Influence of Population Income on Energy Consumption and CO2 Emissions in Buildings of Cities

sustainability Article Influence of Population Income on Energy Consumption and CO2Emissions in Buildings of Cities Irene M. Zarco-Soto, Fco. Javier Zarco-Soto and Pedro J. Zarco-Periñán *   Citation: Zarco-Soto, I.M.; Zarco-Soto, F.J.; Zarco-Periñán, P.J. Influence of Population Income on Energy Consumption and CO2 Emissions in Buildings of Cities. Sustainability 2021,13, 10230. https:// doi.org/10.3390/su131810230 Academic Editors: Massimiliano Capezzali, Jakob Rager and Antonio Caggiano Received: 23 July 2021 Accepted: 8 September 2021 Published: 13 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Departamento de Ingeniería Eléctrica, Escuela Superior de Ingeniería, Universidad de Sevilla, 41092 Sevilla, Spain; [email protected] (I.M.Z.-S.); fjzar[email protected] (F.J.Z.-S.) *Correspondence: pzar[email protected] Abstract: More than half of the world’s population lives in cities. A large part of the emissions and energy consumption corresponds to buildings, both in the residential sector and in the service sector. This means that a large part of the measures taken by governments to reduce energy consumption and greenhouse gas emissions are focused on this sector. With this background, this paper studies energy consumption in city buildings and the CO 2 emissions they produce. It only makes use of publicly available data. The analysis is made from the point of view of income per inhabitant, and the results are obtained per inhabitant and household. To facilitate the analysis of the results, an index has been defined. The main contributions of this work are to analyze energy consumption and emissions due to buildings, study them from the point of view of the income of their inhabitants, and consider cities individually. The proposed methodology has been applied to the case of Spain. A total of 145 Spanish cities that have more than 50,000 inhabitants have been studied. The results show that the higher the income, the higher the consumption and emissions. Electricity consumptions are almost inelastic, while those of thermal origin are greatly influenced by the level of income. Regarding CO 2 emissions, the percentage of emissions of electrical origin with respect to total emissions is higher than that of thermal origin. In addition, the lower the income, the higher the percentage of emissions of electrical origin. Keywords: income; CO2emissions; energy consumption; cities; buildings; Spain 1. Introduction Among the main greenhouse gases (GHG), CO 2 is the most worrying since it represents 80% of total emissions. Furthermore, more than 80% of emissions are due to energy, with only a third corresponding to transport [ 1 ]. A very important part of this energy consumption and of these emissions corresponds to buildings, both in the residential sector and in the service sector. They account for 40% of emissions and 36% of energy consumption [2]. More than half of the world’s population lives in cities. They consume between 60 and 80% of energy and produce 75% of carbon emissions [ 3 ]. Therefore, it is clear how important cities are to achieve efficient use of energy and a reduction in GHG emissions. In fact, within the United Nations sustainable development goals, goal 11 focuses specifically on them, although it is also present in goals 7, 12, and 13 [3]. The COVID − 19 pandemic has mainly affected cities, where more than 90% of cases have occurred [ 4 ], and also caused a major economic crisis. Because of this, the European Union has launched a EUR 750 billion recovery plan, Next Generation EU, based on The European Green Deal with the aim of building a greener, more inclusive, digital, and sustainable Europe [ 5 ]. To achieve this, it has issued an action plan for boosting the circular economy. This plan focuses on six fields of action, one of which is construction and buildings [6]. Therefore, considering the importance that cities in general and buildings have on saving energy and reducing GHG emissions, policy makers have focused their attention on Sustainability 2021,13, 10230. https://doi.org/10.3390/su131810230 https://www.mdpi.com/journal/sustainability Sustainability 2021,13, 10230 2 of 18 them. For this reason, this article studies the consumption of electrical and thermal energy (natural gas) in city buildings. It should be taken into account that these types of energy are those usually consumed in buildings [ 7 ]. This knowledge of the relationship between population income, emissions, and energy consumption aims to be one more element that helps utilities and policy makers in their decision-making task to favor the reduction of emissions, energy savings, and infrastructure planning. Other studies have provided this help by providing information on cities through the geographical information system to characterize the energy performance [ 8 ] and consider them as positive energy districts [ 9 ]. Considering the whole city as an entity, this study presents a methodology to analyze the influence that income has on both energy consumption and emissions in each city. In addition, it makes use of public information in order not to have to invest time or money in studies and surveys to obtain the information. The information exists, but it must be treated appropriately. As in other studies, the objective is to obtain results from some variables, in this case, from population income. For this, the method used is based on others that, although classic, have still been used recently. These are based on the use of information from statistical databases [10–12] and on the creation of synthetic populations [13]. Finally, the application of the proposed methodology to the specific case of Spanish cities with more than 50,000 inhabitants is shown. To the knowledge of the authors, a similar analysis has not been carried out. The main contributions of this paper are to analyze energy consumption and emissions due to buildings at the city level, study them from the point of view of the income of their inhabitants, and take into consideration all the cities of a country separately and not in aggregate. The article is structured as follows: Section 2presents studies that relate energy consumption and emissions in cities with the income of their inhabitants; Section 3describes the proposed methodology, based on the use of information available from different official databases; Section 4shows the detail of the proposed application to the case of Spanish cities with more than 50,000 inhabitants; Section 5presents the results and their discussion; and finally, Section 6summarizes the conclusions of the study. 2. Literature Review The works that study what is investigated by this paper are reviewed, and income is the variable whose influence is analyzed, so this review focuses on it. This study analyzes, based on the income of its inhabitants, the consumption of energy in the form of electricity and natural gas, and the CO 2 emissions produced in cities. However, what appears in the literature is the influence that certain variables have on each other. Among them are gross domestic product (GDP), CO 2 emissions, energy, and other variables that, in one way or another, may influence the geographical area of study. Even when the work refers to the variable economic growth, it essentially refers to GDP. The geographical areas considered are normally extensive: at the country level or even larger. Therefore, they do not descend to the city level and do not give them the importance that, progressively more, they are given by public administrations. Usually, the studies that use the income variable what they do is analyze the behaviors of households with low [ 14 ] or high income [ 15 ]. For the other cases, GDP is the variable used. Two types of research are those that are frequently found with this variable: prediction of consumption and degree of influence of it and other variables on certain parameters of interest. In this way, it is used for the prediction of electricity consumption [ 16 ], gas [ 17 ], electricity and gas [ 18 ], energy in general [ 19 ], or other resources in addition to energy [ 20 ]; additionally, it is used to analyze the influence of extreme events [ 21 ]. Other works investigate how GDP affects residential electricity consumption [ 22 ] or industry [ 23 ]. A large study covering 160 countries analyzes the relationship between GDP, electricity consumption, supply risk, and urbanization rate and concludes that the relationship is very sensitive to differences between countries [24]. A review of the studies that relate to the environment, economic growth, and energy (electric, nuclear, renewable, and non-renewable) has been carried out [ 25 ]. It concludes Sustainability 2021,13, 10230 3 of 18 that the results are not homogeneous or obvious. The review of the studies that relate to economic growth and energy consumption has also been carried out [ 26 ]. The analysis distinguishes between four types of energy consumption: total, electric, nuclear, and renewable. Depending on the study being analyzed, there is bidirectional causality, unidirectional, or even no relationship between them. Therefore, there are also no conclusive results. It is assumed that the cause may be due to the data used or the econometric approaches applied. As with electricity, the studies that analyze gas consumption do not include income as a variable either but rather GDP. In addition, what they do is a prediction of consumption or an analysis of different variables. Prediction is carried out both at the national level, using GDP, population, and gas price [ 27 ], and at the sector level, such as residential, making use of GDP, the price of gas, and heating degree days as variables [ 28 ]. The influence that some variables of interest have on others, such as energy consumption on economic growth, has also been analyzed, reaching the conclusion that natural gas has a lot of influence on GDP [29]. Regarding CO 2 emissions, ref. [ 30 ] reviews the studies on household CO 2 emissions and states that the importance of households on reducing emissions is reflected in the increase that is occurring in these studies. At the country level, household emissions in Ireland were studied. The conclusion was that the higher the income, the higher the emissions [ 31 ]. A similar study was carried out for France and the USA, obtaining identical conclusions [ 32 ]. Similar studies have also been conducted for China [ 33 , 34 ] and for a panel of 170 countries [ 35 ]. Income has also been used to assess the influence it has on energy consumption and emissions in the Chinese residential sector [ 36 ]. Thus, 31 provincial regions have been analyzed, reaching the conclusion that income and education level are the most important parameters. Regarding CO 2 emissions, predictions have been made, using GDP, in five West African countries [ 37 ] or the building sector [ 38 ]. However, the most common is once again the analysis of the influence that certain variables, including GDP, have on emissions. The relationship between emissions, GDP, and energy consumption has also been studied in different scenarios [ 39 ] and in different countries: in 54 African countries [ 40 ], in 17 countries in Europe and Asia [ 41 ], and in 30 Chinese provinces [ 42 ]. The results show that non-renewable energies have a positive causality on CO 2 emissions. However, if the energy comes from renewable sources, the effect is the opposite. GDP, considered as economic growth, is used to analyze the influence on CO 2 emissions. A review of the papers that relate CO 2 emissions and economic growth has been carried out by [ 43 ], finding that there is bidirectional causality between both variables. The relationship in different countries has also been analyzed: South Africa [ 44 ], Pakistan [ 45 ], China [ 46 ], and 47 countries around the world [ 47 ]. In all cases, the results obtained conclude that economic growth implies an increase in emissions. Even [ 48 ] analyzed 83 middle-income countries. In this work, it was found that energy use is the variable that most influences the increase in emissions and that the higher the income, the greater the emissions. Additionally, the different parameters that influence CO 2 emissions in the residential sector of China and India were investigated. The conclusion reached is that per capita income level is the factor that most influences CO 2 emissions [ 49 ]. However, the use of GDP, as it is a more generic variable, does not allow more detailed analysis, as it occurs when using income as a variable. Even the few works that indicate that they analyze the influence of income, what they essentially study is GDP per capita and not the concrete income of the population. Finally, at the city level, little research has been carried out. Emission studies in 49 Japanese cities [ 50 ], comparison of GHG emissions in 10 cities considering geophysical and technical factors [ 51 ], residential building sector in 4 Chinese cities [ 52 ], and 73 Chinese cities [ 53 ] have been realized. The findings show that the results varied widely from city to city. Therefore, the studies that deal with energy consumption do not focus on the influence that population income has on such important elements as buildings neither in its energy consumption nor its CO 2 emissions, and even less at the city level considered independently. Rather, they focus on the prediction of consumption in certain areas and the influence that Sustainability 2021,13, 10230 4 of 18 some variables have on others, using GDP as a variable. Therefore, to the knowledge of the authors, the investigation carried out in this work, considering energy consumption and CO2emissions of each city and the income of its inhabitants, is novel. 3. Materials and Methods In the proposed methodology, the study area to be considered is first established. Next, the criteria for selecting the cities to be analyzed within that area are defined. These cities are then classified according to the income of their inhabitants. Subsequently, the consumption data in each city are obtained. These consumptions are calculated per inhabitant and per household. Finally, CO2emissions are calculated from these consumptions. The data used are obtained from public sources and are provided by different administrations. All that available information must be treated appropriately to reach the desired results: know energy consumption and emissions due to buildings at the city level from the point of view of the income of their inhabitants. This information may be one more to be taken into account by utilities and policy makers. The methodological approach is shown in Figure 1. Although other factors (for example, the type of building or if household is owned or rented) may influence energy consumption in buildings, their removal is beyond the scope of the study. Therefore, it is possible influence has not been eliminated. Sustainability 2021, 13, x FOR PEER REVIEW 4 of 18 Finally, at the city level, little research has been carried out. Emission studies in 49 Japanese cities [50], comparison of GHG emissions in 10 cities considering geophysical and technical factors [51], residential building sector in 4 Chinese cities [52], and 73 Chinese cities [53] have been realized. The findings show that the results varied widely from city to city. Therefore, the studies that deal with energy consumption do not focus on the influence that population income has on such important elements as buildings neither in its energy consumption nor its CO2 emissions, and even less at the city level considered independently. Rather, they focus on the prediction of consumption in certain areas and the influence that some variables have on others, using GDP as a variable. Therefore, to the knowledge of the authors, the investigation carried out in this work, considering energy consumption and CO2 emissions of each city and the income of its inhabitants, is novel. 3. Materials and Methods In the proposed methodology, the study area to be considered is first established. Next, the criteria for selecting the cities to be analyzed within that area are defined. These cities are then classified according to the income of their inhabitants. Subsequently, the consumption data in each city are obtained. These consumptions are calculated per inhabitant and per household. Finally, CO2 emissions are calculated from these consumptions. The data used are obtained from public sources and are provided by different administrations. All that available information must be treated appropriately to reach the desired results: know energy consumption and emissions due to buildings at the city level from the point of view of the income of their inhabitants. This information may be one more to be taken into account by utilities and policy makers. The methodological approach is shown in Figure 1. Although other factors (for example, the type of building or if household is owned or rented) may influence energy consumption in buildings, their removal is beyond the scope of the study. Therefore, it is possible influence has not been eliminated. Figure 1. Methodological approach. 3.1. Classification of Cities by the Income of Their Inhabitants First, the size of the cities to be studied must be defined. Its size may vary depending on the scope of the study. Next, the cities within the study area are selected. The income corresponding to its inhabitants is assigned to each of the cities. Income can be considered according to different criteria, one of which can be from the national minimum wage Classification of cities according to their income Electricity consumption of buildings in each city Thermal consumption in the form of natural gas of buildings in each city Energy consumption per household Energy consumption per inhabitant CO 2 emissions CO 2 emissions Figure 1. Methodological approach. 3.1. Classification of Cities by the Income of Their Inhabitants First, the size of the cities to be studied must be defined. Its size may vary depending on the scope of the study. Next, the cities within the study area are selected. The income corresponding to its inhabitants is assigned to each of the cities. Income can be considered according to different criteria, one of which can be from the national minimum wage (NMW) or a relationship with it. Subsequently, cities with similar incomes are grouped together. Figure 2presents the monthly NMW of the states of the European Union as of January 1, 2020. It should be noted that six of them do not have NNW: Denmark, Italy, Cyprus, Austria, Finland, and Sweden [54]. Sustainability 2021,13, 10230 5 of 18 Sustainability 2021, 13, x FOR PEER REVIEW 5 of 18 (NMW) or a relationship with it. Subsequently, cities with similar incomes are grouped together. Figure 2 presents the monthly NMW of the states of the European Union as of January 1, 2020. It should be noted that six of them do not have NNW: Denmark, Italy, Cyprus, Austria, Finland, and Sweden [54]. Figure 2. National minimum wages in the European Union. The main statistical data of electric, thermal, and total energy consumptions for each group of cities are presented: Number of cities: 𝑛∑1  (1) Mean: 𝐸 ∑    (2) Standard deviation: 𝑠  ∑󰇛     󰇜   󰇛  󰇜 (3) Median: 𝑀𝑒𝑑𝑖𝑎𝑛󰇣   󰇤𝑡ℎ term if the total number of the elements is an odd number, otherwise 𝑀𝑒𝑑𝑖𝑎𝑛󰇡   󰇢   󰇡   󰇢   (4) Maximum: 𝐸  max 󰇛𝐸󰇜 (5) Minimum: 𝐸  min 󰇛𝐸󰇜 (6) where n i is the number of cities that belong to group i; 𝐸  is the mean energy consumed in group i; E ij is the energy consumption of city j, which is in group i; and s i is the standard deviation of the energy consumed in the cities of group i. The energy consumed will be thermal, electric, or total depending on the case study, and cities consumptions should be listed in ascending order to calculate the median. An index has been defined to analyze the variations in energy consumption. This index is analogous to the one defined in [55]. It allows evaluating the variations in consumption in each group. The income variation index (IVI) is defined as follows: 𝐼𝑉𝐼𝐸 /𝐸  (7) where 𝐼𝑉𝐼 is the index of the group of cities that have size i, 𝐸  is the energy consumption mean value of group I, and 𝐸  is the mean energy consumption of all cities (of all groups). This index makes it easy to identify the groups of cities with the highest consumption rates. To compare consumption in cities, a common reference must be selected. The most basic is to calculate per capita consumption. A second criterion is to use equivalized disposable income. As the article analyses energy consumption and emissions according to income both per inhabitant and per household, the latter criterion is, in the opinion of the authors, the most appropriate and is the one used to carry out the analyses. Figure 2. National minimum wages in the European Union. The main statistical data of electric, thermal, and total energy consumptions for each group of cities are presented: Number of cities: ni=∑j1 (1) Mean: Ei=∑jEij ni (2) Standard deviation: si=v u u t ∑ijEij −Ei2 (ni−1)(3) Median: Mediani= [ni+1 2]th term term if the total number of the elements is an odd number, otherwise Mediani=(ni 2)th term + (ni 2+1)th term 2 (4) Maximum: Ei max =maxEij(5) Minimum: Ei min =minEij(6) where n i is the number of cities that belong to group i; Ei is the mean energy consumed in group i;E ij is the energy consumption of city j, which is in group i; and s i is the standard deviation of the energy consumed in the cities of group i. The energy consumed will be thermal, electric, or total depending on the case study, and cities consumptions should be listed in ascending order to calculate the median. An index has been defined to analyze the variations in energy consumption. This index is analogous to the one defined in [ 55 ]. It allows evaluating the variations in consumption in each group. The income variation index (IVI) is defined as follows: IVIi=Ei/E(7) where IVIi is the index of the group of cities that have size i, Ei is the energy consumption mean value of group I, and E is the mean energy consumption of all cities (of all groups). This index makes it easy to identify the groups of cities with the highest consumption rates. To compare consumption in cities, a common reference must be selected. The most basic is to calculate per capita consumption. A second criterion is to use equivalized disposable income. As the article analyses energy consumption and emissions according to income both per inhabitant and per household, the latter criterion is, in the opinion of the authors, the most appropriate and is the one used to carry out the analyses. Sustainability 2021,13, 10230 6 of 18 3.2. Equivalized Disposable Income Equivalized disposable income is based on considering the economy of scale that occurs in a household based on the number of members of the household and their ages: both the overall household income after taxes and other deductions, as well as the expenses they share. To do this, people are converted to equivalent consumption units using the modified OECD scale. This scale attributes a weight of 1 to the first adult, 0.5 to the other adults in the household, and 0.3 to those under 14 years of age. Equivalent income is calculated by dividing the total disposable income of the household by the number of equivalent consumption units that compose it. This income is allocated equally to all members of the household [ 56 ]. In this case, it is necessary to know the published data regarding disposable income per inhabitant and city, as well as that of inhabitants per household and city. Note that the word income used in this paper will refer from now on to the equivalized disposable income. 3.3. Electric and Thermal Energy Consumption When conducting the study, the city has been considered as a living being that consumes energy and produces waste. In addition, the criteria set by international organizations for giving great importance to buildings are followed. Therefore, the energy consumption of the industries that may exist in the cities is not considered. These consumptions could distort the results since they would be higher or lower depending on the level of industrialization that the city could have. Another energy consumption not considered is due to means of transport. However, the consumption of the tertiary sector is considered. People spend a significant part of their lives in buildings that are not properly their home, but that without them, they would not be able to live in cities. Therefore, both household consumption and that of public administration and commercial buildings are considered since they are essential for the development of the human activity. For this reason, the consumption of these buildings will be added to those corresponding to each person for the development of their daily life in the city. To calculate electricity and thermal consumption per inhabitant and household, the information available on the population and consumption per city is used. This information is usually published grouped, so it must be previously processed to obtain disaggregated data. The electricity consumption data are itemized according to the classification of the Statistical Classification of Economic Activities in the European Community, commonly referred to as NACE (for the French term “nomenclature statistique des activitéséconomiques dans la Communautéeuropéenne”) [ 57 ]. The following items have been considered: from 36 to 39, 53, 60, 61, 72, from 84 to 88 (exc. 85.5 and 85.6), 91, 99, from 45 to 47, 58.2, 59, from 62 to 71, from 73 to 75, from 77 to 82, 85.5, 85.6, 90, from 92 to 98. The data used to calculate the consumption of thermal energy are those corresponding to natural gas with a pressure equal to or less than 4 bar. They are the ones that are consumed in homes, stores, public administrations, and services. The consumption of these supplies is between 5000 and 50,000 kWh per year. 3.4. CO2Emissions To know the CO 2 emissions produced by electricity consumption, it is necessary to know the country’s generation mix. The same happens to know those corresponding to the consumption of thermal energy. Therefore, from the knowledge of the sources of electrical and thermal generation, the emissions produced by them can be obtained. 4. Application of This Study to the Case of Spain The methodology proposed in this work is applied as an example to the case of Spain. To the knowledge of the authors, no work has been carried out in Spain identifying the consumption and emissions of Spanish cities with this level of analysis. The scope of the study is all Spanish cities with more than 50,000 inhabitants. Sustainability 2021,13, 10230 7 of 18 To relate the level of income that exists in Spain and locate it in reference to the neighboring countries, Figure 3shows the mean income of Europe. With the income of Spain and Malta, the median is obtained, this being EUR 16,674. The highest income is that of Switzerland with EUR 50,859, the lowest that of Romania with EUR 2746, and the average income is EUR 17,907. Therefore, Spain is below the average of the neighboring countries. The data used in this study are those corresponding to 2016 [58]. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 18 consumption and emissions of Spanish cities with this level of analysis. The scope of the study is all Spanish cities with more than 50,000 inhabitants. To relate the level of income that exists in Spain and locate it in reference to the neighboring countries, Figure 3 shows the mean income of Europe. With the income of Spain and Malta, the median is obtained, this being EUR 16,674. The highest income is that of Switzerland with EUR 50,859, the lowest that of Romania with EUR 2746, and the average income is EUR 17,907. Therefore, Spain is below the average of the neighboring countries. The data used in this study are those corresponding to 2016 [58]. Figure 3. Mean equivalized disposable income in Europe. 4.1. Classification of Spanish Cities by Equivalized Disposable Income The population of Spain amounts to 46.5 million, and more than 50% (24.5 million) live in cities with more than 50,000 inhabitants. Of these, 9 million live in the 10 most populous cities (almost 20% of the population) [59]. Hence, the fact that emissions and energy consumption are so important in cities, as in the rest of the countries. Thus, knowing the CO 2 emissions, the necessary measures can be taken in each city to mitigate them through renewable energy sources, and knowing the consumptions in each city, the necessary facilities can be planned to cover their energy demand. The scope of the study is that of cities with more than 50,000 inhabitants. In Spain, this number of cities is 145. For each city, the following have been considered: number of inhabitants, income, consumption of electricity in their buildings, consumption of thermal energy in the form of natural gas in them, and CO 2 emissions produced by both types of consumption. To carry out the studies, the cities have been grouped into five groups according to their income. The groups have been formed from the NMW, which in 2016 was established at EUR 7429.97 [60]: Group 1 is made up of cities with incomes less than 2 times the NMW; Group 2 for those who have it between 2 and 2.5 times; Group 3 includes cities with incomes between 2.5 and 3 NMW; Group 4 for those who have it between 3 and 4; and Group 5 for those with incomes greater than 4 NMW. Table 1 shows the cities belonging to each of the five groups arranged alphabetically. Figure 3. Mean equivalized disposable income in Europe. 4.1. Classification of Spanish Cities by Equivalized Disposable Income The population of Spain amounts to 46.5 million, and more than 50% (24.5 million) live in cities with more than 50,000 inhabitants. Of these, 9 million live in the 10 most populous cities (almost 20% of the population) [ 59 ]. Hence, the fact that emissions and energy consumption are so important in cities, as in the rest of the countries. Thus, knowing the CO 2 emissions, the necessary measures can be taken in each city to mitigate them through renewable energy sources, and knowing the consumptions in each city, the necessary facilities can be planned to cover their energy demand. The scope of the study is that of cities with more than 50,000 inhabitants. In Spain, this number of cities is 145. For each city, the following have been considered: number of inhabitants, income, consumption of electricity in their buildings, consumption of thermal energy in the form of natural gas in them, and CO2emissions produced by both types of consumption. To carry out the studies, the cities have been grouped into five groups according to their income. The groups have been formed from the NMW, which in 2016 was established at EUR 7429.97 [ 60 ]: Group 1 is made up of cities with incomes less than 2 times the NMW; Group 2 for those who have it between 2 and 2.5 times; Group 3 includes cities with incomes between 2.5 and 3 NMW; Group 4 for those who have it between 3 and 4; and Group 5 for those with incomes greater than 4 NMW. Table 1shows the cities belonging to each of the five groups arranged alphabetically. Sustainability 2021,13, 10230 8 of 18 Table 1. Classification of Spanish cities by equivalized disposable income. Equivalized Disposable Income Cities Group 1: income less than 2 times the NMW Alcaláde Guadaíra, Alcoy/Alcoi, Arona, Arrecife, Benalmádena, Benidorm, Chiclana de la Frontera, Dos Hermanas, Ejido (El), Elche/Elx, Elda, Estepona, Fuengirola, Gandía, Jerez de la Frontera, Linares, Línea de la Concepción (La), Lorca, Marbella, Mijas, Motril, Orihuela, Parla, Puerto de Santa María, Roquetas de Mar, San Bartoloméde Tirajana, San Fernando, San Vicente del Raspeig, Sanlúcar de Barrameda, Santa Coloma de Gramenet, Santa Lucía de Tirajana, Talavera de la Reina, Telde, Torremolinos, Torrent, Torrevieja, Utrera, Vélez-Málaga, Group 2: income between 2 and 2.5 times the NMW Albacete, Alcaláde Henares, Alcorcón, Algeciras, Alicante/Alacant, Almería, Aranjuez, Arganda del Rey, Ávila, Avilés, Badajoz, Badalona, Cáceres, Cádiz, Cartagena, Castellón de la Plana, Ceuta, Ciudad Real, Collado Villalba, Córdoba, Cornellàde Llobregat, Coslada, Cuenca, Ferrol, Fuenlabrada, Getafe, Gijón, Granada, Guadalajara, Huelva, Huesca, Jaén, Las Palmas, Leganés, L’Hospitalet de Llobregat, Lleida, Logroño, Lugo, Málaga, Manresa, Mataró, Melilla, Mérida, Molina de Segura, Mollet del Vallès, Móstoles, Murcia, Ourense, Palencia, Palma de Mallorca, Paterna, Pinto, Ponferrada, Pontevedra, Prat de Llobregat (El), Reus, Rubí, Sabadell, Sagunto/Sagunt, Salamanca, San Cristóbal de la Laguna, Sant Boi de Llobregat, Santa Cruz de Tenerife, Segovia, Sevilla, Siero, Terrassa, Torrejón de Ardoz, Torrelavega, Valdemoro, Valencia, Vigo, Viladecans, Vilanova i la Geltrú, Vila-Real, Zamora Group 3: income between 2.5 and 3 times the NMW A Coruña, Barakaldo, Burgos, Cerdanyola del Vallès, Girona, Granollers, Irún, León, Oviedo, Pamplona/Iruña, Rivas-Vaciamadrid, San Sebastián de los Reyes, Santander, Santiago de Compostela, Tarragona, Toledo, Valladolid, Zaragoza Group 4: income between 3 and 4 times the NMW Alcobendas, Barcelona, Bilbao, Castelldefels, Getxo, Madrid, San Sebastián/Donostia, Vitoria/Gasteiz Group 5: income greater than 4 times the NMW Boadilla del Monte, Majadahonda, Pozuelo de Alarcón, Rozas de Madrid (Las), Sant Cugat del Vallès 4.2. Thermal and Electric Energy Consumption of Spanish Cities The data used for the study come from the following government agencies: Spanish National Statistics Institute [ 61 ], National Commission on Markets and Competition [ 62 ] (they are both dependent on the Ministry of Economic Affairs and Digital Transformation), and Secretary of State for Energy [ 63 ], which depends on the Ministry for Ecological Transition and Demographic Challenge. The data used are those corresponding to the year 2016. 4.3. CO2Emissions of Spanish Cities In 2010, the European Union issued Directive 2010/31/UE of the European Parliament and the Council of 19 May 2010. It establishes the requirements for the energy performance of buildings. In application of this directive, the government of Spain established the CO 2 emission factors: for electricity points of consumption considering all types of generators and sources, it is 0.291 tCO 2 /MWh; for natural gas, it is 0.252 tCO 2 /MWh. These factors are applicable from January 14, 2016 [64]. 5. Results and Discussion 5.1. Sample of the Study The study sample covers the 145 cities with more than 50,000 inhabitants in Spain. Their buildings were considered, which include both households and the tertiary sector, public services, and government agencies. The data used come from official published sources and have been appropriately processed for the study. The cities have been separated into five groups according to the criteria established in Section 4.1 based on the NMW. The energy consumption considered is that normally used in buildings: electrical and thermal in the form of natural gas. Figure 4shows the number of cities in each group. The largest group corresponds to cities with incomes between 2 and 2.5 NMW (Group 2), followed by cities with incomes below 2 NMW (Group 1). The number of cities in groups 3, 4, and 5 decreases as income Sustainability 2021,13, 10230 9 of 18 increases. Regarding the number of inhabitants of each of the groups, the most populated corresponds to the one with the largest number of cities (Group 2), followed by Group 4, even though the latter is only made up of eight cities. The group with the lowest number of inhabitants is Group 5, which is also the one with the fewest cities (Figure 5). The main statistical data of the five groups are presented in Table 2. It shows both population and household data. The statistical data of the households follow a similar behavior to that of the population. Sustainability 2021, 13, x FOR PEER REVIEW 9 of 18 NMW. The energy consumption considered is that normally used in buildings: electrical and thermal in the form of natural gas. Figure 4 shows the number of cities in each group. The largest group corresponds to cities with incomes between 2 and 2.5 NMW (Group 2), followed by cities with incomes below 2 NMW (Group 1). The number of cities in groups 3, 4, and 5 decreases as income increases. Regarding the number of inhabitants of each of the groups, the most populated corresponds to the one with the largest number of cities (Group 2), followed by Group 4, even though the latter is only made up of eight cities. The group with the lowest number of inhabitants is Group 5, which is also the one with the fewest cities (Figure 5). The main statistical data of the five groups are presented in Table 2. It shows both population and household data. The statistical data of the households follow a similar behavior to that of the population. Figure 4. Number of cities of each group. Figure 5. Population of each group of cities. Table 2. Statistical data by population and household of the five defined groups of cities. POPULATION NUMBER OF HOUSEHOLDS Equivalized Disposable Income Total Mean Std. Dev. Median Maximum Minimu m Total Mean Std. Dev. Median Maximum Minimum Group 1 3,310,409 87,116 38,473 76,624 228,675 52,620 1,220,128 32,109 13,571 29,249 83,182 18,927 Group 2 11,954,158 157,292 140,553 104,380 787,808 50,334 4,517,519 59,441 53,452 41,936 312,339 17,901 Group 3 2,960,859 164,492 142,846 112,815 664,938 57,723 1,188,755 66,042 59,001 46,331 269,347 21,470 Group 4 5,841,470 730,184 1,116,856 216,673 3,182,981 65,954 2,345,167 293,146 445,649 90,617 1,262,282 23,811 Group 5 392,954 78,591 17,530 85,605 95,071 51,463 122,900 24,580 5963 26,291 29,937 15,434 5.2. Total Energy Consumption The statistical data of the consumption of the cities of each group are presented in Table 3. The total, thermal, and electrical energy consumption are shown in MWh per Figure 4. Number of cities of each group. Sustainability 2021, 13, x FOR PEER REVIEW 9 of 18 NMW. The energy consumption considered is that normally used in buildings: electrical and thermal in the form of natural gas. Figure 4 shows the number of cities in each group. The largest group corresponds to cities with incomes between 2 and 2.5 NMW (Group 2), followed by cities with incomes below 2 NMW (Group 1). The number of cities in groups 3, 4, and 5 decreases as income increases. Regarding the number of inhabitants of each of the groups, the most populated corresponds to the one with the largest number of cities (Group 2), followed by Group 4, even though the latter is only made up of eight cities. The group with the lowest number of inhabitants is Group 5, which is also the one with the fewest cities (Figure 5). The main statistical data of the five groups are presented in Table 2. It shows both population and household data. The statistical data of the households follow a similar behavior to that of the population. Figure 4. Number of cities of each group. Figure 5. Population of each group of cities. Table 2. Statistical data by population and household of the five defined groups of cities. POPULATION NUMBER OF HOUSEHOLDS Equivalized Disposable Income Total Mean Std. Dev. Median Maximum Minimu m Total Mean Std. Dev. Median Maximum Minimum Group 1 3,310,409 87,116 38,473 76,624 228,675 52,620 1,220,128 32,109 13,571 29,249 83,182 18,927 Group 2 11,954,158 157,292 140,553 104,380 787,808 50,334 4,517,519 59,441 53,452 41,936 312,339 17,901 Group 3 2,960,859 164,492 142,846 112,815 664,938 57,723 1,188,755 66,042 59,001 46,331 269,347 21,470 Group 4 5,841,470 730,184 1,116,856 216,673 3,182,981 65,954 2,345,167 293,146 445,649 90,617 1,262,282 23,811 Group 5 392,954 78,591 17,530 85,605 95,071 51,463 122,900 24,580 5963 26,291 29,937 15,434 5.2. Total Energy Consumption The statistical data of the consumption of the cities of each group are presented in Table 3. The total, thermal, and electrical energy consumption are shown in MWh per Figure 5. Population of each group of cities. Table 2. Statistical data by population and household of the five defined groups of cities. POPULATION NUMBER OF HOUSEHOLDS Equivalized Disposable Income Total Mean Std. Dev. Median Maximum Minimum Total Mean Std. Dev. Median Maximum Minimum Group 1 3,310,409 87,116 38,473 76,624 228,675 52,620 1,220,128 32,109 13,571 29,249 83,182 18,927 Group 2 11,954,158 157,292 140,553 104,380 787,808 50,334 4,517,519 59,441 53,452 41,936 312,339 17,901 Group 3 2,960,859 164,492 142,846 112,815 664,938 57,723 1,188,755 66,042 59,001 46,331 269,347 21,470 Group 4 5,841,470 730,184 1,116,856 216,673 3,182,981 65,954 2,345,167 293,146 445,649 90,617 1,262,282 23,811 Group 5 392,954 78,591 17,530 85,605 95,071 51,463 122,900 24,580 5963 26,291 29,937 15,434 5.2. Total Energy Consumption The statistical data of the consumption of the cities of each group are presented in Table 3. The total, thermal, and electrical energy consumption are shown in MWh per year. The highest consumption occurs in Group 2, corresponding to cities with incomes between 2 and 2.5 NMW, followed by Group 4, of cities with incomes between 3 and 4 NMW. The next group with income between 2.5 and 3 NMW has a consumption that is half of the previous one. Group 5 is the one with the lowest consumption. The two groups with the highest consumption Sustainability 2021,13, 10230 16 of 18 Informed Consent statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors would like to thank the eCitySevilla project for providing facilities to conduct the research. The authors would like to acknowledge the support of the Spanish State Research Agency under Grant No. PID2020-116433RB-I00 (PID2020-116433RB-I00/AEI/ 10.13039/501100011033). Conflicts of Interest: The authors declare no conflict of interest. References 1. European Parliament News. 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