**4. Results**

Through the application of modelling techniques, the regional model was summarized, which generated statistically significant coefficients for all 15 regions studied, with the level of statistical significance exceeding 98% for these regions, with a standard maximum error estimate of 8% at the level of the sample studied, as shown in Table 2.

This validates hypothesis H1. At the global level, the policy to combat microplastic water pollution is directly and proportionally oriented towards the reduction of regional pollution, with the awareness that this approach will have an effect of at least 98% in the total reduction of microplastic water pollution.

Table 2 shows the distribution of the statistical F function values between the minimum value of 236.93 points and the maximum value of 5058.97 points, thus demonstrating a significant regional disparity of policies to combat microplastic water pollution. The maximum values are attributed to the regions: USA, OECD Latin America, Other Africa and non-OECD Asia. With respect to these regions, the analysis of the interval between 1990–2019 shows that the value of the circular economy's impact on microplastic water pollution reaches the maximum value at the correlation function.

At the opposite pole, the lowest values were recorded for the regions: OECD America, OECD Non-EU and OECD Asia. It should be mentioned that the level of Sig. coefficients assimilated to the F function is lower than the selected error representativeness threshold α = 0.05, which allows for all 15 regions analyzed to reject the null hypothesis and maintain the alternative hypothesis. This allows the validation of the regional model to combat microplastic pollution. The ANOVA test is presented in Table 3.

The ANOVA statistical test for the regional models shows the statistical weight of the regression squares is allocated to the correlational function (98.5%), while the residual variable has an allocation of only 99.5%. This demonstrates that the model is valid and representative of the phenomenon studied, validating hypothesis H2. At the global level, plastic recycling mechanisms were created assuming that this approach will have a direct impact on reducing microplastic water pollution.

To prove hypothesis H3, we projected in Figure 5 the dynamic distributions of the evolution of the dependent variable in relation to its predicted values according to the regional PP-Plot distribution graph. From Figure 5, it follows that the error distribution of the dependent variable at the regional level has different error peaks (*y*).

Figure 5 shows that, as experience increases and the current period approaches, the experience gained by policy makers in the field of pollution control helps straighten the trend curves, which proves hypothesis H3. From the point of view of coherence of water pollution abatement policies, there is an increasing trend to reduce the correlation errors of the indicators as the overall experience of implementing these policies increases.



In order to prove hypothesis H4, the authors analysed the results obtained by the proposed model (Equation (1)), finding that, at the level of the dependent variable assimilated to the circular economy, the correlation with the impact of water pollution caused by microplastics was strong, i.e., reducing the amount of plastic waste has the effect of reducing water pollution caused by microplastics by 3.8 times. It can be seen from equation 1 that the impact of the circular economy on the regional size of plastic consumption in the EU is inversely proportional, which can be presumed to have a causality with the high level of regional disparity in plastic consumption. In order to determine the level of regional disparity of plastic use in the EU, we accessed the Our World in Data database [54] for the year 2019 (the latest year for which official statistical data are available) and selected Member States for which we performed an algorithm to plot regional averages against the overall average for the following indicators: share of EU average mismanaged plastic waste (%); share of EU average mismanaged plastic waste to ocean (%) and share of EU average mismanaged plastic waste per capita (%) (see Table 4).

#### **Table 3.** ANOVA test.



**Figure 5.** P-P Plot diagrams of regional distribution.


**Table 4.** Analysis of the degree of regional disparity on policies to combat plastic water pollution in the EU.

The overall pollution disparity index calculated based on the standard deviation of the three regional data sets in Table 4 is 65%. Thus, there is a significant difference between policies to reduce plastic water pollution and policies to reduce plastic consumption per capita in the EU. At a regional level, the share of the EU average mismanaged plastic waste indicator has a disparity of 136%, close to the value of the disparity of the share of the EU average mismanaged plastic waste per capita indicator, which means that, in terms of communicating the effects on the environment, there is a successful communication effort in the EU, with 90% of European citizens aware of the consequences of pollution on the deterioration of environmental quality. This results from a comparison of regional disparities between the two indicators.

On the other hand, the Share of EU average mismanaged plastic waste to ocean indicator shows an increase in regional disparities, up to 169.2%, mainly due to the regional territorial configuration. The most advanced countries in implementing environmental policies in this area are Malta, Finland, Ireland and Slovakia. At the opposite end are Croatia, Greece, the Netherlands, Estonia and Portugal.

In the overall ranking for the three indicators, the highest levels of disparity were assessed for Croatia, Germany and Romania, with these countries showing the most fluctuating variations from the calculated EU average. These aspects demonstrate hypothesis H4. In the EU, as efforts to promote the circular economy intensify, the disparities in combating water pollution caused by microplastics are widening, especially for countries where the implementation of the circular economy is at an early stage.
