Fair and Efficient Implementation of Extended Producer Responsibility Legislation

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Extended Producer Responsibility (EPR) is a policy tool that holds producers financially responsible for the post-use collection, recycling and disposal of their products. EPR is rapidly becoming the preferred policy tool in the U.S. for managing electronic waste. The prevalent form of implementation is a collective system, where one central program is responsible for processing all the e-waste collected in the state. The resulting cost is then allocated to the producers selling in the state by market share or return share. These allocation schemes do not take into account producer heterogeneity or network effects and are thus inherently unfair, which has been a significant barrier to implementation. Hence, the first objective of this research is to develop fair but practical cost allocation mechanisms that ensure widespread producer participation in the central program so as to maximize the welfare gains from EPR legislation. Moreover, effective market mechanisms that do not rely on direct cost allocation and that capture network effects will be designed. There is uncertainty inherent in every aspect of collection and recycling networks. Consequently, cost allocations and market mechanisms that function effectively under uncertainty will be developed. E-waste bills in the U.S. vary widely in their specifics relating to collection and recycling targets, product scope, ability to opt out of the state plan, etc. Hence, an important objective of this research is to evaluate the range of existing policy choices in order to assist in the design of effective legislation. Our approach captures the linkage between the operational and policy levels in EPR programs by embedding collaborative network game analysis within policy evaluation. If successful, the results of this research will enable the fair and efficient implementation of EPR legislation in the United States by developing the knowledge base to provide decision support to the involved parties. The proposed research will extend the knowledge base at the intersection of robust optimization, cooperative game theory, and mechanism design. Finally, this effort will provide a wealth of data for further analysis by the researchers and other academics.