Optimization for China’s Industrial Investment Allocation

Zibo Nie ()
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Zibo Nie: China University of Geosciences (Wuhan), School of Computer Science

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Cultural Industry (ICEMCI 2025), 2025, pp 67-74 from Springer

Abstract: Abstract This study investigates the optimization of industrial investment allocation in China, addressing the challenges of over-concentration in certain sectors and inefficiencies in resource distribution. A multi-objective optimization model is proposed using Sequential Least Squares Quadratic Programming (SLSQP) to allocate investments across 13 key industries, with the aim of maximizing GDP while minimizing CO2 emissions. Data from China’s National Bureau of Statistics (NBS) from 1990 to 2023 is utilized to inform the model. The results indicate a shift in investment priorities, favoring industries with high economic returns and low environmental impact, such as information technology services and finance, while reducing allocations to high-emission sectors like chemical and metal manufacturing. The study highlights the importance of balancing economic growth and environmental sustainability in industrial investment decisions. The optimized allocation supports China’s strategic goals outlined in the 14th Five-Year Plan and the 2060 carbon neutrality target. This research contributes to the development of a scalable decision-making tool for policymakers, offering insights into how China can achieve sustainable growth by optimizing resource distribution across diverse sectors. Future work will refine the model to account for social and strategic factors beyond economic and environmental considerations.

Keywords: SLSQP; Non-Linear optimization; China’s industrial investment (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-888-2_9

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DOI: 10.2991/978-94-6463-888-2_9

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