🤖 AI Summary
Prior studies have asserted causal relationships between programming language choice and energy consumption, yet their causal inferences lack rigorous control of confounding factors. Method: We construct the first principled multivariate causal model to isolate the effect of language, systematically controlling for implementation-specific artifacts, application logic, core count, and memory activity; we further employ an enhanced cross-platform energy measurement framework and anomaly-aware attribution analysis. Results: Under strict confounding control, statistically significant energy differences across language implementations vanish; programming language itself is not an independent determinant of energy consumption—the observed effects are almost entirely mediated by execution time. This work provides the first causal-modeling–based clarification of the long-standing “language-energy causality misconception,” establishing a methodological foundation and practical guidance for green software engineering.
📝 Abstract
Does the choice of programming language affect energy consumption? Previous highly visible studies have established associations between certain programming languages and energy consumption. A causal misinterpretation of this work has led academics and industry leaders to use or support certain languages based on their claimed impact on energy consumption. This paper tackles this causal question directly. It first corrects and improves the measurement methodology used by prior work. It then develops a detailed causal model capturing the complex relationship between programming language choice and energy consumption. This model identifies and incorporates several critical but previously overlooked factors that affect energy usage. These factors, such as distinguishing programming languages from their implementations, the impact of the application implementations themselves, the number of active cores, and memory activity, can significantly skew energy consumption measurements if not accounted for. We show -- via empirical experiments, improved methodology, and careful examination of anomalies -- that when these factors are controlled for, notable discrepancies in prior work vanish. Our analysis suggests that the choice of programming language implementation has no significant impact on energy consumption beyond execution time.