Predicting Sustainable Development Goals (SDG) outcomes for 2030: a machine learning approach using ARIMAX and linear regression models

Forecast of Sustainable Development Goals (SDG) outcomes for 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 the global challenges of health, education, inequality, environmental degradation, and climate change. Despite extensive research tracking progress towards these goals, more work needs to 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 the SDGs that are likely to be influenced by ai in the future were used to improve the model performance. The forecast results indicate that OECD and Eastern Europe and Central Asia countries 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 show lower levels of achievement.

Sustainable development emphasizes the achievement of intergenerational equity and the optimization of resource consumption to meet future needs. Following the definition of the Brundtland Commission, it became clear that economic growth alone cannot ensure sustainability due to the depletion of natural resources. Sustainable development requires a balance between environmental, financial and social sustainability. With 193 UN member states adopting the SDGs in 2015, there is international consensus on the need to address global challenges. The introduction of smart technologies, particularly ai, has the potential to accelerate the implementation of the SDGs. ai can have a significant impact on several SDGs, including health, education and climate action. However, concerns about privacy, cybersecurity issues and social biases need to be managed through regulatory standards and international guidelines to mitigate potential adverse effects. The findings of this study highlight the importance of identifying priority areas for action and formulating specific policies to improve SDG scores globally.

Materials and methods:

This study develops forecasting models using predictors identified through a literature review on the influence of ai on the SDGs. Systematic searches in Scopus using specific keywords yielded 33 relevant articles from 1994 to 2023. Predictor selection used filtering techniques, and the final predictors were chosen from SDGs related to health, education, clean energy, and climate action. Forecasting 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 improves accuracy. Data from the Sustainable Development Report 2023 was used, focusing on regional clusters to minimize missing data issues.

Analysis of ARIMAX and LR models for SDG scores:

The ARIMAX and LR models predict SDG outcomes for six regions between 2022 and 2030. The ARIMAX model generally provides more accurate forecasts, particularly for “OECD countries,” which show the highest accuracy and lowest margins of error. In contrast, “Sub-Saharan Africa” has the lowest scores and the greatest variability. Both models predict similar trends, with “OECD countries” showing the highest growth and “Sub-Saharan Africa” the lowest. Over time, regions such as “Latin America and the Caribbean” and “Eastern and Southern Asia” show moderate improvements, while “Eastern Europe and Central Asia” exhibit stable growth.

Discussion:

Predicting SDG outcomes using smooth linear regression and ARIMAX methods reveals a nuanced picture of global progress. ai’s role in enhancing the SDGs is two-fold: while it contributes to reducing energy consumption, monitoring the environment, and improving health, it also poses risks such as privacy violations, rising inequality, and technological unemployment. The predicted SDG scores for 2030 show mixed 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 lagging regions in achieving the SDGs while addressing socioeconomic and political factors that influence development.

Conclusion:

This study uses machine learning models to forecast SDG scores for world regions through 2030, indicating a general upward trend. Regions such as 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 still have lower scores. Strong political, cultural and socioeconomic structures correlate with higher SDG scores. Limitations include uncertainty in predictions and the changing 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.

Review the PaperAll credit for this research goes to the researchers of this project. Also, don't forget to follow us on twitter.com/Marktechpost”>twitter and join our Telegram Channel and LinkedIn GrAbove!. If you like our work, you will love our Newsletter..

Don't forget to join our Over 47,000 ML subscribers on Reddit

Find upcoming ai webinars here