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The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

On 8th August 2017, a magnitude Ms 7.0 earthquake struck the County of Jiuzhaigou, in Sichuan Province, China. It was the third Ms ≥ 7.0 earthquake in the Longmenshan area in the last decade, after the 2008 Ms 8.0 Wenchuan earthquake and the 2013 Ms 7.0 Lushan earthquake. The event did not produce any evident surface rupture but triggered significant mass wasting. Based on a large set of pre- and post-earthquake high-resolution satellite images (SPOT-5, Gaofen-1 and Gaofen-2) as well as on 0.2-m-resolution UAV photographs, a polygon-based interpretation of the coseismic landslides was carried out. In total, 1883 landslides were identified, covering an area of 8.11 km2, with an estimated total volume in the order of 25–30 × 106 m3. The total landslide area was lower than that produced by other earthquakes of similar magnitude with strike-slip motion, possibly because of the limited surface rupture. The spatial distribution of the landslides was correlated statistically to a number of seismic, terrain and geological factors, to evaluate the landslide susceptibility at regional scale and to identify the most typical characteristics of the coseismic failures. The landslides, mainly small-scale rockfalls and rock/debris slides, occurred mostly along two NE-SW-oriented valleys near the epicentre. Comparatively, high landslide density was found at locations where the landform evolves from upper, broad valleys to lower, deep-cut gorges. The spatial distribution of the coseismic landslides did not seem correlated to the location of any known active faults. On the contrary, it revealed that a previously-unknown blind fault segment—which is possibly the north-western extension of the Huya fault—is the plausible seismogenic fault. This finding is consistent with what hypothesised on the basis of field observations and ground displacements.

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China's new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China's construction land presents a \"point–line–plane\" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China's construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing–Tianjin–Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land.

Traditional villages are repositories for preserving human artifacts and cultural heritage. An investigation of the spatial distribution characteristics and factors influencing traditional villages in provincial administrative regions can provide new insights regarding the protection of traditional villages and rural development. This study focused on 275 traditional villages in Henan Province. Using ArcGIS and GeoDa software, we analysed the spatial autocorrelation and heterogeneity of the nearest neighbour index, Gini coefficient, Moran’s I, and kernel density of the villages. Additionally, in conjunction with the Python sklearn library and GeoDetector, 15 indicators were selected to construct a decision tree model, spatial lag regression model, and geographic detector. Then the influence and interaction mechanisms of each indicator were analysed. The results revealed that (1) the spatial distribution of traditional villages in Henan Province was clustered and uneven, with a spatial layout comprising “3 high-density areas + 1 medium-density belt”; (2) overall, the number of traditional villages was negatively correlated with altitude, slope, rainfall, population density, proportion of the minority population, and historical-cultural intensity; and (3) the decision tree model results demonstrated that the selected 15 indicators had good predictive ability and that population density was particularly important. The spatial lag regression model results showed that the spatial distribution of traditional villages was positively correlated with distance from rivers, urbanization rate, and tourism resources, and negatively correlated with population density, per capita GRP, historical-cultural intensity, and NDVI. (4) The GeoDetector results indicated that historical-cultural intensity and population density were the two factors with the most significant explanatory power for the spatial differentiation of traditional villages in Henan Province. In terms of interactive factors, population density ∩ population was the strongest interactive driving force, followed by population ∩ historical-cultural intensity.

This study was carried out to develop a conceptual framework for determining the best interpolation method which mainly is employed to calculate the variability maps of electrical conductivity (EC) in neighboring regions. The considered case study is parts of the Khorasan Razavi province, Iran (including five aquifers Kashmar, Fariman, Doruneh, Sarakhs and Joveyn). In the first step, the empirical variogram (semi-variogram) was computed for the study area. The methods of the variability of a variable with spatial or temporal distance were considered to measure the semi-variogram function. In the next step, the best variogram model (e.g. spherical, exponential or Gaussian) was considered in the Geographic Information System (GIS) environment and f for the Environmental Sciences (GS+) software. By plotting the semi-variogram in GS + program based on different method as Global Polynomial Interpolation (GPI), Inverse distance weighing (IDW), Radial basis function (RBF), Kriging method, Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), the best variogram model fitted to spatial structure of the EC. Finally, by considering the acceptable range for different parameters which impact on EC and evaluating their impacts by scaling, the best interpolation method has been selected for that area for employing their neighborhood basin. Result indicated that the precipitation located within the range of 140 to 180 mm, RBI has the priority. This process is continued for all 14 parameters and eventually one method gets the most points.

Rubus idaeus is a pivotal cultivated species of raspberry known for its attractive color, distinct flavor, and numerous health benefits. It can be used in pharmaceutical, cosmetics, agriculture and food industries not only as fresh but also as a processed product. Nowadays due to climatic changes, genetic diversity of cultivars has decreased dramatically. However, until now, the status of wild R. idaeus resources in China have not been exploited. In this study, we investigated the resources of wild R. idaeus in China to secure its future potential and sustainability. The MaxEnt model was used to predict R. idaeus suitable habitats and spatial distribution patterns for current and future climate scenarios, based on wild domestic geographic distribution data, current and future climate variables, and topographic variables. The results showed that, mean temperature of the coldest quarter (bio11), precipitation of the coldest quarter (bio19), precipitation of the warmest quarter (bio18), and temperature seasonality (bio4) were crucial factors affecting the distribution of R. idaeus . Presently, the suitable habitats were mainly distributed in the north of China including Xinjiang, Inner Mongolia, Gansu, Ningxia, Shaanxi, Shanxi, Hebei, Beijing, Liaoning, Jilin, Heilongjiang. According to our results, in 2050s, the total suitable habitat area of R. idaeus will increase under SSP1-2.6 and then will be decreased with climate change, while in the 2090s, the total suitable habitat area will continue to decrease. From the present to the 2090s, the centroid distribution of R. idaeus in China will shift towards the east and the species will always be present in Inner Mongolia. Our results provide wild resource information and theoretical reference for the protection and rational utilization of R. idaeus .

The Yangtze River Basin (YRB) is the birthplace of Chinese civilization and is rich in traditional village resources. Studying their spatial distribution characteristics and influencing factors can guide the protection, inheritance, and development of traditional villages in YRB. This study takes 5 batches of 3346 traditional villages in YRB since 2012 as the research object. Using the nearest neighbor index, kernel density analysis, standard deviation ellipse, and Geodetector model, we analyzed the spatial distribution characteristics of traditional villages in YRB and detected their influencing factors. The results show that the distribution of traditional villages in YRB exhibited a spatial pattern of cohesive clusters, forming a high-density area and development center in the junction zone between Guizhou and Hunan provinces and southeast of Anhui Province, and secondary-density areas in Northeast Yunnan Province and east Jiangxi Province. The results of the Geodetector show that the formation of the spatial distribution pattern of traditional villages in YRB is affected by the combined effects of natural and socio-economic factors, among which elevation and NDVI were the main factors, and the interaction of multiple factors showed an enhanced trend. The findings of this study can provide scientific decision-making support for the development and protection of traditional villages in YRB.

Traditional villages hold significant historical and cultural value as the precious heritage of China’s agricultural civilization. Currently, against the backdrop of increasing urbanization and rapid expansion of urban construction land, the spatial patterns of traditional villages across various regions in China are being encroached upon and damaged, with protection pressures growing daily. As one of the important cradles of Chinese civilization, the Lower Yangtze River Basin (LYRB) has traditional villages closely linked with its water systems, forming a unique human-land relationship and spatial distribution pattern. However, influenced by the rapid urbanization process, the spatial patterns of traditional villages in this region also face a crisis, and the contradiction between protection and development is becoming increasingly prominent. How to balance this contradiction and ensure the reasonable protection and sustainable development of traditional villages has become an urgent issue to address. Therefore, this study focuses on the LYRB. Using ArcGIS tools and combined with mathematical analysis methods, the spatial distribution characteristics and essential influencing factors of traditional villages in this area were screened and analyzed. The objective was to examine the spatial structural relationship between traditional villages, four water system types, and nine sub-basin units, intending to reveal the unique interdependence between the water system and traditional villages in this area. This would provide scientific support for the formulation of scientific conservation strategies. The research results show that: (1) Traditional villages in The LYRB form two core clusters spatially and exhibit substantial spatial accumulation; (2) Water system characteristics are the main factors affecting the distribution of traditional villages; (3) In the LYRB, the spatial distribution of the nine sub-basins is closely related to the spatial distribution of traditional villages, resulting in typical regional spatial differentiation of traditional villages in this area. This study is based on a watershed perspective, and the results highlight the importance of the water system network in the development of traditional villages, revealing a unique spatial dependency relationship between traditional villages and the water network in the LYRB. In order to ensure the comprehensive protection of the traditional village system in this region, it is essential to adhere to the fundamental principles that govern its spatial configuration. A tripartite collaborative protection system based on the watershed should be formulated from the perspective of the overall distribution relationship between the water network and the traditional villages. This system would serve to protect the overall landscape, the water network pattern, and the traditional villages. Establishing an overall pattern view of integrating the water system network and the traditional villages is essential.

The diffusion of new technologies is often mediated by spatial and socioeconomic factors. This article empirically examines the diffusion of an important renewable energy technology: residential solar photovoltaic (PV) systems. Using detailed data on PV installations in Connecticut, we identify the spatial patterns of diffusion, which indicate considerable clustering of adoptions. This clustering does not simply follow the spatial distribution of income or population. We find that smaller centers contribute to adoption more than larger urban areas, in a wave-like centrifugal pattern. Our empirical estimation demonstrates a strong relationship between adoption and the number of nearby previously installed systems as well as built environment and policy variables. The effect of nearby systems diminishes with distance and time, suggesting a spatial neighbor effect conveyed through social interaction and visibility. These results disentangle the process of diffusion of PV systems and provide guidance to stakeholders in the solar market.

•DTI carried out over several months on phantom (100 measurements), 50 healthy control (HC) volunteers and 50 multiple sclerosis (MS) patients proved.•The existence of systematic errors with repetitive spatial characteristics and their statistically significant impact on diffusion tensor metrics (DTMs).•The effective elimination of their impact on DTMs by means of the BSD method.•DTMs statistically significantly differentiate the HC and MS groups in both the standard and BSD approaches,however, the latter provides more realistic values. Non-invasive and effective differentiation along with determining the degree of deviations compared to the healthy cohort is important in the case of various brain disorders, including multiple sclerosis (MS). Evaluation of the effectiveness of diffusion tensor metrics (DTM) in 3T DTI for recording MS-related deviations was performed using a time-acceptable MRI protocol with unique comprehensive detection of systematic errors related to spatial heterogeneity of magnetic field gradients. In a clinical study, DTMs were acquired in segmented regions of interest (ROIs) for 50 randomly selected healthy controls (HC) and 50 multiple sclerosis patients. Identical phantom imaging was performed for each clinical measurement to estimate and remove the influence of systematic errors using the b-matrix spatial distribution in the DTI (BSD-DTI) technique. In the absence of statistically significant differences due to age in healthy volunteers and patients with multiple sclerosis, the existence of significant differences between groups was proven using DTM. Moreover, a statistically significant impact of spatial systematic errors occurs for all ROIs and DTMs in the phantom and for approximately 90 % in the HC and MS groups. In the case of a single patient measurement, this appears for all the examined ROIs and DTMs. The obtained DTMs effectively discriminate healthy volunteers from multiple sclerosis patients with a low mean score on the Expanded Disability Status Scale. The magnitude of the group differences is typically significant, with an effect size of approximately 0.5, and similar in both the standard approach and after elimination of systematic errors. Differences were also observed between metrics obtained using these two approaches. Despite a small alterations in mean DTMs values for groups and ROIs (1–3 %), these differences were characterized by a huge effect (effect size ∼0.8 or more). These findings indicate the importance of determining the spatial distribution of systematic errors specific to each MR scanner and DTI acquisition protocol in order to assess their impact on DTM in the ROIs examined. This is crucial to establish accurate DTM values for both individual patients and mean values for a healthy population as a reference. This approach allows for an initial reliable diagnosis based on DTI metrics. [Display omitted]