scholarly journals Exploring the Spatial Determinants of Rural Poverty in the Interprovincial Border Areas of the Loess Plateau in China: A Village-Level Analysis Using Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 345
Author(s):  
Tao Li ◽  
Xiaoshu Cao ◽  
Menglong Qiu ◽  
Yu Li
Keyword(s):  
Water Resource ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Poverty Reduction ◽  
Rural Poverty ◽  
Weighted Regression ◽  
County Level ◽  
The Loess Plateau ◽  
The Impact

The spatial pattern of rural poverty and its influencing factors are unique in regions located in the “double zone”, overlaying the Loess Plateau landform and interprovincial border socioeconomic zone. Using Huining County, located in the interprovincial border area of the Loess Plateau, as a case study, this paper examines the spatial heterogeneity of rural poverty patterns and poverty-causing factors by using geographically weighted regression (GWR) modeling. The potential accessibility indicator is employed to identify the formative mechanism of rural poverty. The results show that rural poverty is significantly correlated with county-level accessibility, water resource accessibility, and town-level accessibility. County-level accessibility and town-level accessibility have significant border effects on rural poverty. The arid characteristics in certain areas of the Loess Plateau mean that the impact of water resource accessibility on the incidence of rural poverty is second only to that of county-level accessibility. Forestland resources have a positive correlation with the incidence of rural poverty in the region dominated by farming. Finally, targeted poverty reduction policies are proposed based on the results of the analysis of poverty-causing factors. The findings derived from this paper can help other developing countries in designing their own poverty reduction policies.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

scholarly journals Pengelolaan Dana Desa di Kecamatan Balong Kabupaten Ponorogo

2019 ◽  
Vol 7 (1) ◽  
pp. 63
Author(s):  
Maretha Berlianantiya Muhammad Ridwan Eka Wardani
Keyword(s):  
Rural Communities ◽  
Rural Areas ◽  
Urban Areas ◽  
Poverty Reduction ◽  
Agricultural Sector ◽  
Rural Poverty ◽  
Infrastructure Development ◽  
The Impact ◽  
The Village

<p><em>Poverty often occurs in rural areas rather than urban areas, low education which results in low quality of human resources and lack of access is often the cause of rural poverty. In addition, most of the economies of rural communities rely solely on the traditional agricultural sector. Various poverty reduction policies have been implemented, including village fund policies. This study aims to examine the management of village funds in the Balong sub-district of Madiun Regency with a case study in the villages of Tatung and Karangmojo villages covering the management of village funds in Tatung village and Karangmojo village. Balong Subdistrict and the impact of empowerment in the villages of Tatung and Karangmojo, Balong District. This research was conducted in Balong Subdistrict, Ponorogo Regency with a Case study in Tatung Village and Karangmojo Village with qualitative methods. In the village of Tatung village funds are managed as tourist villages with a focus on Paragliding tourist rides. Whereas in Karangmojo village it is used for Bumdes in the form of Lovebird birds, providing Gapoktan assistance, and infrastructure development.</em></p>

scholarly journals Driving Factors of Land Change in China’s Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications

2020 ◽  
Vol 12 (3) ◽  
pp. 453 ◽  
Author(s):  
Yanjiao Ren ◽  
Yihe Lü ◽  
Bojie Fu ◽  
Alexis Comber ◽  
Ting Li ◽  
...  
Keyword(s):  
Land Use ◽  
Ecological Restoration ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Well Being ◽  
Weighted Regression ◽  
Dominant Factor ◽  
Land Change ◽  
Negatively Associated ◽  
The Impact

Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement).

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