Literature DB >> 30633859

Solid Waste Management Policy Implications on Waste Process Choices and Systemwide Cost and Greenhouse Gas Performance.

Megan K Jaunich1, James W Levis1, Joseph F DeCarolis1, Morton A Barlaz1, S Ranji Ranjithan1.   

Abstract

Solid waste management (SWM) is a key function of local government and is critical to protecting human health and the environment. Development of effective SWM strategies should consider comprehensive SWM process choices and policy implications on system-level cost and environmental performance. This analysis evaluated cost and select environmental implications of SWM policies for Wake County, North Carolina using a life-cycle approach. A county-specific data set and scenarios were developed to evaluate alternatives for residential municipal SWM, which included combinations of a mixed waste material recovery facility (MRF), anaerobic digestion, and waste-to-energy combustion in addition to existing SWM infrastructure (composting, landfilling, single stream recycling). Multiple landfill diversion and budget levels were considered for each scenario. At maximum diversion, the greenhouse gas (GHG) mitigation costs ranged from 30 to 900 $/MTCO2e; the lower values were when a mixed waste MRF was used, and the higher values when anaerobic digestion was used. Utilization of the mixed waste MRF was sensitive to the efficiency of material separation and operating cost. Maintaining the current separate collection scheme limited the potential for cost and GHG reductions. Municipalities seeking to cost-effectively increase landfill diversion while reducing GHGs should consider waste-to-energy, mixed waste separation, and changes to collection.

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Year:  2019        PMID: 30633859     DOI: 10.1021/acs.est.8b04589

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  Toward a cleaner and more sustainable world: A framework to develop and improve waste management through organizations, governments and academia.

Authors:  Rafaela Garbelini Anuardo; Maximilian Espuny; Ana Carolina Ferreira Costa; Otávio José Oliveira
Journal:  Heliyon       Date:  2022-04-01

2.  Applying a deep residual network coupling with transfer learning for recyclable waste sorting.

Authors:  Kunsen Lin; Youcai Zhao; Xiaofeng Gao; Meilan Zhang; Chunlong Zhao; Lu Peng; Qian Zhang; Tao Zhou
Journal:  Environ Sci Pollut Res Int       Date:  2022-07-26       Impact factor: 5.190

  2 in total

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