An alternative approach to estimation of the energy efficiency gap in food processing

This paper proposes an alternative approach to the estimation of the "practical" energy efficiency gap, using the US food processing industry as an empirical example. Starting with stochastic frontier energy demand functions estimates based on non-public, plant level data from the U.S. Census Bureau, the approach controls for plant level inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. This approach generates an estimate of the cumulative distribution of energy efficiency across all plants in the industry. While the estimates of the distributions are sensitive to the methods, the analysis consistently shows that few plants come close to 100% efficiency. Defining a "practical level of energy efficiency" using the 95th percentile of the estimated efficiency distributions, instead of assuming 100% efficiency, results in an similar to 20% estimate of the efficiency gap. Most of the reductions in energy use from closing this practical efficiency gap are from plants that are "low hanging fruit," i.e.. plants in the bottom half of the efficiency distribution. Two-thirds of the estimated 20% practical efficiency gap can be achieved by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industries, ranging from a low of 0.4% to a high of 5.7%.