Are we moving toward an energy-efficient low-carbon economy? An input-output LMDI decomposition of CO₂ emissions for Spain and the EU28

Serrano-Puente, Darío Article Are we moving toward an energy-efficient low-carbon economy? An input-output LMDI decomposition of CO₂ emissions for Spain and the EU28 SERIEs - Journal of the Spanish Economic Association Provided in Cooperation with: Spanish Economic Association Suggested Citation: Serrano-Puente, Darío (2021) : Are we moving toward an energy-efficient lowcarbon economy? An input-output LMDI decomposition of CO₂ emissions for Spain and the EU28, SERIEs - Journal of the Spanish Economic Association, ISSN 1869-4195, Springer, Heidelberg, Vol. 12, Iss. 2, pp. 151-229, https://doi.org/10.1007/s13209-020-00227-z This Version is available at: https://hdl.handle.net/10419/286533 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ SERIEs (2021) 12:151–229 https://doi.org/10.1007/s13209-020-00227-z ORIGINAL ARTICLE Are we moving toward an energy-efficient low-carbon economy? An input–output LMDI decomposition of CO2 emissions for Spain and the EU28 Darío Serrano-Puente1 Received: 3 June 2020 / Accepted: 24 December 2020 / Published online: 10 February 2021 © The Author(s) 2021 Abstract Spain is on a path toward the decarbonization of the economy. This is mainly due to structural changes in the economy, where less energy-intensive sectors are gaining more relevance, and due to a higher use of less carbon-intensive primary energy products. This decarbonization trend is in fact more accentuated than that observed in the EU28, but there is still much to be done in order to reverse the huge increases in emissions that occurred in Spain prior to the 2007 crisis. The technical energy efficiency is improving in the Spanish economy at a higher rate than in the EU28, although all these gains are offset by the losses that the country suffers due to the inefficient use of the energy equipment. There is an installed energy infrastructure (in the energyconsumer side) in the Spanish economy that is not working at its maximum rated capacity, but which has very high fixed energy costs that reduce the observed energy efficiency and puts at risk the achievement of the emissions and energy consumption targets set by the European institutions. We arrive to these findings by developing a hybrid decomposition approach called input–output logarithmic mean Divisia index (IO-LMDI) decomposition method. With this methodological approach, we can provide an allocation diagram scheme for assigning the responsibility of primary energy requirements and carbon-dioxide emissions to the end-use sectors, including both economic and non-productive sectors. In addition, we analyze more potential influencing factors than those typically examined, we proceed in a way that reconciles energy intensity and energy efficiency metrics, and we are able to distinguish between techThis article was published as the working paper n. 2104 of the Bank of Spain - Working Paper Series in January 2021. The views expressed in this paper are those of the author and do not necessarily coincide with the views of the Bank of Spain or the Eurosystem. Data sources are free for scholars and they are listed in the document. Stata replication files are available upon request. BDarío Serrano-Puente [email protected] https://sites.google.com/view/darioserranopuente/ 1Banco de España, Madrid, Spain 123 152 SERIEs (2021) 12:151–229 nical and observed end-use energy efficiency taking into account potential rebound effects and other factors. Keywords CO2emissions ·Energy efficiency ·Decomposition analysis · Input–output ·LMDI JEL Classification C67 ·O13 ·Q4 ·Q5 1 Motivation There is huge evidence and consensus that global emissions of greenhouse gases are causing global air temperatures to increase, resulting in climate change.1At a global level, the potential consequences include rising sea levels, increased frequency and intensity of floods and droughts, changes in biota and food productivity, and upstream trends in diseases.2Thus, climate change has posed a severe threat to the sustainable development of the human society, the economy, and the environment. At the particular level of the European Union (EU28, hereafter), conforming to the European Environment Agency (2015), more than 80% of the total greenhouse gas emissions are encountered to be a consequence of energy production and energy consumption by the end-use sectors (agriculture, industry, commercial and public services, households, and transport).3These energy-related greenhouse gas emissions are mainly compounded by carbon dioxide (CO2) emissions, an essential environmental pollutant that has greatly contributed to global climate change, as shown by Ozturk and Acaravci (2010).4Despite not being the world’s largest emitter of energy-related CO2, the EU28 contributes to the mentioned global emissions by 10%, which indicates that it has a non-insignificant role in the global warming trends.5 Hence, while efforts to mitigate the adverse effects of climate change are partly focused on limiting the emissions of all greenhouse gases, particular attention is being also paid to energy production and consumption due to its crucial importance for the evolution of the energy-related CO2emissions. There is a clear interrelationship between energy consumption, the share of low-carbon energy sources in such consumption, energy efficiency, and greenhouse gas emissions. Therefore, the energy and climate targets set by supranational bodies and national authorities approach all these elements. For instance, at an United Nations conference in August 2007, it was agreed 1Greenhouse gas emissions are those covered by the Kyoto Protocol and include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and three fluorinated gases, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6). 2See the report published by the Intergovenmental Panel on Climate Change (2007) for a more detailed description of the causes of climate change and its adverse effects. 3Emissions coming from energy consumption by international maritime bunkers and international aviation are usually not included in national total emissions. 4In 2017, according to the Air Emission Accounts published by Eurostat (2020a), more than 95% of the European energy-related greenhouse gas emissions were anthropogenic emissions of CO2. 5According to Our World in Data (2020), China alone is responsible for 29% of the total energy-related CO2emissions, United States for 15%, and Asia and Pacific Ocean for 14%. 123 SERIEs (2021) 12:151–229 153 that an emission reduction in the range of 25–40% with respect to 1990 levels is necessary to avoid the most catastrophic forecasts. More recently, “doubling the global rate of improvement in energy efficiency” or “increasing substantially the share of renewable energy in the global energy-mix” were set as key objectives by the United Nations (2015) in their “2030 Agenda for Sustainable Development”. Turning again to the European sphere, together with the well-known targets established by the European Comission (2012-10-25, later modified in 2013) in its Europe 2020 Strategy or Horizon 2020 (H2020, hereafter), the European Union authorities have defined an even more ambitious climate scenario that is amongst their main priorities. For 2030, (1) greenhouse gas emissions must be reduced by 40% with respect to 1990 levels (20% for H2020), (2) primary energy use must experience a 32.5% reduction to be achieved by improving energy efficiency (20% for H2020), and (3) a share of 32% in the final energy-mix in favor of renewable energies must be reached (20% for H2020). Furthermore, the European Comission (2019-10-31) declared in a report to the European Parliament and the Council that the objective is to achieve climate neutrality by 2050, i.e., net-zero greenhouse gas emissions in 2050. This translates into a plan to decarbonize the European economy by 80–95% with respect to the emission levels of 1990, accompanying this with a strong reduction of energy consumption, which points out again the relevance of making progress toward energy efficiency. Within those forming the EU28, Spain is another country that, due to its geographical location and socioeconomic characteristics, is also vulnerable to climate change, as shown by the Ministerio de Medio Ambiente (2005). Conjointly with the rest of the EU28 member states, Spain faces strong commitments derived from the ambitious European climate targets for 2020 and 2030. Each member state can set its own targets as long as they match those defined at European level. In this sense, according to the Ministerio de Turismo, Energía y Agenda Digital (2017) and the Ministerio de para la Transición Ecológica (2017), the targets fixed by the Spanish authorities would entail (1) achieving a 42% share of renewable energies in the final energy use for 2030 (20% for 2020),6(2) improving the country’s energy efficiency by 39.5% for 2030 (20% for 2020), and (3) reducing greenhouse gas emissions by 23% with respect to 1990 levels for 2030 (10% with respect to 2005 levels for 2020).7 Aiming to comply with the targets set by the European Union as well as by the national authorities, both Spain and the EU28 as a whole adopted different policies and measures. An overview of these policy trends is recovered from the ODYSSEE database published by ODYSSEE-MURE (2020b). Some of these measures are (1) the promotion of renewable energy (including electricity from renewable sources), (2) the creation of the EU emissions trading scheme (a market for carbon dioxide allowances to ensure that emissions reductions can be made where it is most economically efficient), (3) the development of combined heat and power, (4) the improvement in the energy efficiency performance of buildings, (5) the stimulus to use alternative fuels in 6For the case of electricity generation, the percentage of renewable energies in 2030 must be 74%. 7The Spanish emission target translates into a reduction of 38% with respect to the 2017 levels for 2030. 123 154 SERIEs (2021) 12:151–229 A: Greenhouse Gas Emissions 40 60 80 100 120 % of Target Compliance 2005 2007 2009 2011 2013 2015 2017 Panel B: Primary Energy Consumption 40 60 80 100 120 2005 2007 2009 2011 2013 2015 2017 Spain EU28 Panel C: Share of Renewable Sources 40 60 80 100 120 2005 2007 2009 2011 2013 2015 2017 Fig. 1 Compliance with H2020 Targets. Note: Levels above 100 indicate target compliance transport (in particular biofuels), (6) the reduction of the average CO2emissions of new passenger cars, and (7) the taxation of certain energy products and electricity.8 Following the implementation of these measures, mainly after the 2007 crisis, it can be noted that both the EU28 and Spain were progressively moving toward meeting the H2020 targets in recent years. This is shown in Fig. 1. Further, in Fig. 2we observe that Spain has done a great effort in reducing emission levels since 2005. However, this positive evolution cannot compensate the huge increase of emissions occurred from 1995 to 2005, which still leaves Spain in 2017 with higher emission levels than those observed in 1995. On the contrary, the EU28 has experienced a long-term downward trend, but at a lower decreasing growth rate than the last years of the Spanish trend. Considering the year 2017, the last year of analysis in this study, we recognize how greenhouse gas emissions (in Panel A of Fig. 1) are the only magnitude that meets its European target H2020 both in Spain and in the EU28. The other two H2020 targets (the share of renewables in the final energy-mix and the use of primary energy) are not met, either in Spain or in the EU28 (in Panels B and C of Fig. 1, respectively). We can only notice how the reduction target for primary energy use was fulfilled in Spain during the years 2013 to 2015, but in the last two years the magnitude is again not complying with the H2020 target. Consequently, although the reduction of greenhouse gas emissions is on a positive trend that leads Spain (in 2017) to accomplish the European target H2020 for such magnitude, both the EU28 and Spain have to continue making efforts to fulfill the rest of the 2020 targets. Furthermore, Spain should be careful with the last developments of CO2emissions, which experienced a slight increasing trend that could lead to a deviation form the target compliance. In addition, both regions must continue working vigorously in a direction that permits them to later satisfy the 2030 targets, which are even more ambitious than those for 2020, as we have seen above. Besides, according to some analyses published by the World Bank and ClimateWorks Foundation (2014), this line of work to control the emissions can offer opportunities for the economic performance of the country, generate new jobs, benefit agriculture, and boost the development of better technologies for the supply of energy. Obviously, one of the major areas to be addressed in order to effectively control emissions is the efficient use of energy. Improving energy efficiency seems very handy to offer a win-win situation, as it decreases energy costs, energy use, and at the same 8See the report published by the Directorate-General for Climate Action (European Commission) et al. (2016) for a detailed description of the main legislation developments on energy and climate issues. 123 SERIEs (2021) 12:151–229 155 80 100 120 140 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Spain EU28 Fig. 2 Energy-related CO2emissions. Note: This figure is depicted using the estimation approach presented inthisdocument.Theenergy-relatedCO2emissionsshownarethoseassociatedtofinalenergyconsumption. This final energy consumption has been climate-adjusted in order to abstract from potential weather effects, which results in a magnitude that is comparable across regions time, negative impacts related to such energy use, like CO2emissions. Further, using less energy for a certain task gives better possibilities to use energy sources with a predictable price development, which in practice means domestic energy sources, especially in countries that heavily depend on energy imports, like Spain. These arguments clearly highlight the need to implement measures in this regard. However, not all the increase in energy efficiency is translated into energy savings. Some energy equipment could experience an efficiency increase, but if this equipment is not utilized at its maximum rated capacity, sometimes the efficiency improvement is not translated into energy savings. Moreover, technological or efficiency improvements generate cost savings, but these savings could be devoted to new energy consumption and investment, which also requires more energy services, which could consequently increase energy-related emissions. Both pathways generate more activity and may reduce, and even eliminate, the environmentally positive effects of the improvements. This is the so-called rebound effect. Indeed, this effect may be large enough to exceed the maximum expected energy savings from technological or efficiency improvements. Hence, for a better understanding of the impacts of efficiency improvements on our process of energy-use reduction, rebound effects must be incorporated to our analyses. Therefore, care about these rebound effects needs to be taken by policy-makers when calculating the energy saving potential of different measures oriented to improve energy efficiency. Freire-González and Puig-Ventosa (2015) argue that for energy-efficiency-improving policies to be effective, they must be accompanied by other measures such as an effective communication and awareness of the citizens, regulatory instruments and/or an appropriate taxation. An effective combination of traditional efficiency measures with new policies oriented to tackle the rebound effect would maximize the effectiveness of the policy objective of reducing energy consumption. For Vivanco et al. (2016), it is crucial to establish economic instruments for the energy efficiency measures to be completely effective and deal with rebound 123 156 SERIEs (2021) 12:151–229 effect problems. These authors suggest that economy-wide cap-and-trade systems as well as energy and carbon taxes, when designed appropriately, emerge as the most effective policies in setting a ceiling for emissions and addressing energy use across the economy. In addition, these rebound effects vary across end-use sectors. In this sense, Medina et al. (2016) intends to identify the Spanish economic sectors where investment from energy-efficiency-improving measures should be allocated in order to reach the targeted energy efficiency levels in the overall economic system. Besides, only if these energy-efficiency-improving measures are always pursued alongside the decarbonization of the energy system, the carbon-reducing potential of such measures can be guaranteed, as suggested by Malpede and Verdolini (2016). However, these efforts to develop an adequate energy efficiency policy and to promote the use of a lower-carbon energy-mix should not damage the domestic competitiveness of the economy. The relationship between economic growth, energy consumption and CO2emissions is an essential issue that we face in the 21st century, and it is of far-reaching concerntoscholars worldwide.Toinvestigatethismatter,severalmethodologies have been traditionally applied. Zhang et al. (2018) list some of the main ones: the Kuznets curve theory, the Granger causality analysis and co-integration tests, the vector auto-regressive models used to analyze the long-term dynamics, and the decoupling models. The latter approach is followed by Fernández-González et al. (2014), who show that there is a usually a coupling process between energy consumption and economic growth in advanced economies. Therefore, in these economies is more difficult to reduce energy consumption and alternative efforts should be made in order to achieve the decarbonization of the economy, as suggested by Román-Collado and Colinet (2018). Nevertheless, we must bear in mind that the above-mentioned measures to promote efficiency do not explain or influence by themselves alone the evolution of the energy-related CO2emissions. There may be many potential factors underlying the progression observed both in Spain and in the EU28 and their convergence to the established targets, irrespective of the impact of the energy efficiency policies and measures, as suggested by Economidou and Román-Collado (2019). Some of these factors could be the economic activity level, the efficiency of the conversion sector, the demography, lifestyle changes, the weather, etc. For example, the 2007 crisis could have a profound impact on the industrial sectors and services which in turn could affect energy consumption and consequently energy-related CO2emissions. Another example includes weather fluctuations, which could affect the heating and air cooling demand provoking that, in a particular warm year, energy consumption may simply drop due to lower heating demand in the residential and services sectors. Therefore, in order to support the most appropriate energy policy decisions, an integrated analytical method to understand the driving forces behind the observed developments of energy-related CO2emissions, energy consumption and energy efficiency (thethree main energy and climate targets previously presented) is irremediably needed. It is precisely here where our work enhances the available related literature, since we develop a methodological framework to investigate the contributions of various influencing factors to the evolution of the energy-related CO2emissions between 1995 and 2017 both in Spain and in the EU28. With our proposed method, in addition to many macro and efficiency influencing factors discussed before, we are able to cap123 SERIEs (2021) 12:151–229 157 ture the role that the primary energy consumption and the share of renewable sources in the energy-mix play in the developments of the energy-related CO2emissions. This implies that all magnitudes for which the main energy and climate targets are defined and their interrelationships can be monitored within one comprehensive methodological framework. Our period of analysis, 1995–2017, is determined by the availability of data. We should mention that for the findings about the changes that occurred between 1995 and 2017 to be representative of what certainly happened, we must identify two clearly distinct sub-periods, as shown in Fig. 2. These sub-periods are delimited by the year 2007, since it marks the end of a economic expansion period and the beginning of a deep recession followed by a posterior recovery. In this way, we first analyze the 1995-2007 sub-period, and subsequently the 2007–2017 sub-period, both for the EU28 and for Spain. The results that we present give interesting information related to the drivers and inhibitors of the energy-related CO2emissions in both the Spanish economy and the European economy as a whole. These results are useful not only for researchers, but also for private utility companies and policy-makers, as they can contribute to construct and implement the optimal saving and efficiency measures to achieve the mentioned climate and energy targets. In fact, this paper speaks directly to Spanish and European authorities in the field of energy and climate. The remainder of the document is organized as follows. Section 2sheds light on the relevance of our analysis by reviewing the existing literature. Section 3presents the methodology and the databases utilized in our work. Section 4reports the results. And finally, Sect. 5concludes. 2 Conceptual and empirical framework In this Section, we revise the existing literature and remark the contributions of our work. We first introduce the rationale behind our hybrid approach in Sect. 2.1. Second, we propose an allocation diagram scheme for assigning the responsibility of primary energy requirements and CO2emissions to end-use sectors in Sect. 2.2.Third,we present the selected influencing factors to be analyzed in Sect. 2.3. Fourth, we discuss about the differences between energy intensity and energy efficiency metrics in Sect. 2.4. Fifth, we propose and describe a method to distinguish between technical and apparent end-use energy efficiency in Sect. 2.5. Finally, we overview the main contributions of this work in Sect. 2.6. 2.1 Hybrid approach mixing SDA and IDA There are several methodologies to assess the developments of certain energy or environmental magnitudes like emissions. Among others, in a very enriching survey work by Wang et al. (2017), we find methods based on econometric models, system dynamics approaches, computable general equilibrium (CGE) models, and decomposition analyses. Our work focuses on the latter, and more precisely, on two different methods: the structural decomposition analysis (SDA, hereafter) and the index decomposition 123 158 SERIEs (2021) 12:151–229 analysis (IDA, hereafter). In recent times, many researchers are using SDA and IDA techniques as tools for analyzing energy or environmental trends. Both decomposition techniques have been compared in many survey papers, e.g., Su and Ang (2012), Hoekstra and van den Bergh (2003), and Wang et al. (2017). The comparison encounters that the IDA approach is more flexible in its formulation and has a relatively lower data requirement than the SDA approach. However, the IDA method only provides information about the direct effects, ignoring the indirect and final demand effects, as shown by Zeng et al. (2014). On the other hand, the SDA, a framework based on the development of input–output models/tables, provides a wider range of information regarding technical concerns, including final demand effects, and more detailed explanation of the structural factors, such as the Leontief effect (or technical effect), as argued by Cansino et al. (2016) and Xie (2014). Further, the SDA method can shape socioeconomic drivers from both production (or supply) and final demand (or end-use) perspectives. When it particularly comes to the IDA method, we find several decomposition techniques that are documented extensively in a survey paper by Ang and Zhang (2004). Among others, we find the Laspeyres decomposition method and the Divisia index decomposition method. The latter contains the logarithmic-mean Divisia index (LMDI, hereafter) and the arithmetic mean Divisia index (AMDI), both in the additive and multiplicative formulations (leading to redundant results). As suggested by Ang (2015), the logarithmic-mean Divisia index in its additive formulation is the most recommended IDA approach due to its theoretical foundation, robustness, adaptability, ease of use, and result interpretation. It provides a perfect decomposition (i.e., the results do not contain any residual term), permits the investigation of more than two factors, provides a simple and direct association between the additive and the multiplicative decomposition form, and is consistent-inaggregation (i.e., the estimates of an effect at the subgroup level can be aggregated to give the corresponding effect at the group level). Through these techniques, many research works attempt to identify quantitatively the contributions of many influencing factors to the evolution of some energy or environmental aspects. For example, an increasing proportion of the thermal power in the end-use sectors will increase the energy-related CO2emissions, while increasing end-use energy efficiency will reduce them. These driving forces can be analyzed within this type of methodologies, which have been widely used in the literature. Focusing on the performance assessment, we can classify these research works into three different types. The first type deals with assessments over time in a specific country, i.e., single-country temporal analysis. This category accounts for most of the developed studies in the literature. The second type gathers studies that analyze the performance of more than one country. A temporal analysis like the one in the first type is here applied independently for several countries or regions in a way that the results can be compared between countries, i.e., multi-country temporal analysis. The third type of studies focuses on comparative analyses between countries using the data of a specific year, i.e., single-year spatial or cross-country analysis. The first type of studies comprises the conventional IDA and SDA studies applied to one single country or region, where no further elaboration is required. When it particularly comes to applying SDA techniques for the Spanish case, we find different works.Forinstance,ButnarandLlop(2007)investigatethecompositionofgreenhouse 123 SERIEs (2021) 12:151–229 165 Fig. 3 Decomposition of apparent end-use energy efficiency efficiencyimprovementhappened.This decrease in thecost oftheenergy service could provoke increases in energy consumption that can occur through a price-reduction or other behavioral responses. In this way, the observed energy efficiency may not reflect actual changes in the technical energy efficiency. This is one of the main reasons why it is unavoidable to separate the technical efficiency from the observed (or apparent) energy efficiency. Mathematically,as shownin Eq. 1, wedefinethe rebound effect(RE) as the fraction of the potential energy savings (PES) that is not translated into actual energy savings (AES). RE := 1−AES PES (1) The PES are given by the evolution of the technical energy efficiency, which is typicallyestimatedbyengineeringmodels thatassumenoeconomicresponsestoimproved energy efficiency and non-reversible improvements. The AES are usually depicted by observed changes in the apparent energy efficiency once we have controlled for potential rebound effects and other factors like the infra-utilization of the energy equipment installed. This formula could seem very simple and handy. However, the priceor cost-induced rebound effect is a very complex element. It is the umbrella term for a variety of economic mechanisms that comprises every reaction of the agents when they face an effective price reduction. Every potential reaction can be identified as a different type of rebound effect. Hence, the identification of every type of rebound effect is a very complicated process that depends on many aspects. Here, as shown in Fig. 3and explained as follows, we provide a classification of the different types of 123 166 SERIEs (2021) 12:151–229 price-induced rebound effect following the influential works of Greening et al. (2000), Sorrell (2007), Azevedo (2014) and Freire-González et al. (2017). 1. Direct rebound effect It was first defined by Khazzoom (1980) as the increase in the demand of an energy service caused by improvements in the efficiency of that particular energy service. It encompasses (1) pure substitution effects derived from theincentivetousemoreenergyinputoftheenergywhoseeffectivepricehasfallen. This effect is typically given by the own-price elasticity of demand for a particular energy service. The direct rebound effect also covers potential (2) income effects. Thecostreductionderivedfromthetechnicalefficiencyimprovementmayincrease real incomes, which will positively impact on consumption of all commodities, including that of the energy product whose effective price has fallen as a result of the technical efficiency improvement. 2. Indirect rebound effect It is usually defined as the increase in the demand for other goods and services that also require energy for their production and distribution and that are affected by the reduction in the effective cost of the energy service considered and the associated increase in disposable income. This indirect rebound effect can originate from a number of sources. As it can be observed in Fig. 3,it covers: (1) output effects (producers may use the cost savings from energy efficiency improvements to increase output, increasing consumption of capital, labor and materials, which themselves require additional energy to provide); (2) substitution effects (given by the cross-price elasticities of demand for non-energy services); (3) income effects (increased real incomes will impact on consumption of all commodities, which will indirectly enhance an increase in the energy consumption); (4) compositional effects (relatively energy-intensive products benefit more from the fall in the effective energy prices); (5) competitiveness effects (the fall in supply prices of commodities that use energy as an input for production could stimulate their demand, increasing energy needs); and (6) embodied energy, (energy needed to implement the technical efficiency measure that leads to the technical change). 3. Economy-wide rebound effect It accounts for every increase in the demand of energy services causedbya higher economic growth andconsumptionat a macroeconomiclevelasaconsequenceofatechnicalefficiencyimprovementoftheenergy service considered. It comprises all sub types of rebound effects. The economywide rebound effect takes into account not only direct and indirect rebound effects, but also general equilibrium rebound effects. The latter effects account for the adjustments of prices and quantities of goods and services on the whole economy afteran energyefficiencyimprovement.As the technicalefficiencyimproves, there will be a reduction in the price of the energy services, which in turn will lead to a new overall equilibrium of supply and demand for all goods and services in the economy. There is a variety of interpretations of the rebound effect depending on the magnitude and sign of the effect. (i) For values below zero, we encounter negative rebound effects or super-conservation effects. It means that the technical energy efficiency improvement is over realized, i.e., the energy consumption declines in a greater proportion than the extent to what the technical energy efficiency improves. (ii) When 123 SERIEs (2021) 12:151–229 167 the value of the rebound effect is zero, we can say that the technical energy efficiency improvement is fully realized, i.e., the energy consumption drops in the same proportion than the extent to what the technical energy efficiency improves. (iii) We find partial rebound effects for values between zero and one hundred. In this case, the technical energy efficiency improvement is partially offset by an increased demand for energy. Finally, (iv) for values of the rebound effect greater than one hundred, we encounter the so-called backfire effect. In this particular case, the technical energy efficiency improvement is outweighed by an increased demand for energy, i.e., the energy consumption increases in a greater proportion than the extent to what the technical energy efficiency improves. There is an open discussion regarding the actual magnitude of the rebound effect. FortheconcretecaseoftheSpanisheconomy,severalresearchstudiesestimatingdirect rebound effects exist. Using panel data from the period 1991–2003, Freire-González (2010) estimates the magnitude of direct rebound effect for all energy services using electricity in households of Catalonia (Spain) using econometric techniques. He finds an estimated direct rebound effect of 35% in the short term and 49% in the long term. Gálvezetal.(2014)estimatethedirectreboundeffectintheresidentialsectorforSpain. They analyze electricityand natural gas direct rebound effects using data on residential heating and domestic hot water consumption in 2012 and encounter direct rebound effects of 70–80% for electricity and of more than 100% for natural gas. Finally, in the most recent work addressing this topic, Bordon Lesme et al. (2020) estimate shortand long-run direct rebound effects with data on households’ electricity consumption in Spain. Using a two-step Error Correction Model through GMM estimation, they find direct rebound effects between 26 and 35% in the short-run and around 36% in the long-run. After reviewing the literature on rebound effects for Spain one can note how the empirical works do not offer a consensus about the magnitude of the direct rebound effect. Moreover, these studies focus exclusively on the residential sector of the economy and on certain specific energy products. However, for our analysis, we would need to learn what the total rebound effect of the economy is, for every sector (as a whole and separately for each of them) and for every energy product. That is why it is undoubtedly necessary to study what the economy-wide rebound effect is. In this way, we will be able to quantify the rebound effect of the total economy, which will capture the influences not only of direct and indirect rebound effects, but also of general equilibrium rebound effects, as shown in Fig. 3. In other words, we will move the core of this discussion toward the magnitude of the economy-wide rebound effect. Sorrell and Dimitropoulos (2008) state that the economy-wide rebound effect from energy efficiency improvements may be expected to be larger than the direct rebound effect. However, the mechanisms involved are complex, interdependent, and difficult to conceptualize, and the magnitude of this effect is extremely difficult to estimate empirically. While both direct and indirect rebound effects are microeconomic and can be tested empirically, the magnitude of the economy-wide rebound effect should be estimated by the use of Computable General Equilibrium (CGE) models or macroeconometricmodels.Thesemodelscarefullycapturethe dynamics of a entire economy and, as a consequence, calibrating such models to replicate current conditions and running them under alternate conditions is a daunting task. As pointed out by Azevedo 123 168 SERIEs (2021) 12:151–229 (2014), these theoretical frameworks rely on assumptions about price, income, substitution elasticities, cost-minimizing behavior from producers, utility-maximizing behavior from consumers. But once these setup conditions for building the theoretical framework are defined, one could perform an analysis of the economy-wide rebound effects which microeconomic or bottom-up analyses may be inappropriate to handle with. Colmenares Montero et al. (2019) review the state-of-the-art of energy and climate modeling vis-a-vis the rebound literature and they find that worldwide research works report, on average, economy-wide rebound effects around 58%. When we look at the European countries, we encounter the work by Malpede and Verdolini (2016). They estimate the economy-wide rebound effect for 5 major European economies (Germany, France, Italy, the UK and Spain) over the years 1995-2009 and show a range of estimates of 50–60%. Other work reviewing economy-wide rebound effects for a number of countries is Adetutu et al. (2016). They use a combined stochastic frontier analysis (SFA) and two-stage dynamic panel data approach to explore the magnitude of the economy-wide rebound effect for 55 countries over the period 1980 to 2010. They find economy-wide rebound effects of 50–60% for both Spain and Europe. Finally, placing the focus on the Spanish sphere, we find three important papers that calculate the economy-wide rebound effect. Guerra and Sancho (2010) build a CGE model and show that the use of engineering savings instead of general equilibrium potential savings downward biases economy-wide rebound effects and upward-biases backfire effects. Duarte et al. (2018) also construct a dynamic CGE model, but only covering the residential sector, and estimate economy-wide rebound effects for Spain of the order of 50–70%. Finally, Peña-Vidondo et al. (2012) present a static CGE model describing an open economy disaggregated into 27 production sectors, with 27 consumer goods, a representative consumer, the public sector and a simplified rest of the world and accounting for every group of energy products. This model also has the particular feature of including unemployment in labor markets, given the high level of unemployment in the Spanish economy. With this very complex and complete model, they estimate economy-wide rebound effects in Spain of 60–70%. One can see how there is a greater consensus on estimates of the economy-wide rebound effect. In this sense, in our work we will use these estimates from the literature to identify the economy-wide rebound effect in Spain and in Europe and thus be able to decompose the effect of technical energy efficiency on the observed evolution of the apparent energy efficiency of the end-use sectors. 2.5.2 Other factors: infra-utilization Apart from changes in technical efficiency and their possible rebound effects, an observed increase in the unit energy consumption (or decrease in apparent energy efficiency) may be due to other factors. As shown in Fig. 3, the apparent end-use energy efficiency is influenced by other factors that are calculated as a residual from differences between the evolution of the apparent efficiency and the evolution of the technical efficiency and its potential rebound effects. 123 SERIEs (2021) 12:151–229 169 Among this other-factors category, we find that decreases of the apparent energy efficiency may be due to an inefficient use of the equipment, as it is often observed during economic recessions. This is particularly true in industry or freight transport. For instance, as documented by ODYSSEE-MURE (2020a), in a period of recession, the energy consumption of the industry does not decrease proportionally to the activity as the efficiency of most equipment drops, as they are not used at their maximum rated capacity. It means that part of its energy consumption is independent of the production level. This infra-utilization is also well documented by the Ministerio de Turismo, Energía y Agenda Digital (2017). In that case, the technical energy efficiency does not decrease as such, as the equipment is still the same, but it is used less efficiently. This is another of the main reasons why it is unavoidable to separate the technical efficiency from the observed (or apparent) energy efficiency. 2.6 Overview of the main contributions of this study In sum, we are convinced that the present work, which (i) Mixes features and benefits from both IDA and SDA decomposition techniques, (ii) Provides an allocation diagram scheme for assigning the responsibility of primary energy requirements and CO2emissions to the end-use sectors including both economic and non-productive sectors, (iii) Analyzes more potential influencing factors than those typically examined, (iv) Proceeds in a way that reconciles energy intensity and energy efficiency metrics, (v) And distinguishes between technical and observed end-use energy efficiency taking into account potential rebound effects and other factors represents a novelty and offers clear value added to past studies devoted to the study of the energy-related CO2emissions trends both in Spain and in the EU28. In addition, to the best of our knowledge, there is no previous study for Spain and the EU28 that uses such recent and disaggregated data. 3 Methodology and data In this Section, we first introduce the primary energy conversion factor (KPEQ)in Sect. 3.1 and the primary carbon dioxide emission factor (KC) in Sect. 3.2. Then, these key parameters are adopted to develop an LMDI decomposition method suitable for analyzingallinfluencingfactorsdrivingtheevolutionofenergy-relatedCO2emissions inSect. 3.3. Finally, afurtherdecompositionfor theapparentend-useenergyefficiency is presented in 3.4. All data used for these calculations are briefly introduced in the course of this Section. 3.1 Primary energy conversion factor Any estimation of primary energy must first establish factors for conversion between energy magnitudes. Here, the primary energy quantity conversion factor (KPEQ), which was suggested by many authors in previous studies, is the key parameter for 123 170 SERIEs (2021) 12:151–229 establishing the connection between final energy consumption and primary energy consumption.12 KPEQ is defined as the total number of units of primary energy that must be consumed to produce one unit of final energy. There are several methods to calculate this primary energy quantity conversion factor. The European Comission (2016) conducted a survey about some of the methodologies available, applying them to the specific case of electricity, but valid for other types of energy. The purpose of the strategyistobeabletoexpressfinalenergyconsumptioninbothstandardquantity(SQ) form and primary energy quantity (PEQ) form. The SQ form denotes the heat value of final energy consumed by the end-use sectors while the PEQ form denotes the total primary energy consumed to produce such final energy by compensating all energy losses upstream. However, the compensating process for energy losses upstream is complex and involves many interacting conversion sub-sectors. Thus, we follow an input–output method in the spirit of the theoretical framework used by Alcántara and Roca (1995) and Ma et al. (2018) to acquire the KPEQ of each energy product. The input–output method has been widely applied to reveal internal relationships among the economic sectors. The development of an input–output table can reflect the balance of material or capital flows among all sectors while the Leontief inverse matrix of the table can establish the connection between the end-use consumption and the total consumption (which includes both intermediate and end-use consumption) of the flows. Therefore, using the input–output method, we can here construct an energy input–output table of energy sectors to establish the connection between final energy consumption and primary energy consumption by using the Leontief inverse matrix. 3.1.1 Establishment of the energy input–output table To estimate the primary energy required for final energy consumption, a first approximation (an underestimation, as it will be discussed below) is based on the existing interrelationships in the Spanish energy sector so that each final energy consumption (primary or secondary) corresponds to a primary energy vector containing all primary energy sources that must be consumed to make such final consumption available.13 For this purpose, making use of the Complete Energy Balances of the European countries published by Eurostat (2020c), which provide detailed data on energy supply, energy conversion, and final energy consumption, we can modify such energy balance table into an energy input–output table as shown in Table 1(all table entries are expressed in SQ form). The complete energy balance involves 63 energy products (the complete list of products can be shown in Table 13 of Appendix). These energy sources can be either primary or secondary and can be consumed either (1) directly by the end-use sectors to cover their energy needs (final demand of energy i,Yi) or (2) by the conversion 12 See Chong et al. (2015a) and Ma et al. (2018) for an application of this concept to China, Chong et al. (2015b) for an application to Malasya, and Alcántara and Roca (1995) for an application to the Spanish case. 13 We understand as primary energies those directly extracted from the nature and as secondary energies those coming from the transformation of primary (and also secondary) energies. 123 SERIEs (2021) 12:151–229 171 sector to produce final energy that will be later consumed by the end-use sectors (this refers to the intermediate consumption part, where Qi,jis the quantity of energy i consumed to produce energy jin the transformation sector). However, we should also take into account that many secondary energy products (oil derivatives and electricity, among others) could be directly imported from abroad. In our analysis, we consider that an imported energy unit is offset by an exported unit, so we only focus on what the net balance is (the difference between imports and exports).14 When there is a positive net import balance in one secondary energy product, we must obviously consider that this entry of energy means a greater availability of primary energy.15 To do this, we use the methodology proposed by Alcántara and Roca (1995) and we treat these positive net import balances of secondary products as a primary energy source valued for its energy content. In other words, in addition to the 63 energy types, we must augment our input–output table to incorporate the positive net import balances of secondary products. It means that we would have as many new primary energy sources (denoted by Ns) as secondary products with a positive net import balance.16 We should note that the final demand of those positive net import balances of secondary products is 0, i.e., Yi=0, for i={63 +1,...,63 +Ns}, since such positive net import balances would just enter the input–output table in the intermediate consumption part. For example, if electricity were the energy product 1, the positive net import balance of electricity, say it would be the energy product 63+3, would just appear as an input for production of electricity. It means that Q63+3,1would report such positive net import balance quantity and that the row would be filled with zeros elsewhere. The final demand of energy iis denoted by Yi. This quantity includes several elements according to the Sankey Diagrams for Energy Balances developed by Eurostat (2020l). It results from the sum of (1) final energy consumption of energy i(including also final energy iconsumption of the energy branch, i.e., energy iconsumed to operate installations for energy production and transformation), (2) final non-energy consumption of energy i(for instance, oil used as timber preservative), (3) distribution and transmission losses of energy i(energy losses due to transport or distribution of electricity, heat, gas, as well as pipeline losses), (4) energy iconsumed by international maritime bunkers (fuel consumption of ships during international navigation), (5) energy iconsumed by international aviation (fuels delivered to aircrafts for international aviation), and (6) positive net export balances of energy i(when the quantity 14 Nevertheless, while electricity can be considered a homogeneous product (and even in this case an electricity Kw/h generated at one point of time is not the same as a Kw/h generated at another moment), oil derivatives are very heterogeneous products. This could explain the strong import and export balances that the oil derivatives experience. 15 Note that when there is a positive net export balance in a secondary product, the problem mentioned above does not appear and in this case we do not need to consider it since it would not mean a greater availability of primary energy. 16 Another approach would be to estimate how much primary energy is needed to obtain these energies in the countries of origin or estimate it assuming that the technology in other countries is the same as in Spain, but due to a non-easy access to this information and because the differences between the use of this method and the use of the previous one are irrelevant, as shown by Roca et al. (2007), we perform here the first presented alternative. 123 172 SERIEs (2021) 12:151–229 Table 1 Energy input–output table 123…j…63 63+163+263+3…63+NsYQ 1Q1,1Q1,2Q1,3…Q1,j…Q1,63 000…0 Y1Q1 2Q2,1Q2,2Q2,3…Q2,j…Q2,63 000…0 Y2Q2 3Q3,1Q3,2Q3,3…Q3,j…Q3,63 000…0 Y3Q3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iQ i,1Qi,2Qi,3…Qi,j…Qi,63 000…0 YiQi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Q63,1Q63,2Q63,3…Q63,j…Q63,63 000…0 Y63 Q63 63 +1Q63+1,1Q63+1,2Q63+1,3…Q63+1,j…Q63+1,63 000…0 0Q63+1 63 +2Q63+2,1Q63+2,2Q63+2,3…Q63+2,j…Q63+2,63 000…0 0Q63+2 63 +3Q63+3,1Q63+3,2Q63+3,3…Q63+3,j…Q63+3,63 000…0 0Q63+3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 +NsQ63+Ns,1Q63+Ns,2Q63+Ns,3…Q63+Ns,j…Q63+Ns,63 000…0 0Q63+Ns 123 SERIEs (2021) 12:151–229 173 of energy iproduced or transformed in the territory which is sent abroad is larger than the quantity of energy icoming from outside the territory). The energy balances report 27 different types of energy transformation or conversion processes in the transformation sector section (see Table 17 of Appendix for a detailed description of all of them). These processes involve all activities where one energy commodity (either primary or secondary) is transformed into a secondary energy commodity (e.g., natural gas transformed into electricity in a power plant). For these 27 types of energy transformation processes, Eurostat (2020c) reports the energy inputs that they require to produce the energy transformation output. Therefore, the transformation inputs reported in the balances would be the quantities that would fill the intermediate demand part of our input–output table (elements Qi,j,for i,j={1,...,63}). However, there is a limitation coming from many of these transformation processes resulting in more than one energy output.17 Thus, within a unique transformation process, we could not identify exactly which part of the transformation input is dedicated to produce which energy output. To overcome this issue, we assume that the inputs of each transformation process are distributed proportionally to each energy output in case that the transformation process results in more than one energy output. For example, if a transformation process X results in an output of 2 units of energy A, 2 units of energy B and 1 unit of energy C, the inputs of the transformation process X would be assigned in the following way: 40% to produce energy A, 40% to produce energy B, and 20% to produce energy C. In this way, we manage to allocate an intermediate energy demand to each type of energy output, which would allow us to fully identify our input–output table in the intermediate demand part. Finally, Qidenotes the total output of energy i, i.e., the total energy ineeds. It can be calculated from two perspectives. From the demand side, the total energy needs result from the sum of the intermediate consumption and the final demand. This mathematical relationship is expressed in Eq. (2) for the case of energy i. Further, Eq. (3) shows the matrix form containing all energy products. 63+Ns  j Qi,j+Yi=Qi(2) ID+Y=Q,(3) where Qi,jis the i,j-element of the matrix of intermediate demand, ID,Qis the column vector of total output, and Yis the column vector of final demand. On the other hand, from the supply side, Qiresults from the sum of (1) the primary production of energy i(extraction from natural sources into a usable form), (2) the quantity of energy irecovered or recycled (e.g., the supply of renewable energy commodities produced in other fuel balances or certain petroleum products which are reprocessed and recycled), (3) the stock changes of energy i(difference between the opening stock level and closing stock level for stocks held on national territory), (4) the transformation output of energy i(quantity of energy obtained as a result of all 17 For instance, while the charcoal production plants produce charcoal as the only energy output, the refineries produce more than 20 energy outputs (e.g., ethane, fuel oil, gasoline, petroleum coke, among others). 123 174 SERIEs (2021) 12:151–229 transformation processes), and (5) the positive net import balance of energy i(when the energy quantities produced or transformed in the territory which are sent abroad are smallerthantheenergyquantitiescomingfromoutsidetheterritory).Bothcalculations lead to the same quantity of energy needs, Qi.18 3.1.2 Leontief inverse matrix of energy input–output table We define the direct consumption efficiency (or transformation coefficient) ai,jas the energy iconsumed to produce one unit of energy j, which is shown in Eq. (4). ai,j=Qi,j Qj (4) Hence, Eq. (3) can be further expressed as Eq. (5). AQ +Y=Q,(5) where ai,jis the i,j-element of the matrix A. Further, Eq. (5) can be rewritten as Eq. (6), where (I−A)−1is the Leontief inverse matrix, which is denoted with symbol L,asshowninEq.(7). Q=(I−A)−1Y(6) Q=LY(7) In the Leontief inverse matrix, the i,j-element, L i,j, indicates the total number of units of energy ithat should be consumed as transformation input in the energy sector in order to provide one unit of energy jfor final energy consumption of the end-use sectors. Now, as we are interested in knowing just how much primary energy is necessary to make a unit of energy available for consumption of the end-use sectors, we must ignore the coefficients L i,jfor which iis a secondary energy product. In other words, we must drop the rows of the matrix Lthat correspond to secondary energy products. Obviously, it does not refer to the rows included to incorporate positive net import balances of secondary energy products. Thus, we can further calculate the total units of primary energy that should be consumed in the conversion sector in order to provide one unit of energy jfor end-use by using Eq. (8). KPEQ,j= 63+Ns  i=1 L i,j·1i/∈S(8) where Sis the subset of secondary energy products, 1i/∈Sis an indicator variable that takes value 1 when the energy product iis not part of the subset of secondary products and 0 otherwise, and KPEQ,jis the primary energy quantity conversion factor 18 See Table 12 of Appendix for a numerical example of the input–output table. This is done in a fictitious way in order to facilitate the comprehension of the table. 123 SERIEs (2021) 12:151–229 181 Small Value Strategy proposed by Ang and Choi (1997) and we substitute the zero values by values smaller than 10−20.26 Following this scheme, CT POP,AGRI describes the change in the energy-related CO2emissions of the agricultural sector from t=0tot=Tthat is associated to changes in population, CT INC,AGRI is the change associated to the variations in income per capita (or GVA per capita), CT STR,AGRI represents the change attributed tothe economic structure,CT INTR,AGRI providesinformation about thechange related to composition variations of the agricultural sector (or intra-structure), CT OUT,AGRI denotes the changes linked to the structural elements influencing the energy intensity (or variations in the ratio of physical activity driver to economic output), CT EFF,AGRI stands for changes associated to the physical end-use energy intensity (or apparent end-use energy efficiency), CT MIX,AGRI describes the change attributed to variations in the composition of the end-use energy-mix, CT CONV,AGRI represents the change linked to the technical efficiency of the conversion sector (or variations in the primary energy requirements), and CT EMI,AGRI denotes the change associated to the share of carbon primary energy sources used to make the final energy consumption available. 3.3.2 Industry Regarding the industrial sector, we can implement the same extension of the energyrelated CO2identity that we perform for the agricultural sector, since the granularity of the data is the same. In this sense, following Eq. 15, the energy-related CO2emissions coming from the industrial sector and the influential factors to be analyzed can be derived from Eq. 18. Ct IND = m 63  j=1 Pt·Vt TOT ·χt Pt·Vt IND ·χt Vt TOT ·χt·Vt m,IND ·χt Vt IND ·χt·Dt m,IND Vt m,IND ·χt   (1) ·Et SQ,m,IND Dt m,IND   (2) · Et SQ,m,IND,j Et SQ,m,IND ·Kt PEQ,j   (3) ·Kt C,j  (4) = m 63  j=1 POPt·INCt·STRt IND ·INTRt IND,m·OUTt IND,m·EFFt IND,m·MIXt IND,m,j·CONVt j·EMIt j (18) where IND stands for industry, m={EEI,FBT,TL,...,CON}indexes the industrial sub-sector, EEI stands for energy sector and extractive industries, FBT stands for food, beverages and tobacco, TL stands for textile and leather, WWP stands for wood and wood products, PPP stands for paper, pulp and print, CPC stands for chemical and petrochemical industry, NMM stands for non-metallic minerals, BM stands for basic metals, MAC stands for machinery, TE stands for transport equipment, OI stands for other industry, CON stands for construction, and the rest of notations describe analogous aspects to those shown in Eq. 15. In this regard, the only difference from the agricultural sector is the definition of physical activity drivers for each of the industrial sub-sectors. Here, following the matching sector scheme presented in Table 16 of Appendix and based on the strategy 26 There is another strategy called Limit Strategy and proposed by Wood and Lenzen (2006), but we decline to use it because it requires more calculation and is not distinguished from the one we use in the results it offers. 123 182 SERIEs (2021) 12:151–229 proposed by ODYSSEE-MURE (2020a), we compute the physical activity driver of each sub-sector with the Production Volume Index for each of them, respectively. This is an index reported by Eurostat (2020j) that approximates the output of each sub-sector in physical terms.27 Therefore, applying the LMDI decomposition technique to Equation 18, we can derive the change in the energy-related CO2emissions of the industrial sector from t=0tot=Tby using Eq. 19. CT IND =CT IND −C0 IND =CT POP,IND +CT INC,IND +CT STR,IND +CT INTR,IND +CT OUT,IND +CT EFF,IND +CT MIX,IND +CT CONV,IND +CT EMI,IND (19) where the notations represent analogous aspects to those shown in Eqs. 16 and 17,but now for the industrial sector. 3.3.3 Commercial and public services The granularity of the data in the commercial and public services sector follows a different perspective. Whereas we were able to gather the energy consumption and the economic output of the different sub-sectors within the agricultural and industrial sectors directly from the Eurostat (2020c) energy balances, in the case of commercial and public services, we are unable to observe a similar breakdown at the sub-sector level. However, within the conglomerate of activities that constitutes the commercial and public services sector (see Table 16 of Appendix), we can impute the fraction of energy consumption that is devoted to each type of energy end-use making use of the ODYSSEE-MURE (2020b) database.28 In other words, we can see how much of the final energy consumption (with a breakdown by energy product) of the commercial and public services sector is allocated to space heating (SH), hot water (HW), cooking (COOK), air cooling (AC), and lighting (LIGHT). Therefore, given the breakdown of the data for this sector, the extension of the energy-related CO2emissions identity for the commercial and public services sector to include the influencing factors would be as shown in Equation 20. Ct CPS = u 63  j=1 Pt·Vt TOT ·χt Pt·Vt CPS ·χt Vt TOT ·χt·Dt CPS Vt CPS ·χt·Et SQ,CPS Dt CPS 27 The Production Volume Index missing data is fulfilled with the linear observed trend. 28 See Reuter et al. (2019) for a detailed description of the ODYSSEE-MURE (2020b) data imputation and the missing-data filling process. 123 SERIEs (2021) 12:151–229 183 ·Et SQ,u,CPS Et SQ,CPS   (1) ·HDDt HDDref u=SH ·CDDt CDDref u=AC   (2) · Et SQ,u,CPS,j Et SQ,u,CPS ·Kt PEQ,j·Kt C,j = u 63  j=1 POPt·INCt·STRt CPS ·OUTt CPS ·EFFt CPS ·USEt CPS,u ·WEAt CPS,u·MIXt CPS,u,j·CONVt j·EMIt j(20) where CPS stands for commercial and public services, u={SH,HW,COOK, AC,LIGHT}indexes the particular energy end-use, HDDt(CDDt) denotes the heating (cooling) degree days during the year t, HDDref (CDDref) stands for the reference value of heating (cooling) degree days for the whole period of analysis (from 1995 to 2017), the ratio HDDt HDDref is 1 for u= SH and the ratio CDDt CDDref is 1 for u= AC, and the rest of notations describe analogous aspects to those shown in Eq. 15. We can notice that in this sector we do not find the intra-structural component, since there is no disaggregation by sub-sectors as there was in the previous two sectors. However, we find two new influential factors that we did not have before: (1) the share of the different end-uses in the total final energy consumption of the sector and (2) the climate factor. The latter is included so that the magnitudes of both regions (Spain and the EU28, in our case) are comparable and to ensure that the differences between regions are not due to purely climatic systemic differences. In this sense, the final energy consumption for space heating and air cooling is adjusted following Reuter et al. (2019), since variations in weather are a determining factor for this type of end-uses and we must take this into account.29 For that purpose, we access the data regarding the heating (cooling) degree days published by Eurostat (2020d). Finally, it should be commented that because we do not have disaggregation by subsectors, because the data coverage of the production volume index does not include the entire commercial and public services sector, and given that other indicators such as the surface area of the sector’s installations, the number of offices or other technical aspects are not available for the sector as a whole, the only statistic that we consider valid to act as a physical activity driver for the sector is the number of employees provided by Eurostat (2020h). Therefore, the apparent end-use energy efficiency (or physicalend-use intensity) of this sector will be determined by the energyconsumption per employee. Hence, applying the LMDI decomposition technique to Eq. 20, we can derive the change in the energy-related CO2emissions of the commercial and public services sector from t=0tot=Tby using Eq. 21. 29 Effects of changes in annual average temperature play a minor role in other sectors like industry and transport, as shown by Reuter et al. (2019). 123 184 SERIEs (2021) 12:151–229 CT CPS =CT CPS −C0 CPS =CT POP,CPS +CT INC,CPS +CT STR,CPS +CT OUT,CPS +CT EFF,CPS +CT USE,CPS +CT WEA,CPS +CT MIX,CPS +CT CONV,CPS +CT EMI,CPS (21) where the notations represent analogous aspects to those shown in Eqs. 16 and 17, but now for the commercial and public services sector. Furthermore, following this scheme, CT USE,CPS describes the change in the energy-related CO2emissions of the commercial and public services sector from t=0tot=Tthat is associated to changes in the share of the different end-uses in the total final energy consumption of the sector and CT WEA,CPS is the change associated to the variations in the climate conditions.30 3.3.4 Households For the household sector (denoted by HH), the approach is designed based on the energyend-usesinthatsector,asimilarstrategytothatfollowedforthecommercialand public services sector. In this case, the difference comes from the energy consumption of households not being associated with any economic activity (included in the NACE list), but coming from a private activity. Again, in the case of households, we are not able to observe a breakdown by energy end-uses directly from the Eurostat (2020c) energy balances. However, we can impute the fraction of energy consumption that is devoted to each type of energy end-use making use of the ODYSSEE-MURE (2020b) database. In this sense, we can observe how much of the final energy consumption of the households is allocated to space heating (SH), hot water (HW), cooking (COOK), air cooling (AC), and lighting (LIGHT). In addition, due to a different definition of the apparent end-use energy efficiency factor for each end-use type of the residential final energy consumption and given the breakdown of the data for this sector, the extension of the energy-related CO2emissions identity for the household sector to include the influencing factors would be separated in this case into two different expressions, as shown in Eqs. 22 and 23. Equation 22 displays the extension of the energy-related CO2emissions identity for the case of space heating as energy end-use. Ct HH,SH = 63  j=1 Pt·Ht Pt  (1) ·At Ht  (2) ·Et SQ,SH,HH At·HDDt HDDref · Et SQ,SH,HH,j Et SQ,SH,HH ·Kt PEQ,j·Kt C,j = 63  j=1 POPt·SOCt·COMt·EFFt HH,SH ·WEAt HH,SH ·MIXt HH,SH,j·CONVt j·EMIt j (22) where Htdenotes the number of dwellings in the region at year t,Atrepresents the total area of dwellings in the region (in m2), and the rest of notations describe 30 Note that CT WEA,CPS,uwill be zero for u/∈{SH,AC}. 123 SERIEs (2021) 12:151–229 185 analogous aspects to those shown in Eqs. 15 and 20. In this case, data for Atand Htare extracted from the ODYSSEE-MURE (2020b) database. Therefore, the factor EFFt HH,SH is defined as energy consumption for space heating use per m2of dwelling in the region. In the case of the household sector, we do not find influencing factors like income per capita, economic structure and intra-structure, structural factors affecting the energy intensity of the sector nor a factor calibrating the share that the different energy end-uses have in the total energy consumption of the sector. However, we discover two new influencing factors that we had not found before: (1) the social factor (i.e., less people living together in one dwelling) and (2) the comfort factor (or factor related to living standards, i.e., an increasing/decreasing area per dwelling). On the other hand, Eq. 23 displays the extension of the energy-related CO2emissions identity for the case of household energy end-uses different from space heating. Ct HH,SH = u=SH 63  j=1 Pt·Ht Pt  (1) ·Et SQ,u,HH Ht·CDDt CDDref u=AC · Et SQ,u,HH,j Et SQ,u,HH ·Kt PEQ,j·Kt C,j = u=SH 63  j=1 POPt·SOCt·EFFt HH,u·WEAt HH,u·MIXt HH,u,j·CONVt j·EMIt j (23) where the factor EFFt HH,uis defined here as energy consumption for end-use uper dwelling in the region and the rest of notations describe analogous aspects to those shown in Equations 15 and 20. It can be noticed that we do not incorporate the factor COMtfor energy end-use types different from space heating, but only the factor SOCt(1). We should also note that the ratio CDDt CDDref is 1 for u= AC. Therefore, applying the LMDI decomposition technique to Eqs. 22 and 23, we can derive the change in the energy-related CO2emissions of the household sector from t=0tot=Tby using Eq. 24. CT HH =CT HH,SH +CT HH,SH =(CT HH,SH −C0 HH,SH)+(CT HH,SH −C0 HH,SH) =CT POP,HH +CT SOC,HH +CT COM,HH +CT EFF,HH +CT WEA,HH +CT MIX,HH +CT CONV,HH +CT EMI,HH (24) where the notations represent analogous aspects to those shown in Eqs. 16 and 17, but now for the household sector. Furthermore, following this scheme, CT SOC,HH describes the change in the energy-related CO2emissions of the household sector from t=0tot=Tthat is associated to changes in social factors and CT COM,HH is the change associated to comfort or behavior developments.31 31 Note that CT WEA,HH,uwill be zero for u/∈{SH,AC}and that CT COM,HH,uwill be zero for u= SH. 123 186 SERIEs (2021) 12:151–229 3.3.5 Transport As far as the transport sector is concerned (denoted by TRA), the strategy is based on the final energy demands coming from the different existing transport modes. Again, these energy consumptions are not associated with any economic activity (included in the NACE list), but are taken as energy consumption derived from private activity.32 In terms of energy consumption data availability for this sector, the Eurostat (2020c) energy balances present a disaggregation by transport mode (rail, road, aviation, navigation, and pipelines), but no distinction is made on the share of the energy consumption of each transport mode that corresponds to freight (FR) and passenger transport (PASS). This distinction is very relevant as the most appropriate indicator to express activity is passenger-kilometers (PKM, hereafter) in the case of passenger transport and tonne-kilometers (TKM, hereafter) in the case of freight transport. As the conversion of PKM to TKM is not possible, alternative sources like the ODYSSEEMURE (2020b) database must be considered. In this sense, since domestic navigation (NAVI) and pipeline transport (PIPE) are freight transport by definition and domestic air transport + other (AVI) is passenger transport by definition, using the shares offered by the ODYSSEE-MURE (2020b) database, we calculate which part of the road transport (ROAD) and train transit (RAIL) is due to passenger transport and which part is due to freight transport.33 Once we have defined it, we have a complete disaggregation of the transport energy consumption by transport modes. Therefore, the extension of the energy-related CO2emissions identity for the transport sector to include the influencing factors would be given by Eq. 25. Ct TRA = p q 63  j=1 Pt·Kt p Pt  (1) ·Kt p,q Kt p  (2) ·Et SQ,p,q,TRA Kt p,q·Et SQ,p,q,TRA,j Et SQ,p,q,TRA ·Kt PEQ,j·Kt C,j = p q 63  j=1 POPt·SOCt p·STRt p,q·EFFt p,q·MIXt p,q,j·CONVt j·EMIt j (25) where Kt pdenotesthePKMatyeartinthewholepassengertransportif p=PASSand theTKMatyeartinthewholefreighttransportif p=FR,Kt p,qstandsfor thePKM of the passenger transport mode q(for q={ROAD,RAIL,AVI}) at year tif p=PASS and the TKM of the freight transport mode q(for q={ROAD,RAIL,NAVI,PIPE}) atyeartif p=FR,andtherest of notations describe analogous aspects tothoseshown in Equation 15. PKM and TKM data are gathered from the ODYSSEE-MURE (2020b) databaseforeverytransportmodeexceptforpipelinetransport,whoseassociatedTKM 32 In our analysis, we consider that the energy consumption associated with transportation activities appearing in the NACE list of economic activities is only that consumption related to installations (e.g., lighting in train stations). These energy demands will therefore appear under the consumption associated to the commercial and public services sector. 33 Road transport consumption includes all energy consumed by cars, motorcycles and buses for the passenger transport and trucks and light vehicles for the case of freight transport. Domestic aviation only includes energy used by all domestic aeroplanes (e.g., private and commercial planes). Domestic navigation only includes energy consumed for river and coastal maritime domestic transport. 123 SERIEs (2021) 12:151–229 187 are taken from a report published by Eurostat (2020m).34 Therefore, the apparent enduse energy efficiency of the transport sector is measured as energy consumption per PKM (if passenger transport) or per TKM (if freight transport). We do not find in this sector influencing factors like income per capita, intra-structure, weather, comfort, structural factors affecting the energy intensity of the sector nor a factor calibrating the share that the different energy end-uses have in the total energy consumption of the sector. However, two influencing factors that we have presented above are here redefined: (1) the factor SOCt pis here constructed as PKM or TKM per capita and (2) the factor STRt p,mdescribes the modal composition of the passenger or freight transport structure. Hence, applying the LMDI decomposition technique to Equation 25, we can derive the change in the energy-related CO2emissions attributed to the transport sector from t=0tot=Tby using Eq. 26. CT TRA =CT TRA −C0 TRA =CT POP,TRA +CT SOC,TRA +CT STR,TRA +CT EFF,TRA +CT MIX,TRA +CT CONV,TRA +CT EMI,TRA (26) where the notations represent analogous aspects to those shown in Eqs. 16 and 17,but now for the transport sector. 3.3.6 Factor aggregation scheme and data After carefully explaining the different LMDI decomposition strategies that we have performed for each of the five sectors presented above and given the aggregation property of the LMDI formulation, the obtained sectoral results are summed up to review the composition of the energy-related CO2emissions as a whole. Based on Eq. 13, where it is stated that the total energy-related CO2emissions is equivalent to the aggregation of the different sectoral estimates of such magnitude, we can obtain an aggregate LMDI decomposition for the change in the total energy-related CO2 emissions from t=0tot=Tby using Eq. 27. CT TOT =CT AGRI +CT IND +CT CPS +CT HH +CT TRA = s CT s =CT POP,TOT +CT INC,TOT +CT SOC,TOT +CT COM,TOT +CT STR,TOT +CT INTR,TOT +CT OUT,TOT +CT EFF,TOT +CT USE,TOT +CT WEA,TOT +CT MIX,TOT +CT CONV,TOT +CT EMI,TOT (27) 34 For the EU28 case, pipeline TKM data is only available until 2015, therefore years 2016 and 2017 are extrapolated from the data. 123 188 SERIEs (2021) 12:151–229 where the factor CT EFF,TOT, as an example, would be constructed as shown in Eq. 28 (and in analogous manner for other factors). CT EFF,TOT = s CT EFF,s(28) for s={AGRI,IND,CPS,HH,TRA}. However, as we have seen in the above narrative, not all sectors (or sub-sectors) imply a change for the aggregate magnitude. For example, the weather factor at the aggregate level is only determined by how the climate shapes the energy consumption devoted to space heating or air cooling in the services and residential sectors. In any case, an overview of how the factors are aggregated is shown in Fig. 4. Finally, to check the validity of the decomposition, we estimate the annual change of the total energy-related CO2emissions from t=0to t=Tand we compare it with the quantity obtained by aggregating the changes in the different factors and sectors. In this regard, our check reveals 0% differences for the vast majority of cases, with the difference never exceeding 2%, which may be due to the problem that the LMDI approach has in dealing with close-to-zero values. 3.4 Further decomposition of apparent end-use energy efficiency AfterperformingthedecompositionpresentedinthepreviousSect.3.3,wecanseehow much the apparent end-use energy efficiency contributes to the evolution of energyrelated CO2emissions in Spain and the EU. However, as we commented in Sect. 2.5, this observed end-use energy efficiency may be driven not only by the technical efficiency itself, but also by other influences such as possible rebound effects resulting from technical efficiency improvements or other factors such as the infra-utilization of installed energy equipment. For this reason, we consider it necessary to develop a methodology of decomposition that allows us to know what is really driving the apparent end-use energy efficiency (the observed energy unit consumption). Firstly, we define what we understand as apparent end-use energy efficiency. For each sub-sector of the economy previously presented, m, belonging to a sector, s,the apparent end-use energy efficiency at year t,AEE t m,s, is determined by the physical activity driver of said sub-sector, Dt m,s, divided the final energy consumption of said sub-sector, Et SQ,m,s. Note that since a decrease in the specific unit energy consumption is an increase in the apparent energy efficiency, such observed energy efficiency will be given by the inverse of the mentioned specific unit energy consumption. That is, AEEt m,s=1 Et SQ,m,s Dt m,s =Dt m,s Et SQ,m,s .(29) In order to make the evolution of all these apparent energy efficiency indicators comparable across sub-sectors, we calculate an index with base 100 in 1995 (the beginning of our analysis period), i.e., AEE1995 m,s=100. In addition, once presented for eachsub-sector, the apparent end-useenergyefficiencyindexofthe sector sas awhole, AEEt s, is determined by the average of whose sub-sector indexes pondered by the 123 SERIEs (2021) 12:151–229 189 POP INC Agriculture SOC Economic sectors Agriculture and forestry Fishing Energy and extractive industry Other industry Space heating Hot water Cooking Air cooling Lighting Space heating Hot water Cooking Industry Commercial and public services Air cooling Lighting Households Road Aviation Road … Pipeline Freight tranport Passenger tranport … … Transport STR INTR COM OUT EFF USE WEA MIX CONV EMITotal change Fig. 4 Aggregation scheme over factors and sectors 123 190 SERIEs (2021) 12:151–229 weight of each of them in the final energy consumption of the sector, ωt m,s=Et SQ,m,s Et SQ,s . That is, AEEt s=mAEEt m,s·ωm,s. Analogously, the total or national apparent enduse energy efficiency index will be given by AEEt TOT =sAEEt s·ωt s, with ωt sbeing in this case the share of the sector sin the total final energy consumption, Et SQ,s Et SQ,TOT . Secondly, we present the calculation of the technical end-use energy efficiency index. Following the definition and calculations provided by ODYSSEE-MURE (2020a), for each sub-sector of the economy previously presented, m, belonging to a sector, s, the technical end-use energy efficiency index (also called ODEX index) at year t, TEEt m,s, will be defined as the apparent end-use energy efficiency index assuming non-reversible efficiency improvements. A decrease in the specific unit energyconsumption(anincreaseof the apparent energyefficiencyindex)indicates that energy efficiency has been improving. However, in some cases the observed indicator shows an increase (an decrease in the apparent energy efficiency index), resulting in a negative energy efficiency improvement. Since we assume non-reversible technical efficiency improvements, this increase in the specific unit energy consumption may be due to an inefficient use of the equipment (part of the energy consumption is independent of the production level), as it is often observed during economic recession, or due to rebound effects derived from a fall in the effective energy cost. In this case, the apparent energy efficiency index can be replaced by technical energy efficiency index, by considering that if the apparent energy efficiency index for a given sub-sector decreases at year tits value will be kept constant in the calculation of the technical efficiency, i.e., the considered apparent energy efficiency index will be that from year t−1. Thus, the technical end-use energy efficiency index of the sub-sector min the sector sat year twill be depicted by Eq. 30. TEEt m,s=TEEt−1 m,s· Et SQ,m,s+Dt m,s·Et−1 SQ,m,s Dt−1 m,s−Et SQ,m,s Dt m,s Et SQ,m,s =TEEt−1 m,s· Et SQ,m,s+Dt m,s·1 AEEt−1 m,s−1 AEEt m,s Et SQ,m,s =TEEt−1 m,s·Et SQ,m,s+PESt SQ,m,s Et SQ,m,s (30) where PESt SQ,m,sdenotes the potential energy savings (PES) from t−1totand is calculated by multiplying the physical activity driver at tby the variation in the specific unit energy consumption between t−1tot, and it is assumed that PESt SQ,m,s=0 for AEEt m,s≤AEEt−1 m,s. It means that the technical end-use energy efficiency index of the sub-sector min the sector sat year twill be obtained by multiplying the index in the previous period, TEEt−1 m,s, by the ratio between the final energy consumption of the sub-sector without potential energy savings (PES) at t,Et SQ,m,s+PESt SQ,m,s, and the actual energy consumption at t,Et SQ,m,s, assuming that these PEScannot be 123 SERIEs (2021) 12:151–229 197 Table 5 Energy-related CO2emissions associated to total final energy demand Concept Spain EU28 1995 2017 1995 2017 Final energy demand (Gg CO2) 307.06 374.52 4720.11 4719.49 International maritime bunkers 3.29% 5.63% 2.33% 2.85% International aviation 2.13% 3.57% 1.97% 3.30% Distribution losses 2.51% 2.16% 2.70% 2.21% Final energy consumption 79.55% 74.37% 84.54% 78.64% Final non-energy consumption 7.98% 3.92% 6.94% 6.64% Positive net export balance 3.78% 9.78% 1.46% 5.96% Statistical differences 0.76% 0.57% 0.05% 0.40% in Table 5. We find that the mentioned emissions have increased by 22% in Spain from 1995 to 2017, while they remain in the levels of 1995 in the EU28. We also detect that the emissions derived from final energy consumption account for the largest proportionofthetotalCO2emissions(around80%),butthisproportionhasdiminished in favor of the weight gained by the CO2emissions associated to energy consumption for international maritime bunkers, non-domestic aviation, and positive net export balances. This is an evolution that can be observed both in the EU28 and in Spain. Moreover, applying our estimated KC,SQ,jelevation factor to the consumption of each energy product j, we can obtain the energy-related CO2emissions derived from each energy product in the final energy consumption, which has been adjusted by the heating and cooling degree days of each region in order to make the magnitudes comparable across regions. This is the reference magnitude to study the evolution of the emissions, since it does not incorporate the energy-related CO2emissions related to international energy activities, energy distribution losses and positive net energy export balances. In other words, this is the most appropriate magnitude because it solely reflects the energy-related CO2derived from national energy activities. For this purpose, we show in Table 6how much the energy-related CO2emissions related to each energy product group (a compendium of similar energy products) contribute to the total energy-related CO2emissions stemming from the adjusted final energy consumption. Foremost, we should note that the estimation of the energy-related CO2 emissions associated to final energy consumption reveals a number that is equivalent to the magnitude reported in the Air Emission Accounts published by Eurostat (2020a), which highlights the strength of our estimation approach.38 Further on, when reading Table 6we realize that while the CO2emissions related to final energy consumption have increased by 16.2% from 1995 to 2017 in Spain, they havedropped by 7.4% intheEU28.Wealso observehowthe CO2emissions associated with natural gas and renewables have increased in both regions (due to a higher use of these energy products), accounting both of them for a smaller weight in Spain than 38 The magnitude in the Air Emission Accounts published by Eurostat (2020a) that is equivalent or comparable with our estimate is the aggregate of the CO2emissions that takes into account all economic activities (including transport) and households. 123 198 SERIEs (2021) 12:151–229 Table 6 Energy-related CO2emissions associated to final energy consumption (I) Energy Spain EU28 1995 2017 1995 2017 Final energy consumption (Gg CO2) 241.13 280.09 4005.41 3710.42 Solid fossil fuels 2.46% 0.71% 6.19% 2.84% Manufactured gases 2.22% 1.11% 3.85% 2.29% Peat and peat products 0.00% 0.00% 0.11% 0.05% Oil shale and oil sands 0.00% 0.00% 0.01% 0.00% Oil and petroleum products 54.72% 46.99% 36.87% 35.36% Natural gas 6.22% 13.59% 14.46% 15.97% Renewables and biofuels 5.25% 7.32% 4.61% 9.23% Non-renewable waste 0.20% 0.01% 0.24% 0.59% Nuclear heat 0.00% 0.00% 0.00% 0.00% Heat 0.00% 0.00% 6.90% 6.95% Electricity 28.93% 30.27% 26.74% 26.72% Table 7 Energy-related CO2emissions associated to final energy consumption (II) Sector Spain EU28 1995 2017 1995 2017 Final energy consumption (Gg CO2) 241.13 280.09 4005.41 3710.42 Agriculture 3.31% 3.00% 2.69% 2.38% Industry 39.96% 32.12% 38.12% 31.94% Commercial and public services 8.39% 13.98% 11.63% 13.79% Households 15.75% 16.51% 26.04% 24.68% Transport 32.59% 34.39% 21.53% 27.20% in the EU28. At the same time, we can see that derived heat does not represent a significant weight in Spain, while in the EU28 it has a not insignificant relevance. Finally, we can discern how the weight of the CO2emissions derived from solid fossil fuels and oil and petroleum products is evolving in a downward direction, but being oil products still more relevant in Spain than in the EU28 in terms of associated CO2 emissions. From a different perspective, we can also compute the responsibility of each enduse sector in the energy-related CO2emissions associated to the weather-adjusted final energy consumption. In Table 7, we can notice that the industry was typically the sector with the highest CO2emissions both in Spain and in the EU28, but its prominent role has been decreasing and we encounter that the transport sector overpassed the weight of the industry in the energy-related CO2emissions attributable to the weather-adjusted final energy consumption in Spain. The transport sector has more relevance in Spain than in the EU28, despite the increase of its weight in the latter region. Finally, the share attributed to the commercial and public services has slightly 123 SERIEs (2021) 12:151–229 199 Fig. 5 Factorcontributionsto thetotal changein energy-relatedCO2emissions. Note: Positivecontributions refer to an increase of the energy-related CO2emissions associated to the evolution of the factor. Negative contributions refer to a decrease of the energy-related CO2emissions associated to the evolution of the factor increased both in Spain than in the EU28 from 1995 to 2017. The most noticeable differences between the two regions in terms of the sectoral structure of emissions are found in households (with a greater weight in the EU28) and in transport (with a larger share in Spain). This is clearly a result of the weather (Spanish households contributing less to emissions) and the systemic structure of transport (the main mode of transport in Spain is road transport, which is much more carbon-intensive). 4.3 Decomposition of the evolution of carbon dioxide emissions After having over-viewed the general picture of the energy-related CO2emissions estimation, we move on to our decomposition analysis to identify what factors have been the most relevant influences underlying the observed evolution of said total energy-related CO2emissions (that shown in Fig. 2of Sect. 1). To do this, we believe that it would be appropriate to divide our entire analysis period into two sub-periods determined by the outbreak of the 2007 crisis, i.e., we will have a sub-period 19952007 and another sub-period 2007–2017. Figure 5shows the contributions (in %) of each of the thirteen influencing factors considered to the aggregate evolution of the CO2emissions associated with final energy consumption. In addition, Table 6presents the actual evolution of the energy-related CO2jointly with the hypothetical evolution that such magnitude would have had if each contributing factor would have acted independently. The CO2emissions associated with final energy consumption increased by 43% in Spain (from 241.12 Gg CO2to 345.07 Gg CO2) and by 4% in the EU28 (from 4005.45 123 200 SERIEs (2021) 12:151–229 Gg CO2to 4171.02 Gg CO2) from 1995 to 2007.39 The population growth, a rising per capita disposable income and other social factors were the main drivers behind this development. These effects were much greater in the EU28 than in Spain (in both periods). These large effects in the EU28 reversed the very positive effect on emissions reduction that the increase in the apparent or observed energy efficiency of the enduse sectors had, resulting in an increase of the total emissions during the mentioned sub-period. However, this is not what can be observed in Spain, since despite the first factors mentioned above not contributing to the same extent as in the EU28 to the increase in emissions, the evolution of the apparent energy efficiency in the Spanish end-use sectors, unlike in the EU28, was driving further the increase in total emissions. Onthe other hand,from 2007 to2017 (the lastyearfor which wehave disaggregated data), CO2emissions associated with final energy consumption fell by 19% in Spain (from 345.07 Gg CO2to 280.10 Gg CO2) and by 11% in the EU28 (from 4005.45 Gg CO2to 3710.51 Gg CO2). At the EU28 level, this evolution is mainly determined by (i) the increase in the apparent end-use energy efficiency and in the improvement of the efficiency in the energy transformation sector (which means that less and less primary energy is required to produce the necessary energy demanded by the enduse sectors), (ii) the evolution of the productive structure toward sectors that generate fewer emissions, and by (iii) a lower use of fossil fuels for energy transformation. These factors offset the increases in emissions related to population growth, increased income and other social factors, resulting in a decrease in aggregate emissions. Spain has experienced a similar evolution, but the gains in the apparent end-use energy efficiency and in the efficiency of the energy conversion sector that can be observed in the EU28 are not detected in Spain. This means that the Spanish emissions have not been reduced from 2007 to 2017 as much as they could potentially have been if the same energy efficiency improvements (both in apparent end-use efficiency and in efficiency of the conversion sector) as in the EU28 had been observed in Spain. In Spain, the main factor behind the reduction of emissions is the economic structural transition toward less emission-generating sectors and its shift toward higher value products (captured by the monetary to physical output relation factor), changes that are not observed to the same extent in the EU28. Finally, we must note that the apparent energy efficiency is influenced by many factors and do not uniquely depend on the actual technical efficiency, hence we must be cautious when interpreting these results. A more detailed explanation in this regard will be presented in Sect. 4.7. Analyzing these same developments from a sectoral perspective (see Fig. 7), we can note how the transport and services sectors were the main contributors to the increase in emissions that occurred from 1995 to 2007 in the EU28. In Spain, the transportation and the services sector, although to a lesser extent than in the EU28, also contributed to the increase in emissions. Contrarily, despite households and industrial sectors being an inhibitor of the increase in emissions in the EU28, they were a clear driving force of the Spanish emissions during said sub-period. However, during the sub-period 2007– 2017, households and especially industry were clear inhibitors and led to a decline in emissions both in Spain and the EU28. In this latter sub-period, the transport sector 39 Recall that these emissions are computed from a weather-adjusted magnitude. See Fig. 12 of Appendix to check the evolution of the weather factor, which seems to be an upward-driver of the emissions during the sub-period 2007–2017 and an inhibitor of emissions during the sub-period 1995–2007. 123 SERIEs (2021) 12:151–229 201 Panel A.1: Spain (I) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual Population Income per capita Social factors Comfort factors Weather Panel B.1: EU28 (I) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual Population Income per capita Social factors Comfort factors Weather Panel A.2: Spain (II) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual Structure Intra-structure Monetary-to-physical End-use efficiency Panel B.2: EU28 (II) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual Structure Intra-structure Monetary-to-physical End-use efficiency Panel A.3: Spain (III) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual End-use type shifts Final energy mix Efficiency of transf. Primary low-carbon sources Panel B.3: EU28 (III) 80 100 140 120 160 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Actual End-use type shifts Final energy mix Efficiency of transf. Primary low-carbon sources Fig. 6 Evolution of energy-related CO2emissions and contributors. Note: End-use efficiency refers to the apparent end-use efficiency also contributed significantly to the fall in emissions, with this contribution being much greater in Spain than in the EU28. It remains open and what is behind the evolution of each sector, whether structural changes, efficiency changes, final energy-mix, etc.. Thus, after the identification of the most influential factors and sectors in the evolution of the aggregate CO2emissions associated to the final energy consumption, we analyze in more detail each of them in the following Subsections. 123 202 SERIEs (2021) 12:151–229 Fig. 7 Sectoral contributions to the total change in energy-related CO2emissions. Note: Positive contributions refer to an increase of the energy-related CO2emissions associated to the evolution of the factor. Negative contributions refer to a decrease of the energy-related CO2emissions associated to the evolution of the factor Fig. 8 Population, income and other social/comfort factors 4.4 Population, income and other social factors We have previously shown in Fig. 5that the effects of population growth, rising per capita disposable income and other social elements were emission-augmenting factors during both sub-periods considered in Spain and in the EU28. In Fig. 8we can see how the population grew throughout the period considered, both in Spain and in the EU28, although it is true that this growth is slightly more pronounced in the Spanish case. Obviously, the larger the population, the greater the energy consumption and, consequently, the higher the energy-related emissions. Therefore, the population is a driving force for emissions throughout our period of analysis. When it comes to the GVA per capita, we can notice that it also experiences an upward trend if we analyze the beginning and the end of the period. However, it is true that there are a few years after the 2007 crisis (in Spain until 2014 and in the EU28 until 2009) for which the income per capita fell, which could make households and businesses consume slightly less energy during this sub-period, driving emissions down. But from an aggregate perspective in time, the GVA per capita emerges as a driver of emissions, since the higher the income per capita, the greater the energy consumption by the agents of the economy and the greater the consumption of other goods, which consequently increases the energy demand that is necessary to cover their production. 123 SERIEs (2021) 12:151–229 203 In terms of social factors, the number of dwellings per capita increased almost steadily throughout the period, which would lead to higher emissions. On the other hand, the comfort factor, which is measured by the area per dwelling, fell during the period analyzed, but this fall does not translate into a significant contribution to the decrease in emissions. Finally, other social factors such as per capita PKM and per capita TKM, which indicate how much we travel per capita or how much goods are moved per capita, are observed to have increased from 1995 to 2007. This means that, for this sub-period, as there is an increasing transit of people and goods, there is a higher energy consumption of transport, which leads to rising emissions, i.e., PKM and TKM per capita being an upward pressure on emissions. However, this trend ceases abruptly with the arrival of the 2007 crisis and, immediately afterwards, the PKM and TKM per capita fall (to a greater extent in the case of goods) for a few years until their posterior recovery, with the fall being much more pronounced and the recovery more delayed in Spain than in the EU28. This discrepancy between regions in the evolution of the aforementioned magnitudes derived from the impact of the 2007 crisis is the reason why, while the social factors were emission inhibitors in Spain during the 2007–2017 sub-period, they were emission driving forces in the EU28. In aggregate, taking into account all population, income and social factors, we can say that all their related effects offset each other and give rise to a contribution to the emissions that make them increase. In other words, the conglomerate of these factors could be considered as an emission-generating element. 4.5 Economic structure As we have clearly shown in Fig. 5, the economic structure factor is an inhibitor of energy-related CO2emissions for both sub-periods of analysis. The logic behind this result is that the economic structure of both Spain and the EU28 (economically advanced regions) has undergone a process of tertiarization. This mentioned process can be evidently characterized by the changes in the different sectoral shares observed in Table 8. In this Table, the sub-sector shares refer to the weight that each sub-sector has in its particular sector. Analogously, the sector shares refer to the weight that each sector has in the total production. It can be noticed how the industry (a traditionally emission-generating sector) has decreased its weight in favor of the commercial and public services. In this way, activities requiring less energy needs have become more relevant, which leads to a reduction in emissions. By reading this Table 8we can also explain why the intra-structural factor is an emission-driving force in Spain for the 2007–2017 sub-period, while in the EU28 this factor drives the pressure down. Within industry (or in the intra-industrial structure), the activities of the energy sector and the extractive industries have increased their share of the total industrial GVA in Spain, while they have reduced it in the EU28. These industries are traditionally very energy-intensive, and therefore very emissionintensive. Hence, as their weight within the industry increases, the intra-structural factor becomes an upward-pressure on emissions for the Spanish case. 123 204 SERIEs (2021) 12:151–229 Table 8 GVA share Sector Spain EU28 1995 2007 2017 1995 2007 2017 Agriculture 2.87% 2.72% 2.93% 1.86% 1.54% 1.56% Agriculture and forestry 88.31% 94.47% 95.23% 95.06% 96.39% 96.80% Fishing 11.69% 5.53% 4.77% 4.94% 3.61% 3.20% Industry 29.05% 27.14% 20.83% 27.03% 25.17% 23.05% Energy sector and extractive industries 10.47% 10.94% 15.39% 13.60% 11.56% 11.09% Food, beverages and tobacco 11.32% 10.75% 12.18% 8.92% 8.17% 8.76% Textile and leather 3.92% 3.42% 4.26% 4.11% 2.65% 2.27% Wood and wood products 1.32% 1.19% 0.87% 1.38% 1.37% 1.22% Paper, pulp and print 3.17% 3.01% 2.62% 2.96% 2.72% 2.60% Chemical and petrochemical 6.22% 5.67% 7.21% 6.93% 8.17% 8.68% Non-metallic minerals 3.88% 3.59% 2.45% 2.74% 2.65% 2.35% Basic metals 1.70% 1.37% 2.13% 2.45% 2.09% 2.25% Machinery 11.08% 12.17% 11.62% 16.87% 20.38% 20.90% Transport equipment 5.91% 5.89% 7.49% 6.26% 7.78% 10.43% Other industries 4.78% 4.80% 4.91% 5.84% 6.14% 6.28% Construction 36.22% 37.20% 28.87% 27.95% 26.33% 23.16% Commercial and public services 68.09% 70.14% 76.25% 71.11% 73.20% 75.36% Activities of households as employers (with NACE code T) is the only economic activity group with no match in our scheme and therefore its value added (0.9% of the total in 2017 for Spain) is not included in this table 4.6 Transport sector composition Performing an analogous exercise to the one carried out on the GVA shares in the previous Subsection, we analyze in Table 9what the compositional change of the transport sector has been during our analysis period. It should be recalled that modal shifts in the transport sector are included within the contribution of the structural factor to the evolution of emissions, although it is true that changes in the economic structure play a more significant role in the structural factor than what the change in the modal composition of transport plays. We must recall from Fig. 7that the transport sector affects the change in aggregate emissions in an augmenting manner during the 1995–2007 sub-period and in a downward way during the 2007–2017 sub-period. This is perfectly consistent with what we learn from Table 9. During the 1995–2007 sub-period, there is an increase in the share of aviation (for passengers) and road transport (for goods), which are typically energyand emission-intensive transport modes, hence inducing an upward pressure on emissions both in Spain and in the EU28. On the other hand, during the 2007–2017 sub-period, the share of rail transport for passengers increased both in Spain and in the EU28, and since this is a more energy-efficient mode, it leads to downward pressure on emissions. 123 SERIEs (2021) 12:151–229 205 Table 9 Transport mode composition Mode Spain EU28 1995 2007 2017 1995 2007 2017 Passenger transport (% of total PKM) Road 90.17% 88.57% 87.37% 90.41% 89.84% 89.04% Rail 6.37% 6.14% 8.05% 8.52% 8.62% 9.49% Aviation 3.46% 5.29% 4.58% 1.06% 1.55% 1.46% Freight transport (% of total TKM) Road 80.66% 84.26% 80.76% 67.34% 72.61% 73.54% Rail 3.95% 2.68% 3.03% 20.28% 17.05% 16.11% Navigation 13.16% 10.92% 13.42% 6.38% 5.49% 5.64% Pipeline 2.23% 2.14% 2.80% 6.00% 4.85% 4.71% 4.7 End-use energy efficiency Consistently with Fig. 5, the influence of the apparent or observed end-use energy efficiency on emissions is one of the major differences between the EU28 and Spain. While in the EU28 the apparent end-use energy efficiency (measured as energy unit consumption, i.e., final energy consumption per physical output/item) is increasing throughout the period under consideration, and is a major inhibitor of emissions, in Spain such apparent efficiency has not improved at all (for any of the sub-periods), which means that emissions are not reduced in Spain as much as they could have been if an apparent end-use efficiency trend such as that observed in the EU28 had been observed. However, as discussed previously in Sect. 2.5, there are many driving forces driving the apparent energy end-use efficiency from behind. One must note that the observed physical or apparent end-use energy efficiency need not be an accurate measure of the actual technological progress. Therefore, as discussed in Sect. 3.4, it is necessary to discern between what is actually driving the apparent or observed energy efficiency. And to do so, we subject such observed or apparent end-use energy efficiency to a further decomposition and we examine the role played by (1) technical energy efficiency or actual energy savings, (2) rebound effects, and (3) other factors (where the infra-utilization of the installed energy equipment can be a key contributor) in its developments. Firstly, on the basis of the technical end-use energy efficiency indexes calculated in Sect. 3.4 and the rebound effect estimates of Adetutu et al. (2016), we can analyze the aggregate evolution of the apparent end-use energy efficiency both in Spain and in the EU28 and discover which components are effectively driving this evolution. What we can observe in Fig. 9is that, while the apparent or observed end-use energy efficiency has decreased notably in Spain, it has increased considerably in the EU28 from 1995 to 2017. One could think that the EU28 is becoming more energyefficient than Spain, but this is completely misleading. What we observe is that end-use technical energy efficiency has improved steadily even more in Spain than in the EU28 123 206 SERIEs (2021) 12:151–229 Panel A: Spain 75 100 125 150 Index (base 1995) 1995 1998 2001 2004 2007 2010 2013 2017 Apparent EE Technical EE Rebound effect Other factors Panel B: EU28 75 100 125 150 1995 1998 2001 2004 2007 2010 2013 2017 Apparent EE Technical EE Rebound effect Other factors Fig. 9 Contributors to aggregate apparent end-use energy efficiency as a whole. So what could be making apparent energy efficiency decrease in Spain and increase in the EU28? Our simplest explanation is that this difference is due to a greater infra-utilization of energy equipment installed in Spain, among other factors. Although the observed rebound effect is a somewhat more negative influence in Spain than in the EU28, what really differentiates these regions in their apparent energy efficiency are other factors.40 Decreases of the apparent energy efficiency that cannot be explained by rebound effects may be due to an inefficient use of the equipment, as it is often observed during economic recessions. This is consistent with the Ministerio de Turismo, Energía y Agenda Digital (2017). They state that, the energy consumption of does not decrease proportionally to the activity in Spain as the observed efficiency of most equipment drops, as they are not used at their maximum rated capacity. It means that part of its energy consumption is independent of the production level. This is why we believe that infra-utilization is a key component of the other-factors contributor, although we cannot state it with certainty since we cannot decompose further said contributor. On the other hand, it can be seen that the influence of other factors on the evolution of apparent energy efficiency is not only non-negative in the EU28, but contributes positively to this evolution. However, we are not able with our analysis to discern what these other possible factors might be. This makes the Spanish case particular in terms of observed energy efficiency. That is why, since we have more disaggregated data for Spain on the basis of the study by Peña-Vidondo et al. (2012), we analyze which sectors of the economy would be conducting the evolution of end-use energy efficiency, both apparent and technical. In Fig. 10, we can observe the index-points change in the apparent end-use energy efficiency of each sector that is attributed to each factor. Itcan beseenthattheapparent energyefficiency fellinallsectorsin Spainduringthe period1995–2007,thisfallbeingespeciallyaccentuatedintheagriculturalandservices sectors, with decreases in the apparent energy efficiency of 23 and 18 index points, respectively.However,technicalefficiencyincreased inall sectors.Butrebound effects and other factors (mainly, the infra-utilization of energy equipment) lead to a decrease inapparent efficiency,withtheinfluenceofthese otherfactorsbeing especiallyrelevant in the agricultural and household sectors. Nevertheless, households are the only ones 40 Rebound effect estimates by Adetutu et al. (2016) for Spain and the EU are around 60% during the whole period of analysis. 123 SERIEs (2021) 12:151–229 213 employed (studied through IDA decomposition methods) have in the evolution of the energy-related CO2emissions both in Spain and in the EU28. Thus, we refer to this hybrid integrated approach, which benefits from the advantages of both SDA and IDA techniques, as input–output logarithmic mean Divisia index (IO-LMDI, hereafter) decomposition method. Further, with our methodological approach, we also provide an allocation diagram scheme for assigning the responsibility of primary energy requirements and CO2emissions to the end-use sectors including both economic and non-productive sectors. Moreover, we are able to (3) analyze more potential influencing factors than those typically examined. In addition, we (4) proceed in a way that reconciles energy intensity and energy efficiency metrics. Finally, we (5) distinguish between technical and observed end-use energy efficiency taking into account potential rebound effects and other factors. Therefore, we believe that our work represents a novelty and offers clear value added to past studies devoted to the study of the energy-related CO2emissions trends both in Spain and in the EU28. To report of our findings, we make a distinction between two clear sub-periods: 1995-2007 and 2007-2017. In the first mentioned sub-period, the CO2emissions associated with final energy consumption increased by 43% in Spain (from 241.12 Gg CO2 to345.07 GgCO2)andby4% intheEU28(from 4005.45GgCO2to4171.02Gg CO2). The population growth, a rising per capita disposable income and other social factors were the main drivers behind this development. These effects were much greater in the EU28than in Spain. Theselargeeffectsin theEU28reversedthe verypositiveeffecton emissions reduction that the increase in apparent or observed energy efficiency of the end-usesectorshad,resultinginanincreaseofthetotalemissionsduringthementioned sub-period. However, this is not what can be observed in Spain, since although the first factors mentioned above did not contribute to the same extent as in the EU28 to the increase in emissions, the evolution of apparent end-use energy efficiency in the Spanish end-use sectors, unlike in the EU28, was driving further the increase in total emissions. Nevertheless, we cannot say that Spain experienced a decrease in its technical end-use energy-efficiency. Indeed, Spain witnessed an increase in such technical enduse energy efficiency. However, the infra-utilization of the installed energy equipment andthe reboundeffectsdrovedowntheapparentor observedend-useenergyefficiency. On the other hand, from 2007 to 2017, the CO2emissions associated with final energy consumption fell by 19% in Spain (from 345.07 Gg CO2to 280.10 Gg CO2) and by 11% in the EU28 (from 4005.45 Gg CO2to 3710.51 Gg CO2). At the EU28 level, this evolution is mainly determined by the increase in energy efficiency both in final consumption (apparent end-use efficiency) and in the energy transformation sector (which means that less and less primary energy is required to produce the necessary energy demanded by the end-use sectors), by the evolution of the productive structure toward sectors that generate fewer emissions, and by a lower use of fossil fuels for energy transformation. These factors offset the increases in emissions related to population growth, increased income and other social factors, resulting in a decrease in aggregate emissions. Spain has experienced a similar evolution, but the gains in apparent end-use energy efficiency in both final consumption and energy transformation that can be observed in the EU28 are not detected in Spain. This means that the Spanish emissions have not been reduced from 2007 to 2017 as much as they could potentially have been if the same apparent end-use energy efficiency improvements as 123 214 SERIEs (2021) 12:151–229 in the EU28 had been observed in Spain. In Spain, the main factor behind the reduction of emissions is the economic structural transition toward less emission-generating sectors and its shift toward higher value products (captured by the monetary to physical output relation factor), changes that are not observed to the same extent in the EU28. However, as in the previous sub-period, the infra-utilization of the installed energy equipment (mainly in industrial and services sectors) and the rebound effects drove down the apparent or observed end-use energy efficiency resulting in an increase of the CO2emissions associated with final energy consumption. Analyzing these same developments from a sectoral perspective (see Fig. 7), we can note how the transport and services sectors were the main contributors to the increase in emissions that occurred from 1995 to 2007 in the EU28. In Spain, the transportation and the services sector, although to a lesser extent than in the EU28, also contributed to the increase in emissions. Contrarily, despite households and industrial sectors being an inhibitor of the increase in emissions in the EU28, they were a clear driving force of the Spanish emissions during said sub-period. However, during the sub-period 2007– 2017, households and especially industry were clear inhibitors and led to a decline in emissions both in Spain and the EU28. In this latter sub-period, the transport sector also contributed significantly to the fall in emissions, with this contribution being much greater in Spain than in the EU28. As a final conclusion we can say that Spain is on a path toward the decarbonization of the economy. However, despite the fact that this trend is more accentuated than in the EU28, there is still much to be done in order to reverse the huge increases in emissions that occurred in the period of time prior to the 2007 crisis. Furthermore, we can state that the technical energy efficiency of the Spanish economy is improving even more than that of the EU28, although all these gains are exceeded by the losses that the country suffers due to the installation of energy equipment above its potential. That is, there is an energy infrastructure that does not yield its maximum potential, but which has very high fixed energy costs that reduce the observed energy efficiency and puts at risk the achievement of the emissions and energy consumption targets set by the European institutions. The results that we present give interesting information related to the drivers and inhibitorsof the energy-relatedCO2emissionsboth in Spain andinthe European economy as a whole. These results are useful not only for researchers, but also for private utility companies and policy-makers, as they can contribute to construct and implement the optimal saving and efficiency measures to achieve the mentioned climate and energy targets. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Compliance with ethical standards Conflict of interest This article is entirely the author’s own work and no sources or aids other than the ones listed have been employed. This article has not been published before and it is not under consideration for publication anywhere else. The author declares that he has no conflict of interest. Informed consent This article does not contain any information that requires informed consent. 123 SERIEs (2021) 12:151–229 215 Human and animal rights This article does not contain any studies with human participants or animals performed by the author. Open Access ThisarticleislicensedunderaCreativeCommonsAttribution4.0InternationalLicense,which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Appendix See Tables 12,13,14,15,16 and Fig.12 123 216 SERIEs (2021) 12:151–229 Table 12 Example of energy input–output table Qi,jMariAviFECiFNEC iDLiDi f fiExpiYiQi(demand) 12 3 456788+18+2 Coal and coal products 1 0.5 0 0 1.2 0 0 0 0 0 0 0 0 1 0.1 0 0 1 2.1 3.8 Crude, LNG and raw materials 2 0 0.3 12.3 0 0 0 0 0 0 0 0.1 0 0 0.2 0 0 0 0.3 12.9 Oil derivatives 3 0 0 0.4 0.1 0 0 0 0 0 0 0.3 0.2 16 0.1 0.1 0 0 16.7 17.2 Electricity 4 0 0 0 0.7 0 0 0 0 0 0 0 0 6 0 1 0 0 7 7.7 Hydroelectric power 5 0 0 0 3.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.2 Renewables 6 0 0 0.1 0.2 0 0.1 0 0 0 0 0 0 3 0 0 0 1.5 4.5 4.9 Natural gas 7 0 0 0 2.9 0 0 1.1 0 0 0 0 0 12 0 0 0 0 12 16 Nuclear 8 0 0 0 1.3 0 0 0 0 0 0 0 0 0 0 0 0.1 0 0.1 1.4 Refined oil imports 8+1 0 0 0.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2 Electricity imports 8+2 0 0 0 0.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 Primary productionj–39.60 03.24.9101.300– –– – – – – 32 Recycled and recoveredj– 0.50.10 000000 0 – – – – – – – – 0.6 Stock changej–00.2−0.4000000 0 – – – – – – – –−0.2 Transformation outputj–0.31 17.47.4000000– –– – – – – – 26.1 Positive net import balancej– 0 2 0.2 0.3 0 0 6 0.1 0.2 0.3 – – – – – – – – 9.1 Qj(supply) – 3.8 12.9 17.2 7.7 3.2 4.9 16 1.4 0.2 0.3 – – – – – – – 67.6 Qidenotes the total energy needs of energy i,Qi,jdenotes the intermediate consumption of each energy ito produce energy j,Maridenotes the consumption of energy ifor international maritime bunkers, Avidenotes the consumption of energy ifor international aviation, FECidenotes the final consumption of energy i(including final consumption of the energy branch), FNECidenotes the final non-energy consumption of energy i,DL idenotes the distribution losses of energy i,Diffidenotes the statistical difference between Qicalculated from the supply side and Qicalculated from the demand side, Expidenotes the positive net export balance of energy i,andYidenotes the final demand of energy i 123 SERIEs (2021) 12:151–229 217 Table 13 List of energy products and their carbon content i Product Group vi 1 Anthracite Solid fossil fuels 26.8 2 Coking coal Solid fossil fuels 25.8 3 Other bituminous coal Solid fossil fuels 25.8 4 Sub-bituminous coal Solid fossil fuels 26.2 5 Lignite Solid fossil fuels 27.5 6 Patent fuel Solid fossil fuels 26.6 7 Coke oven coke Solid fossil fuels 29.2 8 Gas coke Solid fossil fuels 29.2 9 Coal tar Solid fossil fuels 22.0 10 Brown coal briquettes Solid fossil fuels 26.6 11 Gas works gas Manufactured gases 12.1 12 Coke oven gas Manufactured gases 12.1 13 Blast furnace gas Manufactured gases 70.9 14 Other recovered gases Manufactured gases 14.9 15 Peat Peat and peat products 28.9 16 Peat products Peat and peat products 28.9 17 Oil shale and oil sands Oil shale and oil sands 24.6 18 Crude oil Oil and petroleum products 20.0 19 Natural gas liquids Oil and petroleum products 17.5 20 Refinery feedstocks Oil and petroleum products 20.0 21 Additives and oxygenates Oil and petroleum products 49.6 22 Other hydrocarbons Oil and petroleum products 21.0 23 Refinery gas Oil and petroleum products 15.7 24 Ethane Oil and petroleum products 16.8 25 Liquefied petroleum gases Oil and petroleum products 17.2 26 Motor gasoline Oil and petroleum products 18.9 27 Aviation gasoline Oil and petroleum products 19.1 28 Gasoline-type jet fuel Oil and petroleum products 19.1 29 Kerosene-type jet fuel Oil and petroleum products 19.5 30 Other kerosene Oil and petroleum products 19.6 31 Naphtha Oil and petroleum products 20.0 32 Gas oil and diesel oil Oil and petroleum products 20.2 33 Fuel oil Oil and petroleum products 21.1 34 White spirit Oil and petroleum products 20.0 35 Lubricants Oil and petroleum products 20.0 36 Bitumen Oil and petroleum products 22.0 37 Petroleum coke Oil and petroleum products 26.6 38 Paraffin waxes Oil and petroleum products 20.0 39 Other oil products n.e.c. Oil and petroleum products 20.0 40 Natural gas Natural gas 15.3 123 218 SERIEs (2021) 12:151–229 Table 13 continued i Product Group vi 41 Hydro Renewables and biofuels 0.0 42 Tide, wave, ocean Renewables and biofuels 0.0 43 Wind Renewables and biofuels 0.0 44 Solar photovoltaic Renewables and biofuels 0.0 45 Solar thermal Renewables and biofuels 0.0 46 Geothermal Renewables and biofuels 0.0 47 Primary solid biofuels Renewables and biofuels 27.9 48 Charcoal Renewables and biofuels 30.5 49 Biogases Renewables and biofuels 14.9 50 Renewable municipal waste Renewables and biofuels 27.3 51 Pure biogasoline Renewables and biofuels 19.3 52 Blended biogasoline Renewables and biofuels 18.9 53 Pure biodiesels Renewables and biofuels 19.3 54 Blended biodiesels Renewables and biofuels 20.1 55 Pure bio jet kerosene Renewables and biofuels 19.3 56 Blended bio jet kerosene Renewables and biofuels 19.5 57 Other liquid biofuels Renewables and biofuels 21.7 58 Ambient heat (heat pumps) Renewables and biofuels 0.0 59 Industrial waste (non-renewable) Non-renewable waste 39.0 60 Non-renewable municipal waste Non-renewable waste 25.0 61 Nuclear heat Nuclear heat 0.0 62 Heat Heat 0.0 63 Electricity Electricity 0.0 The list of products is that appearing in the energy balances published by Eurostat (2020c). viis the carbon content per unit of calorific value of the energy product i, expressed in kg-CO2/GJ, and is extracted from the Intergovenmental Panel on Climate ChangeIntergovenmental Panel on Climate Change (2006). The vi associated to oil shale and oil sands is the mean of the vifor shale oil and oil shale and tar sands. The viassociated to primary solid biofuels is the mean of the vifor wood (and wood waste), sulphite lyes (black liquor), and other primary solid biomass. Finally, the viassociated to blended biofuels is calculated assuming that 90% of the value is given by the carbon content of conventional fuel and 10% of the value is given by the carbon content of the pure biofuel 123 SERIEs (2021) 12:151–229 219 Table 14 Annual total change of emissions CO2and its influencing factors (in KTOE) in Spain Year Total POP INC SOC COM STR INTR 1996 −0.57089 0.977923 2.133976 8.439514 0.074566 0.877572 −0.85301 1997 4.217855 1.038496 1.758623 3.735246 0.072389 0.790313 1.751609 1998 14.42058 0.997271 3.84462 1.785072 0.074529 0.441027 0.354878 1999 21.27917 1.088614 6.442058 4.525198 0.096206 0.585414 1.052812 2000 11.32436 1.36754 5.990556 4.291115 0.09868 0.207863 0.487957 2001 0.378722 2.604685 7.002508 2.80018 0.099789 0.674091 −0.8573 2002 16.7021 5.628298 1.735142 −0.17505 0.040184 −1.71855 −1.44396 2003 14.00605 5.32808 7.367625 3.259047 0.037436 −0.21626 −1.81705 2004 14.50217 5.669869 3.667058 0.301037 0.045451 −2.13189 0.114553 2005 18.25691 5.59958 3.110804 1.269109 0.05476 −1.62132 −2.41935 2006 −12.3097 6.015616 1.147315 0.129558 0.049727 −1.53684 −0.84673 2007 3.380313 6.688847 3.508711 3.308696 0.046628 −2.35154 −2.6347 2008 −21.7683 4.132925 5.491006 −5.36107 0.051359 −3.45298 −1.48687 2009 −28.8785 1.63411 −1.36701 −6.98107 0.051066 −6.81982 −4.52604 2010 −7.02718 1.115428 0.434884 −3.26922 0.047804 −3.23759 8.017159 2011 −5.00705 0.910755 −1.33279 −1.14252 0.038391 −4.32005 2.295485 2012 1.227012 −0.53827 −7.65934 −4.95822 0.045652 −5.98065 3.922819 2013 −25.9982 −1.23103 −1.40452 0.525003 0.041614 −0.62327 1.246895 2014 −3.54988 −0.34154 −1.45966 −0.9979 0.040155 0.030052 2.275833 2015 21.25216 −0.05316 1.79948 4.335375 0.038067 −0.42413 0.859243 2016 −8.56551 0.50846 5.541196 2.749434 0.037597 1.394246 2.638563 2017 14.88578 0.761135 3.972913 0.445944 0.038149 −0.26939 −6.77186 Year OUT EFF USE WEA MIX CONV EMI 1996 −5.47709 −0.2488 0.033838 2.207881 0.041264 −0.59201 −8.18652 1997 1.358993 −1.05467 0.078376 −4.71392 0.060313 −6.29808 5.640164 1998 0.000318 4.326382 0.053743 6.56696 −1.90976 0.137305 −2.25177 1999 −4.19961 −1.25093 −0.01448 0.833054 0.489081 3.285742 8.346014 2000 0.100046 6.988206 −0.03254 −1.69302 −2.02124 −3.89675 −0.56405 2001 −6.59002 6.689092 −0.06315 0.685032 0.745956 −7.40629 −6.00585 2002 0.277748 −0.42473 0.146934 −4.4999 0.048431 8.381744 8.705813 2003 −4.36544 9.156711 0.596855 8.474452 −1.73521 −6.90104 −5.17916 2004 −0.70014 8.839238 −0.0135 −1.36603 −1.22468 −0.71666 2.017847 2005 1.559912 4.22147 −0.07223 1.189202 0.418171 −0.28427 5.231076 2006 4.768466 −9.64875 −1.47655 −4.02787 1.973498 −1.83976 −7.01741 2007 0.921259 −2.24513 0.593257 −3.39057 −0.60567 −3.74075 3.281269 2008 −13.5392 2.516414 0.17381 2.273922 0.868287 −1.93181 −11.504 2009 −8.20418 5.288846 −0.12586 1.189062 1.319104 −5.60236 −4.7344 2010 −5.3461 6.513455 −0.10805 3.987472 −1.21364 0.222256 −14.191 2011 −1.34947 −5.37416 −0.10641 −8.51115 0.47094 2.158318 11.2556 2012 −0.07935 8.009776 0.253437 8.519753 −0.39211 0.346412 −0.26291 2013 0.300082 −8.81855 −0.1374 −2.50927 −0.88733 −2.95221 −9.54822 2014 1.055807 −3.27937 0.023094 −7.81771 −0.74986 6.519039 1.152182 2015 −0.01951 −0.53873 −0.74693 7.151668 0.003283 2.439702 6.407794 2016 −8.27973 2.394104 −0.40087 1.379293 −0.4182 −5.82534 −10.2843 2017 6.33422 1.583135 0.267753 −1.21506 −0.20436 1.249768 8.693434 123 220 SERIEs (2021) 12:151–229 Table 15 Annual total change of emissions CO2and its driving factors (in KTOE) in the EU28 Year Total POP INC SOC COM STR INTR 1996 238.1859 6.619692 32.4255 18.82297 5.428458 −19.6768 2.123793 1997 −194.711 20.20656 42.01313 24.07666 2.462667 −8.37472 9.374708 1998 51.52232 5.862608 54.41516 28.28837 2.642897 −11.3007 −7.46749 1999 −144.159 5.629107 50.81539 28.16658 3.059867 −4.86625 2.839164 2000 11.23863 7.962826 68.95427 32.61742 3.705448 −4.77716 −8.02215 2001 127.2738 5.950427 41.83606 22.28843 2.419093 −13.8093 −1.49202 2002 −65.2608 14.32531 18.5426 19.69544 2.273714 −8.21993 5.826487 2003 240.5691 15.69937 17.02448 9.160892 2.758919 −8.03006 −18.7642 2004 −53.562 17.53439 43.6581 46.00667 1.902231 14.38634 −7.62641 2005 36.95267 15.69573 35.47033 10.70141 2.589056 −20.9884 −8.69967 2006 6.458884 15.94102 59.7578 25.42597 4.262218 2.489451 −25.9133 2007 −88.6637 16.83316 58.11647 21.66927 1.258312 −1.28395 −15.0002 2008 −21.9231 14.86762 7.213549 −1.0821 1.779377 −27.15 −14.7542 2009 −244.62 8.640492 −91.0741 −40.1201 1.08125 −75.1633 18.96444 2010 268.8639 −1.6495 41.60523 26.22048 11.26409 25.5327 5.623849 2011 −323.967 8.605468 32.82243 −6.29369 2.202521 −4.6967 −15.418 2012 117.3094 8.623308 −10.4338 −15.7536 1.744806 −32.81 25.51959 2013 −115.603 15.98759 −0.56431 7.435797 3.759218 −14.5583 4.982199 2014 −296.494 9.350781 26.34362 9.127094 −0.93058 9.787204 −7.72821 2015 146.4297 11.81807 29.31857 19.72769 0.926677 −0.53102 −4.50732 2016 −4.76435 8.654791 30.84689 22.43054 0.528465 7.20809 0.951889 2017 14.19114 7.184669 40.50846 28.66168 −0.67499 5.188793 −16.6166 Year Total OUT EFF USE WEA MIX CONV EMI 1996 238.1859 −25.3244 157.8292 −1.50417 89.25372 −18.4454 8.948447 −18.3152 1997−194.711 7.907139−150.19 2.311971−108.183 −4.06257 −7.42021 −24.8336 1998 51.52232 4.785894−51.7745 0.967348 39.83442 −13.8982 −5.40895 4.575565 1999−144.159 −17.4421 −93.6559 0.656572−46.7834 −6.29877 −44.1169 −22.1625 2000 11.23863 0.283031−86.8499 3.648241−42.4988 −0.00664 24.70678 11.51532 2001 127.2738 −25.7618 52.59604 −3.77281 73.44724 −1.86531 −17.0517 −7.51061 2002 −65.2608 −9.40715 −75.93 3.197478−54.9415 3.016273 5.016098 11.34431 2003 240.5691 9.199883 76.11225 −1.28505 92.2456 4.353734 25.7537 16.3396 2004 −53.562 −19.2259 −56.0335 0.593244−49.7137 6.464886−30.7124 −20.796 2005 36.95267 1.763631−19.633 0.98623 5.570676 −1.9314 10.33633 5.091802 2006 6.458884 11.58578 −87.326 0.216632−18.1294 10.37654 −8.1247 15.89699 2007 −88.6637 −11.7445 −138.429 7.558154−42.1978 6.785569−22.1727 29.94356 2008 −21.9231 −6.86816 49.64785 −2.17602 14.20892 −0.7833 −12.8956 −43.931 2009−244.62 −37.2138 0.702598 0.902307 26.2053 1.365501−32.1886 −26.7217 2010 268.8639 −2.51741 59.60244 −1.38025 128.2929 0.600412−15.2259 −9.10508 123 SERIEs (2021) 12:151–229 221 Table 15 continued Year Total OUT EFF USE WEA MIX CONV EMI 2011−323.967 −4.27679−195.819 3.24699−172.369 6.442389 6.821793 14.7651 2012 117.3094 −28.3081 50.9394 −2.03061 109.678 −0.71502 −9.43263 20.28819 2013−115.603 −8.12284 −23.5995 −1.7896 −46.0613 −3.35182 −14.9633 −34.7571 2014−296.494 −20.1963 −186.825 2.613144−116.342 7.827374 3.045326 −32.5665 2015 146.4297 −3.86455 28.93652 −0.58847 75.68251 −9.11639 2.407016 −3.77961 2016 −4.76435−18.9427 10.26233 −0.89885 8.435162 −5.10129 −42.6591 −26.4805 2017 14.19114 5.347963−27.5449 −1.12335 15.89771 −8.54656 −28.5034 −5.58827 Table 16 Sector matching scheme noitpmusnoclaniFECANrotces-buSrotceSpuorgrotceS Economic sectors Agriculture Agriculture and forestry A01, A02 Agriculture and forestry consumption from energy balances secnalabygrenemorfnoitpmusnocgnihsiF30AgnihsiF Industry Energy sector and extractive industries B, C19, D Energy branch + mining and quarrying consumption from energy balances Food, breverages and tobacco C10 - C12 Food, beverages and tobacco consumption from energy balances Textile and leather C13 - C15 Textile and leather consumption from energy balances Wood and wood products C16 Wood and wood products consumption from energy balances Paper, pulp and print C17, C18 Paper, pulp and printing consumption from energy balances Chemical and petrochemical C20, C21 Chemical and petrochemical consumption from energy balances Non-metallic minerals C23 Non-metallic minerals consumption from energy balances Basic metals C24 Iron and steel + non-ferrous metals consumption from energy balances Machinery C25, C26, C27, C28 Machinery consumption from energy balances Transport equipement C29, C30 Transport equipment consumption from energy balances Other industries C22, C31, C32 Not elsewhere specified industry consumption from energy balances secnalabygrenemorfnoitpmusnocnoitcurtsnoCFnoitcurtsnoC Commercial and public services Space heating C33,E,G-S,U Commercial and public services + not elsewhere specified consumption from energy balances and end-use shares -nocdefiicepserehwesleton+secivrescilbupdnalaicremmoCretawtoH sumption from energy balances and end-use shares -nocdefiicepserehwesleton+secivrescilbupdnalaicremmoCgnikooC sumption from energy balances and end-use shares -nocdefiicepserehwesleton+secivrescilbupdnalaicremmoCgninoitidnoCriA sumption from energy balances and end-use shares Electric appliances / lighting Commercial and public services + not elsewhere specified consumption from energy balances and end-use shares Households Households esu-dnednasecnalabygrenemorfnoitpmusnocsdlohesuoH-gnitaehecapS shares esu-dnednasecnalabygrenemorfnoitpmusnocsdlohesuoH-retawtoH shares esu-dnednasecnalabygrenemorfnoitpmusnocsdlohesuoH-gnikooC shares esu-dnednasecnalabygrenemorfnoitpmusnocsdlohesuoH-gninoitidnoCriA shares Electric appliances / lighting esu-dnednasecnalabygrenemorfnoitpmusnocsdlohesuoHshares Transport Passenger -edomdnasecnalabygrenemorfnoitpmusnoctropsnartdaoR-tropsnartdaoR shares -edomdnasecnalabygrenemorfnoitpmusnoctropsnartliaR-tropsnartliaR shares Domestic aviation transport secnalabygrenemorfnoitpmusnocnoitaivacitsemoDFreight -edomdnasecnalabygrenemorfnoitpmusnoctropsnartdaoR-tropsnartdaoR shares -edomdnasecnalabygrenemorfnoitpmusnoctropsnartliaR-tropsnartliaR shares Domestic navigation transport secnalabygrenemorfnoitpmusnocnoitagivancitsemoDsecnalabygrenemorfnoitpmusnoctropsnartenilepiP-tropsnartenilepiP Activities of households as employers (with NACE code T) ise the only economic activity group with no match in our scheme 123 222 SERIEs (2021) 12:151–229 Fig. 12 Heating and coolind degree days See Table 17 and Figs 13 and 14. 123 SERIEs (2021) 12:151–229 229 Peña-Vidondo S, Arocena P, Gómez-Plana AG (2012) 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