Abstract
Future development in cities needs to manage increasing populations, climate‐related risks, and sustainable development objectives such as reducing greenhouse gas emissions. Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between risks and other objectives. To address this, a spatial optimization framework has been developed. This uses a spatially implemented genetic algorithm to generate a set of Pareto‐optimal results that provide planners with the best set of trade‐off spatial plans for six risk and sustainability objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. The framework is applied to Greater London (U.K.) and shown to generate spatial development strategies that are optimal for specific objectives and differ significantly from the existing development strategies. In addition, the analysis reveals tradeoffs between different risks as well as between risk and sustainability objectives. While increases in heat or flood risk can be avoided, there are no strategies that do not increase at least one of these. Tradeoffs between risk and other sustainability objectives can be more severe, for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. The results highlight the importance of spatial structure in modulating risks and other sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development.
Highlights
Urbanization and the increased frequency of climate-change-induced extreme events are driving a move to designing increasingly resilient cities globally.[1,2] The historical development of urban areas has led to a spatial form that is poorly adapted to hazards[3] while major cities are frequently lo-cated within high-risk areas such as coastal zones.[4]
In this article a spatial optimization framework has been developed to provide planners with a means of producing the evidence base for constructing spatial planning strategies that are optimal against multiple criteria and objectives
The efficacy and applicability of the framework is demonstrated for a real-world planning case study for a complex urban area, Greater London (U.K.), which covers a large area of 1,572 km2
Summary
Urbanization and the increased frequency of climate-change-induced extreme events are driving a move to designing increasingly resilient cities globally.[1,2] The historical development of urban areas has led to a spatial form that is poorly adapted to hazards[3] while major cities are frequently lo-cated within high-risk areas such as coastal zones.[4]. Caparros-Midwood, Barr, and Dawson to mitigate the drivers of climate change.[10] Legislation and policies, which in the United Kingdom include the 2008 Climate Change Act,(11) place binding targets on policymakers to reduce greenhouse gas (GHG) emissions (reduction in CO2 of 26% by 2020 and 80% by 2050 against a 1990 baseline in the United Kingdom) Much of this reduction will have to be delivered in cities, which make the largest contributions to energy use.[12] cities around the world are the “front line” for reducing energy and resource usage while reducing risk from climatechange-induced hazards.[7,12]. This requires more sophisticated methods of analyzing risk and sustainability objectives, such that coherent planning decisions can be made that can subsequently be implemented by key stakeholders, such as developers and utility operators
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