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. We propose a novel framework that uses domain-adapted NLP foundation model to estimate Scope 3 emissions by leveraging financial transactions as a proxy of embodied emission of purchased goods and services and recommends appropriate climate actions to reduce scope3 emission through counterfactual queries. 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). We also show how the proposed framework can identify Scope 3 hotspots and explain the factors that create them. Finally, we carry out what-if analysis to take climate actions that help achieve SDG 13. We present a case study demonstrating how this framework can be used by a real estate enterprise to take Scope 3 climate actions.