Use of Life-Cycle Analysis To Support Solid Waste Management

Jan 29, 2009 - Mathematical models of integrated solid waste management. (SWM) are useful planning tools given the complexity of the solid waste syste...
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Environ. Sci. Technol. 2009, 43, 1264–1270

Use of Life-Cycle Analysis To Support Solid Waste Management Planning for Delaware P. OZGE KAPLAN,* S. RANJI RANJITHAN, AND MORTON A. BARLAZ Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695

Received July 3, 2008. Revised manuscript received November 29, 2008. Accepted December 2, 2008.

Mathematical models of integrated solid waste management (SWM) are useful planning tools given the complexity of the solid waste system and the interactions among the numerous components that constitute the system. An optimization model was used in this study to identify and evaluate alternative plans for integrated SWM for the State of Delaware in consideration of cost and environmental performance, including greenhouse gas (GHG) emissions. The three counties in Delaware were modeled individually to identify efficient SWM plans in consideration of constraints on cost, landfill diversion requirements, GHG emissions, and the availability of alternate treatment processes (e.g., recycling, composting, and combustion). The results show that implementing a landfill diversion strategy (e.g., curbside recycling) for only a portion of the population is most cost-effective for meeting a countyspecific landfill diversion target. Implementation of waste-toenergyoffersthemostcost-effectiveopportunityforGHGemissions reductions.

Introduction The cost and environmental implications (e.g., energy consumption, greenhouse gas (GHG) emissions) of solid waste management (SWM) are important societal issues. SWM costs are borne by the public, either through use fees or taxes. SWM also has environmental impacts resulting from waste collection, separation, treatment processes such as composting and combustion, and landfill disposal (1). The beneficial use of waste, for either energy recovery or material recovery, can result in both revenue and avoided emissions (2, 3). An integrated analysis must be conducted to assess the net cost and net environmental effects of (1) an SWM program constituted of a set of municipal solid waste (MSW) process choices that interactively affect system-wide waste flow and (2) SWM policies that constrain the system (e.g., banning items such as yard waste from landfills and banning waste processing options such as waste combustion). Thus, policymakers face the challenge of developing and implementing integrated SWM programs that represent an appropriate use of public funds while considering emissions and energy consumption. * Corresponding author present address: Research Fellow, National Risk Management Research Laboratory, U.S. Environmental Protection Agency, Mail Drop E305-02, Research Triangle Park, NC 27711; phone: (919) 541-5069; fax: (919) 541-7885; e-mail: Kaplan.Ozge@ epa.gov. 1264

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Mathematical models of integrated SWM can serve as planning tools given the complexity of the system, the interactions among the numerous components that constitute the system, and the number of potential SWM alternatives. While numerous models have been described (4-9), and several case studies have been conducted in Europe (10-14), the number of case studies applying SWM planning models in the United States is limited (15). The objective of this study was to evaluate alternative plans for integrated SWM in the State of Delaware considering cost and environmental performance, particularly GHG emissions. This study was conducted to assist the Delaware Solid Waste Authority (DSWA) conduct its periodic (every 10 years) evaluation of the statewide SWM program and development of a long-term plan. The next section summarizes the integrated solid waste management decision support tool (ISWM-DST), a life-cycle model that was utilized to analyze potential SWM programs considering combinations of curbside recycling, yard waste composting, and combustion with energy recovery, i.e., waste-to-energy (WTE), to divert waste from landfills (16-18). The subsequent section describes the modeling approach tailored for urban and rural counties in Delaware and input data development. Analyses are then presented in which system cost and environmental performance are explored at increasing diversion constraints. Initially cost and then GHG emissions are used as the model objective. Finally, we describe how the model results can be applied to advance SWM planning for Delaware.

Model Description The ISWM-DST is a steady-state deterministic optimization model that represents the flow of individual MSW components from generation through collection, separation for recycling at materials recovery facilities (MRFs), treatment (e.g., yard waste composting and WTE), and landfill disposal as described previously (16-18). A summary is provided here, and Table S1 of the Supporting Information (SI) gives additional resources. The ISWM-DST includes (1) process models for estimating cost (including revenue from recyclables and energy recovery), energy consumption, and lifecycle emissions associated with each SWM unit operation, (2) a mathematical programming-based integrated system model that embeds the waste mass flow equations, and (3) a linear programming (LP) model solver (CPLEX) (Figure S1, SI). The process models compute a set of cost and life-cycle emission coefficients per mass of waste item handled in a process using a combination of default and site-specific data. There are process models for waste collection, separation, treatment, and disposal. In addition, there are process models for electrical energy production and the conversion of recyclables into new products (i.e., remanufacturing). An offset analysis is used to calculate the environmental benefits or added burdens from the conversion of recycled materials to new products and from the generation of electricity from landfill gas and WTE (19). All unit processes are integrated, and the mass balance is represented by a series of waste flow equations that may be solved for the minimum value of cost, net energy consumption, or emissions of selected pollutants. The ISWMDST tracks 30 air- and water-borne pollutants and optimizes on seven air pollutants (CO, CO2, CH4, NOx, SOx, PM, and greenhouse gas equivalents [GHEs]), cost, and energy consumption. Recently, the capability to consider the effect of uncertain input parameters on model outputs was 10.1021/es8018447 CCC: $40.75

 2009 American Chemical Society

Published on Web 01/29/2009

TABLE 1. Description of Model Scenarios case

description

Model Objective: Least Cost (1) landfill only all waste buried in a landfill (2) current practice recyclables recovered through voluntary drop-off only (3) recycling waste diversion by both curbside recyclables collection and waste sorting at a mixed waste MRF are enabled (4) recycling + as in case 3 plus the separate composting curbside collection of yard waste is enabled (5) recycling + as in case 4 plus WTE is enabled composting + WTE Model Objective: Least Greenhouse Gas Equivalents (6) recycling + as in case 5 composting + WTE (7) recycling + as in case 4 composting

incorporated (20), which enables a post-optimization uncertainty analysis to be conducted. The functional unit for the system is the management of 1 Mg of MSW set out for collection. MSW includes waste generated in single-family residential, multifamily residential, and commercial sectors as defined by the U.S. EPA (21). Unique waste generation data may be provided for each of two distinct areas in the residential and multifamily sectors and ten distinct commercial generation points.

Modeling Approach and Data Development for the State of Delaware Scenario Definition. Model scenarios were constructed to represent current practice and to explore the implications of increased landfill diversion by recycling, composting, and WTE on total system cost and emissions (Table 1). In the first set of scenarios, the goal was to identify cost-effective SWM plans for different levels of waste diversion, which were modeled as incrementally increasing diversion requirements. As described in Table 1, cases 1-5 were analyzed with different combinations of unit processes enabled. In the second set of scenarios, the goal was to determine SWM plans to minimize GHEs in megagram carbon equivalents (eq 1) for different levels of cost. GHE (Mg) ) [mass of CO2-fossil (Mg)] × 12/44 + 21 × [mass of CH4(Mg)] × 12/16 (1) where 21 is used to convert the mass of CH4 to CO2 equivalents and 12/44 and 12/16 convert CO2 and CH4 to an equivalent mass of C, respectively. SWM plans were identified at incrementally increasing cost targets starting with the cost of current practice (case 2). All scenarios were evaluated separately for each of Delaware’s three counties. A follow-up paper will describe how these county-specific strategies were combined to construct and analyze statewide integrated strategies. Data Development. Delaware is comprised of three counties (Figure S2, SI). New Castle County (NCC) is the most densely populated with 64% of the state’s 783600 people. Kent and Sussex Counties are largely rural. The waste generation rate and composition data were adopted from state waste characterization reports (22, 23). Per capita waste generation was estimated to be 1.04 kg person-1 day-1, excluding durable items. This rate was assumed to be constant statewide and independent of whether a resident lived in the residential or multifamily sector. Totals of 21%, 10%, and

10% of the residential population reside in multifamily dwellings in NC, Kent, and Sussex Counties, respectively. The number of collection locations in the multifamily sector was calculated by estimating that one dumpster will serve 40 multifamily housing units, resulting in 1028 collection locations in NCC. The per-location commercial MSW generation rate was computed from the ratio of commercial MSW generation to the number of commercial locations. Commercial waste generation data and the number of commercial locations were obtained from public records (23, 24). Waste generation and composition data are summarized in Tables S2 and S3 (SI). Waste generation in NCC was modeled using two residential sectors, one multifamily sector and one commercial sector. Two residential sectors were required to represent differences in average distances from collection routes to the facilities (i.e., transfer station, landfill) as ∼10% of the county’s waste flows through a transfer station. Residential sector 2 in NCC represents the southern region that is served by a transfer station. Kent and Sussex Counties were represented by one residential, one multifamily, and one commercial sector. Approximately 20% of MSW generated in Delaware is currently recovered via the state’s drop-off program plus the recovery of source-separated recyclables from the commercial sector (22, 23). There was essentially no curbside collection of recyclables or WTE at the time of this study. For evaluation of future SWM scenarios in which curbside collection of recyclables and composting were enabled, it was necessary to estimate capture rates for these programs. It was assumed that if a residential curbside recycling program were to be implemented, then recovery rates would be higher than the national average rates, which represent the average of all states, including some that do not have a recycling program. Material-specific recovery rates in Delaware were set 20% greater than the national average rates (21). In addition, the rate of participation in potential future residential curbside collection programs was assumed to be 80%. Additional input data are presented in Tables 2 and S4 and S5 (SI). Uncertainty Analysis. For a given countywide strategy, uncertainty in the cost and life-cycle emissions was estimated using uncertainty propagation procedures (20). Probability density functions (PDFs) for selected model inputs were on the basis of experience and expert judgment. Cumulative density functions (CDFs) for cost and GHE were used to assess the robustness of the countywide SWM strategies. Finally, a correlation analysis was conducted to understand the relative significance of uncertainty in each input parameter. The uncertain inputs and their assumed PDFs are presented in Table S6 (SI). Surrogate Environmental Indicator Parameters. A representative indicator parameter for environmental performance was identified to (1) simplify the presentation and analysis of the results and (2) be consistent with the ability of ISWM-DST to minimize only one pollutant at a time. Analyses were conducted in which GHEs were minimized for different cost constraints, and the results show that GHE is a reasonable surrogate for emissions of multiple pollutants (Figure 1). Correlation coefficients (r2) were above 0.9 when considering the trend of GHE with that of energy consumption and all air pollutants except CO, which did not correlate well with any other pollutant (Table S7, SI).

Results Cost-Effective SWM Strategies. The results of model analyses in which different combinations of unit operations were enabled are presented in this section. (As the results for Kent and Sussex Counties were similar, detailed results for Kent County are presented in the SI.) When all waste is buried in VOL. 43, NO. 5, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Summary of Key Model Inputs parameter

default value

Collection refuse collection 1 time per week frequency curbside recyclables 1 time per week collection frequency time from collection 10 for urban, 30 for rural to transfer station, min time from collection 15 for urban, to MRF, min 30 for rural time from collection 15 for urban, to compost, min 30 for rural time from collection 10 for urban, to WTE, min 80 for rural time from collection 15 for urban, to LF, min 30 for rural time from transfer station 45 to WTE, km MRF materials market prices Table S4 (SI) separation efficiency 55 for each for mixed waste MRF (%) recyclable separation efficiency 94 for glass, 100 for for commingled MRF (%) all other items basic design heat rate, BTU/(kW h) ferrous recovery rate (%) utility sector offset

FIGURE 1. Correlation between GHE and other pollutants. The data plotted represent least-GHE SWM strategies for New Castle County in which all unit operations were enabled. A negative value means that the avoided emissions exceeded the emissions from waste management.

WTE mass burn 18 000 (∼19% efficiency) 90 baseload coal and natural gas

Landfill basic design per EPA regulations time frame for emissions 100 estimates, years gas collection 0 in years 1-2, efficiency (%) 50 in year 3, 70 in year 4, 80 in years 5-100 gas management conversion to scheme electrical energy utility sector baseload coal and offset natural gas

a landfill (case 1), the resulting annual cost is calculated to be $37.2 million, $19.8 million, and $34.8 million for NC, Kent, and Sussex Counties, respectively. The corresponding emissions are presented in Figure 2 and in Tables S8-S10 (SI). Interestingly, the avoided emissions associated with the conversion of landfill gas (LFG) to energy resulted in net negative emissions for PM, SOx, and CO2-fossil, with the largest benefit occurring in NCC where travel distances are the shortest. A negative emission means that the avoided emissions exceed the emissions attributable to waste collection and processing. For current practice (case 2), which results in 18-20% diversion using recyclables drop-off and collection of commercial recyclables, net system costs including revenues from the sale of recyclables for NC, Kent, and Sussex Counties were $39.7 million, $21.4 million, and $36.9 million, respectively. The corresponding emissions are presented in Figure 2 and in Tables S11-S13 (SI). The ISWM-DST was next used to identify cost-effective waste management strategies in which landfill diversion was constrained to match current practice as well as higher levels. Solid waste operations that were enabled in addition to those currently used include (1) a mixed waste MRF in which recyclables are recovered from MSW using a combination of hand sorting and mechanical separation and (2) curbside 1266

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FIGURE 2. Variation of mass flow and GHE based on use of landfill only (0% diversion, case 1), current practice (case 2), and an alternative in which a mixed waste MRF and curbside collection of commingled recyclables are enabled (case 3): (a) New Castle County, (b) Sussex County. collection of recyclables that are processed in an MRF (case 3). Within curbside collection, alternatives to sort at either the curb during collection or an MRF were enabled. In NCC, all recyclables were recovered through the dropoff program with increasing use of a mixed waste MRF to achieve up to 28% diversion (Figure 2a) because utilization of a mixed waste MRF was estimated to be cheaper than implementation of curbside recycling. As the diversion rate

increased to 28.5%, curbside collection of recyclables was utilized in residential sector 1 and the multifamily sector to recover more material. When the model objective was set to maximize diversion, commingled recycling was utilized in residential sector 2 to increase diversion to 28.91%. The MRF being farther away from the collection routes resulted in longer transportation distances, which escalated the total cost (Figure 2 and Table S11, SI). At maximum diversion (28.91%), the use of a mixed waste MRF decreased slightly, and the recyclables collected at curbside were sorted by the crew rather than at a MRF. The shift in waste processing choices between 28.5% and 28.91% diversion is controlled by slightly different assumptions about material losses in MRFs that receive commingled versus presorted recyclables. While mathematically correct, this diversion increase is likely insignificant in practice. Of course, the maximum attainable diversion rate depends on the model inputs specifying participation and capture rates (Table S4, SI). Between 20% and 28% diversion, the cost increases uniformly, after which it increases sharply with the implementation of curbside recycling (Figure 2). GHEs decrease consistently as diversion increases due to benefits from remanufacturing offsets and reduced landfill emissions (Figure 2a). The results for Sussex and Kent Counties are similar to those for NCC except that GHEs and several other pollutants reach minima at less than maximum diversion (Figure 2b, Tables S12 and S13, SI). GHEs increase with the implementation of curbside recycling due to the rural character of these counties, causing increased emissions associated with additional collection vehicles. Yard waste composting was enabled for the next set of cost-effective analyses (case 4). As in case 3, drop-off recycling and a mixed waste MRF were utilized until 28% diversion, after which composting and then finally curbside recyclables collection were implemented at the maximum diversion (Figure 3a). Composting, which was relatively cheaper, was utilized before curbside recycling. Again, the cost increases sharply when curbside recycling is included. Between 28% and 32% diversion, GHE does not change because no additional recyclables are recovered, resulting in no changes in the corresponding remanufacturing offsets. Composting results in increased CO2-fossil associated with collection and facility operation (Tables S14 and S15, SI). These CO2-fossil emissions are approximately balanced by the reduced mass buried in a landfill though this result is sensitive to the manner in which LFG is managed (i.e., flare vs energy recovery) and its collection efficiency. While there are benefits associated with compost as a product in certain applications, this study did not attribute offsets to the use of compost. The results for Sussex and Kent Counties (Figure 3b, Table S15, SI) show trends similar to those for NCC, but GHEs increase with the implementation of composting and then curbside recyclables collection due to the greater transport distances. To complete the scenarios with cost as the objective function, WTE was enabled with curbside recycling and composting (case 5). A new WTE facility is assumed to be located in NCC, and transfer stations are assumed to be available in Kent and Sussex Counties (Figure S2, SI). Landfill diversion is now defined to include waste processed by WTE, excluding the resultant ash. For NCC, once the maximum diversion achievable via only the drop-off program is realized, WTE is increasingly utilized to achieve 85% diversion (Figure 4a). Increased diversion above 85% was obtained by first utilizing a mixed waste MRF, followed by composting and then curbside recyclables collection. Composting was selected over WTE to maximize diversion because of additional disposal needs for the ash generation in WTE. In practice this is inconsequential. Curbside recycling increases diversion as noncombustibles (e.g., glass and aluminum), which would otherwise be counted as ash for landfill disposal, are diverted

FIGURE 3. Variation of mass flow and GHE for alternate SWM strategies in which curbside recyclables collection and yard waste composting are enabled: (a) New Castle County, (b) Sussex County (case 4). from WTE. The sharp cost increase at 88% diversion is due to the inclusion of more costly programs to capture more material (Figure 4). Interestingly, these programs result in only slight increases in diversion, but with a sharply higher cost and an increase in GHEs. The major difference in the results for the rural counties in case 5 is that a mixed waste MRF was utilized at smaller diversion targets. Recyclables were recovered by sorting mixed waste, after which the residual was transported to a WTE facility. This is cost-effective because the waste must be transported, at greater cost, to northern Delaware for combustion while a mixed waste MRF is located closer to the point of waste generation (Figure S2, SI). The capital costs of WTE are such that, realistically, only one facility would be located in Delaware, and a location near the industrialized area (i.e., NCC) was assumed. The increase in presorted recycling at 87% diversion in Sussex County is from presorted commercial material. Composting and curbside recycling were only selected at the maximum diversion rate (Figure 4b). As for NCC, GHE achieved a minimum at 88% diversion before composting and curbside recycling were utilized (Figure 4a). Minimum GHE SWM Strategies. The objective of this analysis was to minimize GHE at increasing cost targets, starting with the cost of current practice. First, all processes were enabled as in case 5. In NCC, WTE was the most costeffective way to minimize GHEs until $60 million year-1, after which more expensive processes were utilized with a slight decrease in GHEs (Figure S3, SI). The use of curbside recycling results in the recovery of slightly more recyclable materials than a mixed waste MRF, yielding increased remanufacturing GHE offsets. Interestingly, although composting was enabled, it was not utilized. WTE is the most effective GHE-reducing VOL. 43, NO. 5, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Correlation Factors for Uncertain Input Parameters That Are Strongly Correlated to Cost, Energy Consumption, and GHG Emissionsa New Castle cost

Sussex

commercial residual collection-loading time at one service stop 0.745 heat rate in combustion facility

compacted waste density in the landfill -0.956 energy compacted waste consumption density in the landfill 0.855 -0.932 greenhouse gas CO2-fossil emissions compacted waste equivalents savings from aluminum density in the remanufacturing landfill -0.603 -0.747 a Negative correlation indicates an inverse relation between the input parameter and the output.

FIGURE 4. Variation of mass flow and GHE for alternate SWM strategies in which curbside recyclables collection, yard waste composting, and WTE are enabled: (a) New Castle County, (b) Sussex County (case 5). The cost and GHE for the 88% diversion case for NCC were disaggregated into individual components of the waste management system in Table S17 (SI). option because the recovered energy offsets the generation of electricity from fossil fuels. In contrast to waste management choices for NCC, increasing quantities of waste were processed in a mixed waste MRF prior to entering WTE in Kent and Sussex Counties (Figure S3, Table S18, SI). The utilization of both the mixed waste MRF and WTE depended on the cost constraint. When GHE is minimized without a cost constraint, only a minimal improvement in GHEs is achieved by using commercial and multifamily recycling (Figure S3, Table S19, SI). A scenario was explored in which GHEs were minimized without WTE to represent current regulations that prohibit WTE use in Delaware. In NCC, recyclables drop-off is utilized initially followed by the use of a mixed waste MRF, curbside recyclables collection, and finally composting as the cost target is increased (Figure S4, SI). Composting is selected over a landfill at costs higher than $55 million year-1 although no emissions offset is assigned to the compost product. This is explained by the assumed decay rate of grass in landfills and the LFG collection efficiency that dictates how much gas is captured over time. The assumed decay rate for grass is relatively high (k ) 0.09 year-1), and no gas is assumed to be collected during the first 2 years. As such, some gas production attributable to grass is released to the atmosphere in the early years, making composting more favorable from a GHE standpoint. In the rural counties, only a base case and minimum GHE scenario were considered because the difference in cost among these scenarios was less than 5%. For Sussex County, the primary difference between least GHE and the base case 1268

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scenario is the utilization of commingled curbside recycling in the multifamily sector along with some additional recovery at a mixed waste MRF (Figure S4, SI). Both of these unit operations serve to complement the existing residential dropoff recycling program with decreasing GHEs. Diversion in the least GHE case is 34.5%, 23.1%, and 24.5% for NC, Kent, and Sussex Counties, respectively. Sensitivity to Varying Recyclable Market Prices. Selected strategies in case 5 for NCC were analyzed to evaluate whether increased revenue from recyclable material sales would increase the use of curbside recycling. The original and updated recyclables market prices are presented in Table S5 (SI). Case 5 scenarios with diversion rates of 40% and 85% were rerun (Figure S5, SI). Despite the increased prices, curbside recycling was not selected, and WTE was still preferred for meeting the diversion targets. The changes in system cost were 3-4%, which is insignificant relative to the accuracy of the model. Uncertainty Analysis. For NCC, the expected cost of current practice when considering uncertainty is $39 million, with a range of $32.6 million to $47.4 million and a 38% likelihood of exceeding the deterministic cost of $39.7 million (Figure S6, SI). The expected GHE of current practice is 18830 MTCE year-1, with a range of 15674-21415 and a 3% likelihood of exceeding the deterministic estimate of 20726 MTCE year-1 (Figure S7, SI). Table 3 shows a subset of the most strongly correlated uncertain input parameters to the selected model outputs (cost, energy consumption, and GHE). These results can be used to prioritize the input parameters for which better data are most needed. Comparison of CDFs for multiple SWM alternatives can be used to consider robustness as part of SWM alternative selection.

Discussion Differences among SWM Strategies for Urban and Rural Counties. The higher population density in NCC resulted overall in less costly SWM strategies. When identifying costeffective diversion strategies with curbside recyclables collection enabled, unit costs at maximum diversion were $174, $507, and $631 Mg-1 in NC, Kent, and Sussex Counties, respectively. There are two caveats to this analysis. First, urban areas in Kent County (e.g., Dover) may behave more like NCC in some respects. Second, DSWA does not control the manner in which cities and counties collect refuse and recyclables, but rather manages the waste after collection. Cities and counties may implement a variety of collection alternatives that are not optimal. The strategy with the lowest GHE for NCC (case 6) results in a 74665 MTCE year-1 reduction at an incremental cost of

TABLE 4. Cost, Emissions, and Diversion for a Waste Management Strategy Displaying Near-Optimal Characteristicsa cost, millions of dollars year-1

GHE, MTCE year-1

diversion, %

current practice case 3 case 4 case 5

New Castle County 39.7 20700 45.6 13900 51 11200 57.8 -33000

20 28 35 85

current practice case 3 case 4 case 5

Sussex County 36.9 11400 36.8 8500 38 4400 41.6 -5600

19 26 27 87

a Data are for a diversion level just prior to the level at which costs escalate sharply.

$33.4 million year-1 relative to current practice. In contrast, the net decrease in GHEs and cost increase for Sussex County are 21695 MTCE year-1 and $6.4 million, respectively. While the GHE reduction in Sussex County is slightly more costeffective, there is less waste and therefore a smaller overall reduction potential. In NCC, emission reductions were realized via both curbside recycling and WTE. In contrast, the implementation of curbside recycling increased emissions in Sussex County (case 3) relative to scenarios with diversion lower than the maximum diversion. Effects of Cost and Diversion Targets on Waste Process Choices. The ISWM DST is not constrained to apply a single waste process to an entire sector equally. In most solutions (e.g., Figure 2), a process that is more expensive than dropoff is only utilized to the extent required to meet a diversion constraint. Thus, only a fraction of the total population may be served by, for example, curbside collection to achieve the target diversion. Similarly, WTE was used for only a fraction of the total waste when minimizing GHE subject to a cost target (Figure S3, SI). In practice, it may be difficult to convince a community of the rationale for providing only some residents with, for example, curbside collection while expecting others to utilize drop-off bins, or having some waste disposed in a landfill while other waste is treated by WTE. This is an example of a situation where the optimal strategy could be judged politically or socially infeasible. Alternative strategies that are only incrementally more expensive than the optimal solution, but utilize maximally different sets of facilities, can be developed (16). This is expected to yield more efficient strategies that may include politically or socially more viable options. Counterintuitive Insights Gained through Modeling. One advantage of a mathematical analysis of a complex system is that it may result in outcomes that are not intuitive. When the objective was to minimize cost at a desired diversion level, the model was able to identify a creative approach in which some waste was first processed through a mixed waste MRF prior to flowing to WTE (Figure 4b). This tandem processing accomplishes the following: (1) allows for recovery of noncombustible recyclables (e.g., glass and aluminum) that were not captured via the drop-off program and (2) reduces the quantity of waste to be transported to the WTE facility from the rural counties. Ultimately, a decision-maker must determine the most suitable SWM plan in consideration of competing cost, environmental, and social/political considerations. While there are many cases that could be examined, Table 4 summarizes three key parameters for an SWM strategy at a diversion level just prior to where the cost increases sharply. Clearly, the most significant reductions in GHEs can be

realized when WTE is utilized, albeit at a higher cost. While the objective of case 5 was to minimize cost at varying diversion constraints, the objective of case 6 was to minimize GHE emissions. For an expenditure of $50 million year-1, GHE emissions of -17200 and -31300 MTCE year-1 are realized for NCC in cases 5 and 6, respectively, at diversion levels of 60% and 57% (Figures 4 and S3, SI). This study quantifies the tradeoffs among cost, diversion, and environmental performance by using a life-cycle planning tool to evaluate multiple alternatives for SWM in Delaware. The resultant trends are similar to results reported for several European case studies (10-14). While this study provides a quantitative and systematic basis for evaluating cost, diversion, and GHE objectives for SWM choices and their tradeoffs, specific decisions must be made as to the direction of future SWM. Such a decision may also involve political and other subjective considerations. The quantitative results here are envisioned to provide the necessary information to screen for technically superior strategies that could form the basis for such a decision-making process. When making final decisions that constrain the array of alternatives to be considered, a more narrow set of SWM alternatives should be selected for detailed engineering analysis before a strategy is implemented. In subsequent work, methods are described to develop optimal statewide strategies based on combinations of the county-specific alternatives described here.

Supporting Information Available Waste composition and recyclables capture rate, uncertain parameters and their distributions, and tables of mass and emissions data for each scenario. This material is available free of charge via the Internet at http://pubs.acs.org.

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