Food security is a current research topic, and most scientific papers in this field focus on its analysis on a global level.The presented paper focuses on the analysis of food security in European countries and its features.It emphasizes the need for objective indicators that will suit the specifics of European countries, which may seem food-secure from a global point of view.The analysis used the definition of food security and its pillars according to Food and Agriculture Organization of the United Nations (FAO) and used available data for 12 variables in 4 pillars of food security over the period of 2012-2020 for all accessible countries to produce composite indicators for European countries.Performance according to this indicator was compared with the ranking produced by the global food security index (GFSI).The comparison showed some limitations of the GFSI for the analysis of food security in European countries.The major problems were the smaller numbers of countries included in the GFSI, its complexity (which may hide some specific food security problems in the region), and weights derived from the subjective opinions of expert panels.However, the GFSI can be very useful for identifying the main issues at the world level, and the presented analysis recommends deriving objective weights for this kind of analysis in European countries by data envelopment analysis with dummy input.Derived weights can also be used as a measure of performance in each food security pillar.Analysis identified weak food security conditions in Eastern and Southeastern Europe.Surprisingly, weak performance compared to other European countries was recorded by France and Slovakia.Measuring food security in European countries: limitations of the global food security index and its comparison with the DEA approach 643

The effects of urbanisation in developing countries like India call for measuring regional disparities in Quality of Life (QOL).Though there exists, several internationally recognized indexes to assess QOL, a regional QOL assessment is yet not studied in India.The study attempts to develop such an assessment framework for measuring objective QOL at the regional level using a composite index, taking the case of Kozhikode, Kerala, India, as a case example.Firstly, a set of 19 variables under five domains of QOL was arrived at through several steps of screening processes, refinement from a master list, and a final expert opinion survey.Secondly, a Composite Quality of Life Index (CQOLI) was formulated by conducting a series of statistical analyses on the data set, including Principal Component Analysis (PCA).Thirdly, a spatial analysis of QOL of the study area was conducted by mapping the CQOLI scores to understand the pattern and analyse inequities.Accordingly, the spatial study units were grouped into High, Medium, and Low QOL.Out of the total settlements, it was found that 20% fell in high QOL, 46.25% in medium QOL, and 33.75% in low QOL.The pattern was spatially analysed based on the topographical divisions, namely lowlands, midlands, and highlands, and it was found that the QOL decreased from lowlands towards highlands.Finally, solutions and strategies which may be used as policy directives were proposed for each QOL group.The insights from the study are helpful for planners and decision-makers while implementing interventions in the study region.The methodology adopted here can be replicated in other regions of similar scale by altering the variables suited to the context.

permanent road tests.The ranking of Hungarian cities based on AV preparedness is also specified in this study.Based on this database, we established a neural network in which the number of trucks per 1,000 inhabitants, the current situation, the existence of plans and the number of registered businesses per 1,000 inhabitants in the commercial and vehicle repairs sector are the most likely factors to predict expectations.

This study examines the digitalisation levels of European countries between 2017 and 2022 using the digital economy and society index (DESI).In this study, four main dimensions of the DESI were considered.Initially, hierarchical and non-hierarchical clustering analysis methods were used to group countries according to similar digitalisation levels.Then, the stability of clusters and transitions between clusters were determined by applying association rule analysis.Findings show that European countries are clustered at five levels of digitalisation.Most countries have remained in the same cluster over the years, while some countries have moved to neighbouring clusters.Moreover, common digitalisation characteristics of the countries in each cluster are revealed.The results of this study will guide countries in evaluating their digitalisation levels and devising strategies.This study is expected to contribute to policymakers' decisionmaking processes in the field of digital transformation.Convergence or divergence?Trends in the digitalisation index cluster over the years 1051

the MLE estimator in the TSpVARX model.After we conduct theoretical studies and simulations, the TSpVARX model is applied to predict the inflation and money outflow of Yogyakarta, Solo, and Semarang.Our study shows that TSpVARX is better than SpVARX in predicting the inflation of those three cities and the money outflow of Semarang based on the root mean square error (RMSE) and and symmetric mean absolute percentage error (SMAPE).

Energy poverty is a complex, multidimensional problem that affects people's quality of life.Its study has become an important pillar of the public policy agenda and the literature in Latin America.Using the 10% indicator, this paper aimed to model the energy consumption of households in Argentina to estimate the energy expenditure of each and thus determine which households are in a situation of energy poverty.For the empirical analysis The authors used the 2017-2018 National Household Expenditure Survey of Argentina, and urban households in the interior of the province of Buenos Aires were selected as a case study.After characterizing the main energy consumption habits of the sample, energy poverty was assessed.The analysis shows that for the study period (2017-2018), a significant number of households suffered from energy poverty; that is, they spent more than 10% of their income on energy expenditures.The findings indicate that the Demand Model is consistent, as the degree of adjustment is 0.1% in the demand for electricity and natural gas and 0.3% in the demand for liquefied petroleum gas.In addition, 462,143 households (28.3% of the sample analyzed) in the interior of the province of Buenos Aires experienced energy deprivation when considering the 10% indicator.On the other hand, by means of the 2M indicator, the results show that 518,686 households were in energy poverty (31.8% of the sample analyzed).

An efficient health care system is a priority for all developed countries, and it is an important prerequisite for economic prosperity.Efficiency is measured by the ratio of inputs to outputs of a given system.In the case of health care, inputs include health care expenditure and human and material resources.There are several outputs that can be used to assess efficiency.Among the most important and frequently used are avoidable mortality rates.The consequences of avoidable mortality are understandably very unpleasant for those involved.From a national perspective, avoidable mortality also has serious macroeconomic consequences.The aim of this paper is to assess the efficiency of the health care systems of the member states of the European Union using avoidable mortality rates.Data were taken from the latest available online datasets on avoidable mortality published by Eurostat in 2019.Correlation analysis and multivariate statistical analysis were used.Assessing the health system efficiency by using avoidable mortality indicators in European countries, 2019 1125

The stochastic mortality model of Lee–Carter (1992) has become extremely popular over the past decades. As a result, many extended versions, for example, multi-population
\nmodels, exist nowadays. In the original model, the logarithm of the mortality rates is a linear function of a time-dependent factor called the mortality index, which can be
\nconsidered a long-term trend describing the improvement in mortality. The prediction of mortality rates is based on forecasting thislong-term trend. Multi-population mortality
\nmodels were developed to jointly model related populations. This research fit five multi-population
\nmortality models to Hungarian regional data by sex, and compared the models’ results to each other and to the results of the original Lee–Carter model. This paper is the first to examine the possibility of applying multipopulation mortality models to Hungarian
\nregional data. The selected mortality models are discussed in a standardized methodological
\nframework. The author use maximum likelihood estimation to observe the models
\non the base period of 1970–2021. Regarding the number of deaths, a Poisson distribution is
\nassumed. As a result, the age-specific mortality rates are forecasted until 2050, and the future
\nlife expectancies of the regions of Hungary are estimated by sex. The author consider the
\npresence of the COVID-19 pandemic during fitting and predicting the models similarly to Lee and Carter, who treated the effect of the Spanish flu in their study.