The  effects of terrain factors on the drainage area threshold: comparison of principal component analysis and correlation analysis

Liu, Jinhong; Luo, Mingliang; Chen, Zhongsheng; Gou, Jiaojiao; Yan, Zihong

doi:10.1007/s10661-022-09843-7

Cited by 2 publications

(1 citation statement)

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“…To avoid the effect of multicollinearity among variables, factor analysis is used to extract common factors for regression analysis of the seven variables [67]. First, after dimensionless processing of the raw data, the KMO and Bartlett's tests are performed on the seven indicators using SPSS 20.0 software, and the results showed that the value of KMO was 0.735 (a KMO value > 0.7 indicates suitability for factor analysis) and the p-value was 0.000 (p < 0.05 indicates the existence of a correlation between indicators) [68], indicating the existence of a correlation between indicators and suitability for factor analysis.…”

Section: Stirpat Model Processmentioning

confidence: 99%

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Fan

Chen

2022

IJERPH

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Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu’s ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The “H-H” agglomeration is mainly concentrated in Lianyungang and Suqian, while the “L-L” agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, −0.127%, −0.116%, 0.192%, −0.110%, −0.114%, and −0.123% in Jiangsu’s ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

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Section: Stirpat Model Processmentioning

confidence: 99%