Comparing the statistical distributions of energy efficiency in manufacturing: meta-analysis of 24 Case studies to develop industry-specific energy performance indicators (EPI)
Abstract
There is growing interest among policy makers and others regarding the role that industrial energy efficiency can play in mitigation of climate change. For over 10 years, the US Environmental Protection Agency (EPA) has supported the development of sector-specific industrial energy efficiency case studies using statistical analysis of plant level on energy use, controlling for a variety of plant production activities and characteristics. These case studies are the basis for the ENERGY STAR® Energy Performance Indicators (EPIs). These case studies fill an important gap by estimating the distribution of efficiency using detailed, sector-specific, plant-level data. These estimated distributions allow Energy Star to create “energy-efficient plant benchmarks” in a variety of industries using the upper quartile of the estimated efficiency distribution. Case studies have been conducted for 14 broad industries, 2 dozen sectors, and many more detailed product types. This paper is a meta-analysis of the approach that has been used in this research and the general findings regarding the estimated distribution of performance within and across industries. We find that there are few sectors that are well represented by a simple “energy per widget” benchmark that less energy-intensive sectors tend to exhibit a wider range of within-industry efficiency than energy-intensive sectors, but changes over time in the level and range of energy efficiency do not reveal any single pattern.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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