The relevance of multi-country input-output tables in measuring emissions trade balance of countries: the case of Spain

Statistics & Operations Research Transactions SORT 40 (1) January-June 2016, 3-30 Statistics & Operations Research Transactions © Institut d’Estad´ıstica de Catalunya [email protected] ISSN: 1696-2281 eISSN: 2013-8830 www.idescat.cat/sort/ The relevance of multi-country input-output tables in measuring emissions trade balance of countries: the case of Spain Teresa Sanz1,∗, Roc´ıo Y˜niguez1and Jos´e Manuel Rueda-Cantuche2 Abstract As part of national accounts, input-output tables are becoming crucial statistical tools to study the economic, social and environmental impacts of globalization and international trade. In particular, global input-output tables extend the national dimension to the international dimension by relating individual countries’ input-output tables among each other, thus providing an opportunity to balance the global economy as a whole. Concerning emissions of greenhouse gases, the relative position that countries hold among their main trade partners at the global level is a key issue in terms of international climate negotiations. With this purpose, we show that (official) Multi-country input-output tables are crucial to analyse the greenhouse gas emission trade balance of individual countries. Spain has a negative trade emissions balance for all three gases analysed, being the most negative balances those associated to the bilateral trade with China, Russia, United States and the rest of the European Union as a whole. MSC: 91F. Keywords: WIOD, Emissions Trade Balance, Spain, GHG footprint, GHG. 1. Background and statistical context The latest meeting of the Group of Experts on National Accounts of the United Nations Economic Commission for Europe (UNECE, 7-9 July 2015), was devoted to data collection and compilation methods in respect to global production activities. It was jointly ∗Corresponding author. 1University of Seville, Dpt. Economic Analysis and Political Economy, Avda. Ram´on y Cajal, 1. 41018 Sevilla. Phone: +954557524/954 554481. Fax: 954557629. [email protected]/[email protected] 2European Commission, Joint Research Centre, Institute for Prospective and Technological Studies, Inca Garcilaso, 3, 41092-Edificio EXPO. The views expressed in this paper belong to the authors and should not be attributed to the European Commission or its services. Received: December 2014 Accepted: November 2015 4The relevance of multi-country input-output tables in measuring emissions... organized with Eurostat and the Organization for Economic Co-operation and Development (OECD). The meeting was attended by representatives from more than thirty countries worldwide and representatives from the European Commission (EC), International Monetary Fund (IMF), OECD, the United Nations Conference on Trade and Development (UNCTAD), United Nations Statistics Division (UNSD) and World Trade Organization (WTO), among others. According to the experts at this UNECE meeting, in order to measure global production and global value chains it is no longer sufficient to look only at what a firm does, but to also to consider how the firm does its activities and with whom. For instance, linking business statistics and trade statistics on a micro level should provide new dimensions to the data as long as new balancing challenges at the macro level data (e.g. national accounts). Indeed, statisticians have not always been able to keep up to date with business practices and must find ways to be forward looking and provide the information that meets future policy needs. Traditional measures of trade in goods and services have to be progressively supplemented with information on income and financial flows. Foreign direct investment statistics (FDI) should be further developed and complemented with foreign affiliate statistics (FATS) in order to improve their clarity, usefulness and coverage, and to provide better insights into global value chains. In this respect, the UNECE Report emanating from this meeting supported new global initiatives, such as the extensions to Trade in Value Added and Global Input- Output Tables (OECD), the construction of the European Multi-Country Input-Output Framework (EC and Eurostat) as well as the elaboration of a new Handbook on a System of Extended International and Global Accounts (UNSD). Hence, there is no doubt that globalization is currently affecting the way statisticians are measuring national production of countries and international statistical organizations are indeed very busy working on it in order to meet the policy needs at the worldwide level. As national accounts and input-output tables became an integral part of the production activities of national statistical institutes in the past, very soon multi-country and international input-output tables will become a crucial statistical tool to measure global production, trade in value added, environmental footprints and/or employment effects of export activities with official statistics (e.g. carbon footprint estimated by Eurostat). Bearing all this in mind, we would like to illustrate in this paper the usefulness of global/world input-output tables in measuring the greenhouse gas footprints of individual countries and its external emission trade balance with respect to others. Hopefully, these types of indicators will soon become regularly produced in the future by statisticians using official global input-output tables instead of using other databases produced as one-off projects (e.g. World Input-Output Database, WIOD – www.wiod.org). This paper is structured in five sections. Following this background, there is an introductory section on the related literature on greenhouse gases emissions footprints. Next, the third section introduces the methodology and the database. The fourth section presents the results obtained and discusses them. The fifth section concludes. Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 5 2. Introduction to GHG footprints Greenhouse gas emissions (GHG) are considered to be one of the main causes of climate change. This is the reason why governments are increasingly making efforts to implement policies aiming to reduce GHG emissions. National climate policies are mainly driven by international negotiations and these are strongly linked to the amount of emissions produced within a country or the so called producer’s responsibility principle. Within this context, exporting (producing) countries are responsible for their GHG emissions, irrespective of where the demand for such products comes from. On the other hand, the interest in the so called consumer’s responsibility principle has been growing since Leontief (1970) described the environmental impacts of the final consumer as a negative externality of the production process. This concept has been endorsed by the OECD’s Green Growth Strategy (2011). According to this principle, the GHG emissions are allocated according to countries’ domestic demand of goods and services, irrespective of where they were produced. Different approaches have been used to analyse this new concept of responsibility, such as general balance models, dynamic models and the analysis of structural decomposition, i.e. Peters and Hertwich (2006), Peters (2008) Peters et al. 2011), Druckman and Jackson (2009), Davis and Caldeira (2010), Zhou and Imura (2011) and Edens et al. (2011), Kanemoto et al. (2012), among others. Among others, Rueda-Cantuche and Amores (2010) noted that developed countries may reduce their emissions produced but at the same time, they may increase their consumption-based emissions. This is due to the different technologies used in the production processes of developing countries, generally less clean than those of the developed countries. In the end, some environmental policies might result in a global increase in GHG emissions. At the national level, the difference between the production-based emissions and the consumption-based emissions lead to the so called emission trade balance (ETB) of a country or of a certain industry. This analysis will determine the surplus/deficit that a country/industry has. It is expected that developing countries have surpluses and developed countries, deficits. Within this context, the aim of this paper is to calculate the Emission Trade Balance (ETB) of Spain in 2008 at a worldwide level and bilaterally with respect to 39 countries, 35 industries and one additional region as the “rest of the world” for the three main GHGs (CO2, N2O and CH4). In order to do so, we have used multi-regional input-output analysis (MRIO) and the World Input-Output Database (WIOD) (Dietzenbacher et al., 2013). Input-output analysis (IOA) has been generally used to study environmental problems (Miller and Blair, 2009). Particularly, there are numerous related studies devoted to the analysis of polluting GHG emissions, i.e. Minx et al., (2009), Su et al. (2010), Chen et al., (2010), Liang et al. (2010), Chang et al. (2010), Zhu et al., (2012) and Mattila et al. (2013), among others. 6The relevance of multi-country input-output tables in measuring emissions... Likewise, there are also many studies about GHG emissions associated with the international trade of specific countries, such as China, (Liang et al., 2007, Liu et al., 2009, Zhao et al., 2009, Xu et al., 2011, Hongtau et al., 2010, Chen et al., 2010 a, b, Chen and Zhang 2010); Finland (Maenpaa and Siikavirta 2007); Ireland (Llop and Tol, 2012); Italy (Cellura et al., 2013, Mongelli et al.,2006); Japan (Nansai et al., 2009); the United Kingdom (Wiedman et al., 2010, Druckman and Jackson 2009)) and Turkey (Tunc¸ et al., 2007). The work of Musksgaard and Pedersen (2001) for Denmark was the first one that linked the input-output methodology to the consumer’s responsibility principle related to GHG emissions. It was followed by Ahmad and Wyckoff (2003) for OECD countries and Peters and Hertwich (2006) for the Norwegian economy and for three different gases (CO2, NO2and SO2). IOA has also been applied to study GHG emissions associated to consumption in the case of Spain. Taranc´on and del Rio (2007) used a combination of IOA with sensitivity analyses; Cadarso et al. (2010) study the effect of international trade of the Spanish emissions balace under DTA assumption; S´anchez-Choliz, and Duarte (2004), Serrano and Roca (2008a, 2008b), Serrano and Dietzenbacher (2010) used IOA assuming domestic technology in monetary terms while Arto (2009) and Arto et al., (2012) do the same but in physical terms; Lopez et al. (2013) analyse the existence of pollution haven hypothesis in a bi-regional input-output model and Cadarso et al. (2012) defined a shared responsibility criterion to analyse the impact of international trade in CO2emissions on an industrial basis, such as the food industry in Lopez et al. (2015). But none of them has used a homogeneous multi-country IO database such as WIOD (Dietzenbacher et al., 2013), nor has the analysis been carried out with high industry resolution and bilateral trade flows as in the present study. This work covers 35 industries and 41 different geographical areas for each of the three GHGs considered. Therefore, the originality and interest of this work lies in the details and the extension of the results in terms of higher industry breakdown, homogeneity of the multi-country database, country coverage and pollutants covered (CO2, CH4and N2O) rather than the topic itself, which has already been addressed in the literature. 3. Methodology and database 3.1. Input-output analysis Input-output analysis revolves around the so called input-output tables, which reflect the supply and demand of the economy in terms of products, industries and final users. By using the so called Leontief quantity model (Rueda-Cantuche, 2011), the total output of an economy can be broken down into final and intermediate demand, as indicated in (1): x=Ax +y(1) Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 7 where xis the total industry output vector for nindustries (n×1);Z=Ax is a matrix describing the intermediate uses of industries; Ais a matrix (n×n)of input-output coefficients showing the inputs needed per unit of output by each industry; and ystands for a final demand vector (n×1)showing the sum of consumption, investment and exports of all goods and services. Within this framework, we use industry by industry IO tables from the WIOD database (Dietzenbacher et al., 2013) with the same number of industries and commodities (n). Reordering (1), it yields x= (I−A)−1y(2) where Iis the identity matrix and (I−A)−1, the so called Leontief inverse matrix that shows the total requirements of the economy for the production of goods and services to satisfy a certain level of final demand. Moreover, with appropriate emission levels (s) per unit of total industry outputs (x), c=sˆ x−1(where ˆ denotes diagonalization of the vector x), the Leontief model can serve to estimate the absolute levels of emissions for the production of a certain level of total output needed to satisfy changes in final demand, e.g. emissions of the car industry to produce vehicles due to changes in households demand. It is important to note that this paper is focused on the production phase of emissions alone and it does not include those emissions derived from the use phase of a product (e.g. households driving cars). That is: s−ˆ c(I−A)−1y(3) 3.2. Multi-regional input-output analysis Multi-regional input-output analysis is based on a set of interconnected input-output tables of various countries (Miller and Blair, 2009). While equation (3) refers to one single country with nindustries, we will express hereafter the same equation for a threeregion model with nindustries in each region, namely: Spain (u), rest of the EU (r) and rest of the world (w). The result is a fully fledged input-output table with three times n industries and its main components are described below. A=  Auu Aur Auw Aru Arr Arw Awu Awr Aww  Y=  yuu yur yuw yru yrr yrw ywu ywr yww   L= (I−A)−1=  Luu Lur Luw Lru Lrr Lrw Lwu Lwr Lww  ˆ C=  ˆ cu0 0 0ˆ cr0 0 0 ˆ cw   8The relevance of multi-country input-output tables in measuring emissions... Matrix Aand vector ystand for input-output coefficients and final uses, respectively. The subscript on the left corresponds to the exporting region and the subscript on the right refers to the importing region. Doing so, these two elements include bilateral exports and bilateral imports of intermediate and final uses, too. Besides, each of the submatrices of the Amatrix has nrows and ncolumns, so the fully-fledged matrix Ais of order (3n×3n). For one single final demand category, the matrix Yis therefore of order (3n×3). Moreover, it is straightforward that the Leontief inverse is a square matrix of the same dimension as A, being eventually matrix ˆ Ca diagonal matrix with three diagonalized vectors of n-dimension each. The latter corresponds to different emission coefficients by country of origin (or region), which is quite relevant for our analysis. These emission coefficients have been calculated as the total emissions of each country and industry over their corresponding total output, both provided by the WIOD database (Dietzenbacher et al., 2013). With these new matrices, we re-define equation (3) but also allowing for a fullyfledged decomposition of the final demand by region. Subsequently, equation (4) is split up into as many components as number of regions the model has (i.e. three). As a matter of fact, the sum of all the elements of each component is nothing else but the footprint of each of the regions (e.g. carbon footprint). As in Lopez et al., (2013), Cadarso et al., (2012) or Skelton (2013), we have estimated matrices of emissions (see equation 5), where the sum by rows allocate the responsibility to industries that supply intermediate and final goods and the sum by columns allocate the responsibility to agents/industries that consume them. More precisely, the focus of our analysis is based on the sum of the elements of each row in each of the three fully-fledged matrices of equation (5), which yields three vectors of emissions. ˆ C(I−A)−1  yu0 0 0yr0 0 0 yw  = =  ˆ cu0 0 0ˆ cr0 0 0 ˆ cw    Luu Lur Luw Lru Lrr Lrw Lwu Lwr Lww    yuu 0 0 0yru 0 0 0 ywu   +  ˆ cu0 0 0ˆ cr0 0 0 ˆ cw    Luu Lur Luw Lru Lrr Lrw Lwu Lwr Lww    yur 0 0 0yrr 0 0 0 ywr   +  ˆ cu0 0 0ˆ cr0 0 0 ˆ cw    Luu Lur Luw Lru Lrr Lrw Lwu Lwr Lww    yuw 0 0 0yrw 0 0 0 yww   (4) Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 9 Being:   yu0 0 0yr0 0 0 yw  =  yuu +yur +yuw 0 0 0yru +yrr +yrw 0 0 0 ywu +ywr +yww   Properly extended, equation (4) becomes into:   ˆ cuLuuyuu ˆ cuLuryru ˆ cuLuwywu ˆ crLruyuu ˆ crLrryru ˆ crLrwywu ˆ cwLwuyuu ˆ cwLwryru ˆ cwLwwywu  +  ˆ cuLuuyur ˆ cuLuryrr ˆ cuLuwywr ˆ crLruyur ˆ crLrryrr ˆ crLrwywr ˆ cwLwuyur ˆ cwLwryrr ˆ cwLwwywr  +   ˆ cuLuuyuw ˆ cuLuryrw ˆ cuLuwyww ˆ crLruyuw ˆ crLrryrw ˆ crLrwyww ˆ cwLwuyuw ˆ cwLwryrw ˆ cwLwwyww   and summing row-wise:   gdom uu gimp ru gimp wu  +  gexp ur gdom rr gexp wr  +  gexp uw gexp rw gdom ww  = =  ˆ cuLuuyuu ˆ cuLuryru ˆ cuLuwywu ˆ crLruyuu ˆ crLrryru ˆ crLrwywu ˆ cwLwuyuu ˆ cwLwryru ˆ cwLwwywu    1 1 1   +  ˆ cuLuuyur ˆ cuLuryrr ˆ cuLuwywr ˆ crLruyur ˆ crLrryrr ˆ crLrwywr ˆ cwLwuyur ˆ cwLwryrr ˆ cwLwwywr    1 1 1   +  ˆ cuLuuyuw ˆ cuLuryrw ˆ cuLuwyww ˆ crLruyuw ˆ crLrryrw ˆ crLrwyww ˆ cwLwuyuw ˆ cwLwryrw ˆ cwLwwyww    1 1 1   (5) with the following definitions (only some of them are presented as illustrative purposes): (a) ˆ cuLuuyuu stands for the emissions produced in Spain derived from the Spanish final demand of domestically produced commodities (e.g. purchase of a Spanish car by a Spanish resident); (b) ˆ cuLuryru represents the emissions produced in Spain for the production of an exported commodity that will be used by the rest of the EU (r) to produce something else that Spain will import (e.g. exports of Spanish electronic components for the production of Czech cars that will be imported by Spain). 10 The relevance of multi-country input-output tables in measuring emissions... (c) ˆ cuLuwywu shows the emissions produced in Spain for the production of an exported commodity that will be used by the rest of the world (w) to produce something else that Spain will import (e.g. exports of Spanish electronic components for the production of American cars that will be imported by Spain). (d) gdom uu is the sum of (a), (b) and (c); the sum of emissions emitted in Spain coming from the final demand of Spanish residents. (e) ˆ crLruyuu stands for the emissions produced in EU countries (r) derived from the imported intermediate inputs needed to satisfy the Spanish final demand of domestically produced commodities (e.g. purchase of a Spanish car by a Spanish resident that involves imports of electronic components from the Czech Republic); (f) ˆ crLrryru shows the emissions produced in EU countries (r) to satisfy the Spanish final demand of commodities produced in the EU (e.g. imports of German cars by Spanish residents); (g) ˆ crLrwywu shows the emissions produced in EU countries (r) to produce an intermediate export to a non-EU country that will serve as input to produce something to be exported to Spain (e.g. purchase of a Japanese car by a Spanish resident that involves imports of electronic components from the Czech Republic); (h) gimp ru is the sum of (e), (f) and (g); the sum of emissions emitted in the rest of Europe coming from the final demand of Spanish residents. (i) gimp wu is, analogously, the sum of emissions emitted in the rest of the world coming from the final demand of Spanish residents. (j) ˆ cuLuuyur shows the emissions produced in Spain to satisfy the EU final demand of Spanish commodities (e.g. imports of a Spanish car by a German resident); (k) ˆ cuLuryrr shows the emissions produced in Spain derived from the imported inputs of the rest of the EU needed to satisfy their own final demand of domestically produced commodities (e.g. purchase of a German car by a German resident that involves imports of electronic components from Spain); (l) ˆ cuLuwywr shows the emissions produced in Spain derived from the imported intermediate inputs of the rest of the world needed to satisfy the final demand of EU residents (e.g. purchase of a Japanese car by a German resident that involves imports of electronic components from Spain); (m) gexp ur is the sum of (j), (k) and (l); the sum of emissions emitted in Spain coming from the final demand of EU residents. (n) gexp uw is, similarly, the sum of emissions emitted in Spain coming from the final demand of the rest of the world. Therefore, the total emissions produced in the region u, is: gdom uu +gexp ur +gexp uw (6) Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 11 and the total of emissions caused by the final demand of region u(carbon footprint), is: gdom uu +gimp ru +gimp wu (7) The difference between the two is the so called Emission Trade Balance (ETB), which can be calculated here by the difference between the emissions actually produced in Spain (6) and the Spanish footprint (7). In a bilateral model (i.e. dropping region win equations 6 and 7), the ETB yields: gexp ur −gimp ru which is equal to (from equation 5): ˆ cuLuuyur +ˆ cuLuryrr −ˆ crLrryru −ˆ crLruyuu And therefore, ˆ cu(Luuyur +Luryrr)−ˆ cr(Lrryru −Lruyuu) where the expressions in parentheses are indeed the sum of intermediate and final exports and imports, respectively. Thus, the ETB (positive or negative) highly depends on both the trade balance and the different pollution (emission) intensity of goods traded in both regions (Rueda-Cantuche, 2011; L´opez et al., 2013). Furthermore, multi-country input-output tables also allow a detailed separate analysis about trade on intermediate and final goods and services and thus, global value chains in the emissions balance. For instance, the total emissions generated in the country of reference due to Spanish imports of final goods and services (gimp ru ) can be decomposed into: (a) Emissions generated in the country of reference for the production of the final goods and services exported to Spain (%) - ˆ crLrryru; (b) Emissions generated in the country of reference for the production of the intermediate inputs that will be exported to Spain for the domestic production of a final good or service demanded by Spanish residents (%) - ˆ crLruyuu; (c) Emissions generated in the country of reference for the production of the intermediate inputs that will be exported to a third country for the domestic production of a final good or service to be exported to Spain (%) - ˆ crLrwywu; And similarly, the total emissions produced in Spain due to imports of the country of reference (gexp ur ) can be split up into: 18 The relevance of multi-country input-output tables in measuring emissions... this work lies on the details and the extension of the results in terms of higher industry breakdown, homogeneity of the multi-country database, country coverage and pollutants covered (CO2, CH4and N2O). Spain produced 316.6 million tons of CO2equivalents in 2008 and its final demand led to 494 million tons of CO2equivalents elsewhere in the same year. The emission trade balance of Spain of GHG resulted therefore in -177.7 million tons of CO2equiv- alents. Spain is the fifth EU country with the largest negative emission trade balance, behind Germany, France, United Kingdom and Italy. Moreover, Spanish exports of final goods and services to France, Germany and UK are those that contribute most to the GHG emissions produced by Spain. On the other hand, the final demand of Spanish residents (GHG footprint) leads to 65.5 million tons of CO2equivalent of GHG in China; followed by Russia and US with 20.7 and 13.7 million tons of CO2equivalents. As a result, the largest positive balances are found in France (24 millions of tons of CO2equivalents) and Portugal (18.3 millions of tons of CO2equivalents), while the largest negative emission trade balances of Spain are found for China, Russia and US. The analysis also gives some details by polluting industry. Finally, special attention should be devoted to the emissions trade balance between Spain and China. China is the country that produces more CO2, CH4and N2O emissions due to Spanish imports. In particular, Chinese GHG emissions due to intermediate imported inputs by Spain are much more than those produced for exporting final goods and services to Spain (as in L´opez et al., 2013). This result could be explained by the reallocation of (less clean) production activities and international supply chains across the world (Cadarso et al., 2012). Interestingly, future work might be focused on whether this trend of re-allocation of production activities to less developed countries will continue in time. Policy options like stimuli of technology transfers and the spread use of cleaner technologies through standard regulations would also be worthwhile to investigate. Reducing emissions of greenhouse gases (GHG) has become one of the main objectives of the current climate policies of countries. The relative position that countries hold among their main trade partners is also a key issue in terms of international climate negotiations and this paper hopefully contributes to raise the awareness of national statistical institutes and statistical international organizations about the necessary construction of official global multi-country input-output tables that would pave the way for further detailed studies on the economic, social and environmental impacts of globalization and international trade. Acknowledgements The first and second authors acknowledge the funding received from the SEJ 132 project of the Andalusian Regional Government, ECO2014-56399-R Project of Spanish Ministry of Economy and Competitiveness and the “C´atedra de Econom´ıa de la Energ´ıa y Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 19 del Medio Ambiente” (Department for Energy Economics and the Environment) at the University of Seville and the “Fundaci´on Roger Torn´e” (Foundation). Annex I. Tables Table A.1: WIOD Industries and Commodities.1 WIOD Sectors 1 Agriculture, Hunting, Forestry and Fishing 2 Mining and Quarrying 3 Food, Beverages and Tobacco 4 Textiles and Textile Products 5 Leather, Leather and Footwear 6 Wood and Products of Wood and Cork 7 Pulp, Paper, Paper , Printing and Publishing 8 Coke, Refined Petroleum and Nuclear Fuel 9 Chemicals and Chemical Products 10 Rubber and Plastics 11 Other Non-Metallic Mineral 12 Basic Metals and Fabricated Metal 13 Machinery, Nec 14 Electrical and Optical Equipment 15 Transport Equipment 16 Manufacturing, Nec; Recycling 17 Electricity, Gas and Water Supply 18 Construction 19 Sale, Maintenance and Repair of Motor Vehicles and Motorcycles; Retail Sale of Fuel 20 Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles 21 Retail Trade, Except of Motor Vehicles and Motorcycles; Repair of Household Goods 22 Hotels and Restaurants 23 Inland Transport 24 Water Transport 25 Air Transport 26 Other Supporting and Auxiliary Transport Activities; Activities of Travel Agencies 27 Post and Telecommunications 28 Financial Intermediation 29 Real Estate Activities 30 Renting of M&Eq and Other Business Activities 31 Public Admin and Defence; Compulsory Social Security 32 Education 33 Health and Social Work 34 Other Community, Social and Personal Services 35 Private Households with Employed Persons 1.Commodities and industries are the same provided that the World IOTs used are square. 20 The relevance of multi-country input-output tables in measuring emissions... Legends to read Tables A.2, A.3 and A.4 A1: Total emissions generated in the country of reference due to Spanish imports of final goods and services (GHG footprints) - gimp ru B1: Cumulated share of A1 over the total amount of emissions (%) C1: Share of emissions generated in the country of reference for the production of the final goods and services exported to Spain (%) - ˆ crLrryru D1: Share of emissions generated in the country of reference for the production of the intermediate inputs that will be exported to Spain for the domestic production of a final good or service demanded by Spanish residents (%) - ˆ crLruyuu E1: Share of emissions generated in the country of reference for the production of the intermediate inputs that will be exported to a third country for the domestic production of a final good or service to be exported to Spain (%) - ˆ crLrwywu A2: Total emissions produced in Spain due to imports of the country of reference - gexp ur B2: Cumulated share of A2 over the total amount of emissions (%) C2: Share of emissions produced in Spain for exports of final goods and services - ˆ cuLuuyur D2: Share of emissions produced in Spain for exports of intermediate goods and services to the country of reference for the production of final goods in the same country - ˆ cuLuryrr E2: Share of emissions produced in Spain for exports of intermediate goods and services to a third country that will use them for the production of goods and services to be exported to the country of reference - ˆ cuLuwywr Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 21 Table A.2: Industries with larger CO2footprints and commodities. Thousands of tons CO2, 2008. COUNTRY TOP INDUSTRIES A1 B1 C1 D1 E1 OF WITH MORE (Th. tons) (%) (%) (%) (%) REFERENCE CO2EMISSIONS gimp ru Final Interm. Interm. CHN (-) TOTAL 50 135 100.0 37.2 49.6 13.2 TOTAL INDUSTRIES WITH MORE EMISSIONS 39 150 78.1 36.9 49.5 13.6 Electricity, Gas and Water Supply 26 815 53.5 38.4 48.2 13.4 Basic Metals and Fabricated Metal 5 826 11.6 32.9 51.7 15.5 Other Non-Metallic Mineral 3 301 6.6 31.5 58.3 10.2 Chemicals and Chemical Products 3 208 6.4 36.9 48.0 15.1 RUS (-) TOTAL 14 450 100.0 4.4 69.7 25.9 TOTAL INDUSTRIES WITH MORE EMISSIONS 13 482 93.3 4.8 70.5 24.7 Electricity, Gas and Water Supply 5 850 40.5 5.5 69.2 25.2 Mining and Quarrying 2 798 19.4 1.6 75.2 23.2 Inland Transport 2 129 14.7 1.4 72.1 26.5 Basic Metals and Fabricated Metal 1 868 12.9 3.0 69.4 27.6 Coke, Refined Petroleum and Nuclear Fuel 836 5.8 22.9 59.7 17.5 DEU (-) TOTAL 11 170 100.0 35.9 51.6 12.4 TOTAL INDUSTRIES WITH MORE EMISSIONS 8 779 78.6 33.5 53.6 12.9 Electricity, Gas and Water Supply 4 174 37.4 39.7 47.6 12.7 Basic Metals and Fabricated Metal 2 087 18.7 23.3 60.9 15.8 Chemicals and Chemical Products 1 160 10.4 33.3 53.3 13.4 Other Non-Metallic Mineral 710 6.4 23.2 67.5 9.3 Air Transport 647 5.8 38.3 54.1 7.6 USA (-) TOTAL 10 084 100.0 29.7 50.8 19.5 TOTAL INDUSTRIES WITH MORE EMISSIONS 7 332 72.7 32.2 49.1 18.7 Electricity, Gas and Water Supply 2 981 29.6 31.5 47.8 20.8 Chemicals and Chemical Products 1 256 12.5 44.7 38.0 17.3 Air Transport 1 043 10.3 28.8 59.2 12.0 Coke, Refined Petroleum and Nuclear Fuel 843 8.4 35.2 51.1 13.7 Inland Transport 625 6.2 21.8 56.8 21.5 Basic Metals and Fabricated Metal 585 5.8 22.0 49.9 28.1 IND (-) TOTAL 5 178 100.0 35.4 45.2 19.4 TOTAL INDUSTRIES WITH MORE EMISSIONS 4 095 79.1 68.5 47.7 −16.2 Electricity, Gas and Water Supply 2 550 49.2 40.7 41.3 17.9 Basic Metals and Fabricated Metal 687 13.3 56.5 22.8 20.8 Mining and Quarrying 573 11.1 64.5 10.8 24.7 Chemicals and Chemical Products 286 5.5 55.4 23.6 21.0 COUNTRY TOP INDUSTRIES A2 B2 C2 D2 E2 OF WITH MORE (Th. tons) (%) (%) (%) (%) REFERENCE CO2EMISSIONS gexp ur Final Interm. Interm. FRA (+) TOTAL 8 735 100.0 47.0 46.4 6.6 TOTAL INDUSTRIES WITH MORE EMISSIONS 7 162 82.0 45.0 48.3 6.8 Electricity, Gas and Water Supply 2 120 24.3 51.2 41.9 6.9 Other Non-Metallic Mineral 1 373 15.7 20.3 76.0 3.7 Coke, Refined Petroleum and Nuclear Fuel 906 10.4 43.7 49.3 7.0 Basic Metals and Fabricated Metal 905 10.4 37.8 51.9 10.3 Inland Transport 724 8.3 44.3 47.1 8.6 Agriculture, Hunting, Forestry and Fishing 608 7.0 86.8 10.0 3.3 Chemicals and Chemical Products 526 6.0 51.0 40.0 9.0 PRT (+) TOTAL 4 970 100.0 49.8 48.9 1.3 TOTAL INDUSTRIES WITH MORE EMISSIONS 4 150 83.5 45.5 53.1 1.4 Electricity, Gas and Water Supply 1 185 23.8 55.4 43.1 1.5 Other Non-Metallic Mineral 720 14.5 22.8 76.3 0.9 Coke, Refined Petroleum and Nuclear Fuel 553 11.1 30.9 68.0 1.1 Basic Metals and Fabricated Metal 473 9.5 38.0 59.5 2.4 Agriculture, Hunting, Forestry and Fishing 340 6.8 72.7 26.8 0.5 Inland Transport 334 6.7 58.1 39.8 2.1 Chemicals and Chemical Products 288 5.8 42.1 56.1 1.8 Air Transport 256 5.1 60.2 38.8 1.0 22 The relevance of multi-country input-output tables in measuring emissions... Table A.3: Industries with larger CH4footprints and types of commodities. Tons CH4, 2008. COUNTRY TOP INDUSTRIES A1 B1 C1 D1 E1 OF WITH MORE (tons) (%) (%) (%) (%) REFERENCE CH4EMISSIONS gimp ru Final Interm. Interm. CHN (-) TOTAL 542 790 100.0 40.0 48.3 11.7 TOTAL INDUSTRIES WITH MORE EMISSIONS 534 042 98.4 40.1 48.3 11.6 Mining and Quarrying 261 244 48.1 34.4 50.6 14.9 Agriculture, Hunting, Forestry and Fishing 142 116 26.2 56.5 33.0 10.5 Other Community, Social and Personal Services 130 682 24.1 33.4 60.3 6.2 RUS (-) TOTAL 287 495 100.0 2.4 73.0 24.7 TOTAL INDUSTRIES WITH MORE EMISSIONS 274 574 95.5 2.2 73.3 24.5 Mining and Quarrying 146 779 51.1 1.6 75.2 23.2 Inland Transport 81 814 28.5 1.4 72.1 26.5 Electricity, Gas and Water Supply 45 980 16.0 5.5 69.2 25.2 BRA (-) TOTAL 127 954 100.0 10.2 71.8 18.0 TOTAL INDUSTRIES WITH MORE EMISSIONS 126 645 99.0 10.2 71.9 17.9 Agriculture, Hunting, Forestry and Fishing 112 374 87.8 10.7 72.7 16.7 Mining and Quarrying 8 006 6.3 2.6 70.3 27.0 Other Community, Social and Personal Services 6 265 4.9 12.0 60.1 27.9 USA (-) TOTAL 104 801 100.0 35.9 47.8 16.3 TOTAL INDUSTRIES WITH MORE EMISSIONS 101 962 97.3 36.0 47.8 16.2 Agriculture, Hunting, Forestry and Fishing 46 960 44.8 47.5 38.8 13.7 Mining and Quarrying 37 936 36.2 27.8 53.0 19.3 Other Community, Social and Personal Services 10 578 10.1 23.1 63.9 13.1 Inland Transport 6 489 6.2 21.8 56.8 21.5 IND (-) TOTAL 59 613 100.0 33.5 42.0 24.5 TOTAL INDUSTRIES WITH MORE EMISSIONS 58 575 98.3 33.4 42.0 24.6 Agriculture, Hunting, Forestry and Fishing 28 941 48.5 44.1 31.1 24.8 Mining and Quarrying 16 850 28.3 10.8 64.5 24.7 Other Community, Social and Personal Services 12 784 21.4 39.1 37.0 23.9 COUNTRY TOP INDUSTRIES A2 B2 C2 D2 E2 OF WITH MORE (tons) (%) (%) (%) (%) REFERENCE CH4EMISSIONS gexp ur Final Interm. Interm. DEU (+) TOTAL 56 511 100.0 83.3 9.6 7.1 TOTAL INDUSTRIES WITH MORE EMISSIONS 53 102 94.0 85.7 7.8 6.4 Agriculture, Hunting, Forestry and Fishing 48 682 86.1 90.2 4.5 5.4 Other Community, Social and Personal Services 4 419 7.8 37.0 44.9 18.2 ITA (+) TOTAL 34 812 100.0 70.0 25.0 4.9 TOTAL INDUSTRIES WITH MORE EMISSIONS 31 434 90.3 73.6 21.7 4.6 Agriculture, Hunting, Forestry and Fishing 26 808 77.0 80.6 15.2 4.2 Other Community, Social and Personal Services 4 626 13.3 33.1 59.5 7.4 GBR (+) TOTAL 35 302 100.0 77.3 14.5 8.3 TOTAL INDUSTRIES WITH MORE EMISSIONS 32 730 92.7 80.3 11.8 7.9 Agriculture, Hunting, Forestry and Fishing 28 958 82.0 86.5 6.3 7.2 Other Community, Social and Personal Services 3 772 10.7 32.5 54.3 13.3 Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 23 Table A.4: Industries with larger N2O footprints and types of commodities. Tons N2O, 2008. COUNTRY TOP INDUSTRIES A1 B1 C1 D1 E1 OF WITH MORE (tons) (%) (%) (%) (%) REFERENCE N2O EMISSIONS gimp ru Final Interm. Interm. CHN (-) TOTAL 12 652 100.0 51.5 37.7 10.9 TOTAL INDUSTRIES WITH MORE EMISSIONS 12 268 97.0 52.0 37.2 10.8 Agriculture, Hunting, Forestry and Fishing 9 561 75.6 56.5 33.0 10.5 Chemicals and Chemical Products 1 183 9.3 36.9 48.0 15.1 Other Community, Social and Personal Services 857 6.8 33.4 60.3 6.2 Electricity, Gas and Water Supply 668 5.3 38.4 48.2 13.4 BRA (-) TOTAL 6 326 100.0 10.7 72.5 16.8 TOTAL INDUSTRIES WITH MORE EMISSIONS 6 216 98.3 10.7 72.7 16.7 Agriculture, Hunting, Forestry and Fishing 6 216 98.3 10.7 72.7 16.7 FRA (-) TOTAL 5 742 100.0 49.1 44.7 6.2 TOTAL INDUSTRIES WITH MORE EMISSIONS 5 598 97.5 49.3 44.5 6.2 Agriculture, Hunting, Forestry and Fishing 4 888 85.1 49.9 44.2 5.9 Chemicals and Chemical Products 710 12.4 45.1 46.7 8.2 USA (-) TOTAL 4 523 100.0 45.8 39.7 14.4 TOTAL INDUSTRIES WITH MORE EMISSIONS 4 259 94.2 47.2 38.7 14.1 Agriculture, Hunting, Forestry and Fishing 3 704 81.9 47.5 38.8 13.7 Chemicals and Chemical Products 556 12.3 44.7 38.0 17.3 COUNTRY TOP INDUSTRIES A2 B2 C2 D2 E2 OF WITH MORE (tons) (%) (%) (%) (%) REFERENCE N2O EMISSIONS gexp ur Final Interm. Interm. GBR (+) TOTAL 1 828 100.0 79.9 12.2 7.9 TOTAL INDUSTRIES WITH MORE EMISSIONS 1 682 92.0 82.7 9.6 7.7 Agriculture, Hunting, Forestry and Fishing 1 564 85.5 86.5 6.3 7.2 Chemicals and Chemical Products 118 6.5 32.5 52.8 14.7 PRT (+) TOTAL 1 713 100.0 70.5 28.9 0.6 TOTAL INDUSTRIES WITH MORE EMISSIONS 1 589 92.8 69.0 30.4 0.6 Agriculture, Hunting, Forestry and Fishing 1 475 86.1 72.7 26.8 0.5 Chemicals and Chemical Products 115 6.7 42.1 56.1 1.8 24 The relevance of multi-country input-output tables in measuring emissions... Annex II. Figures Figure A.1: GHG Global emission trade balance of Spain. 2008. Thousands of tons of CO2-equivalents. Source: Based on data from WIOD (Dietzenbacher et al., 2013). Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 25 Figure A.2: Bilateral ETB of CO2(Millions of tons) in Spain, 2008, decomposed into emissions associated with imports and exports. Source: Based on data from WIOD (Dietzenbacher et al., 2013). 26 The relevance of multi-country input-output tables in measuring emissions... Figure A.3: Bilateral ETB of CH4(Thousands of tons) in Spain, 2008, decomposed into emissions associated with imports and exports. Source: Based on data from WIOD (Dietzenbacher et al., 2013). Teresa Sanz, Roc´ıo Y˜ niguez and Jos´ e Manuel Rueda-Cantuche 27 Figure A.4: Bilateral ETB of N2O (Thousands of tons) in Spain, 2008, decomposed into emissions associated with imports and exports. Source: Based on data from WIOD (Dietzenbacher et al., 2013).