Author(s)

Miaomiao Du

Corresponding Author

Miaomiao Du

Abstract

This paper develops a comprehensive analytical framework to optimize government investment allocation across industries in China, integrating both GDP growth and employment objectives. We establish an Industrial Relationship Analysis Framework examining interconnections among 13 major industries through correlation analysis, Granger causality tests, and trend analysis. Our findings identify the financial sector, chemical industry, and IT services as core economic drivers. We then develop an Investment-GDP Relationship Model combining efficiency metrics and regression analysis, revealing the financial sector’s highest investment efficiency (1.41) and service sector’s strong investment elasticity (2.14). Finally, we construct a Genetic Algorithm-based Investment Optimization Model with dual objectives. For GDP growth, financial services (16.8%), IT services, and construction are prioritized; for employment, real estate (34.8%), financial (27.0%), and service industries (26.9%) are recommended. Our enhanced model with sigmoid-transformed metrics achieves balanced allocation across primary industry (36.22%), wholesale and retail (33.16%), and chemical industry (30.62%) in restricted scenarios.

Keywords

Industrial Investment, Economic Growth, Employment Enhancement, Genetic Algorithm, Multi-Objective Optimization