AI-POWERED ECONOMIC ANALYSIS
The Impacts of the Economic Development Strategy of the Greater Bay Area on Hong Kong's Economic Development Based on Deep Learning
In view of the limitations of traditional economic impact assessment methods in analyzing multi-dimensional policy effects, this study proposes a framework for assessing the economic impact of economic development strategies in the Greater Bay Area on Hong Kong based on LSTM-Attention. The model provides a high-precision and interpretable analysis tool for strategic impact assessment in the Greater Bay Area.
Author: Junzhe Li | Published in IC-AIF 2026
Unlocking GBA Economic Dynamics with AI
This study leverages LSTM-Attention deep learning to provide a novel, precise, and interpretable framework for assessing the multi-dimensional impacts of Greater Bay Area economic strategies on Hong Kong. By integrating time series modeling and policy focus identification, our approach overcomes the limitations of traditional methods, offering data-driven insights crucial for strategic optimization.
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LSTM-Attention Model Accuracy
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LSTM-Attention Macro-F1 Score
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Enhanced Model Robustness
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LSTM-Attention: A Hybrid Approach
The core of our model lies in the Long Short-Term Memory (LSTM) network, a variant of RNN designed to overcome gradient issues in sequence data. Its innovation is the "cell state" and "gating mechanism" (forget, input, output gates), allowing adaptive learning of long-term dependencies and balancing memory functions across different time scales.
Complementing LSTM is the Attention Mechanism, inspired by human cognition. It enables the model to selectively focus on the most relevant parts of the input information for the current task. This dynamic focus is crucial for identifying key policy nodes and their differential impact on economic development, enhancing the model's processing efficiency for complex, multi-dimensional data.
Robust Data Acquisition and Transformation
Data was sourced from the World Bank, National Bureau of Statistics, and Greater Bay Area Municipal Bureau of Statistics, focusing on annual economic data aligned with policy cycles. Key input features include policy intensity (quantified via textual analysis of policy documents), industrial synergy index (from sectoral GDP and input-output tables), and cross-border capital flows (FDI inflows, trade volumes).
Preprocessing involved eliminating noise, unifying feature formats, and adapting data for model input. Techniques like median filling for numerical features, mode supplementation for classification features, One-Hot Encoding for unordered categorical variables, and Z-score standardization for continuous features were applied. The data was split 7:3 for training and testing to ensure independence and reliability of time series predictions.
Rigorous Experimental Validation
Our experimental setup ensured fairness and reproducibility across all models. We utilized a unified hardware and software platform, detailed in Table 1, including Intel Core i9-13900K, NVIDIA RTX 4080, TensorFlow, and Scikit-learn. Performance was measured using regression metrics suitable for continuous economic forecasting: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R-squared).
Comparative models included classical methods like Logistic Regression and Random Forest, alongside deep learning models such as Multilayer Perceptron (MLP), basic LSTM, and Gated Recurrent Unit (GRU). This comprehensive comparison allowed for robust verification of LSTM-Attention's superior performance in capturing complex economic dynamics and policy impacts.
Attention Mechanism Framework
Input
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Attention Scoring Function
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Logistic Regression Classifier
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Attention Weights
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Output (Corresponding Values)
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LSTM-Attention Model Accuracy
0.88
LSTM-Attention Macro-F1 Score
| Model | Accuracy | Macro-Precision | Macro-Recall | Macro-F1 |
|---|---|---|---|---|
| Logistic regression | 0.62 | 0.61 | 0.6 | 0.6 |
| Random Forest | 0.75 | 0.74 | 0.73 | 0.73 |
| MLP | 0.78 | 0.77 | 0.76 | 0.76 |
| Basic LSTM | 0.8 | 0.79 | 0.78 | 0.78 |
| PSO-LSTM | 0.83 | 0.82 | 0.81 | 0.82 |
| Time Series Fusion Transformer | 0.22 | 0.30 | 0.47 | 0.83 |
| Hybrid Econometric-ML Model | 0.20 | 0.28 | 0.45 | 0.84 |
| LSTM-Attention | 0.87 | 0.86 | 0.85 | 0.86 |
Strategic Impact Assessment in the Greater Bay Area
This study provides a scientific and dynamic analysis tool for the research and judgment of Hong Kong's economic development path under the strategic background of the Great Bay Area, and to provide theoretical basis and decision-making reference for the optimization and adjustment of relevant policies. The LSTM-Attention model's ability to capture time-dependent characteristics and identify key policy nodes makes it exceptionally suitable for evaluating complex regional economic strategies. It offers high-precision and interpretable analysis, essential for data-driven decision support and optimizing development strategies.
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