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:

The Sustainable Development Goals (SDGs) set by the United Nations aim to eradicate poverty, protect the environment, combat climate change, and ensure peace and prosperity by 2030. These 17 goals address global health, education, inequality, environmental degradation, and climate change challenges. Despite extensive research tracking progress towards these goals, more work must be done to forecast SDG scores. This study aims to predict SDG scores for different global regions by 2030 using ARIMAX and Linear Regression (LR), smoothed by the Holt-Winters’ multiplicative technique. Predictors identified from SDGs likely to be influenced by AI in the future were used to enhance model performance. Forecast results indicate that OECD countries and Eastern Europe and Central Asia are expected to achieve the highest SDG scores. At the same time, Latin America and the Caribbean, East and South Asia, the Middle East and North Africa, and Sub-Saharan Africa will exhibit lower levels of achievement.

Sustainable development emphasizes achieving intergenerational equity and optimizing resource consumption to meet future needs. Following the Brundtland Commission’s definition, it became clear that economic growth alone cannot ensure sustainability due to the depletion of natural resources. Sustainable development requires balancing environmental, financial, and social sustainability. With 193 UN member states adopting the SDGs in 2015, there is an international consensus on addressing global challenges. The introduction of smart technologies, particularly AI, has the potential to accelerate SDG implementation. AI can significantly impact various SDGs, including health, education, and climate action. However, privacy concerns, cybersecurity issues, and social biases must be managed through regulatory standards and international guidelines to mitigate potential adverse effects. This study’s findings highlight the importance of identifying priority areas for action and formulating targeted policies to improve SDG scores globally.

Materials and Methods:

This study develops forecasting models using predictors identified through a literature review of AI’s influence on SDGs. Systematic searches in Scopus using specific keywords yielded 33 relevant papers from 1994 to 2023. Predictor selection utilized filter techniques, and the final predictors were chosen from SDGs related to health, education, clean energy, and climate action. Forecast models, including ARIMAX and LR with Holt-Winters smoothing, were built using Python in Google Colab. The ARIMAX model handles non-stationary data, while LR with Holt-Winters enhances accuracy. Data from the Sustainable Development Report 2023 was used, 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 ARIMAX model generally provides more precise forecasts, particularly for “OECD countries,” which show the highest accuracy and lowest error margins. In contrast, “Sub-Saharan Africa” has the lowest scores and greatest variability. Both models predict similar trends, with “OECD countries” showing the highest growth and “Sub-Saharan Africa” the lowest. Over time, regions like “Latin America and the Caribbean” and “East and South Asia” show moderate improvements, while “Eastern Europe and Central Asia” exhibit stable growth.

Discussion:

Forecasting SDG scores using ARIMAX and smooth linear regression methods reveals a nuanced picture of global progress. AI’s role in enhancing SDGs is dual-faceted: while it contributes to reducing energy consumption, monitoring the environment, and improving health, it also poses risks such as privacy violations, increased inequality, and technological unemployment. The forecasted SDG scores for 2030 show varied regional progress, with OECD countries leading, followed by Eastern Europe, Asia, and Latin America. Sub-Saharan Africa faces significant challenges but shows potential for improvement with AI. Policymakers should 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. Regions like OECD countries, Eastern Europe and Central Asia, Latin America, and the Caribbean are expected to lead with higher scores. At the same time, East and South Asia, the Middle East, and North Africa will improve but remain lower. Strong political, cultural, and socio-economic structures correlate with higher SDG scores. Limitations include uncertainty in predictions and the evolving impact of AI. Future research should explore economic, social, and environmental predictors, refine forecasting models, and assess the influence of policy changes on SDG outcomes.

Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. If you like our work, you will love our newsletter..

Don’t Forget to join our 47k+ ML SubReddit

Find Upcoming AI Webinars here

Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.