In recent years, the world has faced increasingly severe climate change and ecological environmental problems. As an important part of the ecological system, rural areas also face many challenges. Rural ecological construction and carbon neutrality, as a solution, have attracted widespread attention. However, achieving the coordinated development of rural ecological construction and carbon neutrality requires more in-depth research and effective methods.
This study aims to explore how to promote the coordinated development of rural ecological construction and carbon neutrality through the combination of a Transformer-RNN model and cross-attention mechanism. We propose a deep learning framework that combines the parallelism and global dependency capturing capabilities of the Transformer model with the temporal information handling capabilities of the RNN model. By integrating these two models, we leverage their respective strengths to improve the performance of the model. Furthermore, we introduce a cross-attention mechanism that enables the model to simultaneously focus on the relationship between rural ecological construction and carbon neutrality. Through cross-attention, the model accurately captures the impact of rural ecological construction measures on carbon neutrality and the feedback effect of carbon neutrality on the rural ecological environment. In our experiments, we collected relevant data on rural ecological construction and carbon neutrality, including environmental indicators, socio-economic factors, land use patterns, energy consumption, and carbon emissions.
We preprocess the data and train the combined Transformer-RNN model with the cross-attention mechanism. The trained model demonstrates promising results in capturing the complex dependencies and relationships between rural ecological construction and carbon neutrality. The significance of this study lies in deepening the understanding of the coordinated development relationship between rural ecological construction and carbon neutrality and providing a novel deep learning-based method to solve related problems. By introducing the Transformer-RNN model with a cross-attention mechanism, we provide decision-makers with more scientific and accurate decision support, promoting the improvement of the rural ecological environment and the achievement of carbon neutrality goals.