sociodemographics & sustainability value propositions in dsm programs
TRANSCRIPT
DNV GL © 2014 SAFER, SMARTER, GREENER DNV GL © 2014 SAFER, SMARTER, GREENER DNV GL © 2014
Ungraded
Monday, October 19, 2015
Alexander Novie
ENERGY
Sociodemographics & Sustainability Value Propositions in DSM Programs
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BECC 2015 Conference
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Outline
Equity in mature EE/DSM programs • Articulating sustainability value propositions to diverse audiences
Sociodemographic approach • Increasing diversity and identifying opportunities
New policy levers in CPP/CEIP • Using EE/DSM as a resource for “vulnerable” communities
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Theoretical frameworks & background
Social and behavioral aspects of energy use
•“Where and who are the people in energy policies?” vs. PTEM
•Use and behavior patterns based on socioeconomic, lifestyle, race/ethnicity and gender
Analyzing demographics in EE/DSM
•Participation equity
•Applying residential segmentation to commercial energy users
Looking at unique needs of vulnerable groups
•Local market nuances used to define “hard-to-reach” populations
•Value propositions & EE as economic development tool
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DSM program participation: equity & engagement
DSM programs are maturing
• Most of the low-hanging fruit has been eaten
Utilities are shifting their focus
• “Meeting goal” broader strategies of customer engagement and satisfaction
• Using EE/DSM as economic development tool
A nuanced view of equity is key
• Sociodemographic overlays can provide additional insight to the traditional focus on customer load, size and market verticals
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Sociodemographics: what factors?
Culture, ethnicity and race
• Different notions of trust with energy providers and installation contractors
Language matters
• Non-English speaking households are much less likely to be aware of potential energy-saving appliances (Murray & Mills 2011)
Urbanity matters
• Rural facilities often have different consumption profiles; reach of contractor network and campaigns
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Changing sociodemographics: increasing diversity
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Source: Pew Research Center (2015)
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Total population & Hispanic/Latino
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25%
35%
13% 10% 10%
46%
82%
25%
28%
43%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
AZ NV NM CA All US
Percent Population Increase from 2000 - 2010: Total & Hispanic or Latino
Total Population Hispanic/Latino Population
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Foreign-born persons since 1980
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0
5
10
15
20
25
30
NM AZ NV CA US NM AZ NV CA US NM AZ NV CA US NM AZ NV CA US
1980 1990 2000 2010
% o
f P
op
ula
tio
n t
hat
is F
oreig
n-B
orn
Growth in Foreign-Born Populations in NM, AZ, NV, & CA: 1980-2010
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Public opinion: views on the climate problem
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Source: Pew Research Center (2015)
70
56
44
50
19
26
22
23
11
17
31
25
Hispanic/Latino
African American
White
U.S. Adults
Because of human activity Because of natural patterns
There is no solid evidence
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Public opinion: support for climate initiatives
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Source: PPIC (2015)
76% 75%
71%
65%
African American Latinos Asians Whites
Support for GHG reduction in California
(SB-32)
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Other “hard-to-reach” in SW
Low-income
Minority-owned firms
Older adults
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Tucson area: kWh savings & per-capita income
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Identifying opportunity: small businesses in Southern Arizona
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Small Businesses & Program Potential in Santa Cruz County, Arizona
Zip Code Total C&I
Businesses
Businesses
with 1 to 4
Employees
Businesses
with 5 to 9
Employees
Ratio of
Businesses
<10
Employees
Projects
Completed
ITD
Potential
Market
Remaining
85621 725 381 166 75% 73 86%
85646 74 51 12 85% 5 92%
85648 197 104 34 70% 16 88%
Santa Cruz
Summary 996 536 212 77% 134 87%
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Takeaways for DSM programs
Understanding the communities within the marketplaces
• Defining “hard-to-reach” groups to focus on equity & engagement
Analyzing data-driven marketing
and outreach
• Digging deeper into data analytics and looking at the “who” and “why” behind the models
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Question and refine how sustainability value propositions are communicated
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Broader implications: CPP & CEIP
Clean Power Plan (CPP) framed in environmental justice language
• Resources for “vulnerable” and “overburdened” communities
Clean Energy Investment Program (CEIP)
• 2-1 ERCs for EE projects in low-income and communities of color
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2-1 in 2020
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Areas of future research
How do we define low-income and communities of color for program design?
Rethinking program eligibility?
• Low-income individuals are already overburdened by paperwork!
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Remember the people behind the data
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