The increasing integration of artificial intelligence into real-world systems has intensified concerns about the ecological footprint of computational processes. As the capabilities of AI expand and their applications spread into diverse areas of society, questions of efficiency are no longer confined to algorithmic performance alone but extend to the broader impact of computation on energy usage. Within this context classical planning provides a particularly relevant case since it is a core technique in automated planning. Research in this field has traditionally emphasized runtime efficiency, plan quality and algorithmic design while the energetic dimension has remained largely neglected. This thesis examines that omission by shifting the focus from planners to the domain models that constitute their input. Through systematic modifications of syntactic, semantic and solvability related features it demonstrates that modeling decisions can exert a measurable influence on energy consumption. Rather than viewing energy use as an inherent property of planners, the study shows it to emerge from the interaction between algorithmic behavior and representational form. The work introduces a replicable framework that combines controlled domain transformations with fine grained energy measurements, thereby enabling systematic evaluations of energy usage in symbolic AI. The empirical analysis indicates that syntactic variations usually result in only minor fluctuations, whereas modeling inefficiencies can increase energy demand, with operator arity standing out as a recurring factor. The most pronounced effects arise from solvability constraints which, depending on the planner and the domain, can lead to substantial increases in energy usage or in some cases reductions. Taken together the results highlight that domain modeling is not only a matter of syntactic correctness or semantic adequacy but also of energetic efficiency. The contribution of this thesis is twofold. It establishes a framework for investigating the energy implications of domain features and provides empirical evidence that modeling choices shape the energy profile of planning systems. These findings offer a foundation for further research and provide practical guidance for approaching domain modeling with energy consumption in mind.