Risk forecasting for shortfalls in achieving sustainable development goals: A corruption perspective

Abstract

Understanding the relationship between corruption and Sustainable Development Goals (SDGs) is essential for comprehensively addressing sustainable development challenges. Corruption, with its damaging impact on governance, institutions, and public trust, poses a substantial barrier to achieving the SDGs. This study investigates the interconnections between corruption risk at the country level and the risks associated with achieving the SDGs. A Bayesian belief network model is developed using two datasets related to country-level sustainability and corruption performance. The model yields an 86.3 % accuracy in predicting outcomes for the two extreme levels of corruption risk. The findings indicate that the “high risk” state of corruption can significantly hinder progress on the “good health and well-being,” “zero hunger”, and “peace, justice and strong institutions” SDGs. Conversely, the “low risk” state of corruption can significantly enhance performance on the “sustainable cities and communities”, “zero hunger”, and “no poverty” SDGs. This study's exploration of the interconnected relationship between corruption and SDG risks offers valuable insights for policymakers. Its contribution lies in examining the dependencies between corruption and sustainability from a risk science perspective, capturing interactions across all 17 SDGs.