4.7 Article

Carbon footprint of integrated waste management systems with implications of food waste diversion into the wastewater stream

Journal

RESOURCES CONSERVATION AND RECYCLING
Volume 133, Issue -, Pages 263-277

Publisher

ELSEVIER
DOI: 10.1016/j.resconrec.2018.02.021

Keywords

Solid waste management; Wastewater management; Carbon footprint; Carbon credit

Funding

  1. Lebanese National Council for Scientific Research (LNCSR)
  2. American University of Beirut (AUB)
  3. Dar Al-Handasah (Shair Partners)

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This paper introduces a comprehensive model developed to assess the carbon footprint of integrated solid waste management systems including the diversion at source of the food waste component into the wastewater/sludge management systems using household food waste disposers. In addition to the current state of practice in developed economies, the model includes emissions from waste management processes still practiced in developing economies (such as open dumping, open burning, poorly operated landfills with flaring systems and auxiliary fuel needed to satisfy the low heating value (LHV) during incineration) commonly not considered in most life cycle assessment (LCA)-based models. It can disaggregate emissions by source (from collection to final disposal), or type (direct-operating, indirect-upstream, indirect-downstream), or gas (CH4, CO2, N2O) and offers users the flexibility to select processes or modify input parameters while examining their impact on uncertainty in model simulations. Equally important is a clarity in deriving and applying emission factors used to quantify emissions from waste management systems. The model was tested in the context of developed and developing economies to assess the impact of waste composition, management processes, energy consumption and other parameters on variations in emissions. The results demonstrated that best practices through material recycling, biological treatment, food waste diversion, and/or energy recovery can contribute to significant savings in emissions that ranged between 24 and 95%, depending on the tested systems. In closure, we argue the benefits of the model application in providing guidelines for policy planning and decision making about process viability for investing in carbon credit.

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