Abnormal event detection models are based on state-of-the-art neural networks containing millions of artificial neurons organized into complex architectures that try to simulate the human brain. However, considering the progress of neural networks over the recent years, e.g. the evolution from the AlexNet convolutional neural network (CNN) to vision transformers, we observe a prominent pattern towards enlarging the models to increase accuracy, while ignoring the increasing demand for computational resources. Unfortunately, this generic trend in AI applies to abnormal event detection as well. Some recent studies showed that state-of-the-art neural networks require significant computational resources. These studies even went down to estimating the CO2 emissions of such models, showing that training a single model can generate as many CO2 emissions as 5 cars for their entire life span. To our knowledge, there are no previous studies on green abnormal event detection. Our aim is to apply the principles of Green AI and to research and develop more efficient (and equally effective) deep neural models for abnormal event detection in video. We believe this challenge can be achieved by exploring novel architectures in conjunction with newly developed learning paradigms and optimizers that will allow us to train lighter architectures effectively. We will start from our preliminary anomaly detection models published at CVPR 2021 and PAMI 2022 (with results shown in the above figure) and explore the possibility of transferring knowledge from the existing neural models to smaller and efficient models, through knowledge distillation. Jointly with the novel learning paradigms, we will study various neural architectures, from CNNs to transformers, that are likely to be more efficient. Neural architectures can become more efficient in multiple ways, for example by reducing the depth (number of layers) or width (number of units per layer), while replacing existing layers or units with more efficient components.