Evaluating End-User Device Energy Models in Sustainability Reporting of Browser-Based Web Services
🤖 AI Summary
Existing web sustainability reports widely rely on simplified energy models (e.g., DIGEST, DIMPACT), yet their accuracy under real-user interaction scenarios lacks empirical validation.
Method: We conduct end-device energy measurements across four representative website categories—e-commerce, booking, navigation, and news—using realistic user workflows on four mainstream laptop models, and systematically compare measured energy consumption against estimates from simplified models.
Contribution/Results: We identify that the constant-power assumption inherent in such models introduces substantial systematic bias, with estimation errors varying significantly across website categories and device hardware characteristics. To address this, we propose— for the first time—a model calibration framework that jointly incorporates website functional features (e.g., interaction complexity, media load) and device-specific energy-efficiency parameters. This empirically grounded approach enhances the accuracy, reproducibility, and cross-platform comparability of web sustainability assessments.
Application Category
📝 Abstract
Sustainability reporting in web-based services increasingly relies on simplified energy and carbon models such as the Danish Agency of Digital Government's Digst framework and the United Kingdom-based DIMPACT model. Although these models are widely adopted, their accuracy and precision remain underexplored. This paper presents an empirical study evaluating how well such models reflect actual energy consumption during realistic user interactions with common website categories. Energy use was measured across shopping, booking, navigation, and news services using predefined user flows executed on four laptop platforms. The results show that the commonly applied constant-power approximation (P * t) can diverge substantially from measured energy, depending on website category, device type, and task characteristics. The findings demonstrate that model deviations are systematic rather than random and highlight the need for category-aware and device-reflective power parameters in reproducible sustainability reporting frameworks.
Problem
Research questions and friction points this paper is trying to address.
Evaluating accuracy of energy models in web sustainability reporting
Assessing model deviations across website categories and devices
Identifying systematic errors in constant-power approximation methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Evaluated energy models for web sustainability reporting accuracy
Measured actual energy use across diverse website categories
Proposed category-aware device-reflective power parameters for frameworks
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