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The chapter aims to evaluate the efficacy of stakeholder participation in the solid waste management system of Mauritius in view of providing a possible mechanism to attain the goals of a sustainable waste management framework.

The study employs qualitative indicators, namely, User Inclusivity and Producer Inclusivity of the Wasteaware Benchmark Indicators. Secondary data are used to conduct a critical and comprehensive analysis of the sub-indicators falling under each of the two main indicators to determine the overall compliance level with respect to stakeholder engagement of the waste management sector of Mauritius.

The results of the study show a LOW/MEDIUM compliance level for both User Inclusivity and Provider Inclusivity indicators, which indicates that improvement is required in the stakeholder engagement mechanism in Mauritius. The main weaknesses identified comprise of lack of an adequate legal framework with clear definition of waste types with regards to segregation, especially for non-hazardous wastes, low efficiency of sustainable waste management awareness campaigns and lack of inclusion of the informal sector. The main strengths identified consist of a proper bidding mechanism in place and a good level of equity in the provision of waste management services with respect to comingled waste collection. Suggested improvement areas include a revamping of the existing legal framework related to waste management to cater for higher inclusivity of all stakeholders together with including sustainable waste management topics in the formal education curriculum.

The User Inclusivity and Producer Inclusivity indicators were previously applied only to cities to measure the level of stakeholder participation, but this study has demonstrated that these indicators can also be adopted on a nation-wide level to evaluate stakeholder engagement. The use of these indicators together with secondary data presents a less time-consuming method to assess stakeholder participation in the waste sector, which can be particularly useful for Small Island Developing States.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.