Date of Award

10-2013

Embargo Period

2-18-2014

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Civil and Environmental Engineering

Advisor(s)

Scott Matthews

Abstract

While renewable energy is in the process of maturing, energy efficiency improvements may provide an opportunity to reduce energy consumption and consequent greenhouse gas emissions to bridge the gap between current emissions and the reductions necessary to prevent serious effects of climate change and will continue to be an integral part of greenhouse gas emissions policy moving forward. Residential energy is a largely untapped source of energy reductions as consumers, who wish to reduce energy consumption for monetary, environmental, and other reasons, face barriers. One such barrier is a lack of knowledge or understanding of how energy is consumed in a home and how to reduce this consumption effectively through behavioral and technological changes.

One way to improve understanding of residential energy consumption is through the creation of a model to predict which appliances and electronics will be present and significantly contribute to the electricity consumption of a home on the basis of various characteristics of that home. The basis of this model is publicly available survey data from the Residential Energy Consumption Survey (RECS). By predicting how households are likely to consume energy, homeowners, policy makers, and other stakeholders have access to valuable data that enables reductions in energy consumption in the residential sector. This model can be used to select homes that may be ripe for energy reductions and to predict the appliances that are the basis of these potential reductions. This work suggests that most homes in the U.S. have about eight appliances that are responsible for about 80% of the electricity consumption in that home. Characteristics such as census region, floor space,
income, and total electricity consumption affect which appliances are likely to be in
a home, however the number of appliances is generally around 8. Generally it takes
around 4 appliances to reach the 50% threshold and 12 appliances to reach 90% of
electricity consumption, which suggests significant diminishing returns for parties
interested in monitoring appliance level electricity consumption.

Another way to improve understanding of residential energy consumption is
through the development of residential use phase energy vectors for use in the
Economic Input-Output Life Cycle Assessment (EIO-LCA) model. The EIO-LCA
model is a valuable scoping tool to predict the environmental impacts of economic
activity. This tool has a gap in its capabilities as residential use phase energy is
outside the scope of the model. Adding use phase energy vectors to the EIO-LCA
model will improve the modeling, provide a more complete estimation of energy
impacts and allow for embedded energy to be compared to use phase energy for the
purchase of goods and services in the residential sector. This work adds 21 quads of
energy to the residential energy sector for the model and 15 quads of energy for
personal transportation. These additions represent one third of the total energy
consumption of the United States and a third of the total energy in the EIO-LCA
model. This work also demonstrates that for many products such as electronics and
household appliances use phase energy demands are much greater than
manufacturing energy demands and dominate the life cycles for these products.

A final way in which this thesis improves upon the understanding of how use
phase energy is consumed in a home is through the exploration of potential energy reductions in a home. This analysis selects products that are used or consumed in a
home, and explores the potential for reductions in the embedded manufacturing and
use phase energy of that product using EIO-LCA and the energy vectors created in
Chapter 3. The results give consumers an understanding of where energy is
consumed in the lifecycle of products that they purchase and provide policy makers
with valuable information on how to focus or refocus policies that are aimed and
reducing energy in the residential sector. This work finds that a majority of the
energy consumed by retail products is consumed in the use phase of electronics and
appliances. Consequently the largest potential reductions in residential energy use
can be found in the same area. The work also shows that targeting reductions in the
manufacturing energy for many products is likely to be an ineffective strategy for
energy reductions with the exception of a select few products. Supply chain energy
reductions may be more promising than manufacturing energy reductions, though
neither is likely to be as effective as strategies that target use phase energy
reductions.

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