Thin Rules, Thick Energy Realities: Citizen Acceptance of Algorithmic Energy Governance in the Climate Transition

Decarbonization and climate resilience are accelerating the digitalization of energy systems, expanding the use of AI-enabled and automated decision-making (ADM) in utility governance. Smart meters, dynamic tariffs, demand response, fraud detection, and automated eligibility screening for energy assistance or retrofit subsidies increasingly shift discretion from frontline caseworkers and customer-service staff to modelers, vendors, and code—an emerging form of algorithmic energy bureaucracy. Yet citizen acceptance of algorithmic decisions remains volatile, particularly when climate-motivated interventions impose immediate burdens (e.g., remote disconnection, peak-time restrictions, or load curtailment during heatwaves). Vignette experiments are well-suited to identify causal determinants of acceptance. Still, many designs either oversimplify energy contexts—erasing distributive and dignity concerns central to the “just transition”—or overcomplicate scenarios, undermining internal validity.

Building on the conceptual tension between thin, standardized algorithmic rules and thick, context-dependent governance, and on procedural justice theory, this article proposes a parsimonious vignette architecture that preserves the normative thickness of energy governance while enabling clean causal inference. We argue that minimal, theoretically grounded manipulations should isolate: (1) decision locus (human vs algorithmic vs hybrid), (2) context sensitivity and exception handling (e.g., medical device reliance, extreme weather vulnerability), (3) transparency as accessibility (disclosure) versus explainability (comprehensible rationale), (4) opportunities for voice and appeal, and (5) climate-and-equity framing (emissions reduction and grid stability benefits versus bill impacts and hardship risk).

An illustrative high-stakes scenario—smart-meter–triggered remote electricity disconnection or automated peak curtailment targeting households flagged as “high-risk” for arrears—demonstrates how simplification can retain climate-policy relevance without conflating “algorithmic” with “opaque,” “inflexible,” or “unaccountable.” The framework yields testable hypotheses about when climate-benefit narratives fail to compensate for losses in contextual legitimacy, and how explainable justifications and meaningful recourse can strengthen contextual legitimacy in the eyes of citizens.