No abstract

Solar energy has become one of the most important sources of energy all around the world. Only in the European Union, between 2010 and 2019, solar photovoltaic (PV) electricity generation capacity increased from 1.9 to over 133 GW. Throughout this work, an economic analysis of the production of photovoltaic solar energy utility scale facilities is performed, previously defining some theoretical concepts relating to electricity generation by means of photovoltaic modules, as well as commenting on studies that have inspired the project. In order to carry out this economic analysis, the locations of twenty capital cities within European Union countries are selected, in order to estimate their yearly solar PV energy produced under specific conditions. The Levelized Costs of Energy (LCOE) is calculated with the goal of comparing the profitability of each photovoltaic tracking technology: fixed, one-axis tracking systems (vertical or inclined) and two-axis tracking systems; including LCOE maps country-wise for each technology. A sensitivity analysis is also presented, in order to evaluate the significance and impact of the main variables involved in the analysis. The results show that one-axis tracking systems are the best option in all countries, reducing LCOE by more than 20% when compared to two-axis tracking system. The impact of wages is also significant. In higher latitudes, in most cases, wages also increase, hence the LCOE is higher and consequently less interesting for a potential investor.

Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in the European Union, and in the second quarter of 2018 there is a 15.2% unemployment rate, some 3.4 million unemployed. Construction is one of the activity sectors that have suffered the most from the economic crisis. In addition, the economic crisis affected in different ways to the labour market in terms of occupation level or location. The aim of this paper is to discover how the labour market is organised taking into account the jobs that workers get during two periods: 2011-2013, which corresponds to the economic crisis period, and 2014-2016, which was a period of economic recovery. The data used are official records of the Spanish administration corresponding to 1.9 and 2.4 million job placements, respectively. The labour market was analysed by applying unsupervised machine learning techniques to obtain a clear and structured information on the employment generation process and the underlying labour mobility. We have applied two clustering methods with two different technologies, and the results indicate that there were some movements in the Spanish labour market which have changed the physiognomy of some of the jobs. The analysis reveals the changes in the labour market: the crisis forces greater geographical mobility and favours the subsequent emergence of new job sources. Nevertheless, there still exist some clusters that remain stable despite the crisis. We may conclude that we have achieved a characterisation of some important groups of workers in Spain. The methodology used, being supported by Big Data techniques, would serve to analyse any alternative job market.