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Farmers are the major participants in rural development process and their willingness to settle in urban areas directly affects the implementation of rural revitalization strategy. Based on Ostrom’s institutional analysis and development (IAD) framework, we analyzed farmers’ willingness to settle in urban areas and its influencing factors by binary Logistic regression and cluster analysis of survey data of 190 rural households in Sihe village of Gansu Province of China. The results show that: (1) In Sihe village, farmers’ willingness to settle in urban areas was low in general and influenced by their neighbors’ decisions or behaviors. Households willing and unwilling to migrate to urban areas both presented significant spatial agglomeration. (2) The factors influencing farmers’ willingness to settle in urban areas were analyzed from six aspects: individual characteristics, family characteristics, residence characteristics, cognitive characteristics, institutions, and constraints. The main influencing factors were found to be age, occupation, number of non-agricultural workers in the family, household cultivated land area, annual household income, house building materials, degree of satisfaction with social pension, homestead and contracted land subsidies, income constraints, and other constraints. (3) Individual heterogeneity and difference in economic basis determined the difference in farmers’ willingness to settle in urban areas. Institutions and constraints played different roles in the migration willingness of different groups of farmers (Note: More details on the sample as well as further interpretation and discussion of the surveys are available in the associated research article (“Village-Scale Livelihood Change and the Response of Rural Settlement Land Use: Sihe Village of Tongwei County in Mid-Gansu Loess Hilly Region as an Example” (Ma, L.B.; Liu, S.C.; Niu, Y.W.; Chen, M.M., 2018)).

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Rural poverty has received extensive attention worldwide. Eliminating poverty and achieving common prosperity are the major tasks for China to build a well-off society in an all-round way. Based on the evaluation results of quality of rural life (QRL) and relative poverty index (RPI), this paper identifies and classifies the poor objects using importance–Performance analysis (IPA) method and poverty degree model. The results were as following: (1) QRL has obvious regional differences, and its value gradually decreases from west to east, which is opposite to the spatial distribution pattern of RPI. In areas with high QRL value, the RPI is lower. (2) Fifty counties and districts are clustered in the second quadrant of IPA quadrant map, i.e., Low QRL-High RPI, and the lower quality-of-life corresponds to the higher degree of relative poverty. (3) The coincidence between the poverty-stricken counties and the poverty-stricken counties of the country identified as 84.48% by IPA method, which indicates that the accuracy of poverty delineation based on income is high. (4) Gansu Province is dominated by highly impoverished areas, accounting for 60% of the total number of impoverished counties. The results of comprehensive poverty classification are in line with the actual situation of impoverished counties. In counties with higher CPL, the poverty level is deeper. It is more difficult to get rid of poverty. This study can provide theoretical basis and decision-making reference for the formulation and implementation of Poverty Alleviation Policies in the late stage of underdeveloped areas in western China.

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Evaluating the quality of rural human (RH) settlements and clarifying its spatial differentiation characteristics have the most direct guiding role for the formulation of regional construction policies and the optimization of RH settlements. In this study, the index model of environmental quality of RH settlement was established. ImPACT and trade-off analysis methods were used to quantitatively identify the spatial differentiation of RH settlement and dominant impact human factors of it in Gansu Province in 2017. Then, the driving type of RH settlement environment was identified. Results are as follows. (1) The overall environmental quality of RH settlement in Gansu Province shows a decreasing trend from “west to east.” (2) The environmental quality of RH settlement is mainly affected by eight factors, of which the effect of quantity index per capita is more significant. (3) The trade-off relationship between the environmental quality index and these eight dominant factors is mainly low–low, which indicates that the human factors are the main reason for the environmental quality of the RH settlement. (4) Based on the dominant human factors, Gansu Province is classified into four driving types, of which the proportion of comprehensive type accounts for 77.01%.

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