Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling

Chen, Jing; Zhou, Chunshan; Wang, Shuanjin; Hu, Jincan

doi:10.1016/j.envpol.2018.05.083

Cited by 78 publications

(22 citation statements)

References 52 publications

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“…Therefore, geographically weighted regression (GWR) is one of the models commonly used to consider spatial variation in the local region. GWR has been widely used in crop or fruit yields estimation [45][46][47][48][49][50], industrial economy [50][51][52][53], animal and vegetation distributions [54][55][56][57][58][59][60], and environmental security [61][62][63][64]. Therefore, this study used GWR as another yield estimation model for paddy rice.…”

Section: Introductionmentioning

confidence: 99%

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Shiu

Chuang

2019

Remote Sensing

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Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.

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Section: Introductionmentioning

confidence: 99%

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Shiu

Chuang

2019

Remote Sensing

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“…Therefore, China must seize the opportunity to build an innovative country, enhance disruptive technological innovation, and improve the level of technological innovation. China can learn from the literature on developed countries and consider effective and advanced governance technology that can be absorbed and reformed and applied to China's PM 2.5 governance [34]. Talent is the basis of technological innovation; thus, it is possible to increase technological innovation personnel through incentive measures.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Effect of Energy Development and Technological Innovation on PM_2.5 in China: A Spatial Durbin Econometric Analysis

Xiao-hong

2018

Discrete Dynamics in Nature and Society

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Using the panel data of 29 provinces and regions in China, this paper analyzes the temporal and spatial characteristics of PM2.5 concentration. The spatial Durbin model is used to empirically examine the impact of energy development and technological innovation on PM2.5. The results show that the interprovincial PM2.5 in China showed an inverted “N” trend. There is a spatial agglomeration of interprovincial PM2.5, and the spatial autocorrelation is significant. The spatial autoregression coefficients of adjacent weights and distance weights are significantly positive, and PM2.5 pollution in adjacent areas has a positive effect on the region. From the view of energy development, the increase in energy intensity will significantly increase the concentration of PM2.5 in adjacent areas, which will increase the global PM2.5 concentration. The increase in the proportion of coal consumption increases the concentration of PM2.5 in the region and globally. The increase in energy consumption prices will reduce the PM2.5 concentration in the region and globally. From the view of technological innovation, the increase in sales revenue of new products of industrial enterprises above the scale will reduce the concentration of PM2.5 in the region by 0.1285 percentage points and the concentration of PM2.5 in adjacent areas by 0.3058 percentage points, which will reduce the global PM2.5 concentration by 0.4343 percentage points. The increase in the number of granted patent applications will reduce the PM2.5 concentration of the adjacent regions and globally. The increasing level of infrastructure for technological innovation in the region will reduce the concentration of PM2.5 in adjacent areas. The increase in technical market turnover will reduce the concentration of PM2.5 in the region and the concentration of PM2.5 in adjacent areas, which will reduce the global PM2.5 concentration. In order to reduce the concentration of PM2.5 in China, we should continue to promote PM2.5 regional collaborative governance. In terms of energy development, it is necessary to reduce energy intensity, optimize energy structure, implement energy substitution, and rationalize energy prices. In terms of technological innovation, it is essential to increase sales revenue of new products of industrial enterprises, raise number of granted patent applications, vigorously improve level of infrastructure for technological innovation, and enhance technical market turnover.

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“…Considering the differences in PM 2.5 pollution levels and the characteristics of agglomeration, governments should implement differentiated regional pollution control strategies [7]: Prevention and control require cooperation [12]. Studies have already established hierarchical policies based on differentiated socioeconomic development conditions and requirements across Chinese cities [13,14]. Chen et al [8] analyzed the socioeconomic factors of joint control using a geographically weighted regression method, establishing a strategy of joint control.…”

Section: Introductionmentioning

confidence: 99%

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

Zhao

et al. 2020

Sustainability

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China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

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Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling

Cited by 78 publications

References 52 publications

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Effect of Energy Development and Technological Innovation on PM_2.5 in China: A Spatial Durbin Econometric Analysis

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

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Identifying the socioeconomic determinants of population exposure to particulate matter (PM2.5) in China using geographically weighted regression modeling

Cited by 78 publications

References 52 publications

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Effect of Energy Development and Technological Innovation on PM2.5 in China: A Spatial Durbin Econometric Analysis

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

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Effect of Energy Development and Technological Innovation on PM_2.5 in China: A Spatial Durbin Econometric Analysis