“A cluster-based spatial analysis of recycling boundaries aligning anaerobic digestion infrastructure with food waste generation in California”

Abstract

In 2016, California passed Senate Bill (SB) 1383 to reduce short-lived climate pollutants, including methane gas. Towards this end, the law specifically mandates a 75% reduction of organic waste, including food waste (FW), from landfills by 2025. However, current infrastructural capacity to treat this diverted organic waste is limited throughout the state, so new facilities will need to be built to treat these valuable waste flows. The purpose of this study is to investigate ideal size and scale of new facilities that maximize FW treatment and minimize GHG emissions. To do so, this study uses a case study of Los Angeles County to model a decentralized network of small-scale, containerized anerobic digestors (ADs) for treatment of FW in the region. A spatial FW dataset developed for this study is used with a novel iterative-descent clustering model to simulate potential “FW-sheds” of ADs using Geographic Information Systems (GIS). Monte Carlo simulation was used to generate a range of model results and a GHG analysis of FW collection is used to compare systems of two different AD capacities. The results of this analysis show that food waste is ideal for recycling at relatively small spatial scales as hauling burden of FW is reduced in these systems. The proposed infrastructure modeling approach is a first step of developing a zero net energy infrastructural solution that promotes a circular economy of food in direct response to SB 1383 and, more broadly, global climate change.