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In this section, I outline the research design and present the organizational structure of my dissertation.
Chapter 1
Chapter 1
Toward a Causal Theory of Energy Justice: Developing a State Regulatory Environment Typology for Utility Planning in the U.S.
Toward a Causal Theory of Energy Justice: Developing a State Regulatory Environment Typology for Utility Planning in the U.S.
Chapter 2
Chapter 2
Toward a Causal Theory of Energy Justice: Uncovering Plausible Pathways in the U.S. Investor-Owned Utility Sector
Toward a Causal Theory of Energy Justice: Uncovering Plausible Pathways in the U.S. Investor-Owned Utility Sector
Chapter 3
Chapter 3
Toward a Causal Theory of Energy Justice: The Conditional Role of Ownership for Advancing Energy Justice in the U.S. Utility Sector
Toward a Causal Theory of Energy Justice: The Conditional Role of Ownership for Advancing Energy Justice in the U.S. Utility Sector
Research Question
Research Question
What are the distinct types of state regulatory environment in the U.S. that influence utility planning and may influence energy justice outcomes?
What institutional, regulatory, and contextual conditions shape energy justice outcomes among U.S. IOUs, and how do these conditions interact to promote or inhibit justice?
How does utility ownership type interact with institutional, regulatory, and market conditions to shape energy justice outcomes in the U.S. utility sector?
Theoretical Framework
Theoretical Framework
Theoretical Framework
Theoretical Framework
Constellations of policy ambition, market structure, institutional capacity, and equity mechanisms—rather than single statutes—create distinct regulatory environments that shape IOU planning and condition energy-justice outcomes.
Theoretical Framework
Theoretical Framework
A plural, institutional lens viewing IOU energy‑justice performance as the emergent result of interacting regulatory rules, political incentives, market structures, and civic pressures, filtered through utility strategies.
Theoretical Framework
Theoretical Framework
Treats ownership not as destiny but as a conditional lever within a broader governance ecosystem, where publicness, agency problems, participatory norms, and market incentives jointly shape a utility’s ability to deliver energy justice.
Unit of Analysis
Unit of Analysis
Utility-Year
Utility-Year
Utility-Year
Sampling Strategy
Sampling Strategy
All IOUs in the US reporting to EIA form 861 (n = 158)
All IOUs in the US reporting to EIA form 861 (n = 158)
All IOUs in the US reporting to EIA form 861 (n = 158)
Variables
Variables
Cluster Identifying Variables: State government political leaning, Market structure, PUC bipartisan requirement & administrative capacity, Policy implementation, Equitable governance, IRP requirement
Outcome Variable: Energy Justice Index (EJI)
Explanatory Variables: Policy & political context, Stakeholder influence, Market/regulatory design, Public Utility Commission, Policy implementation, Equitable governance, Utility characteristics, Community/customer context, Geography & climate
Outcome Variable: Distributional Justice
Moderator: Ownership Type
Latent explanatory blocks: Regulatory Environment, Institutional Factors, Market Conditions
Data Sources
Data Sources
Secondary Data: Ideology Index (Berry et al., 1998); Market Structure, IRP (State PUC and Legislative websites); PUC (State PUC websites; Insight Engine; NARUC; LinkedIn); Policy Implementation (Insight Engine; DSIRE); Equitable Governance (ACEEE, 2022); Legislature Professionalism (Squire, 2024); Policy Uncertainty (Baker, Davis, & Levy, 2022); Stakeholder Influence (U.S. Bureau of Economic Analysis (BEA); EIA-906; EIA-920; & EIA-923; Earth Focus Group, 2023); Energy Justice (EIA-861; Meier and Mitchell, 202; SEC filing; Census); Utility (SEC filings; Proxy Statements; Utility annual report; EIA-861); Community (EIA, Census); Geography & Location (EIA, NOAA)
Analytical Methods
Analytical Methods
Diagnostics: VIF (multicollinearity), Correlation matrix, PCA (dimensionality)
Clustering: K-Means, Elbow method (optimal k)
Validation: ANOVA (Kruskal-Wallis, Chi-Square), Pairwise tests (Welch’s t, Bonferroni), Effect sizes (η², Cramér’s V, Cohen’s d)
Discriminant Analysis: LDA, QDA (cluster separability)
Validation: Decision Trees, Random Forests, Permutation Importance
Energy Justice Index (EJ Index): Composite of distributional, procedural, and recognition dimensions
Variable Selection: Decision Trees, Random Forests
Causal Structure Learning: NOTEARS (for linear relations), CAM (Causal Additive Models) if nonlinear patterns are present
Validating Chapter 2 pathways: Structural Equation Modeling (SEM)
Expand the Model Across Ownership Types: Structural Equation Modeling (SEM)
Test Moderation by Ownership Type: Structural Equation Modeling (SEM)
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