Does More Federal Energy Funding Increase the
Adoption of Energy Efficiency Policies in U.S.
Local Government?
Taekyoung Lim
Levin College of Urban Studies and Public Affairs
Cleveland State University
August 20, 2015
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Research Question and Purposes
• Research Questions:
– What factors explain variation in energy efficiency policy
adoption at the level of local governments?
– Does more federal funding for energy efficiency policies
increase adoption of energy efficiency policies in local
governments?
• Research Purposes:
– To examine the impact of ARRA funding on local government
action to adopt energy efficiency policies
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Energy Efficiency and American
Recovery and Reinvestment Act (ARRA)
• Energy Efficiency Conservation Block Grant (EECBG)
– EECBG is a nation-wide direct investment for energy efficiency
and renewable energy technologies at the state and local level.
– ARRA appropriated $2.7 billion to EECBG for 2009-2012.
• Weatherization Assistance Program (WAP)
– WAP is an energy efficiency program that provides energy audits
and weatherization for low-income households.
– ARRA appropriated $5 billion to WAP for 2009-2012.
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Theoretical Framework
• Fiscal Federalism
– Federal governments played a role in establishing a comprehensive
framework and local governments then implemented their own
programs for meeting the performance targets written into federal goal
statements (Rabe, 2011).
– Federal government policies are designed to integrate all levels of
governments to adopt and implement policies and programs at each
level of government (Handley, 2008).
– Local governments have different capacities and preferences.
– Faced different sets of circumstances in which to adopt and implement
federally funded projects.
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Hypotheses
• H1. Financial Support by Federal Government
– Cities with more funds related to energy efficiency projects issued by
the federal government under the ARRA are more likely to adopt more
energy efficiency policies.
• H2. Government Type
– Cities with mayor-council forms of government are more likely to
adopt more energy efficiency policies.
• H3. Political Circumstance
– Cities with more legislature seats occupied by Democrats are more
likely to adopt more energy efficiency policies.
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Hypotheses
(continued)
• H4. Interest Groups
– Cities with more non-government organizations related to energy
resource conservation are more likely to adopt more energy efficiency
policies.
• H5. City Characteristics
– Cities with larger land area/higher education level/higher median
household income/ICLEI membership/larger proportion of oil industry
are more likely to adopt more energy efficiency policies.
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Research Design: Dependent Variables
• Policy Adoption
– Number of contracts, grants adopted for EECBG and WAP programs at
the local government level, 2009-2012
– Data: Department of Energy Data reported by the American Recovery
and Reinvestment Act (www.recovery.gov)
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Research Design: Independent Variables
• State level variables
– NGOs Support
•
Number of non-profit organizations
related to energy resources conservation
& development
• Local level variables
– Financial Support by Federal
Government
•
– Manufacturing Influence
•
Percentage of each state’s domestic
product that come from manufacturing
– Oil Industry Influence
•
Percentage of each state’s domestic
product that come from oil and gas
industry
– Political Support
•
•
Percentage of House and Senate seats
occupied by Democrats
1 indicating the governor is a Democrat
and 0 if otherwise
The amount of cities’ obligated ARRA
funds that were actually issued for
EECBG and WAP programs each year
by the federal government
– Government Types
•
Cities with the presence of a mayor
council form (1=presence, 0=otherwise)
– Cities Characteristic
•
•
•
•
•
Land area in square miles
Percent of BA degrees
Number of jobs in the oil industry per
1000 residents
Median household income
The presence of ICLEI membership
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Research Design: Analytical Model
• Multilevel Regression Model:
– Local governments nested within state government
– Unit of analysis: Local governments that received ARRA funds related
to EECBG and WAP
– from 2009 to 2012
– n=976, States=50
– HLM (Hierarchical Linear Modeling) 7.01 software program
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Research Design: Model Specification
•
•
•
Yij is the number measured for
contracts and grants adopted for
EECBG and WAP under the ARRA
measured for city i in state j
Xij is the federal government
financial support, government
types, and cities’ own
characteristics for city i in state j
Zj is the NGO’s support,
manufacturing influence, oil
industry influence, political
support, in state j
• Multilevel Regression Model:
– Level-one model
Yij =β0j + β1j Xij + eij
– Level-two model
β0j = γ00 + γ01Zj + μ0j
β1j = γ10 + γ11Zj + μ1j
– Multilevel model
Yij = [γ00 + γ01Zj + γ10Xij + γ11Zj Xij] +
[μ0j + μ1j Xij + eij]
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Thank you
Questions and Comments
Email address: t.lim17@csuohio.edu
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