Supply chain emission estimation using large language models
This addresses the problem of tracking emissions for enterprises aiming to meet Sustainable Development Goals, though it appears incremental as it applies existing NLP methods to a new domain-specific task.
The paper tackled the challenge of estimating Scope 3 supply chain emissions for large enterprises by proposing a framework using domain-adapted NLP foundation models with financial transaction data as a proxy, which outperformed state-of-the-art text mining techniques and matched subject matter expert performance.
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.