Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques - Muyiwa Lawrence Adedara; Ridwan Taiwo; Olusola Olaitan Ayeleru; Hans-Rudolf Bork (Recycling)
This study investigates the effectiveness of the Lagos Recycle Initiative (LRI) on landfill diversion (LFD) in Lagos, Nigeria, where evidence-based assessments of such initiatives are lacking. It evaluates the recycling diversion rate (RDR) of household recyclables (HSRs) across local government areas using field surveys and population data. Machine learning algorithms (logistic regression, random forest, XGBoost, and CatBoost) refined with Bayesian optimisation were employed to predict household recycling motivation. The findings reveal a low RDR of 0.37%, indicating that only approximately 2.47% (31,554.25 metric tonnes) of recyclables are recovered annually compared to a targeted 50% (638,750 metric tonnes). The optimised CatBoost model (accuracy and F1 score of 0.79) identified collection time and the absence of overflowing HSR bins as key motivators for household recycling via the SHapley Additive exPlanations (SHAP) framework. This study concludes that current LRI efforts are insufficient to meet recycling targets. It recommends expanding recovery efforts and addressing operational challenges faced by registered recyclers to improve recycling outcomes. The policy implications of this study suggest the need for stricter enforcement of recycling regulations, coupled with targeted financial incentives for both recyclers and households to boost recycling participation, thereby enhancing the overall effectiveness of waste diversion efforts under the LRI. This research provides a benchmark for assessing urban recycling initiatives (RIs) in rapidly growing African cities.