Teacher job satisfaction and well-being have a significant impact on educational outcomes, considering that teaching is the main objective of the educational process. The aim of this study is to examine the relationship between teacher job satisfaction and four main categories of determinants: self-efficacy, relational aspects (colleague collaboration, student behavior, school management), work-related aspects (administrative workload, teaching tasks), and working conditions, in order to identify various implications for teachers’ well-being. The study employs a survey delivered to 658 K-12 (pre-university) teachers, from the North-West region of Romania. We used factorial analysis and a structural equation model to test eight proposed hypotheses. The results showed that self-efficacy, promotion, positive student behavior, and working conditions have significant effects on job satisfaction. These factors influence job satisfaction and well-being in the teaching profession because they ensure a positive work environment in which teachers and students thrive, thus leading to higher levels of involvement from teachers, students, and parents alike. An efficient work environment decreases attrition, burnout, emotional exhaustion, and teacher turnover, while increasing job satisfaction, well-being, and teacher retention.

National and global health policies are increasingly recognizing the key role of the environment in human health development, which is related to its economic and social determinants, such as income level, technical progress, education, quality of jobs, inequality, education or lifestyle. Research has shown that the increase of GDP (Gross Domestic Product) per capita can provide additional funds for health but also for environmental protection. However, often, economic growth is associated with the accelerated degradation of the environment, and this in turn will result in an exponential increase in harmful emissions and will implicitly determine the increasing occurrence of non-communicable diseases (NCDs), mainly cardiovascular diseases, cancers and respiratory diseases. In this paper, we investigate the role and effects of economic growth, environmental pollution and non-communicable diseases on health expenditures, for the case of EU (European Union) countries during 2000–2014. In order to investigate the long-term and the short-term relationship between them, we have employed the Panel Autoregressive Distributed Lag (ARDL) method. Using the Pedroni-Johansen cointegration methods, we found that the variables are cointegrated. The findings of this study show that economic growth is one of the most important factors influencing the health expenditures both in the long- and short-run in all the 28 EU countries. With regards to the influence of CO₂ emissions on health expenditure, we have found a negative impact in the short-run and a positive impact on the long-run. We have also introduced an interaction between NCDs and environmental expenditure as independent variable, a product variable. Finally, we have found that in all the three estimated models, the variation in environmental expenditure produces changes in NCDs’ effect on health expenditure.

The development of tourism is usually associated, in positive terms, with economic development, foreign currency inflows, employment opportunities, infrastructure improvements, sustainable development and poverty alleviation. However, the nature of the tourism-growth relationship is still a matter of academic debate, and, perhaps, an expression of the inconsistencies and contradictions of public policies designed to support this industry. Researchers and practitioners have not yet come to an agreement on a number of fundamental questions: does tourism stimulate economic growth or the converse, and whether the causality, if it does exist, is uni or bidirectional, is constant or can change its direction in the medium - or long run. The present paper investigates the relationship between Gross Domestic Product (GDP) per capita and international tourism in Romania, over the 1995–2016 period. Our results show that the causal effect of the GDP on the international tourist arrivals and on the international tourism receipts is significant in the long run in Romania. In the short-run, we find a unidirectional causal relationship from the international tourism receipts to GDP, and a bidirectional causal relationship between GDP and the number of international tourist arrivals.

Tourism has become one of the most important sectors in many countries, significantly contributing to their economic growth and development. However, the expansion of tourism has also brought about various environmental and social challenges. The relationship between tourism, economic growth, trade openness, and the environment is diverse and complex. The objective of this paper is to investigate the relationship between the international tourism development index, GDP per capita, CO₂ emissions, trade openness index as well as the energy intensity index in EU 27, over the 1995–2019 period. A composite index for international tourism was developed using the Principal Component Analysis (PCA). Panel Autoregressive distributed lag (ARDL) approach is used to reveal the long- and short-run impact of GDP per capita, CO₂ emissions, trade openness index as well as the energy intensity index on the tourism development index. Panel ARDL estimates confirm some of our research hypotheses: at the level of EU countries, there is a short-run relationship between tourism and GDP per capita, but only in a few EU countries, trade openness influences tourism development index. Dumitrescu-Hurlin causality test confirms long-run feedback relationship between tourism development index and trade openness, between tourism development index and CO₂ emissions, and between tourism development index and GDP and unilateral causality running from tourism development index towards energy efficiency.

Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing comparative analysis in the same industry and between industries. This paper aims to design a composite financial index to determine the financial performance of listed companies, further used in predicting business performance through neural networks. Principal components analysis was used to build a composite financial index, employing four traditional accounting indicators and four value-based indicators for the period 2011–2018. Five experiments were conducted to predict business performance through the composite financial index. The results showed that observations from two years, of the first three experiments, indicate a better predictive behavior than the same experiments using observations from one year. Therefore, we concluded that observations from more than one year are necessary to predict the value of the financial performance index. Findings led us to the conclusion that recurrent neural networks model predicted better financial performance composite index when taken into consideration more real data for the financial performance index (2012–2018) instead of just for one year (2018).

The positive impact of the tourism industry on economic growth, revenues, infrastructure, employment, social inclusion and poverty reduction, although widely recognised, has been lately weighted against the appearance and exacerbation of several problems, such as: environmental footprint, increase of income inequality, cost increases related to solid waste collection, energy consumption, increased global CO2 emissions. On the other hand, the tourism sector is not just an active economic, societal, or environmental change agent; in turn, the tourism sector supports or is highly influenced by various factors, such as climate change, economic, political, or social factors. More recently, this industry has been highly impacted by the pandemic, technological developments and cultural trends. In this article we examined both the short and long-run relationship between tourism development and economic growth, CO2 emissions and energy consumption in European Union member states (EU27), by using the Principal Component Analysis (PCA) technique and autoregressive distributed lag (ARDL) model for panel data. The findings suggest that economic growth and energy consumption have a statistically significant impact on the tourism index both in the short and long-run, whereas CO2 emissions only have a significant impact upon the tourism index on the long run.