Analysis of Sustainable Development Goals (SDGs) of Russian regions according to the United Nations (UN) criteria. Case of the Rostov region
The UN SDGs are a set of 17 goals for the environmentally, socially and economically sustainable development and conservation of social, cultural, biological and physical systems. 1. eradicate poverty; 2. eradication of hunger; 3. Good health and well-being; 4. Quality education; 5. Gender equality; 6. Clean water and sanitation; 7. Affordable and clean energy; 8. Decent jobs and economic growth; 9. Industrialization, innovation and infrastructure; 10. Reducing inequality; 11. Sustainable cities and human settlements; 12. responsible consumption and production; 13. Combating climate change; 14. Conservation of marine ecosystems; 15. Conservation of terrestrial ecosystems; 16. Peace, justice and effective institutions; 17. Partnerships for sustainable development. As a result of the practice implementation, the following was done: 1) Analyzed the quality of the baseline indicators of the SDGs of the RO, eliminated gaps and outliers; 2) SDG indicators were normalized, necessary data transformations were carried out; 3) Correlations between indicators and SDGs were identified on the basis of correlation and other types of analysis; 4) Clustering of indicators and regrouping of SDGs of the RO were carried out; 5) Analyzed the dynamics of indicators of RO SDGs; 6) The possibility of applying models for time series analysis (ARIMA, ARCH, GARCH) was checked; 7) Interval estimation of the values of the RO SDG indicators for 2023-2024 using ARIMA, ARCH, GARCH models for time series analysis was obtained; 8) The reliability of the obtained forecasts was assessed based on the results of the analysis of the state and dynamics of the RO SDGs for 2023 (big data). The following technology stack was used: PostgreSQL 11 MADlib 2.1.0 Python Java 17, Spring 6
Artificial Intelligence And Digital Services
Big data storage and analysis
2 years
Regions of the Russian Federation, according to the SDGs UN criteria on case of the Rostov region
Competitive advantages of the development practice Data quality: quality analysis, gap and outlier elimination (ensure high accuracy and completeness of data by eliminating anomalies and missing values, thereby increasing the validity and reliability of conclusions). Data normalization and transformation: standardization of SDGs data ensures that they are comparable and correct for further analysis. In-depth analysis of relationships: correlation analysis and other types of statistical analysis (identifies hidden dependencies and relationships between indicators, allowing for a better understanding of the factors affecting the SDGs). Advanced clustering and regrouping: indicator clustering and regrouping SDGs (optimizing the clustering of indicators to improve strategic planning and decision making). Dynamic analysis: analyzing indicator dynamics (examining changes in the region SDG indicators over time to help identify trends and patterns for forecasting and planning). Forecasting using advanced models: ARIMA, ARCH, GARCH models (applying advanced time series models to produce accurate and interpretable forecasts of indicator values for 2023-2024). Interval evaluation of forecasts: providing a range of possible values for better understanding and risk management. Forecast validity assessment: state and trend analysis for 2023 (verification and validation of forecasts based on real data, confirming their reliability and accuracy).
Winning the contest “Artificial Intelligence for Sustainable Development Goals”. The practice was presented by the laboratory member Sergey Vladimirovich Kushukov https://sdg.centrinvest.ru/#contest
List of scientific works and IP connected with the Practice: https://lab-ai.ru
