Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration.

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors-advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value-that affected the distribution of RECC. Other factors, including the interactions between residents' consumption level and environmental regulation, residents' consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Spatial heterogeneity of urban-rural integration and its influencing factors in Shandong province of China.

Based on nighttime light data and statistical data, this study calculated the level of urban-rural integration (URI) of Shandong province, researched spatial heterogeneity of URI levels by local spatial autocorrelation analysis, Geodetector, and geographically weighted regression, and analyzed its influencing factors and spatial heterogeneity. The results concluded that: (1) The spatial pattern of urban-rural integrated level is consistent with the level of regional economic development in Shandong province. The level of URI is higher along the Qingdao-Jinan railway and along the coast, whereas the level is lower in southwest Shandong and northwest Shandong. (2) The cities of Yantai and Weifang are High-High cluster areas of urban integration, and Jining is a Low-Low cluster area. The spatial agglomeration characteristics are not significant in other cities. (3) Among the main factors affecting URI, the explanatory power of the rural population with high school or technical secondary school education or above, the area of urban construction land, and the secondary and tertiary industry GDP to the spatial pattern of URI in Shandong province are 73.58%, 62.08%, and 58.66%, respectively. As the key factors, spatial heterogeneity, such as north-south differences, southwest-to-northeast differences, and east-west differences, is evident.