Trade-offs between climate change mitigation, adaptation, and costs are challenging the sustainable transformation of urban neighborhoods. Guiding urban planners in dealing with this multi-objective problem promotes balanced decision-making in the early planning phases. We use a highly interconnected neighborhood simulation model to quantify trade-offs between the three aspects and investigate case study areas in Munich, Germany. Following the concept of Urban Systems Exploration, a multi-objective optimization (MOO) algorithm is utilized to search for Pareto-optimal solutions with specific characteristics. Thereby, the Pareto fronts for the best possible trade-off in row, block, and detached typologies are identified. Comparing the results indicates strongly pronounced trade-offs between global warming potential and outdoor thermal comfort for detached typologies. The MOO analysis sensitizes urban planners to such interdisciplinary considerations and provides decision-making support in the early design phases.
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Trade-offs between climate change mitigation, adaptation, and costs are challenging the sustainable transformation of urban neighborhoods. Guiding urban planners in dealing with this multi-objective problem promotes balanced decision-making in the early planning phases. We use a highly interconnected neighborhood simulation model to quantify trade-offs between the three aspects and investigate case study areas in Munich, Germany. Following the concept of Urban Systems Exploration, a multi-object...
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