Environ. Sci. Technol. 2010, 44, 8678–8683
Design and Optimization of Photovoltaics Recycling Infrastructure JUN-KI CHOI* AND VASILIS FTHENAKIS Brookhaven National Laboratory, Upton, New York 11973, United States
Received May 19, 2010. Revised manuscript received September 17, 2010. Accepted September 22, 2010.
With the growing production and installation of photovoltaics (PV) around the world constrained by the limited availability of resources, end-of-life management of PV is becoming very important. A few major PV manufacturers currently are operating several PV recycling technologies at the process level. The managementofthetotalrecyclinginfrastructure,includingreverselogistics planning, is being started in Europe. In this paper, we overview the current status of photovoltaics recycling planning and discuss our mathematic modeling of the economic feasibility and the environmental viability of several PV recycling infrastructure scenarios in Germany; our findings suggest the optimum locations of the anticipated PV take-back centers. Short-term 5-10 year planning for PV manufacturing scraps is the focus of this article. Although we discuss the German situation, we expect the generic model will be applicable to any region, such as the whole of Europe and the United States.
Introduction Photovoltaics (PV) have two different major waste streams: end-of-life (EoL) uninstalled waste and manufacturing scraps. The PV market has been growing by an average of more than 40% a year over the last ten years, and we expect sustainable growth of at least 30% a year for the next two decades (1, 2). There are various types of PV manufacturing technologies. The lifetime of the usual PV product is considered approximately 25+ years. However, there are emerging technologies such as thin-film polymer/organic PV that are considered to having much shorter lifetime. Recent technology development and the up-scaling issues of the manufacturing of these technologies are discussed in several studies (3-7). Currently, the scaled up recycling processes for c-Si and some thin-film modules (i.e., CdTe, CIGS) are developed and they have been recycling the PV manufacturing scraps and the EoL PV modules. Economically feasible recycling technologies and infrastructure for the emerging polymer/ organic PV could be developed in parallel with the rapid commercialization of these new technologies. Therefore, different time horizons and strategies must be considered to efficiently manage the complex waste flows generated from past installations, and the current and future production from various PV technologies. Such waste analyses and prognoses provide insights for planning to set up recycling infrastructure in various years. Figure 1 shows a tentative planning scheme for the management of PV waste with a time-horizon starting from the current year. We note that * Corresponding author phone: +1-631-344-2723; fax: +1-631344-3957; e-mail:
[email protected]. 8678
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the regions where major PV manufacturers are sited are different than locations of major installations of PV systems. In addition, there are long time lags from manufacturing PV modules to their EoL (25+ years). Therefore, in the relatively short term (∼5 years), planning for the PV recycling infrastructure should focus on optimizing the locations of recycling facilities based on the amount of PV waste-flow of manufacturing scraps for both crystalline silicon- (c-Si) and thin film-cell/module/systems. A special PV recycling infrastructure for the EoL polymer/organic PV can fall into this short-term analysis because of the relatively short lifetime of these modules. After 2015, this approach should change as major numbers of EoL c-Si modules expectedly will be generated, and different midterm planning strategies will be required; the amount of retired modules from major installations then should be considered. During this midterm period, not many EoL thin-film modules are being produced, but the amount of thin-film manufacturing scraps will keep increasing, paralleling the expected rise in the thin-film module production. Long-term planning, however, must consider waste from thin-film EoL modules, along with all other types of PV wastes. Germany is the world’s largest photovoltaic (PV) market in terms of installed capacity, and is Europe’s leading PV manufacturer. Several key factors ensure that Germany’s PV cluster is successful, even though solar irradiation levels there are well below those in many other countries. These factors include supportive government policies and incentives, the availability of a skilled labor force, a high-quality infrastructure, significant investment in R&D, the presence of highly developed supporting industries, and the depth and breadth of enabling industry associations. Germany accounted for about 40% of total globally installed PV capacity in 2008, and it is expected to keep growing (8). Concurrent with this increasing production and installation, several proactive PV manufacturers established recycling programs to maximize the recovery of valuable materials and minimize the environmental impacts associated with producing PV systems. General issues related to setting up recycling infrastructure cover decisions across the microscale process optimization and macro-scale reverse logistics planning. Generally, both micro- and macro-scale planning deal with the same goal: maximization of profit and minimization of cost associated with the recycling process and reverse logistics. Many studies have focused on the specific recycling process-level optimization of various products in microscale (9-11). However, there are few studies addressing the issues related to PV recycling infrastructure. Fthenakis (12) proposed adopting a holistic approach for designing a PV recycling infrastructure in light of experiences from other industries, and discussed several qualitative schemes for a PV recycling infrastructure that mimics practices for other products, such as electronics, utilities, and batteries. However, the quantitative economic feasibility and environmental viability of PV recycling paradigms were not explored in the early study. Choi and Fthenakis (13) surveyed several studies of mathematical modeling of the recycling processes and developed an optimization model for analyzing the economic feasibility of a CdTe PV recycling process. In addition to the microscale process-level planning, these authors posed the following questions about setting up efficient recycling infrastructure planning of PV in macro-scale: Who participates in the reverse logistics network (i.e., manufacturers, retailers, logisticsservice providers, secondary material dealers)?; where is the network channel located?; which function should be carried out (collection, testing, sorting, transportation, and process10.1021/es101710g
2010 American Chemical Society
Published on Web 10/01/2010
FIGURE 1. Time horizon for setting up PV recycling infrastructures. ing)?; and, how much PV waste can a PV take-back center (PVTBC) handle?. There are a few studies reviewing general issues related to the recycling infrastructure of other products (14-16). These reviews described the management of the recovery and distribution of EoL products, production planning and inventory management, and issues of managing the supply chain in reverse logistics (i.e., collecting and transporting used products and packages). Such activities generally deal with locating collection points or developing strategies to collect used products through reverse-logistics providers. Some studies proposed conceptual frameworks for designing reverse-logistics networks of various products (17-19). Louwers (20) proffered an allocation model of locating facilities to collect and preprocess carpet waste. Planning vehicular routings was resolved by Schultmann et al. (21) for establishing a closed-loop supply chain for the end-of-life vehicle (ELV) treatment. In studying “green” reverse-logistics management, which supports an efficient level of environmental impact through the entire reversesupply chain network, Efendigil et al. (22) offered a methodology for selecting the most appropriate third-party reverse-logistics provider, via a multicriteria decision-making framework. Nonetheless, there are no studies that deal with optimizing the reverse-logistics network for PV recycling. Our work deals with deciding where to locate facilities within a finite set of sites, and how to optimize certain economic criteria. Along with assuring the best level of economic-logistics planning, the environmental viability of the planned PV recycling infrastructural management must be guaranteed. The major environmental issues involved in reverse logistics are the emissions associated with transportation. Therefore, it is crucial to minimize the distance traveled while maximizing the amount of PV modules collected and delivered to recycling centers. We utilized a mathematical modeling scheme to solve the problem of discrete locations in planning for an efficient PV recycling infrastructure and to guide the decision-making process. Although we discuss the German example, we anticipate that our model will be applicable to any region, such as the whole of Europe and the United States. In this paper, we consider for recycling manufacturing scrap from c-Si modules during the years 2010-2015. PV Reverse-Logistics Network. Figure 2 shows a general scheme for designing a network of collection points and PVTBCs. In this case, a central authority monitors all network information, including allocating reverse-logistics services, assessing PV recycling processing capacities, and financial information. The organization has the authority to determine the decision variables of the whole recycling system. A governmental agency or a private PV recycling program might act as a central planner in determining the network’s behavior. Each entity registers as a member of the network, and the central authority monitors and manages the system,
FIGURE 2. Regional recycling infrastructure. assuring the exchange of logistics information and monetary transactions among all within the PV recycling infrastructure. There are two flows of PV waste in this network: EoL PV modules and manufacturing scraps. Damages from packaging and transportation should be considered in the economic analysis of the network. First, PV installers are in charge of uninstalling or replacing PV modules after receiving permission from the PV manufacturers or wholesalers to dispose of them; thereafter, the installers take back damaged modules and transport them to a central collection point free of charge. Additionally, the individual owners of PV systems themselves can bring back nonfunctional modules to the nearest central collection points. Some authorized wholesalers serve as central collection sites in this network, and will store EoL PV modules temporally. The central planning agency supplies them with recycling containers wherein to dispose of EoL modules brought by module installer and owners. When the containers are full, the wholesalers contact authorized logistics contractors who regularly take full truckloads of EoL modules to a designated PVTBC. In some cases, it is much more efficient to transport EoL waste modules directly to a recycling center, especially when some installation sites are located much closer to the recycling center than to the central collection point. The second waste stream comprises defectively manufactured modules/scraps, and modules damaged during packaging operations, both of which can be collected directly from each PV manufacturer. Manufacturers also are supplied with recycling containers and the authorized reverse-logistics companies regularly pick up full loads of such waste from each manufacturing site. The designated recycling centers pay for the reverse-logistics service, while they themselves get revenue from reclaiming valuable parts and materials from the returned modules. In some cases, certain PV manufacturing facilities might be expected to function as an integrated PVTBC that offers total service, including both centralized collection and recycling. The decision on whether any individual PV manufacturer does so may depend on the financial decisions of each one. A manufacturer might analyze the profitability of building an integrated PV recycling facility by considering the trade offs among the amount of manufacturing scraps that might offer revenue by reclaiming various materials/components, and the costs associated with their own processing, their capital investment for setting up a recycling facility, and the inventories/reverse logistics. However, if too many PV manufacturers wish to act as integrated PVTBCs, this will raise concerns about competition inside the PV recycling network to obtain as many waste modules as possible, and the profitability of the whole recycling infrastructure might be lost. On the other hand, if only a few PV manufacturers VOL. 44, NO. 22, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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take this route, there may be a monopoly and the system might lose efficiency. Generic Mathematical Modeling. The central authority selects the optimal sites for the PV recycling plants to encompass both manufacturing scrap and EoL modules. The objective is optimally to establish the location of the central recycling plant to maximize the benefit by minimizing logistics costs. The model aids in deciding where the recycling facilities should be located from a finite set of sites, taking into account the maximum amount of PV modules that will be transported from various collection sites, and the capacity of the recycling center. A flowchart for the model’s algorithm is provided in the Supporting Information. Input to the model includes information about the cost of transportation, the cost of reverse-logistics services, and the distance matrix among various collection/and recycling sites. The model finds the optimal total-system costs by considering the expenses for transportation and logistics, and the capital investment required to open the designated PVTBCs. The model iterates until it identifies the most favorable number of PVTBCs to open, subject to the amount of supply from each collection point and the capacity limit of each PVTBC. The decision variables are the optimal PV waste flow allocated within the system and the binary variable to model the choice of “opening” a recycling facility at a certain designated site. The mathematical form of the objective function and constraints along with nomenclature used for the modeling are described in the Supporting Information; the general form is as follows: Minimize (1) The total system cost (Capital costs and Reverse logistics costs) Subject to (2) The satisfaction of the supply from collection facility (3) Capacity limit of each facility to be opened (4) Material flow balance between facilities Using the optimization model, it chooses the manufacturing facilities that might serve as collection facilities supplying a fixed amount of manufacturing scraps to a designated PVTBC. Otherwise, they could be selected as integrated PVTBCs that handle the collected manufacturing scraps from other manufacturers, along with those generated at their own manufacturing facilities. The model finds the best potential location of the recycling facilities, based on the travel distances, transportation and logistics costs, and capital costs. Case Study: German PV Recycling. This study describes the German case for two reasons, with the intention to expand it to different regions. First, Germany is one of the leading countries in PV manufacturing and installation as of 2010. Second, some of the leading manufacturers there have initiated PV recycling; expectedly, PV CYCLE (23) will start operating central planning in 2011. There are more than 99 full and associated members involved in this program as of 2010 and the number keeps increasing. The goal of PV CYCLE is to set up a voluntary take back- and recycling-program for end-of-life (EoL) modules. To optimize the routing scheme and the location of collection/recycling sites, we examined the location of the major PV manufacturers and the major installation sites; we noted a considerable discrepancy between the two. Information about the percentage installation of PV in the sixteen provinces of Germany from 2001 to 2008 and the location of major PV manufacturers is provided in the Supporting Information. There are major installation sites in the province of North Rhine-Westphalia, and two in the southern part of Germany, viz., BadenWu ¨ rttemberg and Bavaria, while some other provinces also have a reasonable portion of the country’s total installation. Major installation sites correlate directly with the population density. Together, these regions that cover more than 70% 8680
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FIGURE 3. Reverse logistics cost when each manufacturer acts as the sole PVTBC. of the total annual installation will need collection sites and recycling centers for handling large numbers of EoL modules. However, the greatest amount of used EoL modules will become available from 2025 and thereafter, considering the historical installation data and the lifetime of PV (25+ years). Therefore, there is no immediate need for setting up large numbers of collection and recycling facilities in these regions to recycle EoL modules. Meanwhile, it may be possible to set up individual integrated central collection and recycling facilities therein to take care of short-term manufacturing scraps generated from the few leading manufacturers located in this region, along with the unwanted modules damaged during transportation to installation sites, and nonfunctioning modules. Some places near the capital of each region, Dusseldorf, Stuttgart, and Munich, where there are some leading PV manufacturers, may suffice for the time being. Besides the EoL PV waste, because a growing number of new PV manufacturers might decide in the future to build their manufacturing facilities in southern Germany, plans should be considered for expanding the optimal location of the collection/recycling facilities in this region. We focus on the manufacturing scraps from the eastern German region because the German PV manufacturing cluster is concentrated in the former East German states of Saxony-Anhalt, Brandenburg, Thuringia, Saxony, and Berlin, areas where the German Government offered extensive incentive programs to stimulate the regional economy. PV manufacturers involved in this PV cluster produced more than 90% of the total German capacity in 2009. Crystalline silicon technology continues to dominate the PV market with an 85% share, although expectedly it will decline with increasing interest in thin-film technologies. In addition, current recycling technology for thin-film PV (i.e., First Solar) is quite different from that for c-Si recycling (i.e., Solar World). Therefore, plans may require having separate recycling facilities for the two types of PV modules. A map indicating the specific location and the name of the major leading manufacturers of c-Si cell-, module-, and integrated systems in the eastern part of the country, their annual capacity data, and assumptions for the projection to year 2015 is available in the Supporting Information. We also calculated the exact distance between manufacturers from GoogleEarth and constructed a distance matrix (see Supporting Information) for modeling.
Results Economic and Environmental Aspects. For the base model, we employed the following parameters. For transportation, we adopted a fuel price USD 1.82/liter with a 10 tonne truck with a fuel efficiency of 4.2 km/L. The logistics service costs were $21/h salary for each truck driver, driving on average 60 km/h; a service-fee factor of 1.5 accounted for the overhead logistics costs (see Supporting Information for details). Figure 3 shows the total reverse logistics cost when each PV manufacturer acts as the sole PVTBC in the entire recycling
TABLE 1. Optimization Scenario
S1 S2 S3 S4
capital cost/ PVTBC ($K)
annual capacity/ PVTBC (tonne)
total reverse logistics cost ($K)
optimal system cost ($K)
selected PVTBCs
4,000 2,000 1,000 500
20,000 10,000 5,000 2,500
1,237 820 334 266
5,237 4,820 4,334 3,766
R16 R13 + R15 R13 + R14 + R15 + R16 R1 + R6 + R7 + R13 + R14 + R15 + R16
network (i.e., all others serve as collection points). These values are the same, regardless of the amount of marginal capital investment because every PVTBC is set to be the only available recycling facility and all available manufacturing scraps are transported to this designated one. No optimization process is necessary for this scenario; Figure 3 gives the sensitivity of the combination of two parameters: distance traveled and quantity of waste. In this scenario, R1 and R16 are the best candidates in terms of the costs of reverse logistics (see Supporting Information for the description of each R no.). They are located in the central part of the region and have a relatively large production capacity (annual capacity, respectively, of 400 and 540 MW). The entire network system saves money by selecting a single integrated PVTBC that generates a large amount of its own manufacturing scraps that can be recycled onsite. R13 also is a good tentative location since it has the largest annual manufacturing capacity (1100 MW), although the plant’s site is in the southern part of the region. R5 and R8, located in the far north of the region, have smaller manufacturing capacities (130 and 120 MW, respectively) and pay the highest reverselogistics costs. For the optimization, we solved the mixed integer programming model with the goal of minimizing the total costs of the recycling network system. The CPLEX solver was used to generate solutions. Table 1 shows the result of the optimization for four scenarios. The model selects the optimized number and the location of the PVTBC while varying the capital cost to open up one PVTBC. As the capital costs to open up a PVTBC become more expensive, the number of PVTBCs that are opened declines, and the network becomes simpler while the optimal costs of the system rise because of the increase in the total reverse-logistics cost. When the capital cost is more than $4 M, with the relatively high annual recycling capacity of 20,000 tonnes, the model suggests opening only R16 to minimize the total expenses of the system at $5.5 M, wherein the total reverse-logistics cost is $1.5 M. With a relatively small-scale recycling processing capacity (2500 tonnes/year), the model allocates seven different manufacturing facilities as PVTBCs, to minimize the optimal cost of the system to $3.8 M. Therefore, assuming that capital cost is proportional to annual capacity, this finding implies that it is better to have many decentralized PVTBC rather than one large capacity PVTBC in the network; it saves the total expenses by reducing the distance traveled. However, the decision whether or not specific manufacturers act as PVTBCs requires analyzing additional costs and benefits, comparing and deciding between trade-offs among the costs of reverse logistics, inventories, revenues from reclaiming the PV, and processing costs, all of which have economies of scale. This work does not consider integrating the profitability from optimizing the processing, but only from optimizing the reverse-logistics scheme. Figure 4 illustrates the flow of PV manufacturing scrap and the amount of PV waste (tonnes) allocated to each PVTBC for scenario 4 (S4) where seven PVTBCs are opened (marked as stars). The parameter Rj in the GAMS output represents the PVTBC if it is chosen from the model, otherwise it denotes the collection points of the manufacturing scraps. Waste flows that loop back to their own number signify that each PVTBC
recycles a certain portion/whole of its own manufacturing scraps. Since the annual recycling capacity of each PVTBC is set at 2500 tonnes, the network is quite complicated. Some high-production-capacity manufacturing plants should send a certain portion of their own PV waste to the adjacent PVTBCs since they have insufficient recycling capacity to handle it. For example, until the year 2015, R15 generates 14,819 tonnes of its own manufacturing scraps but sends 2319 tonnes of PV waste to R1 and R6 because its maximum capacity of each PVTBC is set at 12,500 for this scenario. The full list of PV waste flow allocated to each recycling center is available in Supporting Information. We assessed the environmental implications of each transportation scenario, based on the greenhouse gas
FIGURE 4. Assignment of PVTBCs and the PV waste flows for scenario 4.
TABLE 2. Emission and Global Warming Potential of Four Scenarios
scenario
total travel (1000 km)
CO2 (tonnes)
CH4 (kg)
N2O (kg)
total GWP (tonne-CO2eq)
S1 S2 S3 S4
1,283 625 346 275
82 54 22 18
345 226 93 74
302 198 82 65
180 118 49 39
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TABLE 3. Example of the Optimized Solution When the Capacity of Plant is 20,000 t/year marginal capital cost ($K)
optimal system costa ($K)
reverse logistics cost ($K)
total capital costa ($)
selected optimal location of PVTBC
479 and up 295-479 174-295 144-174 48-144 31-48
1,715 1,352 993 868 393 294
1,237 762 471 293 153 108
479 590 522 576 240 186
R16 R13 + R15 R13 + R15 + R16 R13 + R14 + R15 + R16 R5 + R13 + R14 + R15 + R16 R6 + R8 + R13 + R14 + R15 + R16
a
Corresponds to the lower bound of the marginal capital cost for each range.
inventories from Ecoinvent V. 2.1 (24) for the operation stage of the truck. The geography of the data refers to average European transport conditions. The inventory includes the supply of diesel and petroleum. Direct airborne emissions of gaseous substances, particulate matter, and heavy metals are accounted for. This is the average data for operating 50% loaded heavy-duty vehicles (3.4-16 tonnes) in Europe. We adopted the following emission factors from the database: 64.74 g-CO2/truck-km, 0.27 g-CH4/truck-km, 0.24 g-N2O/ truck-km. Table 2 shows emissions of the three greenhouse gasses considered for each scenario. To assess the total global warming potential (GWP) as CO2-equivalent, GWP over 100 years (GWP100) is used (25). Compared to S1, the percentage reduction of GWP for other scenarios, S2, S3, and S4 were calculated, respectively, as 34%, 73%, and 79%. The reduction in GWP is directly proportional to the total distance traveled. By setting up three more PVTBCs of smaller capacity (i.e., R16 f r13 + r14 + r15 + r16), the total distance traveled and the GWP is reduced by 73% in this scenario. In the above example, certain large-capacity integrated PVTBCs send some portion of their manufacturing scraps to other PVTBCs. However, if certain manufacturers decide to open a PVTBC, then it is reasonable for them to cover all of their own manufacturing scraps, at least since it is cumbersome for the manufacturers to ship some waste separately to different locations. Table 3 displays the optimal cost and the selected location of the recycling centers, based on a fixed maximum recycling processing capacity of 20,000 ton per year for each PVTBC. It shows the breakeven point of the marginal capital cost for opening each additional recycling center in the optimal location. For example, the model recommends opening only one recycling plant (R16) in the network if the capital cost to open up a PVTBC is more than $479K. Furthermore, it suggests operating two recycling facilities (R13 and R15) when this capital cost ranges from $295K to $479K, and so on. The optimal system cost is the summation of the total reverse logistics cost and the total capital cost. Total capital cost is the multiplication of the number of the selected PVTBC and the marginal capital cost to open up each selected recycling center. The reverse logistics cost decreases exponentially and it reaches zero when all 16 PVTBCs are selected by the model (i.e., each PVTBC recycles its own manufacturing scraps). The graph showing this result can be found in the Supporting Information. Basically, our model resolves the trade-off decisions among the quantity of PV waste at the specific location, the distance between the collection/recycling centers, the parameters of the reverse-logistics cost, and the capital cost for opening up a PVTBC.
Discussion Our general integrated framework can guide policy makers (or central planners) who wish to set up an economically feasible and environmentally viable PV recycling infrastructure in any region. Our short-term 5-10 years forward analyses provide the insight that adding smaller processing 8682
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capacity (low marginal capital-cost) PVTBCs in optimized decentralized locations offers better economical and environmental benefit throughout the network system as the total travel distance and the logistics cost are lower. However, a certain level of sufficient recycling processing capacity should be guaranteed for the midterm and longterm planning. The amount of capital costs for opening up a certain processing level of PVTBCs is an important variable. Therefore, midterm/longterm planning is crucial when the large amount of end-of-life (EoL) PV becomes available for recycling; we will expand our model to consider this in future work. Our study did not consider the amount of thin-film manufacturing scraps that will be available, but it is expected to grow rapidly with the growth of thin-film production. Some integrated PVTBCs may develop a combined recycling process to recycle mixed PV waste (i.e., crystalline based and thin-film based). Another choice is that the integrated PVTBC runs separate recycling technologies at the same site, but here, the capital costs could be the problem. Yet another alternative is to establish specialized recycling facilities for thin film technology, such as First Solar’s (13), in the optimized location.
Acknowledgments This research is supported by the Solar Technologies Program, Energy Efficiency and Renewable Energy, USDOE Contract DE-AC02-76CH000016. We also thank members of PVCYCLE and IEA PVPS Task 12 for useful discussions.
Supporting Information Available Detailed description of the mathematical model and data used for the case study. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Fthenakis, V. M. Sustainability of photovoltaics: The case for thin-film solar cells. Renewable Sustainable Energy Rev. 2009, 13, 2746–2750. (2) Zweibel, K.; Mason, J.; Fthenakis, V. M. A Solar Grand Plan. Sci. Am. 2008, 64–73. (3) Krebs, F. C.; Tromholt, T.; Jorgensen, M. Upscaling of polymer solar cell fabrication using full roll-to-roll processing. Nanoscale 2010, 2 (6), 873–886. (4) Krebs, F. C.; Nielsen, T. D.; Fyenbo, J.; Wadstrom, M.; Pedersen, M. S. Manufacture, integration and demonstration of polymer solar cells in a lamp for “Lighting Africa” initiative. Energy Environ. Sci. 2010, 3 (5), 512–525. (5) Roes, A. L.; Alsema, E. A.; Blok, K.; Patel, M. K. Ex-ante environmental and economic evaluation of polymer photovoltaics. Prog. Photovoltaics: Res. Appl. 2009, 17 (6), 372–393. (6) Garcı´a-Valverde, R.; Cherni, J. A.; Urbina, A. Life cycle analysis of organic photovoltaic technologies. Prog. Photovoltaics: Res. Appl. 2010, DOI: 10.1002/pip.967. (7) Jung, J.-Y.; Guo, Z.; Jee, S.-W.; Um, H.-D.; Park, K.-T.; Lee, J.-H. A strong antireflective solar cell prepared by tapering silicon nanowires. Opt. Express 2010, 18 (S3), A286–A292. (8) GTAI. The Photovoltaic Industry in Germany: Industry Overview; German Trade and Investment: Berlin, 2009.
(9) Boon, J.; Isaacs, J. A.; Gupta, S. M. Economic Impact of Aluminum-Intensive Vehicles on the U.S. Automotive Recycling Infrastructure. J. Ind. Ecol. 2001, 4 (2), 117–134. (10) Lambert, A. D. J. Disassembly sequencing: a survey. Int. J. Prod. Res. 2003, 41 (16), 3721–3759. (11) Sodhi, M. S.; Reimer, B. Models for Recycling Electronics EndOf-Life Products. OR Spektrum 2001, 23, 97–115. (12) Fthenakis, V. M. End-of-life management and recycling of PV modules. Energy Policy 2000, 28, 1051–1058. (13) Choi, J. K.; Fthenakis, V. M. Economic feasibility of recycling photovoltaic modules: Survey and model. J. Ind. Ecol. 2010, DOI: 10.1111/j.1530-9290.2010.00289.x. (14) Rubio, S.; Chamorro, A.; Miranda, F. Characteristics of the research on reverse logistics (1995-2005). Int. J. Prod. Res. 2008, 46 (4), 1099–1120. (15) Field, F. R. Automobile Recycling Policy: Findings and Recommendations; Massachusetts Institute of Technology: Cambridge, MA, 1994. (16) Fleischmann, M.; et al. A Characterization of Logistics Networks for Product Recovery. Omega 2000, 28 (6), 653–666. (17) de Figueiredo, J. N.; Mayerle, S. F. Designing minimum-cost recycling collection networks with required throughput. Transport. Res., Part E 2008, 44 (5), 731–752.
(18) Srivastava, S. K. Network design for reverse logistics. Omega 2008, 36 (4), 535–548. (19) Min, H.; Jeung Ko, H.; Seong Ko, C. A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega 2006, 34 (1), 56–69. (20) Louwers, D.; et al. A facility location allocation model for reusing carpet materials. Comput. Ind. Eng. 1999, 36 (4), 855–869. (21) Schultmann, F.; Zumkeller, M.; Rentz, O. Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry. Eur. J. Oper. Res. 2006, 171 (3), 1033– 1050. (22) Efendigil, T.; Onut, S.; Kongar, E. A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Comput. Ind. Eng. 2008, 54 (2), 269–287. (23) PVCYCLE. http://www.pvcycle.org. Last accessed April 2010. (24) Ecoinvent Centre. Overview and Methodology. In Final Report Ecoinvent Data V2.1; Swiss Centre for Life Cycle Inventories: Dubendorf, CH, 2009. (25) Intergovernmental Panel on Climate Change. IPCC Fourth Assessment Report (AR4); Pauchauri, R. K., Reisinger, A., Eds.; 2007.
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