Explaining High Output Efficiency in Strategic Emerging Industrial Spaces: A Spatial Economic Analysis of 78 Domestic and International Cases
DOI:
https://doi.org/10.71204/vkw73b72Keywords:
Industrial Space, Industrial City, Urban Spatial Performance, Urban Spatial Organization, Rent GradientAbstract
At present, China attaches great importance to the development of new quality productive forces. As the spatial projection carriers of these forces, strategic emerging industrial spaces exhibit high-output efficiency that calls for theoretical explanation. Existing research has largely focused on the internal efficiency of industries, with insufficient attention paid to the mechanisms of spatial attributes—particularly the systematic analysis of spatial economic factors such as intensification, functional mixing, and location–bid rent relations. To address this gap, this study draws upon 78 samples of strategic emerging industrial spaces from China and abroad. By employing quantitative analyses of the density–scale relationship, calculating functional mixing through the information entropy model, and assessing spatial bid rent effects, the study uncovers the underlying causes of the high efficiency observed in new industrial spaces. The results demonstrate that high development intensity and high functional mixing are distinctive characteristics of strategic emerging industrial spaces. Their bid rent capacity in core urban areas exceeds traditional land rent gradients, presenting an empirical challenge to the Alonso model. This high-density, high-mixing spatial pattern fosters reverse industrial clustering in urban cores through mechanisms such as knowledge spillovers, industrial chain collaboration, and innovation network agglomeration, thereby reshaping the theoretical framework of spatial economics. The findings provide a partial explanation for the high-output performance of new industrial spaces and offer a theoretical foundation for optimizing industrial space policies and planning supply strategies.
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Copyright (c) 2025 Yiyao Yang, Qi Wang, Leixian Guo, Qijun Li (Author)
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