Exploring the impact mechanism of low-carbon multivariate coupling system in Chinese typical cities based on machine learning.

Low-carbon city construction is one of the key issues that must be addressed for China to achieve high-quality economic development and meet the Sustainable Development Goals. This study creates a comprehensive evaluation index system of low-carbon city multivariate system based on carbon emission data from 30 typical Chinese cities from 2006 to 2017 and evaluates and analyzes the trend of city low-carbon levels using the CRITIC-TOPSIS technique and MK method. Meanwhile, the influence mechanism of the multi-coupled system is investigated using the coupling coordination degree model and random forest algorithm.