The mitigation of climate change through ambitious greenhouse gases emission reduction targets constitutes a current priority at world level, reflected in international, regional and national agendas. Within the common framework for global climate action, an increased reliance on renewable energy sources, which would assist countries to reduce energy imports and cut fossil fuel use, emerged as the solution towards achieving worldwide energy security and sustainability through carbon-neutrality. As such, this study is aimed to investigate the heterogeneous effects of relevant economic and environmental driving factors for renewable energy consumption (REC) that emerge from current policy objectives (GDP per capita, carbon intensity, and research and development) through an empirical analysis of a wide panel of 94 countries, and five income-based subpanels, over the 1995–2019 period, by using heterogeneous panel data fixed-effects estimation techniques (static and dynamic) with robust Driscoll–Kraay standard errors. The results unambiguously indicate that CO₂ intensity has a significant mitigating effect on REC at world level, and this relationship is stronger for low-income and very high-income countries. Moreover, GDP per capita promotes REC when it surpasses the 5000 USD threshold, whereas research and development is a major contributor to increase in renewable energy consumption in very high-income countries. As such, for the policy makers, it is necessary to consider the heterogeneity of the drivers of REC in order to issue effective and congruent policies. The effective employment of post-COVID-19 recovery funds constitutes a timely, ideal occasion.

The integration of Artificial Intelligence (AI) in higher education has the potential to significantly enhance the educational process and student outcomes. However, there is a limited understanding of the factors influencing AI adoption among university students, particularly in economic programs. This study examines the relationship between students’ perceptions of the efficacy and usefulness of AI tools, their access to these tools, and their concerns regarding AI usage. A comprehensive survey of Romanian university students, focusing on economics students, was undertaken. This study identifies critical latent factors and investigates their interrelationships by employing advanced analytical techniques, such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM), with robust standard errors. The results suggest that formal training and integration, concerns regarding AI, perceived utility, and positive attitudes towards AI are positively influenced by general awareness and familiarity with AI tools. The frequency of AI tool usage is substantially increased by perceived usefulness, positive attitudes, and formal training and integration. Conversely, positive attitudes and perceived utility are adversely affected by AI-related concerns. Indirect effects suggest that formal training and positive attitudes indirectly increase the usage frequency by increasing general awareness. This research is relevant to computer science, as it helps to build strategies to integrate AI technologies into educational processes. Increasing students’ awareness and access to AI tools and addressing their concerns can facilitate the widespread adoption and effective integration of AI technologies, improving academic experiences and outcomes.

To achieve sustainable development, massive changes towards fostering a clean and pollution-reducing industrial sector are quintessential. The textile industry has been one of the main contributors to water pollution all over the world, causing more than 20% of the registered levels of water pollution in countries like Turkey, Indonesia and China (among the G20 group of countries) and also in Romania and Bulgaria (in the Eastern European area), with even more than 44% in Macedonia. Given the controversy created by the textile industry’s contribution to pollution at a global level and also the need to diminish pollution in order to promote sustainable development, this paper comparatively investigates the contribution of the textile industry to the water pollution across Central and Eastern European countries, as well as developed countries. In addition, we employ the Holt–Winters model to forecast the trend of the total emissions of organic water pollutants, as well as of the textile industry’s contribution to pollution for the top polluters in Eastern Europe, i.e., Poland and Romania. According to our estimates, both countries are headed towards complete elimination of pollution caused by the textile industry and, hence, toward a more sustainable industrial sector, as Greenpeace intended with the release of its 2011 reports.

Climate change and pollution fighting have become prominent global concerns in the twenty-first century. In this context, accurate estimates for polluting emissions and their evolution are critical for robust policy-making processes and ultimately for solving stringent global climate challenges. As such, the primary objective of this study is to produce more accurate forecasts of greenhouse gas (GHG) emissions. This in turn contributes to the timely evaluation of the progress achieved towards meeting global climate goals set by international agendas and also acts as an early-warning system. We forecast the evolution of GHG emissions in 12 top polluting economies by using data for the 1970–2018 period and employing six econometric and machine-learning models (the exponential smoothing state-space model (ETS), the Holt–Winters model (HW), the TBATS model, the ARIMA model, the structural time series model (STS), and the neural network autoregression model (NNAR)), along with a naive model. A battery of robustness checks is performed. Results confirm a priori expectations and consistently indicate that the neural network autoregression model (NNAR) presents the best out-of-sample forecasting performance for GHG emissions at different forecasting horizons by reporting the lowest average RMSE (root mean square error) and MASE (mean absolute scaled error) within the array of predictive models. Predictions made by the NNAR model for the year 2030 indicate that total GHG emissions are projected to increase by 3.67% on average among the world’s 12 most polluting countries until 2030. Only four top polluters will record decreases in total GHG emissions values in the coming decades (i.e., Canada, the Russian Federation, the US, and China), although their emission levels will remain in the upper decile. Emission increases in a handful of developing economies will see significant growth rates (a 22.75% increase in GHG total emissions in Brazil, a 15.75% increase in Indonesia, and 7.45% in India) that are expected to offset the modest decreases in GHG emissions projected for the four countries. Our findings, therefore, suggest that the world’s top polluters cannot meet assumed pollution reduction targets in the form of NDCs under the Paris agreement. Results thus highlight the necessity for more impactful policies and measures to bring the set targets within reach.

The COVID-19 pandemic related government interventions produced rapid decreases in worldwide economic and social activity, with multifaceted economic and social consequences. In particular, the disruption of key industries and significant lifestyle changes in the aftermath of the pandemic outbreak led to the exponential adoption of web and video conferencing Software as a Service (SaaS) programs and to the solutions-led video conferencing market growth. However, the magnitude and persistence of the COVID-19 pandemic impact on the video conferencing solutions segment remain uninvestigated. Building on previous evidence linking population web-search behavior, private consumption, and retail sales, this study sources and employs Google Trends data as an analytical and forecasting tool for the solutions segment of the videoconferencing market. It implements a univariate forecast evaluation approach that assesses the predictive performance of several statistical and machine-learning models for the relative search volume (RSV) in the two SaaS program leaders, Zoom and Teams. ETS is found to provide the best forecast of consumer GT search interest for both RSV series. A baseline level for the consumer interest over the first pandemic wave is subsequently produced with ETS and further serves to estimate the excess search interest over the February 2020–August 2020 period. Results indicate that the pandemic has created an excess or abnormal consumer interest in the global web and videoconferencing SaaS market that would not have occurred in the absence of the pandemic. Other findings indicate that the impact is persistent as the excess interest stabilized at higher levels than in the pre-pandemic period for both SaaS market leaders, although a higher saturation of the Zoom market is detected.

With customers’ increasing reliance on e-commerce and multimedia content after the outbreak of COVID-19, it has become crucial for companies to digitize their business methods and models. Consequently, COVID-19 has highlighted the prominence of e-commerce and new business models while disrupting conventional business activities. Hence, assessing and forecasting e-commerce growth is currently paramount for e-market planners, market players, and policymakers alike. This study sources data for the global e-commerce market leader, the US, and proposes an integrated framework that encompasses automated algorithms able to estimate six statistical and machine-learning univariate methods in order to accomplish two main tasks: (i) to produce accurate forecasts for e-commerce retail sales (e-sale) and the share of e-commerce in total retail sales (e-share); and (ii) to assess in quantitative terms the pandemic impact on the size and structure of the e-commerce retail sales sector. The results confirm that COVID-19 has significantly impacted the trend and structure of the US retail sales sector, producing cumulative excess (or abnormal) retail e-sales of $227.820 billion and a cumulative additional e-share of 10.61 percent. Additionally, estimations indicate a continuation of the increasing trend, with point estimates of $378.691 billion for US e-commerce retail sales that are projected to account for 16.72 percent of total US retail sales by the end of 2025. Nonetheless, the current findings also document that the growth of e-commerce is not a consequence of the COVID-19 crisis, but that the pandemic has accelerated the evolution of the e-commerce sector by at least five years. Overall, the study concludes that the shift towards e-commerce is permanent and, thus, governments (especially in developing countries) should prioritize policies aimed at harnessing e-commerce for sustainable development. Furthermore, in light of the research findings, digital transformation should constitute a top management priority for retail businesses.