Djula Borozan’s research while affiliated with University of Osijek and other places

Energy companies are facing increasing pressure from institutional and industry stakeholders to prioritize their responsibility to the environment and society, including providing accurate, reliable, and comprehensive reports on their sustainability practices. Three metrics were developed in this study: the average sustainability reporting score and two sustainability performance reporting indices based on two different performance measurement methodologies. These were designed to assess the effect of mandatory non-financial disclosure on sustainability reporting and the level of transparency and engagement of energy companies. The study also examined the relationship between the level of sustainability reporting and sustainability performance in the period of 2016–2019 by correlating these metrics. The analysis sheds light on the effectiveness of non-financial disclosure regulations in promoting sustainability practices in the energy industry. The results revealed no difference in metric scores prior to, or even following, the adoption of Directive 2014/95/EU. Energy companies performed better in terms of sustainability when more indicators were reported. Their primary focus was on the economic aspect of sustainability, particularly corruption. They gave less importance to the environmental aspect, mainly reporting on emissions. The social aspect received the least attention, although indicators for employee education and training were mentioned most frequently. The analysis showed that the metrics are statistically significantly correlated and complement each other, highlighting the need to consider a variety of metrics when assessing sustainability performance in the energy industry.

In response to the global demand for robust yet environmentally sensitive economic growth, this paper assesses environmentally adjusted technical efficiency and productivity for European Union (EU) countries from 2000 to 2018. It sets efficiency and productivity in a multivariate framework and compares them across the EU and between advanced (EU-17) and post-transition (EU-11) countries, distinguishing between the periods marked by expansion (2000–2006) and turbulence and crises (2007–2018). Based on data envelopment analysis and the Malmquist productivity index, the results confirm the difference in efficiency between the EU-17 and the EU-11, which is in favor of the former. Although all technically inefficient countries have higher environmentally adjusted efficiency than the non-adjusted ones, the catching-up effect is observed only within the EU-17. A favorable trend in environmentally adjusted productivity is detected over time for both groups, primarily due to the innovation effect. In contrast, managerial and organizational practices failed to keep pace with changes in economic conditions and technology. The results also signal a countercyclical nature of productivity and show that while adopting renewable energy is essential for sustainability, an increasing share of this type of energy is an important but not a sufficient condition for achieving optimal technical efficiency.

This paper examines the impact of climate policy uncertainty (CPU) on carbon dioxide (CO 2 ) emissions of 1007 listed firms over 2003–2021. As the urgency to combat climate change and implement effective policies may create uncertainty, it focuses on the Unites States, a significant emitter with a complex policy landscape. Using the two‐step system generalized method of moments estimation, the analysis reveals a negative CPU–CO 2 emissions relationship, indicating that increased CPU incentivizes firms to adopt sustainable practices, leading to emissions reductions. Capital expenditures have similar effects on emissions, accompanied by increased corporate sustainability engagement. However, no significant relationship is found between research and development (R&D) expenditure and CO 2 emissions, nor does CPU significantly moderate the R&D–CO 2 emissions nexus. Firms with substantial fixed assets exhibit higher CO 2 emissions, while heavy emitters demonstrate limited CPU responsiveness. These findings provide valuable insights for policymakers encouraging corporate participation in emissions reduction efforts.

This study explores low-carbon behavior (LCB), considering a number of psychological predictors deemed important according to the theory of planned behavior and the norm-activation model. Four distinct clusters were identified by conducting a cluster analysis of data collected from an online survey of young people in Croatia in 2022, revealing both consistent and inconsistent patterns of LCB. The study highlights the complexity of factors influencing LCB and utilizes a fuzzy-set qualitative comparative analysis to identify specific configurations of psychological variables that contribute to high and not-high levels of LCB within each cluster. The results validate the significance of established psychological determinants in explaining variations in low-carbon intentions and behaviors among young people, challenging the assumption of intention as the single best determinant of LCB and underscoring the presence of multiple causal complexities and equifinalities. Furthermore, the study demonstrates the asymmetric effects of different psychological conditions on high and not-high levels of LCB, suggesting that consistent and inconsistent LCBs cannot simply be viewed as opposite poles of the same continuum and that a variety of pathways can be explored to enhance carbon reduction activities.

Climate policy uncertainty (CPU) has gained prominence in renewable energy research, yet its determinative role remains largely overlooked. This study intends to bridge this gap by exploring whether CPU in the USA affects renewable energy consumption (REN), with a focus on the period 2000–2021. Employing the “Bayesian model averaging approach,” it first identifies the key drivers of REN, accounting for competing theories and empirical evidence. The findings underline not only the importance of economic activity, population, and research and development as factors influencing REN, but also the role of CPU. The “autoregressive distributed lag bounds testing approach” is then used to assess their short- and long-run impacts on REN. The results show that CPU negatively impacts REN, and this impact is even more pronounced in the long run. Consequently, the transition to renewable energy as a climate solution faces challenges due to climate change sensitivity and unclear energy policy. The negative effect is also observed in gross domestic product, industrial production, and oil prices, reflecting the fact that the US economy requires a large, stable, and controllable amount of energy currently unmet by renewables. Encouragingly, intensified international trade and research and development activities foster competitiveness and technological progress in REN. The key implication is that policymakers must prioritize stable climate policies to stimulate sustainable investments in renewable energy. Graphical abstract

Young people are generally well aware of the importance of transitioning to a low-carbon economy. However, there is a significant gap between awareness of the importance of a low-carbon transition and actual behavior. The main aim of this paper is to explore the relationship between these two dimensions and, accordingly, using the k-means cluster method, create a typology of young people, Croatian university students. In addition, the paper is focused on examining the association between cluster membership and selected demographic variables. The application of the cluster method on the data collected by a survey in 2021 resulted in three significantly different, stable, and meaningful clusters. They differ in the extent to which respondents are aware of the importance of a low-carbon economy and the extent to which they engage in low-carbon behavior. The first cluster includes about 5% of respondents who are quite skeptical but often engaged in low-carbon activities. The second cluster includes trusting respondents who sometimes engage in low-carbon activities. This cluster consists of about 68% of respondents. The last cluster includes skeptical respondents who are mostly opposed (rarely active) to low-carbon behavior. The non-parametric chi-square tests suggest that, unlike the selected demographic variables (e.g., gender, financial situation, previously completed study program, field of education, or household size), there is not enough evidence to confirm the existence of the association between rural/urban living environment and cluster membership.KeywordsEnvironmental awarenessLow-carbon behaviork-means cluster analysisCroatian university students

Given the underrepresentation of the information on the size and composition of personal carbon footprints, this paper explores the relationship between the students’ socio-demographic characteristics such as education, gender, age, and place of residence or income level related to their carbon footprint. In addition, the paper also addresses the effect of barriers and incentives to reduce carbon footprints. The analysis is based on a convenient sample using data collected through a self-administered online questionnaire completed by 182 students from Josip Juraj Strossmayer University of Osijek, Croatia, in 2021. The results of the multiple linear regression method, which was applied to assess the influence of explanatory variables, show a significant effect of disposable income and a place of residence on the students’ carbon footprints. In addition, among various examined barriers and internal incentives aimed at low-carbon behaviour, the lack of knowledge and financial savings has a favourable effect on personal carbon footprints. The study contributes to empirical evidence on the personal or system-level hotspots that influence carbon footprint reduction in the context of higher education. Consequently, raising students’ carbon footprint awareness can have a multiplicative effect on carbon emissions in the future.