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Quantifying the global eco-footprint of wearable healthcare electronics

Nature volume 649, pages 73–82 (2026)Cite this article

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Abstract

Wearable healthcare electronics are rapidly emerging as a distinct electronics sector in the digital era1,2,3,4,5,6, offering substantial economic opportunities and crucial medical benefits. However, their interactions with environmental and social systems remain poorly understood7,8,9, leaving critical sustainability challenges unaddressed. Although current efforts have focused on material-level improvements, broader system-level dynamics remain unexplored. Here we present an integrated systems engineering framework based on de novo life-cycle inventories and diffusion-linked scaling to quantify global eco-footprint hotspots and identify effective mitigation strategies. Cradle-to-grave analysis of representative wearable healthcare electronics (glucose, cardiac and blood pressure monitors and diagnostic imagers) generates full-spectrum environmental impact metrics, identifying warming impacts of 1.1–6.1 kgCO2-equivalent per device. The global device consumption is projected to increase 42-fold by 2050, approaching 2 billion units annually and generating 3.4 MtCO2-equivalent emissions alongside ecotoxicity and e-waste issues. Contrary to the conventional sustainability emphasis on plastics, this work demonstrates that recyclable or biodegradable plastics offer only marginal benefits, whereas substituting critical-metal conductors and optimizing circuit architectures can significantly reduce impacts without compromising performance. This systems engineering-based life-cycle assessment framework holds promise for establishing ecologically responsible innovation in next-generation wearable electronics.

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Fig. 1: Overview of the framework for quantifying the global eco-footprint of wearable electronics.
Fig. 2: Quantifying the full-spectrum eco-footprints of a wearable glucose monitor.
Fig. 3: Comparative environmental impacts for representative wearables.
Fig. 4: Global eco-footprint of wearable healthcare electronics.
Fig. 5: Balancing environmental sustainability and functional performance for wearables.
Fig. 6: Quantifying the environmental benefits of life-cycle-based mitigation strategies.

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Data availability

The research findings presented in this study are substantiated by the data included within the main body of the article as well as its Supplementary Information. Additional LCI, circuit designs and other related modelling processes can be found in the Supplementary data, Source data and a public repository (https://osf.io/bfxck/). Source data are provided with this paper.

Code availability

Scripts utilized for integrated assessment model and global mapping analysis based on Python 3.10 are available at https://osf.io/bfxck/. Source files are also provided.

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Acknowledgements

We thank Z. Cheng and X. Zhong for helpful discussions. This work made use of the Pritzker Nanofabrication Facility at the Pritzker School of Molecular Engineering at the University of Chicago. B.T. acknowledges support from the US Army Research Office (W911NF-24−1-0053) and the University of Chicago startup grant. C.Y. acknowledges the support from the Suzuki Postdoctoral Fellowship. F.Y. and B.W. were supported in part by a National Science Foundation (NSF) grant (grant EFMA-2029327), the Karin Bain & John Kukral Foundation, and the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a programme of Schmidt Sciences, LLC.

Author information

Author notes

  1. These authors contributed equally: Chuanwang Yang, Bingzheng Wang

Authors and Affiliations

  1. The James Franck Institute, University of Chicago, Chicago, IL, USA

    Chuanwang Yang, Ji Wan, Ananth Kamath & Bozhi Tian

  2. College of Engineering, Cornell University, Ithaca, NY, USA

    Bingzheng Wang & Fengqi You

  3. Cornell AI for Sustainability Initiative, Cornell University, Ithaca, NY, USA

    Fengqi You

  4. Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA

    Fengqi You

  5. Department of Chemistry, University of Chicago, Chicago, IL, USA

    Bozhi Tian

  6. The Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA

    Bozhi Tian

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Contributions

B.T. and F.Y. supervised the research. C.Y. and B.T. conceived of the idea and concept. C.Y. and B.W. developed the LCIA and diffusion methodologies. C.Y. and B.W. conducted the investigation, with input from A.K. C.Y. and B.W. performed the data analysis and visualization. J.W. and C.Y. conducted the electric circuit design and analysis. C.Y. drafted the paper with contributions and revisions from all authors. All authors reviewed and approved the final version of the paper.

Corresponding authors

Correspondence to Chuanwang Yang, Fengqi You or Bozhi Tian.

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Extended data figures and tables

Extended Data Fig. 1 Uncertainty analysis for nCGM in terms of configuration change.

Probability distributions from uncertainty analysis of nCGM and its subcomponents (sensor, FPCB assembly, packaging, and e-waste) across GWP, FETP, TETP, HTPc, HTPnc, SOP, FFP, and WCP, based on Monte Carlo simulations (n = 10,000).

Source Data

Extended Data Fig. 2 Sensitivity analysis of nCGM under configuration variations.

Overall GWP (b, e, h) and FETP (c, f, i) variations are shown for different configurations, including seven sensor designs (a-c), six FPCB modules (d-f), and five packaging scenarios with associated e-waste treatments (g-i), evaluated at the device level. Error bars denote 95% uncertainty bounds (2.5th–97.5th percentile) derived from the Ecoinvent pedigree matrix (log-normal approximation).

Source Data

Extended Data Fig. 3 Attributional system boundary and simplified consequential effects for nCGM.

The attributional system boundary covers the life cycle of wearable continuous glucose monitors, including material sourcing, manufacturing, transportation, use, and end-of-life. Beyond this scope, the consequential system boundary considers healthcare system-level responses triggered by nCGM adoption. Such effects include the expansion of digital healthcare, which may displace conventional glucose monitoring (e.g., finger-prick blood glucose meters, test strips, in-clinic tests, hospital visits), but also increase upstream demands for cloud computing, flexible PCB feedstocks, and other supply-side resources. A potential rebound effect with accelerated device replacement may offset environmental gains.

Source Data

Extended Data Fig. 4 Projected e-waste generation from wearable healthcare electronics.

(a) Weibull distribution parameters for 4 wearables. (b) Cumulative distribution functions (CDF) of waste generation as a function of time since market entry (n–t). (c) Projected annual e-waste generation of the four wearables under the moderate market scenario by 2050. Colored regions representing the bounds of 95% uncertainty intervals (n = 10,000). (d) Cumulative e-waste generation from 2025 to 2050 under conservative, moderate, and aggressive scenarios. Uncertainties derive from the variation of conservative, moderate, and aggressive scenarios. Error bars: standard deviations (n = 3).

Supplementary information

Supplementary Information (download PDF )

Supplementary Notes 1–9, Figs. 1–40, Tables 1–64 and References.

Reporting Summary (download PDF )

Supplementary Data 1 (download XLSX )

Life-cycle inventories for four wearable healthcare electronics.

Supplementary Data 2 (download XLSX )

Market diffusion data by 2050.

Supplementary Data 3 (download ZIP )

Stagewise life-cycle inventory across raw-material extraction, manufacturing, transportation, use and EOL.

Supplementary Data 4 (download XLSX )

Market-lifespan model for e-waste prediction.

Peer Review File (download PDF )

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Yang, C., Wang, B., Wan, J. et al. Quantifying the global eco-footprint of wearable healthcare electronics. Nature 649, 73–82 (2026). https://doi.org/10.1038/s41586-025-09819-w

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