The threat of the negative consequences of global warming makes the discussion about the relationship between economic growth, productivity, and increasing renewable energy involvement an important topic. Hence, the aim of this study is to analyze the impact of renewable energy and energy supply on economic growth and productivity at the national level using stochastic frontier analysis and the aggregate production function framework. In doing so, we analyzed a panel of annual data from 133 countries from 2008 to 2014. We apply a generalized stochastic frontier model, which allows us to differentiate between persistent and transient inefficiency, as well as individual effects. Our results indicate a threshold level in terms of a country’s development that needs to be obtained to benefit from increasing renewable energy involvement over time. However, if this threshold level is obtained, productivity gains are evident. We also found that the role of the energy supply in aggregate production is nontrivial. That is, its inclusion changes the relationship between key input factors (capital and labor) by decreasing their overall elasticities and increasing the observed economies of scale.

The aim of this paper is to define the factors influencing the level of energy poverty among students. The analysis of these factors is based on the results of a survey conducted among a group of 937 students at the Cracow University of Economics. The study takes into consideration the changes in the attitudes and behavior of students resulting from the introduction of distance learning during the COVID-19 pandemic. The switch to distance learning resulted in a significant increase in the number of responses related to feeling ill or sick due to inadequate temperature (from 24% before a lockdown to 32% after the introduction of a lockdown). Students experienced temporary surges in their overall living costs due to the pandemic, especially during the first wave. The respondents who experienced inappropriate temperatures (inadequate heating) due to excessive costs felt ill or became sick more often than others. The study demonstrated that those who pay more for energy (defined as a surplus payment in excess of 10%) tended to be, on average, less energy-aware than others. The following indicators of energy poverty among the students were distinguished: high living costs, small degree of influence over the choice of living quarters, as well as concerns over energy efficiency and environment. The conclusions drawn from the conducted studies may be utilized to design public policies aimed at curtailing the phenomenon of energy poverty among students. This issue is particularly prominent in large urban agglomerations where the costs of living are high and result in the feeling of pressure regarding the need to save money on thermal energy consumption.

Advanced efficiency measurement methods usually fall within Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), or their derivatives. Although SFA has some theoretical advantages, it has been criticized for relying on arbitrary and potentially restrictive assumptions about model specification. One strand of the literature suggests the use of nonparametric SF models to cope with the issue. We follow an alternative path and demonstrate that it is possible to deal with specification uncertainty and potentially restrictive assumptions while maintaining the advantages of the parametric approach. First, we develop a flexible stochastic model based on generalized t and generalized beta of second kind distributions, which encompasses virtually all known parametric SFA specifications. Second, we apply Bayesian inference methods, which are less restrictive than those used so far, and propose feasible approximate alternatives based on maximum likelihood. Third, we pool results from alternative specifications using model averaging. Our focus is on the distributional assumptions regarding the compound error in SFA since this aspect has not been addressed so far in a satisfactory way. However, extensions to other elements of specification uncertainty, like the choice of the frontier functional form, are straightforward. Finally, we show simulations results and analyze two well-researched datasets, for which we obtain probabilistic (density) estimates of efficiency scores that take into account the estimation error and model uncertainty in a formally justified manner.

The objective of this paper is to investigate the impact of foreign direct investment (FDI) on economic growth and productivity in sectors of the Visegrad Group one decade after their accession to the EU. In order to account for sample heterogeneity, as well as productivity differences, we construct a generalized true random-effects model with varying efficiency distribution. We find that FDI has a positive impact on the Visegrad Group’s sectors and that its effectiveness depends upon the technological gap between the host and home economy. There are three sources of this positive impact: (i) sectoral output and labour productivity growth, (ii) more effective use of input factors, and via (iii) higher efficiency component of the total factor productivity (TFP). These sources form a three-way transmission mechanism through which FDI can impact economic growth conditioned upon FDI effectiveness due to the technological gap.

This paper discusses a Bayesian approach to analyzing cost efficiency of Distribution System Operators when model specification and variable selection are difficult to determine. Bayesian model selection and inference pooling techniques are adopted in a stochastic frontier analysis to mitigate the problem of model uncertainty. Adequacy of a given specification is judged by its posterior probability, which makes the benchmarking process not only more transparent but also much more objective. The proposed methodology is applied to one of Polish Distribution System Operators. We find that variable selection plays an important role and models, which are the best at describing the data, are rather parsimonious. They rely on just a few variables determining the observed cost. However, these models also show relatively high average efficiency scores among analyzed objects.