A Hybrid PCA-Stacking Framework for Multidimensional Assessment of Development Trajectories: Evidence from China's Modernization Process

Hanrui Wang; Yile Wang

doi:10.6981/FEM.202507_6(7).0018

A Hybrid PCA-Stacking Framework for Multidimensional Assessment of Development Trajectories: Evidence from China's Modernization Process

Hanrui Wang, Yile Wang

Frontiers in Economics and Management, (2025), Vol.6, No.7, pp.166-174

Published: July 12, 2025

DOI: 10.6981/FEM.202507_6(7).0018

PDF: Download Full Text PDF

Abstract

China's multidimensional modernization since 2000 necessitates comprehensive assessment frameworks. This study constructs a novel China Development Index (CDI) through integrated analytical approaches. Principal Component Analysis first distilled 14 indicators across economic, social, and governance domains into two dominant components explaining 94.39% cumulative variance: an innovation-education-governance nexus (84.88% weight) and economic fundamentals (15.12% weight). Using Nonlinear Autoregressive Neural Network (NAR) neural networks optimized via Levenberg-Marquardt algorithms (12 hidden neurons, 2-step delays), secondary indicators like service sector growth were forecasted with 0.026 MSE. Stacking fusion modeling combining Linear Regression, k-Nearest Neighbors (KNN), and Random Forest base learners then projected primary indicators, achieving 93.02 Mean Squared Error (MSE) – 7.2% lower than individual models. The entropy weight-variation coefficient method synthesized these projections into the CDI, revealing three historical phases: Founding (1949-1978), Reform (1978-2000), and Modernization (2000-2022). Longitudinal analysis demonstrates accelerated development post-2000, with projections to 2062 indicating transition toward sustainable development characterized by environmental decoupling. A Stacking classification model integrating LightGBM, XGBoost, and Support Vector Machine (SVM) achieved 95.6% accuracy in phase identification.

Keywords: Principal Component Analysis; China Development Index; NAR Neural Network; Stacking Ensemble Modeling; Sustainable Development Transition; Phase Identification Model; Entropy Weight-Variation Coefficient.

APA Citation: Hanrui Wang, Yile Wang (2025). A Hybrid PCA-Stacking Framework for Multidimensional Assessment of Development Trajectories: Evidence from China's Modernization Process. Frontiers in Economics and Management, 6(7), 166-174. https://doi.org/10.6981/FEM.202507_6(7).0018

References

Ruder M ,White K ,Lai A , et al.Using principal component analysis in assessing stride characteristics for footwear and bra preference in female runners[J].Footwear Science,2025,17(sup1):S75-S76.
Li Y ,Bian X ,Sheng J , et al.Macroscopic properties and air pores of tailings concrete under dry-wet cycles of chloride attack based on principal component analysis (PCA)[J].Construction and Building Materials,2025,489142233-142233.
Zhao Z ,Zhu L ,Zheng W , et al.Development of a multicommunity index of biotic integrity for ecological health assessments of water-A case study of a mountain watershed in eastern China[J].Ecological Indicators,2025,176113737-113737.
Computing M A C W .RETRACTION: Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network[J].Wireless Communications and Mobile Computing,2025,2025(1):9807384-9807384.
Vitiana L ,Benedetta E ,Daniela S , et al.Fueling the circular transition: an empirical exploration of sustainable development goal performance in the oil and gas industry[J].Measuring Business Excellence,2025,29(2):285-305.
Esily R R ,Chi Y ,Ibrahiem M D , et al.What policies do the clean energy transition and green innovation tracks dictate for the MENA region’s sustainable development goals?[J].Clean Technologies and Environmental Policy,2025,(prepublish):1-25.
Liu D ,Giraldo S J ,Palensky P , et al.A siamese neural network model for phase identification in distribution networks[J].International Journal of Electrical Power and Energy Systems,2025, 169110718-110718.
Liu Y ,Shu Y ,Xu Z , et al.One-dimensional convolutional neural network model driven intelligent operation phases identification of hydraulic press using energy data[J].Engineering Applications of Artificial Intelligence,2025,155111079-111079.
Ji M ,Wang X ,Wu D , et al.Deep Learning Research on Quantitative Evaluation Model of Tea Taste Based on NAR Neural Network[J].International Journal of Pattern Recognition and Artificial Intelligence,2025,39(07):
Qin Y ,Cai X ,Ji Y , et al.Development and application of a core competency evaluation index system for pediatric asthma specialist nurses in China: a mixed-method study[J].BMC Nursing,2025, 24(1):560-560.
Chen J ,Zhang C ,Li X , et al.An integrative approach to enhance load forecasting accuracy in power systems based on multivariate feature selection and selective stacking ensemble modeling[J]. Energy,2025,326136337-136337.
Li A Z ,Li L Q ,Liang H J , et al.Stacking ensemble surrogate modeling method based on decomposed- coordinated strategy for structural low-cycle fatigue life reliability estimation[J].Reliability Engineering and System Safety,2025,257(PA):110811-110811.