A Screening Life Cycle Metric to Benchmark the Environmental

Jul 13, 2010 - While recycling of many MSW materials is certainly preferable, recycling rate is not suitable as a measure of overall waste management ...
0 downloads 4 Views 3MB Size
Download PDF
Environ. Sci. Technol. 2010, 44, 5949–5955

A Screening Life Cycle Metric to Benchmark the Environmental Sustainability of Waste Management Systems SCOTT M. KAUFMAN,* NIKHIL KRISHNAN, AND NICKOLAS J. THEMELIS Earth and Environmental Engineering (HKSM), Columbia University in the City of New York, New York, New York 10027

Received February 14, 2010. Revised manuscript received June 6, 2010. Accepted June 24, 2010.

The disposal of municipal solid waste (MSW) can lead to significant environmental burdens. The implementation of effective waste management practices, however, requires the ability to benchmark alternative systems from an environmental sustainability perspective. Existing metricsssuch as recycling and generation rates, or the emissions of individual pollutantssoften are not goal-oriented, are not readily comparable, and may not provide insight into the most effective options for improvement. Life cycle assessment (LCA) is an effective approach to quantify and compare systems, but full LCA comparisons typically involve significant expenditure of resources and time. In this work we develop a metric called the Resource Conservation Efficiency (RCE) that is based on a screening-LCA approach, and that can be used to rapidly and effectively benchmark (on a screening level) the ecological sustainability of waste management practices across multiple locations. We first demonstrate that this measure is an effective proxy by comparing RCE results with existing LCA inventory and impact assessment methods. We then demonstrate the use of the RCE metric by benchmarking the sustainability of waste management practices in two U.S. cities: San Francisco and Honolulu. The results show that while San Francisco does an excellent job recovering recyclable materials, adding a waste to energy (WTE) facility to their infrastructure would most beneficially impact the environmental performance of their waste management system. Honolulu would achieve the greatest gains by increasing the capture of easily recycled materials not currently being recovered. Overall results also highlight how the RCE metric may be used to provide insight into effective actions cities can take to boost the environmental performance of their waste management practices.

1. Introduction The management of municipal solid waste (MSW) can have significant effects on the environment. The three major treatment optionssrecycling (including composting), waste to energy (WTE), and landfillingseach carry their own environmental burdens. Though there is a desire across the waste management industry to manage wastes sustainably, conversations on how to achieve this goal tend to be subjective in nature. The successful implementation of * Corresponding author e-mail: [email protected]. 10.1021/es100505u

 2010 American Chemical Society

Published on Web 07/13/2010

effective waste management strategies, therefore, requires the existence of benchmarking tools that allow for a goaloriented comparison of alternative systems from an environmental sustainability perspective. The most commonly used metric for measuring the environmental effectiveness of waste management systems is the so-called “recycling rate” (also known as diversion rate, or the percentage of materials diverted from landfills or incinerators to recycling facilities for reprocessing into new materials). While recycling of many MSW materials is certainly preferable, recycling rate is not suitable as a measure of overall waste management sustainability. There are several reasons for this, but a few stand out as being the most significant. First, it does not account for the differences between landfilling and waste-to-energy for nonrecycled wastes. Perhaps more importantly, it omits materials that are not recyclable at all (e.g., many plastics, contaminated wood, etc.) with contemporary technologies. In other words, the maximum possible recycling rate is well below 100%. Without a theoretical maximum, it becomes nearly impossible to benchmark different systems against one anothersit is not a clear, goal-oriented approach to waste management. Life Cycle Assessment As a Measurement Tool. Life cycle assessment (LCA) (1) has long been used to model and evaluate the environmental impacts of various products and services, and is often used by MSW stakeholders to compare technologies and/or treatment regimes (2). The problem with conducting full-scale LCA for these purposes is that it is expensive, time-consuming, and requires specialists to complete (3). Furthermore, it is difficult to benchmark different systems against one another. Many “workarounds” have been suggested and used to substitute for full LCAs (4-8). These approaches, all of which combine some forms of quantitative and qualitative assessments, are useful for particular applications, but are perhaps not intuitive enough to serve as a screening metric for more general audiences. To present our proposed solution to this problem, we must first introduce a lifecycle impact assessment method known as cumulative energy demand (CED), also known as lifecycle embodied energy (LEE). CED is a cradle to grave account of the energy inputs and consumption necessary to manufacture, use, and dispose of a product (9). Each stage in the life cycle is accounted for in energy terms including direct energy inputs, feedstock materials, and capital goods. It is effective as a screening indicator both because it is quantitative and it captures overall energy requirements, which often drive life cycle impacts (10). In this study, we develop a CED-based metric called Resource Conservation Efficiency (RCE) to evaluate the effectiveness of MSW management systems with respect to lifecycle energy utilization and resources (i.e., materials) conservation. As will be explained in the methodology and discussion sections of this study, the metric allows for a fair comparison of the performance of different waste management systems, even where different technology suites and strategies are employed. This method grew out of the common credo that recycling of materials “saves energy” over manufacturing from virgin sources plus landfilling and/or incineration of waste materials that cannot be recycled (11). Our research (and that of others before us) has shown that this is in fact the case for many familiar materials. However, it is also clear that not all MSW is currently recyclable and those materials that are not recyclable are often high in calorific value and, thus, excellent (or at least passable) fuels. VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5949

Furthermore, even recyclable materials are not fully captured by any city. Any system of waste management analysis must account for the studied systems’ tendency to capture these energy potentials. Additionally, it is hoped that any such evaluation system also factors in the environmental benefits or hazards associated with waste management choices. A system that does this rationally, objectively, and intuitively would be an invaluable addition to the world of waste management decision-making. This paper outlines the development, rationale, and applicability of this new evaluation process. First, the quantifiable underpinnings of the RCE system are developed and examined. This is primarily accomplished through the use of SimaPro LCA software (12), with its many included databases. Materials typically found in MSW are input to the software, and the relevant data are recordedsnamely, CED and the environmental effects of manufacture and waste treatment of the examined materials, measured by means of EcoIndicator99 and other impact assessment tools. Once these factors are tabulated, a correlation is drawn between the CED and indicator scores, in effect vetting a CED-based evaluation score for use as a proxy for overall waste management system performance measurement. Next, the CED values are used to calculate material-specific RCE values, which are also vetted by means of regressions against indicator scores. As a practical means for determining the suitability of this new metric to evaluate the MSW management systems of different localities or regions, RCE is applied to the actual treatment regimes of two American cities: San Francisco, CA and Honolulu, HI. These cities are chosen primarily because they rely on different and opposing methods of final disposal for nonrecycled wastesslandfilling in the case of San Francisco, and waste to energy (WTE) in the case of Honolulu. Use of CED. As discussed earlier, cumulative energy demand (CED) is a useful survey-level indicator for the environmental performance of products and services (10, 13). It has been used to evaluate the environmental and energy impacts of several different sectors. It is frequently employed to determine energy payback periods for alternative generation technologies such as solar (14) and wind (15); and is also commonly used to evaluate the efficacy of efforts to produce energy from biomass (16). It is additionally used to assess the life cycle environmental impact of buildings (17, 18). Though these studies and others like it include waste management as a phase of the overall lifecycle of the systems they are exploring, there has not yet been a CED/LCA-based metric that focuses specifically on evaluating MSW management systems. The RCE method described in this study has been developed to address these issues.

2. Methodology and Development of the RCE Metric Before actually using CED to develop an RCE metric, we first sought to verify the appropriateness of the use of CED as a screening assessment for a full LCA of waste management systems. The goal was to determine whether there is a correlation between CED and life cycle environmental impact indicators for evaluated waste systems. We therefore used LCA tools to model different materials under competing end-of-life scenarios (recycling, WTE, and landfilling). We conducted our LCAs using SimaPro (12), with Eco-Invent (19) and BUWAL 250 (20) as our life cycle databases, and Eco-Indicator 99 (21) as an impact assessment method. We sought to take advantage of the wide range of different materials that have been modeled in existing LCA databases to get as broad a coverage of materials and comparisons as possible. Materials modeled include different grades of paper and plastic, steel, and aluminum. Each of the associated CED values is cradle-to-grave. 5950

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

FIGURE 1. Aggregated test model CED vs EcoIndicator 99. Test Model Results. We tested the correlation between CED and EcoIndicator single scores across all the different materials examined in the test model (Figure 1). The results show a reasonable correlation (R 2 ) 0.931, across 12 materials and 35 data points), leading to the conclusion that CED is a reliable metric to screen for environmental impacts in waste treatment systems. We also performed tests comparing CED with another life cycle indicator metric, IMPACT 2000+ (22) and EcoIndicator using a different weighting factor. The results showed similarly strong correlations to modeled CED values and are included in the Supporting Information (Figures S1-S2). To further refine the application of CED as a screening indicator, the CED values utilized in Figure 1 were plotted against the “resource use” metrics in EcoIndicator 99 (Supporting Information Figure S3). Resource use accounts for the surplus energy required to extract minerals and fossil fuels resulting from the product system being examined. Material Life Cycle Inventories and Recycling Offsets. All materials included in this study were examined from a life cycle inventory perspective to determine cumulative energy demands and energy savings from recycling. Multiple sources were consulted (19, 20, 23-25), and the aggregated literature values for life cycle energy savings are presented in Table 1. A complete table of all consulted literature values can be found in the Supporting Information (Table S1). Combustion with Energy Recovery (WTE) and Landfilling with Methane Recovery (LFGE). Net energy values for materials combusted in WTE facilities are also necessary for RCE calculations. Net energy and heating values for materials referenced in this study come from a report on municipal solid waste management in New York City (27). Landfilling net energy values are adapted from a paper on landfill gas generation in the U.S. (28). All net energy calculations account for conversion efficiencies from thermal energy to electricity production. Energy values for WTE and landfilling for the various materials can be found in Table 1. Resource Conservation Efficiency Metric. With CED established as a screening indicator for full LCAs, and with material-specific energy savings established, it is now necessary to detail the development and structure of the metric we have developed: Resource Conservation Efficiency (RCE). RCE can be used to quickly assess options for waste treatment of different materials; to compare these options against one another; and to assess and compare the overall waste treatment systems of different municipalities or regions (eq 1). The equation for RCE is shown below: RCEi )

where

[

(ELF,i*mLF,i) + (EWTE,i*mWTE,i) + (EREC,i*mREC,i) maxj{Eimi}

]

(1)

TABLE 1. RCE Reference Tablea EREC, rec energy (GJ/ton)

material newspaper kraft paper waxed OCC high grade paper mixed low grade paper polycoated paper soiled paper other paper glass PET HDPE PP aluminum Steel yard waste food waste

EWTE, WTE energy (GJ/ton)

ELF, LF energy (GJ/ton)

EMAX,sav, process

5.5 5.5 5.5 5.5 5.5

1.4 1.4 1.4 1.4 1.4

recycling recycling WTE recycling recycling

5.5 1.8 5.2

1.4 1.4 1.4

8.9 16.4 12.6 170.1 14.8 1.8 1.8

1.4 1.4

WTE recycling WTE recycling recycling recycling recycling recycling recycling recycling recycling

12.7 13.3 21.8 13.3 2.0 4.0 33.1 38.7 34.2 188.1 16.4 2.0 2.0

a Note: Due to the relatively small sample sizes for recycling energy, there can be a large range of values for EREC. A more comprehensive table, including confidence intervals, can be found in the Supporting Information. Note also that recycling values do not include collection and transport, which might have an effect on the overall energy savings (but still does not change the fact that recycling issin all cases for included materialssthe most energy efficient end of life option.

RCEi ) material-specific RCE i ) index for materials j ) index for waste treatment process mj ) mass of material recovered (tons) ELF ) energy recovered from LF gas (GJ/ton) EWTE ) energy recovered from WTE (GJ/ton) EREC ) energy saved by recycling (EVP - ERP) (GJ) maxj{Ei} ) energy saved from best practice (GJ/ton) The maxj{Ei} term is simply the maximum energy saved or recovered from a given waste material. For example, any material that is recyclable and for which the energy saved from recycling is greater than the energy realized from combustion with energy recovery (WTE), or landfilling with methane recovery (LFGE) will have a maxj{Ei} equal to EREC. For any nonrecyclable materialsor for those materials that have EWTE or ELF values higher than their EREC valuesmaxj{Ei} is equal to whichever of these values is highest. (Note that these values do not include collection and transportation of wastesthis is discussed in the Supporting Information.) In Figure 2, four scenarios are presented, each representing the hypothetical management of 100 tons of wastepaper. The first is maximum recovery, i.e., 100% recycling of paper. The second assumes 50% recovery and a 25/25 split of the remaining paper between combustion and landfilling. The third scenario is for 100% combustion with energy recovery and the fourth is for 100% landfilling of paper. RCE Applied to Cities or Regions. RCE has been developed as an indicator at the materials-level scale. We now establish a methodology to extend the metric to model the efficiency of waste management systems of entire cities or regions (eq 2). p

m

∑ ∑E m ij

RCETOTAL )

ij

i)1 j)1

(2)

p

∑ max{E m } i)1

j

ij

ij

where RCETOTAL ) RCE for city/region Eij ) energy savings for material i in process j (GJ/ton) mij ) mass of material i in process j (tons) i ) index for materials j ) index for treatments

3. Application and Results: Modeling the Cities We demonstrate the use of RCE as a benchmarking and improvement tool through a case study applying the methodology to waste management practices in two cities: San Francisco and Honolulu. These cities were chosen for several reasons. First and most importantly, San Francisco relies upon landfilling for all nonrecycled wastes, whereas Honolulu utilizes WTE for the majority of theirs. They are also similar in size, and each recycles better than the national average for major U.S. municipalities (29). Waste Data Acquisition. The San Francisco Department of the Environment, in a waste characterization report published in 2006 (30), delineated the materials considered recyclable under their citywide program. We referenced the corresponding materials in LCA databases (18, 20, 24) and generated a standardized list of waste materials (Table 1) that agrees well with materials chosen for other LCA studies of MSW management (31). Tonnages managed for San Francisco were taken from internal reports provided by the Department of the Environment (32). Honolulu’s data were mostly culled from online reports provided yearly by the municipal government (33). RCE Calculations. Typical materials collected as part of MSW curbside programs are included in the analysis. Calculations are made as follows: First, it is necessary to determine the value of maxi{Ei} (eq 2), which can be chosen by consulting the RCE Reference Table (Table 1). This table is a sample set of reference data from the literature, combining the recycling and landfilling energy values referenced earlier in this paper with material-specific combustion values (28). Energy generated from WTE plants and landfills is assumed to offset average U.S. electric grid emissions, and this assumption is worked into the values in Table 1. It is our hope that this table can be further developed and populated with more publicly available and, perhaps, region-specific data in the future. The values for “kraft paper” and “mixed low grade paper” are equal because mixed low-grade paper is assumed to be feedstock for recycled linearboard production. The energy savings from recycling are therefore equal to GJ saved from producing a ton of recycled linearboard instead of virgin linearboard. The recycled energy value for “soiled paper” (known as “compostable/soiled paper” in San Francisco’s characterization study) is chosen by assuming that this material is composted in a windrow based system. The energy savings from compostings the same values assigned to “yard waste” and “food waste”sare determined by offsetting the production of the equivalent amount of chemical nitrogen, phosphorus, and potassium that would be found in one ton’s worth of composted organic waste (19). Finally, the WTE energy values for “aluminum” and “steel” are both 10% lower than the recycling energy values for the same materials. H-Power, the WTE facility operating in Honolulu, claims to recover “virtually all” of the recyclable ferrous and nonferrous metals entering the facility (33). We assumed 10% losses with the rest of the incoming metals waste being diverted to recycling. These values can be easily adjusted for other cities and facilities where metals are not recovered from WTE processes. Using the RCE Reference Table and the collected waste management data, it is now possible to calculate RCE values VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5951

FIGURE 2. Calculation of RCE for paper.

TABLE 2. Data on Waste Management in Honolulu and San Francisco with Corresponding RCE Values Honolulu

San Francisco

material

recycle rate (%)

WTE rate (%)

landfill rate (%)

RCE (%)

recycle rate (%)

landfill rate (%)

RCE (%)

newspaper kraft paper waxed OCC high grade paper low grade paper polycoated paper soiled paper other paper total paper PET HDPE PP other plastics total plastics recyclable glass other glass total glass aluminum steel other metals total metals yard waste food waste total organics grand total

29.9 50.6 0.0 24.4 8.9 0.0 0.0 0.0 22.0 20.1 12.7 11.6 0.0 3.4 85.0 0.0 59.4 70.9 39.2 0.0 36.2 48.5 23.4 36.0 27.2

69.3 46.5 0.0 75.1 87.5 0.0 98.8 90.8 75.8 76.1 83.2 87.1 91.5 89.5 13.5 95.5 38.2 28.6 34.6 50.1 37.5 47.6 75.3 61.3 67.9

0.9 2.9 0.0 0.5 3.6 0.0 1.2 9.2 2.1 3.8 4.1 1.3 8.5 7.2 1.5 4.5 2.4 0.5 26.2 49.9 26.4 3.9 1.3 2.6 4.9

55.8 69.9 0.0 43.2 45.1 0.0 99.6 2.3 58.0 40.4 47.9 43.8 91.5 67.5 85.5 0.0 85.0 99.5 73.8 0.0 96.0 51.2 24.3 37.8 73.1

62.5 80.9 0.0 66.7 6.2 0.0 0.0 0.0 49.5 34.9 61.7 44.6 0.0 10.5 45.7 0.0 42.6 74.1 39.7 0.0 40.5 80.5 5.7 25.1 34.4

37.5 19.1 100.0 33.3 93.8 100.0 100.0 100.0 50.5 65.1 38.3 55.4 100.0 89.5 54.3 100.0 57.4 25.9 60.3 100.0 59.5 19.5 94.3 74.9 65.6

66.6 82.9 25.1 68.8 15.9 25.1 68.5 25.1 63.3 34.9 61.7 44.6 0.0 23.2 45.7 0.0 45.7 74.1 39.7 0.0 67.3 93.9 70.3 76.4 60.4

for each material and for the overall performance of the two cities (Table 2 and Figure 3). The results demonstrate that the environmental performance of a city’s waste management system depends on more than just the traditional recycling rate. While San Francisco enjoys a very high recycling rate of 34.4%, which places it above the US national recycling rate according to BioCycle (34) as well as among the top five major cities in the US (15), its RCE score is significantly lower than that of Honolulu’s. While RCE is particularly useful as a descriptive metric, it can also be applied as a prescriptive tool, where goals are defined and progress toward them is measured. Since these two cities have highly contrasting approaches to waste management, it is interesting to see what they might be able to do from an RCE perspective to improve their 5952

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

scores. Thus, three alternative scenarios (two for San Francisco and the other for Honolulu) are modeled and described in Figure 4. The first assumes that San Francisco successfully captures a large fraction of its food waste for diversion to composting facilities but still landfills the remainder of its nonrecycled waste. The second scenario still assumes higher food waste composting rates, but this time the nonrecycled waste is sent to a standard-sized U.S. WTE facility that would easily handle this waste stream (35). The alternative scenario for Honolulu assumes increased recycling for several materials that are efficiently captured in San Francisco and could presumably be handled similarly in Hawaii. The results (Figure 4) show that with the current state of technology and composition of the MSW stream, a combination of strong recycling programs plus the use of

FIGURE 3. San Francisco vs Honolulu waste management and RCE percentages.

FIGURE 4. San Francisco and Honolulu alternative Waste Management and RCE Percentages. WTE plants with advanced pollution control technology is the best way to achieve efficient management of solid waste from an energy and environmental perspective.

4. Discussion This paper shows that the RCE allows a user to effectively benchmark and conduct improvement analyses for municipal waste management systems with minimal (and publicly available) data. Many of these data have been compiled for quick reference in this paper (Table 2). The results indicate that, contrary to popular belief, there is no “one size fits all” solution to MSW management in cities. Different regions and cities require unique solutions at given points in their development. This was clearly shown in the case study comparing San Francisco and Honolulu: while the former would clearly benefit from the addition of a WTE plant to its management mix, Honolulu is bestserved by improving their recycling efficiency. And, though their “optimized” results were quite similar, it is conceivable that other mitigating factors could lead to more disparate RCE calculations in other cities with similar technological mixes. For example, landlocked cities in the middle of the U.S. operating far from recycling markets could see their energy savings from recycling decline

dramatically, lowering the relative RCE scores for some materials. Transportation is likely to play a strong part in these scenarios, and this issue has been addressed in the Supporting Information. It is useful to point out here a fundamental difference between RCE and CED, upon which RCE is based. CED is a measure of energy usage, while RCE is a measure of energy savings. RCE essentially takes the results of CED studies and applies them as a tool to help implement goaloriented waste management strategies (36). It is our belief that an efficiency metric of this kind gives users a realistic target to aspire to, which (hopefully) makes it easier to explain policy choices designed to reach those targets. It also complements some other tools (most notably EPA’s WARM and MSW Decision Management Tool models) that have been developed (37-42) by adding a goal-oriented approach. (Note that we have modeled Honolulu and San Francisco scenarios in EPA WARM for reference. The resultssincluded in the Supporting Informationsdo show improvement for both San Francisco and Honolulu when the alternative scenarios are run.) We feel that there is great potential for using the data generated by WARM energy models in combination with the RCE equations to make WARM a more goal-oriented tool. VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5953

There are, however, some potential limitations of the metric. It is assumed that the waste treatment systems being used are the best-available technologies (BAT) (e.g., WTE with advanced flue gas treatment, landfills with methane collection, etc.). Perhaps as a result, this method is somewhat weak in its consideration of toxic substances. It is conceivable, therefore, that dirty processes (e.g., WTE plants without pollution controls or landfills without liners and gas collection/treatment) would show a lesser CED/environmental indicator correlation than those materials and processes in the present study. A related concern is the fact that this analysis considers only currently available technologies. If a suite of more advanced technologies were to be introduced to mainstream waste management, the related material-specific CED scores would have to be added to the RCE Table, and it may take some time to build up enough data to support such additions. Furthermore, it is possible that a new technology could disrupt the CED-EcoPoints correlation discussed in this paper. Our philosophy in using BAT, however, is that policy discussions are crippled by a lack of quantitative and easy-to-understand arguments on the merits of the technologies that are available to treat the problem now. If new technologies begin to redefine the status quo, we believe it will be possible to adjust RCE accordingly, keeping in mind that RCE is a screening metric and that deeper analyses may be needed to supplement the results of its application. A similar question is the fact that the life cycle (both environmental and energy) advantages of composting have not been firmly established. We conservatively estimated an energy advantage based on offsets of fertilizer production, but it is possible that future research will show that the advantages are greater (orsthough this is less likelyslower). Any future iterations of RCE input values would have to account for more robust findings in this area. One further shortcoming of the energy values utilized in this study is that they do not include “local effects,” such as distance to materials recovery facilities (MRFs), transfer stations, and other waste treatment centers. Though this is meant to be a screening metric, it would still be beneficial to include these local effects in the values used for RCE calculationssand users are encouraged to do so. (We have included a discussion of transport effects in the Supporting Information.) Finally, in developing this metric, we deliberately focused on environmental and energy effects of waste treatment. It would be attractive to consider economic factors as well. For example, it would be helpful for a decision maker to know the most cost-effective way for their city to achieve an incremental increase in RCE. This is identified as a subject of future research.

Acknowledgments The help, support, and advice of Professor Paul H. Brunner was invaluable in developing and refining this work.

Supporting Information Available Full sets of data that allow for the recreation of the models presented in this paper. This information is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) EIOLCA Economic Input-Output Life Cycle Assessment. http:// www.eiolca.net. (2) Doka, G.; Hischier, R. Waste Treatment and Assessment of Long Term Emissions. Int. J. Life Cycle Assess. 2005, 10 (1), 77–84. (3) Graedel, T. E. On the Concept of Industrial Ecology. Annu. Rev. Energy Environ. 1996, 21 (1), 69–98. 5954

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 15, 2010

(4) Graedel, T. E. Streamlined Life-Cycle Assessment; Prentice Hall: New York, 1998; p 310. (5) Dickinson, D.; Mosovsky, J.; Caudill, R.; Watts, D.; Morabito, J. M. Application of the Sustainability Target Method: Supply Line Case Studies; IEEE International Symposium on Electronics and the Environment, San Francisco, CA, 2002; IEEE: San Francisco, CA, 2002; pp 139-143. (6) Henrickson, C.; Horvath, A.; Joshi, S.; Lave, L. Economic InputOutput Models for Environmental Life-Cycle Assessment. Environ. Sci. Technol. 1998, 32 (7), 184–191. (7) Rechberger, H.; Brunner, P. H. A New, Entropy Based Method To Support Waste and Resource Management Decisions. Environ. Sci. Technol. 2002, 36 (4), 809–816. (8) Kaufman, S.; Krishnan, N.; Kwon, E.; Castaldi, M.; Themelis, N.; Rechberger, H. Examination of the Fate of Carbon in Waste Management Systems through Statistical Entropy and Life Cycle Analysis. Environ. Sci. Technol. 2008, 42 (22), 8558–8563. (9) Blok, K. Introduction to Energy Analysis; Techne Press: Amsterdam, 2006; p 256. (10) Huijbregts, M. A. J.; Rombouts, L. J. A.; Hellweg, S.; Frischknecht, R.; Hendriks, A. J.; vandeMeent, D.; Ragas, A. M. J.; Reijnders, L.; Struijs, J. Is Cumulative Fossil Energy Demand a Useful Indicator for the Environmental Performance of Products? Environ. Sci. Technol. 2006, 40 (3), 641–648. (11) Porter, R.; Roberts, T. Energy Savings by Wastes Recycling; Taylor & Francis: Abingdon, Oxon, England, 2005; p 243. (12) Goedkoop, M. SimaPro, 7.0; Pre Consultants: Amserfoort, 2007. (13) Klopffer, W. In defense of the cumulative energy demand. Int. J. Life Cycle Assess. 1997, 2 (2). (14) Knapp, K.; Jester, T. Empirical investigation of the energy payback time for photovoltaic modules. Solar Energy 2001, 71 (3), 165–172. (15) Wagner, H. J.; Pick, E. Energy yield ratio and cumulative energy demand for wind energy converters. Energy 2004, 29 (12-15), 2289–2295. (16) Kim, S.; Dale, B. E. Cumulative Energy and Global Warming Impact from the Production of Biomass for Biobased Products. J. Ind. Ecol. 2004, 7 (3), 147–162. (17) Scheuer, C.; Keoleian, G. A.; Reppe, P. Life cycle energy and environmental performance of a new university building: modeling challenges and design implications. Energy Build. 2003, 35 (10), 15. (18) Thormark, C. A low energy building in a life cycle--its embodied energy, energy need for operation and recycling potential. Build. Environ. 2002, 37 (4), 429–435. (19) EcoInvent. EcoInvent data v1.1 in Final reports EcoInvent 2000 No. 1-15; Swiss Center for Life Cycle Inventories: Dubendorf, Switzerland, 2004. ¨ koinventare fu (20) BUWAL 250, O ¨ r Verpackungen, Schriftenreihe Umwelt 250, Bern, 1996. (21) Goedkoop, M.; Spriensma, R. The Eco-indicator 99 A damage orientedmethodforLifeCycleImpactAssessment,MethodologyReport; Pre Consultants: Amserfoort, The Netherlands, 2001; p 144. (22) Jolliet, O. IMPACT 2002+: A New Life Cycle Assessment Methodology. Int. J. Life Cycle Assess. 2003, 8 (6), 324–330. (23) Weitz, K., Life Cycle Inventory Data Sets for Material Production of Aluminum, Glass, Paper, Plastic, and Steel In North America; US EPA Development, Ed.; RTI International: Research Triangle Park, NC, 2003; p 246. (24) McDougall, F. R. White, P. Integrated Solid Waste Management: A Life Cycle Inventory, 2nd ed.; Blackwell Science: Oxford, UK; Malden, MA, 2001; pp xxvii, 513. (25) Porter, R. Roberts, T. Energy Savings by Wastes Recycling; Taylor & Francis: Abingdon, Oxon, England, 2005; p 243. (26) Franklin Associates USA. LCI Database Documentation; Franklin Associates: Prairie Village, KS, 1998. (27) Themelis, N. J. Life After Fresh Kills: Moving Beyond New York City’s Current Waste Management Plan; Columbia University: New York, 2001. (28) Themelis, N. J.; Ulloa, P. A. Methane Generation in Landfills. Renewable Energy 2007, 32 (7), 1243–1257. (29) Anonymous. Municipal Recycling Survey. Waste News, 2006. (30) Waste Characterization Study Final Report; Department of the Environment: San Francisco, CA, March 2006; p 95. (31) Finnveden, G.; Johansson, J.; Lind, P.; Moberg, A. Life Cycle Assessments of Energy from Solid Waste; Stockholms Universitet: Stockholm, Sweden, August 2000. (32) Drew, K. San Francisco Tonnage Report. In Emailed report on SF MSW Tons; Kaufman, S. M., Ed.; San Francisco Department of the Environment: San Francisco, CA, 2007. (33) Opala Solid Waste. www.opala.org.

(34) Simmons, P.; Goldstein, N.; Kaufman, S. M.; Themelis, N. J.; Thompson, J. The State of Garbage in America. BioCycle 2006, 47 (4), 26–43. (35) Themelis, N. J. An overview of the global waste-to-energy industry. Waste Manage. World 2004, (Review Issue), p. 40–47. (36) Brunner, P. H. Rechberger, H. Practical Handbook of Material Flow Analysis; Lewis: New York, 2004; p 317. (37) den Boer, J.; den Boer, E.; Jager, J. LCA-IWM: A decision support tool for sustainability assessment of waste management systems. Waste Manage. 2007, 27, 1032–1045. (38) Lang, D. J.; Scholz, R. W.; Binder, C. R.; Wiek, A.; Sta¨ubli, B. Sustainability potential analysis (SPA) of landfills - a systemic approach: theoretical considerations. J. Cleaner Prod. 2007, 15 (17), 1628–1638.

(39) Carlsson Reich, M. Economic assessment of municipal waste management systems - case studies using a combination of life cycle assessment (LCA) and life cycle costing (LCC). J, Cleaner Prod, 200513, 253–263. (40) Thorneloe, S. A.; Weitz, K.; Jambeck, J. Application of the US decision support tool for materials and waste management. Waste Manage. 2007, 27, 1006–1020. (41) Dijkgraaf, E.; Vollebergh, H. R. J. Burn or bury? A social cost comparison of final waste disposal methods. Ecol. Econ. 2004, 50, 233–247. (42) USEPA. Warm Model. http://www.epa.gov/climatechange/wycd/ waste/calculators/Warm_home.html.

ES100505U

VOL. 44, NO. 15, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

5955