By Horace Herring, Steve Sorrell (eds.)
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Additional resources for Energy Efficiency and Sustainable Consumption: The Rebound Effect
Studies estimating ηε (E), ηε (S) or ηPS (S) for personal transport vary considerably in terms of the data used and specifications employed. Most studies use aggregate data which can capture long-term effects on demand such as fuel efficiency standards, while household survey data can better describe individual behaviour at the micro level. , 1993), while disaggregate studies produce results that are more difficult to generalise. While all studies use vehicle kilometres as the measure of useful work, this may either be measured in absolute terms or normalised to the number of adults, licensed drivers, households or vehicles (Sorrell and Dimitropoulos, 2007b).
Baer, P. and M. Mastrandrea (2006) High Stakes: Designing Emissions Pathways to Reduce the Risk of Dangerous Climate Change, London: Institute for Public Policy Research. Berndt, E. R. (1978) ‘Aggregate energy, efficiency and productivity measurement’, Annual Review of Energy, 3: 225–73. Brookes, L. G. (2000) ‘Energy efficiency fallacies revisited’, Energy Policy, 28(6–7): 355–66. Cleveland, C. , R. K. Kaufmann and D. I. Stern (2000) ‘Aggregation and the role of energy in the economy’, Ecological Economics, 32: 301–17.
Concepts, indicators and methodological issues’, Energy Policy, 24(5): 377–90. Princen, T. (2005) The Logic of Sufficiency, Boston: MIT Press. Pye, M. and A. McKane (1998) Enhancing Shareholder Value: Making a More Compelling Energy Efficiency Case to Industry by Quantifying Non-energy Benefits, Proceedings 1999 Summer Study on Energy Efficiency in Industry, Washington, DC. Saunders, H. D. (1992) ‘The Khazzoom-Brookes postulate and neoclassical growth’, Energy Journal, 13(4): 131. Schipper, L. and M.
Energy Efficiency and Sustainable Consumption: The Rebound Effect by Horace Herring, Steve Sorrell (eds.)