The phenomenon of digitisation of the public sector is an irreversible process that affects both the way public institutions are organised and the communication relationships between people and institutions. The COVID-19 pandemic represented a challenge and a strong impetus in accelerating the digitisation process of public administration at the global level such that it is currently difficult to make a clear distinction between governance and e-governance. The purpose of this research is to investigate the impact of the intensification of the digitisation process of public services in increasing the efficiency of governments at the level of the member states of the European Union, based on a cluster analysis. A robust least squares regression method was used to estimate the effects of the three dimensions of the e-government development index (EGDI) on government effectiveness. The results of the analysis highlighted the fact that the skills of the population in using online services determine the increase in a double percentage of government efficiency in intensively digitised states compared to states where the digitisation of public services is less developed. The development of the telecommunications infrastructure also has a significant positive impact on the efficiency of the government. However, online services offered by public authorities have proven to negatively influence government efficiency in both clusters.

In response to climate change that threatens both economic and social sustainable development, governments adopt strict environmental policy measures to reduce greenhouse gas emissions and encourage the use of energy from renewable sources. The main purpose of this study is to investigate to what extent the strictness of environmental policy can influence the level of greenhouse gas emissions and the consumption of renewable energy in selected Organisation for Economic Co-operation and Development (OECD) countries. The Fully Modified Ordinary Least Squares (FMOLS) method and Granger causality test were employed in order to investigate the long-run relationship between the main components of the environmental policy stringency index and the evolution of greenhouse gas emissions and renewable energy consumption. The results indicate significant influences of the Market-based instruments sub-index and the Technology Support policies sub-index on greenhouse gas emissions reduction, while the Non-Market Based instruments index, which includes policies that impose emission limits and standards, does not exert any significant influence in this regard. Regarding the impact on renewable energy consumption, the results of this study indicate significant positive influences from the perspective of the three sub-indices used in the analysis. These results should send a signal to decision-makers on the effectiveness of policies that impose emission limits and standards, in the sense that their improvement will generate significant influences in mitigating climate change risks.

This study investigates the relationship between artificial intelligence (AI), industrial robots, and renewable energy consumption, driven by the rapid technological advancements and widespread adoption of AI tools in various industries. This research aims to evaluate the environmental implications of these technologies, specifically their impact on renewable energy usage. Employing a comprehensive analytical framework, this study utilizes advanced methodologies, including regularization factors, to accurately estimate the effects of these variables. Through a thorough data analysis, the research quantifies how AI and industrial robots influence the shift towards renewable energy sources. The findings reveal that investments in AI significantly enhance renewable energy consumption, as demonstrated by both conventional estimation techniques and those that integrate regularization factors. Conversely, the use of industrial robots is found to have a detrimental effect on renewable energy consumption. These results have important implications for policymakers, industry leaders, and sustainability researchers. This study encourages policymakers and investors to prioritize funding for AI solutions that promote renewable energy adoption, while it advises industry managers to strategically modify their use of industrial robots to reduce their environmental impact. Ultimately, this research lays a critical foundation for future inquiries and policy initiatives aimed at aligning technological advancements with sustainable energy practices.

To make steps in society towards a more sustainable future, countries must meet the targets established by the United Nations’ Sustainable Development Goals. Thus, factors that could impact the environmental indicators should be analysed. Therefore, this study aims to identify the correlations between financial development and environmental performance in Romania, during the period 1995–2018. Using composite indexes to assess financial development and the Environmental Performance Index (EPI) to express environmental performance, important results are obtained through the Canonical Cointegrating Regression (CCR) method. The explanatory variables used in the study are specific to the evaluation of the financial development of a country: access, depth and efficiency. The results show that, in the case of financial institutions, the access index and the depth index have a negative influence on the environmental performance index, while in the case of financial markets, the negative impact of the access index of financial markets can be observed. The efficiency index, both in the case of financial institutions and financial markets, generates a positive influence on environmental performance in Romania. Financial markets also address positive influences on the evolution of the environmental performance index.

This research aims to identify the influence of fiscal policy, financial development and economic growth on the increase of renewable consumption in Romania. To achieve our objective, we employ bivariate regressions through the Autoregressive Distributed Lag method, over the 2000–2020 period, to examine these influences. We find clear evidence that the variables observed (implicit tax rate on energy, external debt stocks, real GDP per capita, environmental tax revenues from energy taxes, and market capitalisation of listed domestic companies) have significant effects on the use of renewable energy. Four unidirectional causal relationships were identified in the long run: two from independent variables towards the dependent variable and two from the dependent variables towards two other independent variables. The importance of this study is that its results can contribute to the finding of the most suitable solutions to improve renewable energy consumption in Romania and mitigate the impact of climate change. Consequently, the results of this study reveal significant conclusions and policy recommendations for Romania moving towards sustainable and green economic growth, through a balanced set of policies and measures smartly applied, accompanied by a solid rate of absorption of green funds.

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.