Study On Identification And Cooperative Governance Of Relative Poverty In The Area Surrounding Beijing And Tianjin

2020 is the final year for China to win the fight against poverty in an all-round way,and 832 poor counties are all out of poverty,laying a solid foundation for building a moderately prosperous society in an all-round way.With the elimination of absolute poverty,it means that China has entered a new era of relative poverty governance.The Fourth Plenary Session of the 19 th Central Committee of the Communist Party of China proposed,"Consolidate the achievements of poverty alleviation and establish a long-term mechanism for alleviating relative poverty." This marks the focus of China’s poverty alleviation work after 2020 from eliminating absolute poverty to alleviating relative poverty.The report of the 19 th National Congress of the Communist Party pointed out that "the main contradiction in our society has been transformed into the contradiction between the people’s ever-growing needs for a better life and the unbalanced and inadequate development",and this unbalanced and inadequate development is also reflected in the relative regional poverty.During the "13th Five-Year Plan" period,China fully implemented coordinated regional development,and the relative poverty of the region has become a key factor restricting the coordinated development of the region.In this context,on the basis of summarizing and analyzing relevant research results at home and abroad,according to the theories of regional relative poverty,spatial poverty,sustainable development and collaborative governance,this thesis takes the region of Hebei Province around Beijing and Tianjin as the study area,and systematically studies relative poverty identification and collaborative governance issues.From a multi-dimensional perspective,the relative poverty index calculation model is constructed,and the relative poverty index of 71 counties in Hebei province around Beijing and Tianjin is calculated.According to the calculation results,the relative poverty in this area is identified,and its spatiotemporal characteristics are analyzed.The factors that cause relative poverty in the study area are analysed and governance strategies are proposed.Finally,the multi-dimensional relative poverty index estimation model of the study area is constructed to monitor the relative poverty at the township scale.The SFIC model is used to analyze the coordinated management of relative poverty at the township scale and the policy implications are also given.The specific contents are as follows:(1)Based on the concepts of relative poverty and multidimensional poverty,the multidimensional poverty index MPI combined with "two no worries,three guarantees",and the multidimensional relative poverty calculation model was constructed by using FAHP and entropy weight method.The multidimensional relative poverty index of 71 counties in the Beijing-Tianjin region from 2012 to 2019 was calculated,and the types of relative poverty were divided.This thesis takes 71 counties under the jurisdiction of Hebei province around Beijing and Tianjin as the study area,refers to the multidimensional poverty index MPI,combines China’s national conditions,and takes " free from worries over food and clothing and have access to compulsory education,basic medical services and safe housing " as the basis.Thirteen indicators are selected from five dimensions: economic development,quality of life,education level,basic medical care and social security.The 13 indicators are as follows:disposable income of rural residents,public finance income,employment,food production,tap water benefit village rate,rate of mobile phone users,road network density,teacher education level,level of education,number of health care beds,proportion of medical technicians,basic endowment insurance and basic medical insurance.The multi-dimensional relative poverty index calculation model of 71 counties around Beijing and Tianjin from 2012 to 2019 is constructed by using the fuzzy analytic hierarchy process(FAHP)and the entropy weight method.The multidimensional relative poverty index of 71 counties in the area surrounding Beijing and Tianjin from 2012 to 2019 was calculated.Taking the median of 60% of each dimension as the criterion,the county-level relative poverty in the area surrounding Beijing and Tianjin from 2012 to 2019 is divided into four categories: non-relative poverty,mild relative poverty,moderate relative poverty and severe relative poverty.(2)Based on the theory of regional relative poverty and spatial poverty,with the help of spatiotemporal data analysis method,this thesis analyzes the space-time evolution of county-level relative poverty in the Beijing-Tianjin region,and explores the spatial correlation characteristics of county-level relative poverty using spatial autocorrelation analysis.According to the identification results of county-level relative poverty in the area surrounding Beijing and Tianjin,using exploratory spatial data analysis methods,the spatiotemporal evolution of county-level relative poverty in this region is analyzed.Comparing the identification results of relative poverty from 2012 to 2019 with the list of counties in the poverty-stricken belt around Beijing and Tianjin,it is found that there are some differences between the relative poverty counties identified by the multi-dimensional standard and the absolute poverty counties determined by the income standard.Spatial correlation analysis is used to analyze the spatial correlation of relative poverty at the county level in the area surrounding Beijing and Tianjin.The results of the global autocorrelation analysis show that there is a significant global autocorrelation between the relative poverty at the county level in the area surrounding Beijing and Tianjin,and they show spatial aggregation characteristics.The results of local autocorrelation analysis show that the relative poverty at the county level in the area surrounding Beijing and Tianjin from 2012 to2019 shows the clustering characteristics of "high-high" and "low-low".Among them,the "high-high" agglomeration types are mainly concentrated in Langfang City and Tangshan City,and the "low-low" agglomeration types are mainly concentrated in Baoding City and Zhangjiakou City.(3)By using the index contribution,geographical detector and PGTWR model,the factors causing relative poverty at the county level in the Beijing-Tianjin region are analyzed,and the governance strategies for relative poverty are given.According to the identification results of relative poverty in the area surrounding Beijing and Tianjin,this thesis summarizes the poverty-causing factors of relative poverty in this region from 2012 to 2019 using the index-contribution analysis method.The poverty-causing factors are employment,disposable income of rural residents,rate of mobile phone users,teacher education level,level of education,number of beds in health institutions,basic endowment insurance and basic medical insurance.In Dachang county,Mengcun county and Wangdu county,road network density is also a poverty-causing factor.Secondly,using geographic detectors to explore the impact of six environmental-topographic factors including fertilizer use,pesticide use,terrain relief,annual precipitation,annual average temperature,and total annual PM2.5concentration on relative poverty.The factor detection results show that terrain relief and annual average temperature have a significant impact on relative poverty,and have a greater explanatory power for relative poverty.The interaction detector results show that the factor influence by the interaction of any two factors show bilinear or nonlinear enhancement.This indicates that the level and spatial distribution of relative poverty at the county level are influenced by environmental-topographical factors.Finally,using the panel space-time geographic weighted regression model based on holographic mapping explores the impact of industrial development on relative poverty at the county level in the area surrounding Beijing and Tianjin.The results show that most of the relative poverty counties in the area surrounding Beijing and Tianjin are mainly affected by the development of primary and tertiary industries,and these counties are less affected by the per capita public financial expenditure.Haixing County,Kuancheng County,Mengcun County and Dachang County are mainly affected by the secondary industry and are greatly affected by the per capita public financial expenditure.Finally,based on the analysis of the above,governance strategies for relative poverty in this region are proposed.(4)Based on the synergetic governance theory,a synergetic governance framework of relative poverty at the township level is proposed.The nighttime light data is used to monitor the relative poverty at the township level,and the SFIC model is used to analyze the collaborative governance,and the policy implications are given.By calibration of the NPP/VIIRS nighttime light data from 2012 to 2020,combined with the multi-dimensional relative poverty index in the region around Beijing-Tianjin from 2012 to 2019,the multi-dimensional relative poverty index estimation model in this region is constructed and passes the error test.Then,based on the model,the relative poverty monitoring at the township scale is realized based on night light data,which provides a feasible method for large-scope relative poverty monitoring at the township scale.Finally,selecting Q town in D county in the area surrounding Beijing and Tianjin as the case target,using the SFIC model to analyze the coordinated governance of relative poverty in Q town,and summed up policy implications.