At present, China's economic development has entered a "new normal." Exploring Industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the Super-Efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that: The IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

Rapid urbanization has led to the increasing scarcity of land resources in China. Exploring the spatial-temporal characteristics and influencing factors of urban land use efficiency (LUE) is of great significance for optimizing the allocation efficiency of land resources and promoting regional sustainable development. In this study, the Super-SBM model was used to calculate the urban LUE of the Yellow River Basin from 2009 to 2018. The regional differences and agglomeration characteristics of LUE in the Yellow River Basin were analyzed. Moreover, a panel regression model was used to analyze the influencing factors of LUE. The results showed that the LUE in the Yellow River Basin experienced a process of fluctuation decline during the study period. The regional difference of LUE in the Yellow River Basin was as follows: upper reaches > middle reaches > lower reaches. The hot and cold spots of LUE were relatively stable in spatial distribution during the study period. The hot spots were mainly distributed in Ordos in the upper reaches and Yulin in the middle reaches, while the cold spots were mainly distributed in Henan Province in the lower reaches. Globalization had a positive impact on LUE in the lower reaches. Marketization had a positive impact on LUE in the whole basin and lower reaches, and a negative impact on LUE in the middle reaches. Decentralization had a positive impact on the LUE of the whole basin and the upper reaches, and a negative impact on the LUE of the lower reaches.

At present, China's economic development has entered a "new normal." Exploring Industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the Super-Efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that: The IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend whereby Yellow River Basin’s regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.