Agriculture is a very important sector of the economy that has a great influence on rural areas. Almost sixty years of the Common Agricultural Policy (CAP) in the European Union (EU) may impose a question on the impact of this policy on job creation and economic growth in rural areas. By analyzing key indicators, one should note that although there are still many problems to tackle, the CAP has been successful to some extend in increasing employment rate and Gross Domestic Product (GDP) per capita as well as decreasing poverty rate in the EU rural areas. There are also more and more households connected to the next-generation broadband network and there is still a significant number of bed places in the EU rural areas. Although, the situation in specific EU Member States and their rural regions may be different, the overall performance of the EU is satisfactory and shows positive prognostics for the current EU financial perspective of 2021–2027. This paper also provides an empirical approach which checks whether there is a connection between the employment rate and the rural development measured in GDP per capita based on the data from the Baltic States: Estonia, Latvia and Lithuania in the period of 2000–2020. This research confirms that when increasing the level of rural employment, the economic growth rises in rural areas.
The subject of the article concerns changes in the structure of consumption in the Visegrad countries in the years 1996-2016. The focused was on expenditure on services and food expenditure of households in the Czech Republic, Poland, Slovakia and Hungary. The systematic improvement of the socio-economic situation and institutional changes in the Visegrad Group countries resulted in an increase in expenditure on services and food. This contributed to changes in the structure of household consumption, where the share of expenditure on services in final expenditure increased in the examined period in each of the countries studied, and the share of expenditure on food systematically decreased. The main research objective of the article is to identify trends in the share of household expenditure on selected types of goods in their final expenditure. The different trends in the shaping of expenditure shares for goods considered in the article result from their different nature, where services are a higher order good, while food is a good satisfying basic existential needs of man. The study also attempted to determine the limit values of the share of expenditure on services and the share of expenditure on food in the structure of household consumption in selected countries. The limit values were determined for both types of goods on the basis of estimation of parameters of the Tӧrnquist function of the first type. The limit values established in the study should be treated as the saturation level for the share of goods that the countries are able to achieve with the current trends in their socio-economic development.
The paper presents a spatial approach to the analysis of the relationship between air pollution, economic growth, and renewable energy consumption. The economic growth of every country is based on the energy consumption that leads to an increase in national productivity. Using renewable energy is very important for the environmental protection and security of the earth’s resources. Promoting environmentally friendly operations increases awareness of sustainable development, which is currently a major concern of state governments. In this study, we explored the influence of economic growth and the share of renewable energy out of total energy consumption on CO2 emissions. The study was based on the classical environmental Kuznets curve (EKC) and enriched with the spatial dependencies. In particular, we determined the spatial spillovers in the form of the indirect effects of changes in renewable energy consumption of a specific country on the CO2 emissions of neighboring countries. A neighborhood in this study was defined by ecological development similarity. The neighborhood matrix was constructed based on the values of the ecological footprint measure. We used the spatio-temporal Durbin model, with which the indirect effects were determined in relation to the spatially lagged renewable energy consumption. The results of our study also show the strength of the effects caused by imitating actions from the states with high levels of environmental protection. The study was conducted using data for 75 selected countries from the period of 2013–2019. Cumulative spatial and spatio-temporal effects allowed us to determine (1) the countries with the greatest impact on others and (2) the countries that follow the leading ones.
This article presents the analysis of the convergence of energy use from renewable sources among chosen European countries using a spatio-temporal approach. The high energy dependence of European countries on the economies of other continents makes the development of the use of renewable sources for energy production an important factor of their economic and social progress. The economic growth of every country is determined, among other factors, by an increase in the energy inputs. Therefore, in order to avoid excessive degradation of the environment, the use of renewable energy sources is increasingly becoming the crucial goal of governments worldwide. The analysis was conducted using data for 32 selected European countries in the years 1995–2019. In order to check progress in the case of the homogenization of renewable energy use, the β-convergence models for pooled cross-sectional and time-series data (TSCS) and also spatio-temporal β-convergence models were estimated. Absolute and conditional convergence was considered. Based on the literature review, the gross domestic product (GDP) per capita level and CO2 emissions per capita level as processes conditioning the convergence in the case of the renewable energy use were chosen. Moreover, the spatial dependencies between neighboring countries were included in the models, and the neighborhood was defined in two ways. The neighborhood was quantified using the connection matrices: (1) based on the common border criterion (geographical neighborhood) and (2) based on the well-being level similarity (economic neighborhood based on the HPI index values).
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