The Industrial Investment Layout in China: A Data-Driven Study on the Balance between GDP, Employment, and Sustainable Development

This paper presents a comprehensive mathematical modeling approach to determine how government investment in major Chinese industries should be allocated to maximize GDP, promote employment, and support sustainable development. The study begins with an exploration of inter-industry relationships, leveraging correlation and network analyses. We then develop theoretical models (from linear regressions to spillover-augmented frame-works) linking investment levels in different industries to their respective GDP outputs. Using optimization methods, we propose optimal allocations under two scenarios: (1) no limit on the number of industries to invest in, and (2) restricting the government to pick only three industries. These results are refined by incorporating employment objectives. We conclude with long-term policy recommendations on which sectors—particularly high-technology, green energy, and advanced services—should be prioritized to ensure steady wealth growth and strong employment rates.

Authors

Publication Details

Journal
Mathematical Modeling and Algorithm Application
Published
2025-03-25
DOI
https://doi.org/10.54097/ypwbv609
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
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article

The Industrial Investment Layout in China: A Data-Driven Study on the Balance between GDP, Employment, and Sustainable Development

Ziyao Xian, Yuqi Shan, Chang Liu, Guanliang Chen et al.
Mathematical Modeling and Algorithm Application
Energy, Environment, Economic Growth
article

The Industrial Investment Layout in China: A Data-Driven Study on the Balance between GDP, Employment, and Sustainable Development

Ziyao Xian, Yuqi Shan, Chang Liu, Guanliang Chen, Min Luo, Chuxiao Ye, Cai Rui-bin
article en

Abstract

This paper presents a comprehensive mathematical modeling approach to determine how government investment in major Chinese industries should be allocated to maximize GDP, promote employment, and support sustainable development. The study begins with an exploration of inter-industry relationships, leveraging correlation and network analyses. We then develop theoretical models (from linear regressions to spillover-augmented frame-works) linking investment levels in different industries to their respective GDP outputs. Using optimization methods, we propose optimal allocations under two scenarios: (1) no limit on the number of industries to invest in, and (2) restricting the government to pick only three industries. These results are refined by incorporating employment objectives. We conclude with long-term policy recommendations on which sectors—particularly high-technology, green energy, and advanced services—should be prioritized to ensure steady wealth growth and strong employment rates.

Mathematical Modeling and Algorithm ApplicationVol. 4(2)
Openalex Percentile: Top 91%
Energy, Environment, Economic Growth
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