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Policy Analysis

A Systematic Evaluation of Industrial, Commercial, and Institutional Food Waste Management Strategies in the U.S. Keith L Hodge, James William Levis, Joseph F DeCarolis, and Morton A Barlaz Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00893 • Publication Date (Web): 07 Jul 2016 Downloaded from http://pubs.acs.org on July 7, 2016

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Environmental Science & Technology

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A Systematic Evaluation of Industrial, Commercial, and Institutional Food Waste

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Management Strategies in the U.S.

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Keith L. Hodge,a James W. Levis,*,b Joseph F. DeCarolis,b and Morton A. Barlazb

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a. TeamAg, Inc.,120 Lake St., Ephrata, PA 17522

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b. North Carolina State University, Department of Civil, Construction, and Environmental Engineering, Campus Box 7908, Raleigh, NC 27695-7908

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*

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ABSTRACT

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New regulations and targets limiting the disposal of food waste have been recently enacted in

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numerous jurisdictions. This analysis evaluated selected environmental implications of food

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waste management policies using life-cycle assessment. Scenarios were developed to evaluate

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management alternatives applicable to the waste discarded at facilities where food waste is a

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large component of the waste (e.g., restaurants, grocery stores, and food processors). Options

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considered include anaerobic digestion (AD), aerobic composting, waste-to-energy combustion

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(WTE), and landfilling, and multiple performance levels were considered for each option. The

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global warming impact ranged from approximately -350 to -45 kg CO2e Mg-1 of waste for

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scenarios using AD, -190 to 62 kg CO2e Mg-1 for those using composting, -350 to -28 kg CO2e

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Mg-1 when all waste was managed by WTE, and -260 to 260 kg CO2e Mg-1 when all waste was

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landfilled. Landfill diversion was found to reduce emissions, while diverting food waste from

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WTE generally increased emissions. The analysis further found that when a 20-year GWP was

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used, instead of a 100-yr GWP, every scenario including WTE was preferable to every scenario

Corresponding author. Phone: (919) 515-7823; fax: (919) 515-7908; email: [email protected]

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ACS Paragon Plus Environment


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including landfill. Jurisdictions seeking to enact food waste disposal regulations should consider

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regional factors and material properties before duplicating existing statutes.

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Keywords: solid waste, food waste, life-cycle assessment, decision support, waste-to-energy

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INTRODUCTION

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In 2013, food waste represented the largest component of discarded municipal solid waste

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(MSW) in the U.S. at 21%.1 Non-MSW sources (e.g., industrial food manufacturers) in the U.S.

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generated an estimated 22 million additional tons of food waste.1 California and several New

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England states and West Coast cities have implemented policies to limit landfill disposal of food

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waste and promote biological treatment (as summarized in Table S1 in the Supporting

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Information [SI]). While city-level regulations often include the residential sector, laws at the

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state level currently focus on large volume, high food waste content industrial, commercial, and

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institutional (HFW-ICI) waste generators such as restaurants, hotels, supermarkets, and

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conference centers. Once separated, the most common alternative for food waste treatment in the

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U.S. is aerobic composting (AC). An emerging alternative in the U.S. is anaerobic digestion

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(AD) of source-separated food waste in which the generated biogas can be used for energy.

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Evaluating the system-wide environmental consequences of solid waste management

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(SWM) decisions is important for guiding policy and achieving environmental goals. Life cycle

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assessment (LCA) is a framework for systematic environmental evaluation of processes and

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systems that has been applied to SWM systems in general, and food waste management systems

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in particular. Laurent et al. reviewed LCA studies of organic waste management alternatives and

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found that landfills typically had the worst overall environmental performance, while AD and

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WTE typically outperformed composting.3 Morris et al. performed a meta-analysis of source-

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separated food waste management LCA studies and reported that composting and AD were

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preferable to various configurations of WTE and landfilling in consideration of climate change

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impacts.4 Fruergaard and Astrup concluded that WTE with combined heat and power (CHP) was

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generally preferable to AD with CHP in a Danish setting for source-separated organics,5 and

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Evangelisti et al. found that AD with CHP outperformed landfilling and WTE of source-

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separated organics in the UK when considering climate change and acidification impacts.6 Ebner

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et al. found that co-digestion of food waste and manure reduced GHG emissions by 71%

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compared to conventional treatment.7 Levis and Barlaz is one of a limited number of studies to

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have analyzed commercial food waste management alternatives in a U.S. context.8 The study

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concluded that AD was superior to composting and landfilling, but WTE was not considered.

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These studies all used source-separated food waste as their functional unit rather than

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considering a waste composition corresponding to the total waste that HFW-ICI generators must

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manage. The discarded composition from HFW-ICI generators is on average only 36-75% food

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waste (Table S3), so there are large quantities of other materials that must also be managed (e.g.,

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cardboard, plastic film). Not considering these materials is a significant omission when

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evaluating the environmental implications of food waste policies because the manner in which

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food waste is separated and managed will affect the cost and environmental implications

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associated with collecting and treating the remaining waste (i.e., landfilling and WTE both

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perform differently when food waste is removed).

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The objective of this study was to evaluate selected environmental implications of food

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waste management policies in the U.S by comparing alternatives for the management of

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discarded waste from HFW-ICI entities. Considered treatment alternatives include landfilling,

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mass burn WTE, composting, and AD.

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MODELING APPROACH

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Functional Unit and System Boundaries

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A life-cycle approach was used to quantify emissions and selected environmental impacts

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associated with several alternatives for HFW-ICI solid waste management. The functional unit

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was 1 Mg (1000 kg) of HFW-ICI waste with a composition that includes 58% food waste, as

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detailed in Section 2 of the SI. A mixed waste functional unit (i.e., one that includes 42% non-

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food waste) was chosen to capture interactions of food waste management alternatives with the

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larger SWM system. It was assumed that HFW-ICI generators already participate in a recycling

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program independent of the food waste management decision, so the as-discarded mixed waste

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composition did not include the portion of recycled materials that were already separated out by

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the generators, such as plastic and glass containers, ferrous and aluminum cans, and paper and

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cardboard. Waste component material properties assumed for this study are provided in Table S5.

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System boundaries were chosen to include all activities from waste collection through

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treatment and final disposal, as illustrated in Figure 1 based on existing technology in a U.S.

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context. The analysis used a 100-year time horizon for environmental emissions, which is long

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enough to include over 99.9% of landfill gas (LFG) generation from food waste using a decay

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rate of 0.096 yr-1.9

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Selected life cycle impacts were considered based on their relevance to food waste

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management and our confidence in the inventory estimates underlying these impact categories.

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Global warming potential (GWP) was selected given the fugitive methane emissions from

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landfills and AD, as well as offsets from beneficial recovery of energy and nutrients. Cumulative

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fossil energy demand is important for the energy recovery technologies (i.e., WTE, AD, and

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landfill). Eutrophication and acidification potential are important when considering the

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application of nutrients as a fertilizer offset and photochemical smog formation is a potentially

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significant issue for waste combustion activities.

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All impacts were calculated using the Tool for the Reduction of and Assessment of

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Chemical and other environmental Impacts (TRACI) methodology10 with the exception of GWP,

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which was calculated using the Intergovernmental Panel on Climate Change (IPCC) method,11,12

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and cumulative fossil energy demand, which was calculated using the methodology described by

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Frischknecht et al.13 GWP weighting values are inherently uncertain and have changed as

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atmospheric science has progressed. GWP was estimated in the base cases using the IPCC 2007

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methodology (25 kg CO2e kg-1 CH4), which is still the standard in LCA, even though IPCC 2013

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recommends a GWP of 28 for CH4. Sensitivity cases analyzed the impact of using a range of

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possible GWPs as described in the Results. The modeling approach described herein was

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implemented using components of the Solid Waste Optimization Life-Cycle Framework

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(SWOLF) (go.ncsu.edu/swolf). SWOLF is a multi-stage optimization model that was developed

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to identify efficient alternatives for solid waste management in consideration of cost, net energy

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utilization, and a number of environmental emissions.14

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Scenario Descriptions

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The food waste management alternatives considered in this study included landfill, WTE,

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composting, and AD. The mixed waste functional unit dictated a split between source-separated

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food waste and residual waste (i.e., the remaining non-food waste) in scenarios involving

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composting or AD, so 6 scenarios were analyzed (Table 1). For each scenario, facilities with a

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range of performance were considered (Table 2). In addition, for each facility, there is the

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potential for the beneficial use of products (e.g., methane in landfill gas, nutrients in compost), so

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each facility configuration was modeled under multiple assumptions regarding beneficial use

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(Table 2) for a total of 36 scenarios.

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Facility and Process Modeling

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SWOLF model components are summarized in this section, and additional description of modeling parameters is provided in Section 4 of the SI.

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A mixed waste collection process for the full combined functional unit was applied to the

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landfill and WTE scenarios, while both source-separated food waste and residual mixed waste

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collection processes were required for scenarios that include composting or AD. Emissions

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associated with collection were estimated using a previously developed collection model.15

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Mixed waste, separate food waste, and residual collection were estimated to require 3.4, 5.3, and

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4.3 L diesel Mg-1 of waste, respectively (Section 4.6 of the SI).

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The landfill (LF) receives mixed or residual waste from HFW-ICI generators as well as

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residual material from composting or AD, depending on the scenario. The modeled landfill was

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based on Levis and Barlaz (2011) and updated with default values from Levis and Barlaz (2014)

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to estimate emissions, material use, and energy use associated with construction, operations,

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closure and post-closure activities, landfill gas and leachate management, and carbon storage.15,17

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Landfill configurations were differentiated by landfill gas collection system parameters, and

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divided into “Flare” and “Energy Recovery” beneficial use options. “Flare” assumed that all

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collected landfill gas was burned with no beneficial use, while “Energy Recovery” assumed use

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of an internal combustion engine to produce electricity when greater than 10 m3 min-1 of gas was

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available. Table 3 defines the landfill gas collection system parameters used for each landfill

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configuration. In the base case, biogenic carbon remaining in the landfill after 100 years was

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considered stored. Further details on the landfill model and data are provided in Section 4.1 of

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the SI.

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The WTE facility receives mixed or residual waste from HFW-ICI generators as well as

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residual material from composting or AD facilities, depending on the scenario. Estimates of

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emissions, mass flows, and resource use for the WTE facility were based on an updated version

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of the model presented by Harrison et al.18 Model updates include consideration of heat lost to

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moisture and ash in each material, the ability to recover aluminum (in addition to ferrous metal)

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from bottom ash, and the option to beneficially use steam in addition to that used for electricity

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generation. The inclusion of moisture effects is particularly important for this study given the

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emphasis on a high moisture content waste stream. In all modeled configurations, energy

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released during waste combustion was recovered to produce electricity. For the “CHP” beneficial

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use option, the WTE plant was assumed to also recover heat released during combustion for use

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in either a district heating system or an industrial process. Table 3 defines energy and metals

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recovery values used in the WTE configurations. Direct stack emissions of particular pollutants

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also varied across configurations, and are described in Section 4.2 of the SI. Transportation and

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management of ash were included in the LCA.

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The composting facility model was described by Levis and Barlaz.19 The model estimates

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process-related direct emissions, emissions associated with land application of finished compost,

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as well as emissions associated with energy and material inputs required for the composting

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process. Soil carbon storage was estimated for the finished compost material. Additional soil

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carbon storage due to increased humus formation may also occur, but was not included in the

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base case because the level of this impact is uncertain and dependent on soil, climate, crops, and

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land management. The importance of this assumption was explored in a sensitivity case.

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Beneficial use (BU) of the available nitrogen, phosphorus, and potassium in the compost were

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counted as credits to offset mineral fertilizer production, including the relative differences in

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emissions between mineral fertilizer and cured solids during and after land application. The no

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BU (NBU) cases did not consider offsets for mineral fertilizer. For AD and composting, the

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associated use-on-land model also includes fuel use during land application and emissions of

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NH3, N2O, and NO3- after compost or equivalent fertilizer application. Other studies have

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evaluated multiple composting technologies, but only windrow configurations were selected for

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this study because they are the most common process in the U.S. The difference between the two

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windrow composting facilities was that one included an odor control system and the other did

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not. Odor was not quantified in this study, but energy associated with operation of the odor

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control system was included in the Better and Moderate configurations (Table 2). Residual from

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the composting operation (e.g., removed glass, plastic, or metal contamination) was directed

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either to landfill or WTE, depending on the scenario. Further details on the composting model

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and data are provided in Section 4.3 of the SI.

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Emissions and energy and material inputs associated with AD and downstream material

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flows were estimated using the AD model developed by Levis and Barlaz.8,20 The modeled AD

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facility was based on a wet digestion system, as reflected in the assumed moisture content and

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digestate mass flows. The default data were developed from a continuous single-stage wet

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mesophilic digester. Land application-related emissions and carbon storage for separated

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digestate solids were estimated in the same manner as for composting. Three configurations were

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developed by varying the heat rate of the internal combustion engine used for energy recovery

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(Table 3). Residual from AD (e.g., removed glass, plastic, or metal contamination) was treated as

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in the composting scenarios. Further details on the AD model and data are provided in Section

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4.4 of the SI.

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Each of the food waste management facilities has one or more opportunities for

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beneficial use of energy and/or fertilizer, which can displace consumption of an equivalent

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product produced by conventional means. For facilities that generate electricity, the marginal

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grid mix from the Southeastern Electricity Reliability Council (SERC) reported by Siler-Evans et

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al.21 was used for the base case with emissions estimated using a tool developed as part of this

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study using eGRID2012 and ecoinvent v.3.01 data on U.S. electricity generation

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technologies.22,23 The SERC marginal grid mix is 55% coal and 45% natural gas, and is the

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region that most closely resembles the U.S. national mix.21 Coal and natural gas marginal fuel

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sources were also modeled as bounding cases on emissions for electricity generation. The

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corresponding electrical energy CO2e intensities are 0.89, 1.3, and 0.74 kg CO2e kwh-1 for the

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base case, marginal coal case, and marginal gas case, respectively. Impact intensities for fossil

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energy demand, acidification, eutrophication, and photochemical oxidation are given in Table

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S30. Heat production in WTE CHP cases was assumed to offset an equivalent quantity of heat

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generated in a natural gas-fired boiler, using emission factors obtained from the ecoinvent v3.01

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database.23 Where specified, available nitrogen, phosphorous, and potassium in compost and

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cured, digested solids were assumed to avoid conventional N, P, and K fertilizer production,

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application and use respectively, using emission factors adapted from ecoinvent v3.01 (Tables

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S16 – S18).23 Mineral fertilizer equivalents for N, P, and K of 0.4, 1.0, and 1.0 were used as

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described by Hansen et al.24 That is, 1 kg of N in compost offsets 0.4 kg of N in mineral

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fertilizer.

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RESULTS

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Selected LCA results for the Base Case scenarios are presented first, followed by

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sensitivity analyses of additional cases, and finally policy implications of the results. The net

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GWP calculated for all configurations in all scenarios is shown in Figure 2. A contribution

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analysis of the AD-WTE scenario by process and key sub-processes is shown in Figure 3, and

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contribution results for the other scenarios are shown in Figures S1 to S6. As expected, LF

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scenarios resulted in higher GWP than the other scenarios, primarily due to emissions of methane

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prior to installation of a gas collection system.

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Scenarios involving AD perform better than those involving composting due to electricity

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offsets from AD. Figure 3.b also shows that fugitive biogas is responsible for 63% of the gross

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GWP for AD (excludes house electricity use), which highlights the importance of modeling

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assumptions regarding biogas leakage (3% in this study).25 The largest contributor (25 kg CO2e

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Mg-1) to gross GWP for composting is direct gaseous emissions associated with biodegradation

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during composting and solids curing, and after land application (86% of gross emissions).

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Additionally, biogenic carbon storage reduces GHG emissions in AD and composting by 6.3 and

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9.0 kg CO2e Mg-1, respectively. The largest benefits from carbon storage occur in scenarios that

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include a landfill: 220 to 230 kg CO2e Mg-1 when accepting residual waste (i.e., AD-LF and AC-

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LF), and 280 kg CO2e Mg-1 when accepting mixed waste (LF).

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While electrical energy generation from WTE is standard, additional heat recovery at

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existing WTE facilities is relatively rare in the U.S.26 The WTE (NBU) scenario, which directs

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all waste to the WTE facility, is comparable in GWP performance to scenarios including AD.

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This suggests that if a state-of-the-art WTE facility is operating in a given region, the most

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beneficial strategy in terms of GWP could be to continue directing HFW-ICI waste to WTE. In

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contrast, there is a large benefit associated with diverting food waste from landfills to AD,

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composting, or WTE.

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Comparing the emissions from the landfill in the LF scenarios versus the AD-LF and AC-

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LF shows the importance of modeling each waste component separately. In the LF scenarios,

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LFG emissions dominate net GWP (370 kg CO2e Mg-1) as a result of rapidly-degrading food

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waste producing methane that is released prior to installation of the LFG collection system. In

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the AD-LF and AC-LF scenarios, LFG emissions are reduced (170 kg CO2 Mg-1), because most

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of the food waste has been diverted, and the remaining degradable materials (mostly cardboard

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and paper products) degrade more slowly and less completely, so more of the gas is collected and

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treated (using the gas collection schedule in Table 3), and more carbon is stored. Studies that

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consider food waste only, without the other waste components generated at HFW-ICIs, will not

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capture changes in the performance of facilities from which the food waste is diverted. Similarly,

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studies that do not consider the decay rates, staged LFG collection, and carbon storage factors for

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individual waste components will also miss changes in the performance of facilities from which

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the food waste is diverted.

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The disaggregated results indicate that waste collection from HFW-ICI generators

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contributes 14 kg CO2e Mg-1 in the mixed waste cases and 19 kg CO2e Mg-1 in the separate

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collection cases (Section 4.6 of the SI). Disaggregation also shows that mineral fertilizer offsets

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only reduce AD and composting GHG emissions by 14 kg CO2e Mg-1. Surprisingly, use of CHP

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in the WTE scenario incurs a GWP penalty (59 kg CO2e Mg-1) compared to generating only

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electricity (Figure 2). This is because less electrical energy is produced in the WTE CHP

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scenario and the recovered steam offsets natural gas in the base case, which has a lower GHG

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intensity than electrical energy generation. These results are sensitive to the assumptions

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associated with CHP (e.g., maximizing heat production) and could vary with other CHP systems

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and in regions where district heating from CHP is more widely used (e.g. northern Europe).

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Figure 4 shows net photochemical oxidation potential (as NOX-eq) for all scenarios. NOx

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and non-methane volatile organic compounds (NMVOC) emissions are the major contributors to

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this impact category. Comparison of the moderate configurations for each scenario shows that

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LF (NBU) and AC-LF scenarios have the lowest NOx-eq. emissions. WTE and AD facilities

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result in relatively high smog formation potential because biogas engines tend to have higher

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NOx and NMVOC emissions than flares, which were used in the LF (NBU) scenario (Table S8).

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Scenarios involving the Worse Case and Fleet Average WTE configurations have four times

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higher NOx emissions compared to state-of-the-art (SOTA) WTE configurations that include a

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greater level of NOx control.

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Scenario-level results for other impact categories (acidification, eutrophication, CED-

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fossil) are presented in the SI (Figures S7-S9). The rankings for these categories tend to be

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similar to GWP. Scenarios involving WTE consistently outperform those involving a landfill,

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and AD typically outperformed composting. Sensitivity analyses are presented in the following

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section to evaluate the robustness of the results and to develop policy-relevant insights into the

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waste management system.

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Sensitivity Analysis

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Sensitivity analyses were conducted on selected model parameters including the marginal

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fuel for electricity offsets, carbon storage and peat substitution, inclusion of CHP in LF and AD

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scenarios, and alternate carbon accounting protocols and the results are summarized in Figure 5.

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Electricity offsets are major contributors to net GWP in all scenarios involving WTE, AD, or LF

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(BU). The base case assumes that each unit of electricity produced avoids one marginal unit of

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electricity from the SERC regional grid, but sensitivity analyses were performed on electricity

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generation from 100% coal and 100% natural gas (1.3 and 0.74 kg CO2e kWh-1). Resulting GWP

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for all scenarios for the natural gas case are shown in Figure 6, with comparisons to base case

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results. Switching to natural gas increases the GWP of all scenarios involving electricity

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generation by 11 to 75 kg CO2e Mg-1. Results for the coal case showed that a more carbon-

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intensive grid resulted in greater benefits to scenarios that produce electricity, particularly AD-

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WTE (both) and WTE (NBU). The change in marginal emissions offset was sufficient for WTE

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(NBU) and AD-WTE (NBU) to both outperform AD-LF by approximately 33%. These results

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suggest that as marginal emissions decrease over time, the benefits of energy recovery from

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waste will also decline.

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Sensitivity cases were conducted on moisture content and anaerobic conversion of biogenic carbon to explore how food waste material properties affect environmental

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performance. Sensitivity to food waste moisture content was evaluated by increasing and

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decreasing the moisture contents of vegetable and non-vegetable food waste (i.e., 77% and 57%,

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respectively), by 15 percentage points. Increasing moisture content improves landfill

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performance by 60 to 110 kg CO2e Mg-1 and significantly reduces the benefits of AD and WTE

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(e.g., AD-WTE [NBU] GHG emissions increase by 77 kg CO2e Mg-1). This is because increasing

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the moisture content effectively reduces the methane yield and the LHV of the waste. Decreasing

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the moisture content has the opposite effect, making WTE [NBU] the top performing scenario.

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These results indicate that there may be a role for drying pretreatment processes to improve WTE

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performance with wet materials.

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The anaerobic conversion of biogenic carbon in AD and the landfill was varied from 50

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to 100%. The methane yields for the 50% conversion case were 223 and 264 m3 CH4 dry Mg-1

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for vegetable and non-vegetable food waste, respectively, while the base case methane yield was

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369 m3 dry Mg-1. This range of methane yields is similar to the range of reported values (Table

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S29). Changing the percent of biogenic carbon converted to biogas simultaneously changes the

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amount of carbon stored (i.e., lower carbon conversion leads to increased storage). Using a lower

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methane yield reduced the benefits of AD, which made the WTE scenarios and composting more

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competitive alternatives. In the low methane yield case, there was actually a penalty for diverting

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food waste from the landfill with energy recovery to composting because of lost electricity

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generation and carbon storage. Using a high methane yield did not significantly alter scenario

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rankings, though the GWP of the LF scenarios increased by over 110 kg CO2e Mg-1. These

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results illustrate how scenario-level GWP performance and rankings are dependent upon food

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waste material properties. It may be appropriate for regulators to consider these properties in

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determining diversion requirements from particular categories of HFW-ICI generators.

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A sensitivity analysis was also performed on the substitution of peat with increased

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humus formation for compost produced from AD and composting. The use of peat instead of a

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fertilizer offset did not change any of the rankings from the Base Case, and the results for the AD

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and AC scenarios changed by less than 20 kg CO2e Mg-1. Thus, the selection of a fertilizer or

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peat offset does not substantially change the results.

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A sensitivity analysis was performed to explore the potential benefits of adding

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supplemental heat recovery to both AD and landfill. It was assumed that 75% of waste heat could

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be recovered, including a penalty of 25% on the electrical energy heat rate. For the AD system,

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10% of the recovered heat was assumed to be used to heat the digester. As expected, the addition

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of CHP improves AD and landfill performance, which improves the performance of the AD-LF

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(BU) case by (46 kg CO2e Mg-1), while having minimal effect on the rankings.

317

Additional sensitivity analyses were performed on GWP factors to explore the effects of

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different carbon accounting assumptions. In total five sets of GWP factors were considered: (1)

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the base case which used the 2007 IPCC 100-yr GWP (CH4 = 25; Emitted Biogenic CO2 = 0;

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Stored Biogenic CO2 = -1); (2) 2013 IPCC 100-yr GWP with aerosol effects (CH4 = 34; Emitted

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Biogenic CO2 = 0; Stored Biogenic CO2 = -1); (3) 2007 IPCC 100-yr GWP (CH4 = 25; Emitted

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Biogenic CO2 = 1; Stored Biogenic CO2 = 0); (4) 2007 IPCC 20-yr GWP (CH4 = 72; Emitted

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Biogenic CO2 = 0; Stored Biogenic CO2 = -1); and (5) 2007 IPCC 20-yr GWP (CH4 = 72;

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Emitted Biogenic CO2 = 1; Stored Biogenic CO2 = 0). For 100-yr GWP, changing accounting of

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biogenic carbon emissions and storage did not change the rankings of the best seven scenarios

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compared to the Base Case, and did not change the rankings at all when comparing 20-yr GWPs.

327

The results indicate that increasing the GWP of CH4 has the largest effect on the rankings due to

328

the fugitive CH4 emissions from AD and landfills (Table S27). For the 20-yr GWP cases, WTE

329

(BU and NBU) performed the best, followed by AD-WTE (BU and NBU), followed by AC-

330

WTE (BU and NBU). The six scenarios involving landfills performed the worst. The results

331

show that the benefits of WTE were robust no matter the GWP accounting method, while

332

increasing the GWP of CH4 emissions (i.e., 2013 IPCC GWP, and 20-yr GWP) led to significant

333

penalties for scenarios that used landfills.

334

The net GWP results shown in Figure 5 reveal several trends that remain consistent

335

across sensitivity cases. In most cases, AD-LF (BU) was the top performer, but was out-

336

performed by WTE when coal was assumed to be the source of marginal electricity, when AD

337

generated less electricity (low methane yield, high moisture content), and when the GWP of CH4

338

was increased (2013 IPCC GWP, and 20-yr GWP). In ten of the thirteen cases presented, the

339

difference in net GWP between first- and second-ranked scenarios as a percent of the range for

340

that case was less than 10%, which is likely within model accuracy.

341

POLICY IMPLICATIONS

342

The results show that in most cases, it is beneficial to divert food waste from a landfill to

343

AD, composting, or WTE, but often not beneficial to divert food waste from WTE. AD

344

outperformed composting in terms of GWP in most cases, but the treatment options were more

345

comparable when electricity produced at AD offset less GHG-intensive natural gas electricity

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346

and when food waste methane yields were reduced by 50%. Generally, the AD-LF (BU) scenario

347

was the leading alternative in terms of GWP.

348

The results indicate that the benefits of energy recovery are dependent on the GHG

349

intensity of electricity generation. Regions with more GHG intensive electrical grids therefore

350

have more incentive to switch to AD or WTE, and as electrical grids get cleaner over time, the

351

benefits of energy recovery in AD or WTE may decrease.

352

The increased GWP associated with diverting food waste from WTE challenges the

353

assumption that food waste diversion to composting or AD is always beneficial. In particular, a

354

future case study could evaluate the Massachusetts commercial food waste diversion regulation,

355

which requires diversion of commercial organics from combustion facilities. This is especially

356

important since Massachusetts combusts a larger proportion of its waste than any state but

357

Connecticut, and since New England is the most attractive region in the country for district

358

heating systems that can utilize steam produced from CHP.

359

Results of sensitivity analysis on food waste material properties suggest that certain

360

properties (e.g., moisture content and methane yield) have a notable impact on the relative

361

ranking of scenarios. Changing the moisture content effectively changed the LHV, methane

362

yield, and nutrient content and therefore affected all scenarios, while changing the methane yield

363

affected AD and landfill facilities. Based on the possible changes to overall SWM system results,

364

it could be beneficial to consider food waste characteristics (e.g., moisture content, nutrient

365

content, or methane yield), when developing diversion policies for HFW-ICI generators.

366

The analysis showed that the choice of GWP time horizon significantly alters the

367

rankings of the scenarios. When using a 100-yr GWP, AD-LF was typically the best performing

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368

alternative, but in the 20-yr GWP cases, no scenario that included landfills was among the best

369

six scenarios. While 100-yr GWP is still the most used standard metric for global warming

370

impacts, the choice of GWP time horizon is subjective and the best metric will depend on the

371

application and policy context.12 Increasing interest in mitigating short-term climate impacts

372

could increase interest in switching to a 20-yr GWP standard, which would make minimizing

373

fugitive CH4 emissions at landfills the primary global warming concern from SWM systems. If

374

20-yr GWP did become standard, then excluding WTE as a food waste diversion alternative

375

would appear to be even more counterproductive because both WTE scenarios performed the

376

best in the 20-yr GWP cases.

377

The recommendations described above are in large part based on moderate configurations

378

for all scenarios. Comparing better with worse configurations, for example, could produce a

379

different set of results and rankings, which indicates that it is important to evaluate actual or site-

380

specific facility performance in decision making. The conclusions are also primarily based on the

381

global warming impacts. The analysis found that while scenario rankings for fossil energy use,

382

acidification, and eutrophication generally followed global warming, photochemical oxidation

383

did not. Increasing use of WTE and AD could potentially exacerbate photochemical oxidation

384

impacts, so trade-offs among environmental impacts should also be considered when formulating

385

food waste diversion policies.

386

ACKNOWLEDGEMENTS

387 388

This work was supported by Covanta Energy, the National Science Foundation (CBET1034059) and the Environmental Research and Education Foundation.

19



389 390 391

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SUPPORTING INFORMATION Additional methodology details, results, and discussion are provided in the SI. This information is available free of charge via the Internet at http://pubs.acs.org/.

392

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REFERENCES

394 395 396

1. Advancing Sustainable Materials Management: 2013 Fact Sheet; U.S. EPA, Office of Resource Conservation and Recovery: Washington, DC, 2014; http://www.epa.gov/sites/production/files/2015-09/documents/2013_advncng_smm_fs.pdf

397 398 399

2. Analysis of U.S. Food Waste Among Food Manufacturers, Retailers, and Wholesalers; BSR, 2013; http://www.foodwastealliance.org/wpcontent/uploads/2013/06/FWRA_BSR_Tier2_FINAL.pdf.

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3. Laurent, A.; Bakas, I.; Clavreul, J.; Bernstad, A.; Niero, M.; Gentil, E.; Hauschild, M. Z.; Christensen, T. H. Review of LCA studies of solid waste management systems--part I: lessons learned and perspectives. Waste Manag. 2014, 34 (3), 573–588; DOI 10.1016/j.wasman.2013.10.045.

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4. Morris, J.; Matthews, S. H.; Morawski, C. Review and meta-analysis of 82 studies on end-oflife management methods for source separated organics. Waste Manage. 2012, 33(3), 545– 551; DOI 10.1016/j.wasman.2012.08.004.

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5. Fruergaard, T.; Astrup, T. Optimal utilization of waste-to-energy in an LCA perspective. Waste Manage. 2011, 31(3), 572–582; DOI 10.1016/j.wasman.2010.09.009.

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6. Evangelisti, S.; Lettieri, P.; Borello, D.; Clift, R. Life cycle assessment of energy from waste via anaerobic digestion: A UK case study. Waste Manage. 2014, 34(1), 226–237; DOI 10.1016/j.wasman.2013.09.013.

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7. Ebner, J. H.; Labatut, R. A.; Rankin, M. J.; Pronto, J. L.; Gooch, C. A.; Williamson, A. A.; Trabold, T. a. Lifecycle Greenhouse Gas Analysis of an Anaerobic Codigestion Facility Processing Dairy Manure and Industrial Food Waste. Environ. Sci. Technol. 2015, 49 (18), 1199–1208; DOI 10.1021/acs.est.5b01331.

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8. Levis, J. W.; Barlaz, M. A. What is the most environmentally beneficial way to treat commercial food waste? Environ. Sci. & Technol. 2011, 45(17), 7438–7444; DOI 10.1021/es103556m.

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9. De la Cruz, F.B. and Barlaz, M.A. Estimation of Waste Component-Specific Landfill Decay Rates Using Laboratory-Scale Decomposition Data. Environ. Sci. Technol. 2010, 44, 47224728; DOI 10.1021/es100240r.

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10. Bare, J. TRACI 2.0: The tool for the reduction and assessment of chemical and other environmental impacts 2.0. Clean Technol. Envir. 2011, 13, 687–696; DOI 10.1007/s10098010-0338-9.

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11. Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, U.K., 2007.

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12. Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, U.K., 2013.

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13. Implementation of Life Cycle Impact Assessment Methods; Swiss Centre for Life Cycle Inventories, St. Gallen, Switzerland, 2007; http://www.esuservices.ch/fileadmin/download/publicLCI/03_LCIA-Implementation.pdf.

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14. Levis, J. W.; Barlaz, M. A.; DeCarolis, J. F.; Ranjithan, S. R. Systematic exploration of efficient strategies to manage solid waste in U.S municipalities: Perspectives from the solid waste optimization life-cycle framework (SWOLF). Environ. Sci. and Technol. 2014, 48(7), 3625–3631; DOI 10.1021/es500052h.

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15. Jaunich, M. K., Levis, J. W., Barlaz M. A., & Ranjithan, S. R., A Life-cycle Process Model for Municipal Solid Waste Collection, J. Environ. Eng-ASCE. 2016, (in-press); DOI: 10.1061/(ASCE)EE.1943-7870.0001065.

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16. Levis, J.W.; Barlaz, M.A. Is biodegradability a desirable attribute for discarded solid waste? Perspectives from a national landfill greenhouse gas inventory model. Environ. Sci. Technol. 2011, 45(13), 5470–5476; DOI 10.1021/es200721s.

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17. Levis, J.W.; Barlaz, M.A. Landfill Gas Monte Carlo Model Documentation and Results. Report to ICF for the U.S. EPA Waste Reduction Model (WARM), 2014, http://epa.gov/epawaste/conserve/tools/warm/pdfs/lanfl_gas_mont_carlo_modl.pdf.

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18. Harrison, K. W.; Dumas, R. D.; Barlaz, M. A.; Nishtala, S. R. A life-cycle inventory model of municipal solid waste combustion. J. Air Waste Manage. 2000, 50(6), 993–1003; DOI .

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19. Levis, J.W.; Barlaz, M.A., Composting Process Model Documentation. Internal Report, 2013, http://go.ncsu.edu/swolf_composting.

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20. Levis, J.W.; Barlaz, M.A., Anaerobic Digestion Process Model Documentation. Internal Report, 2013, http://go.ncsu.edu/swolf_ad.

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21. Siler-Evans, K.; Azevedo, I.L., Marginal Emissions Factors for the U.S. Electricity System. Environ. Sci. Technol. 2012, 46(9), 4742-4748; DOI dx.doi.org/10.1021/es300145v .

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22. Ninth edition of eGRID with year 2010 data (Version 1.0); U.S. EPA, Washington, DC, 2014; http://www.epa.gov/cleanenergy/energy-resources/egrid/.

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23. Overview and methodology: Data quality guideline for the ecoinvent database version 3; Swiss Centre for Life Cycle Inventories, St. Gallen, Switzerland, 2013; https://www.ecoinvent.org/files/dataqualityguideline_ecoinvent_3_20130506.pdf.

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24. Hansen, T. L.; Bhander, G. S.; Christensen, T. H.; Bruun, S.; Jensen, L. S. Life cycle modelling of environmental impacts of application of processed organic municipal solid waste on agricultural land (EASEWASTE). Waste Manage. Res. 2006, 24(2), 153–166; DOI 10.1177/0734242X06063053.

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25. Sanscartier, D.; Maclean, H. L.; Saville, B. Electricity production from anaerobic digestion of household organic waste in Ontario: techno-economic and GHG emission analyses. Environ. Sci. Technol. 2012, 46(2), 1233–1242; DOI 10.1021/es2016268.

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26. The 2010 ERC Directory of Waste-to-Energy Plants; Energy Recovery Council, Washington, DC, 2010; http://www.energyrecoverycouncil.org/userfiles/file/ERC_2010_Directory.pdf.

467 468

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469

TABLES AND FIGURES

470

Table 1. Waste material routing for each food waste management scenario. Scenario Name LF WTE AD-LF AD-WTE AC-LF AC-WTE

Page 24 of 34

Description All waste collected together and disposed in a landfill. All waste collected together and combusted in WTE with the bottom and fly ash to an ash landfill. Food waste collected separately and treated by AD, residuals to landfill. Food waste collected separately and treated by AD, residuals to WTE, and WTE bottom and fly ash to ash landfill. Food waste collected separately and treated by composting, residuals to landfill. Food waste collected separately and treated by composting, residuals to WTE, and WTE bottom and fly ash to ash landfill.

471 472 473 474

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475


Table 2. Beneficial use options, and facility configurations for each management alternative. Waste Management Facility LF WTE AD AC

476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497

a. b. c. d. e. f. g.

h. i. j. k. l.

Configurations Beneficial Use (BUa / No BU) Energy Recoveryb / Flarec CHPg / No CHP Fertilizer Offseti / Nonej Fertilizer Offsetl / Nonej

Better Moderate SOTAd,e U.S. Nat’l Avg.e SOTAd,h Fleet Avg.h k Better Case Typicalk Windrows, Under Roof with Biofilter

Worse NSPS Limitsf Worse Case h Worse Casek Windrows, Open

BU = Beneficial Use. Energy Recovery. Collected landfill gas is treated and burned in an internal combustion engine to produce electricity when sufficient gas (10 m3 min-1) is collected. All collected landfill gas is flared without beneficial use. State-of-the-art. Definition for SOTA varies by facility. SOTA and U.S. national average MSW landfill gas generation and collection characteristics adopted from Levis and Barlaz.17 New Source Performance Standards set minimum landfill gas collection requirements. Combined Heat and Power. The CHP case here assumes 50% of the energy in steam generated in the WTE boiler is exported for district or industrial process heat and 50% is used in a turbine to generate electricity. The no CHP case assumes all steam from the WTE boiler is used for electricity production. WTE SOTA, fleet average, and worse case emissions and performance data from M. Van Brunt, Covanta Energy (pers. comm.). Beneficial Use. Curing and subsequent land application of dewatered solids from digestate. Land application of solids offsets inorganic fertilizer use and promotes soil quality. None involves land disposal of cured dewatered digestate or compost product, providing carbon storage but no nutrient offsets or soil quality promotion. Better Case, Typical, Worse Case indicate predicted range of performance for AD facilities using food waste as their primary feedstock. Based on variation of biogas engine efficiency. Beneficial use of compost offsets inorganic fertilizer production.

25



498

Table 3. Selected treatment facility parameters for three configurations. Parameters

499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515

Page 26 of 34

Better

Moderate Worse U.S. National Avg.a NSPS Limitsa 2 5 50 50 5 5

SOTAa Landfill Gas Collectiona Time until initial gas collection (yr) 0.5 Initial gas collection efficiency (%) 50 Time to intermediate cover (yr) 3 Gas collection efficiency under intermediate cover 75 75 75 (%)j Time to increased gas collection efficiency (yr) 15 15 15 Increased gas collection efficiency (%) 82.5 82.5 82.5 Time from final waste placement to final cover (yr) 1 1 1 Gas collection efficiency under final cover (%) 90 90 90 Landfill Energy Recovery/ Methane Oxidation Electrical energy recovery efficiencyb (%) 36.5 36.5 36.5 Energy recovery cut-on timec (yr) 5 5 5 Energy recovery cut-off timec (yr) 52 52 52 Energy recovery downtime (%) 3 3 3 Without gas collection or final cover (%) 10 10 10 With gas collection, before final cover (%) 20 20 20 With final cover (%) 35 35 35 WTE Facility SOTAd Fleet Avg.d Worsee f Net Electrical Efficiency (%) (with CHP) 10.3 8.8 7.6 Net Heat Recovery Efficiency (%) (with CHP only) 37.5 33.0 30.6 Net Electrical Efficiency (%) (without CHP) 24.4 20.9 18.2 Ferrous Recovery from Ash (%) 90 90 90 Aluminum Recovery from Ash (%) 65 35 35 Copper Recovery from Ash (%)g 0 0 0 AD Facility Better Case Typical Worse Case Electricity generation efficiency (%)h 40.2 36.5 32.9 Biogas leakage (% of biogas produced)i 3 3 3 Percent of captured gas that is flared without 3 3 3 electricity generation. -1 i Specific electricity usage (kWh Mg ) 58 58 58 a. Values adopted from Levis and Barlaz unless otherwise noted.17 b. Efficiency chosen based on manufacturer specifications. Refer to Table S6 for details. c. Energy recovery cut-on and cut-off times indicate the number of years after initial waste placement that the energy recovery system becomes operational and ceases operation, respectively. The chosen values are based on estimates of the time span over which sufficient gas (10 m3 min-1) is collected from the landfill, with a one-year delay of cut-on to account for system installation. d. Values obtained from M. Van Brunt, Covanta Energy (pers. comm.). e. Values adopted from fleet average, except where noted. Note that stack emission rates for particular pollutants vary between fleet average and worse case. Refer to Table S9. f. This represents the fraction of the LHV of the waste stream that is converted to electricity. The overall energy recovery efficiency would be the value in this row plus the Net Heat Recovery Efficiency. g. Though copper recovery is typically practiced, it was not considered in this study due to the low copper content expected in the HFW-ICI composition. h. Efficiency is expressed as percent of energy in combusted methane, on LHV basis. “Typical” value chosen following procedure described for landfill gas engine. “Better Case” and “Worse Case” are 110% and 90%, respectively, of “Typical.” i. Adopted from Sanscartier et al.25

26


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516 517 518 519


j.

U.S. regulations require 30 cm of intermediate cover in an area of the landfill where additional waste will not be placed for 12 months. This has the effect of improving landfill gas collection efficiency.

27



Page 28 of 34

520

Figure 1 – Simplified process flow diagrams for each of the processing alternatives. As described in Table 1, six scenarios were analyzed and food waste may be collected either separately or with mixed waste. Construction, fuel combustion, and direct emissions are calculated for each process, as well the emissions associated with electricity, fuel, and raw material production. Offset processes represent avoided emissions from beneficial use or recovery of materials or energy. 521 522 523 524 525

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Page 29 of 34

526 527 528 529 530 531


Figure 2 – Net GWP for each scenario and configuration. The bar outlines represent the range of values for all scenarios modeled and the individual symbols correspond to configurations described in Table 2. Selected life-cycle inventory results for the moderate configuration of each of the six scenarios are shown in Tables S21 to S26.

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(a)

(b)

(c)

532 533 534 535 536 537 538 539 540 541

Figure 3 – Contribution analysis for the AD-WTE scenario; (a) the processes involved in the AD-WTE scenario (waste collection, WTE, AD, and material reprocessing for metals recovered from WTE), (b) the AD facility, and (c) the WTE facility as it performs in the AD-WTE scenario. Results are based on moderate configuration, with beneficial use. Processes contributing less than 5% of gross GWP are not shown in (b) and (c) for clarity.

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542 543 544 545 546


Figure 4 – Net photochemical oxidation potential for each scenario and configuration. The bar outlines represent the range of values for all scenarios modeled and the individual symbols correspond to configurations described in Table 2.

547

31



548 549 550 551 552

Page 32 of 34

Figure 5 – Net GWP for the moderate configurations of each sensitivity case excluding GWP cases and the pure food waste case. Associated rankings and GWP values are shown in Table S27, and net GWP results for each scenario are shown in Figures S10 to S17.

553 554

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Page 33 of 34

555 556 557 558 559 560 561


Figure 6 – Net GWP for each scenario and configuration under the natural gas case (0.74 kg CO2e kWh-1). Wide bar outlines present the range of values for all scenarios modeled and the individual symbols correspond to configurations described in Table 2. Thin bars show the range of Base case results using the SERC regional grid (0.89 kg CO2e kWh-1) (Figure 2).

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For Table of Contents Only

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