The article investigates trade flows between the Shanghai Cooperation Organization (SCO) member-states and Belarus before the upcoming Belarus’ joining the organization. The export flows of the countries are modeled using a power function based on the time data. The results of the qualitative and quantitative analysis of foreign trade between the organization and the Republic of Belarus are presented, as well as the quantitative forecast of the prospects open to Belarus in connection with its joining the organization based on three original scenarios using econometric models. The results of the study show that Belarus has certain promising sectors of foreign economic activity, which can contribute to an increase in income from trade. It was found that the integration of the country will have a positive effect on increasing the volume of trade turnover with the participating countries, while in order to maintain sustainable economic growth of the country, domestic development of production should remain a priority, as evidenced by the obtained parameter estimates for the factors. An assessment of potential economic effects can be used to make a decision on whether a country should join an international organization. In particular, based on the assessments in our study in trade with Russia the expected increase in Belarus exports upon joining the Shanghai Cooperation Organization will constitute an increase of nearly 5%, exports to Kazakhstan are expected to increase by almost 75%, and to India and China by almost 90%. In the context of reshaping of international associations and organizations, the problems and issues raised in the study become even more relevant.

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.