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.

翻译：大型企业面临着实现可持续发展目标（SDGs）的关键使命，尤其是聚焦于应对气候变化及其影响的目标13。为减缓气候变化效应，减少企业范围3（供应链排放）至关重要，因其占排放清单总量的90%以上。然而，追踪范围3排放极具挑战性，因为数据必须从数千家上下游供应商处收集。针对上述挑战，我们首次提出一种采用领域自适应NLP基础模型框架的解决方案，通过将金融交易作为采购商品和服务的代理变量来估算范围3排放。我们将所提框架与TF-IDF、word2Vec和零样本学习等最新文本分类模型进行了性能对比。结果表明，经领域自适应的基础模型在性能上优于当前最先进的文本挖掘技术，并可达到与领域专家（SME）相当的水平。该框架有望加速企业级范围3排放估算，从而助力采取适当气候行动以达成SDG 13目标。