Impact of the Economic Structure of Cities on Urban Scaling Factors: Implications for Urban Material and Energy Flows in China

Abstract

Summary We explore the population‐scaling and gross domestic product (GDP)‐scaling relationships of material and energy flow (MEF) parameters in different city types based on economic structure. Using migration‐corrected population data, we classify 233 Chinese city propers (Shiqu) as “highly industrial” (share of secondary GDP exceeds 63.9%), “highly commercial” (share of tertiary GDP exceeds 52.6%), and “mixed‐economy” (the remaining cities). We find that, first, the GDP population‐scaling factors differ in the different city types. Highly commercial and mixed‐economy cities exhibit superlinear GDP population‐scaling factors greater than 1, whereas highly industrial cities are sublinear. Second, GDP scaling better correlates with city‐wide MEF parameters in Chinese cities; these scaling relationships also show differences by city typology. Third, highly commercial cities are significantly different from others in demonstrating greater average per capita household income creation relative to per capita GDP. Further, highly industrial cities show an apparent cap in population. This also translates to lower densities in highly industrial cities compared to other types, showing a size effect on urban population density. Finally, a multiple variable regression of total household electricity showed significant and positive correlation with population, income effect, and urban form effect. With such multivariate modeling, the apparent superlinearity of household electricity use with respect to population is no longer observed. Our study enhances understanding of MEFs associated with Chinese cities and provides new insights into the patterns of scaling observed in different city types by economic structure. Results recommend dual scaling by GDP and by population for MEF parameters and suggest caution in applying universal scaling factors to all cities in a country.

Estimating the potential for industrial waste heat reutilization in urban district energy systems: method development and implementation in two Chinese provinces

“…densely built-up urban areas with low agricultural activities, where DES is a practical option. Our study focuses on these city propers (Shiqu) as representing the cities of China, similar to previous studies noted above (Bettencourt et al 2007, Ramaswami et al 2017a. We study all the city propers in each province (11 in Hebei and 9 in Fujian)-with each representing a different balance of homes and businesses relative to industries.…”

“…The two provinces describe the cases of two different climate zones-Hebei for heating and Fujian for cooling, respectively. We use the word cities to refer to built-up urban areas in China, called city propers or urban administrative districts that have been studied as the city-unit of analysis in many prior studies of Chinese and global cities (Bettencourt et al 2007, Ramaswami et al 2017a. Chinese provinces are generally fully divided geographically into Dijishi, which translates to prefecture-level 'cities', but does not exclusively represent urban areas.…”

Urban form strongly mediates the allometric scaling of airshed pollution concentrations

“…Applying a categorization to the urban settlements-e.g. 'industrial cities', 'commercial cities', etc [19]-has been applied to help diagnose scaling relationships in some settings; we have not been able to find a suitable categorization.…”

“…where coefficient, Y j,o , and power-law exponent, α j , describe the scaling of characteristic Y j with some more commonly measured yardstick of 'scale' such as, in our case, population, P. Other yardstick measures, such as subsets of population [17,18], GDP [19], or network fractal dimension [20], can be used if more appropriate to framing a particular study; we use population because it is a fundamental unit of social organization and to be consistent with prior work [21,22]. The intention of allometric analysis is not to deduce causality, even regression-related Granger causality [23], but rather to search for simple emergent patterns in complex systems as an aid to understanding what is strictly contextual about instances of such systems and what holds more generally about them e.g.…”

“…These scaling relationships prompt explanations in terms of a small set of basic principles that operate locally and describe the intrinsic socio-economic nature of urban living [16], in terms of parsimonious agent-based modeling [15], or via multiple regression model-finding of various kinds, e.g. [19,28].…”