气候变化领域集成服务门户(CCPortal): Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability

CCPortal

DOI	10.3389/fevo.2023.1267259
	Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability
	Li, Tong; Feng, Liangxing
发表日期	2024
ISSN	2296-701X
起始页码	11
卷号	11
英文摘要	Introduction 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.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.Results and discussion 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.
英文关键词	rural ecological construction; carbon neutrality; Swin Transformer; RNN; cross attention mechanism
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001160143300001
来源期刊	FRONTIERS IN ECOLOGY AND EVOLUTION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304677
作者单位	Lanzhou University of Finance & Economics; Lanzhou University of Finance & Economics
推荐引用方式 GB/T 7714	Li, Tong,Feng, Liangxing. Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability[J],2024,11.
APA	Li, Tong,&Feng, Liangxing.(2024).Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability.FRONTIERS IN ECOLOGY AND EVOLUTION,11.
MLA	Li, Tong,et al."Coordinated development of rural ecological construction and carbon neutrality: a deep learning approach for enhanced sustainability".FRONTIERS IN ECOLOGY AND EVOLUTION 11(2024).