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.

提出了一种利用适应领域的自然语言处理基础模型估计企业三阶段的排放量的框架，通过运用财务交易作为购买货物和服务的代理，结果显示该框架的性能超越了现有的文本分类模型，并可与专业人员相媲美。该框架能够加速企业规模的三阶段估计，并协助采取适当的气候行动以实现可持续发展目标 13。

利用大型语言模型进行供应链排放估计

Supply chain emission estimation using large language models

The main source of various religious teachings is their sacred texts which
vary from religion to religion based on different factors like the geographical
location or time of the birth of a particular religion. Despite these
differences, there could be similarities between the sacred texts based on what
lessons it teaches to its followers. This paper attempts to find the similarity
using text mining techniques. The corpus consisting of Asian (Tao Te Ching,
Buddhism, Yogasutra, Upanishad) and non-Asian (four Bible texts) is used to
explore findings of similarity measures like Euclidean, Manhattan, Jaccard and
Cosine on raw Document Term Frequency [DTM], normalized DTM which reveals
similarity based on word usage. The performance of Supervised learning
algorithms like K-Nearest Neighbor [KNN], Support Vector Machine [SVM] and
Random Forest is measured based on its accuracy to predict correct scared text
for any given chapter in the corpus. The K-means clustering visualizations on
Euclidean distances of raw DTM reveals that there exists a pattern of
similarity among these sacred texts with Upanishads and Tao Te Ching is the
most similar text in the corpus.

本文试图通过文本挖掘技术，探索亚洲和非亚洲的宗教经典之间的相似性，并使用监督学习算法，测量其正确预测各种经文的准确性。利用欧几里得、曼哈顿、Jaccard 和 Cosine 等相似性度量方法以及基于文档单词出现频率和规范化文档单词出现频率等相似性度量方法，得到了一些发现。K-means 聚类可视化揭示了宗教经典之间的相似性模式，其中乌梅尼西亚和道德经是语料库中最相似的文本。