Spatiotemporal Patterns and Drivers of Vegetation Carbon Sequestration in Shandong Province, China

At present, most studies focus on the three provinces in Northeast China, the Beijing-Tianjin-Hebei region, and Inner Mongolia, where the basic conditions of ice and snow tourism are relatively superior, while the spatial distribution characteristics of ice and snow tourism in Shandong Province are relatively scarce. In view of this, this study adopts ArcGIS spatial analysis technology to deeply explore the spatial distribution characteristics of ice and snow tourism resources in Shandong Province, and comprehensively analyze its influencing factors, aiming to provide theoretical suggestions and evidence-based policy references for optimizing the spatial layout of ice and snow tourism resources in Shandong Province and promoting the integrated development of ice and snow industry. Method: In this study, ArcMap 10.8 software and geospatial analysis were used to analyze ice and snow tourism resources in Shandong Province as input elements of point data. The research area was 156,700 square kilometers. E distance method was used to analyze the average nearest proximity, geographical concentration index, nuclear density, and standard deviation ellipse of ice and snow tourism resources in Shandong Province. The distribution and structure of ice and snow tourism resources in Shandong Province are drawn. Finally, the factors affecting the spatial distribution of ice and snow tourism resources were analyzed by using geographic detectors. (1) From the overall provincial level, the spatial distribution type of ice and snow tourism resources in Shandong Province is agglomeration, with strong geographical concentration but unbalanced distribution. (2) The ice and snow tourism resources in Shandong Province are influenced by the third GDP, population density, per capita GDP, expenditure on culture, tourism, sports, and media, total population at the end of the year, highway network density, and other factors. (1) Promote synergy among the three economic circles to form synergy for development. (2) Construct a three-dimensional spatial structure of "integrating points into planes and integrating planes into bodies" to upgrade the spatial pattern of ice and snow tourism resources. (3) Enhance the dual driving forces of the government and the market to promote the prosperity and development of the ice and snow industry. (4) Expand the population of ice and snow sports and strengthen the construction of ice and snow sports talents.

Land use/cover change is the second major contributor to carbon emissions, following energy emissions. Studying provincial land-use carbon emissions is crucial for achieving the “double carbon” goal. This study selects 16 prefecture-level cities in Shandong Province as the research object. It analyzes the spatial and temporal distribution pattern of carbon emissions in Shandong Province based on land-use data and energy consumption. In terms of net carbon emissions, this study utilizes the standard deviation ellipse and kernel density estimation to analyze net carbon emissions change from the municipal and regional perspectives. In terms of carbon ecological carrying capacity, not only the carbon ecological carrying capacity of forest and grassland was considered, but also the carbon ecological carrying capacity of crops in Shandong Province, which is a large grain province. Using the geographic detector to explore the drivers. Research findings indicate that carbon sources and sinks show a clear spatial and temporal distribution pattern, with the center of gravity of net carbon emissions extending to the northeast. Areas with high carbon ecological carrying capacity have high forest coverage, grassland coverage, and crop yields. Regarding driving factors, the urbanization rate, economic aggregate, and technological progress demonstrate significant explanatory power through single and interaction tests, suggesting that these factors are critical drivers of land-use carbon emissions within Shandong Province. Based on the spatiotemporal pattern analysis of land-use carbon emissions in Shandong Province, each city's growth rate and spatial distribution characteristics can be clarified, providing a scientific basis for the local government to formulate regional and differentiated emission-reduction policies. In addition, by exploring the driving factors of land-use carbon emissions in Shandong Province, the influence level of factors on carbon emissions can be revealed to provide references for formulating regional sustainable development strategies.

BackgroundDue to recent emergence, severe fever with thrombocytopenia syndrome (SFTS) is becoming one of the major public health problems in Shandong Province, China. The numbers of reported SFTS cases in general and the area with reported SFTS cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of SFTS in Shandong Province have not been investigated yet.MethodsThe surveillance data of SFTS in Shandong Province, China, during 2014–2018 were extracted from China Information System for Disease Control and Prevention (CISDCP). Geoda software was used to explore spatial autocorrelation analysis, and Satscan software was used to identify spatio-temporal clustering of cases. The results were presented in ArcMap.ResultsThe annual average incidence was 0.567/100,000 in Shandong Province during 2014–2018. Results showed that the distribution of SFTS was not random but clustered in space and time. A most likely cluster including 15 counties was observed in the northeastern region of Shandong Province from January 1, 2015 to December 31, 2015 (Relative risk = 5.13, Log likelihood ratio = 361.266, P < 0.001).ConclusionsThe number of SFTS cases in Shandong Province increased overall. Geographic information system analysis coupled with spatial analysis illustrated regions with SFTS clusters. Our results provide a sound evidence base for future prevention and control programs of SFTS such as allocation of the health resources, surveillance in high-risk regions, health education, improvement of diagnosis and so on.

Based on the data of 859 scenic villages in Shandong Province, the spatial pattern of scenic villages in Shandong Province and various prefectural cities and the factors affecting them were quantitatively studied by using spatial analysis methods such as average nearest neighbor index method and kernel density analysis, as well as combining with geodetic detector analysis tools. The results show that: (1) the scenic villages in Shandong Province have significant aggregation distribution characteristics, but there are differences in the spatial types of scenic villages in different prefectural-level cities; (2) the scenic villages in Shandong Province show the spatial distribution characteristics of "three core dense areas and multiple sub-core dense areas", and the spatial distribution in different regions has significant differences. The spatial distribution of scenic villages in Shandong Province is characterized by The "three core intensive areas" are Taian-Jinan, Zibo-Weifang central Luzhong core agglomeration area and Rizhao-Qingdao east Luzhong core intensive area; (3) scenic villages in Shandong Province are affected by a combination of natural conditions, socio-economic conditions, tourism resource conditions, and transportation conditions, among which there is a strong dependence on river density and the number of tourists received.

Improving culture and tourism integration efficiency is an important way to promote the high-quality development of cultural tourism. According to the inherent requirements of high-quality development, this paper constructed an evaluation indicator system for culture and tourism integration efficiency. Then, the culture and tourism integration efficiency of 16 cities in Shandong Province, China during the period from 2010 to 2019 was measured with the benevolent DEA cross-efficiency model. On the basis of exploratory spatial data analysis and dynamic spatial Durbin model, we explored the spatio-temporal evolution characteristics and influencing factors of culture and tourism integration efficiency in Shandong Province. The results show that from 2010 to 2019, the culture and tourism integration efficiency in Shandong Province has experienced three stages of "rapid growth-rapid decline-stable rise period". The spatial pattern has changed from "high in the east and low in the west" to "high in the central and low in the north and south", and regions with high integration efficiency are mainly concentrated in Jiaodong Peninsula. The level of economic development significantly promotes the culture and tourism integration efficiency in local and neighboring cities in the short and long term, while policy environment has a significant negative impact. Traffic conditions and human capital only promote the culture and tourism integration efficiency in local cities. The level of information development and openness degree only have a long-term effect on the culture and tourism integration efficiency, without short-term effect. The research results are of great significance to improve the growth quality and sustainable development of cultural tourism in Shandong Province. Our work could provide a scientific basis for maximizing the allocation benefits of cultural and tourism resources in similar regions in the world.

Combining the Intangible Cultural Heritage and sustainable development has been an important effort of UNESCO since the new century. This study discusses the suitability of educational tourism development of intangible cultural heritage. On the one hand, it was beneficial to improve the comprehensive quality of students; on the other hand, it was conducive to protecting and rationally developing the cultural heritage and avoiding its loss. In this study, an evaluation index system was established according to the analytic hierarchy process, which included two aspects (i.e., intrinsic value and extrinsic conditions) and 16 indices from four criterion layers (e.g., educational value, recreational value, environmental conditions, and relevant facilities and services). Furthermore, we calculated the development suitability and spatial distribution patterns of intangible cultural heritage for educational tourism. At the same time, using the obstacle degree model, the obstacle degrees of each indicator factor were screened and identified to explore the source of obstacles that restrict the suitability of educational tourism development of ICH. Results showed that: (1) The development suitability of intangible cultural heritage for educational tourism was divided into high suitability, middle suitability, and low suitability. A total of 186 intangible cultural heritages were found in Shandong Province, China. Among these intangible cultural heritages, 60 of 186 (32.26%) were low suitability, with values ranging from 0.326 to 0.460; 86 of 186 (46.24%) were middle suitability, with values of 0.460–0.543, and 40 of 186 (21.50%) were high suitability with a range of 0.543–0.689. (2) The spatial distribution patterns of suitability showed that the development suitability of intangible cultural heritage for educational tourism in Shandong Province exhibited a significantly positive spatial correlation that projects with similar suitability levels were clustered into a group and generally distributed with a direction of “southwest to the northeast”. (3) According to the diagnostic results of obstacle factor analysis, from the perspective of the first level indicator, the obstacle degree of the intrinsic value (A1) of the three levels of suitability of ICH was the highest. Among the second-level indicators, educational value (B1) has always been the biggest obstacle factor affecting the educational tourism of ICH, and the relevance of cultural content (C2), representativeness and typicality of the phenomenon (C3), applicability of teaching cases (C4), and uniqueness or rarity (C5) were the greatest among the three suitability factors.

Rural settlements contain rich regional cultural factors and high-quality landscape style and form, which is the essence of China's agricultural civilization. Taking 537 traditional villages in Shandong Province as the research object, this paper systematically analyzes the spatial distribution pattern and influencing factors of traditional villages in Shandong Province by using the methods of GIS, RS and mathematical statistics. The research shows that the traditional villages in Shandong Province are obviously concentrated in space, forming two concentration centers in the mountainous area of central and southern Shandong and the hilly area of eastern Shandong; Traditional villages are concentrated in piedmont/intermountain plain, hills and low mountains. The number of villages varies greatly in different terrain areas. The number of villages is negatively correlated with elevation and slope, and the number of villages is large in plain areas; The distribution of traditional villages has a significant positive correlation with altitude, and the positive characteristics are significant. Traditional villages are mainly concentrated in the sunny slope; Traditional villages are far away from the water area as a whole and are not highly dependent on water sources. The study can provide guidance for the protection and sustainable development of traditional villages in Shandong Province.

The vegetation index research was necessary for monitoring plant growth. Based on the data of 250m spatial resolution NDVI (Normalized Difierence Vegetation Index) of MODIS (Moderate Resolution Imaging Spectroradiometer), this paper analyzed the spatial distribution pattern of NDVI in Summer in Shandong Province in the east of China. With the data of six meteorologic sites and the average NDVI in January, in April, in July and in October in 2006 including the average atmosphere temperature, relative humidity, precipitation, sunlight hours and using the SPSS statistics software, the correlation between NDVI and meteorology factors was researched. The results were showed as follows: there was obvious character of spatial distribution pattern of NDVI in Shandong Province. The value of NDVI in plain region was higher than the value of mountain and hilly region, and moreover the distribution of value of NDVI was even in plain, in which the most of NDVI was greater than 0.7{0.8. In the mountain region, NDVI was falling ofi with the decreasing of the altitude, where NDVI was about 0.6{0.7. Deeply research also showed that NDVI was afiected by human activity distinctly, and so the NDVI in the city was lower than 0.4, as such lower than that of the suburb. The atmosphere temperature and quantity of the precipitation were the two main factors afiecting the change of NDVI, at the same time the seasonal sunlight hours was second-class factor that caused the change of the NDVI. The research results also indicated that with the decreasing of the latitude, the correlation between NDVI and temperature was decreasing too, on contrast, the correlation between NDVI and the quantity of precipitation was increasing.

BackgroundScrub typhus is a significant public health issue with a global distribution. In northern China, Shandong Province is a major endemic area, but its spatiotemporal patterns and influencing factors remain unclear.MethodsThis study collected data on scrub typhus in Shandong Province from the Infectious Disease Reporting System of the Shandong Center for Disease Control and Prevention between 2006 and 2019. Spatiotemporal evolution analysis combined joinpoint regression, spatiotemporal cluster analysis and standard deviation ellipse. GeoDetector was used to identify the impacts of socioeconomic and natural factors on spatial distribution of scrub typhus. Generalised additive model was applied to explore associations with meteorological variables.Results9397 scrub typhus cases were reported in Shandong Province from 2006 to 2019, with an average annual incidence of 0.68 / 100 000, peaking in 2014 (1.53 / 100 000). Cases were concentrated from September to November. Spatiotemporal cluster was mainly in Linyi and Rizhao cities in southern Shandong. The centre of gravity of scrub typhus gradually shifted southeast, and moved back from 2015 to 2019. Nighttime light (q = 0.223), normalised difference vegetation index (q = 0.197), relief degree of land surface (q = 0.230), grassland (q = 0.320), and water (q = 0.180) were all related with scrub typhus, with q indicating the explanatory power of each factor on the spatial distribution of the disease. The strongest relative risks between monthly incidence of scrub typhus and temperature, humidity, precipitation and humidex were 1.528 (lag3), 1.175 (lag3), 1.013 (lag1), and 1.279 (lag3), respectively.ConclusionsScrub typhus in Shandong Province was mainly concentrated in Linyi and Rizhao cities. The occurrence of scrub typhus is influenced by various environmental factors. Humidex is a better composite indicator to reflect the impacts of meteorological factors on scrub typhus in northern China. These findings provide scientific evidence to guide prevention and control strategies for scrub typhus. Limitations include potential underreporting in surveillance data and the absence of vector and host information.

Alteration Information Extraction by Applying Synthesis Processing Techniques to Landsat ETM+ Data: Case Study of Zhaoyuan Gold Mines, Shandong Province, China

The study of spatio-temporal evolution characteristics and factors affecting the coordinated development of population and green economy (CD_PGE) in Shandong province, China, has significant decision-making implications for promoting high-quality and sustainable regional development. Based on 2001 to 2020 panel data for each city and economic zone in Shandong province, this paper constructs an evaluation model for the CD_PGE systems. Using growth elasticity models, geographic concentration models, kernel density estimation models, spatial autocorrelation, analysis of population and regional green economy development in Shandong Province from the perspective of spatial agglomeration coupling, spatial and temporal coupling coordination patterns, and evolutionary characteristics. In addition, we use the fixed effect models and panel quantile models to empirically test the effects of coordinated demographic and green economy development. The results show that: (1) In terms of demo-graphic and economic development characteristics, Shandong's demographic and green economy development trends are good, but there are still many challenges. (2) According to the time series evolution and spatial distribution characteristics, the degree of CD_PGE in Shandong Province is on the rise, and the level of spatial distribution is distinct. (3) From the spatio-temporal dynamical grid evolution of the degree of CD_PGE, the CD_PGE is characterized by significant spatial clustering, but with large regional differences. (4) From an impact factor perspective, both market mechanisms and government intervention have a significant impact on the degree of CD_PGE, but the direction and extent of the impact vary.

Shandong Province is an important part of the Bohai Economic District and the Yellow River Economic Belt， and it is also the only entrance to the sea in the lower reaches of the Yellow River， which is of great significance for ecological protection and flood mitigation. However， in recent years， due to the influence of human activities， the contradiction between ecological protection and socioeconomic development has become increasingly obvious. Taking Shandong Province as the study area， based on the spatial and temporal changes of land use from 2000 to 2020， the spatial and temporal changes of four ecosystem services， namely， water yield， soil retention， carbon storage， and habitat quality， from 2000 to 2020 were explored to assess the trade-off and synergistic relationships among ecosystem services by applying global correlation analysis and local spatial autocorrelation analysis. The study produced several interesting results： ① The reduction of cultivated land area in Shandong Province was most obvious between 2000 and 2020， when it decreased by 1.3033×104 km2， of which 86.1% was transferred to construction land. Land use change was mainly concentrated in the northern coastal areas of Weifang， Dongying， and Binzhou. ② Various ecosystem services in Shandong Province showed a spatial distribution pattern of high in the central and southeastern areas and low in the lower Yellow River Basin. Water production and soil retention showed an increasing trend， with increases of 14.06×108 m3 and 1.06×107 t， while carbon storage and habitat quality showed an overall decreasing trend， with decreases of 7.81×107 t and 0.011， respectively. ③ There were different degrees of correlation between ecosystem services. Among the ecosystem services that correlated， all relationships were synergistic， except for carbon storage-water yield and habitat quality-water yield， which were trade-offs. ④ The global trade-offs and synergistic relationships between ecosystem services and the local trade-offs and synergistic relationships were basically the same， but some ecosystem services had regional variability at global and local scales， having different effects at different scales. This study provides a reference for the scientific management of the lower Yellow River ecosystem and the high-quality development of Shandong Province.

As populations and economies have grown rapidly, questions of land development and use have intensified. It has become a major global concern to achieve sustainable land use practices. This study reveals evolution of the spatiotemporal pattern of land development intensity of counties in Shandong Province by introducing a land development intensity measurement model combined with three-dimensional trend surface and spatial autocorrelation analyses. Geodetector and geographically weighted regression models were employed to demonstrate the interplay and spatiotemporal heterogeneity between development intensity and drivers. The empirical results show that the value of land development intensity of counties in Shandong Province shows a general growth trend, with the number of counties with higher values gradually increasing and the number of counties with lower values gradually decreasing. We also found that the spatial heterogeneity of land development intensity across counties in Shandong Province is significant, and the spatial distribution pattern is basically consistent with the “one group, two centers and three circles” strategy proposed by the Shandong Provincial Government. There is also a positive spatial correlation and clustering effect of land development intensity of counties in Shandong Province. High (low) value clusters are concentrated in core hot (cold) counties, driving some of the surrounding counties towards radial development. The alteration in the intensity of county land development is a complex occurrence that is shaped by numerous factors. Among these, GDP per capita and population density have the primary influence on land development of counties in Shandong Province. To achieve coordinated regional social, economic, and environmental benefits, land development within the county should adhere to the principle of adapting to local conditions and implement differentiated development strategies according to different development intensities.

BackgroundWith the rapid development of China’s economy, air pollution has attracted public concern because of its harmful effects on health.MethodsThe source apportioning of air pollution, the spatial distribution characteristics, and the relationship between atmospheric contamination, and the risk of exposure were explored. The in situ daily concentrations of the principal air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) were obtained from 188 main cities with many continuous air-monitoring stations across China (2014 and 2015).ResultsThe results indicate positive correlations between PM2.5 and SO2 (R2 = 0.395/0.404, P < 0.0001), CO (R2 = 0.187/0.365, P < 0.0001), and NO2 (R2 = 0.447/0.533, P < 0.0001), but weak correlations with O3 (P > 0.05) for both 2014 and 2015. Additionally, a significant relationship between SO2, NO2, and CO was discovered using regression analysis (P < 0.0001), indicating that the origin of air pollutants is likely to be vehicle exhaust, coal consumption, and biomass open-burning. For the spatial pattern of air pollutants, we found that the highest concentration of SO2, NO2, and CO were mainly distributed in north China (Beijing-Tianjin-Hebei regions), Shandong, Shanxi and Henan provinces, part of Xinjiang and central Inner Mongolia (2014 and 2015).ConclusionsThe highest concentration and risk of PM2.5 was observed in the Beijing–Tianjin–Hebei economic belts, and Shandong, Henan, Shanxi, Hubei and Anhui provinces. Nevertheless, the highest concentration of O3 was irregularly distributed in most areas of China. A high-risk distribution of PM10, SO2 and NO2 was also observed in these regions, with the high risk of PM10 and NO2 observed in the Hebei and Shandong province, and high-risk of PM10 in Urumchi. The high-risk of NO2 distributed in Beijing-Yangtze River Delta region-Pearl River Delta region-central. Although atmospheric contamination slightly improved in 2015 compared to 2014, humanity faces the challenge of reducing the environmental and public health effects of air pollution by altering the present mode of growth to achieve sustainable social and economic development.

Ecosystem services (ESs) are essential for human survival and development and serve as key indicators in evaluating ecological civilization progress. This study investigated the spatiotemporal patterns of ESs in Shandong Province from 2000 to 2020, using the Geographical Detector model to assess the impact of diverse influencing factors. The results showd that: (1) From 2000 to 2020, grain production (GP), soil conservation (SC), and carbon sequestration (CS) showed a fluctuating increase of 39.92%, 106.88%, and 32.21%, respectively. Water yield (WY) remained relatively stable, while habitat quality (HQ) declined rapidly after 2010. (2) Land use types and elevation demonstrated high explanatory power regarding the spatial heterogeneity of each ES, with population density also having a significant impact on all services except SC. Precipitation emerged as the strongest explanatory factor for water conservation. Furthermore, all pairwise interactions between factors exhibited a bi-factor enhancement in 2020, indicating a substantial role of multi-factor interactions in ES dynamics. This study provides essential insights to support decision-making, addressing the challenges ESs face and promoting the sustainable, high-quality development of Shandong’s socio-ecological-economic system.