Research Design

This dissertation proposes a multi-method investigation into how regulatory environments, institutional configurations, and utility ownership models shape energy justice outcomes within the U.S. electric utility sector. Centering on investor-owned utilities (IOUs), the project responds to a critical gap in the literature: while energy justice has gained prominence as a normative and evaluative framework, the field lacks a coherent causal theory explaining how just or unjust outcomes emerge across governance contexts. Chapter 1 will construct a data-driven typology of state regulatory environments using cluster analysis. Chapter 2 will use machine learning and structure-learning methods to explore how combinations of regulatory, institutional, and market conditions are plausibly associated with distributional, procedural, and recognition-based energy justice outcomes. Chapter 3 will examine whether and how utility ownership—comparing investor-owned, municipal, and cooperative models—moderates these relationships. Collectively, the dissertation seeks to lay a comparative, explanation-oriented foundation for future causal theorizing in the pursuit of equitable energy transitions.

The introduction positions energy justice—encompassing recognition, procedural, and distributional fairness—as a pressing governance challenge in the U.S. electric‑utility sector. It argues that, despite growing normative attention, the field lacks a causal framework that explains how just or unjust outcomes emerge across diverse regulatory landscapes. Situating the dissertation at this gap, the chapter outlines a three‑article agenda: (1) a typology of state regulatory environments, (2) a machine‑learning and causal‑discovery analysis of how institutional, regulatory, and market factors shape IOU justice performance, and (3) an examination of ownership’s conditional role across all U.S. utilities. It clarifies key concepts, advances a layered theoretical lens that views utilities as strategic actors embedded in institutional ecosystems, and previews the mixed‑methods approach—clustering, random forests, NOTEARS/CAM, and multi‑group SEM—that operationalizes this lens. The chapter concludes by detailing the dissertation’s expected contributions: enhancing causal reasoning in energy‑justice scholarship, providing actionable insights for regulators and advocates, and offering a replicable empirical platform for future research.

The Conclusion chapter of my dissertation will synthesize the key insights from all three articles, highlighting their collective contributions to understanding energy justice in U.S. utility governance. While each article offers distinct findings—on regulatory typologies, causal pathways, and the conditional role of ownership—the chapter will step back to consider their broader theoretical, methodological, and practical significance. It will begin by revisiting the core research problem: the lack of a causal framework explaining how energy justice outcomes emerge within complex regulatory and institutional environments. Drawing from the main findings, I will examine how they interrelate to provide a more holistic account of when and how equitable energy transitions occur, emphasizing cross-cutting themes such as state capacity, policy-implementation alignment, and governance context. The chapter will also reflect on how this work advances a causal theory of energy justice by challenging conventional assumptions—such as the inherent equity of public ownership—and offering more nuanced, context-sensitive explanations. Additionally, it will identify the study’s limitations and suggest directions for future research, including refining justice measures, expanding the typology, and deepening analysis of stakeholder strategies. The chapter will conclude by emphasizing the implications for scholarship and policy, calling for institutional designs that are not only efficient and technically robust but also grounded in a commitment to equity.