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.
It is of great significance to study the interactive relationship between urban transportation and land use for promoting the healthy and sustainable development of cities. Taking Jinan, China, as an example, this study explored the interactive relationship between street centrality (SC) and land use intensity (LUI) in the main urban area of Jinan by using the spatial three-stage least squares method. The results showed that the closeness centrality showed an obvious “core-edge” pattern, which gradually decreased from the central urban area to the edge area. Both the betweenness centrality and the straightness centrality showed a multi-center structure. The commercial land intensity (CLUI) showed the characteristics of multi-core spatial distribution, while the residential land intensity (RLUI) and public service land intensity (PLUI) showed the characteristics of spatial distribution with the coexistence of large and small cores. There was an interactive relationship between SC and LUI. The closeness centrality and straightness centrality had positive effects on LUI, and LUI had a positive effect on closeness centrality and straightness centrality. The betweenness centrality had a negative impact on LUI, and LUI also had a negative impact on betweenness centrality. Moreover, good location factors and good traffic conditions were conducive to improving the closeness and straightness centrality of the regional traffic network. Good location factors, good traffic conditions and high population density were conducive to improving regional LUI.
The coordinated promotion of industry–city integration and carbon emission reduction is of great significance to the construction of a green economic system and deep participation in global environmental governance. Based on the overall framework of the “production–life–ecology” system, the theoretical mechanism of the impact of industry–city integration on carbon emissions is systematically clarified. Taking the Yellow River basin as a sample, the spatiotemporal heterogeneity of the effect of industry–city integration on carbon emissions is empirically tested by using the methods of the dispersion coefficient coordination function, standard deviation ellipse and STIRPAT model. The results show the following: (1) The coordinated integration of industry and city has significant carbon emission reduction effects, thus indicating that industry–city integration and carbon neutralization can achieve both, and that the conclusion is still valid after endogenous treatment and a series of robustness tests. (2) The development of an export-oriented economy and informatization can significantly promote carbon emission reduction. The process of economic development, infrastructure construction and population quality improvement may aggravate regional carbon emissions in the short term. (3) Further analysis shows that the carbon emission reduction effect of industry–city integration has significant spatial heterogeneity, especially in the upper and lower reaches of the Yellow River and regions with moderate carbon emission intensity. Scientific and technological innovation and environmental regulation play a positive role in regulating the carbon emission reduction effect of industry–city integration.
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