Supply chain emission estimation using large language models

Unfortunately, the world remains off track in meeting Development Goals (SDGs), especially goal 13, which focuses the Paris Agreement's target of limiting the temperature rise to on combating climate change and its impacts. To mitigate the effects 1.5 C above pre-industrial levels and reaching net-zero emissions of climate change, reducing enterprise Scope 3 (supply chain by 2050 [14], with a projected temperature rise of around 2.7 C emissions) is vital, as it accounts for more than 90% of total emission above pre-industrial levels by 2100 [22]. To achieve these targets, inventories. However, tracking Scope 3 emissions proves challenging, it is critical to engage non-state actors like enterprises, who have as data must be collected from thousands of upstream and pledged to reduce their GHG emissions, and have significant potential downstream suppliers. To address the above mentioned challenges, to drive more ambitious actions towards climate targets than we propose a first-of-a-kind framework that uses domain-adapted governments [9]. However, a lack of high-quality data and insights NLP foundation models to estimate Scope 3 emissions, by utilizing about an enterprise's operational performance can create barriers to financial transactions as a proxy for purchased goods and services.