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Title: Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies

Abstract

Energy-efficient manufacturing technologies can reduce energy consumption and lower operating costs for manufacturing facilities, but up-front costs and increased process complexity frequently lead to manufacturers being reluctant to adopt such technologies. To avoid overestimating the benefits of advanced energy-efficient manufacturing technologies, it is necessary to account for how quickly and how widely the technology will be adopted by manufacturers. This work develops a method for estimating manufacturing technology adoption rates using quantitative technology characteristics including energetic, economic, and technical criteria that capture both incentives (such as energy and cost savings) and disincentives (such as increased process complexity) for technology adoption. This method is unique in that it can be applied before or after a technology reaches the market; other adoption rate estimation methods require sales data and can only be applied post-market. Eleven technology characteristics are considered, with each characteristic weighted to reflect its impact on the overall technology adoption rate. Technology characteristic data is used to estimate model parameters for the Bass diffusion curve, which quantifies the change in the number of new technology adopters in a population over time. Finally, energy savings at the sector level are calculated over time by multiplying the number of new technology adoptersmore » at each time step with the technology’s facility-level energy savings. The proposed method is demonstrated with an application to glass industry manufacturing technologies using technology data obtained from the U.S. Department of Energy's 2017 bandwidth study. The potential energy savings for each technology and the rate at which each technology is adopted in the sector are quantified and used to identify the technologies which offer the greatest cumulative sector-level energy savings over a period of 20 years.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE) - Advanced Manufacturing Office
OSTI Identifier:
1567137
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Journal of Cleaner Production
Additional Journal Information:
Journal Volume: 232
Country of Publication:
United States
Language:
English
Subject:
Bass diffusion; advanced manufacturing; energy efficiency; technology adoption

Citation Formats

Hanes, Rebecca, Carpenter, Alberta, Riddle, Matthew, Graziano, Diane J., and Cresko, Joe. Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies. United States: N. p., 2019. Web. doi:10.1016/j.jclepro.2019.04.366.
Hanes, Rebecca, Carpenter, Alberta, Riddle, Matthew, Graziano, Diane J., & Cresko, Joe. Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies. United States. doi:10.1016/j.jclepro.2019.04.366.
Hanes, Rebecca, Carpenter, Alberta, Riddle, Matthew, Graziano, Diane J., and Cresko, Joe. Fri . "Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies". United States. doi:10.1016/j.jclepro.2019.04.366.
@article{osti_1567137,
title = {Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies},
author = {Hanes, Rebecca and Carpenter, Alberta and Riddle, Matthew and Graziano, Diane J. and Cresko, Joe},
abstractNote = {Energy-efficient manufacturing technologies can reduce energy consumption and lower operating costs for manufacturing facilities, but up-front costs and increased process complexity frequently lead to manufacturers being reluctant to adopt such technologies. To avoid overestimating the benefits of advanced energy-efficient manufacturing technologies, it is necessary to account for how quickly and how widely the technology will be adopted by manufacturers. This work develops a method for estimating manufacturing technology adoption rates using quantitative technology characteristics including energetic, economic, and technical criteria that capture both incentives (such as energy and cost savings) and disincentives (such as increased process complexity) for technology adoption. This method is unique in that it can be applied before or after a technology reaches the market; other adoption rate estimation methods require sales data and can only be applied post-market. Eleven technology characteristics are considered, with each characteristic weighted to reflect its impact on the overall technology adoption rate. Technology characteristic data is used to estimate model parameters for the Bass diffusion curve, which quantifies the change in the number of new technology adopters in a population over time. Finally, energy savings at the sector level are calculated over time by multiplying the number of new technology adopters at each time step with the technology’s facility-level energy savings. The proposed method is demonstrated with an application to glass industry manufacturing technologies using technology data obtained from the U.S. Department of Energy's 2017 bandwidth study. The potential energy savings for each technology and the rate at which each technology is adopted in the sector are quantified and used to identify the technologies which offer the greatest cumulative sector-level energy savings over a period of 20 years.},
doi = {10.1016/j.jclepro.2019.04.366},
journal = {Journal of Cleaner Production},
number = ,
volume = 232,
place = {United States},
year = {2019},
month = {9}
}