Relationships between Hotel and Restaurant Electricity Consumption and Tourism in 11 European Union Countries

Gil-Delgado, María del Pópulo Pablo-Romero; Sánchez-Braza, Antonio; Sánchez-Rivas, Javier

doi:10.3390/su9112109

Cited by 29 publications

(20 citation statements)

References 77 publications

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“…ATBHs were chosen for this research as there are limited studies in the environmental literature focused on this type of small-scale accommodation. So far, most studies regarding water and energy saving measures in the tourism industry are focused on hotels [42,[97][98][99][100][101], as these firms usually have big accommodation capacities, intensive water and energy consumption, as well as high related costs. ATBHs meet additional challenges in implementing water and energy savings due to their size and limited financial power, but also due to the lower environmental attitudes and behaviors of their owner-managers.…”

Section: Sampling and Data Collectionmentioning

confidence: 99%

Environmental Management Practices within Agritourism Boarding Houses in Romania: A Qualitative Study Among Managers

2019

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Small accommodation businesses such as agritourism boarding houses (ATBHs) provide a large share of accommodating capacity in certain rural destinations. Despite their small accommodation capacity, ATBHs consume substantial quantities of water and energy; this consumption implies high environmental costs. The present study aims to explore opinions of ATBH owner-managers regarding water and energy consumption in their businesses, what motivates them to adopt environmental practices, and the specific saving measures used within the tourist accommodation units they manage. A qualitative research was conducted, using semi-structured interviews among 12 Romanian ATBH owner-managers. Data analysis revealed that the interviewed owner-managers understand the importance of natural resource conservation in tourist destinations but they are not willing or have insufficient financial, time, organizational resources, etc. to engage in serious water and energy saving initiatives. Moreover, most interviewed managers indicated that tourists cannot make a significant contribution to natural resources conservation in accommodation units and perceive water and energy saving systems as more effective than the environmental education of visitors in their facility. These findings may be useful to tourism policy-makers and environmental organizations in order to develop effective environmental strategies in ATBHs.

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Section: Sampling and Data Collectionmentioning

confidence: 99%

Environmental Management Practices within Agritourism Boarding Houses in Romania: A Qualitative Study Among Managers

2019

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“…The tourist sector, despite the presently existent severe constraints imposed by the sanitary emergence, interests almost all the countries and must be considered as an important component of the world's economies, accounting for approximately 10% of total global gross domestic product (GDP), 7% of global trade and 11% of the world's employment [1,2], with a continuously rising trend [3,4]. Accordingly, tourism represent an important contributor to energy consumption both at global and European scale [5,6,7], most of which is consumed in space heating and/or air conditioning (up to 50%), followed by hot water and cooking [8,9]. Hence, tourism exercises a relevant impact on the environment, being responsible for about 5% of the global CO2 amount emitted by human activities [1,10].…”

Section: Introductionmentioning

confidence: 99%

Leading agritourism facilities along Nearly Zero Energy paths: proposal of an easy-to-use evaluation method

et al. 2020

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The tourist sector, despite the present severe constraints imposed by the sanitary emergence, can be considered as an important component of almost all countries’ economies. In Italy agriturism, in particular, has been experiences a continuously rising trend in recent years. Clearly, such great interest towards these businesses also calls for a deep attention by an energy point of view, in sight of an energy efficiency improvement, hence a lowering of the pressure that such facilities exert to the natural environment. On the other hand, the European Union has been engaged, for a long time, in awarding the tourist accommodations environmental excellence brands, like the EU Ecolabel. Unfortunately, the achievement of such excellence brands requires the capability of managing complex energy and environmental data, which is often not the prerogative of people running such facilities. In order of contributing to overcome this difficulty, and with the aim of helping the addressing of agritourism towards a Nearly Zero Energy path, we propose here a simple approach that does not require the modelling and simulation of the energy behaviour of an agritourism, being essentially based on the application of easy-to-use ARERA (Italian Regulatory Authority for Energy Networks and Environment) datasheets scheme. On purpose, an application of the method, involving two typical Sicilian agritourism, is presented. The obtained results showed the viability of the proposed methodology, although the need of an update and/or replacement of some technical datasheets arose.

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“…The energy consumption of the whole society is directly or indirectly affected by many factors. Up to now, many scholars have studied the factors affecting the energy consumption [3][4][5][6]. The problem of electricity consumption is a complex non-linear problem, for which so far scholars have proposed various prediction models, such as grey theory [7][8][9], multiple regression [10][11][12], and time series models [13][14][15]; In recent years, various intelligent algorithms have also been applied to power consumption prediction [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Castillo et al [5] used a unified data set of 13 income and expenditure household surveys to assess changes in electrical and electricity consumption, taking into account income distribution, GDP, population, etc. as indicators of impact assessment; Pablo-Romero [6] analyzed the relationship between electricity consumption and tourism growth in hotels and restaurants in 11 EU countries between 2005 and 2012, and modelled energy use based on three variables: energy price, income and climate. The result showed that both income and climate have a significant impact on increasing electricity consumption, while energy prices have no effect on electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

2019

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The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China’s electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China’s power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China’s renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China’s renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030.

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Relationships between Hotel and Restaurant Electricity Consumption and Tourism in 11 European Union Countries

Cited by 29 publications

References 77 publications

Environmental Management Practices within Agritourism Boarding Houses in Romania: A Qualitative Study Among Managers

Environmental Management Practices within Agritourism Boarding Houses in Romania: A Qualitative Study Among Managers

Leading agritourism facilities along Nearly Zero Energy paths: proposal of an easy-to-use evaluation method

Forecasting China’s Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved Extreme Learning Machine Optimized by a Bacterial Foraging Algorithm

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