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

Ludmila Courtillat--Piazza, Thibault Pirson, Louis Golard, David Bol

TL;DR

This study fills a critical gap by performing a cradle-to-gate LCA of Bitcoin mining hardware, specifically the Antminer S9 with APW3++, to quantify production-phase environmental impacts that are often overlooked in use-focused analyses. By modeling foreground hardware in detail and comparing two major background databases (Sphera LCA and ecoinvent), the authors show that application-specific ICs are the primary production drivers and that production can dominate lifecycle impacts under certain electricity-use scenarios, with the potential to reach up to 80% of total impacts. The work also reveals substantial discrepancies between databases for toxicity-related indicators, highlighting a database-mismatch problem that challenges cross-study comparisons in electronics LCAs. Extending the analysis to alternate electricity mixes and future miners (e.g., Antminer S19 Pro) suggests that larger devices may incur proportionally higher production burdens, particularly when powered by low-carbon grids, underscoring the need to include production-phase impacts and improve background data in environmental assessments of crypto-mining hardware.

Abstract

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.

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

TL;DR

This study fills a critical gap by performing a cradle-to-gate LCA of Bitcoin mining hardware, specifically the Antminer S9 with APW3++, to quantify production-phase environmental impacts that are often overlooked in use-focused analyses. By modeling foreground hardware in detail and comparing two major background databases (Sphera LCA and ecoinvent), the authors show that application-specific ICs are the primary production drivers and that production can dominate lifecycle impacts under certain electricity-use scenarios, with the potential to reach up to 80% of total impacts. The work also reveals substantial discrepancies between databases for toxicity-related indicators, highlighting a database-mismatch problem that challenges cross-study comparisons in electronics LCAs. Extending the analysis to alternate electricity mixes and future miners (e.g., Antminer S19 Pro) suggests that larger devices may incur proportionally higher production burdens, particularly when powered by low-carbon grids, underscoring the need to include production-phase impacts and improve background data in environmental assessments of crypto-mining hardware.

Abstract

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.
Paper Structure (19 sections, 7 figures, 1 table)

This paper contains 19 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related works
  3. Research contributions
  4. Structure
  5. Method
  6. Goal and scope
  7. Modeling principles
  8. Results
  9. Hotspots analysis
  10. Differences between databases
  11. Limitations and uncertainties
  12. Limitations and uncertainties in this LCA study
  13. End-of-life consideration
  14. Discussion
  15. Comparison between production and use phases
  16. ...and 4 more sections

Figures (7)

  • Figure 1: Methodology of the LCA study, including (a) the system boundaries, (b) the two parallel background data collection using Sphera LCA (GaBi) and ecoinvent databases, (c) the foreground data collection for the ASIC miner modeling (presented in Figure \ref{['fig:product-data-model']} and detailed in Support Information SI 2), (d) the computation of the LCI flows, and (e) the computation of the LCIA results using ReCIPe 2016.
  • Figure 2: Architecture of the ASIC miner including a control board, several hashboards and a power supply. In the Antminer S9, there are 3 hashboards with 63 ASICs each, and twice as many heat sinks. Hashboards are used to parallelize the proof-of-work protocol calculations, and the control board distributes the calculations between hashboards and communicates with the network.
  • Figure 3: Foreground and background modeling principles and assumptions for the LCI models using Sphera LCA and ecoinvent databases. It covers the production of each components of the ASIC miner. See supplementary material SI 1 (background) and SI 2 (foreground) for more details.
  • Figure 4: Comparison of the LCIA results (cradle-to-gate) on Antminer S9 miner with APW 3++ power supply, using Sphera LCA (GaBi) and ecoinvent databases. ReCiPe 2016 (H) is the LCIA method used in this study. Data available in the Sheet "data_from_figure_3_in_manuscript" of the Supporting Information SI 3.
  • Figure 5: Discrepancies between the absolute LCIA results obtained with Sphera LCA (GaBi) and ecoinvent. Results are normalized with respect to the ecoinvent ones, i.e., ecoinvent = 1 in each impact category. The ecoinvent results are labeled with their real value obtained with the standard units for each impact category. The abbrevations and units are presented in Fig. \ref{['fig:results']}. The errorbars displayed in the Sphera LCA results are the results of the sensitivity analysis. Data available in the Sheet "data_from_figure_4_in_manuscript" of the Supporting Information SI 3.
  • ...and 2 more figures