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Aim: To establish contact lens wear and care practices during the COVID-19 pandemic in Spain.Method: A 58-item anonymous online survey was distributed during the period 30th April to 10th May via Qualtrics. The survey explored: a) demographic characteristics (age, sex, general health and where they were living during lockdown), b) changes in their contact lens use during lockdown, c) hygiene and contact lens compliance and d) concerns associated with contact lens wear and ways to support wearers during the pandemic.Results: Two hundred and sixty responses were analysed (38.8 ± 11.4 years old, 75% female). Three-quarters of participants reported that they were self-isolating or rigorously following social distancing advice. Sixty-seven percent of participants reported using their contact lenses less during the pandemic. Respondents were found to be compliant with handwashing prior to inserting and removing contact lenses (in both cases 97% doing this 'most times' or 'every time'). However, only 44 % complied with the '20 s rule' and 48 % used a shared towel to dry their hands. A higher proportion of hydrogen peroxide users replaced the lens case monthly compared to multi-purpose users (64% vs.49%; p < 0.001). Twenty-four percent admitted wearing lenses whilst showering and 16% did not consider ceasing lens wear if feeling unwell with flu/cold symptoms.Conclusion: Eye care practitioners should continue to educate contact lens wearers to ensure safe contact lens wear to minimise the chance of developing contact lens related complications during the pandemic. Modifiable factors that need particular attention in Spain include: handwashing for at least 20s before lens handling, drying hands with single use paper towels, including a rub-and-rinse step for reusable lenses, lens case cleaning and renewal, avoidance of water exposure and when to cease lens wear during the pandemic. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has increased the unemployment issue and accelerated the digital transformation. Real-time data specific to ongoing revolution in applied economic analysis are increasingly demanded to anticipate changes in unemployment to improve decision-making. The aim of this paper is to test whether unemployment rate forecasts based on Google Trends data improve the predictions based only on macroeconomic indicators published with a longer time lag. The research has been carried out at the national level for Spain and Portugal, and the main novelty is the analysis of unemployment rate forecasts at the regional level for Spain using dynamic panel data models to implement the best policies to reduce unemployment. The keywords unemployment and job offers have been used in each language. The results obtained demonstrate the capacity of Google Trends data associated with unemployment to improve the predictions of unemployment rates in Spanish regions. Moreover, predictions based on Google Trends data at national level in Spain and Portugal are significantly more accurate than those based on autoregressive models for both countries. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic led to significant changes in domestic water use in many cities. One important question is whether and to what extent the pandemic stressed the resilience of the water supply system. Another question examined in the paper is whether changes in water use during confinement differed according to the sociospatial configuration of cities. We examine these questions in the context of a medium size city of Northern Spain characterized by a mix of residential land uses, and internal variations in education, age composition, and income, producing step gradients in water use. First, we select and analyze influencing factors in domestic water consumption. Second, using statistical regression tests, we assess whether or not and to what extent the same factors influenced consumption during the COVID 19 lockdown. Results indicate that COVID-19 related confinement increased work and study from home and reinforced WASH habits, leading to more water consumption. However, this increase was larger in medium and low-income neighborhoods than in high-income neighborhoods, showing the relevance of sociodemographic factors but also of other variables more relevant for outdoor water use such as precipitation. Despite this rise in consumption, the water supply system was able to withstand the shock caused by confinement showing a robust resilience both in physical and in social terms. However, more challenging scenarios such as the coincidence of a pandemic with a severe drought should be taken into account in future resilience development. • Sociodemographic and urban factors played an important role in explaining domestic water consumption patterns during COVID-19 lockdowns. • Households in multifamily buildings or apartment blocks with medium-low incomes and significant presence of young people recorded the largest relative increases of water consumption during these lockdowns. • Weather conditions are key in domestic water consumption during the COVID 19 lockdown especially in areas with a large presence of single houses. • The water supply system was resilient and could withstand the increase in consumption caused by confinement as well as provide for financial assistance to vulnerable households. Policies should take into account the possible coincidence in time of unexpected phenomena such as COVID-19 with droughts already stressing urban resilience. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and lockdown in Spain during Spring 2020 resulted in a ban on most uses of public space, producing a social redefinition of not only public spaces, but also private spaces such as homes. This paper examines the discourses related to balconies that appeared at this time on Twitter (X), an intermediate microspace and hybrid between the public and private. The study uses data from tweets posted in Spain during the strictest phase of the lockdown (from 15 March to 1 May 2020). Based on a descriptive analytical approach – both quantitative and textual – the study applies text content analysis and natural language processing to identify and analyse the main topics of conversation related to the use of balconies. The study brought to light two primary and complementary results. Firstly, the balcony during the pandemic became a revitalized space in an exceptional situation, partially adapting to the most common practices characteristic of public space and thus reaffirming the importance of the existence of public spaces for citizen encounters. Secondly, the various uses of balconies received different types of attention and generated stronger or weaker interactions on social networks, demonstrating the complexity and diversity of relationships with public spaces. • The impossibility of using public spaces multiplied the importance of balconies • Using Twitter data, we studied the use of balconies in Spain in the spring of 2020 • Balconies became a public microspace through their use towards the outside • Technology did not isolate citizens but was key for the promotion of the new uses • We found important differences by use in attachments to the tweets and interactions [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has suddenly modified the lifestyle of a large portion of the population around the world. This pandemic is also the first one in decades that has severely impacted many countries of the Global North. Governments have had to adopt wide-scope and desperate measures to face the abnormal situation and to reduce the stress of their health care systems. These measures have been based on reducing the physical-social interaction and mobility (closing schools and some economic activities, or fostering telework, among others), increasing the physical distance between people, and recommending washing hands frequently and wearing masks. Thus, the COVID-19 may change many habits of people and the ways we interact with others after the current pandemic. It would also imply changes in mobility habits. Many questions arise about the willingness and acceptability of changes, and who would have to impulse them and how. This paper aims to study and understand individuals' acceptability towards a set of generic measures related to urban mobility in Spain, one of the countries most affected by the COVID-19 pandemic. To that end, we conducted an online survey during the lockdown in Spring (2020). More than 75% of respondents would accept restrictions on car use after the return to normal, and more than 90% agree on increasing the space for pedestrians and cyclists on streets. Furthermore, 75% of respondents would change the primary transport mode towards a more sustainable transport mode if it would decrease the incidence or severity of the COVID-19. These results show that the respondents are overall in favor of a new urban hierarchy that gives more importance to the most sustainable modes, reducing the public space devoted to the car, which means the possibility of turning the COVID-19 crisis into an opportunity to make Spanish cities more sustainable. • Impacts of COVID-19 on mobility patterns and behavior in Spain. • Acceptance of policies to restrict car use and increase soft mode space in a post-COVID-19 scenario. • Policy measures are recommended based on the survey and the Binary Logit models rresults. [ABSTRACT FROM AUTHOR]

Online learning and training continue gaining momentum worldwide resulting in the reduction of the traditional form of face-to-face education with its temporal and spatial limitations. Online education improves access to education and training, as witnessed during the Covid-19 pandemic. This article focuses on online education adoption in Spain. A representative survey on ICT use in households conducted annually by the Spanish National Institute of Statistics is used to construct a panel database for the years 2008–2020. The first objective is to provide an econometric model for adopting online education using this panel data. Next is to measure the effects of relevant observable individual socioeconomic variables on adoption. A Heckman selection model allows for estimating the impact of gender, age, education, digital skills, habitat, and income. The article also measures the effects of Covid-19 in 2020 on different population groups. The drivers and impediments have the expected signs and plausible sizes. The paper concludes with policy recommendations and suggestions for further research. • Adopotion of online education, both before and during the Covid-19 pandemic. • An economic model of online adoption. The main determinants are gender, age, education, digital skills, habitat, and income. • An acceleratio n of nine times in the adoption is apparent after the first wave of Covid-19 compared with the period 200819. • The policy recommendations are related to improving digital skills, which are limited compared with other European countries. [ABSTRACT FROM AUTHOR]

The objective of this paper is to link the literatures on economic complexity, income equality and environmental quality within the context of the Environmental Kuznets Trilemma. Within this framework, it is possible to measure the presence of an impossible trinity of irreconcilable objectives: economic growth, equal distribution of income and environmental sustainability. Our paper revisits this trilemma by focusing on economic complexity (EC) indexes instead of direct measures of economic growth, and applies this analysis to the sub-national level. We link these three cutting-edge topics by means of novel datasets computed for the Spanish economy at the province level (NUTS-3) for a long period (2002–16). Our paper also sheds new light on the spatial patterns of the trilemma's three dimensions and their implications for the future of Spain's more peripheral regions. ⁃ This paper links the literatures on economic complexity, income equality and environmental quality within the context of the Environmental Kuznets Trilemma. ⁃ Our paper focuses on economic complexity indexes instead of direct measures of economic growth, and applies this analysis to the sub-national level, using novel datasets for the Spanish provinces (NUTS-3) for a long period (2002–16). ⁃ Our paper shows a clear tension between economic growth and complexity versus income equality and environmental quality. The econometric analysis verifies the classic trilemma (equality-sustainability-growth), but also of our new version: equality-sustainability-complexity. ⁃ The paper also tests the presence of a quadrilemma, discussing the role to which a province's level of complexity tends to play in the tensions between growth, inequality, and sustainability. The results open new avenues for the smart specialization of regions within countries, something specially relevant in the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

In this paper we refer to the Open Web to the set of services offered freely to Internet users, representing a pillar of modern societies. Despite its importance for society, it is unknown how the COVID-19 pandemic is affecting the Open Web. In this paper, we address this issue, focusing our analysis on Spain, one of the countries which have been most impacted by the pandemic. On the one hand, we study the impact of the pandemic in the financial backbone of the Open Web, the online advertising business. To this end, we leverage concepts from Supply–Demand economic theory to perform a careful analysis of the elasticity in the supply of ad-spaces to the financial shortage of the online advertising business and its subsequent reduction in ad spaces' price. On the other hand, we analyze the distribution of the Open Web composition across business categories and its evolution during the COVID-19 pandemic. These analyses are conducted between Jan 1st and Dec 31st, 2020, using a reference dataset comprising information from more than 18 billion ad spaces. Our results indicate that the Open Web has experienced a moderate shift in its composition across business categories. However, this change is not produced by the financial shortage of the online advertising business, because as our analysis shows, the Open Web's supply of ad spaces is inelastic (i.e., insensitive) to the sustained low-price of ad spaces during the pandemic. Instead, existing evidence suggests that the reported shift in the Open Web composition is likely due to the change in the users' online behavior (e.g., browsing and mobile apps utilization patterns). [ABSTRACT FROM AUTHOR]

CO 2 emissions are one of the major contributors to global warming. The variety of emission sources and the nature of CO 2 hinders estimating its concentration in real time and therefore to adopt flexible policies that contribute to its control and, ultimately, to reduce its effects. Spain is not exempted from this challenge and CO 2 emissions are published only at the end of the year and as an aggregated value for the whole country, without recognising the existing differences between the regions (the so-called, Autonomous Communities). The recent COVID-19 pandemic is a clear example of the need of accurate and fast estimation methods so that policies can be tailored to the current status and not to a past one. This paper provides a method to estimate monthly emissions of CO 2 for each AACC in Spain based on data that are published monthly by the relevant administrations. The paper discusses the approximations needed in the development of the method, predicts the drop in emissions due to the reduced industrial activity during the pandemic in Spain and provides the estimation of future emissions under three recovery scenarios after the pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we decompose a family of energy poverty indices into incidence, intensity and inequality among the poor. When continuous variables such as income are used, it is well known that any income poverty index should take into account these three poverty terms. Therefore, every energy poverty measure should also take into account these three. We propose to follow an analogous idea to decompose a family of energy poverty indices in these three components. The novelty of this proposal is that the multidimensional family of energy poverty measures is applied to multiple dichotomous variables and not to a single continuous variable, when, to date, no decomposition has been proposed based on these types of variables. We then extend this decomposition to a further decomposition by population subgroups. In this way, we will be able to know more precisely the origins of energy poverty in order to implement more specific anti-poverty policies targeting the incidence, intensity or/and inequality among the energy poor. Finally, we apply the proposed decomposition to Spain in 2020 as regards certain individual and household characteristics, so as to identify the individuals most likely to suffer from energy poverty in the first year of the Covid-19 pandemic. • An decomposable index to measure energy poverty. • Poverty Decomposition in Incidence, Intensity and Inequality. • Sources of energy poverty increase in Spain on 2020. [ABSTRACT FROM AUTHOR]

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively. • Estimated polynomial model coefficients serve as COVID19 contagion rate descriptors. • EANNs are used to build valid predictors for different pandemic stages. • Considering curve polynomial descriptors significantly improve the model performances. • Simultaneous Multi-Task EANN forecast perform better in Málaga with simpler models. [ABSTRACT FROM AUTHOR]

• Sleep disorders and anxiety were the most common symptoms. • Most of the participants did not believe they were being adequately supported and informed by their superiors or the institution. • Emotional expression and social support were more frequent among women, and social support seeking was associated with ages 35–50 and was prevalent in Medical, Psychology and Nursing specialization trainees. • The distribution of coping strategies was heterogeneous, which indicates the lack of a proper training in coping with stressful situations. The aim of this paper is to analyze the main coping strategies used by frontline teams during the first days of the COVID pandemic confinement in Spain. This information could be necessary in order to carry out training programs that allow a better handling of future emergency situations, as well as acting more effectively and with less negative emotional impact. A questionnaire was used to identify different psychological profiles for coping, and in turn, other relevant variables were analyzed. The most used strategies by health professionals were problem solving, desiderative thinking and social support. Emotional expression and social support were used more by women. Significantly different behaviors were found in desiderative thinking (lower in people of 35–50 years old, and social support, higher in people 35 years old). The symptoms most commonly experienced by medical personnel were: sleep disorders, anxiety, tension, depressive symptoms, gastrointestinal symptoms and general somatic muscular symptoms. A relationship could be determined between the age/gender of the workers and the participants' overall assessment of their ability to cope with the COVID-19 stress situation they had experienced (men 50 years old and women between 35 and 50 years old, who felt able or very able to cope with the stress caused by the health emergency. However, women <35 years old and >50 years old believed they were able to cope poorly with the circumstances. The advantage of specific training plans in order to help with some stress symptoms could be suggested, aimed at the acquisition of tools based on problem solving, and emotional management in stressful and emergency situations. [ABSTRACT FROM AUTHOR]

The Spanish real estate and its 'sea and sun' tourism model, were profoundly disrupted during the Great Recession of 2008–2014 As a result, hedge funds and their speculative operations have favoured an intense process of urban touristification in the largest Spanish cities, especially over the past ten years. The aim of this paper is to examine how the COVID-19 crisis has triggered shifts in the supply of short-term rentals and the type of demand of such rentals. By taking into account such changes, we will address the potential changes that the current pandemic scenario might bring between the 'classical' real estate market and short-term rentals in Spain. • Its high dependence on tourism makes Spain the best case study to analyze pandemic-related impacts on urban tourism • The exhaustion of the "Sun & Beach" tourism model had been replaced by the touristification of consolidated urban areas • Short-term rental and residential markets have redefined their complex relationship in new, uncertain directions • Platforms are the new frontier of capital accumulation in cities, anda powerful new mechanism to extract rents from housing [ABSTRACT FROM AUTHOR]

With the outbreak of the COVID-19 pandemic, the use of Information and Communications Technology (ICT) came to dominate daily activities (e.g., e-working, e-shopping, and e-leisure). The intensive use of ICT might trigger higher levels of spatial fragmentation of daily activities, having significant consequences for planning purposes. This paper seeks to estimate how ICT use and habits affect the individuals' spatial fragmentation patterns in urban contexts on post-COVID-19 societies, while controlling for socioeconomic and built environment characteristics. The city of Alcalá de Henares (Madrid Metropolitan Area, Spain) serves as the case study. The research design is based on activity diaries obtained by face-to-face interviews, and Tobit and Poisson regression analyses are used to examine the relationships between spatial fragmentation measures (outcome variable) and ICT use (predictors). The results reveal that ICT might originate higher spatial fragmentation for work-related activities than for shopping-leisure purposes. For working activities, higher spatial fragmentation patterns are found among people with higher willingness to e-work and individuals who e-work at least once a month, but rather dependent on the occupation type. Regarding shopping and leisure activities, higher spatial fragmentation patterns are noted with car owners and more frequent consumers of online entertainment. The study provides insight into how ICT use is transforming spaces traditionally adapted for a single purpose into multifunctional spaces as well as the spatial effect of this phenomenon. • Information and Communications Technology (ICT) use become a central part in the post-COVID-19 society. • Working-related activities are more spatially fragmented than shopping-leisure activities. • Working-related activities are more spatially fragmented for individuals e-working monthly. • People consuming online entertainment are more likely to strongly fragment their shopping and leisure activities. • Residential land uses become an important activity hub. [ABSTRACT FROM AUTHOR]

Confronting the COVID-19 health emergency has forced public administrations in Spain to work with various networks as a means of promoting their campaigns to citizens. This paper aims to analyse digital citizens' e-participation by focusing on the state health campaign #EstoNoEsUnJuego - #ThisIsNotAGame. This campaign was launched by the Spanish Ministry of Health in September 2020 via Twitter with the objective of reinforcing protection measures against the virus. A sample consisting of 19,576 tweets, sent from September 2020 to February 2021, was investigated and the results have indicated that, of 9133 users, 64.8% of citizens collaborated in the dissemination of tweets. It was observed that most messages supported the campaign by disseminating information on measures, data and news. Only 0.1% of the messages were aggressive. The conclusion is that, despite not having created a true form of communication between public institutions and citizens, e-participation has generated a functional connection between them. Citizens have acquired a responsible and participatory digital role which, although failing to show personal involvement in their comments, has been the main driving force behind the success of this campaign. • e-participation is a key element in dissemination and sharing information on health security measures. • e-participation shows a collaborative and responsible behaviour during the campaign on Twitter. • Spanish digital citizenship is emotionally neutral in health emergency situations. • Twitter is a useful tool for instant communication in crisis situations. • The impact of a crisis campaign via Twitter diminishes over time. [ABSTRACT FROM AUTHOR]

This paper analyses the influence that the initial actions and strategies pursued by hotel managers have on the recovery of occupancy after a crisis such as the COVID-19 pandemic. To do this, a specific survey is carried out on managers of Spanish hotels. The main findings show that labour actions, especially plans for temporary employment regulations, innovation and differentiation strategies, reorientation to closer markets and obtaining information from official sources as a guarantee of their certainty, are the measures that have a greater impact on the possibilities of recovering hotel activity. In addition, government measures that contribute to the improvement of the financial situation of firms can also play a relevant role in hotel recovery. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel genetic algorithm-based seasonal fractional grey model is proposed. • Monthly electricity generation is forecasted since the Covid-19 lockdown started. • Seasonal grey and machine learning models are used for forecasting. • The selected countries are France, Germany, Spain, Turkey, and the UK. • The share of renewables in total electricity generation is forecasted. Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector. [ABSTRACT FROM AUTHOR]

• Harmonization and quality control of originally heterogenous observational databases. • Large-scale application of machine learning methods in a distributed data network. • Transparent use of open-source software tools and publicly shared analytical code. As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g. , by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world. [ABSTRACT FROM AUTHOR]

• Intensive care unit admission for COVID-19 positive patients is considered. • Fuzzy AHP is used to prioritize 19 criteria. • Multi MOORA is used to rank COVID-19 positive patients. • Which patient is more urgent for ICU is found out. The COVID-19 pandemic results in an intense flow of patients to hospitals especially to the intensive care units (ICUs) to be treated. The ICUs will therefore be confronted with a massive influx of patients (e.g. Spain and Italy). However, if the number of patients is higher than the resources available in ICUs, rationing decisions such as determining and evaluating the criteria for ICU admission becomes essential. In this case, the decision of which patients will be admitted to the ICUs may put significant pressure on healthcare personnel. The goal of this paper is to determine the criteria to be used in the decision of admission of COVID-19 patients to the ICUs. A three-step methodology is applied. In the first step, the evaluation criteria are determined, and then the criteria are prioritized using a fuzzy analytical hierarchy process (AHP) in an uncertain and multiple-criteria environment choice. Finally, COVID-19 patients are ranked using the Multi-Objective Optimization Method by Ratio Analysis to find out which patient is more urgent. According to experts' evaluation of ICU admission criteria, "increment of >2 in SOFA score" seems the most dominant factor among others. The proposed methodology is tested on 10 anonymous COVID-19 positive patients being treated in a public hospital and the ICU admission results are discussed. Obtained priorities and ranking is in line with the hospitals' behavior that potentially depicts the usefulness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

The Covid-19 crisis has had a major impact on electricity markets, affecting power plant input and output prices. In this paper Spanish electricity and natural gas prices and international carbon prices are used to calculate the variable margin of natural gas combined cycles (NGCC), i.e. the Clean Spark Spread (CSS). The stochastic behavior of the CSS is modeled using an Ornstein-Uhlenbeck (OU) process because of its properties. The expected first semester 2020 CSS results based on the fitted model with daily 2016-2019 data, taking the end of 2019 as a starting point, are compared with the actual figures for the same period. In the first half of 2020 electricity and natural gas prices are significantly lower than expected at the end of 2019, but carbon allowance prices have decreased less in percentage terms. The monthly CSS values in the first half of 2020 are significantly lower than expected for March-May 2020, with the April value being -€4.15/WWh lower figure than the €7.16/MWh expected. This work calculates distributions of daily and monthly CSS values. • The Covid-19 crisis is having a strong effect on energy wholesale markets. • The Clean Spark Spread (CSS) is modelled as an Ornstein-Uhlenbeck (OU) process. • Daily and monthly price distributions of expected CSS values are calculated. • The CSS values are lower than expected for March–May 2020. • In April 2020 the mean of actual CSS prices was -€4.15 €/MWh compared to an expected value of €7.16/MWh. [ABSTRACT FROM AUTHOR]

• Human and economic capital increase organisational resilience. • Corporate social responsibility practices increase organisational resilience. • Organisational resilience affects the scope of adoption of anti-COVID-19 measures. • Organisational resilience increases perceived job security. • Perceived job security affects positively organisational commitment. The COVID-19 pandemic will reduce the attractiveness of hospitality occupations. This particularly concerns senior management positions whose holders may substitute hospitality jobs with more secure and rewarding employment in other economic sectors. Organisational resilience of hospitality businesses, including their response to COVID-19, and corporate social responsibility (CSR) practices may, however, affect perceived job security of senior managers and, thus, influence their commitment to remain in their host organisations. This paper quantitatively tests the inter-linkages between the above variables on a sample of senior managers in hotels in Spain. It finds that the levels of organisational resilience and the extent of CSR practices reinforce perceived job security of managers which, in turn, determines their organisational commitment. Organisational response to COVID-19 affects perceived job security and enhances managers' organisational commitment. To retain senior management teams in light of future disastrous events, hotels should, therefore, strengthen their organisational resilience and invest in CSR. [ABSTRACT FROM AUTHOR]