Evaluating the Spatial Aggregation and Influencing Factors of Chinese Medicine Human Resources in China: A Spatial Econometric Approach

2021 ◽  
pp. 576-586
Author(s):  
Fang Xia ◽  
Jinping Liu ◽  
Yanyin Cui ◽  
Hongjuan Wen
Keyword(s):  
Chinese Medicine ◽  
Human Resources ◽  
Influencing Factors ◽  
Spatial Aggregation ◽  
Spatial Econometric ◽  
Econometric Approach

scholarly journals Evaluation and Influencing Factors of Industrial Pollution in Jilin Restricted Development Zone: A Spatial Econometric Analysis

2021 ◽  
Vol 13 (8) ◽  
pp. 4194
Author(s):  
Yanhua Guo ◽  
Lianjun Tong ◽  
Lin Mei
Keyword(s):  
Influencing Factors ◽  
Industrial Pollution ◽  
Production Capacity ◽  
Pollution Index ◽  
Green Technology ◽  
Econometric Analysis ◽  
Ecological Civilization ◽  
Entropy Weight ◽  
Spatial Econometric ◽  
Spatial Econometric Analysis

Winning the battle against pollution and strengthening ecological protection in all respects are vital for promoting green development and building a moderately prosperous ecological civilization in China. Using the entropy weight method, this paper establishes and evaluates a comprehensive industrial pollution index that contains and synthesizes six major industrial pollutants (wastewater, COD, waste gas, SO2, NOx, and solid waste) in the 2006–2015 period. Subsequently, this paper studies the spatiotemporal characteristics and influencing factors of industrial pollution via the Moran index and spatial econometric analysis. The empirical results indicate that (1) the temporal evolution of the industrial pollution index is characterized by an overall trend of first decreasing and then increasing. (2) The industrial pollution index of each county has certain geographical disparities and significant spatially polarized characteristics in 2006, 2009, 2012, and 2015. (3) The Moran test shows that there is a relatively significant spatial autocorrelation of the industrial pollution index among counties and that the geographical distribution of the industrial pollution index tends to show clustering. (4) Spatial regression models that incorporate spatial factors better explain the influencing factors of industrial pollution. The economic development level, technological progress, and industrialization are negatively correlated with industrial pollution, while population density and industrial production capacity are positively correlated. (5) Consequently, as relevant policy recommendations, this paper proposes that environmental cooperation linkage mechanisms, environmental protection credit systems, and green technology innovation systems should be established in different geographical locations to achieve the goals of green county construction and sustainable development.

The peer effect of training on farmers' pesticides application: a spatial econometric approach

2020 ◽  
Vol 12 (3) ◽  
pp. 481-505
Author(s):  
Li Zhou ◽  
Fan Zhang ◽  
Shudong Zhou ◽  
Calum G. Turvey
Keyword(s):  
Peer Effects ◽  
Spillover Effects ◽  
Technical Training ◽  
Pesticide Use ◽  
Spatial Econometric ◽  
Agricultural Technologies ◽  
Content Type ◽  
Spatial Econometric Models ◽  
Estimated Parameters ◽  
Econometric Approach

PurposeThe purpose of this paper is to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.Design/methodology/approachThis study uses survey data from 300 peanut growers in Zoucheng County, Shandong, China, in 2016 and employs spatial econometric models to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.FindingsThis paper reveals that important peer effects can be channeled through technical training and that these peer effects are sufficiently significant to encourage neighboring farmers to reduce the amount of pesticide use, to transform the structure of pesticide use, and to increase the usage amount of low-toxicity, low-residue pesticide use per hectare. The estimated parameters for the peer effects from technical training are significantly larger than those from technical training alone, which suggests that the technical training of neighboring farmers plays a greater role than technical training for farmers individually.Originality/valueThe research finds that technical training within smaller, localized, groups can induce previously unobservable spillover effects, and this provides a scientific, theoretical and empirical justification for agricultural technology extension that can lead to a rapid, effective transformation of applying new agricultural technologies in an environmentally sensitive and economically sustainable manner.

scholarly journals Comparative Changes of Influence Factors of Rural Residential Area Based on Spatial Econometric Regression Model: A Case Study of Lishan Township, Hubei Province, China

2018 ◽  
Vol 10 (10) ◽  
pp. 3403 ◽  
Author(s):  
Xuesong Zhang ◽  
Ju He ◽  
Zhen Deng ◽  
Jiyue Ma ◽  
Guangping Chen ◽  
...  
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Spatial Regression ◽  
Influence Factors ◽  
Water System ◽  
Decisive Role ◽  
Research Direction ◽  
Hubei Province ◽  
Residential Areas ◽  
Spatial Econometric

The influencing factors of rural residential areas have always been a key research direction in addressing rural problems in China. By introducing a spatial regression model combined with Kernel Density Estimation and Buffer Analysis, this study made a comparative study on the quantification of the influencing factors of rural residential areas in 2009, 2012, and 2015 in Lishan Township, Hubei Province, China. The results showed that the elevation and slope of Lishan Township have always played a decisive role in the distribution of rural residential areas, that the influence of the water system is abnormal, and that the influence of roads and townships has been strengthened based on the spatial statistical analysis. Then, based on spatial econometric regression analysis, the coefficients of “Topographic indices” (CTI) were 0.666, 0.719, and 0.439 in 2009, 2012, and 2015, respectively. The coefficients of Road (CR) were 0.170, 0.112, and 0.108, respectively. The coefficients of Town (CT) were 0.120, 0.127, and 0.166, respectively. The coefficients of Water system (CWS) were 0.166, 0.124, and 0.173, respectively. With the change of time, the influence of road decreased and the influence of town increased gradually. Furthermore, the influence of the water system and topography showed volatility.

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