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-Agents 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.
