The annual volume of waste generated in sub-Saharan Africa (SSA) increased from 81 million tonnes to 174 million tonnes per year between 2012 and 2016 and is projected to reach 269 million tonnes in 2030. In 2018, SSA’s municipal solid waste (MSW) collection coverage was estimated at 44%. Concerned that the waste generation rate outweighs the collection pace, we conducted a systematic review of studies on MSW collection to examine the current situation in the region concerning the waste collection and coverage rates and to highlight the impediments to rapid progress in waste collection using the lens of four cities. Findings reveal that, despite the involvement of private waste collectors, collection and coverage rates are still below the desired 100% with backlogs of uncollected waste in public spaces, especially in low-income neighbourhoods where coverage remains abysmally low. This study fortifies the systematic discussion on MSW collection and coverage rates by conducting a meta-analysis. The result of the analysis shows that the waste collection and coverage rates are 65% and 67% in SSA, respectively. Aside from the paucity of data on waste generation rate and characterisation, most available data are incongruent. The review further shows that although several studies have been carried out on waste disposal, waste treatment and recycling in SSA studies directly focused on MSW collection are still few, leaving room for more research in this area. The review offers suggestions on how collection and coverage rates can be increased and equally proposes a strategy for reducing scavenging activities in the region’s unsanitary landfills, given its concomitant health impacts on the scavengers.

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.