Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Summary 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 ga...

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Bibliographic Details
Published in:Journal of industrial ecology 2017-04, Vol.21 (2), p.294-306
Main Authors: Horta, Isabel M., Keirstead, James
Format: Article
Language:English
Subjects:
Accounts
Autocorrelation
Averages
Bayesian analysis
Cities
Consumption
Data collection
Data processing
Decision makers
econometrics
energy
Energy consumption
Energy efficiency
Geography
Global cities
Local government
London
Mapping
Measurement techniques
Metabolism
Methods
modeling
Multilevel
Normalization
Regression analysis
Residential energy
Resource consumption
Spatial distribution
Statistical methods
Studies
United States
Urban areas
Urban metabolism
Waste disposal
Water
Water resources
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Summary:Summary 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.
ISSN:1088-1980
1530-9290
DOI:10.1111/jiec.12428