Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach

TL;DR

This paper introduces a bottom-up life-cycle framework to quantify the carbon footprint of IoT edge devices, revealing significant variability and emphasizing the importance of considering environmental impacts in IoT deployment.

Contribution

It presents a novel parametric framework for assessing IoT device carbon footprints and provides macroscopic estimates highlighting the environmental significance of IoT production.

Findings

Production carbon footprint varies by over 150x between simple and complex devices.

Estimated global IoT device production emissions could reach over 1000 MtCO2-eq/year by 2027.

Results suggest current estimates may underestimate true impacts by about 2x due to methodological truncation errors.

Abstract

In upcoming years, the number of Internet-of-Things (IoT) devices is expected to surge up to tens of billions of physical objects. However, while the IoT is often presented as a promising solution to tackle environmental challenges, the direct environmental impacts generated over the life cycle of the physical devices are usually overlooked. It is implicitly assumed that their environmental burden is negligible compared to the positive impacts they can generate. In this paper, we present a parametric framework based on hardware profiles to evaluate the cradle-to-gate carbon footprint of IoT edge devices. We exploit our framework in three ways. First, we apply it on four use cases to evaluate their respective production carbon footprint. Then, we show that the heterogeneity inherent to IoT edge devices must be considered as the production carbon footprint between simple and complex devices can vary by a factor of more than 150x. Finally, we estimate the absolute carbon footprint induced by the worldwide production of IoT edge devices through a macroscopic analysis over a 10-year period. Results range from 22 to 562 MtCO2-eq/year in 2027 depending on the deployment scenarios. However, the truncation error acknowledged for LCA bottom-up approaches usually lead to an undershoot of the environmental impacts. We compared the results of our use cases with the few reports available from Google and Apple, which suggest that our estimates could be revised upwards by a factor around 2x to compensate for the truncation error. Worst-case scenarios in 2027 would therefore reach more than 1000 MtCO2-eq/year. This truly stresses the necessity to consider environmental constraints when designing and deploying IoT edge devices.