Fengying Yan
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Urban energy inequality and economic growth in China: insights from nighttime light data and spatial machine learning
ABSTRACT Spatial inequality in energy consumption is a key challenge for urban sustainability. This study integrates nighttime light data with a geographically weighted random forest (GWRF) model to map and analyse energy use across over 200 Chinese cities. Unlike aggregate statistics, GWRF captures spatial heterogeneity and reveals inequality patterns missed by conventional methods. Results indicate that within-city disparities dominate overall inequality, surpassing differences between cities.
Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities-From the Perspective of Urban Planning Content and Process
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