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• Assessing spatio-temporal changes in eco-environmental by Gini coefficient and Kernel density estimation method. • Eco-environmental risks are marked by high east and low west and clustered in blocks. • Spatial variability of eco-environmental risks in the study area is narrowing. • High risk and low risk provinces in the Yangtze River Delta region proximity distribution. • Policymakers should govern eco-environmental risks from a synergistic perspective. The Yangtze River Economic Belt is an essential ecological security barrier and demonstration area for ecological civilization construction in China, so it is vital to prevent and resolve eco-environmental risks for its healthy development. However, the state of the development of eco-environmental risks in this region has remained understudied. Based on panel data from 2000 to 2020, this paper analyzes the development status of eco-environmental risks in the Yangtze River Economic Belt using the Dagum Gini coefficient, kernel density estimation, and Moran's I test model. The results show that: (1) the eco-environmental risk index of the Yangtze River Economic Belt ranges from 50.25 to 92.16, which represents a high risk status overall, showing obvious characteristics of a high index in the east and a low index in the west. (2) The overall Gini coefficient of eco-environmental risks in the Yangtze River Economic Belt decreased from 0.059 in 2000 to 0.0502 in 2020, showing an obvious M-type evolution trend. The internal difference in eco-environmental risks of the lower reaches is the largest, followed by those of the upper and middle reaches. The average Gini coefficient in the upper and the lower reaches is 0.0679, while that in the middle and the lower reaches is 0.0645, and that in the upper and the middle reaches is 0.0604. The average contribution rates of inside reaches, different reaches, and hypervariable density in the study cycle are 29.55%, 27.84%, and 42.61%. (3) The kernel density estimation curve of eco-environmental risks in the study area shifts from the lower reaches to the middle and upper reaches, with low-risk areas adjacent to high-risk areas, especially in Zhejiang, Jiangsu, Anhui, and Shanghai, where the high–low/low–high clustering characteristics are more significant. Finally, we put forward some policy implications to provide practical support for the sustainable development of the Yangtze River Economic Belt eco-environment. [ABSTRACT FROM AUTHOR]