Abstract
R Ramyar. Planning Urban Green Infrastructure for Urban Climate Change Adaptation, 161 pages,11 tables, 47 figures, 2022. APA style guide used
Transferring scientific and theoretical research into applicable tools for urban green infrastructure planning is a great challenge for urban ecologists and climatologists. In using this idea as a strategy in urban spatial planning and climate change adaptation, especially urban heat reduction, this research wants to develop methods and tools to facilitate the planning of ecological resources in providing multifunctional ecological resources to cope with climate disruption. My research frames three questions at three levels of planning relating to combining urban green infrastructure planning with urban adaptive planning. At the first scale, I will discuss what urban green infrastructure is, how it could be integrated with urban adaptive planning, and what the decision-making process would be. In the next level, I want to develop a spatial and quantified analysis of social-ecological urban systems to identify not only where we need more green areas regarding social-ecological need but understand what the characteristics of the green areas in different parts of the city are in terms of the ecosystem benefits. This would be very helpful for cities with limited ecological and financial resources. For the last part, I will focus on only urban heat as a climate threat for cities. We know trees and vegetation in general can mitigate air temperature, but in ecosystem service mapping or urban climate mapping, there is a lack of clarity about how to adequately parameterize the cooling benefits of street trees in different urban settings for the purpose of urban planning/climate mapping. Scholars tend to rely on simply counting the number of trees for larger scale maps to measure their cooling benefits based on the idea that the trees have similar cooling benefits in different urban settings and that these benefits are cumulative. I will focus on the cooling benefits of trees in different urban settings and parameterize the indices to make them useful for upscaling the results for mapping at the urban scale.