The number of technological tools used in education is increasing day by day. Studies on mobile learning have increased in recent years. Future teachers’ views on mobile learning are very important. In line with this purpose, the opinions of teacher candidates on mobile learning were taken by using qualitative research method in order to determine in detail their views on mobile learning. The participants of the study consists of 61 teacher candidates studying at Plekhanov Russian University of Economics, I.M. Sechenov First Moscow State Medical University, Gubkin Russian State University of Oil and Gas, Kazan National Research Technological University and Kazan (Volga region) Federal University. The opinions of pre-service teachers selected on the basis of voluntarism were analysed by content analysis method. The research questions were prepared by the researchers by taking expert opinion. The findings obtained as a result of the research are given in detail in the findings and results section.

This research aims at diagnosing such priority areas for the development of petrochemicals in Russia as sustainable development and energy efficiency, at identifying trends and forecasting the development of the industry, taking into account the greening of the industry. Achieving the goal is based on the use of methods such as graphical, comparative, economic and mathematical (neural network modeling, correlation regression analysis), and prognostic. The article contains an assessment of the achievement of the sustainable development goals focused on energy saving and environmental protection; forecasting the level of greenhouse gas emissions in Russia based on the construction of a neural network and a regression model; comparative analysis of the rates of transition to sustainable development of chemical production and production of coke and petroleum products in the Russian economy. The scientific results of the research are a neural network model trained on the indicators of sustainable and energy efficient development of the Russian economy, on the basis of which the relationship between the level of greenhouse gas emissions, the energy intensity of GDP and the share of electricity from renewable energy sources is formalized; a predictive model that made it possible to calculate future values of greenhouse gas emissions depending on the target values of predictive variables; features of the greening of petrochemical industries in Russia.