Socio-Economic Impact Mechanism of Ecosystem
Services Value, a PCA-GWR Approach

ZhenYa, Zhu; Li, Binbin; Zhao, Yongjun; Zhao, Zhongwei; Chen, Lihua

doi:10.15244/pjoes/120774

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Here, the size and direction of the regression coefficients of the geographically weighted regression (GWR) model are mainly used to diagnose the influencing factors. Compared with the traditional OLS model, the GWR model considers spatial factors, and its results can better reflect local characteristics [ 37 ]. In addition, the test results show that regression models involving all the above variable combinations will lead to severe collinearity problems, and the estimated results may lose their explanatory significance [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

Zhang

2022

Foods

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The stability of wheat production is closely related to national food security and agricultural sustainable development, and it has been a major policy concern for China. By analyzing the spatiotemporal factors and causes of wheat production, we can grasp the spatiotemporal distribution law of wheat production to rationally allocate agricultural resources. To this end, this study first conducted a quantitative analysis of the yield differentiation patterns in Huang-Huai-Hai (HHH) wheat based on the 2010–2020 wheat agricultural data, comprehensively using the Theil index and exploratory spatial data analysis. Second, to eliminate the spatial heterogeneity and multicollinearity of the modeling variables, a local model of SCA-GWR combining Spearman correlation analysis (SCA) and geographically weighted regression (GWR) was established. Compared with the traditional global regression model, the superiority and applicability of the SCA-GWR model are proved, and it is a simple and effective new method to detect spatial data nonstationarity. Finally, the factors influencing wheat production in the HHH region were detected based on the SCA-GWR local model, and relevant policy recommendations were put forward. The results show that: (1) The yield difference in different farming areas gradually narrowed, and the wheat production had a significant High-High aggregation trend. The center of gravity for wheat production lies in the southwest of the HHH region. (2) Wheat production still has a strong dependence on irrigation and fertilizer. Effective irrigated areas and temperature are the main driving forces for its production. The inhibitory effect of the proportion of nonagricultural employment on wheat production gradually weakened. Radiation and rainfall were only significantly positively correlated with wheat production in the central and southern HHH region. In response to the findings of the study, corresponding policy recommendations are made in terms of optimizing the allocation of resources, increasing investment in agricultural infrastructure, and vigorously researching and developing agricultural science and technology, and the results of the study can provide a basis for decision-making and management by relevant departments.

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Section: Methodsmentioning

confidence: 99%

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

Zhang

2022

Foods

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“…These principal components (PCs) retain most of the information of the original variables, which are usually expressed as the linear combination of the original variables. The relationship between principal component

and original variable

is as follows [ 42 , 53 , 54 ]:

…”

Section: Methodsmentioning

confidence: 99%

“…Prior to PCA, the Kaiser-Meyer-Olikin (KMO) and Bartlett sphericity tests was used to determine whether the commonality of the variables was high. If KMO > 0.5 and

< 0.01, the variables are suitable for PCA [ 54 ].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Wang

et al. 2022

IJERPH

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Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial–temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014–2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

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“…There are significant differences in urbanization development and ecological construction in different counties, meaning that the impacts of urbanization and ecological construction indicators on the GEP are spatial-temporal heterogeneous in different counties and in different development periods. To further improve and reflect the differences in the impact mechanism in more detail [53], we employed the GWR models. In this study, GWR describes the impacts of urbanization and ecological construction on the GEP and reflects the spatial heterogeneity and the direction of the impact through the regression coefficients within each unit [20].…”

Section: ) Geographically Weighted Regression (Gwr) Modelmentioning

confidence: 99%

Spatial–Temporal Variations of the Gross Ecosystem Product under the Influence of the Spatial Spillover Effect of Urbanization and Ecological Construction in the Yangtze River Delta Region of China

Ji,

Qi,

Jiang

et al. 2024

Land

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Rapid development of urbanization and intense human activities had a profound influence on the ecosystem service functions. As an integrated monetary index for the evaluation of final ecosystem services, the gross ecosystem product (GEP) is widely used in the quantification of ecosystem service value (ESV). This study initially assessed and analyzed the spatial distribution of the GEP at the county-level scale using multisource data spanning 2000, 2005, 2010, 2015, and 2020. Then, the spatial transfer characteristics of the GEP were measured. Finally, the study employed spatial panel econometric models and the geographically weighted regression (GWR) model to investigate the spatial effect of urbanization and ecological construction on the GEP. The results indicated that: (1) In 2020, the GEP in the Yangtze River Delta Region was RMB 15.24 trillion, and the GEP per unit area was RMB 42.58 million per square kilometer. It exhibited a cumulative decrease of RMB 298.72 billion from 2000 to 2020. (2) The spatial transfer efficiency of the GEP in urban agglomerations showed a clear decline trend. During the period of 2000–2020, over 96% of county-level units exhibited a decline with RMB 90,076,103.17/km2, indicating a consistent downward trend from the central regions towards the periphery. (3) Based on the decomposition effects of the spatial Durbin mode, urbanization and the ecological construction indicator showed spatial spillover effects on the GEP, but their impact mechanisms varied substantially. Among them, the urbanization rate (UR), population density (PD), and the proportion of impervious land (ILP) had the largest negative effect on the GEP, and a 1% rise in ILP locally resulted in a 0.044% decline in the local GEP and a 0.078% rise in the GEP of neighboring units. And the area of ecological land had a positive effect on the GEP of both local and neighboring areas. Those conclusions can offer evidence in favor of encouraging ecologically responsible building practices and sustainable growth in urban agglomerations.

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Socio-Economic Impact Mechanism of Ecosystem Services Value, a PCA-GWR Approach

Cited by 9 publications

References 29 publications

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Spatial–Temporal Variations of the Gross Ecosystem Product under the Influence of the Spatial Spillover Effect of Urbanization and Ecological Construction in the Yangtze River Delta Region of China

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