Mismatched Relationship between Urban Industrial Land Consumption and Growth of Manufacturing: Evidence from the Yangtze River Delta

Abstract

Background: The precise allocation and efficient use of industrial land are necessary for the development and optimization of urban production space; however, the mismatches between urban industrial land consumption and the growth of manufacturing are becoming more serious and has become the primary obstacle to sustainable urban development. Methods: Based on a combination of the Boston Consulting Group matrix, spatial mismatch model, decoupling index, GIS, and Geodetector tools, this paper conducts an empirical study on the Yangtze River Delta region in an attempt to reveal the spatio-temporal evolution of the mismatch between urban industrial land changes and the growth of manufacturing and provide a basis for spatial planning and land management in the new era. Results: The distribution of urban industrial land is characterized by high heterogeneity and agglomeration, the coexistence of expansion and contraction, and increasingly complex and diversified changes. Gross domestic product, government revenue, the added value of tertiary industry, and government investment in science and technology indicate that the goal orientation and scale effect of economic growth play a decisive role in the allocation of urban industrial land and that the influence of industrial structures and technological innovation is rapidly increasing. The interaction between the different factors is a bifactor enhancement, for example, land used for logistics and storage, utilities, commercial and other services, and the import and export trade, which have a strong synergistic enhancement effect. The mismatches between urban industrial land changes and the growth of manufacturing are still within a reasonable degree but there is an increasing number of cities with negative mismatches, making it necessary to implement a differentiated spatial adjustment and management policy. Conclusions: Compared with the mismatches of mobile resources such as labor, finance, and capital, the mismatches of immovable land resources have an increasing impact with more serious consequences and it is harder to make optimizations and corrections. However, the academic community has limited knowledge about land resource mismatches. By quantitatively assessing the mismatches between industrial land consumption and the growth of manufacturing in YRD cities, this paper argues that the mismatches can be rectified through spatial and land use planning and suggests the establishment of a zoning management and governance system to achieve the optimal allocation of urban industrial land resources through the implementation of a “standard land + commitment system” and industrial land protection lines.

Evolution Pattern and Spatial Mismatch of Urban Greenspace and Its Impact Mechanism: Evidence from Parkland of Hunan Province

“…where SMI i is the spatial mismatch index between urban-parkland supply and population demand in the i-th city, UPL i and UPL represent the number of resident population in the i-th city and Hunan, PD i and PD represent the area of urban-parkland resource supply in the i-th city and Hunan, respectively, SMI is the sum of the absolute values of the spatial mismatch indexes of all the cities in Hunan, and CRI i is the contribution of spatial mismatch indexes of the i-th city to the spatial mismatch between urban-parkland supply and population demand in Hunan. Therefore, based on the related research and the characteristics of the distribution of the value of SMI i in this study, we classified the spatial mismatch into high negative-spatial mismatch, low negative-spatial mismatch, low positive-spatial mismatch, and high positive-spatial mismatch by thresholds of 0.5 and −0.5 (Table 1) [66].…”

The Hidden Facets: Uncovering the Influence of Region on Social Housing Unit Distribution in Brazil

“…Thus, in addition to the positive economic effects that MCMV projects can provide, the concept of a smart city can be emphasized by creating conditions that encourage occupants' long-term residency through the appreciation of their units and an enhanced quality of life. In this way, the urban environment of a smart city can be developed based on urban planning models that prioritize nature views through windows [63], study the correlation between the presence of high-speed rails and the economic impacts of an urban spatial agglomeration structure [64], identify and quantify deteriorated urban areas that can be repurposed as urban agricultural areas [65], or even allocate urban land with an industrial vocation [66].…”