Transcript 2. Sergi - Center for Climate and Energy Decision Making

Understanding public perceptions of
energy tradeoffs in climate, health,
and economic cost
Brian Sergi
Inês Azevedo
Alex Davis
CEDM Annual Meeting
May 23, 2016
Motivation
• Increasing agreement on need for climate action
• What tradeoffs are individuals willing to make in
order to get there?
2
Previous work on tradeoff perceptions
• Individuals respond more strongly to attributes of
energy use than to source (Ansolabehere &
Konisky, 2014)
• Individuals willing to make tradeoffs analysis in
energy decisions (Fleishman-Mayer et. al., 2014)
• Health frames can motivate changes to energy use
more than economics (Asensio & Delmas, 2014)
3
Research questions
• How do individuals make tradeoffs across the different
attributes of electricity generation?
– climate change
– health related air pollution
– economic costs (electricity bills)
• What is the relative effect of providing climate change
and health information when making these tradeoffs?
4
Discrete choice survey
• Individuals respond to 16 comparisons of discrete
electricity “futures” with different attribute levels
• Well-established method in marketing, transportation
research (Train, 2009)
• Emerging method in the energy & environment space:
–
–
–
–
–
5
Climate change and energy security (Longo et. al., 2008)
Estimating implicit discount rates for lighting (Min et. al., 2014)
Preferences for electric vehicles (Helveston et. al., 2015)
Energy efficiency (Davis & Metcalf, 2014)
Renewable energy and electricity bills in Germany (Kaenzig,
2013)
Example choice screen
6
Example choice screen
Electricity portfolio – ways of meeting a state’s generation
needs.
Levels: five “representative” scenarios
-- coal (41%) (baseline)
-- renewables (42%)
-- natural gas (56%)
-- nuclear (50%)
-- efficiency (14%)
7
Example choice screen
Climate change related emissions – change in annual
CO2 emissions from baseline i.e. current emissions levels
(as percentages).
8
Levels:
-- 70% decrease
-- 30% decrease
-- no change
-- 30% increase
-- 70% increase
Example choice screen
Health related air pollution – change in annual SO2
emissions from baseline i.e. current emissions levels (as
percentages).
Levels:
-- 70% decrease
-- 30% decrease
-- no change
-- 30% increase
-- 70% increase
9
Example choice screen
Monthly electricity bill – change in monthly electricity
bill levels for consumers from baseline i.e. individuals’
current bill payments (as percentages).
Levels:
-- 20% decrease
-- 10% decrease
-- no change
-- 10% increase
-- 20% increase
10
Effect of emissions information
• Randomized controlled trial with different emissions
attributes shown in the task.
• Respondents see…
–
–
–
–
–
Group 1: all four attributes (portfolio, bill, CO2, and SO2)
Group 2: portfolio, bill, and CO2 only (no information on SO2)
Group 3: portfolio, bill, and SO2 only (no information on CO2)
Group 4: portfolio and bill only (no information on CO2 or SO2)
Group 5: all attributes + monetized damages for CO2 and SO2
• CO2 – social cost of carbon of $40 per ton
• SO2 – state averaged marginal damage values from AP2 (Muller, 2014)
11
Example choice screen
No SO2 emissions information
12
Example choice screen
13
Modeling
• Random utility mixed logit model (Train, 2009)
– Model the probability that respondents pick any given scenario
conditional on its attributes and those of the alternative
– Respondent is utility maximizer
– Estimate heterogeneous preferences for emissions and bills
14
Survey demographics
(N = 1006 from Amazon Mechanical Turk)
15
Results
• Probability of support (conditional on attributes)
• Willingness to pay
• Not included
– Heterogeneity in responses by state, demographics
– Verification and consistency checks
16
Probability of support
…more likely to support renewables if the are informed about
the emissions benefits (even if there are increased costs)
Respondents less likely to support renewables if they
imply higher electricity bills, but…
17
Conditional probability
Support for renewables falls if emissions benefits do not
manifest in groups with emissions info
Increased support when
climate and health emissions
benefits are presented (even in
the face of higher bills)
18
Willingness-to-pay
Implicit WTP in $ per ton of emissions reduced:
$100-130 per ton of CO2
$60,000-110,000 per ton of SO2
19
Results summary
• Preferences for lower bills, emissions
– Outcomes (monthly bill, CO2, SO2) seem to matter more than means
(portfolio of sources)
• Climate & health emissions reductions on similar level
– Reductions in both pollutants slightly increases support
• Limitations of stated choice studies
– Hypothetical choices, survey design can affect results (Louviere, 2006)
– Cognitive biases in stated preference preference studies (Fischhoff, 2005)
20
Policy implications
• Technology “neutral” policies for emissions reductions?
– More information on portfolio implications may also be needed
• Communicate information on emissions reductions,
particularly health information
– EPA Clean Power Plan promoted as a CO2 policy but with
massive anticipated health “co-benefits” (EPA, 2014)
• Consider co-optimizing climate mitigation policies across
multiple health and climate objectives
– Many possible ways to achieve CO2 reductions (Driscoll et. al.,
2015)
– Those with health benefits likely to gain more support
21
Acknowledgements
• Funding sources: CEDM, Steinbrenner Institute,
CBDR small grants program
• Special thanks to:
– My advisors Inês Azevedo & Alex Davis
– EPP colleagues
– Pre-testers and survey participants
22
Backup slides
23
Heterogeneity by state
CO2 and SO2 random effects plotted for respondents seeing damage information
24
Political party
25
Probability of Support
26
Future work
• Expand on modeling technique
• Run additional surveys
– Nationally representative sample
– Local sample (in-depth interviews)
– Compare to other regions (e.g. China)
• Repeat experiment using different structures
– Change attribute ranges, include other attributes (employment)
27
Electricity portfolios
28
Electricity portfolios
Coal portfolio
Renewables
portfolio
29
Natural gas portfolio
Nuclear portfolio
Efficiency
portfolio
Choice attributes & levels
30
Efficiency incorporation
• Step 1: Current cumulative annual efficiency savings by
state from the EPA
– Input to Integrated Planning Model analysis for Clean Power
Plan (Technical Support Document 2014)
– Top-down engineering estimates
– State based savings estimated to range from 0-2.2% annually
• Step 2: Add EE savings to total generation to calculate
total “demand
• Step 3: Recalculate percentages using this total demand
31
Energy mix by state
32
Attribute levels and format
33
Monetized emissions damages
• What is the effect of providing information on monetized
emissions damages on CO2 and SO2 tradeoffs?
• Monetary damages for attribute levels for each state
– emissions x marginal damages (per ton) = total damages
CO2
34
SO2
Well mixed global pollutant –
location of emissions does not
affect marginal damage
Marginal damage dependent on
population, meteorology, etc. –
spatial heterogeneity by state
Social cost of carbon –
Interagency Working Group
mean value estimate (3%
discount rate) of $40/ton
AP2 model (Muller, 2014) –
state averages of marginal
damage (mortality & morbidity
only)
Damage calculations
• Step 1: State emissions data for 2014 (EPA CEMS)
• Step 2: Estimate per ton damages
– CO2  social cost of carbon
• $40 in $2014 (average 2015 value given 3% discount)
• (IWGSCC Technical Support Document
– SO2  AP2 model
• State averaged values
• Step 3: emissions x per ton damages = total damages
– Approach by Muller 2014, EPA, and others
35
CEMS data (CO2)
36
CEMS data (SO2)
37
State emissions
•
TX – 226 million tons CO2 in 2013 from the electric power sector alone
– 340 thousand tons SO2, but marginal damages are much lower
– ~35% coal and 40% natural gas
•
PA – 100 million tons CO2, 260 thousand tons SO2
•
WV – 7 million tons CO2, 90 thousand tons SO2
•
CA – 44 million tons CO2, 3 thousand tons SO2
Source: EPA CEMS data & “State Energy CO2 Emissions”
(http://www3.epa.gov/statelocalclimate/resources/state_energyco2inv.html)
38
Social cost of carbon
Taken from EPA: http://www3.epa.gov/climatechange/EPAactivities/economics/scc.html
39
AP2
Integrated assessment model for valuing emissions damages.
Source: (Muller, 2015)
40
41
AP2 per ton SO2 damages
Source: Muller (2014)
42
AP2 state averages
• AP2 marginal damages by county averaged by state
– Counties weighted by current share of state emissions (CEMS)
43
East Coast coal
states: OH, PA,
IN, KY
Source: author calculations using AP2 and CEMS data
44
Relatively high
CO2 costs
Relatively high
SO2 costs
45
Monetized emissions damages
• Electricity dominates social damages of emissions from
energy production (Jaramillo & Muller, 2016)
– Mortality accounts for 95% these costs
• SO2 accounts for around 70% of all social costs from the
power sector (Heo, 2015)
• 2005  2011 total electricity sectors damages decreased
from $154 billion to $100 billion (Jaramillo & Muller, 2016)
– SO2 emissions ~ 5 million tons in 2011 (3 million tons in 2014)
46
Electricity sector damages
Source: Jaramillo & Muller, 2016
47
* Estimate for 2011. Adjusted to $2014
Also includes damages from VOCs, PM2.5, NOx, NH3.
48
EPA use of damages
49
Respondent checks
• Attention checks
– 2 tasks with “dominated” alternatives (second & last task)
• Transitivity check
– 3 tasks with related alternatives (A, B, C)
• Linearity check
– 2 tasks with different scenarios but identical differences between choices
50
Attention checks
• 2 question with dominated alternatives (2nd and last task)
– Portfolio question – “Coal, natural gas, renewables, nuclear, and
energy efficiency are all electricity sources or reductions
considered in this survey.”
– Bill question – “Assuming the amount of electricity you use does
not change, higher electricity prices would lower your monthly
electricity bill.”
51
Attention checks
• 2 tasks with dominated alternatives (2nd and last task)
52
Attention checks
53
Transitivity checks
• Series of 3 tasks
– Choice 1: A vs. B
– Choice 2: B vs. C
– Choice 3: C vs. A
54
Transitivity checks
55
Transitivity checks
• 3 choices  8 possible choice combinations
• ABC, ABA, ACC, ACA, BBC, BBA, BCC, BCA
• 6 of these 8 consistent with
transitive preferences
– ABC, BCA not consistent with
transitivity
– 75% chance of randomly picking
choices consistent with transitivity
56
A
B
C
Portfolio Coal Renew. Gas
CO2
0
-30
70
SO2
0
-30
70
Bill
0
10
20
57
Transitivity checks
• Some thoughts
– Relatively small emissions reductions via renewables may not be
enough to entice coal lovers
– For people with renewables as a first preference, gas preferred
to coal (group 1) unless it brings high emissions (other groups)
• Suggests renewable people support emissions reductions,
gas as a “bridge” strategy
• Will support coal if cheaper + has lower emissions
– “ABA” combination consistent with choosing on bill alone
58
Linearity check
• Two tasks with different baseline levels but identical
differences between the two options
• Linear preferences if respondents choose AB or BA
– 50% chance of responding this way by just guessing
59
Linearity checks
Group
Linearity (%)
Significant difference by group
1
2
3
*
1
90
-
*
2
75
*
-
3
68
*
4
85
5
78
*
*
4
*
*
-
*
*
-
*
Significant differences tested using Welch Two Sample Test
(confidence level 95%)
60
5
*
-
Passed all checks
• Probability of …
– Passing attention checks  25%
– Transitive preferences  75%
– Linear preferences  50%
– All consistency checks  ~ 9 %
• Actual success rates
All
61
Group1
Group 2
Group 3
Group 4
Group 5
0.82
0.71
0.64
0.82
0.74
Conditional probability check
Comparison of predicted and actual probability estimates for 1 task
62
Group1
Group 2
Group 3
Group 4
Group 5
Predicted
probability
0.73
0.71
0.72
0.79
0.76
Actual
probability
0.63
0.64
0.63
0.76
0.73
Number of
respondents
204
192
205
221
184
Pilot test demographics
*Data
63
from U.S. Census, 2015 Gallup Polls
Pilot test demographics
64
Pilot test results
65
Sampling by state
66
Sampling by state
67
Demographics by group
68
Monthly electricity bill
69
Survey completion time
70
Choice set assumptions
• Alternatives must be…
– Mutually exclusive
• Specific generation levels of portfolios other attribute levels
– Exhaustive
• “representative” extreme values
• non-included fuels (e.g. biomass, geothermal) not likely to be
large sources of generations
– Finite number of alternatives
• Infinite number of portfolios, simplified by representative
scenarios
71
Choice modeling
• Logit model
– Random utility model:
“Observed” component
72
“Unobserved” component
Observed component of utility
• Often assumed to be a additively separable linear
function
– X = levels of the attributes
73
Unobserved component of utility
• Assumed iid Gumbel distribution (extreme value)
• Standard Gumbel: μ = 0, β = 1
74
Gumbel distribution
75
Difference in two errors ~ logistic
• Only differences in utility matter
– Gumbel assumption does not affect utility estimation so long as
unobserved components have the same mean
• Difference of two Gumbel random variables is distributed
logistically
76
Logistic distribution
• Similar to assumption of normally
distributed errors but with fatter tails
– Allows for more variability (Train, 2009)
• Outcome of a well specified model
– Unobserved error should be “white
noise”
• Model used in logistic
regression/binary logit models
77
Choice modeling
• Logit model
– Random utility model:
“Observed” component
78
“Unobserved” component
Choice modeling
• Logit model
– Assumed that respondents are utility maximizers
– Choices made based on difference in utility (Train, 2009)
• Choose left if Ui,left > Ui,right
– Since we don’t observe Uij, need to model probabilistically
– Coefficients for Vij estimated using Maximum Likelihood
Estimation
79
Logit Assumptions
• Multinomial logit
– Cannot represent random taste heterogeneity
– Independence of Irrelevant Alternatives,
– Cannot deal unobserved correlation over time
• Mixed logit
– Does not require these assumptions
– Only requires choice proper set construction + error distribution
80
List of models
Multinomial logit
Mixed logit
Base model
Individual random effects
Base model, no intercept
State random effects
State fixed effects
(large states only)
Individual + state random effects
Single model with dummies for group
Individual + state random effects
(SO2 & CO2 at state level)
SO2 indicator
Individual + state random effects, no
intercept
(SO2 & CO2 at state level)
Individual random effects + SO2
indicator
Error variance
• 2 sources of bias:
– Different scale parameter (bias downward)
– Traditional omitted variable bias for any attribute based on
covariance (bias up or down, depending on perception)
• Scale parameter issue neutralized by taking ratio of
coefficients (e.g. WTP)
82
Nonprice Incentives
(Asensio & Delmas, 2014)
• “Environment and health-based information treatments
motivated 8% savings vs. control”
– Families with children achieved ~19% energy savings
– Monetary group increased consumption ~ 3% (rebound)
– Did not address long-term
persistence
Table S1. Treatment Messages
83
Group
Monetary Savings Group
Treatment Message
“Last week, you used 66% more/less electricity than your efficient
neighbors. In one year, this will cost you (you are saving) $34 dollars
extra.”*
Health Group
“Last week, you used 66% more/less electricity than your efficient
neighbors. You are adding/avoiding 610 pounds of air pollutants which
contribute to health impacts such as childhood asthma and cancer.”*
Control Group
None.
* ‘Efficient neighbors’ in this context means households in the top 10th percentile of household weekly
average kWh consumption (households with the lowest electricity use) for similar size apartments in the community.
What motivates public opinion
on energy?
• Cultural model
– Cultural cognition/motivated reasoning; e.g. community identity (Wildavsky,
1981), post-materialism (Ingleheart 1990)
– Demographic identities and social groupings (race, gender, income, etc.)
– Some challenge to the useful explanatory power of demographic variables
(Smith 2002)
• Psychological model
– Dread and nuclear power (Slovic, Fischhoff, and Lichtenstein 1981)
• Political model
– Institutional trust and perceived risk (Slovic 1993)
– Political affiliation and attitudes; e.g. Republicans and nuclear power
(Greenberg and Truelove 2011)
• Other personal values and norms
84
What motivates public opinion
on energy?
• Consumer model
– People evaluate energy choices based on the attributes of the
options (Ansolabehere and Konisky 2014)
– Individuals think about options in terms of tradeoffs between
attributes (Fleishman-Mayer et. al. 2010)
– Most important attributes: economic cost and environment
impact (Ansolabehere and Konisky 2014)
• In this model, two key elements:
– The public’s knowledge of different technologies’ attributes
– The public’s valuation of the tradeoffs between different options
85
Discrete choice: pros & cons
Pros
Cons
Established theory of choice
behavior
Random utility theory assumes
articulated values
Complex choices decomposed
Hypothetical
Control of attribute levels
Subject to cognitive biases (e.g.
scope insensitivity, anchoring,
prospect theory, inattention, etc.)
May not include all relevant
factors
Error variance assumption not
theoretically justified
86
Mixed Logit
87
Multinomial Logit
88
Multinomial Logit (dropped data)
89
Moulton Factors
90
Random effects
• Standard deviations for state level random effects
91
Equations
Logit
definition
Conditional
probability
(at baseline)
92
Odds ratio calculation
93
Odds ratios
94
Conditional probabilities
95
Group 5 results
96
Group 4 results
97
Willingness-to-pay ratio
• Derived from ratio of attribute and price (i.e. bill coefficients)
CO2
SO2
Stated CO2 WTP*
No emissions
info
-
-
8%
CO2 info
18%
(14-22)
-
10%
SO2 info
-
CO2 & SO2 info
(no damages)
16%
(13-20)
21%
(17-27)
15%
(12-19)
9%
12%
CO2 & SO2 info
14%
12%
11%
(with damages) (11-130)
(10-160)
WTP as percentage increase in monthly electricity bill
* Respondents were directly asked how much they would be willing to pay for 30%
CO2 reductions in percent increase of electricity bills
98
Implicit per ton WTP calculation
• Step 1: modeled WTP
– % bill for 30% reduction in CO2 / SO2
• Step 2: multiply by U.S. average monthly electricity bill
– Survey average: $124
– EIA average: $114
• Step 3: multiply by U.S. population (national WTP)
– Could also use number of households
• Step 4: divide by tons emissions reduced
99
Implicit per ton WTP results
Calculated results
Results from other studies
100
In each screen we will ask you to
choose between 2 energy scenarios
just like the ones you see here.
Each scenario is described by 4
characteristics. They are:
• Electricity portfolio
• Climate change related emissions
• Health related air pollution
• Monthly electricity bill
The next screens will provide indepth information on these
characteristics.
Electricity portfolio shows how much of your
state's electricity generation would come from coal,
natural gas, nuclear power, and renewable energy
(renewables include wind, solar, and hydropower).
In addition, energy efficiency programs (using more
efficient appliances or weatherizing houses, for
example) can help reduce the total electricity needed,
and these savings are included in
the electricity portfolio.
Climate change related emissions shows the
percentage change in yearly carbon dioxide (CO2)
emissions compared to today’s levels.
CO2 is a greenhouse gas that contributes to climate
change, and more CO2 will result in increased average
global temperature, more intense storms, more floods
and droughts, and rising sea levels. These effects
occur on a global scale.
An estimate of the additional costs to the planet from
these climate change effects is also provided.
Health related air pollution shows the percentage
change in yearly sulfur dioxide (SO2) emissions compared
to today’s levels.
SO2 can form small particles that can get into the lungs,
meaning that people who live in areas with higher SO2 can
have an increased risk of heart attacks, asthma, and other
respiratory problems. These effects are most severe in the
areas near where the pollution is emitted.
An estimate of the additional costs to polluted areas from
these health effects is also provided.
Monthly electricity bill shows the
percentage change in your electricity bill
relative to what you pay now. Different
scenarios can be more or less expensive
and can affect how much you pay for
electricity.
Throughout the choices you can
click on the yellow “Learn more”
box to see this information again.
Learn&
more&
Learn&
more&
Learn&
more&
Learn&
more&
You can also click the text above to
find out more about the
combinations you are seeing.
Finally, you can indicate the
scenario you prefer using these
buttons below.
When you’re ready, click the blue
arrow below to finish the guided
example.
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