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.The results show that there are 8 cities with a significant increasing trend of low-carbon level, 19 cities with no significant monotonic change trend, and 3 cities with a decreasing trend of low-carbon level. By analyzing the coupling coordination degree, we found that the coupling coordination degree between low-carbon level and economic development in most cities tends to increase year by year, from the initial antagonistic effect to a good coordination development trend, which confirms the “inverted U-shaped” relationship between economy and carbon emission. In addition, industrial pollutant emissions, foreign direct investment, and economic output are the core drivers of low-carbon levels in cities.