The Economics of Energy Market Transformation ProgramsEPA Grant Number: U915383
Title: The Economics of Energy Market Transformation Programs
Investigators: Duke, Richard
Institution: Princeton University
EPA Project Officer: Just, Theodore J.
Project Period: January 1, 1998 through January 1, 2001
Project Amount: $102,000
RFA: STAR Graduate Fellowships (1998) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Economics and Decision Sciences , Fellowship - Economics and Business
The objective of this research project is to develop a dynamic, benefit-cost model and apply it to the World Bank Group's Photovoltaic Market Transformation Initiative (PVMTI), the U.S. Environmental Protection Agency's (EPA) Green Lights (GL) Program, and the federal gasoline excise tax exemption for ethanol.
This research project, designed in conjunction with my advisor Professor Daniel M. Kammen, evaluates three energy-sector market transformation programs: the U.S. EPA's ongoing GL Program to promote on-grid efficient lighting, the World Bank Group's new Photovoltaic Market Transformation Initiative (PVMTI), and the federal tax subsidy to support grain ethanol. We developed a benefit-cost model that uses experience curve theory to estimate unit cost reductions as a function of cumulative production experience. This novel methodology for assessing market transformation programs assumes an isoelastic demand schedule and calculates indirect demand effects based on the induced price differential. Accounting for dynamic feedback between the demand response and learning substantially raises the benefit-cost ratio (BCR) of the first two programs. The BCR of the ethanol program, however, is approximately zero, illustrating a technology for which a market transformation program was not justified. Our results support a broader role for market transformation programs to commercialize new environmentally attractive technologies, but the ethanol experience suggests moderately funding a broad portfolio of technologies that meet strict selection criteria.