  Over recent years, news recommender systems have gained significant attention
in both academia and industry, emphasizing the need for a standardized
benchmark to evaluate and compare the performance of these systems.
Concurrently, Green AI advocates for reducing the energy consumption and
environmental impact of machine learning. To address these concerns, we
introduce the first Green AI benchmarking framework for news recommendation,
known as GreenRec, and propose a metric for assessing the tradeoff between
recommendation accuracy and efficiency. Our benchmark encompasses 30 base
models and their variants, covering traditional end-to-end training paradigms
as well as our proposed efficient only-encode-once (OLEO) paradigm. Through
experiments consuming 2000 GPU hours, we observe that the OLEO paradigm
achieves competitive accuracy compared to state-of-the-art end-to-end paradigms
and delivers up to a 2992\% improvement in sustainability metrics.
