Quantifying the Economic Cost of Reducing GHG Emissions through Changes in Household Demand: A Linear Multi-Sectoral Approach for European Countries

atmosphere Article Quantifying the Economic Cost of Reducing GHG Emissions through Changes in Household Demand: A Linear Multi-Sectoral Approach for European Countries Alfredo J. Mainar-Causapé1,* , Margarita I. Barrera-Lozano 2and Patricia D. Fuentes-Saguar 3 1European Commission, Joint Research Centre (JRC-Seville), 41092 Sevilla, Spain 2Department of Applied Economics III, University of Seville, 41018 Sevilla, Spain; mbarr[email protected] 3Department of Economics, University Pablo de Olavide, Seville, 41013 Sevilla, Spain; [email protected] *Correspondence: [email protected] Received: 6 April 2020; Accepted: 22 May 2020; Published: 24 May 2020   Abstract: The mitigation of Greenhouse Gas Emissions can be approached in various ways: from the supply side, by using improvements in technologies and input uses; and from the changes in the demand for products, by influencing consumer behavior to achieve a more sustainable consumption pattern. Either way it can be approached using multi-sectoral data based on an input–output or on a Social Accounting Matrix (SAM) framework, although a suitable database and the proposal of appropriate indicators are needed. A suitable database is developed through the estimation of new SAMs for the latest possible period, that of year 2015. This paper focuses on the demand approach: that of changes in the demand for products. It analyzes the different impacts among activities and commodities of a change in domestic household consumption patterns, compares the potential reductions in Greenhouse Gas (GHG) emissions obtained through the reduction of specific demands, and considers the consequent reduction in output and employment. For this purpose, a linear multi-sectoral analysis is employed that focuses on the main EU member states. Despite major differences between countries, the results show that a decrease in emissions through demand-reduction policies exerts greater negative effects on those less polluting sectors with a higher intensity in the labor force, and offers a more suitable option for those highly polluting sectors with a lower concentration of the work factor. Richer countries that are based on service sectors therefore suffer a sharper drop in employment using this kind of policy. Keywords: GHG mitigation; effect of adoption of mitigation measures on society; social accounting matrices; multi-sectoral model; impact analysis 1. Introduction The global increase in economic activity has been connected to the general deterioration of the environment and to the increase in greenhouse gas emissions, and has become a growing issue worldwide that unequivocally demands action. A cleaner environment, a more productive and faster-growing economy, and low levels of unemployment cover what any country or region should aim to achieve. However, a better environment implies previously incurred ecological damage, while higher productivity may hide an increase in unemployment, and higher economic growth often points towards environmental destruction. Those countries that have improved their environmental quality at the same time as they have economically developed and advanced it are not actually succeeding in Atmosphere 2020,11, 545; doi:10.3390/atmos11050545 www.mdpi.com/journal/atmosphere Atmosphere 2020,11, 545 2 of 20 covering all those general economic targets. Instead, they have unintentionally succeeded in moving the environmental impact abroad through the demand of their economic agents [1]. The current situation has become an extreme exemplification of the relationship between consumption and environment. Skies become cleaner in an undesirable situation in which economic production and demand suddenly and dramatically halt [ 2 ]. Pollution indicators demonstrate what even the most basic economic literature strives to explain: demand-driven production triggers environmentally harmful effects, not only through the use of resources but also due to the negative externalities that arise from the economic activity. Nevertheless, the effects differ between countries because the technology of production and the weight of value added also differ in each context. This paper presents an appraisal of the impacts of variation in demand on the emissions of Greenhouse Gases (GHGs; GHGs considered are Carbon Dioxide (CO 2 ), Methane (CH 4 ), Nitrous Oxide (N 2 O) and fluorinated gases (hydrofluorocarbons, perfluorocarbons, sulphur hexafluoride, and nitrogen trifluoride) and on employment in order to provide an assessment tool for policies aimed at a reduction of those emissions through reductions in demand of certain commodities. In this respect, the paper compares, as the main objective, the potential reduction in GHG emissions obtained with a reduction in demand, and the corresponding reduction in output and employment. Lineal multi-sectoral analysis is used, while focusing on the EU member states (MS) and using estimated Social Accounting Matrices (SAMs) for 2015. SAMs make the consideration of all the economic agents possible, since all the interrelations between productive economic activities, final demand, and value added are included in the circular flow of income [3]. Demand-driven emissions have been analyzed in a number of studies. Peters [ 4 ] discusses the change in the inventories of the emissions from production-based to consumption-based methods. Wiedmann [ 5 ] estimates emissions embedded in international trade. Davis and Caldeira [ 6 ] analyze the amount of emissions associated with consumption, while focusing on China and developing countries, and find that a substantial quantity of CO 2 emissions is traded internationally. Similarly, Liu et al. [ 7 ] study the environmental impact of several industries through embodied emissions. Yang and Meng [ 8 ] present a general overview of the mapping between fields of studies regarding demand-driven emissions. S á nchez-Ch ó liz et al. [ 9 ] study the environmental impact of the Spanish economy generated by household demand and Duarte et al. [ 10 , 11 ] analyze the relationship between types of consumption and pollution using a SAM for Spain, by examining the composition of final demand and determining the final volume of emissions. They analyze consumption patterns in relation with sustainable growth and development using a SAM-based model in combination with econometric estimations, and observe that the relationship between per capita income and regional responsibility is based on a demand scale effect. Hertwich and Peters [ 12 ] quantify greenhouse gas emissions associated with the final consumption of goods and services, and find that more than 70% of GHG emissions correspond to household consumption, 10% to government consumption, and the remaining GHG emissions correspond to investments, food, shelter, and mobility account for 56% of the total. They demonstrate the major role of consumption in the environmental impact and register greater indirect impact effects than direct impact effects. Other notable research that analyzes the responsibility of household demand for GHG emissions in different ways includes that by Hoekstra and Bergh [13], Gallego and Lenzen [14], and Lenzen and Peters [15]. Onthe otherhand, many studieshaveused SAMstoanalyzeimpactson employment: Allan et al. [16] , Courtney etal. [ 17 ], Khan andThorbecke[ 18 ], SeungandWaters[ 19 ], and Campoy et al. [20] , among others. More specifically, in relation to the link between demand and employment, Philippidis et al. [ 21 ] use employment multipliers for the bioeconomy sectors for 27 EU member states, and find several clusters with a homogeneous structure. Cardenete et al. [ 22 ] calculate employment multipliers of the Spanish bio-based accounts; Meng [ 23 ] and Meng et al. [ 24 ] simulate the effects of taxes on various employment occupations. The contribution of this paper involves the use of a new dataset of SAMs for EU SAMs (year 2015) and the adaption of a well-known input–output and SAM multi-sectoral analysis tool, which enables a broader analysis of this issue to be performed, for the assessment of demand-driven GHG mitigation Atmosphere 2020,11, 545 3 of 20 policies. It could be considered a prior analysis for a more in-depth analysis based on the specific structure and characteristics of each member state, which provides the main lines that describe the issue. In this respect, the main objective of the paper is to estimate the cost, in terms of employment, of reducing GHG emissions through reductions in the demand for goods and services, so that the effectiveness of this measure can be assessed for 1a wide range of countries and commodities. The rest of the paper is structured as follows: Section 2presents the main steps in the estimation of the database (Social Accounting Matrices for the 28 EU MSs, year 2015) and shows the method employed to value the GHG emissions, output, and employment generated by the final demand. Section 3includes the main results and discussion. Finally, conclusions are described in Section 4. 2. Database and Methods 2.1. Social Accounting Matrices for the 28 European Union Member States The database used in this analysis comprises of a new set of Social Accounting Matrices, one for each of the 28 member states of the European Union, and refers to 2015 as the base year. A SAM (a common reference on the origins of SAMs is the work of Sir Richard Stone (see Stone, [ 25 ]). Pyatt and Round [ 26 ] provide a fundamental explanation of the basic structure and potential utilities of SAMs) is a comprehensive and economy-wide database that records data on transactions between all economic agents within an economy in a given period. A SAM is ultimately a square matrix in which activities, commodities, factors, and institutional sectors are represented by specific rows and columns (Social Accounting Matrices improve traditional Input–Output Tables (IOTs), which reflect the production part of the economy, but not the relations between the income and expenditure of institutional agents. In this respect, SAMs expand the explanatory capacity of I–O models and explicitly introduce income and its primary and secondary distributions, and the final consumption of institutional agents (households, government, etc.). SAMs are extensions of the concept of IOTs achieved in an integrated way and not through the addition of satellite accounts). Each cell records the payment by the account in the column to the account in the row (SAMs have a double relevance: they serve as a database to calibrate economic modeling, and they describe, in a simple but exhaustive way, the complete circuit of economic relations of an economy. The concept of the circular flow of income is the foundation of the SAMs ( Mainar et al. [27] ). SAMs are extensions of the concept of IOTs achieved in an integrated way and not through the addition of satellite accounts; several primary databases are employed to populate the cells of a matrix. The main databases include the set of National Accounts systems, household budget, and/or labor market surveys (and other socioeconomic databases), and statistics related to foreign sectors and international trade). Thus, the income of each account is described along its corresponding row while its expenditures are recorded in the corresponding column. The basic structure of a standard SAM is shown in Figure 1(European Commission [ 28 ], Eurostat [ 29 ], Mainar et al. [ 27 ], and Miller and Blair [ 30 ] describe the characteristics of this structure, as well as specific issues regarding its definition and composition). In order to achieve the objective of the analysis, a new SAM was estimated for each MS for the year 2015. These SAMs are completely coherent with the macro-magnitudes of each country, based on statistical data from Eurostat (this entails a two-step procedure for each country. First, a matrix containing aggregates for the main submatrices of the SAM is estimated using official macro-magnitudes. These auxiliary matrices are called MacroSAMs and are estimated with values extracted from the Non-financial Annual Sector Accounts [ 31 ]. These accounts provide values paid and received, aggregated, and per institution for the main macro-variables of the economy. This data set is employed for aggregate production, supply and demand, primary factors (labor and capital), taxes, and institutional accounts (i.e., household, government, corporations, and the rest of the world). In a second stage, the MacroSAM structure is extended by opening up the aggregate accounts to represent specific activities and commodities, thereby obtaining the targeted SAMs. The procedure of opening up these submatrices in the MacroSAM is facilitated by entering information from the Atmosphere 2020,11, 545 4 of 20 2015 Supply-and-Use Tables (SUT) [ 32 ]. Activities and products/commodities in the SUT follow the classifications of Eurostat NACE Rev. 2 (statistical classification of economic activities in the European Community, revised version 2) [ 33 ] and the Classification of Products by Activity (CPA) [ 34 ], respectively, and hence SAMs also use this classification (see Table A1); the Supply-and-Use Tables are the matrices per industry and product that describe production processes and the transactions in products of the national economy. They show the structure of the costs of production and the value added generated, and flows of goods and services produced within the national economy and with the rest of the world. It should be noted that the aggregates from the Supply-and-Use Tables of Eurostat are broadly consistent with the National Accounts system). Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 24 Figure 1. Standard structure of a Social Accounting Matrix (SAM). Source: Authors’ own based on Mainar et al. (2018). In order to achieve the objective of the analysis, a new SAM was estimated for each MS for the year 2015. These SAMs are completely coherent with the macro-magnitudes of each country, based on statistical data from Eurostat (this entails a two-step procedure for each country. First, a matrix containing aggregates for the main submatrices of the SAM is estimated using official macro-magnitudes. These auxiliary matrices are called MacroSAMs and are estimated with values extracted from the Non-financial Annual Sector Accounts [31]. These accounts provide values paid and received, aggregated, and per institution for the main macro-variables of the economy. This data set is employed for aggregate production, supply and demand, primary factors (labor and capital), taxes, and institutional accounts (i.e., household, government, corporations, and the rest of the world). In a second stage, the MacroSAM structure is extended by opening up the aggregate accounts to represent specific activities and commodities, thereby obtaining the targeted SAMs. The procedure of opening up these submatrices in the MacroSAM is facilitated by entering information from the 2015 Supply-and-Use Tables (SUT) [32]. Activities and products/commodities in the SUT follow the classifications of Eurostat NACE Rev. 2 (statistical classification of economic activities in the European Community, revised version 2) [33] and the Classification of Products by Activity (CPA) [34], respectively, and hence SAMs also use this classification (see Table A1); the Supply-and-Use Tables are the matrices per industry and product that describe production processes and the transactions in products of the national economy. They show the structure of the costs of production and the value added generated, and flows of goods and services produced within the national economy and with the rest of the world. It should be noted that the aggregates from the Supply-and-Use Tables of Eurostat are broadly consistent with the National Accounts system). 2.2. Multipliers Analysis The analysis of the sectoral capacity to generate output and employment on the one hand, and GHG emissions on the other, was carried out by comparing the corresponding SAM multipliers. A multiplier, mij, reflects the impact that an exogenous unit of income (for example, demand) of sector j, finally generates on the income of the account i, after going through the circular flow of income an infinite number of times. The sum of the columns of the multiplier matrix would indicate the total effect of an exogenous shock on the rest of the economic activity as received by an endogenous account. Therefore, a column of this matrix whose sum attains a very high value corresponds to an account that Figure 1. Standard structure of a Social Accounting Matrix (SAM). Source: Authors’ own based on Mainar et al. (2018). 2.2. Multipliers Analysis The analysis of the sectoral capacity to generate output and employment on the one hand, and GHG emissions on the other, was carried out by comparing the corresponding SAM multipliers. A multiplier, m ij , reflects the impact that an exogenous unit of income (for example, demand) of sector j, finally generates on the income of the account i, after going through the circular flow of income an infinite number of times. The sum of the columns of the multiplier matrix would indicate the total effect of an exogenous shock on the rest of the economic activity as received by an endogenous account. Therefore, a column of this matrix whose sum attains a very high value corresponds to an account that holds a major influence over the rest of the economy when it receives an exogenous shock, which could be caused by a determined economic policy or by another external event. In order to estimate the data required for the analysis proposed herein, it is first necessary to obtain the output multipliers (see Pyatt and Round [ 35 ], among others) for each of the 28 MSs considered. The SAM multipliers are estimated from the natural extension to a SAM of the classic expression of the Leontief inverse [ 36 ]. The starting point for the analysis is given by the following equilibrium equation: x=Ax+y⇐⇒ x=(I−A)−1=My (1) where x is the vector of total gross output of endogenous (regarding the selection of endogenous and exogenous variables, exogenous variables are commonly considered as those typically determined outside the economic environment (for instance, the foreign sector) or as those that can be used as instruments of economic policy (for instance, public expenditure, investment, certain social transfers, Atmosphere 2020,11, 545 5 of 20 etc.). In the application presented in this work, these are exogenous: public sector accounts, savings and investment accounts, and the rest of the world) accounts and y is the corresponding vector of total final demand. I represents the identity matrix, and A is the usual matrix of coefficients in the SAM framework. Their elements a ij show the share of the sector iin each unit produced by sector j. M is the matrix of SAM multipliers. Although A and M are square matrices with an order determined by the endogenous accounts, only the part concerning production activities in rows and commodities account in columns, submatrix Ma , is considered in order to focus the analysis on the proposed objectives (effects of a demand shock for a commodity on the output generated by an activity). The sum, for each column of Ma , of the values corresponding to the rows of the accounts of activities, results in the sectoral multiplier of production, and shows the increase in output generated by an exogenous shock in one of the exogenous demands for a given commodity. In order to obtain employment and GHG-emission multipliers, it is necessary to consider additional vectors including information regarding ratios of the employed, or of GHG emissions generated per unit of output, [ 37 ]. In this respect, the next step involves the estimation of a vector with the average direct GHG emissions (measured in tonnes of CO 2 ) per unit of output (in this case, one million euros) for each activity. This vector c, which expresses direct emissions (tonnes of carbon dioxide equivalent per million euros of output), is built using Eurostat data on direct emissions per activity (air emissions accounts by NACE Rev. 2 activities, [38]) and the total sectoral output directly observed in SAMs. The matrix of GHG-emission multipliers, MG , is obtained by pre-multiplying the output multiplier matrix, Ma, by the diagonal matrix from c. MG=^ cMa(2) Elements of MG ,m G (i,j) show the total amounts of emissions generated (direct, indirect, and induced) per sector ifor each additional exogenous unit of income in the economic sector j, that is, a multiplier matrix of generated embodied emissions. The total effect on GHG emissions resulting from an exogenous shock in each commodity is obtained by adding up in the same way as that used for output multipliers. Similarly, in order to obtain employment multipliers, a vector e that contains the ratios of the number of jobs per million euros of output value is required [ 39 ]. To populate this matrix, employment data from the Labor Force Survey was used [ 40 ]. A diagonal matrix based on e was multiplied by Ma . The expression of this employment multiplier, Me, is given as: Me=^ eMa(3) Each element in Me ,m e (i,j), shows the increment in the number of jobs of the account iwhen the account jreceives a unitary exogenous injection. In the same way as for GHG emissions, the addition by columns provides the total effect on employment resulting from an exogenous shock in the demand for each commodity. Tables A2–A4 summarize the values of these multiplier matrices that serve to obtain the main results of this analysis. 3. Results and Discussion By means of using the estimated SAMs of the 28 MSs and the necessary additional statistical information, the output, GHG emissions, and employment multipliers have been calculated, following the proposed methodology. Although all the calculations have been performed with the complete disaggregation of the matrices (65 activities and 65 commodities), in this section the results were presented aggregated into five broad sectors in order to facilitate their interpretation: primary-sector commodities, mining and quarrying, manufactured products, construction, and services (the disaggregated results per country are presented in the Appendix A). Tables A2–A4 show the corresponding multipliers of output, employment, and emissions. It is necessary to bear in mind Atmosphere 2020,11, 545 6 of 20 that the objective is to allow a comparison between the benefits, quantified in terms of emission reductions that a reduction in a commodity generates, and the damages that the corresponding loss of employment would entail. Therefore, the presented results show the reductions, both in GHG emissions, and in output and employment, generated by a unit reduction (million euros) in the demand for the corresponding commodities or groups of commodities. In general, it can be seen how the greatest multipliers of output are found in the service and construction sectors, which drive sectors of the developed European economies. However, when considering the employment multipliers, these results are modified, since the commodities of the primary sector are shown as the greatest generators of employment, although this is due to the great importance of this sector and its especially labor-intensive character in countries such as Romania and Bulgaria. Apart from these cases, it can be stated that the service sector is that which shows the greatest employment capacity (and, therefore, the greatest destruction capacity, if applicable), followed by the construction sector. In order to more intuitively balance the benefits of eliminating emissions through reductions in the demand for commodities with the drawbacks of the consequent decrease in output and employment, the quotient of these two multipliers is presented. These ratios show the reductions in output (in millions of euros) and in employment (jobs) that are generated for a reduction of 1000 tonnes of GHG emissions through reductions in the demand for each commodity. These ratios are presented in Figures 2–4and in Tables 1and 2(aggregated sectors) and A5 and A6 (disaggregated sectors). Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 24 3. Results and Discussion By means of using the estimated SAMs of the 28 MSs and the necessary additional statistical information, the output, GHG emissions, and employment multipliers have been calculated, following the proposed methodology. Although all the calculations have been performed with the complete disaggregation of the matrices (65 activities and 65 commodities), in this section the results were presented aggregated into five broad sectors in order to facilitate their interpretation: primary-sector commodities, mining and quarrying, manufactured products, construction, and services (the disaggregated results per country are presented in the Appendix A). Tables A2–A4 show the corresponding multipliers of output, employment, and emissions. It is necessary to bear in mind that the objective is to allow a comparison between the benefits, quantified in terms of emission reductions that a reduction in a commodity generates, and the damages that the corresponding loss of employment would entail. Therefore, the presented results show the reductions, both in GHG emissions, and in output and employment, generated by a unit reduction (million euros) in the demand for the corresponding commodities or groups of commodities. In general, it can be seen how the greatest multipliers of output are found in the service and construction sectors, which drive sectors of the developed European economies. However, when considering the employment multipliers, these results are modified, since the commodities of the primary sector are shown as the greatest generators of employment, although this is due to the great importance of this sector and its especially labor-intensive character in countries such as Romania and Bulgaria. Apart from these cases, it can be stated that the service sector is that which shows the greatest employment capacity (and, therefore, the greatest destruction capacity, if applicable), followed by the construction sector. In order to more intuitively balance the benefits of eliminating emissions through reductions in the demand for commodities with the drawbacks of the consequent decrease in output and employment, the quotient of these two multipliers is presented. These ratios show the reductions in output (in millions of euros) and in employment (jobs) that are generated for a reduction of 1000 tonnes of GHG emissions through reductions in the demand for each commodity. These ratios are presented in Figures 2–4 and in Tables 1 and 2 (aggregated sectors) and A5 and A6 (disaggregated sectors). Figure 2. Average output (millions of euros) reduction caused by a reduction of 1000 tonnes of Greenhouse Gas (GHG) emissions through a reduction in demand by country. 0.00 5.00 10.00 15.00 20.00 25.00 Austria Belgium Bulgaria Croatia Cyprus Czechia Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom EU28 Figure 2. Average output (millions of euros) reduction caused by a reduction of 1000 tonnes of Greenhouse Gas (GHG) emissions through a reduction in demand by country. Atmosphere 2020,11, 545 7 of 20 Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 24 Figure 3. Average employment (jobs) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand per country. 0.00 20.00 40.00 60.00 80.00 100.00 120.00 Austria Belgium Bulgaria Croatia Cyprus Czechia Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom EU28 Figure 3. Average employment (jobs) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand per country. Figure 2shows the average reductions in production for each country necessary to achieve a reduction of 1000 tonnes of GHG emissions. The very different data between countries is the reflection of the different production structures. Countries with a greater orientation towards services (less polluting sectors) need to reduce their production by a greater amount than those whose output contains a greater weight of agricultural or manufacturing activities (highly polluting sectors). For example, the drop in France in output to reduce 1000 tonnes of GHG emissions is more than 15 million euros, this stands at around 12 million in Ireland, Italy, and the United Kingdom, and is 18 million in Sweden, but only 2.5 million in Bulgaria, 2.74 million in Poland, and 5.1 million in Hungary. Thus, the countries with the highest value of this ratio are Austria, Belgium, Finland, France, Ireland, Italy, Luxembourg, and the United Kingdom, with the lowest values observed in Bulgaria, Estonia, Lithuania, Poland, or Romania. On average, EU28 countries would lose 7.6 million euros for every 1000 tonnes of GHG emission reduction through demand. However, in terms of employment, the loss would be 61.5 jobs, now the differences between countries are nuanced, since, to the factor of the production structure, with more or less preponderance of polluting activities, it is necessary to add the intensity of the labor factor, which differs between production branches per se and between countries for the same branch. In this way, included among the countries that would need a greater sacrifice in employment, apart from those that already needed it in production, are Bulgaria, Croatia, Hungary, Latvia, Romania, Slovakia, and Slovenia. These countries, characterized by a greater intensity of the labor factor, both in the cleanest and the most polluting sectors, logically show a greater reduction in employment due to the consequent reductions in production generated by the decrease in demand. Atmosphere 2020,11, 545 8 of 20 Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 24 Figure 4. Average employment (jobs) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand per commodity. 25.3 74.7 37.6 19.1 41.9 77.0 62.3 32.6 66.4 17.4 25.6 66.8 49.1 16.4 16.9 54.7 70.6 57.0 63.7 62.6 71.7 74.9 62.8 5.1 30.2 17.4 61.7 91.7 52.0 44.1 35.4 10.8 9.9 50.0 101.6 92.7 84.5 85.9 72.1 89.0 85.0 86.1 106.0 56.1 56.0 88.6 81.6 73.7 91.0 99.2 68.0 51.7 54.4 109.4 98.9 135.1 110.3 159.6 107.3 87.7 95.8 98.2 128.7 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 C_01 C_02 C_03 C_04 C_05 C_06 C_07 C_08 C_09 C_10 C_11 C_12 C_13 C_14 C_15 C_16 C_17 C_18 C_19 C_20 C_21 C_22 C_23 C_24 C_25 C_26 C_27 C_28 C_29 C_30 C_31 C_32 C_33 C_34 C_35 C_36 C_37 C_38 C_39 C_40 C_41 C_42 C_43 C_44 C_45 C_46 C_47 C_48 C_49 C_50 C_51 C_52 C_53 C_54 C_55 C_56 C_57 C_58 C_59 C_60 C_61 C_62 C_63 Figure 4. Average employment (jobs) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand per commodity. Atmosphere 2020,11, 545 9 of 20 Table 1. Average output (millions of euros) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand. All Sectors Primary-Sector Commodities Mining and Quarrying Manufactured Products Construction Services Austria 10.56 2.13 4.66 7.93 4.64 15.47 Belgium 9.33 2.06 3.74 6.74 4.76 13.74 Bulgaria 1.92 1.44 1.43 1.55 2.15 2.55 Croatia 4.08 1.56 2.70 3.35 0.72 5.59 Cyprus 5.25 2.16 2.81 2.39 0.64 7.43 Czechia 3.95 1.92 0.71 3.72 1.01 4.44 Denmark 6.08 2.03 2.09 9.79 3.24 5.08 Estonia 2.42 1.49 1.84 2.06 1.15 2.77 Finland 7.57 2.53 5.15 5.97 2.24 8.83 France 11.70 2.34 7.59 9.40 2.82 15.03 Germany 7.28 1.78 4.09 7.06 3.56 8.45 Greece 3.54 2.56 3.98 2.42 0.86 4.38 Hungary 4.55 1.64 1.68 4.77 0.78 5.06 Ireland 10.05 0.78 2.53 10.26 1.97 12.00 Italy 9.67 3.85 5.54 8.25 3.21 11.80 Latvia 4.34 1.28 4.65 4.52 1.31 4.89 Lithuania 2.93 1.29 3.75 2.89 0.99 3.08 Luxembourg 22.38 1.27 10.79 11.11 6.06 26.51 Malta 7.61 3.91 8.78 7.07 1.00 7.59 Netherlands 7.42 2.12 4.74 6.19 3.88 10.41 Poland 2.58 1.13 0.94 2.45 1.12 2.74 Portugal 5.28 2.16 3.99 4.22 0.96 6.94 Romania 3.05 1.38 1.60 2.98 0.84 3.89 Slovakia 5.34 3.26 2.43 4.97 1.22 6.51 Slovenia 5.05 1.77 2.28 4.89 3.07 5.13 Spain 7.13 2.61 4.65 5.90 2.33 9.08 Sweden 15.42 3.71 4.94 13.01 6.14 18.69 United Kingdom 10.76 2.17 3.79 8.27 3.43 12.43 EU28 7.57 2.04 3.38 6.37 2.56 9.50 Source: Authors’ own. The results in Figures 2and 3may seem controversial. While there are eight countries that present an above-average loss of output that are also among those with an above-average loss of employment, there are still countries whose loss in production does not reach that in the loss in employment. Those countries with the worst behavior in these two variables are Sweden, France, the United Kingdom, Italy, and Austria. However, Luxembourg, Ireland, and Belgium would be especially damaged in terms of output, but not in terms of employment. Croatia, Hungary, Latvia, Romania, Portugal, Slovakia, Spain, and Slovenia would suffer particularly in terms of jobs lost, but not that much in terms of output. Special mention has to be made regarding Luxembourg, whose production would decrease by nearly 25% of its value, but whose employment would be the least affected by the simulation. Apart from the aforementioned economic structure characteristics, it is interesting to highlight the relationship between these results and the productivity of labor per country. In labor terms, the more productive the country becomes, the more prone it is to suffer mainly in terms of economic growth; in contrast, the less productive the country, the greater the decrease in employment. This is coherent with the results obtained by Duarte et al. [ 41 ], Fuentes et al. [ 39 ], and Philippidis and Sanjuan-L ó pez [ 42 ]. Furthermore, Mainar-Causap é and Fuentes-Saguar [ 3 ] obtain analogous results for the Spanish economy. Those sectors most affected are: C_58, C_56, C_63, C_57, C_54, and C_59 (Figure 4, see codification in the Appendix A). For the analysis of the most disaggregated sectoral differences, Figure 4shows the mean values of job losses in the EU28. Care services (160 jobs), education (135), and health (110), are the sectors that would suffer the greatest losses, followed by artistic activities, financial services, and communications, all of which would suffer in excess of 100 jobs lost. Among the sectors that would allow a reduction of GHG emissions with a lower cost in terms of employment, the most polluting sectors appear: transport (10 jobs), electricity generation (5), Atmosphere 2020,11, 545 16 of 20 Table A5. Average output (millions of euros) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand (disaggregated commodities). C_01 C_02 C_03 C_04 C_05 C_06 C_07 C_08 C_09 C_10 C_11 C_12 C_13 C_14 C_15 C_16 C_17 C_18 C_19 C_20 C_21 C_22 C_23 C_24 C_25 C_26 C_27 C_28 C_29 C_30 C_31 C_32 Austria 1.9 16.2 15.0 4.7 7.6 15.2 15.1 5.7 15.6 4.2 6.5 16.2 12.7 2.8 2.4 8.8 15.3 14.6 14.6 16.5 16.1 11.1 14.8 4.4 8.6 4.6 12.3 18.7 14.6 15.9 5.8 3.6 Belgium 2.0 5.3 3.5 3.7 7.6 12.2 12.2 9.2 10.0 5.3 4.8 16.6 10.0 2.2 4.5 11.6 14.3 13.4 11.5 16.1 13.4 13.6 14.1 1.2 10.7 4.8 11.9 15.2 13.3 14.2 7.8 7.4 Bulgaria 1.4 2.5 3.2 1.4 2.0 2.5 2.0 1.7 2.4 1.9 1.1 1.4 2.1 0.8 1.8 2.1 2.2 2.4 2.2 2.5 2.6 2.4 2.2 0.2 1.1 2.1 2.4 2.5 2.0 2.2 1.2 2.6 Croatia 1.2 3.0 4.4 2.7 3.8 4.8 5.7 4.8 6.0 2.1 1.5 5.1 5.0 1.7 3.8 5.5 4.5 6.0 6.1 4.5 5.9 5.5 5.9 1.3 5.1 0.7 3.8 4.4 4.4 4.3 3.3 2.9 Cyprus 1.8 5.3 4.4 2.8 3.4 5.0 5.7 5.1 5.0 4.8 3.5 4.4 4.0 0.4 4.0 4.8 4.8 5.2 5.4 5.0 5.0 5.0 6.3 0.4 1.1 0.6 3.1 6.2 6.1 4.2 3.4 11.8 Czechia 1.5 6.1 3.1 0.7 3.3 4.3 5.3 3.5 5.6 2.2 1.9 4.9 4.7 2.1 1.4 4.5 5.2 5.1 4.6 7.1 5.8 5.1 3.7 0.5 2.3 1.0 4.9 6.3 4.3 5.2 2.6 2.0 Denmark 1.8 9.2 3.7 2.1 5.2 13.4 12.2 9.5 15.9 3.2 10.7 17.6 13.1 2.7 10.7 13.2 16.1 15.5 16.3 13.6 14.7 16.0 12.2 1.2 14.9 3.2 11.7 12.6 12.7 14.9 3.7 1.3 Estonia 1.0 3.5 2.5 1.8 2.1 3.0 3.4 1.4 2.9 0.6 2.7 3.0 2.9 1.3 2.6 3.6 6.4 4.7 3.6 3.3 3.5 3.2 3.7 0.2 0.8 1.2 3.1 3.3 3.3 2.6 1.8 1.2 Finland 1.5 9.2 2.9 5.1 6.0 9.0 9.2 4.9 9.2 3.4 5.2 9.4 8.4 3.9 2.9 8.8 10.9 11.0 11.9 10.0 10.3 10.0 10.1 1.1 9.0 2.2 9.3 10.8 9.4 8.4 4.0 2.1 France 2.1 10.3 5.1 7.6 6.3 13.2 12.8 8.0 13.4 4.4 5.8 13.5 11.0 3.1 3.0 11.8 14.5 13.0 13.9 14.4 20.6 14.2 14.9 5.1 15.0 2.8 12.7 12.2 17.5 12.5 6.1 6.6 Germany 1.7 8.5 6.3 4.1 5.6 7.7 8.0 5.0 8.0 3.4 5.0 8.2 7.2 2.4 2.9 7.6 9.1 8.5 9.8 10.5 11.1 8.5 10.2 0.7 1.4 3.6 5.9 10.2 8.2 8.1 5.3 2.1 Greece 2.2 3.0 6.2 4.0 3.7 4.5 4.8 4.2 4.6 3.1 3.2 4.9 3.0 0.9 2.1 3.6 4.9 0.8 3.5 4.8 4.7 4.6 3.5 0.3 1.7 0.9 3.4 5.5 4.8 5.7 1.8 4.3 Hungary 1.6 3.6 3.4 1.7 3.4 5.0 4.5 3.9 5.1 2.4 2.2 4.8 5.1 1.8 2.2 5.0 9.2 7.0 5.9 8.6 6.1 5.4 6.1 0.6 3.0 0.8 4.4 4.2 4.4 4.5 3.0 3.5 Ireland 0.7 1.4 3.5 2.5 3.5 8.7 3.4 10.1 11.5 4.2 12.6 23.9 7.5 1.0 1.4 5.3 26.2 8.9 12.2 7.9 7.7 10.7 10.7 0.8 9.7 2.0 7.3 8.8 10.0 7.1 2.4 4.5 Italy 3.6 10.8 6.9 5.5 7.6 9.9 9.0 5.5 9.5 3.9 5.3 9.2 8.5 2.5 4.6 9.9 10.1 10.1 11.3 10.6 11.9 9.8 10.1 1.8 4.3 3.2 10.6 12.4 7.7 11.5 5.6 2.0 Latvia 1.0 5.7 3.7 4.7 3.6 4.5 6.3 2.2 6.3 5.6 4.0 5.8 6.3 1.8 4.1 6.0 7.1 6.8 6.1 6.0 5.7 6.4 5.8 1.6 2.3 1.3 5.9 6.5 6.4 6.7 2.0 5.4 Lithuania 1.1 4.7 1.8 3.8 3.3 4.8 4.4 4.4 5.2 2.8 1.3 4.3 3.6 1.1 4.6 5.0 4.6 4.7 4.8 4.7 4.9 4.9 6.3 1.3 4.3 1.0 5.5 4.7 4.4 5.4 1.0 3.8 Luxembourg 1.2 5.6 27.2 10.8 8.9 16.0 9.1 17.5 33.0 27.1 20.9 26.8 33.1 1.1 7.4 22.2 31.0 22.5 23.0 29.2 2.0 27.6 9.4 3.5 — 6.1 12.9 25.9 27.4 28.2 8.1 20.3 Malta 3.8 — 4.2 8.8 6.5 5.5 7.7 6.7 8.2 5.3 6.0 7.5 7.0 9.0 6.4 7.9 13.5 8.2 6.9 5.7 5.7 6.8 5.7 1.9 4.0 1.0 11.2 6.6 4.7 7.0 3.0 0.1 Netherlands 2.1 5.3 2.9 4.7 6.5 10.4 11.6 8.5 13.6 3.2 3.4 11.7 7.9 5.0 2.4 9.4 17.1 11.3 12.1 12.2 14.4 10.6 12.7 0.6 6.6 3.9 7.9 13.7 12.8 11.7 6.4 2.1 Poland 1.1 3.0 1.3 0.9 2.3 2.9 3.0 2.4 3.3 1.5 1.5 3.0 2.8 1.2 1.3 2.7 3.4 3.1 3.0 3.4 3.3 3.1 3.5 0.4 1.0 1.1 3.1 3.5 2.6 2.8 1.8 2.8 Portugal 1.8 6.4 4.1 4.0 4.6 6.6 4.7 3.3 5.9 2.9 3.3 7.0 6.0 1.2 2.4 6.1 8.2 6.9 5.2 8.8 8.0 6.7 7.4 1.3 4.4 1.0 5.1 7.5 6.5 6.8 2.6 2.0 Romania 1.3 4.3 3.2 1.6 3.0 4.1 3.9 3.0 3.9 2.3 1.7 3.5 3.5 1.2 1.5 2.9 3.7 3.7 3.4 3.9 3.7 4.1 4.1 0.8 2.8 0.8 2.7 3.9 4.3 3.9 2.9 3.8 Slovakia 3.0 8.8 7.2 2.4 4.8 7.9 9.0 6.3 7.6 2.6 3.2 5.9 4.8 2.2 1.0 3.3 11.7 7.4 4.9 9.5 5.9 7.3 6.6 2.3 3.5 1.2 5.5 9.5 7.5 7.9 3.4 5.1 Slovenia 1.5 6.8 7.2 2.3 3.9 7.2 7.6 3.0 7.7 6.2 5.5 8.5 7.2 2.0 3.7 6.7 7.5 8.2 7.8 8.5 7.0 7.6 9.2 0.8 5.6 3.1 7.2 9.2 6.6 7.1 1.3 9.0 Spain 2.6 8.6 3.0 4.6 6.4 8.3 5.7 4.5 8.5 3.2 4.9 8.1 7.9 1.5 3.6 6.7 8.8 7.6 7.7 9.1 8.5 8.6 8.2 1.7 10.8 2.3 8.2 8.9 8.2 9.2 2.8 3.7 Sweden 2.0 6.9 6.3 4.9 10.6 19.0 13.6 11.8 22.1 5.3 8.8 22.6 19.3 3.5 4.6 15.8 23.5 20.0 20.8 22.7 26.9 20.0 18.3 3.6 9.1 6.1 14.2 19.7 — — 9.0 1.4 United Kingdom 1.9 11.7 4.9 3.8 8.1 10.8 9.7 7.4 11.1 3.2 7.2 12.0 7.6 3.7 3.3 9.0 11.5 10.6 10.0 11.5 12.4 10.8 11.1 2.1 8.2 3.4 11.5 12.8 10.3 12.2 5.8 3.1 EU28 1.9 6.9 4.3 3.4 5.4 8.3 6.7 5.3 8.3 3.3 4.7 10.4 6.7 2.1 2.8 7.1 10.4 7.5 9.4 9.4 12.0 8.3 9.7 0.9 3.5 2.5 7.9 9.9 11.3 28.3 3.2 2.4 C_33 C_34 C_35 C_36 C_37 C_38 C_39 C_40 C_41 C_42 C_43 C_44 C_45 C_46 C_47 C_48 C_49 C_50 C_51 C_52 C_53 C_54 C_55 C_56 C_57 C_58 C_59 C_60 C_61 C_62 C_63 C_64 Austria 2.3 15.9 21.9 14.5 18.6 22.9 25.2 19.6 26.1 24.3 26.9 18.3 23.4 17.1 19.7 13.7 21.4 9.3 16.3 16.2 13.1 16.5 19.4 16.8 19.8 17.2 17.4 15.8 17.4 17.5 16.4 22.0 Belgium 1.3 13.0 12.2 11.4 15.4 20.9 24.0 17.0 26.3 28.8 22.8 22.0 28.1 17.4 14.9 12.2 25.5 19.2 14.8 15.0 6.7 14.0 14.7 16.3 17.3 14.2 15.1 14.1 18.2 12.8 12.4 10.2 Bulgaria 2.5 2.2 2.6 2.2 2.9 3.1 2.3 2.8 3.6 4.0 3.4 2.2 4.3 3.2 3.0 3.1 3.5 2.8 2.5 2.7 3.2 2.7 3.0 3.0 2.4 2.9 3.7 3.1 3.8 3.3 2.4 — Croatia 4.6 3.9 6.5 5.4 6.5 6.2 8.0 6.7 8.5 8.0 8.0 3.1 10.6 6.5 6.2 6.0 6.9 4.0 5.5 5.5 5.8 3.2 6.0 5.9 7.1 4.5 7.0 5.5 7.0 5.0 4.6 1.2 Cyprus 6.7 10.3 5.1 3.6 10.3 4.9 7.1 12.5 8.6 4.8 9.3 5.7 6.9 6.8 5.9 5.4 5.5 6.1 6.3 7.0 6.1 5.2 5.4 5.9 5.4 4.2 7.9 4.9 4.3 7.1 5.0 6.7 Czechia 5.9 4.6 5.1 4.5 6.5 8.1 7.1 5.9 6.9 7.3 8.5 3.9 7.7 5.4 6.0 4.9 7.5 6.3 5.7 5.2 7.3 2.2 4.8 4.4 4.4 4.4 5.6 5.2 5.2 3.3 4.6 5.1 Denmark 1.4 11.4 11.8 10.4 16.8 19.5 18.0 16.9 23.8 23.5 23.8 21.5 30.6 17.5 17.4 15.7 21.3 17.1 15.6 16.2 4.9 12.9 15.3 14.6 15.9 13.5 16.3 13.0 16.4 15.7 16.3 15.5 Estonia 3.7 3.0 3.9 2.5 3.5 4.3 4.2 3.5 4.9 5.3 5.4 2.7 6.3 3.4 3.9 2.1 3.9 4.3 2.6 4.3 4.0 3.4 2.8 2.6 3.4 2.3 3.1 2.6 2.8 4.0 2.8 7.0 Finland 1.8 10.7 9.0 7.3 12.3 12.6 13.4 12.6 11.6 12.5 10.9 7.7 11.8 7.6 8.6 10.5 12.2 10.8 5.7 12.3 4.2 9.1 9.4 10.6 11.6 10.3 9.3 7.9 7.6 10.8 9.6 12.4 Atmosphere 2020,11, 545 17 of 20 Table A5. Cont. France 2.0 20.0 13.7 12.0 17.9 15.8 21.3 20.8 24.5 27.1 21.9 23.9 27.6 22.1 20.3 18.7 17.5 17.6 12.1 16.8 20.0 17.5 16.5 14.1 17.1 13.7 17.3 11.7 16.7 13.0 13.8 16.1 Germany 2.0 7.2 7.7 7.3 10.4 12.2 11.5 11.4 12.6 13.8 12.6 11.1 14.4 9.3 8.0 8.9 12.0 10.8 9.8 12.1 17.9 8.5 9.4 9.7 9.2 9.1 10.2 7.0 9.5 9.8 8.5 11.5 Greece 4.0 3.6 5.6 4.7 6.2 4.8 5.9 5.2 6.2 7.1 6.4 6.4 6.8 5.8 5.1 4.8 5.5 5.7 4.0 5.2 5.5 5.9 4.2 4.9 4.7 3.5 6.2 3.6 4.4 5.7 5.1 4.6 Hungary 4.1 2.6 5.9 5.0 6.0 7.8 6.8 6.4 7.6 9.3 7.4 4.4 11.5 6.6 6.5 6.2 6.1 5.4 4.2 6.3 6.9 5.0 5.5 5.8 6.2 5.2 6.6 4.6 6.3 6.1 5.4 6.1 Ireland 1.1 6.9 10.9 8.6 24.1 13.4 13.7 26.2 19.5 26.5 16.1 24.1 — 17.9 10.7 12.6 12.6 7.6 15.6 11.7 4.8 12.0 9.6 9.3 11.6 — 14.1 9.8 12.3 10.2 12.7 16.3 Italy 4.1 8.5 7.1 9.5 11.8 15.4 15.3 14.5 16.0 19.4 15.2 15.2 17.7 14.5 11.7 11.1 12.8 14.2 10.6 12.1 13.0 9.3 11.6 13.6 13.3 13.3 15.1 13.7 10.0 11.5 8.1 13.6 Latvia 2.1 7.4 6.5 6.3 6.6 8.6 8.8 8.1 11.5 10.2 10.7 5.8 11.2 8.0 7.8 6.9 8.9 8.5 4.7 7.9 5.3 6.1 6.9 6.6 6.5 6.3 7.3 6.3 7.9 8.5 6.1 12.8 Lithuania 4.0 3.2 6.5 5.4 5.3 6.4 7.4 6.8 8.2 8.4 7.7 6.7 8.0 7.5 6.8 5.6 7.3 5.3 6.9 6.9 7.3 5.7 6.3 5.7 6.0 5.1 6.6 6.3 7.1 7.0 6.3 5.7 Luxembourg 0.7 4.3 23.1 13.3 49.7 45.3 68.7 51.7 178.2 182.1 289.0 31.5 67.7 42.7 44.6 33.5 39.6 31.7 27.6 27.7 1.2 31.6 34.2 31.2 32.0 30.5 25.5 26.7 35.1 28.4 25.1 52.1 Malta 3.3 7.1 7.8 8.1 9.2 8.8 13.4 11.5 32.9 14.7 13.5 10.6 16.8 23.6 11.2 6.0 15.4 11.2 14.8 8.1 2.4 6.4 7.9 8.9 8.7 6.9 21.9 6.5 8.8 7.2 8.6 13.4 Netherlands 1.6 9.3 16.5 9.6 16.2 19.6 17.5 16.4 23.8 22.3 18.3 13.8 26.2 17.1 13.0 11.8 18.2 17.5 12.8 10.4 8.7 13.1 14.0 13.8 14.1 12.2 10.9 9.1 10.4 14.1 9.8 17.0 Poland 2.1 2.5 2.6 2.7 3.3 4.2 3.8 3.8 3.5 2.4 3.5 1.6 4.3 3.2 3.6 2.9 4.0 3.5 3.1 3.0 3.2 2.6 2.9 2.7 3.2 2.9 3.0 2.4 2.9 3.2 3.1 4.3 Portugal 4.5 8.7 8.9 6.2 8.0 10.1 11.1 9.4 11.0 12.0 10.6 10.3 11.1 9.0 7.3 7.3 8.6 9.2 5.5 8.3 7.5 7.7 6.8 8.3 8.6 5.9 8.2 6.8 7.5 7.5 7.2 8.8 Romania 3.7 3.9 3.6 4.2 4.2 4.1 4.6 4.5 4.6 4.3 4.5 4.7 5.1 4.2 4.8 4.5 4.0 3.8 4.1 3.6 4.2 3.9 3.9 4.2 4.5 2.3 4.6 5.0 4.2 3.4 4.4 — Slovakia 7.9 6.8 3.1 6.9 9.4 10.9 10.2 10.3 12.2 14.3 12.6 8.4 — 6.2 7.9 7.1 9.1 4.7 4.8 7.9 12.0 7.3 7.0 7.6 7.6 7.5 9.2 8.4 4.8 10.4 9.3 — Slovenia 10.6 6.1 7.8 7.0 7.8 11.1 12.8 10.1 10.4 12.6 10.8 5.4 15.3 7.9 9.3 7.9 11.1 6.2 5.6 9.6 9.3 7.3 8.8 7.6 8.2 6.6 9.3 7.7 9.2 8.9 6.0 9.8 Spain 1.9 8.2 10.0 10.3 9.2 11.3 11.6 12.3 13.1 15.7 13.2 10.2 17.9 12.6 9.9 9.7 11.1 8.0 10.7 11.0 7.4 8.7 9.7 11.2 10.8 9.7 12.5 9.4 8.1 10.7 10.9 11.8 Sweden 2.4 12.7 18.1 18.7 32.1 32.8 33.1 28.1 33.8 36.4 28.9 24.2 26.5 19.2 24.5 23.2 33.5 28.4 17.1 13.5 12.1 19.9 24.7 26.7 25.9 24.8 25.1 21.1 23.7 23.1 19.6 28.6 United Kingdom 1.6 11.9 9.7 11.2 12.7 16.8 15.5 14.1 16.3 17.8 15.6 16.0 17.4 15.3 13.1 12.8 15.2 14.5 10.5 14.7 16.0 14.1 13.1 13.6 13.0 13.5 14.8 12.1 15.0 14.4 14.2 14.2 EU28 1.8 6.9 9.6 8.4 13.0 13.4 13.5 14.1 20.7 17.2 15.7 10.6 15.6 13.2 10.7 10.8 12.1 10.8 11.5 13.4 8.8 11.5 10.1 10.5 11.2 11.2 11.5 9.1 9.8 10.4 9.5 13.0 Source: Authors’ own. Table A6. Average employment (jobs) reduction caused by a reduction of 1000 tonnes of GHG emissions through a reduction in demand (disaggregated commodities). C_01 C_02 C_03 C_04 C_05 C_06 C_07 C_08 C_09 C_10 C_11 C_12 C_13 C_14 C_15 C_16 C_17 C_18 C_19 C_20 C_21 C_22 C_23 C_24 C_25 C_26 C_27 C_28 C_29 C_30 C_31 C_32 Austria 26.0 83.5 77.1 22.7 49.3 100.7 74.0 27.7 85.7 20.8 27.5 101.8 69.5 15.8 10.0 51.8 92.5 78.7 76.0 78.1 87.7 71.5 75.3 11.0 39.8 18.8 77.9 118.6 84.9 152.3 33.5 12.3 Belgium 10.6 14.3 15.8 19.1 33.5 63.7 57.0 39.4 52.0 12.8 15.9 66.4 43.3 10.3 14.9 55.4 80.8 64.8 54.5 69.4 60.8 76.9 60.4 4.1 41.9 18.6 56.4 82.2 56.6 125.3 41.1 24.6 Bulgaria 47.7 117.6 54.6 38.3 57.4 122.2 70.8 41.8 61.3 41.8 29.7 39.3 62.2 18.7 33.0 62.6 72.6 62.1 63.0 74.9 63.2 85.4 67.0 4.4 37.7 55.5 65.3 97.6 59.7 99.2 31.3 77.7 Croatia 43.2 97.1 81.9 42.2 76.8 114.1 127.3 88.9 106.1 33.8 24.4 89.5 90.9 30.6 74.7 118.0 94.2 108.2 116.3 98.2 122.1 121.7 110.4 14.2 112.4 15.9 70.8 97.6 90.9 100.9 67.2 90.3 Cyprus 28.3 166.3 60.7 27.3 46.6 82.4 109.9 52.0 82.0 81.6 54.9 57.8 53.6 4.5 47.1 77.0 70.2 65.8 60.5 79.0 76.8 87.7 42.9 2.6 6.3 7.4 39.0 113.3 85.2 78.9 45.3 58.1 Czechia 21.5 84.6 76.1 9.5 42.2 66.4 72.8 38.7 79.2 21.0 21.4 64.6 49.4 26.1 15.4 61.5 49.1 51.1 52.7 62.8 63.9 81.5 45.1 3.8 26.0 13.7 60.8 87.6 47.9 107.6 35.6 65.9 Denmark 10.6 51.5 13.5 5.8 25.5 80.5 71.1 46.2 89.4 7.6 46.9 73.6 67.9 14.1 53.4 72.9 84.0 83.8 76.2 79.5 84.1 77.2 62.8 4.0 77.2 14.4 60.8 86.9 51.2 161.7 18.7 2.5 Estonia 16.0 45.1 19.2 18.2 29.0 54.5 41.4 16.9 44.0 7.6 34.7 41.8 39.1 18.3 27.7 50.9 53.6 55.9 43.7 44.8 37.4 50.6 49.0 1.9 5.1 12.0 52.8 56.3 39.5 59.4 25.3 11.1 Finland 14.6 54.6 11.8 23.6 36.1 60.1 48.6 19.4 58.8 11.6 21.2 54.3 43.4 22.7 10.2 50.6 61.0 57.9 58.6 58.0 57.0 62.8 55.8 4.3 41.5 10.0 53.4 75.2 51.8 74.3 26.0 10.0 France 14.8 62.5 35.4 48.4 37.6 92.0 84.4 44.3 93.8 16.9 30.0 83.3 69.5 18.4 15.3 74.7 96.9 83.2 88.4 85.3 92.6 101.7 70.5 17.1 82.8 18.7 77.8 91.3 95.4 115.7 46.7 26.1 Germany 13.8 54.2 59.3 31.3 38.8 63.7 48.1 28.2 63.4 15.3 26.9 56.4 42.7 15.2 13.1 50.3 65.7 48.9 60.3 54.7 63.6 69.8 56.4 3.6 6.8 19.9 45.5 86.7 54.3 92.7 32.4 9.3 Greece 48.3 84.3 80.1 50.9 52.6 65.5 91.4 46.9 74.2 22.1 36.1 64.0 33.3 9.9 18.7 40.7 66.2 8.6 40.2 66.9 64.5 69.0 35.6 2.6 15.8 7.8 46.2 80.5 49.9 126.2 24.2 35.5 Hungary 31.1 118.9 44.0 31.5 61.4 138.9 107.7 63.1 121.8 26.7 27.6 74.5 70.8 27.5 31.7 88.6 109.1 95.7 85.6 93.1 101.7 118.3 117.0 8.2 75.0 17.0 95.1 97.3 66.4 146.3 61.7 36.4 Ireland 6.9 10.4 26.0 14.5 16.6 73.2 19.0 48.6 84.1 16.1 24.4 49.7 28.8 4.9 8.0 32.9 42.0 36.3 70.0 53.6 50.6 39.8 55.3 2.3 91.4 10.3 43.8 113.3 33.6 78.1 19.6 10.5 Italy 31.6 162.7 53.5 37.1 48.6 65.0 65.9 31.4 65.4 17.2 28.7 59.6 49.6 16.1 24.8 63.4 67.9 61.2 67.0 62.9 71.9 71.3 65.1 7.6 25.1 20.1 74.7 97.3 45.1 108.9 33.7 10.7 Latvia 24.8 108.9 98.6 80.3 65.7 106.8 93.5 29.2 99.6 108.2 68.8 106.2 105.2 28.0 60.1 117.1 116.0 109.2 112.3 96.1 74.1 122.0 115.3 18.7 49.7 22.0 92.1 171.2 87.5 180.2 37.0 154.9 Atmosphere 2020,11, 545 18 of 20 Table A6. Cont. Lithuania 29.3 118.5 36.8 66.8 58.2 113.4 83.0 69.9 87.9 22.9 15.9 78.2 48.0 20.1 76.2 92.0 85.1 78.7 94.6 91.9 85.9 90.3 107.9 15.0 87.1 19.3 116.2 115.7 74.6 153.0 16.4 51.7 Luxembourg 4.5 5.8 45.0 6.2 14.1 24.1 5.4 18.5 36.3 43.7 19.2 42.6 29.1 0.8 6.9 25.8 48.9 26.4 26.7 38.9 2.2 42.6 13.6 4.1 — 4.9 25.3 78.4 31.7 47.5 21.3 23.1 Malta 44.0 — 35.5 70.4 76.3 58.4 60.7 67.3 76.1 78.7 72.3 74.0 69.4 94.7 76.2 103.5 70.3 57.7 67.0 82.4 82.4 92.1 49.5 3.2 40.3 8.3 115.4 95.9 53.0 128.8 34.4 0.9 Netherlands 11.8 21.1 17.1 14.4 28.8 66.5 69.0 38.8 76.2 7.7 11.2 63.2 37.4 25.9 11.3 46.4 70.0 58.6 55.7 60.6 62.8 79.7 54.0 1.7 26.9 18.4 38.3 82.2 61.3 131.2 35.4 6.6 Poland 39.0 62.6 30.4 16.7 43.9 60.1 52.8 34.8 57.5 19.5 21.6 51.8 41.0 20.5 20.1 42.6 53.0 47.0 48.8 49.3 54.8 59.4 52.4 5.0 20.1 18.8 50.1 61.8 40.3 65.4 30.3 56.9 Portugal 45.5 82.3 65.6 49.7 63.7 104.8 59.2 32.6 80.5 21.0 33.0 94.9 63.0 15.0 21.1 84.4 95.8 69.9 59.2 98.2 108.1 105.5 95.6 7.0 47.3 9.8 72.7 127.6 73.1 139.7 33.2 15.8 Romania 97.9 124.7 74.3 39.5 93.7 136.6 97.2 64.3 70.7 41.0 36.9 86.6 74.7 28.1 27.9 68.9 89.1 80.7 78.1 80.1 86.7 110.8 109.1 13.2 94.1 18.5 64.5 114.9 83.8 139.2 66.4 91.8 Slovakia 40.0 120.1 65.4 34.2 65.5 145.5 113.4 73.8 104.9 18.6 36.3 76.6 55.0 29.0 10.2 37.9 104.4 86.9 62.8 73.9 78.8 110.9 62.4 15.5 48.1 18.4 72.5 144.3 89.6 135.1 45.1 115.5 Slovenia 36.9 78.1 70.5 27.5 56.3 92.9 81.1 27.3 94.4 80.6 50.6 81.8 74.1 22.5 32.5 76.7 90.3 88.0 83.3 89.4 83.3 106.9 97.0 7.4 48.2 35.7 76.7 112.8 74.6 117.4 14.2 52.1 Spain 27.4 114.7 29.3 35.4 49.3 78.8 53.3 33.0 74.1 17.8 32.4 74.7 55.9 11.4 19.8 49.9 81.3 55.1 59.5 60.8 55.4 83.5 60.7 7.5 71.0 17.2 65.0 83.3 64.7 136.3 25.2 26.5 Sweden 15.6 36.9 47.5 22.3 55.2 118.5 60.1 46.3 118.8 16.0 37.1 103.8 94.0 15.9 17.8 78.5 124.4 93.4 99.8 96.9 115.4 113.3 95.7 13.1 34.1 30.2 81.6 124.2 — — 50.9 5.2 United Kingdom 13.7 104.4 25.9 18.4 48.0 75.0 63.8 42.2 68.4 13.2 39.1 72.2 43.7 20.6 18.0 54.5 78.3 65.7 60.9 62.7 72.3 74.4 73.7 6.7 50.3 17.8 70.5 79.0 57.2 105.3 40.5 13.0 EU28 25.3 74.7 37.6 19.1 41.9 77.0 62.3 32.6 66.4 17.4 25.6 66.8 49.1 16.4 16.9 54.7 70.6 57.0 63.7 62.6 71.7 74.9 62.8 5.1 30.2 17.4 61.7 91.7 52.0 44.1 35.4 10.8 C_33 C_34 C_35 C_36 C_37 C_38 C_39 C_40 C_41 C_42 C_43 C_44 C_45 C_46 C_47 C_48 C_49 C_50 C_51 C_52 C_53 C_54 C_55 C_56 C_57 C_58 C_59 C_60 C_61 C_62 C_63 C_64 Austria 10.3 98.0 178.5 113.8 113.1 116.3 113.0 122.7 148.3 129.6 173.4 78.8 65.3 102.6 138.7 86.3 126.6 96.4 79.8 65.1 90.5 145.7 167.7 165.3 170.4 207.0 150.5 163.5 146.5 126.6 186.5 646.8 Belgium 5.0 49.0 83.8 77.5 78.7 103.7 84.1 90.3 91.7 104.9 104.4 68.4 48.6 60.8 75.4 69.0 94.0 156.4 91.4 39.8 29.3 116.9 110.9 161.4 121.7 204.9 122.2 96.3 91.4 96.9 130.5 77.1 Bulgaria 46.0 47.9 101.7 107.1 107.9 98.0 47.5 80.2 87.4 98.3 89.6 47.7 49.5 115.9 95.0 94.6 97.4 89.2 59.1 65.5 78.5 144.4 131.3 151.1 99.1 155.4 100.3 109.9 134.9 151.3 100.9 — Croatia 54.9 84.8 188.7 122.6 148.8 125.9 109.5 128.5 114.1 157.0 137.6 54.2 73.9 104.7 114.4 99.6 107.4 117.6 75.6 57.4 86.8 129.9 134.5 200.6 179.5 168.0 153.8 103.9 143.5 123.2 149.4 66.9 Cyprus 48.6 63.8 101.1 48.7 66.2 65.9 61.8 80.8 66.7 41.8 76.8 46.1 39.4 81.3 105.5 71.5 47.5 120.8 95.5 47.7 88.3 140.5 75.2 101.1 77.8 91.1 114.2 62.0 66.9 106.9 105.4 386.7 Czechia 48.2 45.6 117.7 94.3 82.3 84.5 62.0 74.8 74.1 86.0 144.8 41.2 42.3 75.0 73.2 66.5 100.8 83.0 55.2 47.7 75.4 41.2 86.9 111.6 89.1 171.5 102.1 85.9 75.1 61.3 129.5 659.4 Denmark 4.6 51.9 85.7 90.3 95.6 96.6 70.4 100.7 94.3 107.4 82.4 70.0 58.4 103.5 96.3 87.4 121.1 113.9 88.3 45.1 22.9 95.9 98.1 132.7 118.9 149.3 110.0 138.6 145.5 92.8 165.2 100.3 Estonia 25.3 27.4 97.5 53.9 62.2 76.1 48.2 52.2 52.1 48.9 40.3 31.0 32.4 48.3 72.9 27.3 58.0 60.3 28.7 30.0 56.7 68.6 52.3 88.2 78.1 81.5 65.9 42.9 57.0 37.2 101.2 24.9 Finland 7.2 53.2 87.4 57.8 75.4 72.9 60.6 70.9 61.4 65.0 54.9 34.6 31.6 47.5 61.3 72.8 81.4 89.1 33.9 46.4 27.3 96.8 52.6 95.8 101.6 117.5 78.7 65.4 60.2 91.5 99.7 267.4 France 10.3 110.6 162.3 91.7 104.8 81.2 91.5 113.5 125.4 121.3 109.7 108.5 65.4 112.2 128.5 86.2 111.8 135.8 57.1 60.4 123.5 128.0 140.4 146.5 153.6 200.9 129.4 82.3 170.6 79.5 164.1 624.8 Germany 10.0 50.8 61.4 79.5 90.1 68.6 43.9 67.0 76.0 75.7 114.1 44.8 44.8 64.5 60.6 58.3 82.7 100.2 45.4 41.7 94.0 98.6 81.7 109.5 93.0 142.1 89.3 50.3 92.1 142.2 74.0 203.1 Greece 36.5 35.8 84.3 68.2 70.5 74.5 47.8 65.4 65.9 78.0 72.2 48.9 43.2 89.3 117.5 66.2 64.1 62.5 46.2 37.6 63.9 93.9 58.5 96.4 73.8 115.6 70.3 69.9 41.0 75.2 141.0 196.5 Hungary 40.1 40.6 239.7 147.9 125.7 108.4 78.0 121.4 136.0 169.4 154.4 57.1 88.6 140.2 94.4 86.0 135.3 102.5 70.1 64.7 151.1 152.3 167.7 229.8 160.2 222.1 172.9 114.1 117.8 176.0 195.2 751.1 Ireland 3.6 36.7 93.1 121.9 48.7 105.0 70.8 56.4 70.0 66.7 45.6 49.2 — 70.5 111.2 71.4 89.2 51.9 43.8 35.3 49.9 143.4 63.3 95.5 88.7 — 237.1 141.6 94.5 95.6 187.6 2,060.6 Italy 19.6 47.3 96.9 83.5 82.4 88.9 77.5 93.2 92.4 107.8 86.6 69.0 58.3 112.7 96.4 66.6 81.2 113.4 68.8 50.1 80.8 81.3 94.9 170.6 110.6 173.1 102.4 105.0 97.7 109.1 84.1 294.2 Latvia 20.9 83.6 186.4 159.7 125.4 145.6 119.9 138.3 157.5 171.2 180.5 89.2 84.5 192.7 128.2 148.4 144.4 211.9 62.2 74.1 74.9 150.7 147.9 242.7 191.1 301.3 177.7 144.6 156.5 237.8 308.9 73.6 Lithuania 48.9 44.7 191.2 144.8 111.6 82.1 95.1 133.9 136.2 163.2 164.5 98.1 68.7 177.3 131.5 179.7 122.8 117.5 147.9 59.1 151.9 183.1 143.6 242.8 191.0 170.3 191.6 242.6 252.8 159.6 183.7 56.1 Luxembourg 0.9 8.3 14.9 57.4 41.9 53.2 72.3 40.8 75.2 126.0 68.7 36.1 29.7 88.3 42.3 98.7 41.5 41.1 27.0 34.9 3.4 51.3 194.2 213.7 143.0 204.4 60.6 63.0 115.6 34.1 161.1 1,177.8 Malta 21.2 50.7 128.3 98.6 97.1 140.4 91.3 92.2 87.9 105.0 79.7 97.1 59.5 103.5 108.8 68.1 91.4 147.3 57.1 45.4 18.7 99.8 125.8 211.5 140.4 153.0 93.2 89.9 144.6 119.4 172.6 274.9 Netherlands 5.5 39.0 149.4 85.6 89.1 101.5 59.4 82.9 86.2 92.9 95.7 49.1 58.9 83.3 82.5 77.0 109.0 148.9 95.9 31.5 33.1 106.0 86.6 132.3 131.9 156.9 99.5 74.3 79.0 116.6 124.7 101.6 Poland 27.7 36.0 98.4 64.8 54.9 60.4 56.4 61.6 57.3 38.4 55.6 22.1 39.5 54.3 61.3 58.4 58.2 69.4 49.0 33.5 47.2 69.8 74.5 87.1 69.7 88.6 84.7 52.8 59.5 68.2 70.9 140.8 Portugal 37.0 88.2 128.1 92.2 107.2 122.5 109.2 121.5 118.5 120.4 114.1 96.2 67.4 126.3 111.5 111.8 132.3 170.5 63.2 65.6 118.7 139.2 108.4 189.9 135.0 151.5 161.0 118.1 107.9 171.9 166.3 455.6 Romania 72.8 72.3 122.9 125.9 93.5 78.6 90.8 88.9 95.0 99.6 87.8 77.9 69.0 84.9 85.8 87.3 70.1 79.1 60.9 62.7 82.2 126.9 154.8 158.8 136.1 248.7 90.9 117.5 102.8 72.3 124.5 — Slovakia 109.7 63.5 64.8 197.0 110.8 109.2 99.5 131.3 116.2 137.7 147.2 72.4 — 68.7 72.1 117.1 97.1 40.6 63.5 76.1 149.1 96.1 152.3 209.8 156.9 417.5 103.1 143.3 68.0 139.7 218.7 — Slovenia 86.2 49.5 131.6 132.4 98.1 139.9 114.0 136.6 119.8 131.0 117.7 58.9 63.7 92.0 100.5 93.1 124.0 65.8 64.5 60.2 91.9 134.4 136.5 176.5 126.5 155.3 148.1 115.6 117.7 114.8 110.3 59.4 Spain 11.4 59.7 142.5 104.3 82.0 84.1 80.0 103.8 97.4 134.2 97.6 71.3 65.5 130.6 79.5 94.7 92.1 81.8 82.3 54.6 61.0 110.2 110.8 139.5 113.4 134.0 116.4 98.7 83.4 145.6 158.2 393.6 Sweden 9.8 54.4 127.5 145.6 137.0 178.6 125.6 140.1 145.7 155.9 223.7 101.1 69.8 144.3 149.1 101.6 186.8 190.1 125.7 45.8 47.9 150.2 182.4 298.6 195.8 250.8 196.3 167.6 200.6 151.8 198.1 68.7 United Kingdom 7.4 71.2 80.1 93.7 82.7 94.9 72.5 91.5 82.6 86.2 111.4 76.8 56.2 102.3 88.7 88.9 101.9 118.0 77.3 60.6 86.2 118.6 98.2 145.9 106.9 176.3 109.5 158.9 113.3 121.8 135.3 85.9 EU28 9.9 50.0 101.6 92.7 84.5 85.9 72.1 89.0 85.0 86.1 106.0 56.1 56.0 88.6 81.6 73.7 91.0 99.2 68.0 51.7 54.4 109.4 98.9 135.1 110.3 159.6 107.3 87.7 95.8 98.2 128.7 317.1 Source: Authors’ own. Atmosphere 2020,11, 545 19 of 20 References 1. UNFCCC. Kyoto Protocol Reference Manual. On Accounting of Emissions and Assigned Amount; United Nations Framework Convention on Climate Change: Bonn, Germany, 2008. 2. Myllyvirta, L. Analysis: Coronavirus Temporarily Reduced China’s CO2 Emissions by a Quarter; CarbonBrief: London, UK, 2020. 3. Mainar, A.; Fuentes, P. A social accounting matrix approach to appraise sectors with a zero deficit public budget. Revista Galega de Economia 2017,26, 89–96. 4. Peters, G.P. From production-based to consumption-based national emission inventories. Ecol. Econ. 2008 , 65, 13–23. [CrossRef] 5. Wiedmann, T. A review of recent multi-region input–output models used for consumption-based input–output models used for consumption-based. Ecol. Econ. 2009,69, 211–222. [CrossRef] 6. Davis, S.J.; Caldeira, K. Consumption-based accounting of CO 2 emissions. Proc. Natl. Acad. Sci. USA 2010 , 107, 5687–5692. [CrossRef] [PubMed] 7. Liu, L.; Huang, G.; Baetz, B.; Cheng, G.; Pittendrigh, S.M.; Pan, S. Input-output modeling analysis with a detailed disaggregation of energy sectors for climate change policy-making: A case study of Saskatchewan, Canada. Renew. Energy 2020,151, 1307–1317. [CrossRef] 8. Yang, Y.; Meng, G. The evolution and research framework of carbon footprint: Based on the perspective of knowledge mapping. Ecol. Indic. 2020,112, 106125. [CrossRef] 9. S á nchez-Ch ó liz, J.; Duarte, R.; Mainar, A. Environmental impact of household activity in Spain. Ecol. Econ. 2007,62, 308–318. [CrossRef] 10. Duarte, R.; Mainar, A.; S á nchez-Ch ó liz, J. The impact of household consumption patterns on emissions in Spain. Energy Econ. 2010,32, 176–185. [CrossRef] 11. Duarte, R.; Mainar, A.; S á nchez-Ch ó liz, J. Social groups and CO 2 emissions in Spanish households. Energy Policy 2012,44, 441–450. [CrossRef] 12. Hertwich, E.G.; Peters, G.P. Carbon footprint of Nations: A global, trade linked analysis. Environ. Sci. Technol. 2009,43, 6414–6420. [CrossRef] 13. Hoekstra, R.; van der Bergh, J. Comparing structural and index decomposition analysis. Energy Econ. 2003 , 25, 39–64. [CrossRef] 14. Gallego, B.; Lenzen, M. A consistent Input-output formulation of shared producer and consumer responsibility. Econ. Syst. Res. 2005,17, 365–391. [CrossRef] 15. Lenzen, M.; Peters, G. How City dwellers affect their resource Hinterland. J. Ind. Ecol. 2010 ,14, 73–90. [CrossRef] 16. Allan, G.; Mcgregor, P.; Swales, K. The importance of revenue sharing for the local economic impacts of a renewable energy project: A Social Accounting Matrix approach. Reg. Stud. 2011 ,45, 1171–1186. [CrossRef] 17. Courtney, P.; Mayfield, L.; Tranter, R.; Jones, P.; Errington, A. Small towns as ‘sub-poles’ in English rural development: Investigating rural-urban linkages using sub-regional Social Accounting Matrices. Geoforum 2007,38, 1219–1232. [CrossRef] 18. Khan, H.; Thorbecke, E. Macroeconomic effects of technology choice: Multiplier and structural path analysis within a SAM framework. J. Policy Model. 1989,11, 131–156. [CrossRef] 19. Seung, C.; Waters, E. The role of the Alaska seafood industry: A social accounting matrix (SAM) model approach to economic base analysis. Ann. Reg. Sci. 2006,40, 335–350. [CrossRef] 20. Campoy-Muñoz, P.; Cardenete, M.A.; Delgado, M.C. Economic impact assessment of food waste reduction on European countries through Social Accounting Matrices. Resour. Conserv. Recycl. 2017 ,122, 202–209. [CrossRef] 21. Philippidis, G.; Sanju á n, A.; Ferrari, E.; M’Barek, R. Employing social accounting matrix multipliers to profile the bioeconomy in the EU member states: Is there a structural pattern? Span. J. Agric. Res. 2014 ,12, 913–916. [CrossRef] 22. Cardenete, M.A.; Boulanger, P.; Delgado, M.C.; Ferrari, E.; M’Barek, R. Agri-food and bio-based analysis in the Spanish economy using a key sector approach. Rev. Urban Reg. Dev. Stud. 2014 ,26, 112–134. [CrossRef] 23. Meng, X. Will Australian Carbon Tax Affect the Resources Boom? Results from a CGE Model. Nat. Resour. Res. 2012,21, 495–507. [CrossRef] Atmosphere 2020,11, 545 20 of 20 24. Meng, S.; Siriwardana, M.; McNeill, J. The Impact of the Australian Carbon Tax on Industries and Households. Margin 2014,8, 15–37. [CrossRef] 25. Stone, R. Measurement of National Income and the Construction of Social Accounts; United Nations: Geneva, Switzerland, 1947. 26. Pyatt, G.; Round, J. Social Accounting Matrices: A Basis for Planning; The World Bank: Washington, DC, USA, 1985. 27. Mainar-Causap é , A.; McDonald, S.; Ferrari, E. Social Accounting Matrices: Basic Aspects and Main Steps for Estimation; Publications Office of the European Union: Luxembourg, 2018. 28. European Commission. European System of National and Regional Accounts 2010; European Commission: Luxembourg, 2013. 29. Eurostat. Eurostat Manual of Supply, Use and Input-output Tables; Eurostat Methodologies and Working Papers; Office for Official Publications of the European Communities: Luxembourg, 2008. 30. Miller, R.; Blair, P. Input-output Analysis: Foundations and Extensions, 2nd ed.; Cambridge University Press: Cambridge, UK, 2009. 31. Eurostat. Non-Financial Transactions (nasa_10_nf_tr). 2016. Available online: http://ec.europa.eu/eurostat/ data/database (accessed on 1 March 2020). 32. Eurostat. Supply, Use and Input-output Tables (naio_10_cp). 2016. Available online: http://ec.europa.eu/ eurostat/data/database (accessed on 1 March 2020). 33. Eurostat. NACE Rev.2 Statistical Classification of Economic Activities in the European Community; Methodologies and Working Papers; Publications Office of the European Union: Luxembourg, 2008. 34. Eurostat. CPA 2008—Statistical Classification of Products by Activity; Publications Office of the European Union: Luxembourg, 2008. 35. Pyatt, G.; Round, J. Accounting and fixed price multipliers in a Social Accounting Matrix framework. Econ. J. 1979,89, 850–873. [CrossRef] 36. Leontief, W. Quantitative Input and Output Relations in the Economic Systems of the United States. Rev. Econ. Stat. 1936,18, 105–125. [CrossRef] 37. Wiedmann, T. On the decomposition of total Impact multipliers in a supply and use framework. J. Econ. Struct. 2017,6, 11. [CrossRef] 38. Eurostat. Air Emissions Accounts by NACE Rev. 2 Activity Env_ac_ainah_r2. 2020. Available online: http://ec.europa.eu/eurostat/data/database (accessed on 1 March 2020). 39. Fuentes, P.; Mainar-Causap é , A.; Ferrari, E. The Role of Bioeconomy Sectors and Natural Resources in EU Economies: A Social Accounting Matrix-Based Analysis Approach. Sustainability 2017,9, 2383. [CrossRef] 40. Eurostat. Labour Force Survey (lfs_egan22d). 2020. Available online: http://ec.europa.eu/eurostat/data/ database (accessed on 1 March 2020). 41. Duarte, R.; Mainar, A.; S á nchez-Ch ó liz, J. The role of consumption patterns, demand and technological factors on the recent evolution of CO2 emissions in a group of advanced economies. Ecol. Econ. 2013 ,96, 1–13. [CrossRef] 42. Philippidis, G.; Sanju á n-L ó pez, A. A Re-Examination of the Structural Diversity of Biobased Activities and Regions across the EU. Sustainability 2018,10, 4325. [CrossRef] 43. Munksgaard, J.; Pedersen, K.; Wien, M. Impact of household consumption on CO2 emissions. Energy Econ. 2000,22, 423–440. [CrossRef] 44. Moll, H.; Noorman, K.; Kok, R.; Engström, R.; Throne-Holst, H.; Clark, C. Pursuing more sustainable consumption by analyzing household metabolism in European countries and cities. J. Ind. Ecol. 2005 ,9, 259–275. [CrossRef] 45. Kerkhof, A.; Nonhebel, S.; Moll, H. Relating the environmental impact of consumption to household expenditures: An input–output analysis. Ecol. Econ. 2009,68, 1160–1170. [CrossRef] © 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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