As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the environmental impact of recommender systems research by reproducing typical experimental pipelines. Our analysis spans 79 full papers from the 2013 and 2023 ACM RecSys conferences, comparing traditional "good old-fashioned AI" algorithms with modern deep learning algorithms. We designed and reproduced representative experimental pipelines for both years, measuring energy consumption with a hardware energy meter and converting it to CO2 equivalents. Our results show that papers using deep learning algorithms emit approximately 42 times more CO2 equivalents than papers using traditional methods. On average, a single deep learning-based paper generates 3,297 kilograms of CO2 equivalents - more than the carbon emissions of one person flying from New York City to Melbourne or the amount of CO2 one tree sequesters over 300 years.

翻译：随着全球变暖加剧，评估研究的环境影响变得日益紧迫。尽管如此，很少有推荐系统研究论文关注其环境影响。本研究通过复现典型实验流程，评估了推荐系统研究的环境影响。我们的分析涵盖了2013年和2023年ACM RecSys会议的79篇全文论文，比较了传统的“经典AI”算法与现代深度学习算法。我们设计并复现了这两个年份的代表性实验流程，使用硬件能耗计测量能耗，并将其转换为二氧化碳当量。结果表明，采用深度学习算法的论文产生的二氧化碳当量约为传统方法论文的42倍。平均而言，一篇基于深度学习的论文产生3,297千克二氧化碳当量——超过一个人从纽约飞往墨尔本的碳排放量，或一棵树300年固碳量的总和。