Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach

This paper estimated and evaluated the spatial–temporal evolution of the concentration of healthcare resources (HCRs), in 31 provinces in China between 2004 and 2017, by using the entropy method. The spatial Durbin model (SDM) was used to further analyze the mechanisms behind the spatial driving forces at the national and regional levels. The findings revealed that: (i) The concentration of HCRs differed significantly among eastern, central, and western regions. The eastern, followed by the central region, had the highest concentration. Going east to west, the concentration of HCRs in the first echelon decreased, while it increased in the second and third echelons; (ii) places with higher concentrations clustered, while those with lower concentrations agglomerated; and (iii) economic development, population size, and urbanization promoted concentration. Education facilitated HCR concentration in the eastern and central regions, income stimulated HCR concentration in the eastern and western regions, and fiscal expenditure on healthcare promoted HCR concentration in the eastern region. Economic development inhibited HCR concentration in neighboring regions, population size restrained HCR concentration in neighboring areas in the western region, urbanization and income curbed HCR concentration in neighboring areas in the eastern and western regions, and fiscal expenditure on healthcare hindered HCR concentration in neighboring areas in the eastern region. Policy recommendations were proposed toward optimizing allocation of healthcare resources, increasing support for healthcare and education, and accelerating urbanization.

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Spatial-temporal evolution and driving forces of rainfall erosivity in a climatic transitional zone: A case in Huaihe River Basin, eastern China

CATENA ◽

10.1016/j.catena.2020.104993 ◽

2021 ◽

Vol 198 ◽

pp. 104993

Author(s):

Yinyao Xu ◽

Hu Sun ◽

Xu Ji

Keyword(s):

River Basin ◽

Temporal Evolution ◽

Driving Forces ◽

Eastern China ◽

Transitional Zone ◽

Rainfall Erosivity ◽

Huaihe River Basin ◽

Huaihe River

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Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

Remote Sensing ◽

10.3390/rs13214380 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4380

Author(s):

Yi Dong ◽

Dongqin Yin ◽

Xiang Li ◽

Jianxi Huang ◽

Wei Su ◽

...

Keyword(s):

Loess Plateau ◽

Temporal Evolution ◽

Driving Forces ◽

Arid Area ◽

Anthropogenic Factors ◽

The Loess Plateau ◽

Geographical Detector ◽

Humid Area ◽

Detector Model ◽

Semi Arid

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

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Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

Journal of Public Health ◽

10.1093/pubmed/fdn023 ◽

2008 ◽

Vol 30 (3) ◽

pp. 234-244 ◽

Cited By ~ 18

Author(s):

Jin-Feng Wang ◽

George Christakos ◽

Wei-Guo Han ◽

Bin Meng

Keyword(s):

Spatial Pattern ◽

Temporal Trend ◽

Temporal Evolution ◽

Driving Forces ◽

Critical Time ◽

Implicit Associations ◽

Control Factors ◽

Exposure Distribution ◽

Beijing China ◽

Relevant Factors

Abstract Background Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them. Methods A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated. Results The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined. Conclusion It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.

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Acquiring and Geo-Visualizing Aviation Carbon Footprint among Urban Agglomerations in China

Sustainability ◽

10.3390/su11174515 ◽

2019 ◽

Vol 11 (17) ◽

pp. 4515 ◽

Cited By ~ 2

Author(s):

Chuntao Wu ◽

Maozhu Liao ◽

Chengliang Liu

Keyword(s):

Co2 Emissions ◽

Driving Forces ◽

Current Knowledge ◽

Value Added ◽

Chord Diagram ◽

Carbon Footprints ◽

Urban Agglomerations ◽

Factor Va ◽

Annual Total ◽

Kaya Identity

This paper had two main purposes. One was to estimate annual total aviation CO2 emissions from/among all key urban agglomerations (UAs) in China and its changes patterns from 2007 to 2014. The second one was to visualize the aviation carbon footprints among the UAs by using a chord diagram plot. This study also used Kaya identity to decompose the contribution of potential driving forces behind the aviation CO2 emissions using Kaya identity. Especially, it decomposed factor CO2/gross domestic product (GDP), which is wildly used in Kaya identity analysis, into factor CO2/value-added (VA) and factor VA/GDP. Here, VA represents the tourism value added of the corresponding flights. The main results were: (1) The UAs developed a much bigger and stronger carbon network among themselves. (2) There was also an expanding of the flows to less densely populated or less developed UAs. However, the regional disparity increased significantly. (3) Compared with the driving factor of population, the GDP per capita impacted the emission amount more significantly. Our contribution had two folds. First, it advances current knowledge by fulfilling the research gap between transport emissions and UA relationship. Second, it provides a new approach to visualizing the aviation carbon footprints as well as the relationships among UAs.

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KARST ROCKY DESERTIFICATION ANALYSIS BASED ON HISTORICAL DISP DATA TAKE TWO REGIONS FOR EXAMPLE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-871-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 871-874

Author(s):

J. Liu ◽

G. Q. Zhou ◽

B. Jia ◽

T. Yue ◽

X. Y. Peng

Keyword(s):

Southwest China ◽

Temporal Evolution ◽

Driving Forces ◽

The United States ◽

Living Environment ◽

Karst Area ◽

Visual Interpretation ◽

Karst Rocky Desertification ◽

Rocky Desertification ◽

Hydrogeological Data

Abstract. Karst rocky desertification (KRD) is used to characterize the processes that transform a karst area covered by vegetation and soil into a rocky landscape almost devoid of soil and vegetation. This situation seriously affects and threatens the living environment and standards of local people, which results in a series of social problems. In view of the importance and harmfulness of KRD, many scholars have studied the spatial and temporal evolution of KRD and its driving forces. In this paper, the Visual Interpretation Marks of Rocky Desertification in Southwest China in 1960s are constructed by using the DISP image of the United States, combined with DEM data and Hydrogeological data. The area of rocky desertification in Guangnan and Funing counties, where rocky desertification is more serious, is about 2457.729 km2. The area of rocky desertification can be used as the basic data for studying the historical changes in southwestern China by researchers.

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