Life cycle assessment of municipal solid waste management with regard to greenhouse gas emissions: Case study of Tianjin, China

The environmental impacts of municipal solid waste (MSW) management have been highlighted in China, due to the continually increasing amount of MSW being generated and the limited capacity of waste treatment facilities. Of particular interest is greenhouse gas (GHG) mitigation, aided by the Kyoto Mechanisms. China is an important case study for this global issue; however, an analysis of the entire life cycle of MSW management on GHG emissions is not available for China. This study evaluates the current and possible patterns of MSW management with regard to GHG emissions, using life cycle assessment (LCA), based on the Tianjin case. We assess the baseline scenario, reflecting the existing MSW management system, as well as a set of alternative scenarios, five exploring waste treatment technology innovations and one exploring integrated MSW management, to quantitatively predict potentials of GHG mitigation for Tianjin. Additionally, a sensitivity analysis is used to investigate the influence of landfill gas (LFG) collection efficiency, recycling rate and methodological choice, especially allocation, on the outcomes. The results show GHG emissions from Tianjin's MSW management system amount to 467.34 Mg CO2 eq. per year, based on the treatment of MSW collected in the central districts in 2006, and the key issue is LFG released. The integrated MSW management scenario, combining different improvement options, shows the highest GHG mitigation potential. Given the limited financial support and the current waste management practice in Tianjin, LFG utilization scenario would be the preferred choice. The sensitivity analysis of recycling rate shows an approximately linear relation of inverse proportion between recycling rate and total GHG emissions. Kitchen waste composting makes a considerable contribution to total GHG emissions reduction. Allocation choices result in differences in total quantitative outcomes, but preference orders and contributions analysis are found to be robust, suggesting LCA can support decision making