Prediction and Evaluation Model Based on Carbon Emission with RF: A Case Study of Six Coastal City Clusters in China

Abstract Aiming at predicting the problems of social economics, environmental pollution, climate change and marine disasters caused by carbon emission, a predicting model based on carbon emission with the Random Forest (RF) model was constructed. Meanwhile, a novel urban adaptivity evaluation system is put forward considering the above four domains of indicators, hence the two models are integrated. Six coastal city clusters of China are selected as study area and the result of RF model shows that northern city clusters suffer more pollutant loads due to their heavy industry layout, southern cities generally have higher GDP while they’re more vulnerable toward extreme weather and marine disasters. The result of evaluation system indicates that northern city clusters have higher urban adaptivity due to their balance between economics and pollution as well as less vulnerability on climate change because of relatively high latitude. On the contrary, southern cities are supposed to focus on environmental pollution and tropical storms to pursue superior compatibility.