SD-LSTM: A Dynamic Time Series Model for Predicting the Coupling Coordination of Smart Agro-Rural Development in China
The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two int...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/14/1491 |
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| Summary: | The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two into smart agriculture and rural development. Utilizing panel data from 31 Chinese provinces from 2011 to 2022, a comprehensive evaluation index system is constructed to assess development levels. The entropy weight method and kernel density estimation are employed to evaluate indicator performance and capture dynamic distribution patterns. A coupling coordination model is used to analyze the spatio-temporal evolution of the interaction between the two systems, while a hybrid SD-LSTM (System Dynamics–Long Short-Term Memory) model forecasts coordination trends over the next six years. Results reveal a steady upward trend in both systems, with coordination levels improving from “moderate imbalance” to “moderate coordination.” A distinct spatial pattern emerges, characterized by “high in the east, low in the west” and a mismatch between high coupling and low coordination. Forecasts suggest a continued progression toward “good coordination.” The findings offer policy implications for enhancing digital village initiatives, accelerating rural technological diffusion, and strengthening regional collaboration—providing valuable insights into advancing China’s smart rural transformation and agricultural modernization. |
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| ISSN: | 2077-0472 |