Research background: Intensive economic growth in Russian regions during recent decades has been associated with numerous environmental issues, particularly increasing CO2 emissions, as well as income inequality. To achieve sustainable development, it is necessary to resolve these issues. Purpose of the article: To shed light on the impact of income inequality on CO2 emissions based on Russian regional data covering the years 2004?2018. Methods: Gini index and decile dispersion ratio are used to measure income inequality. To study the impact of income inequality on CO2 emissions in the Russian regions, we estimate econometric models with fixed and random effects and apply GMM method. We test the hypothesis of the environmental Kuznets curve to determine the impact of economic growth on CO2 emissions. Findings & value added: The results show that CO2 emissions increase in tandem with growth in income inequality between 10% of people with the lowest income and 10% of people with the highest income. Simultaneously, CO2 emissions decrease with growth of Gini coefficient. The hypothesis of the Environmental Kuznets Curve was confirmed based on GMM method. Our findings underscore that the activities of the extraction and manufacturing sectors, as well as energy consumption, increase CO2 emissions. The chief significance of this paper is the finding that large income gap between extremely rich and extremely poor population cohorts increases CO2 emissions. This implies that economic policy aimed at reducing income inequality in Russian regions will also reduce CO2 emissions, especially if accompanied by increased use of environmentally friendly technologies. From the international perspective, our research can be extended to study other countries and regions.

The degree of income differentiation depends on many factors, including the level of regional economic development, production structure and industrial specialization. In this paper, we assess the impact of the industrial specialization of Russian regions on income inequality measured by the Gini coefficient. Based on the regional data over the period 2005 to 2018, we build an econometric model applying the Arellano-Bover / Blundell-Bond estimation method. We use shares of the main industries in gross regional product to describe production structure of regions. The modelling results show that the classic Kuznets curve is not empirically supported for the regions of Russia. Besides, we find that the larger the share of mining, manufacturing, construction, trade and financial sector in GRP, the higher the inequality in the region, while the share of agriculture does not affect it significantly.

The relationship between investment and cash flow has been extensively studied since the mid-20th century. The aim of our study is to assess the impact of Tobin's ratio and cash flows on the capital investments of Russian companies. For econometric estimation we data on 206 Russian public companies traded on the Moscow Exchange from 2011 to 2020. We apply quantile regression to obtain more detailed results. The results of our study confirm the significance of the Tobin ratio and cash flow on capital investments. We observe these effects in all quantiles however their magnitude varies. This research is valuable and can be utilized by companies to maximize efficiency of their capital expenditures.