Study Of Greenhouse Gas Reduction For Municipal Solid Waste Management System: A Case Of Tianjin

According to the12-five-year energy saving and carbon reduction plan, Tianjinis required to reduce carbon intensity by19%from2010to2015. Meanwhile, sincesolid waste grows7%~9%for each year in China and IPCC introduced solid waste as anew chapter, carbon reduction of waste management system have be put in a crucialposition. In the long run, the waste treatment system can be improved by introducingnew facilities or technologies to reduce the GHG emissions in the process of collectionand transportation, transfer and final treatment. However, for short-term planning,when economic or technical factors are considered, it is not practical to introduce newfacilities for waste treatment system in Tianjin due to the fact that waste treatmentfacilities have met requirements at present.Hence a multi-objective programming method was adapted in this study and wastemanagement system can be optimized by adjusting waste flow allocation withoutconsidering new technology and high budget for short-term planning. Most previousoptimization analyses were respectively devoted to cost and environmental impactexcept GHG emissions, but failed to weigh GHG emissions and costs. The study, takingWMS in Tianjin, China as an example, aims to find an optimal solution for WMS whichfavorably balances the two items. Firstly, waste management system should beinvestigated,and the greenhouse gas and cost were accounted. Secondly, based on thesame case, we set three objective functions (cost minimization、GHG minimization andeco-efficiency minimization) which were subjected to the same constraints. Eachobjective function corresponded to one scenario. Thirdly, GHG emissions and costwere derived from the waste flow of each scenario. When it comes to a more realisticcase, the cost and GHG emissions could hardly reach the value minimization. So threeauxiliary objective functions (cost maximization、 GHG maximization andeco-efficiency maximization) were developed and each objective function stillcorresponded to one scenario. Finally, the range of GHG emissions and cost of the otherpotential scenarios were drawn, when the waste flow was adjusted by infinitelypossible step size according to difference among the above six scenarios. This rangeprovided a comparative basis to determine an optimal scenario. The results suggest that1) the scenarios located on the border between scenariocost minimization and GHG minimization create an optimum curve, and scenario GHGminimization has the smallest eco-efficiency on the curve;2) simple pursuit ofeco-efficiency minimization using fractional programming may be unreasonable; and3)balancing GHG emissions from incineration and landfills benefits Tianjin s WMS as itreduces GHG emissions and costs.