Search Results (6)

The coordination between poverty alleviation and ecological protection is both a crucial requirement and a long-standing challenge for sustainable development. China’s implementation of a targeted poverty alleviation strategy has completed the task of eliminating extreme poverty. However, the evaluation of the corresponding ecosystem changes in the entire poverty-alleviated areas is still insufficient. This study investigated the spatiotemporal changes in ecosystem vulnerability across China’s 832 national poverty-stricken counties from 2005 to 2020. A habitat–structure–function framework was applied to develop an evaluation index, along with a factor analysis of environmental and socio-economic indicators conducted through the Geodetector model. Finally, the implications of China’s practices to balance poverty alleviation and ecological protection were explored. The results show that ecosystem vulnerability decreased from 2005 to 2020, with an even greater decrease observed after 2013, which was twice the amount of the decrease seen before 2013. The post-2013 changes were mainly brought about by the enhancement of the ecosystem function in critical zones such as the Qinghai–Tibet Plateau Ecoregion, Yangtze River and Sichuan–Yunnan Key Ecoregion, and Yellow River Key Ecoregion. From 2013 to 2020, the influence of the gross domestic product (GDP) surpassed that of other factors, playing a significant positive role in diminishing ecosystem vulnerability in the three regions mentioned. The results suggest that China’s poverty-alleviated areas have found a “win–win” solution for poverty alleviation and ecological protection, that is, they have built a synergistic mechanism that combines government financial support with strict protection policies (e.g., more ecological compensation, eco-jobs, and ecological public welfare positions for poor areas or the poor). These findings elucidate the mechanisms behind China’s targeted poverty alleviation outcomes and their ecological implications, establishing a practical framework for coordinated development and environmental stewardship in comparable regions. Full article

Based on the quasi-natural experiment established in China’s national key ecological function areas, this paper takes 102 counties in Hebei Province, China, from 2014 to 2018 as the research object. It uses propensity score matching and difference-in-difference methods to investigate the impact of policy implementation on farmers’ income levels and constructs a mechanism using the air quality index to examine the environmental effect. The results show that when the time and regional fixed effects are not considered, the income level of farmers in the county increased by 3.11% due to the influence of the transfer payment policy, and the policy treatment effect grew over time. Among the control variables, the degree of industrialization and agriculturalization, urbanization rate and government financial scale were all positively related to farmers’ income. Controlling the fixed effects of region and year, the impact of policy on the improvement of farmers’ income was weakened, and the regression coefficient changed from 0.2211 to 0.0366, a drop of 83.45%. This suggests that the policy is greatly affected by the city where farmers live. The “environmental effect” test results showed that transfer payments could increase the income level of farmers in counties affected by the policy. The mechanism is that the priority measure of the ecological compensation policy is to improve the ecological environment, which is conducive to improving local environmental governance and environmental productivity and increasing crop yields, and thus increasing farmers’ incomes. Because the regions where the policy is implemented overlap with highly poverty-stricken areas, it is necessary for the central government to improve transfer payment standards and enrich their content to protect people’s livelihood while promoting ecological protection. As a result, local governments will be encouraged to act ecologically, vigorously develop local ecological industries, and promote the internalization of positive externalities in ecological environmental services, further improving the level of agricultural modernization and ecological sustainability and improving the income levels of farmers and their quality of life. Full article

Accurately identifying the degree of poverty and poverty-causing factors of poverty-stricken farmer households is the first key step to alleviating absolute and relative poverty. This paper introduces a multiobjective spatio-temporal evolution analysis method to examine poverty reduction of poverty-stricken farmer households under different development goals. A G-TOPSIS model was constructed to evaluate poverty-stricken households under short-, medium-, and long-term development goals. Then, GIS analysis methods were employed to reveal the spatio-temporal distribution of poverty-stricken households, and poverty causing factors were detected using the obstacle degree model. Taking Fugong County in Yunnan Province, China, as an example, the empirical results show that: (1) Great progress has been made in poverty reduction during the study period; however, some farmer households which have escaped absolute poverty are still in relative poverty and are still highly vulnerable. (2) Farmers with higher achievement rates under three different development goals are mainly distributed in the central and northern regions of study area, with a pattern of high–high agglomeration under the medium and low development goals, while low–low agglomeration mostly appears in central-southern regions. (3) Under the short-term development goals, the main poverty-causing factors are per capita net income, safe housing, sanitary toilets, years of education of labor force and family health. Under the medium- and long-term goals, per capita net income, labor force education and safe housing are the development limitations. (4) Infrastructure and public service are crucial to ending absolute poverty, and the endogenous force of regional development should be applied to alleviate the relative poverty through sustainable development industries and high-quality national education. Full article

Eliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this paper, a quantitative evaluation model is built and applied to all poverty-stricken counties in Hunan Province. First, based on the SDG global index framework and local index system of China, a local SDG index system for poverty-related goals is designed, and the weights of the indexes are derived using an entropy method. The scores obtained for counties and districts with data available are then taken as the true value for the poverty assessment. Second, using National Polar-orbiting Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light images and land use and digital elevation model data, six factors, including socioeconomic, land cover, terrain and traffic factors, are extracted. Third, we then construct multiple linear evaluation models of poverty targets defined by the SDGs and machine learning evaluation models, including regression trees, support vector machines, Gaussian process regressions and ensemble trees. Last, combined with statistical data of poverty-stricken counties in Hunan Province, model validation and accuracy evaluation are carried out. The results show that the R² and relative error of the localized, multiple linear evaluation model, including all six factors, are 0.76 and 19.12%, respectively. The poverty-stricken counties in Hunan Province were spatially aggregated and distributed mainly in the southeastern and northwestern regions. The proposed method for regional poverty assessment based on multisource geographic data provides an effective poverty monitoring reference scheme for the implementation of the poverty eradication goals in the 2030 agenda. Full article

The implementation of technology training is essential to promote the commercialization of research achievements, and plays a crucial role in poverty alleviation in China. Based on the microcosmic survey data of farmers in four poverty-stricken counties officially assisted by National Forestry and Grassland Administration, the effects of technology training on forest-related income of rural poverty-stricken households is analyzed by using Propensity Score Matching (PSM) method. The study found that after eliminating the deviation from the self-selection and the endogenous issues, the forestry technology training has increased the total forest-related family income and forestry production and operation income by 3.09 times and 2.82 times, respectively. The effect of technology training on income increase is remarkable. Besides, the behavior of poor farmers participating in forestry technology training is significantly affected by the following factors, such as gender, age, family size, managed forestland area, whether they held forest tenure/equity certificate, whether they joined forestry professional cooperatives, and whether they cooperated with forestry enterprises. In order to further improve the effect of technology in poverty alleviation, the following policy recommendations are proposed, including: (1) to encourage poverty-stricken households to actively participate in forestry technology training; (2) to establish a diversified system of forestry technology training; and (3) to ensure the training content is based on the actual needs of the poor. Full article

The rural settlements in poverty-stricken mountainous areas are the "living fossils" of an economic society with the characteristics of spatial dispersion and are slowly changing. Spatial agglomeration is the development direction of rural settlements. In-depth exploration of the spatial agglomeration characteristics and influencing factors of rural settlements in poverty-stricken mountainous areas is a way to provide a basis for rural settlement restructuring. We selected Pengshui County, a national poverty-stricken county in the southwestern mountainous area of China, as the research area. Spatial buffer and kernel density analysis were used to analyze the agglomeration characteristics of rural settlements and influencing factors. The results show that: (1) The rural settlements are small in scale and the space is evenly dispersed. 55.63% of the rural settlements’ sizes are less than 1000 m², 84.15% of the rural settlements’ sizes are less than 2500 m², and 92.81% of the rural settlements are within 200 m. (2) The elevation and slope of topographic factors have a significant agglomeration effect on rural settlements. However, the slope direction has no agglomeration effect. 85.41% of rural settlements (52.75% of rural settlements are gathered between 400 and 800 m above sea level) are gathered at an altitude of 1000 m or less, and 77.59% of rural settlements are gathered with a slope of 6~25°. Additionally, there are few rural settlements with a slope of 0~2°. Moreover, the distribution of residential areas has no agglomeration effect on rural settlements. (3) The cultivated land exerts the most significant effect on rural settlements followed by roads and water sources, while the role of urban land is weak. 99.48% of rural settlements are concentrated in the 100 m area of cultivated land. Therefore, in the poverty-stricken mountainous areas in the southwestern mountainous areas of China, convenient farming is the primary condition for production and living. Rural settlements are highly correlated with cultivated land. Rural settlements are scattered and concentrated with the scattered cultivated land. The rural settlements were leaded by the distribution of cultivated land. Less high-quality cultivated land with less slope were occupied or not by rural residential areas’ people. Full article