In the era of responsible and sustainable AI, information retrieval and recommender systems must expand their scope beyond traditional accuracy metrics to incorporate environmental sustainability. However, this research line is severely limited by the lack of item-level environmental impact data in standard benchmarks. This paper introduces Eco-Amazon, a novel resource designed to bridge this gap. Our resource consists of an enriched version of three widely used Amazon datasets (i.e., Home, Clothing, and Electronics) augmented with Product Carbon Footprint (PCF) metadata. CO2e emission scores were generated using a zero-shot framework that leverages Large Language Models (LLMs) to estimate item-level PCF based on product attributes. Our contribution is three-fold: (i) the release of the Eco-Amazon datasets, enriching item metadata with PCF signals; (ii) the LLM-based PCF estimation script, which allows researchers to enrich any product catalogue and reproduce our results; (iii) a use case demonstrating how PCF estimates can be exploited to promote more sustainable products. By providing these environmental signals, Eco-Amazon enables the community to develop, benchmark, and evaluate the next generation of sustainable retrieval and recommendation models. Our resource is available at https://doi.org/10.5281/zenodo.18549130, while our source code is available at: http://github.com/giuspillo/EcoAmazon/.

翻译：在负责任与可持续人工智能时代，信息检索与推荐系统必须超越传统准确性指标，将环境可持续性纳入考量。然而，该研究方向因标准基准数据集中缺乏细粒度商品环境影响数据而受到严重制约。本文提出Eco-Amazon，一种旨在填补此空白的新型资源。该资源包含三个广泛使用的亚马逊数据集（即家居、服装与电子产品）的增强版本，通过产品碳足迹元数据进行了扩充。二氧化碳当量排放评分采用零样本框架生成，该框架利用大型语言模型基于商品属性估算细粒度PCF。我们的贡献包括三个方面：（i）发布Eco-Amazon数据集，通过PCF信号增强商品元数据；（ii）提供基于LLM的PCF估算脚本，使研究人员能够扩展任意产品目录并复现我们的结果；（iii）展示如何利用PCF估算促进可持续商品的应用案例。通过提供这些环境信号，Eco-Amazon使研究社区能够开发、基准测试和评估新一代可持续检索与推荐模型。本资源可通过https://doi.org/10.5281/zenodo.18549130获取，源代码发布于：http://github.com/giuspillo/EcoAmazon/。