Year: 2026
Type: article
Abstract: Current methods of Economic Growth Quality Index (EGQI) evaluation do not effectively embed multidimensional indicators of regional growth, uncertainty in subjective weightings and non-linear computational relationships in one framework. To address this, this research proposes a novel Framework for multidimensional EGQI Assessment based on Artificial Intelligence and Fuzzy Algorithm (AI-FAF). A multidimensional dataset about provincial-level development covering China’s situation from 1990 to 2021 was used, and 13 indicators were chosen based on their availability and relevance in economic, social, technological, environmental, public-investment and innovation dimensions to serve as empirical analysis. Data processing involves processing outlier data, handling missing values, and aligning spatiotemporal panel data. An AI-FAF integrated with the Entropy Weight Method (EWM) for objective indicator weighting, a Fuzzy Analytic Hierarchy Process (FAHP) model to address uncertainty and qualitative factors, and a Bald Eagle Search-driven Enriched Deep Neural Network (BES-EDNN) to identify complex nonlinear relationships and forecast trends in the quality of economic growth. The AI-enhanced fuzzy evaluation mechanism produces a detailed Economic Growth Quality Index (EGQI) achieved by applying Python 3.11. Experimental results show that the proposed framework provides better performance than the conventional statistical and standalone fuzzy evaluation methods, with Mean Absolute Error (MAE) of 0.023 and Root Mean Square Error (RMSE) of 0.0346. The findings show that there are major regional differences with innovation capability and environmental sustainability as key factors. The proposed framework provides a powerful decision support tool for policy makers aiming at balanced, smart and sustainable economic development. Not applicable.
Source: Discover Computing
Authors: Shuhua Tsao, Wenru Chen
Institution: Nanfang College Guangzhou
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7224548773