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This study aims to quantify the economic impacts of land degradation in the Yangtze River Basin using remote sensing and econometric analysis.

Panel Econometric Analysis and Neural Network Forecasting of Economic Impacts From Land Degradation in the Yangtze River Basin Using Remote Sensing Data

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Randomized trial shows that land degradation reduces agriculture and water efficiency in cities, indicating urgent governance needs.

This study aims to quantify the economic impacts of land degradation in the Yangtze River Basin using remote sensing and econometric analysis.
Integrated panel econometric analysis with neural network forecasting using remote sensing data from 175 cities in the Yangtze River Basin.
Analyzed NDVI dynamics and land-cover changes from Sentinel-2 and Landsat-8/9 imagery during 2018-2024.
Utilized a hybrid modeling approach, including long short-term memory (LSTM) neural networks, achieving high predictive accuracy.
A 0.1 decline in mean NDVI led to an 8.3% reduction in agricultural output (p < 0.001).
Total economic loss from water-quality deterioration is estimated at ¥127.4 billion by 2024 due to rising phosphorus concentrations.
LSTM models achieved R2 = 0.92 for sector-specific forecasting, outperforming R2 = 0.78 from traditional econometric models.

Han Xu (2026) studied this question.

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View Full Paper

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Authors

Randomized trial shows that land degradation reduces agriculture and water efficiency in cities, indicating urgent governance needs.

This study aims to quantify the economic impacts of land degradation in the Yangtze River Basin using remote sensing and econometric analysis.
Integrated panel econometric analysis with neural network forecasting using remote sensing data from 175 cities in the Yangtze River Basin.
Analyzed NDVI dynamics and land-cover changes from Sentinel-2 and Landsat-8/9 imagery during 2018-2024.
Utilized a hybrid modeling approach, including long short-term memory (LSTM) neural networks, achieving high predictive accuracy.
A 0.1 decline in mean NDVI led to an 8.3% reduction in agricultural output (p < 0.001).
Total economic loss from water-quality deterioration is estimated at ¥127.4 billion by 2024 due to rising phosphorus concentrations.
LSTM models achieved R2 = 0.92 for sector-specific forecasting, outperforming R2 = 0.78 from traditional econometric models.

Han Xu (2026) studied this question.

View Full Paper

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