Spatial-temporal Evolution and Influencing Factors of Digital Financial Inclusion: County-level Evidence from China

Zhang, Guojun; Chen, Yu; Wang, Gengnan; Zhou, Chen

doi:10.1007/s11769-023-1333-5

Cited by 19 publications

(19 citation statements)

References 24 publications

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“…Finally, digital financial inclusion can promote the quality of economic growth in neighboring regions. Many studies have confirmed that digital financial inclusion and the quality of economic growth have significant spatial autocorrelation [ 74 , 87 ], and digital financial inclusion has a positive spatial spillover effect on farmers’ income growth [ 88 ]. Aziz and Naima (2021) concluded that although digital services have improved the access to financial services, these services have not been fully utilized due to a lack of basic connectivity, financial literacy, and social awareness [ 89 ].…”

Section: Resultsmentioning

confidence: 99%

“…Tobler's First Law of Geography states that all things are spatially correlated with other things. Many studies have confirmed that digital financial inclusion has the characteristics of cross-space-time transmission and significant spatial effects [ [73] , [74] , [75] ]. Owing to the continuous expansion of the Internet and big data, digital financial inclusion has spillover effects on other industries, resulting in greater value for participants.…”

Section: Literature Reviewmentioning

confidence: 99%

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Digital financial inclusion and quality of economic growth

Xi,

Wang

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Digital financial inclusion and quality of economic growth

Xi,

Wang

2023

Heliyon

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“…where y is the comprehensive CF value; n is the number of evaluation factors; and ∆ i is the importance of evaluation factor i, calculated by the Formula (2). In this study, building upon the foundational deterministic coefficient model, the deterministic coefficient values for each evaluation factor are determined using Equation (1). Utilizing the multi-value extraction to point function in ArcGIS, deterministic coefficient values associated with each evaluation factor are mapped onto each sample dataset.…”

Section: Weighted Linear Combinationmentioning

confidence: 99%

“…Its distinct geographical and geological environment and its topography are the primary causes behind the frequent occurrence of geological disasters in the region. Areas with mountainous plateau terrain are prone to geological hazards such as collapses, landslides, and debris flows, posing significant threats to national economic development, the safety of lives and properties, as well as causing substantial damage to infrastructure and the ecological environment within the affected areas [1]. Among geological disasters in China, slope-related incidents rank as the second most prevalent after earthquakes [2].…”

Section: Introductionmentioning

confidence: 99%

Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model

Zhang,

Tan,

Zhou

et al. 2023

Sustainability

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An assessment of regional vulnerability to geological disasters can directly indicate the extent and intensity of risks within the study area; thus, providing precise guidance for disaster management efforts. However, in the evaluation of geological disaster susceptibility using a single deterministic coefficient model, the direct superimposition of deterministic coefficient values for each evaluation factor, without considering their objective weights, can impact the accuracy of susceptibility zoning outcomes. To address this limitation, this research proposes a novel approach: geological disaster susceptibility evaluation using a random-forest-weighted deterministic coefficient model. In this method, the objective weight of each evaluation factor is calculated based on a deterministic coefficient model and a parameter-optimized random forest model. By weighting and superimposing the deterministic coefficient values of each evaluation factor, a comprehensive deterministic coefficient map is generated. This map is further divided using the natural breakpoint method to obtain a geological disaster susceptibility zoning map. To validate the accuracy of the evaluation results, partition statistics and the ROC (Receiver Operating Characteristic) curve of the test sample points are utilized. The findings demonstrate that the model performs well in evaluating geological disaster susceptibility in Huize County. The evaluation results are considered reliable and accurate, highlighting the effectiveness of the proposed approach for assessing and zoning geological disaster susceptibility in the region.

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“…As a result, the county becomes a regional unit with earlier, faster, more widespread and larger-scale population contraction in China. Meanwhile, the social, economic and ecological environmental effects generated by population contraction are more comprehensive, more typical, more prominent and stronger [2][3][4]. By comparing the data of the sixth and seventh national census, it is found that from 2010 to 2020, the proportion of the country s permanent resident population shrinking in counties was 53%, the working-age population in more than 70% of counties decreased to varying degrees, and the growth rate of the working-age population in more than 96% of counties was lower than that of the permanent resident population [4].…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Spatiotemporal Correlation Effect of County-Scale Population Shrinkage: A Case Study of Northeast China

Xue,

Wu,

Zhang

et al. 2024

Sustainability

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There is a mutual causal relationship between population shrinkage and the level of regional social–economic–ecological development and their coordinated development. It is of great significance to reveal the correlation effect between population shrinkage and regional development for the adjustment and optimization of the relationship between regional population and social, economic and ecological development. Taking 142 counties in the three provinces of Northeast China as samples, the population contraction was identified and classified in different segments, and a comprehensive evaluation index system was constructed. The entropy method, coupled coordination model, grey correlation degree model, bivariate spatial autocorrelation model and other analysis methods were used. This paper measures the level of social, economic, ecological and synthetical development and the coordination degree among the three in different periods, and it analyzes the spatio-temporal correlation with population shrinkage. The obstacle degree model is used to analyze the main factors affecting the coordinated development under different population shrinkage levels. The results show that: (1) The number of counties with a shrinking population accounted for 57.04% from 2000 to 2010, showing a “Nearly half of the increase and half of the decrease” situation; from 2010 to 2020, the number of counties with population contraction type accounted for 99.3%, and the region entered a state of comprehensive contraction, and the contraction amplitude increased significantly. (2) From 2000 to 2010, the degree of population shrinkage was negatively correlated with the level of social, economic, synthetical and coordinated development but positively correlated with the level of ecological development. From 2010 to 2020, the degree of population shrinkage was still negatively correlated with the level of social, economic, synthetical and coordinated development, but it is not significantly correlated with the level of ecological development. During the study period, the correlation between population shrinkage and social development level was strong, while that between population shrinkage and ecological development level was weak. (3) During the study period, the social and economic system factors were the main obstacles in the process of coordinated development. From 2000 to 2010, the common important obstacle factors of the three types of population shrinkage level counties were the number of industrial enterprises above designated size, average night light index and gross regional product, and the common main obstacle factor was population density. From 2010 to 2020, the common important obstacle factors of the three types of population shrinking counties were the number of industrial enterprises above designated size and the per capita balance of loans from financial institutions at the end of the year, and the obstacle levels of indicators in different types of population shrinking counties are significantly different.

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Spatial-temporal Evolution and Influencing Factors of Digital Financial Inclusion: County-level Evidence from China

Cited by 19 publications

References 24 publications

Digital financial inclusion and quality of economic growth

Digital financial inclusion and quality of economic growth

Geological Disaster Susceptibility Evaluation of a Random-Forest-Weighted Deterministic Coefficient Model

Multidimensional Spatiotemporal Correlation Effect of County-Scale Population Shrinkage: A Case Study of Northeast China

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