While deep learning based solutions, including CNNs or transformer-based architectures, have demonstrated promising results for image super-resolution (SR) tasks, their substantial depth and parameters challenge deployment on edge computing AI-enabled devices. To address this issue, we propose a lightweight single image super-resolution (SISR) model named Efficient Spatial and Channel Attentive Network (ESCANet), comprised of Spatial Enhancement Module (SEM) and Channel-wise Enhancement Module (CEM). SEM efficiently captures both local and non-local information by introducing Efficient Self-Attention Approximation Block (ESAB) that does not increase the computational complexity. CEM modulates the local features by channel mixing. Experiments demonstrate that ESCANet achieves a reasonable trade-off between computational efficiency and reconstruction performance on five well-known SR benchmark datasets.