a partial approach to measure the performance of the bioeconomy. The key aim of this study, however, is to contribute to the pressing need for systems-wide databases that fully integrate detailed food, feed, energy and material bio-based activities within established national accounts data. In this regard, the input–output (IO) framework has emerged as a key candidate. In the context of the policy discussion above, this class of data not only provides easily accessible information for descriptive economic analysis, but also facilitates direct short-run comparisons of relative economic performance with non-biobased (rival) activities, as candidates for promoting growth and employment. Moreover, in medium- to long-term simulation experiments, such data constitutes an essential starting point for quantifying the synergies, trade-offs and rebound effects from an array of economic, social and environmental indicators when exploring alternate bioeconomy transition pathways consistent with visions of sustainable growth.1 A first major step in the construction of such a database was initiated by the Joint Research Centre (JRC) of the social accounting matrices (SAMs) were estimated for each 1In this paper, we take a strictly economy-wide approach to the assessment of the bioeconomy. As a result, we do not consider micro-scale non-economic sustainability assessment tools of, such as, for example, life cycle assessment models. ECONOMIC SYSTEMS RESEARCH 135 member state (MS), called ‘AgroSAMs’ for the year 2000, with a detailed disaggregated split of primary agricultural accounts (Müller et al., 2009).2The AgroSAMs were subsequently updated to 2007 and employed to examine the structural properties of said sectors (Philippidis et al., 2014). Elsewhere, Budzinski et al. (2017) estimated a multiregional input – output (IO) model for Germany with detailed splits focussed on analysing the forestry supply chain, whilst Escobar et al. (2018) examined the bioplastics sector modifying the structure of a global model. Taking a further step in this direction, this paper presents a new set of estimated SAMs for 2010 for each of the EU member states, dubbed ´BioSAMs‘, which consolidates and significantly deepens the representation of the bioeconomy. More specifically, as with the AgroSAMs, the definition of non biobased manufacturing and services activities in the BioSAMs is consistent with national accounts data, whilst both databases follow the same nomenclature for the coverage of primary agriculture and food sector accounts. In addition, however, the BioSAMs extend the treatment of bioeconomy activities to encompass further sources of wood based and lignocellulosic biomass, a representative breakdown of liquid biofuels types, solid biomass for electricity and biochemicals. In this way, whilst nonexhaustive, the BioSAMs exhibit a considerably broader representation of biobased sectors beyond the typical feed and food representation found in most economy-wide databases of this type. As is typical to the SAM structure, the BioSAMs extend the inter-industry and final demand relationships found in IO tables, by capturing the complete circular flow of the economic system through explicit linkages between the productive factors and institutional accounts. To the best of our knowledge, this database represents the most detailed, consistent and comprehensive set of EU member states SAM tables for the bioeconomy.3 The rest of this paper is as follows. Section two discusses the structure of the SAM table and details the steps employed to open the biobased activity accounts within the BioSAMs. Section three presents an analytical application of the structural properties of the biobased activities within the BioSAMs employing a sensitivity analysis of three complementary linear multiplier techniques to identify ‘key sectors’. The study concludes that there is considerable relative wealth generating potential within cereals and livestock activities and commodities as well as contemporary bioindustrial activities. Section 4provides some critical discussion of the database and suggests recommendations for further research directions. 2. Material and Methods 2.1. Social Accounting Matrices: Concept and general issues. A SAM is a square matrix in which activities, commodities, factors or institutions are represented through accounts that are presented by rows and columns. Each cell in the matrix shows the payment from the column account to the row account, so the resources of one 2The availability of the AgroSAMs also served as a crucial input for the compilation of the agricultural accounts in the EU- funded EXIOBASE (see Stadler et al., 2018). A table describing the AgroSAM activities and commodities is included in the supplementary materials document. 3There are several IO type databases worldwide, with national and/or international or interregional flows: EORA (Lenzen et al., 2012,2013), EXIOBASE (Stadler et al., 2018), GTAP (Aguiar et al., 2016) or WIOD (Timmer, 2012; Dietzenbacher et al., 2013). All of these are respected and tremendously useful. The contribution of BioSAMs, however, is not only the incorporation of this detailed disaggregation of biobased sectors into a SAM scheme for all EU member states, but the use of the activities by commodities structure and a complete coherence with national accounts data. 136 A. J. MAINAR-CAUSAPÉ ET AL. Figure 1. A Social Accounting Matrix (SAM) structure. account are shown along the row and the uses in the column and, due to the double entry system, total resources of each account match exactly its total uses. Indeed, the SAM can be considered as a natural extension of IO tables. Moreover, the SAM structure overcomes some limitations of the IO tablesby internalising the complete process of production, trade, income generation and redistribution among institutional sectors (Pyatt & Round, 1985; Pyatt & Thorbecke, 1976), which not only enables the analysis of inter-industrial relations, but also social and environmental issues.4 In general, the elaboration of a SAM requires an important volume of statistical information, of which the IO framework provides the core, since it contains the interrelationships between activities and commodities and, at least at the aggregated level, the values of the links with the institutional sectors.5This key information can be derived from the traditional symmetric table or from the more detailed Supply-Use Tables (SUT). In both cases, it is possible to build a SAM that distinguishes between activities and commodities6or that definestheproductivesectorsbasedononlyoneofthetwoconcepts. A Social Accounting Matrix has six basic groups of accounts (see Figure 1): Activities and/or Commodities, Factors, Private Institutions – (Households and Corporations/Enterprises), Government (public institutions), Capital accounts (usually combined) andanaccountfortheRestoftheWorld.Thefinaldimensionofthematrixisdetermined by the level of disaggregation of these six groups. Subject to data availability, this structure can be modified to provide a more explicit representation of specific socioeconomic issues.7 4The concept of a SAM begins with Stone (1947), whose pioneering work includes most of the conventions that would later be adopted by economic and statistical organisations developing this tool. Pyatt and Thorbecke (1976) subsequently formalised the concept of a SAM, which paved the way for its use as a framework for economic analysis and planning (see also Pyatt & Round, 1985) in areas including employment, poverty, growth and income distribution and trade. 5Due to this dependence, most of the SAMs are estimated for the same base year as relevant existing IO frameworks. 6The distinction between productive sectors (activities) and the goods and services produced (commodities) is proposed in the current guidelines of national accounts (i.e. the European System of National and Regional Accounts, ESA 2010 - EC, 2013). 7The general characteristic of this structure, as well as specific issues of its definition and composition can be found in Eurostat (2008a), Miller and Blair (2009), EC (2013) and Mainar-Causapé et al. (2018). ECONOMIC SYSTEMS RESEARCH 137 Table 1. Institutional sectors correspondence in Eurostat and SAM accounts. Eurostat classification of sectors SAM accounts Total economy (S.1) Total Non-financial corporations (S.11) Enterprises / Corporations Financial corporations (S.12) General government (S.13) Government Households (S.14) Households Non-profit institutions serving households (S.15) Rest of the World (S.2) Rest of the World Source: Own elaboration. Table 2. Macro-variables used to build MacroSAMs. Aggregates used in activities/commodities, factors and taxes accounts Aggregates used in the estimation of the closure matrix Output Final consumption expenditure Intermediate consumption Compensation of employees Final consumption expenditure Taxes on production and imports Gross capital formation Subsidies Exports of goods and services Property income Imports of goods and services Current taxes on income, wealth, etc. Taxes on production and imports Net social contributions Subsidies Social benefits other than social transfers in kind Taxes less subsidies on products Adjustment for the change in pension entitlements Saving, gross Other current transfers Current external balance Operating surplus and mixed income, gross Compensation of employees External balance of goods and services Operating surplus and mixed income, gross Saving, gross Current external balance Source: Own elaboration. 2.2. Estimation of ‘first-stage’ SAMs The construction of the BioSAMs for each of the member states consists of two main stages, each with three steps. This section discusses the first stage, which consists of the construction of ‘MacroSAMs’, ‘MicroSAMs’ and finally ‘first-stage’ SAMs. Thus, in the first step, an auxiliary matrix called a MacroSAM is populated with aggregates for the main submatrices of the SAM, employing official macro-economic data. The main data source for this is the non-financial annual sector accounts (Eurostat, 2016a). These accounts provide values paid and received, by institutions, for the main macro-variables of the economy. Table 1shows the relationship between institutions and SAM accounts and Table 2contains the macro-variables used to characterise the MacroSAMs.8Data from this statistical operation are used for aggregate production, supply and demand, primary factors (labour and capital), taxes and institutional accounts (i.e. ‘household’, ‘government’, ‘corporations’ and ‘rest of the world’). Having populated these cells, a so-called closure matrix (cells containing links between institutions) that maintains a strict equality between the aggregate data row and column targets, is estimated employing the bi-proportional RAS method (Miller &Blair,2009). 8Note that the MacroSAMs do not include domestic margins. 138 A. J. MAINAR-CAUSAPÉ ET AL. In the second step, the MacroSAM structure is converted into a MicroSAM, with a full coverage of the specific branches of the activities and commodity accounts. In our application, the procedure of estimation of these extended submatrices from the MacroSAM consists of entering information from the 2010 SUTs (Eurostat, 2016b), with their original structure of 65 sectors/products. In this context, there are also previous examples in the literature showing the construction of SAMs from IO frameworks (either symmetric IO tables or Supply-Use tables). Examples, among others, include Breisinger et al. (2009)and more recently, Alvarez-Martinez and López-Cobo (2018), who build SAMs for the member statesoftheEUfromthemultiregionalWIODtables(Dietzenbacheretal.,2013;Timmer, 2012). Thus, using Eurostat’s SUT structure, it is possible to depict production and transactions processes of the national economy, describing the structure of the costs of production, the value added generated and the flows of goods and services, both domestically produced and from foreign trade. With reference to Figure 1, the Supply table provides values for sub-matrix TA,C (domestic production), imports, taxes less subsidies on products and margins. The Use table is used to populate submatrix TC,A (intermediate consumption), submatrix TF,A (labour and capital remuneration), final consumption (of households and government), gross capital formation, exports and taxes less subsidies on production. With the Eurostat SUT as a key source, the initial set of MicroSAMs follow the activity classifications of Eurostat NACE Rev. 2 (Statistical classification of economic activities in the European Community, revised version 2) (Eurostat, 2008b) and the commodity Classification of Products by Activity (CPA) (Eurostat, 2008c). These MicroSAMs are finalised by correcting (where necessary) minor differences arising from different concepts in the SUT (consumption by residents abroad, payments to labour and indirect taxes and others). In the final step, ‘first-stage’ SAMs are generating by mapping and aggregating all nonprimary agriculture and food (activity and commodity) accounts in the MicroSAM to the classifications in version 9 of the Global Trade Analysis Project (GTAP) database (Aguiar et al., 2016). On the one hand, this allows for a quality check on the coherence of the results with other existing approaches. More crucially, the redefining of the MicroSAMs based on the GTAP products classification facilitates the subsequent opening of additional accounts through the use of other compatible databases (see next sections) and their subsequent reconciliation within a more complete and detailed accounts structure for the BioSAMs (i.e. higher disaggregation of agricultural sectors and the inclusion of institutional accounts). Thus, at the end of this step, Table 3provides the classification of activities and commodities (largely following version 9 of the GTAP nomenclature) used in the final estimated ‘first-stage’ SAMs. 2.3. Estimation of the EU-28 BIOSAMS Starting with the first-stage SAMs which mainly follow the GTAP nomenclature of sectors (32) and products (32) (Table 3), the final major stage is the disaggregation of two groups of bio-based sectors. Firstly, a further split of the aggregate agricultural (arable and livestock activities and products) and food industry accounts. Secondly, a split of select contemporary biobased energy and industrial accounts and their feedstocks, which remain hidden within the Eurostat SUT or standard GTAP accounts definitions. Thus, to facilitate this step, the sectors and products that require further splits are Agriculture (includind ECONOMIC SYSTEMS RESEARCH 139 Table 3. Sectors (activities and commodities) classification in the first-stage SAMs. Agriculture Machinery and equipment nec Forestry Manufactures nec Fishing Electricity and gas Coal Water Food industry Construction Textiles, wearing apparel, leathers. Trade Wood products Transport nec Paper products, publishing Water transport Petroleum, coal Air transport Chemical, rubber, plastic products Communication Mineral products nec Financial services nec Metals Insurance Metal products Business services nec Motor vehicles and parts Recreational and other services Transport equipment nec Public Administration, Defence, Education, Health Electronic equipment Dwellings Source: Own elaboration livestock);Forestry;Food industry;Petroleum, coal; Chemicals, rubber and plastics;Wood products and Electricity and gas. This major stage consists of three steps described in the following subsections. 2.3.1. Splitting out ‘Agriculture’ and ‘Food’ accounts. The information needed to perform the disaggregation9of agricultural activities (and livestock activities and products) and food industry activities, comes from two main databases. Tables 4a and 4b belowshowtheprimarysectorandfoodindustryfinaldisaggregation. The Complete and Consistent (CoCo) database from the Common Agricultural Policy Regionalised Impacts (CAPRI) modelling system (Britz & Witzke, 2014) for each of the MS10 is exploited following the procedure described in Müller et al. (2009)toobtain AgroSAMs, with updated values to 2010. The adapted data from CoCo provides information for intermediate and final consumption of agricultural commodities by institutional sectors (households, government and investment) and for the estimation of the production coefficients and value added of agricultural activities. This step therefore enables the agricultural sector splits within the submatrices11 TC,−,T−,C, TA,−and T−,A in Figure 1. The Economic Accounts for Agriculture (EAA) from Eurostat (Eurostat, 2016c), provides information to check the previous estimations and for additional balances. In this sense, data from the EEA are used as a quality check for primary sectors’ aggregate values (monetary flows) and is used as a proxy for extrapolation in case of data gaps or inconsistencies in specific activities and/or countries. Also, the EEA gives highly disaggregated information for crops and livestock activities on production, taxes and subsidies, 9For the interested reader, in the literature there are several methodological papers describing the disaggregation of IO tables: Aislabie and Gordon (1990), Barrera-Lozano et al. (2015), Lindner et al. (2012)orWenzetal.(2015) among others. 10 The incorporation of the CoCo into the agricultural accounts of the BioSAMs follows the approach of Müller et al., (2009) for the AgroSAMs. Thus for a fuller technical description of the CoCo and EAA data sources and their reconciliation within the accounts, the reader is advised to consult sections 3.7, 3.8 and 7 of their (open access) report. This CoCo database does not include data for Croatia, so values for this country were estimated using its aggregates from Eurostat and the CoCo average neighbourhood values as proxies. 11 TC,−and T−,C (TA,−and T−,A) refer to submatrices with commodities (activities) accounts in rows and columns, respectively. 140 A. J. MAINAR-CAUSAPÉ ET AL. Table 4a. Biobased activities/commodities classification in BioSAMS. a. Agriculture, livestock and food industry activities and commodities Paddy rice Cows and cattle, live Wheat Sheep, goats, horses, asses, mules and hinnies, live Barley Swine, live Grain maize Poultry, live Other cereals Other animals, live and their products Tomatoes Raw milk Other vegetables Meat of bovine animals, fresh, chilled, or frozen Grapes Meat of swine, fresh, chilled, or frozen Fruits and nuts Meat of sheep, goats, and equines, fresh, chilled, or frozen Rapeseeds Meat and edible offal of poultry, fresh, chilled, or frozen Sunflower seed Vegetable oils and fats Soya seed Olive oil Olive for the oil industry Oil-cakes Other seed for the oil industry Dairy products Sugar beet Rice, milled or husked Fibre plants Processed sugar Potatoes Prepared animal feeds Live plants Other food products Fodder crops Wine Tobacco Other beverages and tobacco Other crops b. Non-agricultural biobased activities and commodities Forestry 2nd generation biofuel – biochemical pathway fuels (eth) Plantations (energy crops) 2nd generation biofuel – thermal pathway fuels (ft_fuel) Wood products Biochemicals Pellets Bioelectricity Bioethanol Textiles, wearing apparel and leather Biodiesel Note: In the BioSAMs, there are the same number of activities as commodities, with the same denomination (in the case of activities, it will be: production activity of ...) Source: Own elaboration Table 4b. Non-biobased activities/commodities classification in BioSAMS Paper products, publishing Trade Petroleum, coal Transport nec Mineral products nec Water transport Metals Air transport Metal products Communication Motor vehicles and parts Financial services nec Transport equipment nec Insurance Electronic equipment Business services nec Machinery and equipment nec Recreational and other services Manufactures nec Public Administration, Defense, Education, Health Water Dwellings Construction Note: In the BioSAMs, there are the same number of activities as commodities, with the same denomination (in the case of activities, it will be: production activity of ...) Source: Own elaboration and aggregate agriculture values of output, intermediate consumption, gross and net value added, gross fixed capital formation (GFCF), compensation of employees, other taxes and subsidies on production, net operating surplus or net mixed income, property income and net entrepreneurial income in current prices. ECONOMIC SYSTEMS RESEARCH 147 to the HEM and eigenvector methods, respectively. In the case of ‘bioenergy’, according to HEM its activities are key for 24 of the 28 MS. The eigenvector method counts ‘bioenergy’ askeyinonlysevenMS,whilsttherearenoMSexamplesrecordedundertheRasmussen- Jones method, once again largely due to the effect of the scale of these sectors on national economies. Finally, ‘biomass’ presents similar behaviour, classified as a key activity in 18 member states according to the HEM and eigenvector methods and nine according to the Rasmussen-Jones method, while respective results for the ‘biomass’ commodity account are seven, ten and six member states, respectively. 4. Discussion The EU policy agenda has been radically reformed by the launch in 2019 of the ‘Green Deal’. This umbrella strategy seeks to roadmap an equitable, climate-neutral, and sustainable model of economic development. In promoting fossil-substituting technologies and through the reusage of waste and residue biomass streams, the bioeconomy, which permeates many aspects of the modern economy, forms a core part of the Green Deal. As a basis for conducting policy relevant impact assessments, there is therefore aclearandpresentneedforconsistentandcomprehensiveeconomy-wideframeworks that explicitly represent the interdependences between the bioeconomy and the broader macroeconomy. This paper is a first attempt to bridge that gap by describing the steps to integrate a detailed representation of the biobased and natural resources economy for each of the EU member states, within the official structure of the national accounts for the statistical offices of the European Union. To the best of our knowledge, the resulting social accounting matrices (BioSAMs) are currently the most complete multisectorial database of the bioeconomy that exists for each of the EU member states, with a comprehensive and detailed coverage of bio-based sectors and their links with the rest of the economic activity accounts and institutional accounts (Households, Government and Corporations). Whilst it is expected that the coverage of specific biobased technologies or value chains into respective secondary databases will steadily improve over time, the introduction of new NACE and PRODCOM classifications typically requires many years to come to fruition. Thus, for tackling the immediate pertinent research questions for the EU bioeconomy from an economy-wide perspective, it is hoped that the ‘BioSAMs’ will be a de facto resource for useful descriptive analysis, the application of structural linear multiplier models, or as a basis for calibrating computable general equilibrium foresight studies. In addition, in combination with additional socio-economic or environmental satellite data, they can be used (inter alia) in the detailed analysis of job and value added creation, sustainable growth and use of resources or environmental impacts. A general caveat of the work relates to the paucity of detailed available data to open specific biobased accounts in the BioSAM (Budzinski et al., 2017), which necessitated the need, in some cases, to resort to restrictive assumptions. Thus, whilst the initial construction of the standard SAMs is highly reliable and verifiable due to the direct use of statistical information on the national accounts compiled by Eurostat (subject to publication delays), the subsequent splitting of agricultural and non-food/feed biobased accounts 148 A. J. MAINAR-CAUSAPÉ ET AL. requires, especially in the case of the latter, the use of information from dispersed and even non-published sources of data and expert insights.21 As an illustration of the potential of this database, a structural analysis of the bioeconomy to identify the main motors of biobased growth, denominated as key sectors, has been carried out. As a definitive economy-wide ex-ante quantitative descriptor of the structure of an economy, key sector analysis offers an insightful and easily interpretable summary of the economic performance of a single activity, or collective of activities. For example, to assess the relative (short-run) impacts on wealth generation or employment generation arising from a given demand-driven economic shock. As a result, this class of analysis has garnered considerable interest in policy circles to pinpoint potential ‘champion’ sectors, which could serve as a key input into a better understanding of structural economic change and even policy design. Based on the Rasmussen criterion, a series of three recognised and respected complementary structural multiplier approaches are performed (Rasmussen-Jones, hypothetical extraction method and eigenvector method). The three methods converge on the conclusion that primary bioeconomy sectors ‘cereals’, ‘intensive’ and ‘extensive livestock’ and ‘raw milk’ are strong key sectors for the EU28 aggregate, ‘bioindustry’ activities are also key. As a result, each of these sectors, in relative terms, is capable of generating a cumulative demand driven (backward-linkage) and supply driven (forward-linkage) wealth effect, which exceeds the value of the initial impetus in the economic system. For example, raw milk has diverse outputs (e.g. butter, cheese, skimmed milk, whey) and multiple inputs (feeds, veterinary costs, chemicals, energy etc.), which generate proportionally larger ripple effects throughout the economy. Moreover, ‘biomass’ is revealed as a forward oriented sector, which confirms the notion that the uses of biomass are particularly varied (food, feed, energy, industry), resulting in considerable supply driven effects as biomass is transformed into alternate processed products. Elsewhere, ‘bioenergy’ sectors are not detected as key, but have a significant backward capacity, which indicates that there is a considerable portfolio in the structure of inputs (types of biomass, energy, chemicals) required in the production of biofuels that generate above-average demand driven effects The results obtained are coherent with previous approximations based on the analysis of multipliers and backward and forward linkages (Fuentes-Saguar et al., 2017;Philippidis & Sanjuán-López, 2018). In Fuentes-Saguar et al. (2017)themultipliersofoutput are specified by large groups of bioeconomic activities, although their characterization is not developed as a key sectors analysis within the economic structures of the MS (although their main features can be inferred). Meanwhile, in Philippidis and Sanjuán-López (2018)a more appropriate sectorial disaggregation is used, based on the properties of the backward and forward linkages, although unlike the current paper, they do not consider variations in outcomes that may arise from the use of different structural multipliers to verify the robustness and coherence of the results. 21 At the time of writing, work has already begun to estimate and construct BioSAMs for the year 2015, with a possible update for 2017. The key issue is the availability of coherent, consistent and updated data to perform the split of the non-agricultural/food bio-based sectors from their ‘parent’ activities. This remains a challenge. For agriculture, work to open the agricultural accounts is already underway employing the CoCo database as the principal source, along with Eurostat’s EAA. ECONOMIC SYSTEMS RESEARCH 149 From a policy perspective, the illustrative application in the current paper provides direct input into the fifth pillar of the EU’s bioeconomy strategy (EC, 2018).22 Biomass represents a biologically renewable, albeit not unlimited, specific resource for biobased activity.Thus,inseekingtoguidememberstateandEUpublicpolicydecisionsonhowbest to allocate biomass, our results already provide an initial (short-run) picture of the relative return from different biobased applications. For example, on the activity accounts of the BioSAM, the three multiplier techniques indicate that bioindustry may be capable of generating greater relative wealth than bioenergy. This is consistent with the ‘cascading principle’ (EC, 2012), which prioritises higher value added biomass reuse prior to burning for energy. On the other hand, this is not aligned with current EU policy practise that currently promotesbiomassforenergyovermaterialuse(OECD,2012,Visetal.,2016), which further runs the risk of crowding out private sector investments in promising nascent bioindustrial technologies (Carus et al., 2011). The policy usefulness of the database itself, is further reaching for the Bioeconomy Strategy and by extension, the Green Deal. This becomes apparent when considering the quantification of the trade-offs and synergies between all five pillars of the Bioeconomy Strategy. For example, European Commission annual agricultural market outlooks (EC, 2019c) already exist to examine issues of food security (pillar 1) and competitiveness (pillar five). On the other hand, only a systems-wide macroeconomic market model calibrated to the BioSAMs is able to address pertinent policy questions that combine outcomes for all five pillars at EU and member state level. This could include public policy support initiatives, targeted investments in contemporary bioindustrial activity, or market shocks arising from changing fossil fuel prices or carbon taxes. The resulting outcomes can be evaluated in terms of the pattern of biomass allocation across competing sectors (pillar two), the implications for EU food and energy self-sufficiency (pillar one and three), the role in reducing the manufacturing base’s dependence on carbon based inputs (pillar four) and indeed, market competitiveness (pillar five). Looking ahead, there are still gaps within the bioeconomy that are not currently captured within the BioSAMs. For example, further research could be channelled into improving the representation of the forestry sector, as well as the usage of solid woody biomass for energy (i.e. bioheat), which constitutes a significant portion of biomass for bioenergy usage. Furthermore, a key element of the biobased circular economy is the activity of municipal and industrial waste collection, processing and disposal (composting, recycling, incineration for energy). To internalise to a greater degree the multidimensional policy aims of the bioeconomy, an even more ambitious line of research could seek to incorporate additional transactions accounts to capture non-market ecosystem services within the SAM structure (Vallecillo et al., 2019). In the same vein, a superior measurement of sustainability indicators could also be examined through the lens of material flows of physical biomass units (i.e. dry-matter, wet-matter, carbon content) and their reconciliation and verification with the value transactions in the member state accounting framework (Van Berkel & Delahaye, 2019). 22 The five pillars are: (i) Ensuring food and nutrition security; (ii) managing natural resources sustainably; (iii) reducing dependence on non renewable, unsustainable resources; (iv) mitigating and adapting to climate change and (v) strengthening European competitiveness and creating jobs. 150 A. J. MAINAR-CAUSAPÉ ET AL. Acknowledgments The authors thank Robert M’barek and Emanuele Ferrari (Joint Research Centre-Unit D.4, Seville) who provided helpful comments on an earlier draft of the paper. The authors are also indebted to two anonymousreviewerswhosethoroughcommentshavegreatlyimprovedthequalityofthepaper. Disclosure statement No potential conflict of interest was reported by the author(s). Funding This work was supported by the European Commission under Specific Contract 154208.X16 “Jobs and Growth in the Agri-Food Sector”. This research was also part funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 773297 for the project “Bio-based innovation for sustainable goods and services -supporting the development of a European Bioeconomy (BIOMONITOR)”; by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) (RTA2017-00046-00-00), co-funded by FEDER “Operational Program Smart-Growth” 2014-2020, for the project “Bioeconomía 2030: Un análisis cuantitativo de las perspectivas a medio plazo en España”. ORCID Alfredo J. Mainar-Causapé http://orcid.org/0000-0003-2032-9658 George Philippidis http://orcid.org/0000-0003-1727-2240 Ana I. 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Bioeconomy sectors (activities and commodities) aggregation for the application Aggregate sectors Description of aggregate sectors BioSAMs sectors Cereals Cereals (paddy rice, wheat, barley, maize, other cereals) PARI; WHT; BARL; MAIZ; OCER Vegetables Vegetables (tomatoes, potatoes, other vegetables) TOMA; OVEG; POTA Fruits Fruits (grapes, other fruits) GRPS; FRUI Oilseeds Oilseeds (rape, sunflower and soya seeds) RAPE; SUNF; SOYA Oil plants Oil plants (olives, other oil plants) OLIV; OOIL Industrial Crops Industrial Crops (sugar beet, fibre plants, tobacco) SUGB; FIBR; TOBA Other crops Other crops (live plants, other crops) LPLT; OTCR Extensive livestock Extensive livestock production (live bovine; sheep, goats, horses, asses, mules and hinnies) LCAT; LSGE Intensive livestock Intensive livestock production (live swine, poultry) PIGF; PLTR Other animals/animal products Other live animals and animal products OANM Raw milk Raw milk MILK Fishing Fishing FSH Food Food processing except Dairy (animal feed, fodder crops, biodiesel by-product oilcake, meat of bovine, meat of sheep, goats, meat of swine, poultry, vegetable oils, rice (processed), sugar (processed), olive oil, wine, beverages and tobacco, other food products) FODD; CAKES; ANFD; BFVL; SGMT; PORK; POUM; VOL; RICE; SUGAR; OLIVEOIL; WINE; BEVTOBAC; OFOOD Dairy Dairy products DAIRY Bio-mass Bio-mass supply (energy crops, pellets, forestry) FRS; ENECROP; PELLETS Bioenergy Bioenergy (bioelectricity, biofuel 1st generation (bioethanol, biodiesel), biofuel 2nd generation (biochemical and thermal technology biofuel)) BIOE; BIOG; BIOD; ETH; FTFUEL Bio-industry Bio-industry (wood products, textiles, wearing apparel and leather, biochemicals) WOOD; TEXTILES; BIOCHEM Source: Own Elaboration 156 A. J. MAINAR-CAUSAPÉ ET AL. Table A2. Structural Summary Statistics in four member states (million e2010 prices). Finland Germany Output Value Added Exports Imports Output Value Added Exports Imports Grains 614 40 132 51 10,659 3,676 2,291 4,135 Vegetables 602 278 43 122 1,659 911 903 3,202 Fruits 86 41 55 787 434 319 1,101 11,504 Oilseeds 92 2 14 57 3,854 1,030 949 2,784 Oilplants 1 0 1 7 453 107 42 303 Industrial crops 22 3 5 17 932 89 354 1,706 Other crops 188 87 23 0 2,483 1,166 736 122 Extensive livestock 304 108 6 21 2,219 553 290 343 Intensive livestock 537 149 272 21 6,111 1,642 558 715 Other animals/animal products 536 322 117 3 895 362 100 1,095 Raw milk 1,194 574 12 0 7,982 4,905 0 314 Fishing 171 114 23 141 378 209 273 528 Dairy 1,945 293 470 404 20,887 1,958 4,475 4,902 Food 8,635 2,312 1,185 3,003 142,306 36,805 49,702 45,524 Bio-mass 3,984 2,754 58 356 3,680 1,648 427 872 Bioenergy 266 202 14 7 3,296 641 2,628 1,529 Bio-industry 7,480 1,980 3,432 3,746 61,429 19,859 49,065 59,447 Ireland Spain Output Value Added Exports Imports Output Value Added Exports Imports Grains 348 62 38 173 4,084 2,793 375 2,437 Vegetables 363 106 79 352 7,183 6,322 4,882 1,895 Fruits 42 19 109 677 7,549 6,394 4,806 1,374 Oilseeds 7 0 0 10 363 198 31 1,151 Oilplants 0 0 0 5 1,146 806 53 270 Industrial crops 0 0 0 18 271 211 53 449 Other crops 104 13 69 3 2,970 2,523 418 126 Extensive livestock 1,988 454 617 2 3,205 625 107 185 Intensive livestock 531 98 102 6 7,683 1,004 541 105 Other animals/animal products 222 46 49 27 1,609 422 58 8 Raw milk 1,838 818 42 0 2,697 960 24 0 Fishing 373 188 124 179 2,794 1,205 729 1,229 Dairy 3,421 290 2,909 500 11,638 1,924 603 1,627 Food 19,102 6,122 16,981 6,901 112,064 25,711 23,030 18,423 Bio-mass 448 247 1 1 1,148 960 127 126 Bioenergy 12 4 6 1 587 144 263 256 Bio-industry 9,065 3,516 8,620 4,571 34,284 10,012 18,131 23,212