Local Disproportions of Quality of Life and Their Influence on the Process of Green Economy Development in Polish Voivodships in 2010–2020

Abstract

Voivodships are centres of economic, social, and cultural life—they gather economic and social activities. This research aimed to evaluate the spatial differentiation of the quality of life in voivodships in Poland with the use of a synthetic measure. To achieve the research objective, the research methods used were literature analysis, statistical analysis, and synthetic measure. The Technique for Order Preference by Similarity to an Ideal Solution method was used to build synthetic measures. The choice of variables in 2010–2020 was largely conditioned by the availability of data collected in the regional system at the level of voivodships at the Local Data Bank of the Central Statistical Office. As a result of the analysis of voivodships in Poland, based on the quality of life measure, four groups were distinguished (according to the value of quartiles). In the group of the best voivodeships there were: Pomerania, Masovia, Lower Silesia, and West Pomeranian in 2010, and Masovia, Pomerania, Greater Poland, Lower Silesia, and Lesser Poland in 2020, and in the IV, the weakest group: Lodz Province, Podlasie Province, Lubusz Province, and Holy Cross in 2010, and Lodz Province, Podlasie Province, Holy Cross, and Lublin Province in 2020. The synthetic quality of life ranged from 0.37 to 0.56 in 2010 and from 0.39 to 0.64 in 2020. Regional authorities, taking care to improve economic potential, cause increasing the attractiveness of the area and attracting new entrepreneurs, create new jobs, and improve the quality of life of the inhabitants. Quality of life is shaped by economic activity and working conditions, health, education, free time and social relations, economic and physical security, and the quality of the natural environment. The results of the research conducted allow local governments to make comparisons. The conclusions drawn may allow them to identify potential directions for developing policy optimization.

Evaluation and System Coupling of Beautiful Qinghai–Tibet Plateau Construction Based on Point of Interest Data

“…International scholars have carried out less research on Beautiful China, which was mostly focused on the analysis of the sustainable development dimension [23]. For example, the transformation, development, and path of a green economy have gained wide attention in the lagging countries or European Union countries [24][25][26][27], which may have an impact on the life quality. For Beautiful China construction, some scholars (Table 1) have studied the construction of Beautiful China index system [11,14,16,20,23,[28][29][30][31][32][33].…”

Green Economy and Waste Management as Determinants of Modeling Green Capital of Districts in Poland in 2010–2020

“…The selected primary diagnostic variables are obvious in relation to the main criterion in question: they are stimulating, and destimulating (Table 3) [61]. The nature of the selected variables can be controlled by determining the direction of the correlation of the variables with the decision variable (stimulants should be positively correlated with stimulants and destimulants negatively correlated with destimulants).…”