Impact path of digital economy on carbon emission efficiency: Mediating effect based on technological innovation.

The digital economy (DIE), a new economic form with digitalization at its core, has become an important driving force for promoting regional economy development. In the context of the COVID-19 pandemic, exploring the impact path of the DIE on carbon emission efficiency (CEE) is conducive to giving full play to the "carbon-reduction-and-efficiency-enhancement" role of the DIE, and to promoting the realization the "dual carbon" goal of carbon peak and carbon neutrality. In this paper, the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) are taken as study areas, the panel Tobit model is used to explore the impact of the DIE on CEE, and the intermediary-effect model and threshold-effect model are constructed to test the intermediary and threshold effects of technological innovation, respectively. The results show that the DIE has a U-shaped nonlinear impact on CEE in both the YRB and the YREB and that the impact has regional heterogeneity. Technological innovation can play a mediating effect between the DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. Technological innovation has a threshold effect on the DIE to improve CEE, while the threshold value in the YREB is higher than that in the YRB. Furthermore, this paper proposes some suggestions to guide regional low-carbon and sustainable development.

The Interactive Relationship between Street Centrality and Land Use Intensity-A Case Study of Jinan, China.

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.

Spatial correlation and influencing factors of industrial agglomeration and pollution discharges: a case study of 284 cities in China.

Exploring the spatial correlation characteristics and influencing factors of industrial agglomeration and pollution discharge, which is of great significance to reduce industrial pollution discharge and promote China's construction of an ecological civilization. Taking 284 prefecture-level cities in China in 2017 as the research object, this study used spatial autocorrelation analysis method to explore the spatial agglomeration characteristics and spatial correlation of industrial agglomeration and industrial pollution discharge, and spatial econometric analysis method was used to explore the main factors affecting industrial pollution discharge. The research results showed that the level of industrial agglomeration in China exhibited a spatial distribution characteristic of "high in the east and low in the west". The total discharge and discharge intensity of industrial pollutants showed a spatial pattern of "high in the north and low in the south" in general, and industrial agglomeration, total discharge, and discharge intensity of industrial pollution showed significant spatial autocorrelation. Moreover, industrial agglomeration had a strong local spatial correlation with the total and intensity of industrial wastewater, industrial SO2, and industrial smoke and dust, and the main agglomeration types were high agglomeration-low pollution, low agglomeration-high pollution, and low agglomeration-low pollution. In addition, industrial agglomeration had a positive impact on the total industrial wastewater discharge, and had a negative impact on the total industrial smoke and dust discharge, industrial wastewater discharge intensity, industrial SO2 discharge intensity, and industrial smoke and dust discharge intensity.

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China's economic transformation: spatial-temporal characteristics and influencing factors.

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.