Social-Psychological Factors and
Energy Behaviors
Chien-fei Chen
CURENT course
November 6th, 2014
Knoxville, Tennessee
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Feedback,
Norms, Goalsetting,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
(Barriers)
Why Should You Care?
• Decision-making: understand the process of how
individuals make decisions in energy domain and
the impact of behaviors on energy use and the
environment as a whole.
• Awareness: connection between engineering
profession to the consequences of its activities on
the society.
• Improvement: engineering technology designs and
make broader impact.
• Persuasion: explain and discuss the ramifications
of developments in engineering and engineering
works to the public. Big Picture!
Typical Social Science Studies
Collect Data
Theories
Evidences
(crosssectional
survey;
experiments)
Analyze
Modeling
Interpretation
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Norms, Goalsetting,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Energy and Behavioral Change
• What are the factors affecting energy use?
• How to reduce energy consumption?
• How to change people’s behaviors over
time?
Creating an Energy Revolution
“A revolution doesn’t happen when society
adopts new tools, it happens when society
adopts new behaviors” (Glay Shirky, Digital Guru)
“Mitigate future climate change will be made
by energy consumers, rather than
suppliers… not a straightforward and easily
achievable goal” (EIA, 2009)
U.S. Energy Consumption in 2010
Transportation
-other, 16.90%
Industry,
29.80%
Residential,
22.40%
Personal
Transport,
11.40%
Residential &
personal
transport
account for
34%
Commercial,
19.40%
Source: EIA
Questions
What is your average monthly electricity
consumption?
What is average American household
electricity consumption?
A Household Consumption in TN
2000
1800
1600
1400
1200
1000
Series1
800
600
400
200
0
Jul.
Jun.
May
Apr.
Mar.
Feb.
Jan.
Dec.
Nov.
Oct.
Average Monthly Electricity Consumption per
Household in 2012
US. Average 940 KWh
Tennessee Average: 1217KWh
Highest in the nation (ranked 2nd)
Evidences of Energy Behaviors
• Behavioral approach could save 123 million metric tons
of carbon annually in year 10 from a study of 17
household actions, representing 20% of household direct
emissions or 7.4% of U.S. national emissions.
• Types of household behaviors (based on cost and
frequency of action) could result in a 22% reduction in
household and personal transportation energy use over
a 5-8 year period (Laitner & Ehrhardt-Martinez, 2009).
• In crisis situations, energy saving has resulted in
immediate, community-level electricity savings of 25% in
6 weeks and post-crisis savings of 8-10% (Leighty & Meier, 2010).
• Feedback programs and devices could save electricity
from 4-12% (Ehrhardt-Martinez, et al. 2010).
How Much Can Household Save Energy?
• Researchers identifying more than 100
separate conservation and energy
efficiency measures (all cost-effective) that
could be taken in a short period of time
(Laitner and Martinez, 2009).
• Based on a Monte Carto probability
simulation, an energy saving potential on
the order of about 9 Quads (9% of US
energy use) compared to current use.
Carbon Emission and Behavioral Change
1-14
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Norms, Goalsetting,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Traditional Economic
and Engineering Models
Typical responses to energy crisis:
• Find new energy resources
• Develop technology – engineers’ job
• Provide financial incentives for people to
reduce consumption, to adopt more
efficient technologies
Assumption of rationality
People are instrumental and selfinterested, consistent, cost-benefit based.
Assumptions of Human Behaviors
Without Deliberation
• In-output model
• Behaviors controlled by
environment
Not Empathizing:
• Internal judgments
• Cognitive process
• Interpersonal relationships
With Deliberation
• Emphasize human
agency
• Mindful or cognitive
process
Empathizing:
• Attitudes
• Perceptions
• Motivations
(Asch, 1951, Sherif, 1935)
Actors are mindless robots
Actors are mindful
Decision-Making Models
•
•
•
•
Social Cognitive Theory (Bandura, 1986)
Theory of Planned Behavior (Ajzen, 1991)
Norm Activation Model (Schwartz, 1977)
Social Norm Approach (Claidini,1994 and
others)
• Prospect Theory (Kahneman & Tversky,
1979)
• To identify factors or in targeting
behavioral change (intervention)
Social Cognitive Theory (Bandura, 1986)
• Human agency involves deliberative ability to
make choices & regulate behavior
• A triadic, dynamic, & reciprocal interaction.
Environmental
Factors (norms)
Personal Factors
(cognitive, affective, &
biological events)
Behavior
Theory of Planned Behavior
(Icek Ajzen, 1991)
Norm Activation Model (Schwartz, 1977)
Awareness of
consequences
Ascription of
responsibility
Personal
Norms
Pro-social or
environmental
behaviors
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Norms, Goalsetting,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Social-psychological Factors influencing
Energy Conservation in China
• Investigated how social norms, behavioral
control, attitudes, energy concern, perceived
consequences influence energy saving intention
and behavior
• Sampled 584 employees from electricity
companies in Jiangsu Province, China
• Based on the Theory of Planned Behavior and
the Norm Activation Model.
Factors Predicting Energy Conservation
Behaviors
Results of Social-Psychological Factors Affecting
Electricity Saving Behaviors (241 UTK students)
Barriers to save electricity (-0.26) **
Support of renewable (0.30) *
Energy concern (0.33) **
Global warming consequences
(0.19)*
Economic benefits (0.09)
Electricity
Saving
Behaviors
Personal norms (0.34)*
Social rewards (0.03)
Demographics (gender, age, race,
political orientation, rent)
* P<0.05; ** p<0.01, green boxes on the left were not significant
Result of Social Psychological Factors Affecting
Support of Renewable Energy
Electricity saving behaviors (-0.22)**
Energy conservation attitudes (0.18)*
Energy concern (0.24)*
Global warming consequences(0.18)*
Economic benefits (-0.10)
Support of
Renewable
Energy
Personal norms (0.34)
Social rewards (-0.08)
Familiarity with renewable(0.05)
Political orientation (0.20) *
* P<0.05; ** p<0.01, green boxes on the left were not significant
Symbolic Values: Social Status and Green
Behaviors
• People are more likely to choose green
products rather than non-green products
when status motives were made salient
(Griskevicious et al., 2010)
• Especially when the choice were made in
the public and the green product is more
expensive.
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Norms, Goalsetting,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Advanced Meters
• Meters that measure and record usage data at
hourly intervals or more frequently, and provide
usage data to both consumers and energy
companies at least once daily. Advanced meters
include basic hourly interval meters, meters with
one-way communication, and real-time meters
with built-in two-way communication capable of
recording and transmitting instantaneous data.
• To encourage energy conservation, utilities must
directly or indirectly provide this information to
consumers to contextualize the information,
break down barriers and motivate actions.
Public Acceptance of Smart MetersCURENT Project
• M-turk respondents: 817
• Awareness, Knowledge, Attitudes toward the Installation
of Smart Meters, Interests in having smart meters,
Intention to use smart meters or in-home displays,
Satisfaction, Electricity habit change, Perceived ease of
use and usefulness, Perceived pricing, Privacy
• Health and environmental concerns
• Willingness of reducing electricity use to save
environment
• Money consciousness, trust on utility company
• Lasting change, smart phone application
• Opinions on demand response program: Time of Use
pricing and appliance auto setting
• Demographics
Factors Influencing
Acceptance of Smart Meter
Useful1
Useful2
Useful3
Useful4
Useful5
Useful6
.89
Priv4R
.51
Money2
Money6
Environ1
Environ2
Environ3
Environ4
.91
Priv7R
Price2
.94
Price3
.93
.85
Price
privacy
.52, p < .001
.88
.84
-.14, p < .001
-.093, p = .001
.76
.61
.70
.56
-.059
HabitE
1
Money
Money3
Money4
.92
Usefulness
Useful8
Money1
Priv6R
.74
.89
.87
.80
Priv5R
-.018
HabitC
1
-.007
.091, p = .01
.83
.84
.87
.73
.95
.021, p =
.492
Environ
.80
.014
.035
HabitC
H
.83
Support
.68
Habit
7
Habit6
.95
Trust1
Trust
.89
Trust2
.83
Trust3
.92
Trust4
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Feedback
Program)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Intervention/Strategies of Changing Behaviors
• Feedback programs
• Non-economic motivation strategies can
effectively enhance household energy saving
• Social norms: descriptive and injunctive
norms shape people’s behaviors; developing
strategies in a social context
• Goal setting: define what people are trying to
attain and be able to evaluate their progress
• Commitments: help people to sure their
actions are consistent with their ideals
Layers of Energy Feedback Technologies
Advantages of Feedback Programs
• Eliminates the need for prescriptive
program (financial incentives)
• Maximizes household options
• Allows for targeted recommendations
• Engages people in an active learning
process
• Empowers people to become part of the
energy solution
(Source: Ehrhardt-Martinez from Garrison institute)
Indirect vs. Direct Feedback
• Indirect feedback helps people to see
larger patterns in energy use
• Direct feedback help people to understand
the impact of small changes and the
implications of specific end uses
• Both influence people in different ways
• More and more approaches use both
types
Average Household Electricity Savings of
Historical Program by Feedback Type
Average Household Electricity Savings of
Historical Program by Feedback Type
Potential
Resource
Savings:
20-35%
Real-Time
Plus
Feedback
w/Smart
Program
Plus Smart
Application of S.S.
Insights
Social Norms Approach: Opower
1-40
Goal Framing
• Three main goals (Lindenberg, 2008)



Normative Goal
Gain Goal
Hedonic Goal
• Behaviors can be framed based on
different goals
Effectiveness of Feedback Programs
# of
Studies
Energy
Savings
Sources
Social Norms
14
2-10%
Alcott (2009), Ayers et al. (2009),
Ehrhardt-Martinex (2009), Nolan et al.
(2008), Schultz et al (2007), Wilhite et al.
(1999)
Goal Setting
4
5-17%
Seligman (1978), Winett et al. (1982),
Van Houwellingen (1989), Abrahamse et
al. (2007)
Competitions
2
8-32%
Petersen et al. (2007) (68,300 kWh)
Sintov, et al. (2010)(75,000 kWh)
Commitment
1
5-8%
Staats et al. (2004)
Source: Garrison Institute
Demand Response vs. Overall Conservation
Programs
• A meta analysis reports that energy saving of
Peak Load Shifting Program is 3%, while energy
conservation promotion program is 10%; based
on 36 studies implemented between 19952010(Martinez, Donnelly and Laitner, 2010).
Persistence of Savings
• The evidence from 27 of the 58 studies suggests
that if the feedback is persistent, the savings are
persistent over time (Garrison Institute)
Study
Country
Type
Duration
Months
Savings
Mountain (2006)
Canada
Real Time
Aggregate
13
Persistent conservation effect
Mountain (2008)
Canada
Real Time
Aggregate
24
Persistent conservation
Neilsen (1993)
Denmark
Enhanced Billing
36
Persistent conservation effect
Staats et al
(2004)
Netherlands
Enhanced Billing
36
Energy savings increased from
4.8% (at 8 months) to 7.6% (at
24 months)
Wilhite and Ling
(1995)
Norway
Enhanced Billing
36
Increased from 7.6% at the end
of year two to 19% at the end
of year three.
Wilhite et al.
(1999)
Norway
Enhanced Billing
21
Increased 6-8% right after the
intervention, 4% after 2 years
Persistence of Energy Savings
• Are savings persistent when feedback is removed?
Study
Country
Type
Duration
Savings
Persistence of
savings
Van
Houwellingen
(1989)
Netherland
s
Real time
Aggregate
(the
indicator)
24
12.3%
Energy conservation
did not persist after the
monitors were removed
Study
Country
Type
Duration
Savings
Persistence of
savings
Staats et al.
(2004)
Netherlands
Enhanced
Billing
36
7.6%
Energy savings
increased from 4.8%
(at 8 months) to 7.6%
(at 24 months). Saving
persisted long after
the intervention ended
• The supportive social environment provided by
ecoTeams made the difference
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention
(Norms,
Framing)
Factors Influencing
Public Acceptance
of Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors Influencing
Electricity Savings
Focus Theory of Normative Conduct
• Focus theory posits that norms affect human
behavior systematically and significantly but only
in situations where the norm is salient (focal) for
the individual (Cialdini, Reno, & Kallgren, 1990; Kallgren, Reno & Cialdini, 2000)
• Experiments: descriptive (what is typically done)
vs. injunctive norms (what is approved)
 Littering increased in a littered environment;
littering was reduced only when descriptive norms
was activated in a clean environment.
 Influence of injunctive norms was effective
regardless of whether the environment was clean
or littered.
Message Framing and Norms
Experiment: Sign 1
Source: Goldstein, Cialdini, Griskevicius,
Schultz, Nolan
Experiment: Sign 2
Result of Experiment
Study 1a: Motives to Conserve Energy
• California neighborhood survey (Dr. Noah Goldstein)
3.4
3.3
3.2
3.1
Perceived
amont of
influence
3
2.9
2.8
2.7
2.6
Envrionment
Society
Money
Norms
Study 1b: Social Norms Experiment
• Conducted an experiment and placed signs
on 300 residents’ doors encouraging them to
conserve energy
• Varied the reasons for conserving on the
signs to be in accordance with the various
motivating factors from Study 1a
Result of Study 1b
Energy conservation appeal (A/C):
14.5
14
14.3
14
13.8
13.9
13.5
Energy
Consumption
(kilowatt hours
consumed per day)
13
12.6
12.5
12
11.5
No SignEnvrionmental protection
Benefit to Society
Money
Norms
Conclusions from Studies 1a & 1b
• People estimate social norms as the least
powerful motivators of energy conservation,
this research found signs communicating the
social norms were the most effective.
• Attitudes and behaviors are not consistent
Recycling Towels in Hotels:
Evidence of Descriptive Norms
Below “Please reuse your towels”
Control: HELP SAVE THE
ENVIRONMENT. You can show your
respect for nature and help save the
environment by reusing your towels
during your stay
Social Norm: JOIN YOUR FELLOW
GUESTS IN HELPING TO SAVE THE
ENVIRONMENT. Almost 75% of
guests who are asked to participate in
our new resource savings program do
help by using their towels more than
once. You can join your fellow guests
in this program to help save the
environment by reusing your towels
during your stay.
Goldstein, Cialdini, & Griskevicius (2008, J of Consumer Research)
Back-fire Effect
Back-fire Example
Back-fire Example
Prospect Theory and Message Framing
• Framing (risk-aversion)
 Prospect theory: people respond to loss and
gain messages differently (Kahneman &
Tversky, 1979)

For example- say there are 600 lives in danger
 Program A: "200 people will be saved"
 Program B: "there is a 1/3 probability that 600
people will be saved, and a 2/3 probability that no
people will be saved"
Persuasion Messages
• Loss Framing (risk-taking behavior)
 Program C: "400 people will die"
 Program D: "there is a 1/3 probability that
nobody will die, and a 2/3 probability that 600
people will die”
Message Framing and Electricity SavingCURENT Project
• What kind of messages are more effective
to influence people’s attitudes and
intention to save electricity?
• Four manipulations on how to convey the
benefits of saving electricity



Goal framing: environmental vs. financial
Temporal framing: long-term vs. short-term
Control group
Condition 1: Financial, Long-Term
Condition 2: Financial, Short-Term
Condition 3: Environmental, Long-Term
Condition 4: Environmental, Short-Term
Results of this Experiment
• Environmental message are more effective than
the money saving ones in producing positive
attitudes.
• Short-term and long-term messages did not
influence individuals’ intention to save electricity
but the efficacy of saving electricity.
Demand Response and Incentives
Demand response,
incentives and
behavioral changes
Intervention:
Understanding
Barriers
Factors of
Influencing Public
Acceptance of
Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors of Influencing
Electricity Savings
Example Study: Financial Incentives and DR
• Goal of the study: Financial incentives
behaviors
?
Desired DR
1) Adjusting Heating/AC thermo setting by 2-3oF when at home
2) Adjusting Heating/AC thermo setting by 5oF or more (or
shutting down) before leaving home
3) Allowing utility companies to adjust Heating/AC thermo
setting by 2-3oF when needed
4) Shutting down Heating/AC for 10 (and 30) minutes in
response to emergency messages
• Impact: more accurate estimation of adjustable loads as a
function of financial incentives
• Method: an online survey on 711 U.S. residents across 48
states
Acceptance rates in respondents
Example Study: Financial Incentives and DR
80.0%
71.9%
70.0%
60.0%
54.4%
50.0%
40.0%
33.0%
30.0%
20.0%
10.0%
15.6%
8.6%
36.3%
34.3%
22.5%
16.0%
19.7%
9.3%
30.3%
51.10%
29.1%
14.6%
0.0%
Adjust 2Adjust
Let utility Emergency Emergency
3°F_at home >=5°F/shut company shut down shut down
down_
adjust 2-3°F
10mins
30mins
away
no rewards
no rewards or a
reward <5% monthly
bill
no reward or a
reward <10%
monthly bill
Financial Incentives, DR, and Customer Segmentation
• Extended goal: better marketing for DR, based on customer
segmentation
• Method: cluster analyses & comparisons of measured socialpsychological and demographic variables across clusters
• Impact: possible increase in adopting rates as a result of good
marketing
• Results: three clusters were identified– the most,
medium, and the least cooperative cluster.
Lower
Comfort
Features of the most cooperative cluster ():
Need in
Summer
• Suggestions for utility companies:
 Focus on the cooperative customers
 Appropriately address customers’
concern for comfort
 Build relationship with customers,
enhance trust
More Smaller
Homes
Higher
Concern for
Environmental
Impacts
The most
Cooperative
Cluster
Higher Trust
in Utility
Company
More Renters
Younger
Social Psychological Factors and
Energy Behaviors
Demand response,
incentives and
behavioral changes
Intervention:
Understanding
Barriers
Factors of
Influencing Public
Acceptance of
Smart Meters
Evidence of Energy
and Behaviors
Why should
you care?
Decision-making
Models
Factors of Influencing
Electricity Savings
Recognizing Barriers – Individual Level
Attitudes &
behaviors
are
inconsistent
Difficulty in
changing
established
values,
behaviors
Barriers
Technology
bias/perceiv
ed risk
Rebound
boomerang
effect
Recognizing Barriers – Structural Level
Political
and policy
issues
Socialeconomic and
Infrastructural
factors
Barriers
Geographic
factors
(climate,
suitable
locations
Cultural
factor
Barrier: What Do Americans Think about
Energy and Efficiency?
Attitudes
•
•
•
•
•
•
Behavior (Gallup 2007)
Most Americans believe our country
•
wastes energy in harmful ways despite
many don’t believe in climate change
People are concerned about the
availability and affordability of energy
(77%)
Percentage of people who reported
buying CFLs is 7%
•
Percentage of people who reported
upgrading to energy efficient
appliances over the past year is 4%
•
Most Americans are eager to reduce
energy use and support policies to
reduce the nation’s energy use
People report they should be spending
several thousand dollars to make their
home as energy efficient as possible
(78%)
Percentage of people who reported
making their home more energy
efficient by installing new windows,
insulation, solar panels, etc. is 2%
Most Americans are willing to
participate in transforming the way we
use energy
People report they should install a
solar panel to reduce home energy
(71%) and should buy a hybrid car
(62%)
Source: Malbach et al., Ch8, People-center Initiatives for Increasing Energy Savings
Barriers: Issue of Rebound
Economic incentives can be effective in certain
situations but can also be ineffective
- A California utility provided information about the
cost of running appliances and devices
a coffee pot is 3 cents per pot; a 3-ton
central AC system is 36 cents per hour
- Your low energy use in comparison with your
neighbor’s high energy use could inspire to use
more energy
- New energy end users consume as much as
30% of saving
Barriers: Structural or Organizational Level
• Energy Efficiency Gap - a significant
underinvestment in energy efficiency measures
whose benefits outweigh their costs – among
residential consumers
• Unrealized reductions in energy consumption is
30-60% (Ehrhardt-Martinez, 2011)
• Rigid organizational structures and cultures that
hinder innovation
• Insufficient knowledge and experience regarding
behavior change strategies
Strategies: Applying Social-psychological Elements
1. Targeting:
2. Informing:
policies,
producers,
programs;
4.
Empowering:
removing
barriers to
provide &
enable better
choices
3. Motivating:
norms,
networks, goals
through
communication
channels
specific
audiences
and actions
Targeting: Scale, People and Actions
• Assess specific actions, target audience,
sources of diversity across households
• Community based actions might include:
 home weatherization and deep retrofits
 Smaller homes with greater amenities
 Use PV systems, LEDs
 Transporting choices
Informing: Consumers, Producers
Motivating: Norms, Networks, Commitments
• Community-based Social Marketing
- Identify barriers and benefits
- Commitment: Move good intentions to ACTION
- Prompts: Reminders to conserve energy
- Build social norms
- Marketing your message (Framing; Risk
Communication Strategies)
- Incentives: Enhancing motivation to act
- Removing external barriers
- Evaluate outcomes
(Amy Hollander, 2011)
Empowering: Providing Better Choices
Providing alternative environment:
• Choice architecture: people make decisions in
an environment where many features, noticed
and unnoticed, can influence their decisions.
The person who creates that environment is, a
choice architect (Thaler and Sunstein, 2008).
• Overcoming the status quo bias
• Social Movement and Campaigns
Conclusions (1)
• Human beings are not always rational.
• Traditional economic theories alone fail to
adequately account for energy efficiency gap
(Hofmeister, 2010).
• Advanced meter technologies provide significant
electricity savings; yet, development of energyefficient technologies is not sufficient.
• Smart meters won’t be smart enough by
themselves.
• Understanding social psychological factors that
influence energy decisions is important.
Conclusions (2)
• Information programs may be effective in changing
attitudes but are not very effective in changing
behaviors in some situations.
• Greater rates of savings is possible given the right
combination of program designs and policy support.
• Sometimes behavior change is faster than
technology development; more behavior change
strategies are needed.
• The need to build a culture of energy conservation:
“engaging people crates a culture of mindful as
opposed to mindless consumption” (EhrhardtMartinex, 2011).