The paper introduces ELGC-Net, an efficient framework for remote sensing change detection (CD) that leverages rich contextual information to accurately estimate change regions while reducing model size. ELGC-Net consists of a Siamese encoder, fusion modules, and a decoder, with a key innovation being the Efficient Local-Global Context Aggregator (ELGCA) module. This module captures both global and local contextual information through pooled-transpose (PT) attention and depthwise convolution, respectively. PT attention employs pooling operations for robust feature extraction and minimizes computational cost. Extensive experiments on three challenging CD datasets (LEVIR-CD, DSIFN-CD, and CDD-CD) demonstrate that ELGC-Net outperforms existing methods, achieving a 1.4% gain in intersection over union (IoU) on the LEVIR-CD dataset compared to the recent transformer-based approach ChangeFormer. Additionally, a lightweight variant, ELGC-Net-LW, is introduced, which reduces computational complexity while maintaining comparable performance. The source code is publicly available.The paper introduces ELGC-Net, an efficient framework for remote sensing change detection (CD) that leverages rich contextual information to accurately estimate change regions while reducing model size. ELGC-Net consists of a Siamese encoder, fusion modules, and a decoder, with a key innovation being the Efficient Local-Global Context Aggregator (ELGCA) module. This module captures both global and local contextual information through pooled-transpose (PT) attention and depthwise convolution, respectively. PT attention employs pooling operations for robust feature extraction and minimizes computational cost. Extensive experiments on three challenging CD datasets (LEVIR-CD, DSIFN-CD, and CDD-CD) demonstrate that ELGC-Net outperforms existing methods, achieving a 1.4% gain in intersection over union (IoU) on the LEVIR-CD dataset compared to the recent transformer-based approach ChangeFormer. Additionally, a lightweight variant, ELGC-Net-LW, is introduced, which reduces computational complexity while maintaining comparable performance. The source code is publicly available.