On the use of macro indicators to evaluate the sustainability of urban development
Sur l'utilisation d'indicateurs pour l'évaluation de la durabilité du développement urbain
Résumé
Cities need to project themselves on the long-term to achieve sustainable planning
choices. Scientific community can help through prospective models providing
decision support. To be effective and easily discussed, models outputs can be
summed up through simple and explicit indicators. These indicators make it
easier to compare trajectories and to evaluate the relevance of the modeling
hypothesis with stakeholders. Indeed, to collaborate with urban planners, quite
unused to scientific modelling approaches in France, we understood that a clear
visualisation and representation of the trajectories of the long-term evolution is
necessary. Thus, we propose here an approach computing macro indicators to
study urban sustainable development and assess a land allocation model. The
methodology can be divided in three phases: a long-term land-use allocation
model creating pathways for global evolution of urban main sectors, a spatially
explicit dispatcher recreating the maps from the run and a post-processing tool
computing the macro indicators. All steps are based on optimal tools but were
separated because of deeply different logics. We apply this outline to Bordeaux
Métropole, a French Southwestern metropolitan territory.
The first prospective model is based on an optimal bottom-up paradigm. To
fulfil a final demand of housing, mobility and jobs, the optimiser can invest
in archetypes of various types: urban shapes, that use the land and allocate
it to different uses (natural, residential or activity, roads, etc.), and buildings,
that transform buildable land into housing, jobs and public equipments. These
archetypes were previously characterized through statistical study of 2014 Bordeaux
data (available publicly from national institutes). The optimisation is
based on minimising the artificial land while answering the demand and the
accent is therefore put on the densification. Whereas discussable as density
is not so clearly and unequivocally linked with environmental efficiency, this
choice was motivated by Bordeaux’s situation: the city is located near highly
used agricultural lands (such as wine producers) and is already perceived as too
mineral by its inhabitants. This model is then used to study a high demographic
growth scenario where the population doubles in 2050 compared to 2014 instead
of increasing by 35%.
We project the results of the long-term model at a finer geographical scale
through constrained maps and thereafter compute various indicators such as
the temperature and the overall imperviousness. The computation of the macro
indicators not available in the model is based on a prephase of data collection
and modelling. We present here temperature case that can help study UHI
phenomenon: we retrieved data from MeteoFrance results that we tried to
link with global urban parameters (such as built density, impervious surface,
natural area density, etc.) through bibliography study, statistical correlations
and different machine-learning models. The goal here is to be able to evaluate
the temperature by postprocessing the outputs of the main model and therefore
compare the effects of the two scenarios on Bordeaux warming.
The preliminary results on the first two steps show a strong densification of the
different neighborhoods, result expected as presented above. The model makes
a strong use of buildings elevation in certain regions.
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