CarbonSim: A Lifecycle-Aware Framework for Evaluating Carbon Tradeoffs in Hardware Upgrade Decisions

2026-06-04

AI Summary

This study addresses the common oversight of full lifecycle carbon emissions in hardware upgrade decisions by proposing a lifecycle-aware simulation framework. The framework uniquely integrates workload characteristics, location-specific time-varying grid carbon intensity, and multiple embodied carbon allocation strategies—such as uniform amortization and front-loading—with multi-generation CPU power models to dynamically evaluate the total carbon footprint of different deployment scenarios. Experimental results demonstrate that, particularly under low-utilization conditions or in regions with cleaner electricity grids, extending the operational lifespan of existing hardware can substantially reduce overall emissions. These findings challenge the prevailing assumption that newer hardware is inherently more environmentally sustainable and offer a novel paradigm for greener computing practices.

Abstract

As the demand for information and communication technologies (ICT) continues to rise, the environmental impact of computing systems is becoming an increasingly critical concern. Although newer hardware often improves performance and energy efficiency, these gains do not always offset the carbon cost of premature replacement, particularly under low-utilization workloads or low-carbon electricity grids. We present CarbonSim, a lifecycle-aware simulation framework for evaluating carbon tradeoffs in hardware upgrade decisions. CarbonSim combines workload execution profiles, machine-level power characteristics, embodied carbon inventories, scheduling policies, and time-varying grid carbon intensity to estimate total emissions under alternative deployment scenarios. The framework supports multiple embodied-carbon accounting strategies, including uniform amortization and front-loaded lifecycle attribution, enabling analysis under different hardware lifespan assumptions. Using heterogeneous CPU generations as calibration platforms, we demonstrate that newer machines do not always minimize total emissions: under lightly loaded workloads or cleaner electricity mixes, extending the useful life of existing hardware can reduce lifecycle carbon despite lower operational efficiency. These results highlight that hardware refresh decisions should be workload-aware, location-aware, and lifecycle-aware.

Problem

• carbon tradeoffs
• hardware upgrade
• lifecycle carbon
• embodied carbon
• ICT sustainability

Innovation

• CarbonSim
• lifecycle-aware
• embodied carbon
• hardware upgrade
• carbon intensity