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Predicting Sustainable Development Goals (SDG) Scores by 2030: A Machine Learning Approach with ARIMAX and Linear Regression Models

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Predicting Sustainable Development Goals (SDG) Scores by 2030: A Machine Learning Approach with ARIMAX and Linear Regression Models

Forecasting Sustainable Development Goals (SDG) Scores by 2030

Practical Solutions and Value

The Sustainable Development Goals (SDGs) aim to eradicate poverty, protect the environment, combat climate change, and ensure peace and prosperity by 2030. This study uses ARIMAX and Linear Regression (LR) models to predict SDG scores for different global regions. AI-influenced predictors enhance model performance, highlighting priority areas for action and targeted policies to improve SDG scores globally.

Materials and Methods

The study develops forecasting models using predictors identified through a literature review of AI’s influence on SDGs. ARIMAX and LR models are built using Python in Google Colab, focusing on regional groupings to minimize missing data issues.

Analysis of ARIMAX and LR Models for SDG Scores

The ARIMAX and LR models predict SDG scores across six regions from 2022 to 2030. The forecasted SDG scores reveal varied regional progress, with OECD countries leading, followed by Eastern Europe, Asia, and Latin America. Policymakers are advised to leverage AI to support regions lagging in SDG achievement while addressing socio-economic and political factors influencing development.

Conclusion

This study uses machine learning models to forecast SDG scores for global regions up to 2030, indicating an overall upward trend. Strong political, cultural, and socio-economic structures correlate with higher SDG scores. Future research should explore economic, social, and environmental predictors, refine forecasting models, and assess the influence of policy changes on SDG outcomes.

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