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The main purpose of this paper is to investigate the happiness factors and assess the performance of machine learning techniques on predicting the happiness levels of European immigrants and natives. Two types of machine learning methods, Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN), are employed for analytical modeling. Our results with a total sample size of 196,724 respondents from nine rounds of the European Social Survey (ESS) indicate that the determinants of happiness for immigrants and natives are significantly inconsistent. Therefore, variables should be specifically selected to predict the happiness levels of these two different groups. The sensitivity analysis shows that satisfaction with life, subjective general health, and the highest level of education are the three most prominent determinants that contribute to the happiness of immigrants and natives. The overall accuracies of OLR and ANN baseline models are >80%. This can be further improved by building models for each individual country. The application of OLR and ANN implies that machine learning algorithms can be a useful tool for predicting happiness levels. The greater knowledge of migration and happiness will allow us to better understand the decision-making processes and construct more effective policies.

Human capital and ICT have a significant role in determining human development. The impacts of ICT and human capital on green growth and environmental sustainability should be explored for sustainable economic development. This research contributes to the literature on the role of ICTs and human capital in the determination of green growth and environmental performance. Based on time-series data 1990–2019, the study intends to investigate the impact of ICTs and human capital on environmental and green growth performance for China. The study reports that ICTs tend to reduce CO2 emissions and improve green growth in the long-run. However, education reduces CO2 emissions in the long-run but does not produce any significant impact on green growth in the long-run. It is suggested that government should invest in environmental efficiency and environmental technologies simultaneously with human capital that could significantly contribute to pollution reduction. Lastly, policies to increase human capital should be implemented simultaneously with policies to promote ICTs contribution in order to confirm green growth and environmental protection.

The main purpose of this paper is to investigate the happiness factors and assess the performance of machine learning techniques on predicting the happiness levels of European immigrants and natives. Two types of machine learning methods, Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN), are employed for analytical modeling. Our results with a total sample size of 196,724 respondents from nine rounds of the European Social Survey (ESS) indicate that the determinants of happiness for immigrants and natives are significantly inconsistent. Therefore, variables should be specifically selected to predict the happiness levels of these two different groups. The sensitivity analysis shows that satisfaction with life, subjective general health, and the highest level of education are the three most prominent determinants that contribute to the happiness of immigrants and natives. The overall accuracies of OLR and ANN baseline models are >80%. This can be further improved by building models for each individual country. The application of OLR and ANN implies that machine learning algorithms can be a useful tool for predicting happiness levels. The greater knowledge of migration and happiness will allow us to better understand the decision-making processes and construct more effective policies. [ABSTRACT FROM AUTHOR]