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Geography of Talent in China During 2000-2015:An eigenvector spatial filtering Negative Binomial Approach

Chinese Geographical Science 2021年第2期31卷 297-312页

作者： GU Hengyu Francisco ROWE LIU Ye SHEN Tiyan School of Government Peking UniversityBeijing 100871China Geographic Data Science Lab Department of Geography and PlanningUniversity of LiverpoolLiverpool L69United Kingdom School of Geography and Planning Sun Yat-sen UniversityGuangzhou 510275China Guangdong Key Laboratory for Urbanization and Geo-Simulation Sun Yat-sen UniversityGuangzhou 510275China

The increase in China’s skilled labor force has drawn much attention from policymakers,national and international firms and *** how educated talent locates and re-locates across the country can guide future policy discussions of equality,firm localization and service *** studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in ***,few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing *** released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in *** four-year city-level data from national population censuses and 1%population sample surveys conducted every five years between 2000 and 2015,we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over *** reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and *** also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of *** eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent *** indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate,on the distribution of *** results highlight that urban economic-and amenity-related factors have simultaneously driven China’s talent’s settlement patterns over the first fifteen years of the 21st century.

关键词： talent distribution determinants eigenvector spatial filtering panel data analysis China

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1995-2015年中国老年人口迁移的空间格局与影响因素

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Journal of Geographical Sciences 2022年第12期32卷 2541-2559页

作者：刘晔黄翠盈武荣伟潘泽瀚古恒宇 School of Geography and Planning Sun Yat-sen UniversityGuangzhou 510275China Guangdong Key Laboratory for Urbanization and Geo-simulation Sun Yat-sen UniversityGuangzhou 510275China Guangdong Provincial Engineering Research Center for Public Security and Disaster Guangzhou 510275China Population Development and Policy Research Center Chongqing Technology and Business UniversityChongqing 400067China School of Social Development and Public Policy Fudan UniversityShanghai 200433China Department of Geography and Resource Management The Chinese University of Hong KongHong Kong SARChina

Although China was one of the countries with the fastest-growing aging population in the world,limited scholarly attention has been paid to migration among older adults in *** full picture of their migration in the entire country over time remains *** study examines the spatial patterns of older interprovincial migration flows and their drivers in China over the period 1995 to 2015,using four waves of census data and intercensal population sample survey *** from eigenvector spatial filtering negative binomial regressions indicate that older adults tend to migrate away from low cost-of-living rural areas to high cost-of-living urban and rural areas,moving away from areas with extreme temperature *** location of their grandchildren is among the most important *** findings suggest that family-oriented migration is more common than amenity-led migration among retired Chinese older adults,and the cost-of-living is an indicator of economic opportunities for adult children and the quality of senior care services.

关键词： interprovincial migration older adults eigenvector spatial filtering negative binomial regression models China

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邻里效应对中国自然保护区家庭参与生态系统服务付费项目的影响--方法研究

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Journal of Geographical Sciences 2021年第6期31卷 899-922页

作者：张慧洁安力里查德·比尔斯堡全庸完杨爽戴捷 Department of Geography San Diego State UniversitySan DiegoCAUSA Department of Geography University of CaliforniaSanta BarbaraCAUSA Center for Complex Human-Environment Systems San Diego State UniversitySan DiegoCAUSA Departments of Biostatistics and Geography and Carolina Population Center University of North Carolina at Chapel HillUSA School of Economic Political and Policy SciencesUniversity of Texas at DallasRichardsonTXUSA

Payments for Ecosystem Services(PES)programs have been implemented in both developing and developed countries to conserve ecosystems and the vital services they *** programs also often seek to maintain or improve the economic wellbeing of the populations living in the corresponding(usually rural)*** studies suggest that PES policy design,presence or absence of concurrent PES programs,and a variety of socioeconomic and demographic factors can influence decisions of households to participate or not in the PES ***,neighborhood impacts on household participation in PES have rarely been *** study explores potential neighborhood effects on villagers'enrollment in the Grain-to-Green Program(GTGP),one of the largest PES programs in the world,using data from China's Fanjingshan National Nature *** utilize a fixed effects logistic regression model in combination with the eigenvector spatial filtering(ESF)method to explore whether neighborhood size affects household enrollment in *** comparing the results with and without ESF,we find that the ESF method can help account for spatial autocorrelation properly and reveal neighborhood impacts that are otherwise hidden,including the effects of area of forest enrolled in a concurrent PES program,gender and household *** method can thus uncover mechanisms previously undetected due to not taking into account neighborhood impacts and thus provides an additional way to account for neighborhood impacts in PES programs and other studies.

关键词： neighborhood impacts payments for ecosystem services Grain-to-Green Program eigenvector spatial filtering fixed effects logistic regression model China

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