Serhii Apalkov

In the past few decades, the ever-increasing dynamics of international migration flows can be observed. At this stage, the governments of major countries in the world are striving to balance the needs of their citizens and the support of immigrants. The paper analyzes factors that affect the immigration policies of various countries and determines the role of ecological factors (such as environmental conditions). The objective of the study is to predict the immigration policies of different countries of the world based on the analysis of the influencing factors, including environmental performance. The research method is based on the use of the RapidMiner software package to build two decision tree models and a static index database of more than 150 countries around the world. The results show that in most cases, the immigration policies of various countries will focus on maintaining the current level of immigration and increasing the number of skilled workers. At the same time, one of the key decision-making factors will be the country’s current immigration level, environmental performance, GDP per capita, and the Education index. One of the main conclusions is that the country’s environmental indicators have begun to become one of the priority values that determine the state immigration policy. This can be explained by the rising global community interest in the challenges of climate change.

The need to increase the price of non-green, carbon-emitting goods, as well as the application of new environmental taxes and fees to help solving the global climate crisis, has been actively discussed. However, price is not only a strong impetus for market development, but it can also restrain growth. The price level and population purchasing power belong to the key indexes that define the market capacities in different countries. This paper aims to investigate the impact of income inequality, including price levels and purchasing power, on environmental performance in different countries. The research method is based on RapidMiner’s machine learning programs, applying three modeling algorithms: correlation, clustering, and decision trees with a static index database of more than 150 countries around the world. The results obtained partially confirm the conclusions made by other researchers studying the Environmental Kuznets concept (EKC) effects. In particular, it was found that an important factor influencing the efficiency of the environment in the country’s ecosystem is the level of population’s income. The analysis also shows that environmental performance is strongly dependent on domestic price levels. This may support the hypothesis that the cost of green goods reflects a high benchmark for natural resource costs. However, further research is needed, including such directions as sources of financing for the implementation of circular projects, as well as the associated economic and environmental effects.