## 3 Results
Residents not involved in green practices found the following environmental problems to be important: air and water pollution (4.2 and 4.1, respectively); and household waste generation--4.0 points. The problem of global warming was recognized as the least significant in both groups of the population (Figure 3). Figure 4 shows the attitude to clean energy. The data show how people are willing to bear certain costs in the transition to sustainable development. In order to analyze pro-environmental behavior based on survey data, we built a regression dependence of ecological behavior on the following factors. Environmental behavior = f (respondent's age and education level, respondent's income, environmental parameters). The environmental variables were the amount of waste generated by the family and the electricity costs per month. Looking for correlations between variables, we started with the Granger causality test. This test is a necessary but constitutes an insufficient condition for causality. By conducting the Granger causality test, we can analyze the causal relationships amongst environmental behavior, electricity costs, household income, (level of) education, waste generated and age. The results of the Granger test are presented in Table 4. If P value is below 0.05, the null hypothesis is rejected. It is clear from the data that direct causality of pro-environmental behaviors is detected for electricity costs and for household income for a lag of 2 (Table 4). Other variables show a two-way relationship, except for waste generated. Waste generated exhibits a causality for a lag of 3 (\(p\) = 0.02). Let us now construct the linear relationship between our variables. Table 5 shows the results of the regression analysis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Null Hypothesis** & **F-Statistic** & **Prob.** \\ \hline Electricity costs do not Granger Cause EB & 4.89160 & 0.0077 \\ Household income does not Granger Cause EB & 11.2698 & \(1\times 10^{-5}\) \\ Waste generated does not Granger Cause EB & 2.25490 & 0.1053 \\ AGE does not Granger Cause ECOLOG & 0.41438 & 0.6608 \\ Education does not Granger Cause EB & 0.62286 & 0.5366 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pairwise Granger causality tests. Figure 4: Attitude of respondents to renewable energy sources. The following equation was obtained. \[ \begin{split}\text{Pro-environmental behavior}=0.279+0.002\text{ * }\text{Age}+0.16\text{ * }\text{edu}+0.0000152\text{ * }\text{ Household income}-0.00095\text{ * }\\ \text{Waste generated}-0.0000432\text{ * }\text{ Electricity costs}\end{split}  \tag{1}\]  
According to the results, age, level of education and income have a positive effect on pro-environmental behavior. Waste generated by the family and electricity costs per month are negatively related topro-environmental behavior. Thus, the population that is not involved in green practices generated more waste and did not practice energy saving. Thus, we can argue that the first hypothesis about the positive dependence of pro-environmental behavior on income and age is confirmed. The second hypothesis is also confirmed, i.e., people who are not involved in green practices generate more waste and consume more energy. The chosen model is characterized by a low R Square value (0.07), and this may be due to the complex structure of pro-environmental behavior, which may further require more variables than collected in the survey, and to difficulties in collecting and quantifying factors. We also tested the different forms of relationships between the variables' environmental behavior, electricity costs, household income, (level of) education, waste generated and age. Table 6 shows the Akaike information criterion (AIC) and [PERSON] information criterion (also SIC), which enable comparisons between models with different numbers of parameters. The best model is indicated by the lowest value of the criterion. Importantly, model comparisons have to be based on samples of the same length.