City Governments and Energy Justice
PUAD 934 | S. Mohsen Fatemi
Dec 8, 2021
City Government and Energy Justice
Objectives:
To understand whether local government programs on community energy efficiency can address energy justice.
Research Question:
To what extent are community energy efficiency programs effective tools for local governments to address energy justice?
Theory:
Energy justice as a three-pronged concept:
Distributional, Recognition, Procedural
Jenkins, K., McCauley, D., Heffron, R., Stephan, H., & Rehner, R. (2016). Energy justice: A conceptual review. Energy Research & Social Science, 11.
Energy Burden
Research design [Quantitative]
Empirical setting
Of all U.S. households, 25% (30.6 million) face a high energy burden (i.e., pay more than 6% of income on energy bills) and 13% (15.9 million) of U.S. households face a severe energy burden (i.e., pay more than 10% of income on energy)
Nationally, 67% (25.8 million) of low-income households (≤ 200% of the federal poverty level [FPL]) face a high energy burden and 60% (15.4 million) of low-income households with a high energy burden face a severe energy burden.
Black, Hispanic, Native American, and older adult households, as well as families residing in low-income
multifamily housing, manufactured housing, and older buildings experience disproportionally high
energy burdens.
Leading cities and states have begun to incorporate energy burden goals into strategies
and plans and to create local policies and programs to
achieve more equitable energy outcomes in their
communities. They are pursuing these goals through
increased investment in energy efficiency,
weatherization, and renewable energy.
American Council for an Energy-Efficient Economy (ACEEE)
Hypotheses:
H1: Existence of energy efficiency programs is negatively associated with energy burden.
H2: The more number of energy efficiency programs local governments have is negatively associated with burden.
H3: Energy efficiency programs that directly target low-income households have higher impact on energy burden compared to general programs.
H4: Homeownership positively impacts the effectiveness of EE programs on energy burden.
H5: Effectiveness of EE programs on energy burden is negatively associated with racial diversity.
Research design [Quantitative]
Unit of analysis ⇨ Local jurisdiction 一 Year
Dependent variables ⇨ Energy burden
Key independent variable ⇨ Community Energy Conservation Programs
| Variable | Definition |
DV | Energy Burden | Average % of Household with high energy burden (more than 6% of income) |
IV | Energy Conservation Programs | Energy audits for individual residences [0/1] Weatherization for individual residences [0/1] Heating/air conditioning upgrades for individual residences [0/1] Conservation programs targeted to assist low-income residents [0/1] |
Deslatte, A., & Stokan, E. (2020). Sustainability synergies or silos? The opportunity costs of local government organizational capabilities. Public Administration Review, 80(6), 1024-1034.
Research design [Quantitative]
Control Variables
C | Monitoring programs | Conservation programs tracking [0/1] |
C | Sustainability Actions | Existence of sustainability plan [0/1] Adoption of a climate mitigation plan [0/1] Conducted a greenhouse gas inventory of the community [0/1] |
C | Environmental disasters | Whether they had to respond to a major environmental disaster [0/1] |
C | Government Capacity | If they own their electric utility [0/1] If they have dedicated staff on sustainability [0/1] Funding for reduction of energy consumption in the community (local, state/fed, utility, private grant) [0/1] |
C | Information seeking behavior | Number of sources of information for developing sustainability strategies [#] |
C | Political Environment | Advocates for sustainability efforts by government [#] Opposes sustainability efforts by the government [#] Number of forms of public participation [#] |
C | Housing | Age: whether they were built after 1950 or not [0/1] |
C | Legislations | Motivating legislations: state/fed policies are supportive [0/1] Hindering legislations: state/fed policies are hindering [0/1] |
C | Demographics | Population [#], race [% nonwhite], education [% under bachelor’s degree], homeownership [%] |
Research design [Quantitative]
Case Selection ⇨ 1 Treatment Group + 2 Control Groups
Treatment Group includes the ones who adopted at least one of the programs 2010-2015
Control Group 1 includes governments
that had no programs.
Control Group 2 includes governments that
already had at least, one program before 2010.
Treatment
Control 1
Control 2
2011-2015
2016-2020
Research design [Quantitative]
Dataset
Analysis
Multiple regression analysis.
| Burden (%) | Program 1 | Program 2 | Program 3 | Program 4 | Program 5 |
City 1_05_10 | 37 | 0 | 1 | 0 | 0 | 1 |
City 1_10_14 | 36 | 0 | 1 | 0 | 1 | 1 |
City 1_15_19 | 30 | 0 | 1 | 0 | 1 | 1 |
City 2_05_10 | 22 | 1 | 1 | 0 | 0 | 0 |
City 2_10_14 | 22 | 1 | 1 | 0 | 0 | 1 |
City 2_15_19 | 21 | 1 | 1 | 0 | 0 | 1 |
City 3_05_10 | 43 | 1 | 1 | 0 | 1 | 0 |
City 3_10_14 | 39 | 1 | 1 | 0 | 1 | 0 |
City 3_15_19 | 32 | 1 | 1 | 0 | 1 | 0 |
Research design [Quantitative]
Limitations:
Most respondents have council-manager form
Recession year
more years included would be better
Research design [Quantitative]
Data Management Plan
Data management
Sharing work
Research design [Quantitative]
Research timeline
| Month 1 | Month 2 | Month 3 | Month4 | Month 5 | Month 6 | Month 7 |
Data collection on variables | X | | | | | | |
Survey data cleaning | X | | | | | | |
Survey statistical analysis | X | X | X | | | | |
Manuscript preparation | | | X | X | X | | |
Submitting to journals | | | | | X | X | X |
Lectures at conferences | | | | | X | X | X |
Lectures to Fellows | | | | | X | | |
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