Zhongsheng Chen

The Jinghe River flows through the gully area of the Loess Plateau, where soil erosion is relatively severe. With the intensification of human activities, quantitatively evaluating the impact of land use/cover change (LUCC) on runoff is of paramount importance. This study is based on the Soil and Water Assessment Tool (SWAT) and Patch-generating Land Use Simulation (PLUS) models, and quantitatively analyzes the effect of LUCC on runoff in the Jinghe River Basin (JRB) through land use data from 2000 to 2020 and predicted scenarios for 2030 that encourage development, farmland protection, and ecological protection. The results show that reductions in farmland, grassland, and forest areas promote runoff, while increases in construction land similarly contribute to greater runoff. In all 2030 scenarios, the JRB is dominated by farmland and grassland. The mean annual runoff of LUCC under the three simulated prediction scenarios shows an increasing trend compared to LUCC in 2020, and the distribution of mean annual runoff depth is roughly the same. In addition, there is a strong interconnection between land use types and runoff in their dynamic relationship. Within the LUCC scenario, the decrease in farmland and forest land, along with the growth of construction land area promote runoff, while grassland plays a suppressive role in runoff. The results can offer a scientific foundation for improving soil erosion as well as optimizing land use patterns in the JRB.

Data interpolation methods are important statistical analysis tools that can fill in data gaps and missing areas by predicting and estimating unknown data points, thereby improving the accuracy and credibility of data analysis and research. Different interpolation methods are widely used in related fields, but the error between different interpolation methods and their interpolation fusion optimization have a significant impact on the interpolation accuracy, which still deserves further exploration. This study is based on two different types of point data: PM_2.5 (PM_2.5 refers to particulate matter in the atmosphere with a diameter of 2.5 μm or less, also known as inhalable particles or fine particulate matter) in Xinyang City, Henan Province, and the elevation of typical gullies in Yuanmou County, Yunnan Province. Using relative difference coefficients and hotspot analysis methods, the differences in error characteristics among four interpolation methods, ordinary kriging (OK), universal kriging (UK), inverse distance weighted (IDW), and radial basis functions (RBFs), were compared, and the influence of interpolation fusion methods on the accuracy of interpolation results was explored. The results show that after interpolation of PM_2.5 concentration and gully elevation, the error difference between OK and UK is the smallest in both datasets. For PM_2.5 concentration data, IDW and UK interpolation errors have the largest difference; for elevation data, the differences between RBF and UK interpolation are the largest. The weighted fusion results show that the interpolation error accuracy of PM_2.5 concentration data with an interpolation point density of 0.009 points per square kilometer is improved, and the root mean square error (RMSE) after fusion is reduced from 0.374 μg/m³ to 0.004 μg/m³. However, the error accuracy of the elevation data of the gully with an interpolation point density of 0.76 points/m² did not improve significantly. This indicates that characteristics such as the density of the original data are important factors that affect the accuracy of interpolation. In the case of sparse interpolation points, it is possible to consider fusing the interpolation results with different error patterns to improve their accuracy. This study provides a new idea for improving the accuracy of interpolation errors.

The coupling coordination of new urbanization (NU) and rural revitalization (RR) is the key research that focuses on promoting integrated development between urban and rural areas. The entropy weight method, coupling coordination model, and obstacle model were used to explore the development level, coupling coordination degree, and obstacle factors of NU and RR in the basin area of Sichuan Province from 2013 to 2021 in this study. The results show the following: The levels of NU and RR in the basin area of Sichuan Province show an uptrend. Central cities exhibit higher levels of RR and NU. Under the influence of a central city, regional central cities have obvious growth in RR and NU. There are fluctuations in the RR and NU indicators in node cities. The coupling coordination degree of NU and RR in the basin area of Sichuan Province continues to rise, and the coordination levels are mainly basic coordination and moderate coordination. The coupling coordination degree is higher in central and regional central cities, while the coupling coordination degree of node cities is relatively lower. The levels of agricultural modernization, public infrastructure and medical resources, and rural governance are the main factors influencing the coupling coordination of NU and RR in the basin area of Sichuan Province. Affected by terrain, economy, and other factors, the level of urban–rural integration in the basin area of Sichuan Province is obviously different. With the continuous improvement of policies, the coupling coordination level of NU and RR is gradually developing towards a positive trend.

The frequent occurrence of extreme high-temperature events in the summer under global climate change poses a serious threat to Chinese society. An urban vulnerability evaluation system for counties in the Chengdu-Chongqing Area was constructed to calculate the urban vulnerability and distribution characteristics of each district. In this study, a vulnerability-contribution model was used to analyze the types of urban vulnerability in the Chengdu-Chongqing Area. Additionally, combined with the optimal parameter geographic detector (OPGD) model, the main influencing factors and interactions of urban vulnerability were explored. The results show that: ① The urban vulnerability of the Chengdu-Chongqing Area is high in the east and low in the west, with vulnerability degree mostly below the medium degree. ② Exposure contributes more than 50% to severe and general urban vulnerability in the region, while adaptability contributes the highest proportion to mild urban vulnerability, reaching 47.53%. ③ From the factor perspective, the impact ratio of high-temperature days on urban vulnerability is 39.1%, and the interaction between various meteorological factors and social factors produces an enhancement effect, with the highest interaction q-value reaching 0.7863.

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China’s population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf’s law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies.

It is necessary to alleviate the high temperatures and heat wave disasters in cities in southwest China that are beginning to occur because of global warming. During this study, the spatial and temporal characteristics of heat waves in Nanchong from 1961 to 2022 are analyzed by using the signal smooth method and mutation test. Based on the meteorological data and socioeconomic statistics, the entropy value method is used to obtain the indicator weights to construct a heat wave social vulnerability evaluation index system and conduct vulnerability assessments and classifications. The results show that: ① The heat wave indicators in Nanchong show an increasing trend, although there is a low period of heat waves from 1980 to 1995. Additionally, there are significant mutations in the number of days, frequency, and intensity of high-temperature heat waves from 2009 to 2011, which may be caused by the abnormal high-pressure belt in the mid-latitude. ② The distribution of exposure, sensitivity, and adaptability in Nanchong City, under high temperatures, is uneven in space. Generally, the indicators in the north are lower than those in the south. ③ The high-vulnerability counties are mainly distributed in the east and west of Nanchong, the proportion of the medium social vulnerability index areas are more than a half, while the dominant factor in the distribution pattern is natural factors. ④ The Western Pacific Subtropical High (WPSH) anomaly directly led to the extremely high temperature in Nanchong in the summer of 2022, and the urbanization process index shows a significant positive correlation with the trend of high temperatures and heat waves in Nanchong.

Energy is an important resource for human survival and development. China has become the world’s second largest energy consumer, and its energy consumption growth rate ranks first in the world. With the rapid growth of China’s economy, such a way of energy consumption will inevitably hinder the rapid development of China’s economy in the long run, so it is necessary to improve China’s energy consumption way. In order to explore the impact of energy consumption on regional agricultural economic growth, improve the negative impact of unscientific energy consumption, and improve the level of regional economic development, this paper takes three regions as the research object to study the relationship between energy consumption and economic development. The research results show that there is no one-way or two-way causality in Type I areas with high economic development and high energy consumption, no matter in the short-term or long-term; in Type II areas with high economic development and low consumption, there is a bidirectional causal relationship between energy consumption and economic development in the short term. In the long run, economic growth is a one-way causal relationship. In Type III regions with slow economic growth and low energy consumption, only the single causal relationship between long-term energy consumption and economic development is less than 10% significant. This suggests that the long- and short-term causality between energy consumption and economic development varies across regions. Therefore, in order to coordinate regional economic development and narrow regional gaps, it is necessary to formulate appropriate regional energy consumption policies and strategies.

Although some methods have identified the physical urban area to a certain extent, the driving factors for the identification threshold have not been studied deeply. In this paper, vector building data and road intersection data are used for comparative validation based on the urban expansion curve method to identify the physical urban area using the meso-city scale. The geographical detector technique is used to detect how and to what extent the urban spatial structure factors, geographical environment factors and social economic factors affect the optimal distance threshold of 22 administrative districts in the Chongqing municipality. The results based on the vector buildings are more precise and show the characteristics of the physical urban area of core-periphery distribution and the distribution along the water corridor. From the results of quantitative attribution, it was found that the road network density, building density, urbanization rate and urban population density, and their interaction with regional GDP, play a critical role in the optimal distance threshold, with the index value of influence degree ≥0.79. Under the influence of different factors, the optimal distance thresholds of 22 administrative districts show adaptive characteristics. Looking forward to the future, this study provides ideas for further research on the morphological characteristics and distribution laws of multi-spatial scale cities.

To measure and present urban size urban spatial forms, in solving problems in the rapid urbanization of China, urban territorial scope identification is essential. Although current commonly used methods can quantitatively identify urban territorial scopes to a certain extent, the results are displayed using a continuous and closed curve with medium- and low-resolution images. This makes the acquisition and interpretation of data challenging. In this paper, by extracting discretely distributed urban settlements, road intersections in OpenStreetMap (OSM), electronic maps, and urban expansion curve based on fractal thoughts have been used to present urban territorial scope and spatial form. Guangzhou, Chengdu, Nanjing, and Shijiazhuang cities were chosen as the identification targets. The results showed that the distance threshold corresponding to the principal curvature point of the urban expansion curve plays a vital role in the extraction of urban settlements. Moreover, from the analysis, the optimal distance thresholds of urban settlements in Guangzhou, Chengdu, Nanjing, and Shijiazhuang were 132 m, 204 m, 157 m, and 124 m, respectively, and the corresponding areas of urban territorial scopes were 1099.36 km², 1076.78 km², 803.07 km², and 353.62 km², respectively. These metrics are consistent with those for the built-up areas.