Empirical Analysis and Effect Quantification of Investment and Financing Demands for Rural Revitalization Based on Multimodal Data Fusion

Abstract

This study employs natural language processing, Kalman filtering, and Gaussian process regression to construct an NLP-KF-GPR multimodal data fusion model, conducting empirical analysis across 31 provinces and municipalities nationwide. Focusing on quantifying the synergistic effects of policy instruments and nonlinear responses to environmental constraints, the findings reveal significant regional heterogeneity in the transmission mechanisms of rural revitalization investment and financing. Analysis of spatial differentiation in regional policy elasticity coefficients shows that the eastern region exhibits an average coefficient of 0.42, with significantly shorter policy transmission lags compared to the western region. The median policy transmission time in the west reaches 8.3 months, reflecting gradient differences in infrastructure conditions and policy implementation efficiency. Nonlinear validation of the environmental-economic threshold effect reveals an inverted U-shaped relationship between  emissions and investment-financing demand, with an inflection point at 527,000 tons, providing a basis for dynamically adjusting ecological compensation standards. Furthermore, empirical evidence confirms the significant dampening effect of policy semantic stability on market volatility: a 10% increase in the semantic coherence index reduces market expectation dispersion by 14.8%. Model performance evaluation indicates that the NLP-KF-GPR framework demonstrates outstanding capability in investment and financing demand forecasting, achieving a low normalized root mean square error of 0.124, showcasing excellent predictive accuracy and robustness.