arXiv subjects

Thibault Pirson

Publications and source records attributed to Thibault Pirson.

6 recordsLinked to original sources

arXiv2026

UNCASExt -- A Systematic Computational Framework for Uncertainty Propagation and Scope Consistency in Absolute Environmental Sustainability Assessments (AESA)

Absolute environmental sustainability assessment (AESA) has gained increasing attention in environmental research and policymaking. However, its reliability is challenged by several sources of uncertainty that remain insufficiently accounted for, as well as by scope inconsistencies within the absolute sustainability ratio (ASR), which compares estimated environmental burdens with allocated carrying capacities for a given human activity. This work introduces UNCASExt, an extension of the UNCASE framework for systematically propagating uncertainty and ensuring scope consistency in AESA, together with a supporting open-source Python package, pyaesa. At country and sector levels, the computational framework formalizes allocation procedures that match the scope of allocated carrying capacities with that of estimated environmental burdens across three dimensions: impact pathway modeling; production-based versus consumption-based accounting; business-to-consumer versus business-to-business activities. It also incorporates temporal dynamics, supporting both retrospective and prospective assessments with either static steady-state or dynamic carrying capacities, including greenhouse gas budgets from the Intergovernmental Panel on Climate Change Sixth Assessment Report under Shared Socioeconomic Pathway transition scenarios. The framework is applied to a case study of electricity consumption in France over the period 2019 to 2060. The results show that mismatches between the functional units of estimated environmental burdens and allocated carrying capacities can lead to substantial underallocation, with a median factor of 4.6x across all available sector-region pairs in EXIOBASE 3.10.2. Overall, UNCASExt and pyaesa provide a scalable solution to support AESA harmonization and a versatile way forward to bridge the gap between methodological guidelines and practical application.

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arXiv2025

Purer than pure: how purity reshapes the upstream materiality of the semiconductor industry

Growing attention is given to the environmental impacts of the digital sector, exacerbated by the increase of digital products and services in our globalized societies. The materiality of the digital sector is often presented through the environmental impacts of mining activities to point out that digitization does not mean dematerialization. Despite its importance, such a narrative is often restricted to a few minerals (e.g., cobalt, lithium) that have become the symbols of extractive industries. In this paper, we further explore the materiality of the digital sector with an approach based on the diversity of elements and their purity requirements in the semiconductor industry. Semiconductors are responsible for manufacturing the key building blocks of the digital sector, i.e., microchips. Given that the need for ultra-high purity materials is very specific to the semiconductor industry, a few companies around the world have been studied, revealing new critical actors in complex supply chains. This highlights strong dependencies towards other industrial sectors with mass production and the need for a deeper investigation of interactions with the chemical industry, complementary to the mining industry.

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arXiv2025

More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU

The rapid expansion of Artificial Intelligence (AI) has intensified concerns about its environmental sustainability. Current assessments focus on operational carbon emissions using secondary data, overlooking impacts in other life cycle stages. This study presents a comprehensive multi-criteria life cycle assessment of AI training, examining 16 environmental impact categories using primary data from the Nvidia A100 SXM 40 GB GPU. Results for GPT-4 training show that the use phase dominates 10 categories, contributing 96% to climate change and fossil fuel depletion. Manufacturing dominates 6 categories, including human toxicity (94%) and freshwater eutrophication (81%). The GPU chip is the largest contributor in 10 categories, particularly climate change (81%) and fossil resource use (80%). While primary data produces modest changes in carbon estimates, substantial variations emerge elsewhere, e.g. minerals and metals depletion increases by 33%. This analysis expands the Sustainable AI discourse beyond carbon emissions, challenging current sustainability narratives.

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arXiv2024

A Cradle-to-Gate Life Cycle Analysis of Bitcoin Mining Equipment Using Sphera LCA and ecoinvent Databases

Bitcoin mining is regularly pointed out for its massive energy consumption and associated greenhouse gas emissions, hence contributing significantly to climate change. However, most studies ignore the environmental impacts of producing mining equipment, which is problematic given the short lifespan of such highly specific hardware. In this study, we perform a cradle-to-gate life cycle assessment (LCA) of dedicated Bitcoin mining equipment, considering their specific architecture. Our results show that the application-specific integrated circuit designed for Bitcoin mining is the main contributor to production-related impacts. This observation applies to most impact categories, including the global warming potential. In addition, this finding stresses out the necessity to carefully consider the specificity of the hardware. By comparing these results with several usage scenarios, we also demonstrate that the impacts of producing this type of equipment can be significant (up to 80% of the total life cycle impacts), depending on the sources of electricity supply for the use phase. Therefore, we highlight the need to consider the production phase when assessing the environmental impacts of Bitcoin mining hardware. To test the validity of our results, we use the Sphera LCA and ecoinvent databases for the background modeling of our system. Surprisingly, it leads to results with variations of up to 4 orders of magnitude for toxicity-related indicators, despite using the same foreground modeling. This database mismatch phenomenon, already identified in previous studies, calls for better understanding, consideration and discussion of environmental impacts in the field of electronics, going well beyond climate change indicators.

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arXiv2022

From Silicon Shield to Carbon Lock-in ? The Environmental Footprint of Electronic Components Manufacturing in Taiwan (2015-2020)

Taiwan plans to rapidly increase its industrial production capacity of electronic components while concurrently setting policies for its ecological transition. Given that the island is responsible for the manufacturing of a significant part of worldwide electronics components, the sustainability of the Taiwanese electronics industry is therefore of critical interest. In this paper, we survey the environmental footprint of 16 Taiwanese electronic components manufacturers (ECM) using corporate sustainability responsibility reports (CSR). Based on data from 2015 to 2020, this study finds out that our sample of 16 manufacturers increased its greenhouse gases (GHG) emissions by 7.5\% per year, its final energy and electricity consumption by 8.8\% and 8.9\%, and the water usage by 6.1\%. We show that the volume of manufactured electronic components and the environmental footprints compiled in this study are strongly correlated, which suggests that relative efficiency gains are not sufficient to curb the environmental footprint at the national scale. Given the critical nature of electronics industry for Taiwan's geopolitics and economics, the observed increase of energy consumption and the slow renewable energy roll-out, these industrial activities could create a carbon lock-in, blocking the Taiwanese government from achieving its carbon reduction goals and its sustainability policies. Besides, the European Union, the USA or even China aim at developing an industrial ecosystem targeting sub-10nm CMOS technology nodes similar to Taiwan. This study thus provides important insights regarding the environmental implications associated with such a technology roadmap. All data and calculation models used in this study are provided as supplementary material.

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arXiv2021

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

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.

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