The vast consumption of consumer electronics raises the burden on the environment due to the huge amount of obsolete products after usage. The influence of pollution from obsolete electronics products, known as scrap electronics (e-scrap), on the environment is self-evident since they contain metals and other materials that can be hazardous to the environment if they are not properly managed after usage.
According to the U.S. Environmental Protection Agency (EPA) study, 40% of the lead in the U.S. landfills is from discarded e-scrap products (DFC, 2009). E-scrap has increased rapidly worldwide. For instance, in developed countries, the average lifetime of a computer is 6-year in 1997 but 2-year in 2005; this change leads to a ballpark number of annual e-scrap generation ranging from 20 to 50 million units (Greenpeace, 2009). In Taiwan, there are about two million e-scrap products recycled according to the Taiwan EPA’s statistical data in 2009 (RFMB, 2009a).
In order to relieve the damage to the environment, several regulations are announced. For instance, Waste Electrical and Electronic Equipment (WEEE), Restriction of Hazardous Substances Directive (RoHS), and Eco-Design Requirements for Energy Using Products (EuP) are announced by the European Union. The WEEE indicates that manufacturers bear the responsibility for collecting, recycling, and disposing e-scrap products properly. The RoHS forbids using some specific hazardous substances as raw materials to produce new products. The EuP provides the rules for eco-design to improve the environmental performance of energy-related products (Yen, 2006).
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In general, forward supply chains may involve the manufacturing/importing/selling processes of new products, and reverse supply chains may include the reuse/recovery/recycle operations of end-of-life products. In the past decade, much attention has focused on designing proper forward and reverse (closed-loop) supply chains. For example, Fleischmann et al. (2000) derive a classification scheme for different types of recovery networks by comparing the general characteristics of product recovery networks with traditional logistics structures. Guide and Harrison (2003) indicate that new business models need to be developed by cooperating between industry and academia. Wang and Yang (2007) propose a new mixed integer linear programming model to maximize the overall utilization and revenue for designing an e-scrap reverse logistics network. Hong et al. (2006) propose a mixed integer linear programming model to design an infrastructure to process used televisions, monitors, and computer central processing units in the state of Georgia in the U.S to maximize the system net profit, and then robust solution are found with a min–max robust optimization methodology.
Recycling is a part of the operations in reverse supply chains. It not only decreases the consumption amount of natural resources, but also reduces the impact of obsolete products on the environment. Many researchers have proposed recycling models that maximize total profits and recycling rates by using mathematical programming methodologies (e.g. Inderfurth et al., 2001; Stuart et al.,1999; Uzsoy and Venkatachalam, 1998; Hoshino et al., 1995; Ron and Penev, 1995). Several countries make associated policies to manage the recycling system. For example, the Taiwan EPA imposes taxes, called tax revenues, on the manufacturers, importers and sellers (MIS firms) who are players in forward supply chains. The MIS firms have to pay the e-scrap products processing fee, named as advanced recycling fee
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(ARF) to support the implement of recycling. On the other hand, consumers may bring the e-scrap products to recycling firms and then receive some reward money paid by the recycling firms. To compensate recycling firms for the costs along with recycling and processing the e-scrap products, the EPA uses the tax revenues to subsidize the recycling firms on the basis of fund balance. The ARF in Taiwan is designed in a similar way to the ARF enacted in California, U.S. The state of California assigns an ARF of $8-$25 on all e-scrap products containing hazardous materials depending on the viewable screen size (CalRecycle, 2009). The California EPA uses the tax revenues to establish the Department of toxic substances control (DTSC) besides compensating the recycling firms for the recycling costs incurred.
The DTSC is responsible for inspecting the products for hazardous materials (Gable and Shireman, 2001). Canada and Japan have implemented similar programs (Hicks et al., 2005; HP, 2005; Lee et al., 2000; Shih, 2001; Wen, 2005a). However, the EPA is a non-profit organization. It should consider the total social welfare when making policies. It is reasonable to view the EPA as a role of the government, so our model aims to maximize the total social welfare associated with all participants. In general, the social welfare may be defined as the sum of producer surplus, consumer surplus, tax/subsidy revenue, and the environmental externality cost (Bansal and Gangopadhyay, 2003; Hong et al., 2007).
Our modeling framework assumes that the government establishes the associated fees to maximize social welfare and not the fund balance objective in a competitive system. We further assume that the government considers the fees public information and that the associated players select the optimal response to the government-determined rates. Hence, this thesis presents a Stackelberg-type model where the government is a leader to determine the ARFs and subsidy fees, and parties
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such as MIS firms and recycling firms are the followers, who are competitive participants respectively. The number of recycling firms may affect the value of social welfare, so it is important for the EPA to determine the number of recycling licenses. Therefore, we study how the EPA determines the optimal number of recycling licenses. In this research, we address the following questions:
(i) Is the concept of fund balance the ideal method for determining the level of ARFs and subsidy fees in a competitive system?
(ii) What are the socially optimal ARFs and subsidy fees?
(iii) How might the associated players behave in a competitive system?
(iv) How might the government behave when it determines the number of recycling licenses?
The rest of this thesis is organized as follows. In Chapter 2, we review the current environmental policies and the associated instruments. In Chapter 3, we present the social welfare model and fund balance model. Then we solve the optimization problems of these two models for the equilibrium ARFs and subsidy fees established by the EPA and the decisions made by the MIS firms and recycling firms respectively. In Chapter 4, we utilize a case study to examine the difference in the performance measures between the proposed social welfare model and the current practice model. In Chapter 5, we study the impact of the number of recycling licenses on the value of social welfare, total recycling quantity, reward money, and subsidy fees in the recycling market for the social welfare model and the fund balance model. We conclude this thesis in Chapter 6.
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