The Sustainable Development Goals (SDGs) seek to address complex global challenges and cover aspects of social development, environmental protection, and economic growth. However, the holistic and complicated nature of the goals has made their attainment difficult. Achieving all goals by 2030 given countries' limited budgets with the economic and social disruption that the COVID-19 pandemic has caused is over-optimistic. To have the most profound impact on the SDGs achievement, prioritising and improving co-beneficial goals is an effective solution. This study confirms that countries' geographic location and income level have a significant relationship with overall SDGs achievement. This article applies the Gradient Boosting Machine (GBM) algorithm to identify the top five SDGs that drive the overall SDG score. The results show that the influential SDGs vary for countries with a specific income level located in different regions. In Europe and Central Asia, SDG10 is among the most influential goals for high-income countries, SDG9 for upper-middle-income, SDG3 in low and lower-middle-income countries of Sub-Saharan Africa, and SDG5 in Latin America and the Caribbean upper-middle-income countries. This systematic and exploratory data-driven study generates new insights that confirm the uniqueness, and non-linearity of the relationship between goals and overall SDGs achievement.
Keywords: Co-beneficial SDGs; Data science; Gradient boosting machine; Machine learning; SDG prioritisation; Sustainable development goals.
© The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature 2022.