Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China

Abstract

In the context of economic globalization, the manufacturing production space in the global city-regions of developing countries have presented significant spatial characteristics, attracting attention to the problems of intensive and sustainable development of production space. Taking global city-region in the Pearl River Delta (PRD) as an example, manufacturing production space based on remote sensing (RS) technology and point of interest (POI) data extraction was more precise and continuous, which had more advantages for further analysis of spatial characteristics and influencing factors in multi-scale, and precise policy recommendations. The results show that: (1) under different scales, the distribution characteristics of manufacturing production space and the agglomeration characteristics of spatial form are different. It is not simply extensive agglomeration or diffusion that can accurately explain its diversified spatial characteristics. Meanwhile, for the local manufacturing production space optimization control, the local government should apply advanced experience according to local conditions instead of simply and roughly promotion or containment. (2) Influencing factors show a strong positive correlation with the urbanization rate, the number of foreign direct investment (FDI) enterprises and gross industrial production, and which shows a weak negative correlation with fixed asset investment and the employment population. In conclusion, the spatial characteristics of manufacturing production space in global city-regions in developing countries is significantly different from that in Western countries, and its influencing factors have similarities and differences. Therefore, when conducting multi-scale space optimization and sustainable regulation, the government should consider more about the actual multi-scale spatial characteristics of manufacturing production space and its influencing factors instead of copy the Western experience.

Spatial and functional organizations of industrial agglomerations in China’s Greater Bay Area

“…Specifically, the primary sector is testified as two large-scale concentrations located towards west GBA, while concerning the manufacturing industries, a dual-centre configuration is revealed in the central megacities. This finding is partially inconsistent with the discovery of Liu et al (2019) who analyse remote sensing images and suggest a small number of manufacturing spaces in the metropolitan areas of Shenzhen and Guangzhou. A possible explanation of this is that the remote sensing images cannot reveal the details on the vertical concentration of industrial space, for instance, small workshops and factories can operate inside a downtown industrial building.…”

Research on the Spatio-Temporal Impacts of Environmental Factors on the Fresh Agricultural Product Supply Chain and the Spatial Differentiation Issue—An Empirical Research on 31 Chinese Provinces

“…Additionally, the specific bandwidth of each variable can be sued as the indicator of the spatial scale in the process. Still, the approach featuring multiple bandwidths helps produce the spatial process model that is more real and practical [20].…”

“…The great randomness and uncontrollability of the environment make it very hard to obtain precise information about the distribution of stochastic disturbance terms. Nevertheless, the GMM could arrive at more efficient and robust results without such information [20]. By constructing regional regression equations at each point within the scope of space, the GWR explores the spatial variations and relevant driving factors of the research subject under a certain bandwidth, based on which it effectively solves the problem of spatial heterogeneity, only with certain limitations to the processing of bandwidth.…”