Supply chain emission estimation using large language models
Ayush Jain Manikandan Padmanaban Jagabondhu Hazra Shantanu Godbole Kommy Weldemariam
Large enterprises face a crucial imperative to achieve the Sustainable
Development Goals (SDGs), especially goal 13, which focuses on combating
climate change and its impacts. To mitigate the effects of climate change,
reducing enterprise Scope 3 (supply chain emissions) is vital, as it accounts
for more than 90\% of total emission inventories. However, tracking Scope 3
emissions proves challenging, as data must be collected from thousands of
upstream and downstream suppliers.To address the above mentioned challenges, we
propose a first-of-a-kind framework that uses domain-adapted NLP foundation
models to estimate Scope 3 emissions, by utilizing financial transactions as a
proxy for purchased goods and services. We compared the performance of the
proposed framework with the state-of-art text classification models such as
TF-IDF, word2Vec, and Zero shot learning. Our results show that the
domain-adapted foundation model outperforms state-of-the-art text mining
techniques and performs as well as a subject matter expert (SME). The proposed
framework could accelerate the Scope 3 estimation at Enterprise scale and will
help to take appropriate climate actions to achieve SDG 13.