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|Impact of the Economic Structure of Cities on Urban Scaling Factors: Implications for Urban Material and Energy Flows in China 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.||Impact of the Economic Structure of Cities on Urban Scaling ...||Ramaswami, Anu and Jiang, Daqian and Tong, Kangkang and Zhao, Jerry||Journal Article||2017||
|Downscaling Aggregate Urban Metabolism Accounts to Local Districts Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio-based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.||Downscaling Aggregate Urban Metabolism Accounts to Local Districts||Horta, Isabel M. and Keirstead, James||Journal Article||2017||
Economy-Wide Material Flow Analysis (EW MFA)