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Representation of energy efficiency as a no-regrets option requires an estimate of the costs indirectly imposed by the barriers to their adoption or conversely of the costs of policies and programs that are established to address these barriers. In the third section, we review these issues and summarize the results of the empirical analysis we have conducted on this topic on the quantification of the principal agent barrier in the U.S. residential sector. We also undertake an empirical assessment of the costs and benefits for overcoming these barriers by policies and programs by analyzing the energy efficiency programs implemented by utilities in California, which are one of the world’s largest energy efficiency programs. The section also includes a brief description of the use of such costs and benefits in EC models.

In the fourth section, we focus on the importance of considering the co-benefits of adopting energy efficiency measures. Based on the empirical analysis of the U.S. iron and steel industry, and residential clothes washers, we show how the estimates of the potential of no-regrets options changes when co-benefits are included in the analysis.

In the fifth section, we report on the historical changes in the cost curves in industrial and residential sectors, and how these dynamic changes are likely to influence the costs of carbon emissions reduction. The last section summarizes our findings and provides recommendations for future work.

Representation of Energy Efficiency: Cost Curves and Energy-Climate Models

In this section, we first review the use of cost curves, one of the commonly used approaches to represent and compare various GHG mitigation options including energy efficiency improvements. We then broadly review how energy efficiency improvement options are represented in energy climate models.

Energy Efficiency Cost Curves

The energy sector comprises the major energy demand sectors (industry, residential and commercial, transport, and agriculture), and the energy supply sector, which consists of resource extraction, conversion, and delivery of energy products. Greenhouse gas emissions occur at various points in the sector, from resource extraction to end use, and accordingly, options for mitigation exist at any of these points.

The bottom-up approach involves the development of scenarios based on energy end uses and evaluation of specific technologies that can satisfy demands for energy services. One can compare technologies based on their relative cost to achieve a unit of GHG reduction and other features of interest. This approach gives equal weight to both energy supply and energy demand options. A variety of screening criteria, including indicators of cost-effectiveness as well as non-economic concerns, can be used to identify and assess promising options, which can then be combined to create one or more mitigation scenarios. Mitigation scenarios are evaluated against the backdrop of a baseline scenario, which simulates the events assumed to take place in the absence of mitigation efforts. Mitigation scenarios can be designed to meet specific emission reduction targets or to simulate the effect of specific policy interventions. The results of a bottom-up assessment can then be compared to a top-down analysis of the impacts of energy sector scenarios on the macro-economy.

In this paper, we will focus on energy efficiency options and the representation of their marginal cost curves, typically referred to as cost of conserved energy (CCE) curves. CCE curves were developed about three decades ago to place energy-efficiency cost estimates at a level comparable to that for supply-side options (Meier, 1982). A CCE curve is made up of a combination of several options and can be sector-specific or economy-wide. The CCE is estimated for each mitigation option and plotted against its resulting energy or emissions savings.5 A combination of such calculations yields a curve of CCE for a suite of mitigation options. The CCE calculation is based on investment theory6 and it is expressed as:


One criticism that has been directed at the calculation of CCEs is that they may err by aggregating across the entire stock, without differentiating costs, and discount rates for various classes of consumers. One way to avoid this mistake is illustrated by Sathaye and Murtishaw (2004) in the clothes washer analysis by disaggregating potential adopters by income class.


Stoft (1995) offers an alternative expression that avoids the problem of double counting due to dependence among measures on a curve.

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