This study aims to explore whether consumers’ attention to companies’ green marketing communication influences their green purchase behaviour. It also analyses the importance of consumers’ characteristics, namely gender, education, and green attitudes, in their attention to companies’ green marketing communication. An online survey was carried out on the population residing in Portugal over 18 years of age, allowing us to collect 690 valid responses. Data analysis techniques including descriptive analyses, parametric and non-parametric tests, linear correlation, and regression analysis were used. The achieved results allow us to conclude that consumers are attentive to companies’ green marketing communication. A strong correlation between consumers’ attention to companies’ green marketing communication and green purchasing behaviour was identified. The results also confirm that individuals with higher educational levels and green attitudes and females are the most attentive to companies’ green marketing communication.

In recent years, there has been a growing concern about the environmental impacts of consumers’ behaviour. As this environmental awareness increases, consumers tend to focus more on green products and how purchasing these products represents an effective way to protect the environment. Through the application of the theory of planned behaviour (TPB), in this research, we studied the influence of some key variables on college students’ green purchasing behaviour, namely perceived behavioural control, subjective norms, green attitudes, and green purchase intentions. The TPB model was extended to the analysis of the influence of the companies’ green communication on students’ green purchase behaviour. To achieve the proposed goal, from March to April of 2021, a survey was conducted among the students of a higher education institution (HEI) in Portugal, allowing us to gather 432 valid responses. The findings suggested that all the variables, except the subjective norms, had a positive influence on the students’ green purchase intentions. Moreover, it was observed that students’ intentions had a positive influence on their green purchase behaviour. This research provides important results not only for policymakers to promote more sustainable behaviours among consumers but also for companies to be more aware of the importance of green communication.

The European Regional Development Fund devoted around 66 billion Euros to the financial support of innovation and productivity in European enterprises over the 2014–2020 programming period. In this framework, we assessed the implementation of the Operational Programmes dedicated to fostering research and innovation, particularly in small and medium-sized enterprises. With this aim, we used a network slack-based data envelopment analysis model paired with cluster analysis that encompasses a multitude of performance framework indicators to assess 53 Operational Programmes from 19 countries. Our findings suggest that compared to transition and less developed regions, more developed regions present a higher room for improvement. Also, less developed regions present a better performance when they employ their funding against more developed regions, suggesting that further funding should be channelled for leveraging research and innovation in the former regions. Finally, Operational Programme managers should focus on solving the problems both inherent to the poor outcomes in terms of enhancing the number of researchers working in improved research infrastructures and promoting the technology transfer between research institutions and enterprises.

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation.

We shall deal with specific classes of the second-order reduced bias extreme value index estimators, devised for heavy tails. In those classes, the second-order parameters in the bias are estimated at a level k ₁ of a larger order than that of the level k at which we compute the extreme value index estimator, and by doing this, it is possible to keep the asymptotic variance of the new estimators equal to the asymptotic variance of the Hill estimator, the maximum-likelihood estimator of the extreme value index γ, under a strict Pareto model. On the basis of an adequate pair of this type of extreme value index estimators, we also provide a heuristic adaptive choice of the threshold in reduced bias estimation and we proceed to an intensive computer simulation that enables us to study, through Monte-Carlo techniques, the behavior of the non-adaptive and adaptive proposed estimators. An illustration of the behavior of these estimators for sets of real data in the fields of finance and insurance is also provided.