The bioeconomy in Spain as a new economic paradigm: the role of key sectors with diferent approaches

Vol.:(0123456789) Environment, Development and Sustainability https://doi.org/10.1007/s10668-022-02830-5 1 3 The bioeconomy inSpain asanew economic paradigm: therole ofkey sectors withdifferent approaches ValeriaFerreira1 · LaiaPié1 · AlfredoMainar‑Causapé2 · AntonioTerceño1 Received: 17 March 2022 / Accepted: 5 December 2022 © The Author(s) 2023 Abstract The bioeconomy includes the sectors that use renewable biological resources to produce value added products, such as food, feed, energy, and bio-based products. Its importance has been demonstrated by its inclusion as a priority in specific and related policies such as the bioeconomy strategy, the Sustainable Development Goals, the European Green Deal, and the Next Generation recovery plan. Spain has not lagged behind and considers the bioeconomy as a priority in its policy strategies to achieve a more sustainable economy. Despite its importance, the analysis of the potential of the bioeconomy sectors in Spain is limited. To carry out policy-relevant impact assessment in support of bioeconomy development, specific databases describing bio-based products are required. Hence, this work based on the Bio Social Accounting Matrix (BioSAM) for Spain for the year 2010 with a high disaggregation of bio products to perform a structural analysis based on two different and complementary methods: the traditional and the Hypothetical Extraction approach. The structural analysis results reveal promising products as key wealth generators and growth promoters and allow to identify the most suitable to be stimulated with policies to promote the development of the Spanish bioeconomy. Therefore, this paper provides some proposed avenues that should be considered by policymakers. Keywords Bioeconomy· Structural analysis· Hypothetical extraction method· Rasmussen· Policy· Implications· Spain 1 Introduction The bioeconomy comprises all economic activities based on the use of renewable biological resources to produce food, feed, bioenergy, and bio-based products (European Commission, 2012). This new economic paradigm emerges as the need to focus on a more sustainable economy supporting the achievement of many Sustainable Development Goals * Laia Pié [email protected] 1 Markets andFinancial Analysis Research Group, Department ofBusiness Management, Faculty ofBusiness andEconomics, Universitat Rovira I Virgili, Av. Universitat n1, 43204Reus, Spain 2 Departament ofApplied Economics III, Universidad de Sevilla, Avda. Ramón y Cajal 1, 41018Seville, Spain V.Ferreira et al. 1 3 (SDGs) (Heimann, 2019). Thus, it has become a priority for the European Union (EU) promoted through its own strategy and related policies such as the European Green Deal or the Next Generation Funds. Specifically, the European bioeconomy strategy was published in 2012 and updated in 2018 (European Commission, 2018). In this sense, there has been a significant interest in the bioeconomy demonstrated by the promotion of related strategies in many countries such as Spain, Finland, Germany, Sweden or Netherlands (Lainez etal., 2018; Priefer et al., 2017), as well as an increasing scientific literature (Keswani etal., 2021). In summary, the bioeconomy offers the possibility of finding new ways of producing the same products based on more efficient use of resources, reducing dependence on non-renewable resources and avoiding resource depletion (European Commission, 2012). All of this while promoting the creation of new products, and having an impact on the competitiveness of enterprises and the generation of new jobs (Lainez etal., 2018). Despite its importance, little work has been done to analyse the potential of the bioeconomy sectors due to the lack of suitable databases and methodologies. Although different studies have measured the size of the bioeconomy by calculating the biobased shares of the sectors involved (Efken etal., 2016; Ronzon & M’Barek, 2018; Ronzon etal., 2022; Vandermeulen etal., 2011), or using econometric models (Lochhead etal., 2016), the influence of the analysis with multisectoral models, for the analysis of economic, social and/or environmental variables, is clear, for example for Netherlands (Heijman, 2016), Poland (Loizou etal., 2019), Finland (Lehtonen & Okkonen, 2013), Brazil (Maia & Bozelli, 2022), the EU and member states (Ferreira etal., 2020, 2021; Mainar-Causapé, 2019; Philippidis & Sanjuán, 2018). To this end, the Input–Output tables, and the Social Accounting Matrices (SAM) stand out as databases. With the aim of analysing the potential of bioeconomy products in Spain and promote its development, this article presents on the one hand, a multisectoral structural analysis based on two different and complementary methods: the classical methodology and the Hypothetical Extraction Method (HEM); and, on the other, some policy implications derived from this analysis. Structural analysis allows us to analyse the linkages between the sectors in an economy and to understand the impacts on wealth generation arising from demand-driven economic shocks. Linkages are defined as the relationships that a sector has with the rest of the economy through the purchase of inputs and the sale of its outputs, showing the sectors that are highly connected and identifying key sectors (Cai & Leung, 2004; Miller & Lahr, 2001). The key sectors are important to promote growth in the economy, as they have the highest potential to stimulate other sectors due to their diffusion capacity in response to final demand and cost variations (Cardenete etal., 2010). The different methodologies to identify the most important sectors of an economy can be broadly classified into two groups: the classical and the hypothetical extraction approach. In a nutshell, the classical method measures how key the sectors of an economy are, considering the impact they have on other sectors due to demand or supply variations. The HEM determines the significance of a sector by considering the output loss when extracting the sector from the economy (Dietzenbacher etal., 1993). These different methodologies are considered as complementary for a better analysis of the structure of an economy and useful tools for the analysis of ex-ante policy evaluations, (i.e. ‘what if’ questions) (Iráizoz, 2006; Leung & Pooley, 2002; Mainar-Causapé etal., 2020). The objective of the article is to analyse the bioeconomy structure in Spain using different methodologies and to identify those promising sectors as key wealth generators and growth promoters that will be suitable to stimulate with policies to promote the development of the Spanish bioeconomy. To this end, the symmetric Bioeconomic SAM Matrix The bioeconomy inspain asanew economic paradigm: therole of… 1 3 for Spain 2010 constructed by the authors is used (Ferreira etal., 2021; Mainar-Causapé etal., 2020), and both the hypothetical extraction method and traditional methodologies are applied. The empirical application provides an update of the structural analysis of the bioeconomy in Spain and its improvement by using the new bioeconomy database, and different methodologies that have not previously been applied to the analysis of the Spanish bioeconomy. In this way, the BioSAM provides a comprehensive economy-wide frameworks that explicitly represent the linkages between the bioeconomy and the broader macroeconomy, and the combination of methodologies applied enables a more in-depth analysis of these linkages and therefore, a better interpretation of those most suitable sectors for promoting the bioeconomy (Iráizoz, 2006). As the key sector analysis offers the possibility of assess the relative (short-term) wealth-generating impact of a given demand-driven economic shock, it has attracted considerable interest in policy circles to better understand structural economic change and even for policy design (Mainar-Causapé etal., 2020). Consequently, the results obtained in this article could be very useful when designing future strategies that promote the growth of the bioeconomy in Spain since it provides knowledge of which accounts are most suitable for the effective and efficient assignation of government resources. A clear example is related to the allocation of Next Generation funds provided by the European Commission, with the aim of recovering the economy because of the crisis generated by Covid-19, which should focus on promoting a sustainable economy. This article first describes the methodologies for structural analysis and the database used. Then, on section three, the results obtained with each methodology allow for an identification of the most suitable bioeconomy products to be promoted due to their ability to generate wealth. In the discussion section, the results are interpreted considering the contribution of each of the structural analysis methodologies applied. Based on the results, section five includes policy implications and recommendations of the most appropriate products that could be influenced for the development of the Bioeconomy in Spain. Finally, the conclusions of the article are presented in section six. 2 Data andmethodologies 2.1 Database: bioeconomic social accounting matrix To focus on the study of the bioeconomy, this article works with a SAM that contains accounts related to bio-based products separated from those that are not. A SAM is a comprehensive and economy-wide database that compiles economic and social information on every transaction made between agents in an economy over a period of time, which is generally one year. The origins of SAM are found in the pioneering works of Stone (1962) and Pyatt and Round (1979), among others. Hence, the database used in this article is a symmetric Bioeconomic SAM for Spain 2010 constructed by the authors (detailed in Fig.1) based on the BioSAM produced for the EU members states (Ferreira etal., 2021; Mainar-Causapé etal., 2020). The bioeconomy SAM obtained has a partial aggregation of 36 products, of which 32 are part of the bioeconomy classified in 4 groups: agriculture, food, biomass, bioindustry, and bioenergy. Coding details of the products presented in the results are clarified in Appendix Table6, which includes also the detail of all the accounts in the SAM. V.Ferreira et al. 1 3 2.2 Bioeconomy structural analysis The idea behind structural analysis is to understand the structure of an economy by analysing the linkages between its sectors to identify those that are highly interconnected and to determine the importance of each sector. Those sectors that prove to have the greatest capacity to stimulate others will be key to promoting the growth of the economy. For this reason, carrying out the structural analysis of an economy is considered an important tool for implementing and evaluating economic policies (Cai & Leung, 2004). According to the literature, the different methodologies have their advantages and limitations, but to date no single method is considered to be the most appropriate (Leung & Pooley, 2002). While the traditional methodology stands out for its simplicity and reliability, it has the disadvantage of analysing linkages without considering the size of each sector, whereas the HEM allows the size of the sector to be taken into account and has been the most widely used in recent years. Therefore, the application of different methodologies makes it possible to complement the results obtained and, therefore, to analyse the country’s structural data in more detail. The structural analysis of the bioeconomy has previously focused on the use of traditional methods (Mainar-Causapé, 2019; Philippidis & Sanjuán, 2018; Philippidis etal., 2014; Sancho & Cardenete, 2014) and the HEM has been used only for the analysis of the European bioeconomy (Mainar-Causapé etal., 2020). Outside the bioeconomy field, the literature shows many examples where classical and HEM methodologies have been applied in combination, for example to analyse China (Andreosso-O’Callaghan & Yue, 2004), Europe (Mainar-Cau- sapé et al., 2020; Soza-Amigo & Ramos Carvajal, 2005), Spain and its regions (Campoy- Muñoz etal., 2015; Cansino etal., 2013; Cardenete, 2011; Cardenete etal., 2009; Mainar & Flores, 2013), South East Asia (Ali etal., 2019), and Mexico (Boundi, 2017). The following Production FactorsPrivate Sectors Public Sectors Saving and Investment Rest of the World (RoW)Total Production Bioeconomy Intermediate consumption (S) Private sector Consumption Public sector expenditure Investment and stock changes Exports Demand Non-Bio Factors Labour Remuneration of factors (E) Factor income from RoW Factor income Capital Private Sectors Households Factor income to private sector Transfers between private sector Transfers to private sector Transfers to private sector Private income Corporations Public Sectors Taxes Taxes Factor income to public sector Private sector taxes Transfers to government Public income Government Saving and investment Private sector savings Public sector savings Transfers from RoWSaving Rest of the worldImports Factor income to RoW Transfers private sector to RoW Transfers public sector to RoW Payments to RoW Total Supply Expenditure on factor Private expenditure Public expenditure Investment Income from RoW Fig. 1 Database basic structure: Bioeconomy SAM symmetric product-by-product. Source: based on Mainar etal. (2018) and Ferreira etal. (2021) The bioeconomy inspain asanew economic paradigm: therole of… 1 3 parts of this section will explain the database and the methodologies applied for the structural analysis of the bioeconomy. 2.2.1 Traditional methods The traditional methods analyse the impact that each sector have on the other sectors considering the linkages between them and the direct, indirect, and induced multiplier effects they cause. The traditional methods include the first contributions on structural analysis, published by Rasmussen (1956), and Hirschman (1958) and with some later modifications by other authors such as Augustinovic (1970), Jones (1976) and Beyer (1976). 2.2.1.1 Methodology of Rasmussen (1956) and Hirschman (1958) The starting point is Leontief’s equilibrium equation applied to the case of a SAM as follows Yn =(I−A nn ) −1 X n =M nn X n , where the matrix Ann is the coefficient matrix and Mnn is the multipliers accounting matrix (with elements mij ). Then, Yn represents the income of the endogenous accounts, and Xn is the final demand of the endogenous accounts represented by government expenditure, investment, and exports. Rasmussen (1956) proposes obtaining two indexes based on the multipliers obtained in the model. The index of power of dispersion BLR . j (also called backward linkages, Eq. (1) it represents how a unitary increment in the demand of a sector j is dispersed to the rest of the sectors, measuring the capacity to expand a unitary and exogenous injections of income towards the other endogenous accounts (Andreosso-O’Callaghan & Yue, 2004; Pulido & Fontela, 1993). The sensitivity of dispersion index FLR i. (also called forward linkage, Eq. (2), shows how the output of a sector i is affected by the increment in the final demand of all the sectors. Total linkages (multiplier effects) Variation indices Backward linkage U . j = ∑n i=1 m ij ∀j=1,2,…n BL R .j= U .j 1 n∑ n j=1 U. j (1) V .j= � 1 n−1∑n i=1�mij − 1 n∑n i=1mij �2 1 n∑ n i=1 mij (∀j=1,2,…n) (3) Forward linkage U i.= ∑n j =1m ij ∀i=1,2,…n FL R i.== U i. 1 n∑ n i=1 Ui . (2) V i.= � 1 n−1∑n j=1�mij − 1 n∑n j=1mij �2 1 n∑ n j= 1mij (∀i=1,2,…n) (4) Table1 shows a classification of the sectors according to their BL and FL (Hirschman, 1958) with the aim of determining what the ideal key sectors are for applying economic policies (Andreosso-O’Callaghan & Yue, 2004). A key sector requires more inputs than the others in relative terms and provides the rest of the productive sectors with large quantities of intermediate inputs. Therefore, a boost to a key sector stemming from applying an economic policy propagates more extensively Table 1 Classification of sectors according to their backward and forward linkages Source: Own elaboration based on Rasmussen (1956) BL < Average BL BL > Average BL FL < Average FL Independent sectors Driving sectors FL > Average FL Base sectors KEY sectors V.Ferreira et al. 1 3 to the rest of the economy. A base sector represents a low demand for intermediate inputs, while its outputs that are in great demand by other sectors. This means that the destination of its production serves as inputs for other sectors and, therefore, its variations have important effects on the rest of the sectors. The driving sectors have a huge demand for intermediate inputs from other sectors. In response to exogenous shocks that cause demand for more production they have a huge capacity to drive other sectors, instigating further activities and promoting economic growth. The independent sectors have a lower-than-average incidence in the global economy because their development does not greatly affect the rest of the sectors. This methodology does not provide information on the concentration of the productive sectors, which may be affected by extreme values. To address the limitation, Rasmussen (1956) proposes the calculation of the dispersion of effects to know whether the effects are spread throughout the economy or whether they are concentrated in certain sectors (Cansino etal., 2013). Therefore, for a better analysis of the economy structure, V.j (Eq.3) can complement BLR . j , with high value interpreted as an industry whose impact is concentrated in a few sectors and a low value is interpreted as an industry that has a similar influence on all sectors (Hazari, 1970). Thus, Vi. (Eq.4) can complement FLR i. with a low value indicating that the rest of the industries influence i in a similar way, and a high value, indicating that few sectors influence the sector analysed (Soza-Amigo, 2007). 2.2.1.2 Variation inthecalculation ofFL One of the major discussions in the literature is the calculation of the FL. Authors like Augustinovic (1970), Jones (1976), Beyer (1976), and Dietzenbacher (1997) consider the Ghosh model to be the most suitable for its calculation. This model is based on the use of the distribution coefficient matrix ( Bnn ) and takes into account value added as an exogenous variable, Y � n =V� n( I−B nn)−1 =V� n G nn .1 With this variation, the effect of changes in the value added of a sector that affect changes in its production can be evaluated (Cardenete et al., 2010; Pulido & Fontela, 1993). Therefore, we can consider that if the FL is higher than average it represents an account with the capacity of cost dispersion because changing the values of its added value has an above average effect on the rest of the sectors (Cardenete etal., 2010). The sectors are classified under the same conditions as for Table1, with the new FL value. 2.2.1.3 Hypothetical extraction method The HEM determines the importance of a sector, considering the differences in output upon hypothetically eliminating it from the economy (Dietzenbacher etal., 1993). To this effect, a sector n is extracted from the economy and consequently neither consumes inputs nor sells outputs to the other sectors, impacting on the initial matrix and causing a new output to be obtained (Dietzenbacher etal., 1993). By comparing the level of output for each of the rest of the sectors before and after the hypothetical extraction, it can be seen whether the consequences of the extraction are significant or not for the economy. Various authors have proposed different ways to “extract” the sector. The review of extraction methodologies published by Miller and Lahr (2001) concludes that the arguments about the virtues and limitations of the methods of extraction can be useless if the aim is to know the importance of a sector (Miller & Lahr, 2001). Consequently, we have chosen to use the application published by Dietzenbacher etal. (1993) recognised as “more 1 For more detail see Dietzenbacher (1997) The bioeconomy inspain asanew economic paradigm: therole of… 1 3 evolved and synthesised” (Cardenete & López, 2015) or as the most “paradigmatic” version (Sancho & Cardenete, 2014).2 2.2.1.4 Backward linkage Analysis of the BL serves to indicate the impact of extracting a sector that no longer consumes inputs for its production on the other sectors in terms of reduction in output. Considering the initial equation of Leontief’s demand model: on hypothetically extracting the sector j the rest of the sectors are represented among the group r . Considering the matrix Ann , partitioned between the sector j and the rest r , Yj and Yr represent the total output of each group, respectively, and Xj and Xr represent their respective final demands. On extracting sector j , a new matrix of technical coefficients, A nn , and a new output after the extraction represented by Y j and Y r are obtained. Total reduction in output on hypothetically extracting sector j is proposed as: where Y is the total output prior to extraction and Y the value obtained after extracting the sector j . Carried out n times, each result constructs a new matrix that collects the backward effects of the extracted sector on the rest of the economy and of the rest of the economy on the extracted sector (Cardenete & Sancho, 2006). The elements that do not belong to the diagonal i≠j will represent the actual BL (Cardenete & López, 2015; Cardenete etal., 2009), which reflects the output lost on the other sector on extracting the sector analysed, given by the backward dependency of the extracted sector on the other sectors (Cardenete etal., 2010; Sancho & Cardenete, 2014). 2.2.1.5 Forward linkage FL is understood as the impact on output reduction, which in terms of “opportunity cost” the extraction of a sector will have since it will not be able to supply the inputs to satisfy demand from the other sectors (Cansino etal., 2013). This index allows us to know the effect that extracting a sector j has on the rest in terms of sales of their output (Cardenete etal., 2010). (5) Yn =(I−A nn ) −1 X n =L nn X n (6) Y =(I−A nn ) −1 X (7) [ Yj Yr ] = [ An jj 0 0An rr ][ Yj Yr ] + [ Xj Xr ] (8) [ Yj Yr ] = [[ I0 0I ] − [ An jj 0 0An rr ]] −1 [ Xj Xr ] (9) Y −Y= ( Yj−Y j Yr−Yr ) = {[ Ln jj Ln jr Ln rj Ln rr ] − [ (I−An jj)−10 0(I−An rr)−1 ]}[ Xj Xr ] 2 Cardenete and Sancho (2006) later extended its use to a SAM (Cardenete and Sancho 2006; Sancho and Cardenete 2014), which has been applied in different research (Beltrán etal., 2016; Cansino etal., 2013; Cardenete 2011; Cardenete and López 2015; Cardenete etal., 2009). V.Ferreira et al. 1 3 Considering the initial equation of the Ghosh model: where Bnn is the distribution matrix, Vn represents the primary outputs and Gnn is Ghosh’s inverse matrix. Then, in the partitioned matrix Bnn the sector to be extracted is separated as j and the rest of the economy is represented by r . On extracting the sector j , a new matrix of coefficients of distribution Bnn is obtained, and from this equation a new output is obtained after the extraction represented by Yj and Y r . Total reduction in output on hypothetically extracting sector j is proposed as ( Y−Y )� . Again, sectors are extracted one by one, obtaining new production values for each case and detailing the results of each output in a new matrix. Each element (i,j) will represent the case where the sector was extracted (j) , and will represent the forward linkage between the sector j and the sector i . The difference between the case with the extracted sector and the original situation reflects the economic losses of the rest of the sectors of the economy without the offer of the extracted sector. The sum of the values outside the diagonal of the matrix obtained are the FL, and they quantify the forward relation of the extracted sector j with the global economy. The total output reduction on hypothetically extracting the sector j is proposed as: 3 Results The methods should be used as tools that provides information to describe the structure and linkages between sectors within the Spanish bioeconomy (Cai & Leung, 2004). It provides information on which are the most important sectors to promote the bioeconomy in Spain. Therefore, the approach is useful for descriptive purposes, and these results can be considered preliminary for ex-ante policy decisions (Dietzenbacher & van der Linden, 1997). 3.1 Application oftraditional methodology 3.1.1 Structural analysis according toRasmussen/Hirschman The products codes and sectoral classification can be found in Table6 in Appendix. Table2 shows the values obtained for each product and their structural classification, which is shown visually in Fig. 2. Their analysis indicates that only services, classified outside the bioeconomy, can be considered as a key product. However, several of the products belonging to the (10) Y � n =V� n( I−B nn)−1 =V� n G nn, (11) Y � =V� ( I−Bnn )−1 (12) ( YjYr ) � = [ VjVr ] � [[ I0 0I ] − [ Bjj n0 0Brr n]]−1 (13) ( Y−Y ) � = [( Yj−Yj ) � , ( Yr−Yr ) � ] = [ VjVr ] � {[ Gjj nGjr n Grj nGrr n ] − [( I−Bjj n ) −10 0 ( I−Brr n) −1 ]} The bioeconomy inspain asanew economic paradigm: therole of… 1 3 Table 2 Bioeconomy Product Classification for Spain according to Rasmussen/Hirschman. Source: Authors’ own elaboration based on BioSAM Spain 2010 (Ferreira etal., 2021). Note: Values higher than one mean above average. Each value of the BL shows the income expansion effect generated in the endogenous accounts above average due to a unitary exogenous shock of income into the account. Each value of the FL quantifies the increase in income above average in the account as a result of a unit-income exogenous injection in the economy. The gradient colour scale indicates higher values represented by green and lower values by red Classification ProductBL FL V.jVi. Classification Product BL FL V.jVi. Driving Fruits 1.02 0.27 2.31 1.06 Independent Cereal 0.84 0.49 2.33 0.96 Other Crops1.23 0.38 2.17 0.99 Vegetables 0.95 0.26 2.34 1.08 Extensive livestock 1.31 0.31 1.83 1.05 Oil seeds 0.39 0.28 3.62 1.06 Intensive livestock 1.38 0.37 1.80 1.02 Oil Plant0.95 0.25 2.24 1.10 Other live animals & animal products 1.27 0.24 1.82 1.12 Industrial Crops 0.55 0.24 2.78 1.11 Raw Milk 1.32 0.30 1.86 1.04 Fishing 0.89 0.25 2.28 1.10 Animal feed 1.04 0.68 2.27 0.94 Olive oil0.87 0.41 2.69 1.08 Beverages & Tobacco 1.14 0.39 2.06 1.02 Sugar 0.96 0.26 2.15 1.10 Red meat 1.27 0.28 1.94 1.06 Pellet 0.97 0.21 2.16 1.16 White meat 1.33 0.39 1.92 1.02 Biofuel 1st generation 0.88 0.22 2.02 1.15 Vegetable oils 1.11 0.24 1.87 1.11 Biofuel 2nd generation 0.96 0.22 2.44 1.6 Dairy1.22 0.77 2.40 0.98 Bio- Chemicals0.86 0.27 2.25 1.06 Processing of rice 1.03 0.26 2.11 1.09 Textiles 0.67 0.42 2.72 1.00 Other food 1.11 0.89 2.05 0.92 Natural Resources0.51 0.87 3.75 0.93 Wine 1.09 0.31 2.20 1.03 KeyService 1.27.02 2.85 0.90 Energy Crops1.23 0.21 2.28 1.16 Base Manufacture 0.88 2.19 2.72 0.90 Forestry 1.07 0.25 2.27 1.11 Bioelectricity 1.06 0.22 2.31 1.16 Wood 1.08 0.31 2.29 1.08 Energy 1.15 0.96 2.36 0.93 1 23 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 0.00.2 0.40.6 0.81.0 1.21.4 1.6 Forward Linkage Backward Linkage Base sectors Driving sectors Independent sectors Key sectors Fig. 2 Structural overview of the Bioeconomy sectors, Spain (Rasmussen/Hirschman). Note: Colours by aggregated sector: * Agriculture, * Food industry, * Biomass, * Bioenergy, * Bioindustry V.Ferreira et al. 1 3 the bioeconomy is centred on just a few branches, limiting the dispersion of the effects. According to the results obtained for the bioeconomy in Europe, these products require less support from others to process and distribute one unit to the end-users (Philippidis & Sanjuán, 2018; Philippidis etal., 2014). Continuing with the interpretation of the results, within bioenergy, biofuels are considered as independent products. Although their backward links are close to the average, their forward links are low because their intermediate demand is mainly focused on manufacturing and services, and the final demand is mainly focused on exports and households. We can also discern the products that stood out previously due to their quantitative importance but whose links with the other accounts represent a below average influence, mainly within the bioindustry, textiles, bio-chemicals and wood products. Nonetheless, exclusively analysing their links with the other accounts reveals that only wood products are considered as a product with important relations driving the growth of the economy to an above average extent in terms of sectors, with the influence of work and capital standing out. Both textiles and biochemicals lack higher than the economy average links with the other accounts, considered as independent branches. These products have an important offer in the bioeconomy, which stands out for its imports. 5 Policy implications Due to the current situation of climate change and the health emergency, the bioeconomy has become a necessity and opportunity to drive an economic recovery with a sustainable approach. In this regard, for the adequate use of Next Generation funds, Spain has drawn up the Recovery, Transformation and Resilience Plan. This plan aims to select strategic sectors with a high capacity to drive economic growth and employment in Spain, while seeking to promote the bioeconomy and the ecological transition. For this reason, it is essential to analyse the situation of the Spanish bioeconomy and to know which key sectors should receive political support funds. Considering that our objective is to highlight those sectors of the Bioeconomy that have a greater impact on economic growth in general and on the growth of the Bioeconomy in particular, the summary of Table7 allows us to interpret the tendency of bioeconomic products to behave in a certain way if policy decisions were applied to them. In this sense, the analysis of the linkages shows the importance of the bioeconomy products’ relationships with the other accounts because most of them can be considered as driver, base or key products within the economy. This indicates that an exogenous injection in their demand or primary inputs can rebound on the rest of the economy to a greater degree than on the rest of the sectors. The methods applied allows us to be mainly affirmed that various bioeconomy products are important both for the total they represent in it and for the relationship they have with the other accounts. Hence, this article concludes that to promote the Spanish bioeconomy, the products with the greatest influence identified in this study as "key sectors" by some methodology must be stimulated. As a summary, Table5 shows the case of those bioeconomy products classify as key sector by at least one methodology. These are mainly other crops (7), extensive (8), and intensive livestock and products (9), and raw milk (11) -in the agriculture group-, considering the food industry animal feed (13), vegetable oils (18), olive oil (17), sugar (21), rice (20), and dairy (19). Within the biomass group only energy crops (25) and only wood products (32) within the bioindustry group. As a result, given an initial shock to the economy, each of these products has influence to generate a cumulative The bioeconomy inspain asanew economic paradigm: therole of… 1 3 demand-driven (backward-linkage) and supply-driven (forward-linkage) wealth effect. This means that they are products whose influence on the rest of the accounts is above average and, therefore, they can be considered strategic for the growth of the economy. For example, dairy products use many inputs in their production and their outputs are also highly demanded, generating multiplier effects in the economy. Despite not being considered as key products, the analysis of the products identified as driving or base also allows policy recommendations to be made on them. Considering the biomass group of products, they are mostly considered as base sectors because they are forward oriented as they are products widely used by other sectors. Further promoting its use as a raw material for the biochemical and bioenergy industry is essential to achieve the objectives of the Spanish Bioeconomy Strategy Horizon 2030. From a policy point of view, the primary sector in Spain, have the potential to promote economic growth in the rest of the economy sectors. Hence, up to this point, policies should focus on promoting Spain bioeconomy more traditional sectors because it can help to promote the growth of the bioeconomy thought the relations with other sectors. Furthermore, the promotion of agriculture will influence the development of rural areas, which will have an impact on SDGs such as food security and job creation, also helping to achieve the ‘Empty Spain’4 goal. As the policy implications for bio-economic products will have a global effect on rural areas, this should, as far as possible, be adopted in a coordinated manner. When analysing those sectors of the bioeconomy that can be considered as ‘modern’ (i.e.bioindustry and bioenergy), it can be observed that although some of them do not represent a significant value within the bioeconomy in Spain, their linkages indicate that they can be considered as drivers or base in most cases. This is because they are capable of generating above-average demand-driven effects (bioelectricity) or economic activity very close to the average (biofuels, bio-chemicals, and textiles) in the rest of the economy due to their demand for inputs from other branches and their use of capital and labour. Therefore, the promotion of these products will mean that they will represent a larger quantity in the bioeconomy and that their backward and forward linkages promote the development of other activities within it, promoting economic growth. In the case of biofuels, the Spanish policy aims to reach 10% of biofuels consumption by 2022. However, considering our analysis it can be said that in addition to the identified exports, their use by households and industries should be encouraged to influence the promotion of the sector. Another point is the case of the bioindustry, both textile and biochemical, which represent a high supply in the bioeconomy due to import dependency. This implies that Spain should encourage the production of such biobased products, generating national economic growth and employment. Consequently, the transition to a biobased economy requires that public investment R + D + i and political support focus on the most innovative biobased products. As the bioeconomy is based on the creation of an economy that uses biological resources and avoids fossilbased resources, it is essential to invest in innovation and new technologies to improve existing value chains and create new ways of bio-based production. In so doing, production for their products increases, having a knock-on effect on the rest of the economy growth and employment. In addition, increased production must be accompanied by policies that enable market penetration and favour the use of products with greater biobased content. For example, there could be market incentives for commercialization of biobased products, benefits for companies that use renewable energy and bio-based inputs, or policies that set mandatory percentages for the consumption and sale of some bio-based products. 4 It refers to the territories in Spain affected by the depopulation of rural areas. V.Ferreira et al. 1 3 In summary, the bioeconomy in Spain is a recent paradigm, which is intended to be promoted since the publication of its strategy in 2016: ‘Spanish Bioeconomy Strategy: Horizon 2030’. Therefore, it will be important to invest not only in the traditional sectors previously mentioned that stand out as important in the economy, but also in those that are important at strategic levels because they enable the expansion of the products belonging to the bioeconomy, thus aiding its development in Spain. These results will support one of the main lines of the Spanish bioeconomy strategy, which considers public and private research and collaborations and business investment in innovation in the bioeconomy to be essential (Lainez etal., 2018). 6 Conclusions In recent years, the bioeconomy has emerged as a new economic paradigm which offers the possibility of finding new ways of supplying the same products based on the more efficient use of resources, reducing dependence on non-renewable resources and avoiding resource depletion. It is also based on promoting the creation of new products, developing new economic activities, increasing companies’ competitiveness and thereby generating new jobs (European Commission, 2012). For this reason, it is essential to analyse the situation of the Spanish bioeconomy and to find out about possible actions that could help to promote it. In this way, it is necessary to work with a suitable database that can be used for economic modelling. Therefore, this article presents a structural analysis of the bioeconomy sectors in Spain based on the BioSAM. The significance of this matrix is based mainly on the split of several products within the bioeconomy. Considering the bioeconomy sectors, this article focuses on a new perspective and compliment the structural analysis applying different methodologies with the purpose of examining in-depth the relations within the sectors. In other words, different methodologies have been applied to the BioSAM to analyse the structure of the bioeconomy in Spain and to understand the potential of each bio commodity to Table 5 Bioeconomy products classify as key sector by at least one methodology K, key sectors; B, base sectors, I, independent sectors **Relative value according to the amount extracted Product HEM** Rasmussen/ FL variation Other crops K B Extensive livestock K K Intensive livestock K K Raw milk K K Animal feed K K Olive oil I K Vegetable oils K K Dairy I K Processing of rice. milled or husked K K Sugar K B Energy Crops K B Wood products I K The bioeconomy inspain asanew economic paradigm: therole of… 1 3 generate influence in the rest of the economy, providing a complete analysis of Spanish Bioeconomy. The main purpose of the methodologies applied is to provide more detailed information about the links between the accounts of the Spanish bioeconomy. The descriptive analysis applied allowed us to identify which products stand out most for their economic importance and/or their link with the other accounts, promoting the development of the rest, and also the products whose economic maturity is not yet relevant. This gives the possibility of finding out possible economic drivers that will have the greatest reach and capacity to promote the bioeconomy. Not only can the key products be identified, but also the products which, although they no longer stand out in the market, can be considered as strategic in the development of the bioeconomy because of their relationships with the rest of the accounts and because they are priority for the bioeconomy itself. The results indicate that the more traditional bioeconomy products (such as primary sector) have the potential to promote economic growth in the rest of the economy sectors since they stand out for their importance and/or their relations with the other accounts. Special attention should be paid to those other more ‘modern’ products included in this new matrix, particularly in the bioenergy and bioindustry groups. In the first case, neither biofuels nor bioelectricity represent a significant value in the Spanish bioeconomy. Furthermore, biofuels stand out for their importation and not for their linkages with capital and work. In the case of the bioindustry, it can be observed that their quantity within the bioeconomy is mainly representative due to the influence of textiles. However, both textiles and bio-chem- icals also stand out for their imports, while they do not represent important linkages with the other accounts in the economy. This means that the bioeconomy products considered as more ‘modern’ have not reached their maximum potential in the Spanish bioeconomy. The methodology is a useful tool which allows for an analysis of an economy structure and ex-ante policy assessments. Nevertheless, the different structural analysis methodologies applied work with the Leontief demand model and Ghosh offer model, with their respective limitations (Soza-Amigo & Ramos Carvajal, 2005). These limitations are mainly related to the excess capacity in all sectors and unemployed factors of production (no supply constraints) and fixed prices (not taking substitution effects into account), among others (Miller & Blair, 2009). Therefore, it should be noted that this study cannot address the analysis of the transformation of input use in industries and includes data from 2010. Last and notably, we identified the key or strategic products to promote the Spanish bioeconomy exclusively considering the economic aspect and without considering environmental variable, such a greenhouse gas emissions or land use change (Mougenot & Doussoulin, 2021). As the goal is sustainable development, the bioeconomy should consider not only economic, but also social and environmental impact analysis (Ramcilovic-Suominen & Pülzl, 2018). Therefore, to make comprehensive policy recommendations, future lines of research should focus on the assessment of the bioeconomy, considering also environmental impact. In addition, the impact of the COVID-19 restriction and also the conflict between Ukraine and Russia have important implications on agricultural and food systems and thus on the bioeconomy. Therefore, this could also be an important focus for future research. Appendix See Tables 6 and 7 V.Ferreira et al. 1 3 Table 6 Details of the accounts in the BioSAM including products coding. Source: Authors’ own elaboration based on the BioSAM (Mainar etal., 2020) Products Aggregated Code Account Primary sector 1 Cereal 2 Vegetables 3 Fruits 4 Oil seeds 5 Oil plant 6 Industrial crops 7 Other crops 8 Extensive livestock & products 9 Intensive livestock & products 10 Other live animals & animal products 11 Raw Milk 12 Fishing Food 13 Animal feed 14 Beverages & tobacco 15 Red meat 16 White meat 17 Olive oil 18 Vegetable oils 19 Dairy 20 Processing of rice. milled or husked 21 Sugar 22 Other food 23 Wine Biomass 24 Pellet 25 Energy crops 26 Forestry Bioenergy 27 Bioelectricity 28 Biofuel 1st generation 29 Biofuel 2nd generation Bioindustry 30 Bio-Chemicals 31 Textiles 32 Wood products Non Bioeconomy 33 Natural resources 34 Energy 35 Manufacture 36 Service Other accounts Factors of production Labour Capital Taxes Net production taxes Net products taxes Direct taxes Private and public agents Households Enterprises/Corporations Government Capital account Investment-savings External relations Rest of the world The bioeconomy inspain asanew economic paradigm: therole of… 1 3 Table 7 Structural classification of products according to each methodology. Source: Author’s own elaboration based on BioSAM Spain 2010 (Ferreira etal., 2021) Product Total economy Average of bioeconomy products HEM* HEM** Rasmussen Rasmussen /FL variation HEM* HEM** Rasmussen Rasmussen / FL variation Cereal I B I B B B B B Vegetables I I I I I I I I Fruits I I D I I I D I Oil seeds I B I B I B I B Oil Plant I B I B I B I B Industrial Crops I B I B I B I B Other Crops I K D B B K K B Extensive livestock I K D K I K D K Intensive livestock I K D K K D K K Other live animals & animal products I D D D I D D D Raw Milk I K D K I K D K Fishing I I I I I I I I Animal feed I K D K K B K K Beverages & Tobacco I D D D K D K D Red meat I D D D D D D D White meat I D D D K D D D Olive oil I I I K I I B K Vegetable oils I K D K I K D K Dairy I I D K K I K K Processing of rice. milled or husked I K D K I K D K Sugar I K I B I B I B Other food D D D D K D K D Wine I D D D K D D D Pellet I B I B I B I B V.Ferreira et al. 1 3 Table 7 (continued) Product Total economy Average of bioeconomy products HEM* HEM** Rasmussen Rasmussen /FL variation HEM* HEM** Rasmussen Rasmussen / FL variation Energy Crops I K D B I K D B Forestry I B D B I I D B Bioelectricity I B D B I B D B Biofuel 1st generation I D I I I D I I Biofuel 2nd generation I D I D I D I D Bio-Chemicals I D I I K I I I Textiles I I I I K I B I Wood products I I D K K I D K Natural Resources B I I B K I I B Energy K I D K K I D K Manufacture K I B I K I B I Service K I K D K I K D K, key sectors; D, driving sectors, B, base sectors, I, independent sectors. * Relative value according to the total economy without the amount extracted. ** Relative value according to the amount extracted The bioeconomy inspain asanew economic paradigm: therole of… 1 3 Author contribution Conceptualization: LP, AT; Methodology and data: VF, AM-C; Formal analysis: VF, AM-C; Writing—original draft preparation: VF; Writing—review and editing: VF, AM-C, LP, AT; Funding acquisition: VF. Funding Open access funding provided by Universitat Rovira i Virgili. This project received funding from the European Union’s NextGeneration programme, the Ministry of Universities of Spain and the Universitat Rovira i Virgili, under the Recovery, Transformation and Resilience Plan (programme Margarita Salas). Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships to declare that are relevant to the content of this article. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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