Unveiling Urban Evolution: Assessing Patterns of Transformation in Guangzhou's Net Production

Abstract

Urban evolution refers to the continuous changes and adaptation of urban environments to social, economic, technical, and environmental changes, acknowledging that cities are dynamic systems. Urbanization and land use/cover change (LULCC), which are mostly caused by human activity, which have a big impact on the environment. These two variables also affect urban vegetation when combined with climate change. We choose Guangzhou City to examine how urban growth processes and spatial changes in urban areas affect net production (NP). A Machine learning [ML] approach such as Support Vector Machine (SVM) was utilized to examine the relationship between NP and urbanization intensity. This study examined to analyze urban evolution, focusing on the patterns of change in the NP in Guangzhou City, China. The findings demonstrated between 2011 and 2023, the NP in the studied region decreased overall and had distinct geographic variation. The mean Urban Development Index (UDI) in Guangzhou increased significantly between 2011 and 2023. In both years, metropolitan exurbs constituted the majority of the urban geographic category, but the most notable increase in urban fringe areas, which accounted for about 2,320.24 km2 of the urban exurbs. Regarding UDI and NP, there was an opposite relationship, suggesting that the growing intensity of the growth of urban expansion negatively affected NP. Urban development intensity negatively influenced NP, with urban fringes experiencing the most significant losses due to an increase in urbanization and a decrease in agriculture.