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The Feature Paper can be either an original research article, a substantial novel research study that often involves
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Considering the proposed methodology, at the first stage, all data are checked for stationarity by the Levin-Lin-Chu , Im–Pesaran–Shin , Pesaran’s CADF , and cross-sectionally augmented Im, Pesaran, and Shin (CIPS) tests . The results of stationarity testing in panels are shown in Table 2.
The results of Levin-Lin-Chu, Im–Pesaran–Shin, Pesaran’s CADF and CIPS justify the conclusion that not all data are stationary at the level (
). However, at the first difference, all variables have become stationary, with statistical significance at the 1% level. At the next stage, to avoid inconsistency and biased estimation, the study provides cross-section dependency tests, particularly the Pesaran CD, Breusch-Pagan LM, and Pesaran scaled LM tests. The findings of the Pesaran CD test are shown in Table 3.
The results of the p value in Table 3 allow rejecting the null hypothesis on the existence of the cross-sectional independence and accepting alternative–cross-sectional dependence for the panel data analyzed. Thus, RE, Know, Gov, GDP, TO, and Know×Gov have a cross-sectional dependence. Table 4 contains the empirical results of the Breusch-Pagan LM and Pesaran scaled LM tests. The calculated probability (Table 4) is less than 0.5, which does not allow rejecting the null hypothesis that there is no cross-sectional dependence.
If the data are stationary with cross-sectional dependence, the Westerlund ECM panel cointegration test  could be provided. The results of the cointegration test are shown in Table 5.
Based on the findings in Table 5, the null hypothesis (no cointegration) can be rejected for all selected models. This means that cointegration exists among the variables for all models at 1% statistical significance. At the next stage, heterogeneous parameter models with FGLS techniques are applied. The findings (Table 6) demonstrate the positive effect of Innov on knowledge spillover for all the analyzed countries, which confirms Hypothesis 1. Thus, the growth of patents in environment-related technologies leads to an increase in knowledge spillover by 0.004, with a significance level of 10%. At the same time, Gov has a negative moderating effect on the interconnection between innovation and knowledge spillover. The growth of Gov reduces the knowledge spillover by 0.001. Such results allow rejecting the second hypothesis that environmental regulation has a positive moderating effect on the interconnection between innovation and knowledge spillover.
The empirical results (Table 6) confirm Hypothesis 3 that innovation positively connects with renewable energy. This means that the growth of patents in environment-related technologies enhances renewable energy. In addition, environmental regulation does not have a statistically significant impact on the interconnection between innovation and renewable energy. This allows rejecting the fourth hypothesis. Furthermore, knowledge spillover does not have a statistically significant impact on the interconnection between innovation and renewable energy, and environmental regulation strengthens this relationship. This means that Hypothesis 5 could not be confirmed. The interconnection between environmental regulation and knowledge spillover does not have a statistically significant positive effect on the interconnection between innovation and renewable energy. This allows Hypothesis 6 to be rejected. The results of the FMOLS long-run analysis are shown in Table 7, which demonstrates the impact of Innov, Know, Gov, GDP, and TO on RE within each country.
The empirical results show that Innov has a positive effect on renewable energy in the following countries: Austria, Belgium, Bulgaria, Denmark, Finland, Ireland, Italy, Latvia, The Netherlands, Poland, Portugal, Slovenia, Spain, and Sweden. It bears noting that Belgium, Ireland, The Netherlands, and Poland have not achieved the target share of renewable energy in the primary energy supply. This means that the mentioned countries should stimulate green patents, which increase renewable energy by 1.685 points in Belgium, by 0.589 points in Ireland, by 1.405 in The Netherlands, and by 0.425 points in Poland. At the same time, Innov has a negative effect on renewable energy in Croatia and Cyprus. Thus, for those countries, the financing of green patents will not guarantee the extension of renewable energy. At the same time, in the Czech Republic and Luxemburg, Innov also has a negative effect on renewable energy; however, it is not statistically significant.
Knowledge spillover negatively affects renewable energy in Belgium, Bulgaria, the Czech Republic, France, Greece, Hungary, Italy, Latvia, Malta, Poland, Romania, the Slovak Republic, Slovenia, and Spain. However, this effect is not statistically significant. Furthermore, in the following, the further increase in Know will bring the growth of RE: Austria, Croatia, Denmark, Estonia, Finland, Ireland, Portugal, and Sweden. It stands to mention that The Netherlands, Belgium, Bulgaria, Italy, Latvia, Poland, Slovenia, and Spain have a large gap between innovation and knowledge spillover, which restricts the growth of RE. It is necessary to strengthen the accountability of the social, economic, and ecological effects from attracted knowledge into the countries.
Environmental regulations boost renewable energy in the following countries: Belgium, Croatia, Czech Republic, Denmark, France, Greece, Italy, Poland, and Slovenia. However, environmental regulations decrease renewable energy in Cyprus, Finland, Ireland, Lithuania, Malta, The Netherlands, and the Slovak Republic. This means that the mentioned countries have not developed an effective regulatory framework for spreading renewable energy. For instance, at the first stage, Malta, The Netherlands, Cyprus, and Ireland (countries that have not achieved their targets) should provide appropriate environmental regulations, after which they could provide incentives for attracting investments in green patents and projects that aim to extend renewable energy.
It stands to mention that in most cases, the impacts of GDP and TO on RE are positive and statistically significant. In addition, in general, the growth of GDP and TO provoked an increase in RE, excluding the following case: the growth of GDP by 1 point led to a decline in RE by 0.790 in Belgium and 0.191 in Greece. The values of
(Table 6) are higher than the thresholds in all countries. This leads to the conclusion that the obtained findings are useful for interpretation.
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