Tourism and Electricity Consumption in 9 European Countries: A Decomposition Analysis Approach

Tourism and Electricity Consumption in 9 European Countries: A Decomposition Analysis Approach María del P. Pablo-Romero, Antonio Sánchez-Braza, and Javier Sánchez-Rivas María del P. Pablo-Romero Department of Economic Analysis and Political Economy Faculty of Economics and Business Sciences Universidad de Sevilla Ramón y Cajal 1, 41018 Seville, Spain Tel.: +34 954 557 611. Fax: +34 954 557 629 [email protected] Antonio Sánchez-Braza (corresponding author) Department of Economic Analysis and Political Economy Faculty of Economics and Business Sciences Universidad de Sevilla Ramón y Cajal 1, 41018 Seville, Spain Tel.: +34 954 557 529. Fax: +34 954 557 629 [email protected] Javier Sánchez-Rivas Department of Economic Analysis and Political Economy Faculty of Economics and Business Sciences Universidad de Sevilla Ramón y Cajal 1, 41018 Seville, Spain Tel.: +34 954 557 524. Fax: +34 954 554 629 [email protected]s Acknowledgements The authors wish to acknowledge the funding provided by the following institutions: the RTI2018-096725-B-I00 Project of the Spanish Ministry of Ministry of Science, Innovation and Universities; the “Cátedra de Economía de la Energía y del Medio Ambiente” sponsored by “Red Eléctrica de España” at the “Universidad de Sevilla” (University of Seville); the Andalusian Regional Government (Project SEJ-132); and the “Departamento de Análisis Económico y Economía Política (Universidad de Sevilla)” (Department of Economic Analysis and Political Economy at the University of Seville). 1 Tourism and Electricity Consumption in 9 European Countries: A Decomposition Analysis Approach Abstract Tourism is a major economic activity constituting one of the main sectors in economic terms. European countries traditionally play a significant role in the overall international tourism flow. However, while tourism has a noticeable positive impact on economic development, it also contributes to environmental degradation by increasing energy consumption and therefore emissions. This paper analyses the relationships between Hotel and Restaurant electricity consumption and tourism growth in 9 European countries during 2004-2012, for which there is a sufficient amount of data available. A decomposition analysis based on logmean Divisia index method (LMDI I) is conducted to examine electricity consumption on this sector and their components. Five decomposition factors are considered: energy intensity (EI), physical capital intensity (KI), physical and human capital relationship (KL), human capital intensity (LI) and the tourism factor (T). Results show the evolution of energy consumption related to this sector, identifying the driving forces that have influenced it. Keywords: Tourism growth, electricity consumption, Hotels and Restaurants sector, European countries, LMDI, decomposition analysis. 2 1. Introduction The tourism sector is a major economic activity in the world. Its total contribution to GDP was USD 7,613.3bn, representing 10.2% of GDP and 9.6% of total employment (World Travel & Tourism Council, 2017). Thus, the tourism sector constitutes one of the main sectors in economic terms for the most developed or developing countries, being one of the largest generators of employment in the world. According to the European Commission, tourism is the third largest socio-economic activity in the EU (after the trade and distribution, and construction sectors), and has an overall positive impact on economic growth and employment (Nižić, Grdić & Hustić, 2016). Traditionally, Europe plays a significant role in the overall international tourism flow. International tourist arrivals (overnight visitors) in 2016 grew by 3.9% to reach a total of 1,235 million worldwide, which equates to an increase of 46 million over the previous year. In this context, Europe welcomed 616 million international tourists, equivalent to half the world total, an increase of 13 million from 2015 (United Nations World Tourism Organization, 2017). However, while tourism has a noticeable positive impact on economic development, improving the balance of payments, boosting investments and generating employment, it also contributes to environmental degradation by increasing CO2 emissions, mainly through the sectoral energy consumption. In the same vein, studies such as those by Scott, Hall, and Gössling (2016a, 2016b), and Scott, Gössling, Hall, and Peeters (2016) analyse the effect of tourism in the Climate Change and the implication of the Paris Agreement (COP 21) for the sector, including impacts and mitigation and adaptation policies (United Nations, 2015). So, while energy, labour and capital resources are used to produce desirable goods and services through economic activities, they simultaneously produce undesirable outputs such as GHG emissions (International 3 Panel on Climate Change, 2014; Moutinho, Madaleno & Silva, 2016). Thus, the increase in the number of tourists not only contributes to the economic welfare of the country but also to its energy consumption (Katirciogulu, 2014). In this sense, tourism also needs to be considered from the viewpoint of energy consumption and CO2 emissions, being considered as a new field of study (Frantál & Urbánková, 2017). Regarding energy consumption, some papers focus on the energy use associated with global tourism or derived from specific tourism activities or services. Among the first ones, Gössling (2002) calculated that the use of energy associated with global tourism in 2000 was 14,000 PJ, while Rutty, Gössling, Scott, and Hall (2015) update these data, indicating that energy associated with global tourism was equal to 17,500 PJ in 2005. Among the second ones, it is worth highlighting the studies by Becken, Frampton and Simmons (2001), and Becken, Simmons and Frampton (2003) which analyse the energy use within the accommodation sector and associated with different tourist travel choices, respectively. Likewise, more recently, some papers have pointed out the relationships between energy consumption, tourism and growth (for example, Dogan & Aslan, 2017; and Nepal, al Irsyad & Nepal, 2019), some of them also considering different specifications of the Environmental Kuznets curves (for example, Naradda Gamageet, Hewa Kuruppuge, & Haq, 2017; Pablo-Romero, Pozo-Barajas, & Sánchez- Rivas, 2017, and Pablo-Romero, Sánchez-Braza, & Sánchez-Rivas, 2017). Following on from these previous studies, the aim of this paper is to analyse the relationships between Hotel and Restaurant electricity consumption and tourism growth in 9 European countries for the period 2004-2012, for which there is a sufficient amount of data available. In order to reach this objective, in this study we propose using the logmean Divisia index method (LMDI I) decomposition technique (Ang, 2005) to quantitatively evaluate the performance of electricity consumption in the Hotels and 4 Restaurants sector for the 9 European countries considered. As far as our knowledge is concerned, this technique is the first time it has been used to assess the extent to which tourism and other productive factors affect or determine energy consumption in the tourism sector; specifically, in this study, the electricity consumption of the hospitality sector. Essentially, IDA is an analytical tool designed to quantify the driving forces influencing changes in an aggregate indicator as energy consumption. Such a method may be easily applied to any source of available data at any aggregation level in a given time period. Proposed decomposition factors in this study, include the energy intensity factor (EI), the physical capital intensity factor (KI), the physical and human capital relationship factor (KL), the human capital intensity factor (LI) and the tourism factor (T). The paper is structured in five sections. After the Introduction, in Section 2, the methodology is presented and the factors proposed to identify, quantify and explain the determinant of the evolution for Hotel and Restaurant electricity consumption are justified. In Section 3 data are presented, while in Section 4 the decomposition analysis results are presented and discussed. Finally, Section 5 provides a conclusion. 2. Methodology Studies based on LMDI I are useful for understanding the evolution of energy consumption related to this sector and identifying the driving forces that have impacted these possible changes and evolution, as well as the importance of these variables in the energy consumption of this sector. In this sense, this study follows the criteria of Ang (2004), who evaluated the various decomposition methods. He concluded that LMDI I is a more recommendable method than others thanks to both its theoretical base and its set of properties, which are satisfactory in the case of index decomposition. 5 LMDI I is a “refined” non-parametric IDA (Index Decomposition Analysis) approach based on the Divisia index method, with weighted logarithmic mean. An additional argument that favours LMDII is that it allows perfect decomposition (that is, without residuals) and provides a simple and direct association between the additive and the multiplicative decomposition form (Ang & Liu, 2007). Recent studies using this methodology to analyse energy consumption growth are numerous; referring to different countries, groups of countries and economic sectors. Among these studies, it is worth highlighting those carried out by Chong et al. (2017), Cruz and Dias (2016), Dong, Zhang, Mu, and Su (2016), Ediger and Huvaz (2006), Lima, Nunes, Cunha, and Lucena (2017), and Liu, Zhou, Zhou, and Wang (2018) who referred to the whole economy; those by Duran, Aravena, and Aguilar (2015), Olanrewaju, Jimoh, and Kholopane (2012), and Wang, Ge, Liu, and Ding, (2016) who referred to the industrial sectors and that by Zhang, Song, Li, and Li (2016) who referred to the residential one. Nevertheless, in spite of the recent growth of this technique used in the explanation of the factors affecting energy consumption, to our knowledge there are no previous studies referring to the tourism sector or some specific tourist related sector; although some previous studies have used this technique to analyse the factors affecting the generation of emissions related to the sector (Robaina- Alves, Moutinho, and Costa, 2016, for example). Likewise, it is worth noting that there is no unique expression of factor decomposition for the energy consumption, but different expressions are used depending on the study. Nevertheless, three main types of factors are being incorporated in the previous studies; related to the energy efficiency, to the activity level and more recently, as in Liu et al. (2018), to the production function factors. 6 Based on these three types of factors, in this study, five factors have been proposed to identify, quantify and explain the main determinant of the evolution for Hotel and Restaurant electricity consumption EI, KI, KL, LI and T. Applying the decomposition proposed to n European countries considered, the total electricity consumption for this sector may be presented as follows: i i i i i i i n ii iTur Tur L L K K GAV GAV E E   1 [1] Where Ei represents the electricity consumption of the Hotels and Restaurants sectorof country i; GAVi represents the Gross Added Value registered for this sector of country i; Ki denotes the capital stock for this sector of country i, Li indicates the employment level for this sector of country i measured as the number of hours worked, and Ti represents the nights spent at tourist accommodation establishments, as a measure of tourism level of country i. So, incorporating the five factors proposed, Equation [1] may be expressed as: iiii n iiTLIKLKIEIE   1 [2] The energy intensity factor (EI) corresponds to the ratio of electricity consumption and GAV for the Hotels and Restaurants sector in a given period for each country, representing the energy required in comparison to the output in this sector. EI factor is often used as a measure or aggregate proxy of the energy efficiency level of a country’s economy or sector; as EI grows, energy efficiency decreases. It may be seen as a signal indicating the efficiency of the energy system, technology choices, energy prices, energy conservation techniques and investments for energy saving in this sector (Goldemberg & Johansson, 2004; Voigt, De Cian, Schymura, & Verdolini, 2014). 7 The physical capital intensity factor (KI), the physical and human capital relationship factor (KL) and the human capital intensity factor (LI) represent the way in which the productive activity of the sector affects the energetic consumption. In this sense, recent studies, such as that of Du and Lin (2015), indicate that capital and labour may have relevant substitution effects on energy consumption. The ratio of GAV and the stock of physical capital for the Hotels and Restaurants sector in a given period denotes the capitalization level of this sector for each country, as an indicator of capital productivity. In this sense, several previous studies have highlighted the role of the stock of capital on energy consumption, indicating in some cases, as in Pablo-Romero and Sánchez-Braza (2015), that energy is weakly substitutable with this factor. In other recent studies, increasing stock of capital tends to show an increase in energy use, such as that of in Saidi and Hammami (2015). The ratio of the stock of physical capital and the number of hours worked for the Hotels and Restaurants sector in a given period for each country, indicates the private physical capital stock level per employee hour. This ratio, according to Wu (2012), indicates how the productive structure may affect the energy efficiency, being considered a proxy of the level of technology involved. The human capital intensity factor (LI) denotes the ratio of the number of hours worked for the Hotels and Restaurants sector and the overnight stays for each country, as an indicator of the intensity of labour requirements of this sector. Finally, the tourism factor (T) represents the total number of overnight stays. It is considered as a measure of tourism, therefore is assessed as nights spent at tourist accommodation establishments, which include hotels, holiday and other short-stay accommodation, camping grounds, recreational vehicle parks and trailer parks. Total overnight stays have been used before as a proxy to measure tourism, such as in the 8 studies by Cortes-Jimenez and Pulina (2010), Gómez-Calero, Molina, and Pablo- Romero (2014), and more recently in the study by Pablo-Romero et al. (2017a, 2017b). Therefore, this indicator represents the sectoral level of activity, which is always included in previous studies by different indicators depending on the study in question. Changes in the electricity consumption of the Hotels and Restaurants sector may be assessed by implementing additive or multiplicative decomposition. In this paper, an additive LMDII analysis is carried out. The overall ratio of change in electricity consumption from one period to another may be decomposed as the sum of the considered factors: TLIKLKIEIEEE tt  1 [3] With the right-hand side variables being the representatives of the various contributing determinants as previously defined but now being referred to as changes, and where: )1(, )(, 1)(, ln     ti ti n iti EI EI wEI [4] )1(, )(, 1)(, ln     ti ti n iti KI KI wKI [5] )1(, )(, 1)(, ln     ti ti n iti KL KL wKL [6] )1(, )(, 1)(, ln     ti ti n iti LI LI wLI [7] )1(, )(, 1)(, ln     ti ti n iti T T wT [8] 15 lack of capacity of the sector to adapt to the decrease or increase of activity quickly. This effect also seems to be shown in the case of Spain, since it shows that energy inefficiency is a clear driver of electricity consumption in certain periods. In this sense, it is worth noting that we are facing a sector that needs to properly condition large buildings. If the activity decreases, the conditioning needs remain the same, significantly increasing inefficiency. In this sense, the need to find heating and cooling systems of buildings (hotels, restaurants ...) that allow their conditioning by zones may be appropriate for those countries that may be faced with significant changes in activity, according to the global and local economy evolution. Germany and the United Kingdom are the countries that show a greater variability in the effect of the other factors. There is a great variability in the factors associated with the production mode, implying that over the period they have seen their productive structures change in such a way that they have exerted a notable influence on the electric consumption of the sector. In Germany, the lack of capital investment after the crisis has contributed to the reduction of energy consumption, although once this phase is over, there is a rebound of capital investments that act boosting the energy consumption, once the tourism tends to grow again. On the contrary, in the UK, capital investments do not tend to increase after the crisis, and the evolution of tourism is compensated by employment. Thus, when tourism grows, it acts as a clear driver of energy consumption, but is compensated by a reduction in the service offered to the tourists, i.e. there is not an employment growth in line with the tourism growth. Therefore, the use of work becomes more energetically efficient. 16 In the case of Spain, it seems that its evolution with respect to capital investments is similar to that of Germany, but with lesser effects on energy consumption. Regarding employment, the same effects are observed as in the UK, but also with less intensity. With respect to Italy and France, they are countries that do not show such pronounced effects during the years of financial crisis. In Italy, there is a tendency to decelerate the growth of consumption in the sector, with gains in energy efficiency almost every year, and especially at the end of the period. This gain in efficiency has been acting as a clear compensatory factor. A slight variation of tourism and a decrease in the relative use of capital in the period can justify this behavior. In France, fairly homogeneous values are observed throughout the period with the exception of the growth of energy inefficiency in a year of crisis, and the reverse behavior to recover. The important growth of tourism in 2010 should be noted, acting as an important driver of energy consumption, which is clearly offset, however, by the energy efficiency gained by the employment factor (LI). Finally, in Portugal, the increase in the stock of capital has been a strong driver of electricity consumption at the beginning of the period, but its influence has tended to reduce since 2008. Likewise, it is also worth noting the improvement in energy efficiency throughout the period, which may explain the reduction of the energy consumption rates in this period. Insert Figure 3 here Figure 3. Evolution of the five factors considered in LMDI analysis (2004-2012) by countries. 17 5. Conclusions Tourist activity is valued worldwide by the multiple economic benefits in the countries of destination, and is measured through keeping a record of overnight stays and the demand of direct and indirect services of the different sub-sectors, among them hotels and restaurants, etc. However, it is important to evaluate their net benefits considering also the associated costs in terms of a greater impairment of the heritage, use of local natural resources or greater energy consumption. In this sense, tourism activity must be analysed based on the environmental costs generated by it, and there is a wide variety of works that show that tourism has a significant impact on climate change and also needs to be considered from the viewpoint of energy consumption and CO2 emissions. The relationships between Hotel and Restaurant electricity consumption and tourism growth during the period 2004-2012 have been analysed for Denmark, France, Germany, Italy, Holland, Portugal, Spain, Sweden and the United Kingdom. The use of the log-mean Divisia index method (LMDI I) decomposition technique is then proposed to quantitatively evaluate the extent to which tourism activity and other productive factors affect or determine energy consumption in this sector, including energy intensity (EI), physical capital intensity (KI), physical and human capital relationship (KL), human capital intensity (LI) and tourism activity (T) as the decomposition factors or effects to be considered. In accordance with the obtained results, a heterogeneous behavior of factors may be highlighted, inducing the electricity consumption throughout the period in question, being relevant the change in energy consumption around the financial crisis years and 18 the significance of the tourism factor in explaining the evolution of electricity consumption. Performing the analysis for the different countries by the five considered factors, it may be highlighted that countries with higher tourism attraction, since they have been more affected by the crisis in their tourism sector evolution, have experienced more variability in their results throughout the period. The energetic intensity (or energy inefficiency) acts as a compensating factor according to the moment of the cycle, and the effect of this is particularly demonstrated in the cases of Germany, the United Kingdom and Spain. 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Main figures for factors considered in LMDI analysis 2004-2012 by countries 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010 2010-2011 2011-2012 EIi = Ei / VABi ∆EI (Energy Intensity Factor) Denmark -0.047051 5.893930 -1.146079 0.798035 4.479508 11.306404 2.377621 -9.082396 France 4.605857 5.843456 3.966217 18.491706 70.302400 -83.695577 -8.523175 14.117702 Germany -136.787579 -25.955790 -128.931742 174.492033 275.424148 36.540023 -121.667577 -13.818188 Italy 23.542901 -10.719968 -9.720244 5.024195 20.159507 -2.120936 -48.879035 -50.696469 Netherlands 19.904107 -15.039842 -14.703091 2.631618 21.928089 33.640439 1.581710 14.190341 Portugal -3.421651 -5.753133 -20.807423 24.736953 -3.054732 -30.328786 -21.638048 -22.080856 Spain 66.983738 -3.606902 118.169575 0.899818 190.417565 -21.067346 -1.685758 18.294178 Sweden 5.454694 -2.621816 5.879005 -2.921062 8.723576 -1.491433 3.743783 -3.151382 United Kingdom -33.501006 -47.065077 -161.356934 47.494082 161.920460 -28.319362 -7.974743 -15.236001 ∑∆EIi -53.265992 -99.025141 -208.650716 271.647376 750.300522 -85.536573 -202.665220 -67.463072 KIi = VABi / Ki ∆KI (Physical Capital Intensity Factor) Denmark 0.232305 3.218749 0.837270 2.477001 -1.862371 -2.783484 5.148675 3.308103 France -4.295823 -15.226944 -1.572031 -32.630134 -21.482095 38.862134 20.338292 -8.259711 Germany 9.432555 2.485094 26.773448 -48.511869 -81.442902 28.848539 32.916051 23.062801 Italy -8.404786 -2.776592 -1.122179 -50.321631 -3.122338 -48.169213 -13.511066 -5.126347 Netherlands -0.749008 1.396490 4.531941 -16.246270 -9.545989 -3.925954 12.382450 4.052460 Portugal 7.777262 -85.555821 -67.571229 -29.363450 5.291978 -1.328963 -0.514043 -0.412479 Spain -12.981910 -7.262281 -8.589220 8.554131 15.420580 1.536625 8.852980 8.460454 Sweden 2.586693 2.553483 2.256510 -5.651866 -15.361442 5.297155 10.727777 9.765252 United Kingdom -40.261137 -7.383456 -24.328958 -49.048755 -31.119708 20.843236 11.777694 63.295610 ∑∆KIi -46.663850 -108.551280 -68.784448 -220.742843 -143.224288 39.180075 88.118809 98.146142 KLi = Ki / Li ∆KL (Physical and Human Capital Relationship Factor) Denmark -3.793167 -1.750123 -1.686367 -4.760756 0.856878 2.900256 -2.436783 -4.160256 France 4.036540 19.323460 -5.726814 20.897275 15.702941 -41.311839 11.252922 -0.863335 Germany -29.925795 -24.488372 20.828889 -6.055928 -61.896445 -109.976358 67.748024 9.724156 Italy 11.422233 -45.042009 -23.344297 3.580788 21.770225 53.849217 10.865774 -39.306516 Netherlands 3.609883 -2.499878 -6.640495 -0.578335 -11.501856 -14.864752 -14.904952 -8.393852 Portugal -11.560838 84.192502 64.262594 19.630108 1.384916 10.440962 7.695193 12.193689 Spain -6.715352 -7.345396 -7.792981 -18.685341 -21.399876 10.660877 -7.727584 19.412256 Sweden 2.683318 -5.793341 -1.208577 1.747067 8.649835 -14.697849 -10.377334 -12.308142 United Kingdom 75.414168 26.238667 38.184345 40.666336 14.190056 5.597340 3.926810 -65.912946 ∑∆KLi 45.170992 42.835509 76.876297 56.441213 -32.243326 -97.402147 66.042069 -89.614946 24 LIi = Li / Turi ∆LI (Human Capital Intensity Factor) Denmark 3.400335 0.505333 0.289435 1.570976 -0.152517 -3.008771 2.030983 0.389467 France -20.865719 4.585016 20.102204 16.017376 5.628492 -231.562111 -6.283943 6.164418 Germany -4.688463 -10.274786 185.888037 -8.182602 64.527788 -31.707337 -63.187865 -40.733654 Italy -23.259847 45.332491 26.550858 60.082965 -20.497375 -17.301595 -19.741800 51.235318 Netherlands -0.432180 -5.612855 -5.491708 14.573866 -1.054509 4.138934 9.457487 4.273775 Portugal 0.430231 -2.375123 -5.282821 7.395775 8.450451 -1.479538 -11.883410 -9.471195 Spain 14.508840 -1.011438 15.392358 18.162932 26.847478 -49.705955 -37.604975 -24.723069 Sweden -7.212719 -1.545884 0.526479 12.381111 -4.328900 9.523821 4.333665 5.222935 United Kingdom -173.060835 82.089979 31.320114 24.649698 -72.646021 99.487042 17.360376 -237.471579 ∑∆LIi -211.180357 111.692734 269.294957 146.652098 6.774889 -221.615511 -105.519482 -245.113583 Ti ∆T (Tourism Factor) Denmark 3.400335 0.505333 0.289435 1.570976 -0.152517 -3.008771 2.030983 0.389467 France -20.865719 4.585016 20.102204 16.017376 5.628492 -231.562111 -6.283943 6.164418 Germany -4.688463 -10.274786 185.888037 -8.182602 64.527788 -31.707337 -63.187865 -40.733654 Italy -23.259847 45.332491 26.550858 60.082965 -20.497375 -17.301595 -19.741800 51.235318 Netherlands -0.432180 -5.612855 -5.491708 14.573866 -1.054509 4.138934 9.457487 4.273775 Portugal 0.430231 -2.375123 -5.282821 7.395775 8.450451 -1.479538 -11.883410 -9.471195 Spain 14.508840 -1.011438 15.392358 18.162932 26.847478 -49.705955 -37.604975 -24.723069 Sweden -7.212719 -1.545884 0.526479 12.381111 -4.328900 9.523821 4.333665 5.222935 United Kingdom -173.060835 82.089979 31.320114 24.649698 -72.646021 99.487042 17.360376 -237.471579 ∑∆Ti -211.180357 111.692734 269.294957 146.652098 6.774889 -221.615511 -105.519482 -245.113583