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Amidst the global energy crisis in 2022, the Spanish and Portuguese governments introduced a subsidy to natural gas ("the Iberian exception"), attempting to lower the wholesale electricity market prices, with the understanding that gas-fired-combined cycle gas turbines (CCGTs) are price-setting technologies most of the time, directly or indirectly. The subsidy succeeded in lowering the market price but induced several other effects, such as (1) the increase in cleared energy in the Spanish market (mostly produced with gas), (2) the bias in the import/export cross-border position between Spain and France (Spain became a net exporter to France immediately), or (3) the consequent increase in congestion rents, which serve to lightly finance the subsidy, among other effects. This paper provides a framework for clustering the different effects based on the market participation phases: the subsidy, the market bidding, the market results, and surplus and rents. Moreover, this paper builds on the theoretical market models, with and without subsidies, and with and without cross-border exchanges. Based on the real market bids, the subsidies, and the generators' data, we reconstruct the supply and demand curves and simulate the counterfactual market scenarios in order to illustrate and quantify the effects. We highlight the quantification of the theoretical effect of the transfer of rents, from non-fossil to fossil fuel producers, induced by the gas subsidy. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium–sulfur batteries in the second. These results provide insights into the establishment of research and development (R&D) cooperation strategies by countries and pre-planning by companies in the battery industry. [ABSTRACT FROM AUTHOR]

One of the main identified causes of energy poverty (EP) is the low energy efficiency of housing. In this line, since 2018, public administrations and NGOs collaborating with the Naturgy Foundation's Energy Renovation Solidarity Fund have implemented several shallow renovation interventions in 3660 Spanish vulnerable households. However, the effects of these measures on domestic energy affordability were not evaluated before because of a lack of a proper method. This paper presents a methodology to objectively assess the impact of these interventions on EP. In particular, this work proposes calculating a hidden EP indicator using data from a primary survey and applies it to a local case study (54 vulnerable households in Catalonia—10% of dwellings renovated by the Fund in the region) by processing their characteristics and energy bills before and after the implementation of the interventions. Considering the whole sample of households analysed, the hidden EP indicator drops by 10% in absolute terms (11.2% in relative terms) after the retrofit, and the average EP gap goes from 423 €/year to 313 €/year, thus marking a significant positive effect of the analysed interventions on the EP situation of this population. Eventually, extrapolating the results to the vulnerable population in Spain, this paper points out a series of recommendations that could be useful for decision-makers and organisations when designing and implementing shallow renovation interventions. [ABSTRACT FROM AUTHOR]

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy. [ABSTRACT FROM AUTHOR]

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system's stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance. [ABSTRACT FROM AUTHOR]

Combined Heat and Power (CHP) has been identified by the EU as a powerful resource capable of making substantial contributions to energy savings and reducing GHG emissions. Spain's effort to promote CHP has been prolific since the 1980s. In this regard, there have been various Laws, Royal Decrees (RDs) and European Union (EU) Directives addressed to reach the national objectives set for the CHP sector. Despite these attempts, the evolution and growth of installed CHP capacity has been irregular, compared to other technologies. Likewise, the academic treatment of the Spanish CHP evolution has not deserved the same attention as other technologies such as wind, photovoltaic and thermal solar systems. As a result, this article is aimed at providing a comprehensive overview of the regulatory frameworks applied to the Spanish CHP sector and analysing the reasons behind the variable evolution of the installed CHP capacity. The study covers the legislative context from 1980 to 2020, describing the evolution during both the pre-liberalization and liberalization periods, highlighting the modifications in economic policies that affected self-producers and the so-called Special Regime (SR) for CHP, and examining the challenges faced during the cost containment measures that followed. The manuscript finds and explains the connection between the regulatory framework and the evolution of installed CHP capacity in Spain. Likewise, the connection between the industrial situation and the promotion of CHP, as well as the influence of the Spanish Electricity Sector (SES)'s liberalization on the CHP sector are also pointed out. The paper intends to provide valuable insights for CHP experts and policymakers by showcasing the importance of aligning regulatory measures with the objectives of energy efficiency. It also serves as a reference for countries in various stages of promoting CHP, and provides evidence for the importance of stable energy-policy control mechanisms. [ABSTRACT FROM AUTHOR]

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models. [ABSTRACT FROM AUTHOR]

This paper presents an economic assessment of three drying systems (DS) with different configurations: diesel-powered (DDS), grid-powered (GDS), and photovoltaic-powered (PVDS). The third configuration, PVDS, presents important technological innovations regarding the electric configuration (stand-alone PV, without batteries or grid support) and the heat pump technology used (optimized for drying the recirculated air, which permits a reduction in the electricity consumption by more than 45% if compared to a diesel air-heater or an electric resistance). The profitability of the investment required for the installation of the three systems is proven by calculating the profitability index (PI), the internal rate of return (IRR), the payback period (PBP), and the levelized cost of energy (LCOE). All the indicators show good profitabilities for the three technologies (the PI values range from 6.78 to 10.11, the IRR values range from 24% to 37%, and the PBPs are between 3 and 4 years), but the PVDS clearly offers important economic advantages: its PI and IRR are more than 40% higher, the PBP is 25% shorter, and the LCOE is between 40 and 47% lower than for the two other systems. The results of this analysis correspond to a particular base case but are obviously sensitive to geopolitical and economic changes: the ultimate strength of the PVDS configuration is its resilience against a possible rise in electricity and diesel prices, reducing the uncertainty for investors and policymakers. [ABSTRACT FROM AUTHOR]

Soiling of PV modules is an issue causing non-negligible losses on PV power plants, between 3 and 4% of the total energy production. Cleaning is the most common way to mitigate soiling. The impact of the cleaning activity can be significant, both in terms of cost and resources consumption. For these reasons, it is important to monitor and predict soiling profiles and establish an optimal cleaning schedule. Especially in locations where raining is irregular or where desert winds carry a high concentration of particles, it is also important to know how precipitation and dust events affect the soiling ratio. This paper presents a new model based on environmental conditions that helps the decision-making process of the cleaning schedule. The model was validated by the analysis of five large grid-connected PV plants in Spain over two years of operation, with a total power of 200 MW. The comparison between the model and soiling sensors at the five locations was included. Excellent results were achieved, the mean difference between sensors and model being 0.71%. [ABSTRACT FROM AUTHOR]

In recent years, important regulatory changes have been introduced in Spain in the fields of self-consumption and energy tariffs. In addition, electricity prices have risen sharply, reaching record highs in the last year. This evidences the need to conduct new research studies in order to provide an accurate picture of the profitability of battery energy storage systems and photovoltaic systems. This paper proposes a complex simulation tool developed to assist in the optimal design of these kinds of facilities. The tool is used in this study to analyze the benefits of including batteries in PV systems under different self-consumption models, different consumer profiles and different locations across the country. The research results indicate that at current electricity prices, the use of batteries is less profitable than selling excess energy to the grid, unless the price of batteries drops drastically by more than 50% in all the cases analyzed. However, at current battery prices, they become a valuable resource in facilities that do not feed energy surplus into the grid. [ABSTRACT FROM AUTHOR]

This paper shows a practice to raise the reliability of an electric power system by the installation of distributed generation, taking gasified biomass as fuel. To calculate the reliability index, a probabilistic load flow was used. This index is determined as the fault probability of the system. The resolution of this probabilistic load flow combines the method of cumulants and Gram–Charlier expansion. To achieve the reliability index, simulating a number of contingencies is required; the greater the number of simulated contingencies, the higher the accuracy of the index obtained. This probabilistic technique uses the random variables as starting information, so the two generators and loads are simulated as random variables. The generators of this distributed generation are biomass-fueled gas engines, commonly found in Spain. The simulations carried out on the IEEE 14-bus Test System, including three biomass generators, show that the inclusion of this type of generation improves the overall reliability indices of the electrical system. [ABSTRACT FROM AUTHOR]

In hospitals, operating rooms are energy-intensive spaces, due to the high flow of outside air required to achieve the necessary indoor air quality. Operating rooms demand ventilation continuously, despite periods of low daily surgical activity. However, by controlling ventilation during inactive periods in the operating room, significant energy savings can be achieved, avoiding penalties on IAQ levels. This paper evaluates the energy savings achieved by introducing ventilation flow control and regulation systems in operating rooms of hospitals located in different climatic zones within Spain. In addition, emissions and economic savings have also been evaluated. Two control and regulation strategies of the air flow to be supplied and extracted in the operating rooms, during periods of inactivity, are studied and include regulation by schedule and regulation by occupancy. Data from a 900-bed university hospital center are used as a reference to evaluate the average occupancy of the operating rooms, and the energy consumption, thus validating the model for calculating the demand of a typical operating room. The energy savings for the regulation by occupancy are 37.5%, and the regulation by schedule are 40% of the annual demand, with respect to an operating room working permanently. [ABSTRACT FROM AUTHOR]

The ongoing spread of electric sustainable mobility is transforming the local ways of transport in metropolitan areas. This is meant to be extended outside of big cities in the near future thanks to new technological developments. Little towns should adapt to these changes, as they are located geographically far from the big cities and are generally characterized by low economic and demographic indicators. Hence, little towns must keep pace with these changes in mobility to avoid being isolated from the main cities in a country. People living in the countryside usually move toward big cities for various reasons, either related to work or living necessities. Therefore, it must be possible to conduct usual displacements through the use of electric vehicles (EVs), i.e., reaching the destinations and supplying the batteries through charging infrastructures. This paper studies the full implementation of electric mobility applied in the case of Cuenca, a city located in middle Spain. A brief geographical context is provided, together with the routes and destinations of interest considered. Then, different EVs are considered and an analytical vehicle model is provided. The model was exploited to simulate the electrical energy demand to reach the destinations chosen; the results allow comparing the performances offered by different types of EVs. This aspect is then considered as the basis to propose further upgrades in the charging infrastructures where needed, to comply with the widespread use of electric mobility. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to analyze the effect of industrial agglomeration on the degree of interorganizational cooperation and the innovative performance of firms of the electricity supply sector in Spain. For this purpose, the agglomeration coefficient in each of the 50 provinces of Spain is calculated, based on secondary data from SABI database. Subsequently, primary data are obtained from a sample of 197 companies through a structured questionnaire. In this case, the PLS-SEM technique is used. The results show that there is a positive and significant relationship between the variables analyzed. It is concluded that industrial agglomeration and cooperation are relevant external factors that boost the innovative performance of firms and that business associations foster interorganizational cooperation. [ABSTRACT FROM AUTHOR]

Climate change motivated by human activities constitutes one of the main challenges of this century. To cut carbon emissions in order to mitigate carbon's dangerous effects, the current energy generation mix should be shifted to renewable sources. The main drawback of these technologies is their intermittency, which will require energy storage systems to be fully integrated into the generation mix, allowing them to be more controllable. In recent years, great progress to develop an effective and economically feasible energy storage systems, particularly motivated by the recent rise of demand for electric transportation, has been made. Lithium-ion (Li-ion) battery prices have fallen near 90% over the past decade, making possible the affordability of electric vehicles and transforming the economics of renewable energy. In this work, a study on storage capacity demand previously presented as conference paper is expanded, including a deep analysis of the Spanish generation mix, the evaluation of the energy storage requirements for different low-carbon and carbon-free scenarios in Mainland Spain, and the calculation of the CO2 emissions' reduction and the associated storage costs. [ABSTRACT FROM AUTHOR]

Concentrating solar power (CSP), also known as solar thermal electricity (STE), is increasing its deployment worldwide. One of the potential ways to decrease costs in CSP plants is the improvement of corrosion resistance between the heat transfer fluid (HTF) and storage materials, and the materials used for pipes, tanks, containers, and receivers. This paper assesses the literature on this topic (290 publications) through a bibliometric analysis, identifying the trends of the research, the topics of most interest to researchers, and literature gaps. Most documents are from Spain, Germany, and the United States of America. Results show that the most recent approaches for corrosion migration are selective coatings and the use of nanoparticles to reduce corrosiveness. The use of nitrates is changing to other salts such as chloride mixtures and potassium compounds. In addition, the techniques used to evaluate corrosion results are dominated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and electrochemical testing, but new dynamic techniques are starting to be used, representing the biggest gap that needs to be filled for the testing of components such as solar receivers. [ABSTRACT FROM AUTHOR]

The emergence of energy communities represents a promising option to democratize the energy system by empowering consumers to take a more active role. This can aid in achieving energy and environmental goals as well as encouraging more equitable distribution of costs and revenues between all parties on the energy system. Despite this potential, energy communities are still a nascent solution, the success of which is heavily influenced by regulations. As a result, there are a wide variety of organizational structures for energy communities at this time. This paper provides a review of the policy landscape in Spain as it relates to energy communities. This work also presents a formalized method for characterizing different energy community structures and provides a qualitative assessment of the impacts of different measures to encourage energy communities with respect to their organizational structure. Findings suggest that many market-focused measures, including wholesale, local flexibility, capacity, and multisector market measures favor larger, more integrated communities, while regulatory, legal, and organizational measures, including peer-to-peer trading, aggregation, and self-consumption favor smaller, more distributed communities. Additionally, when developing policies to encourage the growth of energy communities, policymakers should be cognizant of the progression of policies in the context of the desired outcomes for energy community growth specific to the region or country and its goals. [ABSTRACT FROM AUTHOR]

This paper aims at presenting and describing a dimensioning methodology for energy storage systems (ESS), in particular for one based on flywheels, applied for the specific application of reducing power oscillation in a wave energy conversion (WEC) plant. The dimensioning methodology takes into account the efficiency maps of the storage technology as well as the effect of the filtering control techniques. The methodology is applied to the case study of a WEC plant in operation in Spain, using real power generation profiles delivered into the electric grid. The paper firstly describes the calculation procedure of the efficiency maps for the particular technology of flywheels, although it could be extended to other storage technologies. Then, the influence of using future data values in the dimensioning process and the control of the ESS operation is analysed in depth. A moving average filter (MAF) is defined to compensate for power oscillations, studying the difference between considering prediction and not doing so. It is concluded that a filtering control using future values based on a number of samples equivalent to a 4-min time order provides an important reduction in the energy storage requirements for a power generation plant. Finally, and based on the selection of storage modules previously defined, the efficiency maps, and the MAF used for delivering the power into the grid, an optimal operation strategy is suggested for the storage modules, based on a stepped switching technique. The selection of four flywheel energy storage system (FESS) modules achieves a reduction of 50% in power oscillations, covering 85% of the frequency excursions at the grid. [ABSTRACT FROM AUTHOR]

Cross-sectional energy benchmarking in the building domain has become crucial for policymakers, energy managers and property owners as they can compare an immovable property performance against its closest peers. For this, Key Performance Indicators (KPIs) are formulated, often relying on multiple and heterogeneous data sources which, combined, can be used to set benchmarks following normalization criteria. Geographically delimited parameters are important among these criteria because they enclose entities sharing key common characteristics the geometrical boundaries represent. Linking georeferenced heterogeneous data is not trivial, for it requires geographical aggregation, which is often taken for granted or hidden within a pre-processing activity in most energy benchmarking studies. In this article, a novel approach for Linked Data (LD) generation is presented as a methodological solution for data integration together with its application in the energy benchmarking use case. The methodology consists of eight phases that follow the best principles and recommend standards including the well-known GeoSPARQL Open Geospatial Consortium (OGC) for leveraging the geographical aggregation. Its feasibility is demonstrated by the integrated exploitation of INSPIRE-formatted cadastral data and the Buildings Performance Certifications (BPCs) available for the Catalonia region in Spain. The outcomes of this research support the adoption of the proposed methodology and provide the means for generating cross-sectional building energy benchmarking histograms from any-scale geographical aggregations on the fly. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

The growth of the geothermal industry demands the constant search of techniques with the aim of reducing exploration efforts whilst minimizing subsurface uncertainty. The exploration of geothermal resources is fundamental from the exploitation point of view, especially in those regions where this energy is not as widespread as the rest of renewable sources. This research shows how geoelectrical methods can contribute to the investigation and characterization of medium–low enthalpy geothermal resources until about 800 m of depth. A 2000 m long electrical-resistivity tomography profile was performed in a region of Southern Spain with previous evidence of moderate geothermal potential. Results of this geophysical campaign (together with a preliminary geological characterization) allowed for the obtainment of a 2D profile and a pseudo-3D model with extensive information about the subsoil in terms of geological composition and formations. The interpretation of geophysical results denotes the existence of a potential formation constituted by carbonate materials with thickness greater than 300 m, crossing different fractures. Once the ideal location for the geothermal exploitation is defined, the research evaluates the contribution of the possible energy source, deducing that the energy extraction in the potential fracturing area would be double that of the one in the vicinity of the site. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy metrics is essential for efficiently managing renewable energy generation. Given the high variability in load and renewable energy power output, this represents a crucial area of research in order to pave the way for increased adoption of low-carbon energy solutions. Whilst the impact of different neural network architectures and algorithmic approaches has been researched extensively, the impact of utilising additional weather variables in forecasts have received far less attention. This article demonstrates that weather variables can have a significant influence on energy forecasting and presents methodologies for using these variables within a long short-term memory (LSTM) architecture to achieve improvements in forecasting accuracy. Moreover, we introduce the use of the seasonal components of the target time series, as exogenous variables, that are also observed to increase accuracy. Load, solar and wind generation time series were forecast one hour ahead using an LSTM architecture. Time series data were collected in five Spanish cities and aggregated for analysis, alongside five exogenous weather variables, also recorded in Spain. A variety of LSTM architectures and hyperparameters were investigated. By tuning exogenous weather variables, a 33% decrease in mean squared error was observed for solar generation forecasting. A 22% decrease in mean absolute squared error (MASE), compared to 24-h ahead forecasts made by the Transmission Service Operator (TSO) in Spain, was also observed for solar generation. Compared to using the target variable in isolation, utilising exogenous weather variables decreased MASE by approximately 10%, 15% and 12% for load, solar and wind generation, respectively. By using the seasonal component of the target variables as an exogenous variable itself, we demonstrated decreases in MASE of 19%, 12% and 8% for load, solar and wind generation, respectively. These results emphasise the significant benefits of incorporating weather and seasonal components into energy-related time series forecasts. [ABSTRACT FROM AUTHOR]

This study provides an in-depth assessment of the environmental performance of five public schools in the transition towards a low-carbon economy and a more sustainable model of society. Life cycle assessment (LCA) methodology is used to conduct the study. The school system includes several activities and processes clustered in three subsystems: management of the school building, training and learning activities (T&L) and mobility and transport (M&T). A detailed primary data inventory of energy and resources consumption was collected in five schools located in Spain and Portugal. Findings on climate change (CC), water depletion (WD), particular matter (PM), acidification (Ac), and human health (HH), as well as associated external cost (EC), are reported per student in one school year as reference unit, allowing the schools' individual performance comparison and identify the potential improvements. Considering the sample of schools, findings reveal that peculiarities of the schools, such as location, specialization, and level of education, are crucial for the environmental performance. Buildings are a relevant contributor to CC as well as heating and electricity needs, although their relevance is dependent on multiple factors. The M&T subsystem also has relevant weight on the metrics evaluated. Educational activities have a lower impact in absolute terms but, in some schools, it becomes the main contributor to HH due to paper and electricity consumption and manufacturing of equipment. External costs results are in the range of 11 to 38 EUR/student·year mainly caused by heating, electricity and wastes from the building subsystem, and the M&T subsystem. [ABSTRACT FROM AUTHOR]

This paper on Political Economic and Labour Economic Policies in the European Green Deal framework analyses the Recovery Plan and Resilience Facility. It pays attention to the effects on the design of new green jobs and the necessary skills to develop this type of new positions generated, with higher labour wellbeing. The paper is focused on the analysis of the green jobs' opportunity for Europe, at a country level, with a specific analysis made for the Spanish case. A systematisation of the concepts and calculations on the issue is made and analysed (attending the international institutions and forums proposals) to harmonise the recovery plans, apply them beyond the energy sector to other related green activities and align the public and private sector, and other critical stakeholders, in achieving this goal. The following research questions were formulated: (1) what is the estimated number of new green jobs that would be created as a consequence of the implementation of the Recovery Plan in Spain; (2) which new soft skills or re-skilling would be necessary to develop such new green jobs; (3) how much are the new jobs aligned with Wellbeing Economics? The research was conducted by applying a few research methods, i.e., secondary sources, the desk research method and the exploration method, critical and comparative analysis, inductive and deductive reasoning and some mathematical calculations. The research contribution can provide a valuable source of information to coordinate sectoral plans by the policymakers, particularly those responsible for the economy, business, green activities and education. [ABSTRACT FROM AUTHOR]

The role of the energy industry has always been central for one reason or another, being environmentalism the main motive in the last two decades. Therefore, attention and research have been directed in this sense. However, human resources—or human capital—have remained understudied, especially concerning the salaries received. Thus, this study is disruptive as it explored the factors that influence employee remuneration in the energy subsector, using Spain as a case study. For this, the PLS-SEM (Partial Least Squares Structural Equation Modelling) path modelling methodology was used, executing a traditional PLS analysis, bootstrapping and, finally, IPMA (Importance-Performance Analysis). Solid and significant relationships were found among labour conditions, human capital, market and wages, with the relationships between human capital and wages and between human capital and labour conditions being especially relevant. Besides, through IPMA, a series of considerations was made regarding the individual indicators according to their relative importance and performance. Consequently, this paper significantly contributes to the extant literature by analysing the composition of wages in the energy sector, which might allow taking valuable management decisions. Nevertheless, the main limitation of this study lies in the availability of data for Spain and, specifically, for workers in the energy supplying industries. [ABSTRACT FROM AUTHOR]

The paper will examine, in detail, (a) the norms that can be featured under the category "Green Deal" connected to the European Commission, (b) their application to Spain, and (c) the different patterns of action and development models that have been shaped by this framework over the last 20 years. These patterns are particularly relevant currently, as the COVID-19 crisis has highlighted the importance of advancing towards new patterns of local sustainability endowed with higher resilience. The notion of cognitive sustainability will be one of the added values to the current reflections on sustainability in general, and energetic sustainability in particular. [ABSTRACT FROM AUTHOR]

The production of biohydrogen with negative CO2 emissions through the steam methane reforming of biomethane, coupled with carbon capture and storage, represents a promising technology, particularly for industries that are difficult to electrify. In spite of the maturity of this technology, which is currently employed in the production of grey and blue hydrogen, a detailed cost model that considers the entire supply chain is lacking in the literature. This study addresses this gap by applying correlations derived from actual facilities producing grey and blue hydrogen to calculate the CAPEX, while exploring various feedstock combinations for biogas generation to assess the OPEX. The analysis also includes logistic aspects, such as decentralised biogas production and the transportation and storage of CO2. The levelized cost of golden hydrogen is estimated to range from EUR 1.84 to 2.88/kg, compared to EUR 1.47/kg for grey hydrogen and EUR 1.93/kg for blue hydrogen, assuming a natural gas cost of EUR 25/MWh and excluding the CO2 tax. This range increases to between 3.84 and 2.92, with a natural gas cost of EUR 40/MWh with the inclusion of the CO2 tax. A comparison with conventional green hydrogen is performed, highlighting both prices and potential, thereby offering valuable information for decision-making. [ABSTRACT FROM AUTHOR]

Nowadays, great emphasis is rightly given in the scientific community to hydrogen production from electrolysis. However, to achieve the politically stated target ambitions, all low-carbon sources for hydrogen production must be considered. The present work proposes a local production system of negative carbon hydrogen from lignocellulosic residual biomass using gasification and gas separation through H2-selective membranes as enabling technologies. The feedstock is pruning. In addition, the system produces heat and power for a Renewable Energy Community (REC) to increase the economic feasibility of hydrogen production via their sale. A modular basic plant is sized, based on a simplified system envisaged for RECs under the current regulatory framework in Spain (electrical renewable output of 100 kW). A network of these modular basic plants in the province of Huesca (Aragón) is simulated to create a system of hydrogen refueling stations for mobility in that area. A Levelized Cost of Hydrogen (LCOH) is proposed, comprehending the whole production chain from "field to tank", which is significant in areas where there is no infrastructure for the production and distribution of hydrogen for automotive purposes. The resulting LCOH for the whole system is 8.90 EUR/kg. Sensitivity analysis potentially values a lower LCOH, which unveils that hydrogen mobility can be largely competitive with diesel one. [ABSTRACT FROM AUTHOR]

Large deployment of photovoltaic (PV) installation worldwide demands reliability assurance of the systems to maintain the confidence in the markets. With the diversity of PV applications, it is essential the detection of circumstances that can be the root of failure mechanisms, such as transportation and installation of the modules. They are one of the main sources of induced vibrations, which, in its turn, can provoke defects and damages in the PV modules. In this work, we have measured and analyzed tri-axial accelerations and mechanical vibration that photovoltaic crystalline modules withstand during transportation by road, including loading and unloading operations. We have also analyzed the natural oscillation frequencies, the resonance phenomena and the highest impacts. It is concluded that a proper fastening of the load inside the truck is necessary and that high impacts during loading and unloading should be avoided. Packing the modules to reduce vibrations is also recommendable. Several solutions to perform this are proposed. [ABSTRACT FROM AUTHOR]

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France. [ABSTRACT FROM AUTHOR]

Developing a regional power system to achieve a high level of integration of national systems requires sufficient development of the regional transmission grid. This is possible only with appropriate schemes for the complete cost–benefit analyses, and cost allocation of these transmission investments, which plays a critical role in the selection of the most efficient network investment and the proper assignment of their cost to the national systems. Network reinforcements affect the operation of power systems and, therefore, the externalities of power generation. This paper examines the impacts of integrating local air pollution damage from power production within the benefit assessment and cost allocation of transmission investments. The paper describes the methodology followed and illustrates its application in a real-life case study where a simplified version of a European network is considered. Within this case study, we have assessed the impact of considering the reduction in air pollution damage achieved through a particular HVDC project between France and Spain on the benefits, and benefit-driven cost allocation, computed for this project. In this case study, local pollution related benefits are a relevant fraction of the overall benefits of the considered transmission project. However, considering the local air pollution benefits of the project does not affect the net positive benefits of each country significantly, resulting in a limited change in the cost allocation of the project. [ABSTRACT FROM AUTHOR]

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure. [ABSTRACT FROM AUTHOR]

This study presents a novel methodology for determining the apparent thermal diffusivity of subsoil in situ, employing two heat transfer models within the subsurface: one method is based on heat conduction caused by air temperature oscillations, while the other considers heat transmission via both conduction and convection due to groundwater flow. Differential equations were solved, and non-linear regression analysis was employed. This method has direct applications in various engineering and environmental domains, such as underground transmission lines, oil and gas pipelines, radioactive waste management, and geothermal systems, especially in the context of implementing horizontal geothermal collectors (HGC). The apparent thermal diffusivity value of 1.514 × 10−6 m2 s−1, within a 95% confidence interval spanning 1.512 × 10−6 m2 s−1 and 1.516 × 10−6 m2 s−1, was obtained from the section between 1.67 and 3.86 m depth in a research borehole located in Asturias, Northern Spain, using twenty-one temperature sensors. The method allowed for the calculation of the subsoil's apparent thermal diffusivity up to a depth of 14.55 m. [ABSTRACT FROM AUTHOR]

The rapid expansion of solar photovoltaic (PV) generation has established its pivotal role in the shift toward sustainable energy systems. This study conducts an in-depth analysis of solar generation data from 2015 to 2018 in Spain, with a specific emphasis on temporal patterns, excluding weather data. Employing the powerful eXtreme gradient boosting (XGBoost) algorithm for modeling and forecasting, our research underscores its exceptional efficacy in capturing solar generation trends, as evidenced by a remarkable root mean squared error (RMSE) of 11.042, a mean absolute error (MAE) of 5.621, an R-squared (R²) of 0.999, and a minimal mean absolute percentage error (MAPE) of 0.046. These insights hold substantial implications for grid management, energy planning, and policy development, reaffirming solar energy's promise as a dependable and sustainable contributor to the electrical power system's evolution. This research contributes to the growing body of knowledge aimed at optimizing renewable energy integration and enhancing energy sustainability for future generations. [ABSTRACT FROM AUTHOR]

The integration of a CSP tower system with a PV solar field, sharing a thermal energy storage, is modeled and discussed. The tower system uses a new-design solid particle fluidized bed receiver integrated with a thermal storage, where hot particles are directly collected to store daily energy for overnight production of electricity. The PV solar field is aimed to supply the daily energy demand; when there is a surplus of PV energy production, the electric energy is converted to heat and accumulated in the thermal storage too. The integration of the two energy systems is modeled, building efficiency functions for all the sub-components of the integrated plant (heliostat field, receiver, storage, power block, PV field). Yearly simulations are performed for two different locations, Spain and Australia, obtaining that a system with a peak power of 10 MWe CSP + 15 MWe PV can supply—with a limited curtailment—a fraction of more than 60% (respectively, 62% and 68%) of a realistic electric load with a peak demand around 10 MW, to be compared with the 45/47% of the same load obtained adopting a PV-only system with the same overall peak power. In the integrated system, PV directly supplies 40/41% of the load, the remaining 23/28% being produced by the power block (mainly fed by the CSP). [ABSTRACT FROM AUTHOR]

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide. [ABSTRACT FROM AUTHOR]

The Hullera Vasco Leonesa (HVL) underground coal mine in northern Spain is subject to violent methane (CH4) outbursts. Vertical wells are used to extract CH4 from coal layers to improve mine safety. Bentonite suspensions are used as drilling fluids in this degasification system. The relationship between the soil and filter cake permeabilities, the filter cake thickness, and the filtrate loss significantly affects the fluid's rheological properties. Fann mud balance, marsh funnel viscometer, and Fann 300 press filter tests have been carried out to determine the rheological properties of the bentonite suspension. A drilling fluid study was carried out for three drilling zones (across which the rheological properties of the drilling fluid vary for the reasons mentioned above): Zone 1, the lower zone, wherein drilling cuts through the coalbed; Zone 2, the upper zone, wherein drilling cuts through layers of sand; and Zone 3, an intermediate zone consisting mainly of rock. When drilling cuts through the coalbed, the release of methane, which improves the safety of underground operations, depends on the relationship between the permeability of the coal and the permeability of the filter cake of the drilling fluid. The effect of sand contamination increases the filtrate loss, and therefore also increases the permeability of the filter cake. The filtrate reducer decreases filtrate loss by recovering the permeability. [ABSTRACT FROM AUTHOR]

The goal of the research is to assess the minimum requirement of fast charging infrastructure to allow country-wide interurban electric vehicle (EV) mobility. Charging times comparable to fueling times in conventional internal combustion vehicles are nowadays feasible, given the current availability of fast charging technologies. The main contribution of this paper is the analysis of the planning method and the investment requirements for the necessary infrastructure, including the definition of the Maximum Distance between Fast Charge (MDFC) and the Basic Highway Charging Infrastructure (BHCI) concepts. According to the calculations, distance between stations will be region-dependent, influenced primarily by weather conditions. The study considers that the initial investment should be sufficient to promote the EV adoption, proposing an initial state-financed public infrastructure and, once the adoption rate for EVs increases, additional infrastructure will be likely developed through private investment. The Spanish network of state highways is used as a case study to demonstrate the methodology and calculate the investment required. Further, the results are discussed and quantitatively compared to other incentives and policies supporting EV technology adoption in the light-vehicle sector. [ABSTRACT FROM AUTHOR]

In this paper, the energy benefits of switchable insulation systems (SIS) are assessed when applied as shades for windows as well as dynamic insulation for exterior walls of residential buildings located in European countries including Belgium and Spain. A series of analyses is performed for detached houses and apartments representing common Belgian residential buildings to determine the energy performance of SIS when deployed to windows and exterior walls and operated using simplified rule-based controls. The analysis results indicate that SIS-integrated windows can achieve significant energy savings for both dwelling types in Belgium, including the elimination of any mechanical cooling and a reduction of up to 44% of heating energy end-use. Moreover, the results show that SIS can offer even more energy efficiency and thermal comfort benefits when deployed to both windows and exterior walls for residential buildings. These energy efficiency benefits are higher, especially for reducing heating needs, for the milder climates of Belgium and Spain. However, it should be noted that the energy performance of SIS could be affected substantially by windows' orientation and occupants' behavior. [ABSTRACT FROM AUTHOR]

In this paper, the use of HVAC systems and non-HVAC control measures to reduce virus-laden bioaerosol exposure in a highly occupied indoor space is investigated. A simulation tool was used to model the fate and transport of bioaerosols in an indoor space in the hotel industry (bar or pub) with three types of HVAC system (central air handling system (CAHS), dedicated outdoor air system (DOAS), and wall unit system (WUS)). Non-HVAC control measures such as portable air cleaners (PAC) and local exhaust fans were considered. Occupant exposure was evaluated for 1 μm bioaerosols, which transport SARS-CoV-2, for 3 h/day of continuous source and exposure. The combined effects of ventilation (400 l/s of outdoor air), recirculated air filtration (90% efficacy), and a PAC with a capacity up to 900 m3/h mitigated the (normalized) integrated exposure of the occupant by 0.66 to 0.51 (CAHS) and 0.43 to 0.36 (DOAS). In the case of WUS, the normalized integrated exposure was reduced by up to 0.2 when the PAC with a capacity of up to 900 m3/h was used. The corresponding electricity consumed increased by 297.4 kWh/year (CAHS) and 482.7 kWh/year (DOAS), while for the WUS it increased by 197.1 kWh/year. [ABSTRACT FROM AUTHOR]

The energy transition to renewable energy in a democratic way is directly connected to the development of energy communities and community microgrids. Los Molinos del Rio Aguas (LMRA), an ecological community in the south of Spain, offers a promising case study for an off-grid community-owned microgrid. In this paper, the interconnection of autonomous solar home systems is proposed with the addition of community assets in order to create an off-grid community microgrid that is financially beneficial for the community. Based on this scenario, a Local Energy Market (LEM) based on Distributed Ledger (DL) technologies is implemented in order to foster the energy exchange and contribute to the social welfare of the community. The results provide a win-win scenario for the community and provides an example of an off-grid community microgrid in combination with a LEM that takes into consideration the social aspect of the community. [ABSTRACT FROM AUTHOR]

Understanding of energy consumption patterns is extremely important for optimization of resources and application of green trends. Traditionally, analyses were performed for large environments like regions and nations. However, with the advent of Smart Grids, the study of the behavior of smaller environments has become a necessity to allow a deeper micromanagement of the energy grid. This paper presents a data processing system to analyze energy consumption patterns in industrial parks, based on the cascade application of a Self-Organizing Map (SOM) and the clustering k-means algorithm. The system is validated with real load data from an industrial park in Spain. The validation results show that the system adequately finds different behavior patterns which are meaningful, and is capable of doing so without supervision, and without any prior knowledge about the data. [ABSTRACT FROM AUTHOR]

Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-maximization strategy (EPS), the chance-constrained programming-based strategy (CPS) and the multi-objective bidding strategy (ECPS). Analytical solutions under the three strategies are obtained. Comparisons among the three strategies are conducted on a hypothetical wind farm which follows the Spanish market rules. Results show that bid under the EPS is highly dependent on market clearing price, imbalance prices, and also the mean value and standard deviation of wind forecast, and that bid under the CPS is largely driven by risk parameters and the mean value and standard deviation of the wind forecast. The ECPS combining both EPS and CPS tends to choose a compromise bid. Furthermore, the ECPS can effectively control the tradeoff between expected profit and target profit for WPPs operating in volatile electricity markets. [ABSTRACT FROM AUTHOR]

Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly patterns. However, other factors like temperature or social events cause abnormalities in this otherwise periodic behavior. In order to develop an effective load forecasting system, it is necessary to understand and model these abnormalities because, in many cases, the higher forecasting error typical of these special days is linked to the larger part of the losses related to load forecasting. This paper focuses on the effect that several types of special days have on the load curve and how important it is to model these behaviors in detail. The paper analyzes the Spanish national system and it uses linear regression to model the effect that social events like holidays or festive periods have on the load curve. The results presented in this paper show that a large classification of events is needed in order to accurately model all the events that may occur in a 7-year period. [ABSTRACT FROM AUTHOR]

As a new technology, electric mobility has the potential to achieve a reduction in CO2 emissions and contribute to the transition from the current transportation system to a better one, environmentally speaking. The objective of the paper is to aid the necessary decision-making for the adoption and development of electric vehicles in Spain, taking the time horizon of 2020. This will be achieved by building a System Dynamics model for various scenarios that will be used for the analysis and comparison of various dynamic variables, as well as to determine how, and to what extent, they will influence the number of electric vehicles that will run on Spanish roads in the coming years, focusing on the cost variable. [ABSTRACT FROM AUTHOR]

Non-cooperative scheduling games can be used to coordinate residential loads in order to achieve a common goal while accounting for individual consumer's interests, privacy, and autonomy. However, a significant portion of the residential flexibility—Thermostatically Controlled Loads (TCLs) such as water and space heating/cooling appliances—has not been fully addressed under this game theoretic approach: their comfort constraints and integer control were not considered. This paper presents a method for properly including TCLs in this framework and discusses its application in energy communities. Specifically, we propose a general mathematical formulation for considering users' comfort in non-cooperative games. We model the integer nature of the TCLs control with binary variables and show that optimal or close to optimal (less than 1%) solutions are reached. Moreover, different total cost functions can be used depending on the market context and the objective of the demand management program. To illustrate and discuss these aspects in practical applications, we used a case study of an energy community in Spain. The results show that the TC solutions are optimal or only 0.80% worse than optimal; different total cost functions result in different results (load curve smoothing or peak load reduction); consumers' comfort is respected; and the proposed game model cooperates with consumers in order to minimize community's costs. [ABSTRACT FROM AUTHOR]

This paper lays out the role of the first centre in the world for the integration into the electrical grid of electricity coming from renewable energy (the Spanish acronym for which is CECRE (Centro de Control de Energías Renovables; Control Centre for Renewable Energies)) and the industrial development of large energy suppliers and wind turbine manufacturers in Spain. These two initiatives enabled the development of one of the first integrated markets for this type of energy source. The key contributions were the development of two software programs (wind management and management of solar light incidence), their visual implementation, and centralized digital control. An economic and business history approach is used to show the rise and relative failure of the Spanish wind industry during the period 2004–2015, when Spain became the fourth country after China, the US, and Germany in terms of installed capacity of renewable energy and, in relative terms, the second country after Denmark. This study is unique in that it provides an integrated vision of the reasons for the relative fall of Spain in the world ranking of wind energy producers. The methodology of the economic analysis of industrial policies makes it possible to explain the fall in the relative importance of Spain in the international panorama of wind farms. [ABSTRACT FROM AUTHOR]

Islands are widely recognised as ideal pilot sites that can spearhead the transition to clean energy and development towards a sustainable and healthy society. One of the assumptions underpinning this notion is that island communities are more ready to engage with smart grids (SGs) than people on the mainland. This is believed to be due to the high costs of energy on islands and the idea that the sense of community and collective action is stronger on islands than on the mainland. This paper presents findings from a survey conducted to assess people's perception of, and readiness to engage with, SG and demand response (DR) in the communities of three islands taking part in a H2020 project called REACT. The main objective of the survey, conducted in 2020, was to inform the recruitment of participants in the project, which is piloting different technologies required for SGs and DR with communities on the three islands. The results show that many island residents are motivated to take part in SG, to engage with energy saving, and are willing to change some energy-related behaviours in their homes. However, the results also indicate that levels of ownership of, and knowledge and familiarity with, the SG and DR related technologies are extremely low, suggesting that the expected uptake of DR in islands might not be as high as anticipated. This brings into question the readiness of island dwellers for the SG, their role in the deployment of such schemes more widely and the validity of the assumptions often made about island communities. This has significant implications for the design of SGs and DR solutions for islands, including devoting sufficient efforts to build knowledge and awareness of the SG, investing in demonstration projects for that purpose and tailoring interventions based on island communities' motivations. [ABSTRACT FROM AUTHOR]

European institutions have decided to ban the sale of Internal Combustion Vehicles (ICEVs) in the EU from 2035. This opens a possible scenario in which, in the not-too-distant future, all vehicles circulating in Europe are likely to be Battery Electric Vehicles (BEVs). The Spanish vehicle fleet is one of the oldest and has the lowest percentage of BEVs in Europe. The aim of this study is to evaluate the hypothetical scenario in which the current mobility of ICEVs is transformed into BEVs, in the geographical area of the province of Barcelona and in Spain in general. The daily electricity consumption, the required installation capacity of wind and solar photovoltaic energies, and the potential reduction of NOx and particulate matter (PM) emissions are estimated. The daily emission reduction would be about 314 tons of NOx and 17 tons of PM in Spain. However, the estimated investment required in Spain to generate the additional electricity from renewable sources would be enormous (over EUR 25.4 billion), representing, for example, 5.5% of the total national budget in 2022. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of the fall in household consumption after an economic crisis in Spain on greenhouse gas emissions. To this end, household consumption is differentiated by the age of the main provider by using a conversion matrix that relates consumption groups to activity sectors. A multisectoral model was used to quantify and compare the environmental impact caused by the consumption of each age group, indicating that the older the age of the main household provider, the smaller the reduction in GHG emissions associated with their consumption. The results facilitate an analysis of how the greenhouse gas emissions of the different sectors of the Spanish economy, associated with the population under study, varied before and after the 2008 crisis, and confirm that the sectors with the greatest reduction in emissions were, in this order, extractive industries, construction, manufacturing industry, wholesale and retail trade and transport and storage. This is relevant for decision making in the field of environmental policies in crises, akin to the one the world is currently experiencing. [ABSTRACT FROM AUTHOR]