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PCF-RWKV: Large Language Model for Product Carbon Footprint Estimation

Author

Listed:
  • Zhen Li

    (Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Peihao Tang

    (Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Xuanlin Wang

    (Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Xueping Liu

    (Division of Advanced Manufacturing, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China)

  • Peng Mou

    (Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

As global climate change intensifies, assessing product carbon footprints serves as a foundational step for quantifying greenhouse gas emissions throughout a product’s lifecycle, forming the basis for achieving sustainability and emission reduction goals. Traditional lifecycle assessment methods face challenges such as subjective boundary definitions and time-consuming inventory construction. This study introduces PCF-RWKV, a novel model based on the RWKV architecture with task-specialized low-rank adaptations (LoRAs). Trained on carbon footprint datasets, the model minimizes memory use and data interference, enabling efficient deployment on consumer-grade GPUs without relying on cloud computing. By integrating multi-agent technology, PCF-RWKV automates the creation of lifecycle inventories and aligns production processes with emission factors to calculate carbon footprints. This approach significantly improves the efficiency and security of corporate carbon footprint assessments, providing a potential alternative to traditional methods.

Suggested Citation

  • Zhen Li & Peihao Tang & Xuanlin Wang & Xueping Liu & Peng Mou, 2025. "PCF-RWKV: Large Language Model for Product Carbon Footprint Estimation," Sustainability, MDPI, vol. 17(3), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1321-:d:1584975
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    References listed on IDEAS

    as
    1. Kai Rüdele & Matthias Wolf, 2023. "Identification and Reduction of Product Carbon Footprints: Case Studies from the Austrian Automotive Supplier Industry," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
    2. Yong Yang & Xiaogang Yue & Yongle Luo & Li Jin & Buyu Jia, 2024. "Building Information Modeling–Life Cycle Assessment: A Novel Technology for Rapid Calculation and Analysis System for Life Cycle Carbon Emissions of Bridges," Sustainability, MDPI, vol. 16(23), pages 1-23, December.
    3. Laura Vauche & Gabin Guillemaud & Joao-Carlos Lopes Barbosa & Léa Di Cioccio, 2024. "Cradle-to-Gate Life Cycle Assessment (LCA) of GaN Power Semiconductor Device," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
    4. Anika Trebbin & Katrin Geburt, 2024. "Carbon and Environmental Labelling of Food Products: Insights into the Data on Display," Sustainability, MDPI, vol. 16(24), pages 1-28, December.
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