Influence of climate on energy consumption and CO2 emissions: the case of Spain

Abstract

This paper presents a methodology that allows for calculating the thermal and electric energy consumption together with CO2 emissions of cities by inhabitant and household based on climate, only making use of publicly available data. With this aim, climate was analysed and cities were classified based on it. The analysis of those cities' energy consumption and CO2 emissions allowed for drawing conclusions. Once aware of the climate zones in which energy consumption and emissions are higher, these mentioned conclusions could help to take further actions. An index has been defined to facilitate this analysis. This paper shows the case of Spain for illustrative purposes. This type of study has been carried out in some detail in many countries, but not in Spain yet. This paper tries as well to fill the existing gaps in studies that relate climate to thermal and electric energy consumption. For this purpose, it analyses the 145 cities in Spain that have more than 50,000 inhabitants. Knowing all this is essential in all regions and countries. It will allow for taking proper actions for promoting the energy saving and the use of alternative energy sources that reduce CO2 emissions. According to the study carried out in this paper, the extremer the climate of a city is, the higher the thermal energy consumption is. This consumption decreases in softer climates. However, electric energy consumption is similar in all cities independent of the climate they have. With regard to CO2 emissions, it was calculated that the higher the energy consumption of a city is, the higher these emissions are.

Do Higher Education Research and Development Expenditures affect Environmental Sustainability? New Evidence from Thirty-One Chinese Provinces

“…As some tech companies have started using solar and wind for power generation, other sectors will also follow this campaign to reduce their carbon footprint. This result validates the previous studies conducted for China (Akadiri et al, 2020;Munir & Riaz, 2020;Xu et al, 2015;Zhang, 2019); Spain (Zarco-Soto et al, 2020); South Asian economies (Munir & Riaz, 2019); Bangladesh (Shahbaz et al, 2014); ASEAN countries (Lean & Smyth, 2010); Pakistan (Rehman et al, 2019) and BRICS (Haseeb et al, 2019).…”

Do higher education research and development expenditures affect environmental sustainability? New evidence from Chinese provinces

“…As some tech companies have started using solar and wind for power generation, other sectors will also follow this campaign to reduce their carbon footprint. This result validates the previous studies conducted for China (Akadiri et al 2020 ; Munir and Riaz 2020 ; Xu et al 2015 ; Zhang 2019 ); Spain (Zarco-Soto et al 2020 ); South Asian economies (Munir and Riaz 2019 ); Bangladesh (Shahbaz et al 2014 ); ASEAN countries (Lean and Smyth 2010 ); Pakistan (Rehman et al 2019 ); and BRICS (Haseeb et al 2019 ).…”

“…Yorucu and Varoglu ( 2020 ) studied the nexus among industrial production, electricity consumption, economic growth, and CO 2 e in selected small island states. Based on the FMOLS and DOLS estimations, the authors found that a 1% increase in electricity consumption predicted an upsurge of 0.79% in CO 2 e. In the same way, other studies have also reported a positive connection between electricity consumption and CO 2 e for China (Akadiri et al 2020 ; Munir and Riaz 2020 ; Ou et al 2011 ; Xu et al 2015 ; Zhang 2019 ); Spain (Zarco-Soto et al 2020 ); South Asian economies (Munir and Riaz 2019 ); Bangladesh (Shahbaz et al 2014 ); ASEAN countries (Lean and Smyth 2010 ); Pakistan (Rehman et al 2019 ) BRICS (Cowan et al 2014 ; Haseeb et al 2019 ); and Kuwait (Salahuddin et al 2018 ). Table 4 depicts the summary of the selected studies on the nexus between electricity consumption and CO 2 e.…”

Influence of the Population Density of Cities on Energy Consumption of Their Households

“…where n i is the number of cities that belong to group i; E i is the mean energy consumed in group i; E ij is the energy consumption of city j, which is located in group i; 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 analogous to that defined in [41] is used to analyze the energy consumption of cities. The density variation index (DVI) is defined as:…”

“…Although the influence of external factors has not been eliminated, it must be mentioned that the climate is one of those that may be of greater importance. That is why, to have a vision of the types of climates in which cities are located, Figure 2 shows the types of climates in Spain and the number of cities studied that are in each of them [41].…”