Calculation of Farmland Ecosystem Carbon Footprint and Analysis of Influencing Factors at County Level in Plateau Mountainous Area—Taking Luliang County as an Example

2020 ◽  
Vol 09 (03) ◽  
pp. 223-232
Author(s):  
昕妤 段
Keyword(s):  
Influencing Factors ◽  
Carbon Footprint ◽  
Mountainous Area ◽  
County Level ◽  
Ecosystem Carbon ◽  
Farmland Ecosystem

scholarly journals Spatiotemporal variation in global floods with different affected areas and the contribution of influencing factors to flood-induced mortality (1985–2019)

2022 ◽  
Author(s):  
Tian Liu ◽  
Peijun Shi ◽  
Jian Fang
Keyword(s):  
Influencing Factors ◽  
Large Scale ◽  
Human Life ◽  
Flood Frequency ◽  
Spatiotemporal Variation ◽  
Mountainous Area ◽  
Flood Events ◽  
Protection Measures ◽  
Flood Duration ◽  
Level Ii

AbstractFloods are great threats to human life and property. Extensive research has investigated the spatiotemporal variation in flood occurrence, while few have studied the heterogeneity in global flood events of different sizes, which may require different coping strategies and risk reduction policies. In this study, we analysed the spatiotemporal patterns of global flood events with different affected areas (classified in three levels) during 1985–2019 and examined the contribution of different influencing factors to flood-induced mortality using Geodetector. The results show that (1) the increase in global flood frequency was mainly caused by Level II and Level III floods, and the average area affected by flood events has been increasing yearly since 1985. (2) In America and Africa, the frequency of Level III floods has increased monotonically. At the same time, the frequency of Level I floods in Europe and Level II floods in Asia has increased significantly. (3) For Europe and Asia, most of the deaths occurred with Level II floods; while for America and Africa, Level III floods caused the most mortality. (4) The top three factors contributing to the spatial heterogeneity in flood-induced mortality were the affected population, GDP per capita and flood duration. The contribution of each factor varied among the different types of floods. Topographic factors (percentage of mountainous area) magnified flood-induced mortality during extreme events with heavy rainfall, especially for Level III floods. The heterogeneity in flood frequency and flood-induced mortality indicates that flood protection measures should be more targeted. In addition, the increase in large-scale floods (Level III) highlights the need for transregional cooperation in flood risk management.

Why can China maintain a high crop diversity? A spatial-temporal dynamic analysis

2021 ◽  
pp. 030913332110558
Author(s):  
Zhenhuan Liu ◽  
Guoping Tang ◽  
Yi Zhou ◽  
Jing Sun ◽  
Wenbin Wu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Local Level ◽  
National Level ◽  
Social Factor ◽  
Crop Diversity ◽  
Mean Annual Temperature ◽  
Mountainous Area ◽  
County Level ◽  
Diversity Pattern ◽  
Agricultural Sustainable Development

Crop diversity is crucial for producing more food and nutrition in the crowded planet and achieving agricultural sustainable development, and thus it is a hot topic in shaping policies aimed at ensuring food security. Many studies have revealed that enhanced crop diversity can benefit crop productivity. However, research on how to maintain a relatively high crop diversity at regional and national scales remains limited. This study attempts to examine the underlying mechanisms of crop diversity changes in China and eventually answer why China can maintain a high crop diversity from the spatial-temporal perspective. To achieve this end, the county level crop area dataset for the period of 1980–2014 was compiled and used to quantify the spatiotemporal dynamics of crop diversity in China. The result reveals that the China’s crop diversity trended upward over past 35 years, evidenced by more than 7 major crops at national level and 4 major crops at county level having undergone massive planting process to maintain a high crop diversity. Spatially, the crop diversity increased in more than two-thirds of the counties, and its hotspots moved gradually to the south-west mountainous area. The natural factor of slope and the social factor of population density contributed to shape the crop diversity pattern in global effects. In contrast, the irrigation degree, elevation of cropland, mean annual temperature and precipitation affected the spatially non-stationary distribution of crop diversity at the local level. On the whole, the maintenance of a higher crop diversity in China not only was limited by natural conditions, but also subject to adopt the multi-cropping systems strategic choice for the country to agricultural conditions. We argued that crop diversity can be an indicator to draw agricultural zoning, and increasing crop diversity should be recognized as a policy tool to implement agricultural sustainable development strategy.

Temporal and spatial dynamics in the carbon footprint and its influencing factors of farmland ecosystems in Yunnan Province

2018 ◽  
Vol 38 (24) ◽  
Author(s):  
李明琦 LI Mingqi ◽  
刘世梁 LIU Shiliang ◽  
武雪 WU Xue ◽  
孙永秀 SUN Yongxiu ◽  
侯笑云 HOU Xiaoyun ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Carbon Footprint ◽  
Yunnan Province ◽  
Spatial Dynamics ◽  
Temporal And Spatial

Influencing factors and characteristics of nighttime sap flow of Acer truncatum in Beijing mountainous area

2019 ◽  
Vol 39 (9) ◽  
Author(s):  
张婕 ZHANG Jie ◽  
蔡永茂 CAI Yongmao ◽  
陈立欣 CHEN Lixin ◽  
陈左司南 CHEN Zuosinan ◽  
张志强 ZHANG Zhiqiang
Keyword(s):  
Influencing Factors ◽  
Sap Flow ◽  
Mountainous Area

scholarly journals Analysis of the Influencing Factors of the Medical Service Capacity in Key Clinical Specialties at the County Level Based on TOPSIS

2020 ◽  
Author(s):  
Kaixuan Wang ◽  
Jin-Ling Guo ◽  
Wei Wang ◽  
Shui-Chang Zhao ◽  
Miao-Jun Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Medical Service ◽  
Compliance Rate ◽  
Care Quality ◽  
Appropriate Technology ◽  
County Level ◽  
Service Capacity ◽  
Personnel Training

Abstract To acknowledge the medical service capacity of the first batch of county-level key clinical specialties in Henan province, analyze the influencing factors, and make targeted suggestions. TOPSIS method was used to evaluate the medical service capacity comprehensively, and the influencing factors were analyzed by simple linear regression and multiple linear regression. In the multiple linear regression model, the basic conditions, including the total investment funds during the construction period, net usable area of beds and the ratio of bed to nurse, the quality of care—the compliance rate of major clinical diagnosis and pathological diagnosis, and the personnel training—the number of technical promotion trainees, of the first county-level clinical specialties, have a significant influence upon the medical service capacity; while, the actual number of beds, the ratio of doctors and nurses, the proportion of annual expert visits, the number of technical promotion training and sending of learners, percentage of drug expenditure and core journal articles have limited effect. On the basis of the ensured allocation of resources and capital investment, more attention should be paid to personnel training and appropriate technology promotion, and the medical care quality is supposed to be always insisted on, to promote the overall improvement of medical service capacity in county hospitals.

scholarly journals Spatial statistics and influencing factors of the novel coronavirus pneumonia 2019 epidemic in Hubei Province, China

2020 ◽  
Author(s):  
Yongzhu Xiong ◽  
Yunpeng Wang ◽  
Feng Chen ◽  
Mingyong Zhu
Keyword(s):  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Pearson Correlation ◽  
Hubei Province ◽  
County Level ◽  
The Novel ◽  
Land Area ◽  
Novel Coronavirus ◽  
And Control ◽  
Global Spatial Autocorrelation

Abstract An in-depth understanding of the spatiotemporal dynamic characteristics of infectious diseases could be helpful for epidemic prevention and control. Based on the novel coronavirus pneumonia (NCP) data published on official websites, GIS spatial statistics and Pearson correlation methods were used to analyze the spatial autocorrelation and influencing factors of the 2019 NCP epidemic from January 30, 2020 to February 18, 2020. The following results were obtained. (1) During the study period, Hubei Province was the only significant cluster area and hotspot of cumulative confirmed cases of NCP infection at the provincial level in China. (2) The NCP epidemic in China had a very significant global spatial autocorrelation at the prefecture-city level, and Wuhan was the significant hotspot and cluster city for cumulative confirmed NCP cases in the whole country. (3) The cumulative confirmed NCP cases had a very significant global spatial autocorrelation at the county level in Hubei Province, and the county-level districts under the jurisdiction of Wuhan and neighboring Huangzhou district in Huanggang City were the significant hotspots and spatial clusters of cumulative confirmed NCP cases. (4) Based on Pearson correlation analysis, the number of cumulative confirmed NCP cases in Hubei Province had very significant and positive correlations (p<0.01) at the prefecture-city and the county levels with four population indexes (registered population, resident population, regional GDP and total retail sales of consumer goods) during the study period. (5) The number of the cumulative confirmed NCP cases in Hubei Province also had a very significant and positive correlation (p<0.01) on the prefecture-city scale with the Baidu migration index and population density but not with land area, whereas that in Hubei Province had a significant and positive correlation (p<0.05) at the county level with land area but not with population density from January 30, 2020, to February 18, 2020. It was found that the NCP epidemic in Hubei Province had distinctive characteristics of a significant centralized outbreak, significant spatial autocorrelation and complex influencing factors and that the spatial scale had a significant effect on the global spatial autocorrelation of the NCP epidemic. The findings help to deepen the understanding of spatial distribution patterns and transmission trends of the NCP epidemic and may also benefit scientific prevention and control of epidemics such as COVID-19.

Spatial pattern and influencing factors of rural multifunctionality at county level in China

2019 ◽  
Vol 38 (9) ◽  
pp. 1316-1328 ◽  
Author(s):  
Ren YANG ◽  
Xiuli LUO ◽  
Yanchun CHEN ◽  
Keyword(s):  
Spatial Pattern ◽  
Influencing Factors ◽  
County Level
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