Leveraging Machine Learning for Regulatory Intelligence: A Predictive Assessment of Enforcement Gaps in Nigeria's E-Waste Legal Framework

The issue of electronic waste (e-waste) has become one of the most important environmental and regulatory challenges that affects Nigeria, the improvement of which is promoted not only by the increased imports of used electronics but also by the fact that its legal force is weak. Although the National Environmental (Electrical/Electronic Sector) Regulations (2011) exist, the level of compliance is still not the best, as it is limited to the absence of coherent control, an insufficient data environment and analytical potential. The paper set out to use machine learning as regulatory intelligence, creating a prediction system to discover gaps in the enforcement of e-waste legislation in Nigeria. A mixed-method predictive design was used to train the supervised algorithms, such as Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), with compliance datasets trained on other years (2015-24), which were coupled with interviews with experts. Accuracy, F1-score, recall, and ROC-AUC were used as performance metrics to evaluate the model's performance, and SHAP was utilised to identify the importance of features. Observations have indicated that GBM performed better than other models (Accuracy = 0.90, ROC-AUC = 0.91), and the compounds of data completeness, inter-agency coordination, regulatory clarity and budget sufficiency were found to be the important predictors of enforcement success. FCT and Lagos were mapped on the spatial probability as high- compliance regions on one hand, whereas on the other hand, Enugu and Kaduna had high enforcement risk. The paper concludes that machine learning offers a strong avenue in data-driven and anticipatory environmental governance. It suggests creating a National Regulatory Intelligence Unit (NRIU) under NESREA, enhancing systems of data-sharing, and integrating algorithmic literacy, ethical AI, and risk learning structures into the environmental regulatory system of Nigeria.