Factors influencing farmers’ adoption of eco-friendly fertilization technology in grain production: an integrated spatial–econometric analysis in China

2021 ◽  
pp. 127536
Author(s):  
Xiaoxing Qi ◽  
Fachao Liang ◽  
Wenhua Yuan ◽  
Tao Zhang ◽  
Jianchun Li
Keyword(s):  
Econometric Analysis ◽  
Grain Production ◽  
Spatial Econometric ◽  
Factors Influencing ◽  
Spatial Econometric Analysis

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.

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