Efficient image super-resolution (SR) models are essential for achieving high-quality image reconstruction with reduced computational complexity, particularly in resource-constrained environments. In this paper, we introduce a novel self-attention mechanism, Broadcast-Gated Attention with Identity Adaptive Integration (BGAI). Then, based on this mechanism, we design a lightweight super-resolution network that achieves state-of-the-art performance with minimal computational cost. By observing the sparsity and convergence properties of self-attention, BGAI optimizes computational resource utilization through the effective broadcasting of meaningful features across attention heads and network layers. A key innovation in BGAI is the Broadcast-Gated Multi-head Self-Attention (BGMSA) mechanism, which employs a dedicated head to capture and integrate long-range dependencies, broadcasting this broader contextual information to local attention heads. This design enhances long-range interaction modeling while minimizing redundant computations. Additionally, the Identity Attention Adaptive Integration (IAAI) mechanism facilitates efficient feature propagation by leveraging the continuity in dependencies across layers, with a focus on dynamic variations to improve representational efficiency and accelerate convergence. Comprehensive experiments on standard benchmarks demonstrate that BGAI achieves high-fidelity super-resolution while reducing the number of parameters and FLOPs by up to 35% compared with existing lightweight methods. These results establish BGAI as a robust and scalable solution for resource-efficient SR, with significant potential for deployment in real-world, high-resolution image processing applications. The code and trained models are publicly available at https://github.com/bbbolt/BGAI.