A Climate-Sensitive Approach for Determining the Urban Growth Boundaries: Towards a Spatial Exploration for Bursa, Türkiye

Abstract

Population growth is inevitable in urban areas responsible for climate crises worldwide, and urban development is affected by processes fed by many dynamics. Predicting and planning the growth limits of the city effectively is a critical issue for achieving sustainable urban growth and managing climate risks. The study used the cellular automata-Markov chain method to define development areas regarding natural structure and land use/land cover. It aimed to present a method that can be applied to different urban areas by focusing on effective urban growth management with a climate-sensitive approach. It offered a climate-sensitive approach to determining growth limits according to scenarios. The boundaries of 2030 have been determined for the city of Bursa, which exhibited an increased average summer value of the land surface temperature from 24°C to 45°C between 2012 and 2021, stands out with its natural riches, and shows a rapid urban growth trend. The proposed method modeled the limits of urban growth with a climate-sensitive approach, and the model's suitability was demonstrated by Kappa statistics (Klocation = 0.8884). The determined urban boundary will reduce the rate of the urban built-up area from 86% to 70% by 2030. While the climate crisis threatens all life, urban activities, urban growth, and sprawl continue to increase rapidly. However, the growth of urban areas within certain limits is a critical need for engineering and urban planning. For decision makers, spatially determining the limits of human intervention in nature is an important guideline. Extreme weather events and disasters such as floods, tsunamis, and changes in water levels brought about by climate change can potentially cause chaotic crises. Loss of life and economic crises may occur in possible processes. Therefore, measures must be taken on behalf of humanity and life to adapt to climate effects and keep the level of impact under control. Spatial models based on a wide range of temporal contexts have a structure that broadens our perspective. The study provides an approach that can be implemented in various fields with models produced based on scenarios.