Eco-Amazon: Enriching E-commerce Datasets with Product Carbon Footprint for Sustainable Recommendations

2026-02-17

AI Summary

This study addresses the critical absence of product-level carbon footprint data in existing e-commerce recommendation systems, which has hindered research on sustainable recommendations. To bridge this gap, the authors propose a zero-shot framework leveraging large language models (LLMs) to estimate product carbon footprints (PCFs), thereby enriching a dataset of Amazon products across three major categories—home, apparel, and electronics—with PCF metadata. The resulting resource, Eco-Amazon, constitutes the first benchmark dataset designed explicitly for sustainable recommendation research. The work contributes not only the inaugural e-commerce dataset incorporating carbon footprint signals but also a reproducible LLM-driven tool for PCF estimation. Furthermore, it demonstrates the feasibility of integrating environmental sustainability metrics into recommender systems, laying the groundwork for the development of green, intelligent recommendation technologies.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Efficient ML / Green AI
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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/.

Problem

• Product Carbon Footprint
• Sustainable Recommendations
• E-commerce Datasets
• Environmental Impact
• Information Retrieval

Innovation

• Product Carbon Footprint
• Large Language Models
• Zero-shot Estimation
• Sustainable Recommender Systems
• Eco-Amazon Dataset