Incorporating experience curves in appliance standards analysis

Abstract

There exists considerable evidence that manufacturing costs and consumer prices of residential appliances have decreased in real terms over the last several decades. This phenomenon is generally attributable to manufacturing efficiency gained with cumulative experience producing a certain good, and is modeled by an empirical experience curve. The technical analyses conducted in support of U.S. energy conservation standards for residential appliances and commercial equipment have, until recently, assumed that manufacturing costs and retail prices remain constant during the projected 30-year analysis period. This assumption does not reflect real market price dynamics. Using price data from the Bureau of Labor Statistics, we present U.S. experience curves for room air conditioners, clothes dryers, central air conditioners, furnaces, and refrigerators and freezers. These experience curves were incorporated into recent energy conservation standards analyses for these products. Including experience curves increases the national consumer net present value of potential standard levels. In some cases a potential standard level exhibits a net benefit when considering experience, whereas without experience it exhibits a net cost. These results imply that past energy conservation standards analyses without experience curves may have undervalued the economic benefits of potential standard levels, possibly resulting in less stringent standards and reduced energy savings than was economically justified.

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

“…For example, learning curves using price data were incorporated into recent energy conservation standards analyses of key appliances such as room air conditioners, clothes dryers, and refrigerators and freezers [42]. The joint approach of the Energy Protection Agency (EPA) and the National Highway Traffic Safety Administration (NHTSA) to apply learning curves in vehicle regulator impact assessments assumes different rates of learning to reflect likely substantial learning impact of newer technologies in the near future and limited learning opportunities for mature technologies [67].…”

“…The Boston Consulting Group applied learning phenomena to analyze the relationship between the average unit price and cumulative output of 24 industrial products in 1968. Since then, this approach has been used in empirical studies in a wide range of sectors, including manufacturing [3,21], consumer products [22,23], energy supply technologies [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], energy demand technologies [42][43][44] and environmental control technologies [45][46][47].…”

Accounting for Technological Change in Regulatory Impact Analyses: The Learning Curve Technique

“…The DOE framing of the learning curve approach generally lacks the specific citations to the management literature and the underlying phenomenon that are included in the EPA-NHTSA approach, although a research article developed for the energy policy literature concurrently with the regulatory impact assessment chronology provided in Table 4, above, includes a brief literature review and introduction to the concept (see Desroches, Garbesi et al 2013). Within the regulatory impact assessment documents themselves, however, the writing on the DOE approach tends to equate the long-term declines observed in appliance prices with learning as well as 21 experience curves, and does not discuss the complex factors that underlie the learning curve phenomenon.…”

“…First, the DOE approach fits a learning curve to past data for the whole of a given product that is the subject of the regulatory impact assessment. Second, it uses that learning curve to derive a multiplier for a part of the cost projections for the given product under both a business-as-usual case as well as a case in which a new MEPS is set for the product (for more information, see Desroches, Garbesi et al 2013). …”

“…The prices included in the PPI are from the first commercial transaction for many products and some services. Desroches, Garbesi et al (2013) points out that there is an attempt within the PPI to filter out "physical changes (such as capacity, premium features, government-mandated features, etc.) in the product that affect the price."…”