EXPLORING THE MITIGATION OF GREENHOUSE GAS EMISSIONS FROM THE CURRENT MUNICIPAL SOLID WASTE SYSTEM OF KAZAKHSTAN: CASE STUDY OF NUR-SULTAN CITY

Abstract

As we move forward, municipal solid waste (MSW) landfills, particularly in developing countries, contribute notably to global greenhouse gas (GHG) emissions. Therefore, the MSW sector plays a key role in planning strategies for developing countries such as Kazakhstan to decrease GHG emissions. With respect to the Paris Agreement, Kazakhstan has set the target of reducing GHG emissions to 15-25% by 2030 compared to the level of 1991, which will undoubtedly require certain measures in the field of MSW management. Several recent articles have been published on the waste management sector of Kazakhstan; however, none have explicitly focused on the impact of greenhouse gas emissions and possible pathways towards sustainable management. Thus, this paper describes the existing MSW system in Nur Sultan city as representative for the rest of the country. The quantitative evaluation of GHG emissions from the existing MSW system in the capital is carried out based on the IPCC methodology using the SWM-GHG calculator developed by the Institute for Energy and Environmental Research (IFEU). An assessment and cost analysis of a set of several suitable MSW management scenarios, such as scenario 1: existing case (15% recycling rate and 85% disposal), scenario 2: 30% recyclable materials, and 70% sanitary landfill with gas collection; scenario 3: 30% recyclable materials and 70% biological stabilization and landfill without gas collection; scenario 4: 30% recyclable materials, 20% composting and 50% waste to be sent to the WtE plant (incineration). The level of GHG emissions decreases with the introduction of more integrated waste management methods, but requires more financial investments. Therefore, Scenario 3 is the most efficient to implement in terms of the combination of cost of €19.4 million/year and magnitude of GHG emissions of 48 kt of CO2 eq/year. The outcomes of this work will help to extrapolate the model to other large cities in Kazakhstan