Assessing progress toward China's subnational sustainable development by Region Sustainable Development Index

Wang, Chengyuan; Wang, Ling; Zhai, Junjie; Feng, Tiantian; Lei, Yuzhou; Li, Shanfu; Liu, Yuan; Liu, Yuwen; Hu, Zhibo; Zhu, Kun; Chang, Yupeng; Cui, Shuai; Mao, Xuegang

doi:10.1016/j.horiz.2024.100099

Cited by 1 publication

(1 citation statement)

References 70 publications

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“…(i) Conducting the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test to assess the suitability of the PCA model. (ii) Calculating the eigenvalues of the correlation matrix and the variance contribution ratio of each indicator, and determining the number of principal components K with the criterion that the eigenvalue is greater than 1 and the cumulative variance contribution ratio is greater than 80% [57]. (iii) Deriving linear combination coefficients using the factor loading values of the indicator correlation matrix (Equation ( 4)).…”

Section: Farmland Productivity Assessment Modelmentioning

confidence: 99%

Raster Scale Farmland Productivity Assessment with Multi-Source Data Fusion—A Case of Typical Black Soil Region in Northeast China

Liu,

Wang,

Wang

et al. 2024

Remote Sensing

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Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources.

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Section: Farmland Productivity Assessment Modelmentioning

confidence: 99%