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Identifying key technology and policy strategies for sustainable cities: A case study of London

Authors: Richard Dawson; R. Villarroel Walker; M. B. Beck; M. B. Beck; Oliver Heidrich; Jim W. Hall;

Identifying key technology and policy strategies for sustainable cities: A case study of London

Abstract

AbstractAssuming communities in a city may formally express their aspirations for the future sustainability of their city, which technological innovations for changing the city's infrastructure and metabolism might they introduce today, as a first step towards realizing their distant aspirations? What is more, recognizing the diversity of aspirations that may never be reconciled into a consensus, might some innovations and policy interventions be nevertheless more privileged than others, in being non-foreclosing? How might we discover this? These questions are addressed through a computational case study of London. The city's metabolism is modeled as the set of interacting, cross-sectoral (water, food, energy, waste) flows of carbon (C), nitrogen (N), phosphorus (P), water, and energy. Given various degrees of target improvements in an accompanying set of metabolic performance metrics, and given four candidate technological innovations in the water sector, an inverse (or “backcasting”) analysis is implemented in order to identify the key technological, policy, social, and climate-related features determining whether the community's aspirations — through the surrogates of the metabolic performance metrics — are attainable (or not), under substantial uncertainty. From this, the paper proceeds to examine which businesses are currently marketing some of the so-identified key technological innovations. It closes with a brief review of the related status of the economic justifications and social changes that may either promote or stifle the opportunities for London to move towards a higher niveau of sustainability.

Country
United Kingdom
Keywords

urban metabolism, Resource-flow model, Urban metabolism, Inverse analysis, water-food-energy nexus, Water-food-energy nexus, sensitivity analysis, backcasting, inverse analysis, resource-flow model, Sensitivity analysis, Backcasting

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    28
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
Green
hybrid