Unlocking Agenda 2030 progress: Artificial Intelligence for SDGs’ indicator prioritization in OECD nations

As the imminent deadline of the 2030 Agenda approaches, a substantial number of its 17 Sustainable Development Goals (SDGs) remain unattained, necessitating accelerated efforts to make significant progress within the remaining six years. Since the large number of indicators, associated to the SDGs, are intertwined in complex non-linear ways, considering their actual synergies and trade-offs to prioritize them plays a key role in their successful implementation. This prioritization is essential for taking impactful and actionable steps and efficiently utilizing available resources. In particular, indicator-level analysis is of special importance as it offers a more granular perspective enabling the formulation of clear governance recommendations for stakeholders. Efforts in this field have encounter challenges, such as the inadequacy of traditional regression methods due to the inherent correlations and complex interdependencies among the SDGs. Additionally, the scarcity of data from many countries’ SDG indicators has limited the scope of previously performed analyses. This article performs an Artificial Intelligence method, a Random Forest, to address these challenges due to its ability to deal with correlated and non-linear data. Furthermore, it focuses on high-quality data from OECD countries to reduce the necessity for extensive data manipulation that might introduce uncertainties and diminish the validity of the analysis. The obtained results pinpoint income inequality as the most significant factor impacting OECD countries. This underscores income inequality's profound influence on high-income countries and its impact on other SDGs. Further key indicators include SO₂ emissions embodied in imported goods and services (kg/capita), proportion of urban population living in slums and population’s access to clean fuels and cooking technology. These results showcase the multifaceted nature of sustainability challenges in OECD nations and provide new insights into key drivers for OECD members that significantly impact the attainment of the 2030 Agenda. Furthermore, the prioritized indicators found by this study will support stakeholders in developing effective resource allocation strategies.