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Purpose This paper aims to identify the changes in the share of large public interest entities (PIEs) in European Union (EU) Member States providing Sustainable Development Goal (SDG) reporting prior to (2017) and after (2019) the implementation of Directive 2014/95/EU and the factors that influence their decisions to provide SDG reporting in 2019. Design/methodology/approach The authors use the multilevel theory of social change in organizations as the theoretical background. The sample consists of 341 PIEs based in the EU Member States, for which reports published in 2017 and 2019 are available in the global reporting initiative sustainability disclosure database. The authors analyzed the data using the statistical significance test of equal proportions and the logistic regression model. Findings The study findings allow to identify a significant positive change in the share of companies providing a reference to SDGs in 2019 compared with 2017. The research confirms that companies’ engagement in United Nations Global Compact and previous experience in sustainability reporting positively influences the decision to report on SDGs in 2019. Contrary to the expectations, industry, size, SDG implementation score, future orientation of government and corporate governance score do not seem to be relevant factors influencing PIEs’ disclosures. Originality/value The paper adds to the understanding of the differences in SDG reporting within the EU, which is seen as a frontrunner in implementing the 2030 Agenda and the SDGs.

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The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.

* Artykuł powstał w ramach realizacji projektu badawczego sfinansowanego ze środków przyznanych Wydziałowi Zarządzania Uniwersytetu Ekonomicznego w Krakowie w ramach dotacji na utrzymanie potencjału badawczego.