In addition to human capital and creativity, cultural consumption opportunities play an important role in explaining local development and growth. They promote the attraction of visitors, as well as the attraction of the creative class, improving local income and wages. This paper analyses the relative importance of cultural consumption opportunities, as cultural scenes, explaining income differences among Spanish municipalities. Indices to measure talent, creative class, and different kinds of opportunities for cultural consumption at the local level are proposed, using multivariate regression analysis to show their complementary impact on local income. In addition to human capital and the creative class, the main results show that different kinds of opportunities for cultural consumption (cultural scenes) have an independent impact on local income.

人才是推动地区创新发展的核心动力,也是未来城市竞争的关键。基于第六次全国人口普查和2015年全国1%人口抽样调查数据,运用集聚度、基尼系数等空间统计分析以及负二项回归模型方法,对2010—2015年中国城市高学历人才的空间演化特征及驱动因素展开研究。结果发现：① 人才呈现出高度集中且不平衡的空间分布格局,但不平衡的趋势有所缓解,分布方向为“东北—西南”走向。人才分布的空间溢出效应显著,城市群是人才集聚的高地。② 经济机会是影响中国高学历人才集聚的主导力量,其中工资是核心驱动因素,地区的发展水平、产业结构同样产生显著影响。③ 控制经济机会的影响后,以教育和医疗等基础公共服务、交通可达性和城市消费设施为核心的地方品质变量在高学历人才的空间集聚过程中发挥重要作用。④ 城市群与非城市群人才驱动因素存在显著差异：经济机会是城市群和非城市群人才集聚的主要驱动力;教育、消费、交通以及自然环境等地方品质变量对城市群人才集聚的影响更为显著。本研究为城市和区域人才政策和区域发展政策的制定提供借鉴与参考。

Talents are the core driving force for regional innovation and development, and they are also the key to urban competition in the future. Based on the data of the sixth population census and the national 1% population sampling survey in 2015, this paper uses the concentration index (CI), Gini index (GI) and several related spatial analysis methods to examine the spatial patterns of highly educated talents across China and the drivers behind these patterns from 2010 to 2015. The results are as follows: (1) Talents show highly concentrated and unbalanced spatial distribution patterns at the city level over the five years, but the trend of concentration and imbalance has gradually eased. Results from standard deviation ellipse (SDE) indicate that the distribution direction of highly educated people is "NE-SW". Furthermore, there is a significant spatial spillover effect in the distribution of talents, with three major urban agglomerations of China as the highlands. (2) Economic opportunities are the dominant drivers for the distribution of highly educated talents in China. Among them, wages are the core driver, and gross GDP and industrial structure of each city also exert a significant impact. (3) After controlling the impacts of economic opportunities, local quality variables represented by basic public services (e.g., education and medical care), transportation accessibility and urban consumption facilities play an important role in the distribution of highly educated talents. (4) There are significant differences between the driving factors for talents in urban agglomerations and non-urban agglomerations: economic opportunities are the main driving force for the distribution of talents in both urban agglomerations and non-urban agglomerations, while local qualities including education, consumption, transportation and natural environment have a more significant impact on the distribution of talents in urban agglomerations. This study provides references for the formulation of urban and regional talent policies and regional development policies.

自户籍制度放宽以来,中国经历了大规模的人口迁移。其中,异质性劳动力（高技能劳动力、普通劳动力）在迁移过程中表征出不同的空间格局和网络组织特征,对地区经济发展产生各异的影响。本研究基于人口普查和抽样调查微观抽样数据,使用复杂网络理论对1995—2015年中国异质性劳动力迁移的时空格局和网络演化展开分析,并分析了格局背后的可能成因。研究发现：① 省际高技能和普通劳动力迁移均呈现出持续高度不平衡的空间集聚特征,承载大量人口的迁移流主要由中国中西部地区指向东部沿海地区,但这种空间不平衡特征呈现出一定的减弱趋势。高技能劳动力的平均迁移距离比普通劳动力更长;② 两类劳动力迁移网络中均呈现明显的“小世界”特性,但普通劳动力迁移网络的迁移强度和关联程度均高于高技能劳动力迁移网络;③ 两类劳动力迁移网络均呈现出以北京、上海、广东为主要核心节点的网络结构。高技能劳动力网络结构呈现相对稳定的特征,而普通劳动力网络结构则呈现出变化的趋势;④ 地区经济差异、路径依赖效应、异质性劳动力在就业市场上的差异性以及劳动力市场对异质性劳动力需求的差异是导致两类劳动力迁移格局差异性的重要成因。

With the loosing of the household registration (hukou) system, China has witnessed massive volumes of interregional migration. Among all kinds of migration, skilled migration and less-skilled migration present differential spatial patterns and network characteristics over time and may exert distinguished impacts on regional development and innovation. However, due to limited data availability, prior studies only adopted cross-sectional data analysis and examined the patterns of skilled and less-skilled migrations before 2005, whereas few have been done on related topics after 2005. Drawing on previous studies, the present paper uses micro-level datasets from population censuses and sample surveys to assess the spatial evolution patterns of China's skilled and less-skilled internal migrations between 1995 and 2015. Besides, several complex network analysis methods have been employed, together with the network visualization technique. The results of the paper are listed as follows. First, there were spatial clustering and uneven patterns in both skilled and less-skilled internal migrations over the 20 years of 1995-2015, where migration flows containing a large percentage of skilled and less-skilled migrants originated from inland western and central regions to the eastern coastal areas of China. However, the degree of spatial unevenness in the migration patterns has been slightly weakened. In addition, the average migration distance of skilled migration was farther than that of less-skilled migration in the same period. Second, both of the two migration networks have presented a “small-world” property over the 20 years. In comparison, the intensity and connection degree of less-skilled migration networks have prevailed over that of the skilled migration networks. Third, results from the community detection have revealed that both skilled and less-skilled migrations exhibited a network structure where Beijing, Shanghai, and Guangdong served as the core nodes. Skilled migration presented a more stable network structure during the 20 years, while less-skilled migrations showed a persistently changing pattern. Fourth, the paper suggested that regional economic disparity, path dependence effect, the heterogeneity of different labour forces in the job market, and the differences in labour market demand for different labour forces were underpinning factors shaping the spatial evolution pattern and network structure of the two types of migration from 1995 to 2015.

基于2000年、2010年中国人口普查分地级及以上行政区数据和2015年中国各省份1%人口抽样调查数据，采用变异系数、泰尔系数、基尼系数测度中国高学历人才比重分布的空间不均衡程度，并采用面板数据Tobit随机效应模型，识别影响高学历人才比重空间分布的主要因素。结果表明：① 中国的人才比重分布表现出极大的不均衡性，人才比重的高低与城市等级密切相关，主要表现在直辖市、省会城市、计划单列市等行政区吸引了大量人才，而普通地级城市人才比重相对较低；② 10 a间人才比重的变异系数、基尼指数和泰尔指数均有所下降，表明人才比重分布的空间不均衡程度有所下降；③ 平均工资水平、生活成本、城市等级、每万人高校在校学生数、每万人医院卫生院床位数、人均科学事业、教育事业经费与人才比重呈正相关，中学生师比、万人互联网用户数与人才比重呈负相关，失业率、单位面积二氧化硫排放量、绿地率对人才比重没有影响。

Based on the data from the 2000 and 2010 China censuses by prefecture level and above, and the 2015 1% population sample survey data of Chinese provinces, this article studies the spatial pattern evolution of the distribution of highly educated talents and its influence mechanism in China. The coefficient of variation, Theil coefficient and Gini coefficient are used to measure the spatial imbalance of the highly educated talents proportion. The panel data Tobit model is used to identify the main factors affecting the spatial distribution of the proportion of the highly educated talents. The results show that: 1) The distribution of the highly educated talents proportion in China shows a great imbalance, closely related to the city level, which is highlighted in administrative regions such as municipality, provincial capitals, and city specifically designated in the state plan, to which a large number of talents are attracted, while the talents proportion in ordinary prefecture-level cities is relatively low; 2) In the past 15 years, the coefficient of variation, Gini coefficient and Theil coefficient of the talents proportion have all declined, indicating that the spatial imbalance of the talents proportion has decreased; 3) Average wage level, cost of living, city hierarchy, students enrollment of regular institutions of higher education per 10 000 people, the number of hospital beds per 10 000 people, expenditure for education per capita, expenditure for science and technology per capita are positively correlated with talents proportion; The ratio of middle school students to teachers and the number of internet users per 10 000 people are negatively correlated with talents proportion; Unemployment rate, green rate, sulfur dioxide emissions per unit area have no significant impact on the talents proportion.

经济快速增长和社会转型促使中国人才迁移的地理景观正涌现出一些新特征和新趋势。利用中国2005年、2010年和2015年千分之一人口抽样调查微观数据,揭示2000—2005年、2005—2010年和2010—2015年全国高学历人才省际迁移格局的演化趋势,选用零膨胀负二项回归模型解释高学历人才省际迁移格局的演化机理。结果发现：① 中国高学历人才跨省流动性呈先增后减的演化趋势,人才省际迁移规模的空间分布开始趋于均衡化。② 东部发达省市人才净流入活跃,中部、东北和河北等地区人才净迁出活跃,2010年后发达地区的人才高强度净流入及欠发达地区的人才高强度净流失问题均开始缓解。③ 高学历人才由中西部流动至东部地区的主要迁移模式比较稳定,但东部向中西部地区的高学历人才迁移规模持续上升。④ 从影响因素的演化趋势上看,全国人才省际迁移主要为经济驱动型,但收入在人才迁移决策中的重要性开始减弱,产业转型、购房成本、教育服务舒适性及气候舒适性对人才跨省流动性的影响开始增强。⑤ 全国人才跨省流动性的演化机制具有一定的阶段分异性,前期,城镇居民收入差距的扩大是人才跨省流动性升高的主要动因;之后人才跨省流动性的减弱主要受城镇居民收入差距的缩小、发达地区过高的购房成本及小学教育服务设施短缺等因素影响。

The rapid economic growth and social transition in China brings about some new trends of skilled migration. Based on microdata of China's 2005, 2010 and 2015 one-thousandth population sample survey, this paper analyzes the evolution pattern of China's interprovincial migration of highly-educated labors in the periods of 2000-2005, 2005-2010 and 2010-2015, and employs zero inflation negative binomial regression models to explain the evolution mechanism of the interprovincial migration of highly-educated talents. Results show that: (1) the interprovincial mobility of China's highly-educated workers rose first and then declined from 2000 to 2015, and the spatial distribution of migration volume of highly-educated talents tended to be balanced. (2) The net inflow rate of highly-educated talents was high in the eastern developed provinces, while the net out-flow rates were high in the central region, northeastern region and Hebei Province; the high-intensity net inflow of highly-educated talents in developed areas as well as high-intensity net outflow in less-developed areas began to ease after 2010. (3) Highly-educated individuals mainly migrated from central and western regions to eastern coastal areas, while the sizes of highly-educated labors migrating from eastern regions to central and western regions continuously increased. (4) From the perspective of the evolution trend of influencing factors, China's interprovincial migration of highly-educated talents was mainly driven by economic factors, but the importance of income in the decision-making of skilled migration tended to weaken, and the impact of industrial transformation, housing price income ratio, education service facility, and climate on the interprovincial mobility of talents began to increase. (5) The evolution mechanism of the interprovincial mobility of highly-educated talents in China varied in development stages. In the early stage, the widening urban residents' income gap was the main driver for the increase of the interprovincial mobility of highly-educated talents. After that, the decrease of the interprovincial mobility of highly-educated talents was mainly affected by the narrowing urban residents' income gap, the high housing price income ratio and the shortage of primary education service facilities in developed areas.

知识经济时代人才是建设粤港澳大湾区世界级城市群的重要生产要素。本研究采用2005年、2010年和2015年广东省人口普查和1%抽样调查数据,以及香港和澳门对应口径的统计数据,以县市为基本单元,提出从受教育程度和职业技能两个口径测度人才集聚水平,系统解析粤港大湾区高学历与高技能人才集聚的演化格局及影响因素。结果表明：① 粤港澳大湾区作为中国经济高度发达地区,人才集聚优势高度集中在香港、澳门,内地珠三角城市群的人才集聚水平低于京津冀城市群和长三角城市群。② 2005—2015年,粤港澳大湾区高学历人才集聚持续均衡化,而高技能人才集聚优势仍然体现在香港、澳门,内地因为发展教育提升的高学历人力资本尚未完全有效转化为高技能人力资本。③ 香港人才集聚水平处于绝对领先,澳门、广州、珠海和深圳次之,而外围县市相对处于人才洼地,特别是制造业发达的佛山、东莞人才集聚水平相对偏低。④ 面板模型表明,服务业对高技能人才集聚的拉动效应强于高学历人才,而制造业的拉动作用并不突出。高等教育对高技能人才集聚的带动作用要弱于高学历人才。高薪资待遇有利于促进高学历人才集聚,但对高技能人才集聚的促进作用有限。新时期,亟需推动粤港澳三地管理制度衔接、产业转型升级和优质高等教育建设,推动粤港澳大湾区建设国际创新中心。

In the era of knowledge economy, talents concentration plays a key role in the development of a world-class urban agglomeration of Guangdong-Hong Kong-Macao Greater Bay Area (GHM). Based on the population census of Guangdong Province in 2010, the 1% population sampling survey in 2005 and 2015, and employment statistics in Hong Kong and Macao, this study measures the level of talents concentration from two perspectives of educational attainment and occupation on the county scale, and analyzes the evolution pattern and motivations of talents concentration in the GHM. The results show that: (1) GHM is one of the highly developed economic areas in China, and there is the absolute advantage of talents concentration in Hong Kong and Macao, while the level of talents concentration in the Pearl River Delta urban agglomeration is lower than that in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. (2) From 2005 to 2015, the spatial distribution of highly-educated talents in the GHM tends to be balanced, and there is also the advantage of the concentration of highly-skilled talents in Hong Kong and Macao. The increasing human capital defined by educational attainment in the mainland, which is caused by the expansion of college enrollment in China, has not been fully and effectively transformed into the advantage of human capital defined by occupation. (3) The level of talents concentration in Hong Kong plays an absolute leading role, followed by Macao, Guangzhou, Zhuhai and Shenzhen, while the counties and cities on the periphery of GHM have a low level of talents concentration. In particular, although Foshan and Dongguan have some developed manufacturing industries, their talents concentration level is relatively low. (4) The panel model shows that the service industry has a greater promoting impact on the concentration of highly-skilled workers than that of highly-educated labors, and manufacturing industry does not influence the talents concentration. Higher education plays a less important role in promoting the agglomeration of highly-skilled workers than that of highly-educated labors. High salary helps promote the concentration of highly-educated workers, while it does not boost the concentration of highly-skilled labors. In the new era, it is urgent to promote the cohesion of management systems in Guangdong, Hong Kong and Macao, the industrial transformation and upgrading, and the establishment of high-quality higher education, ultimately, building GHM into an international innovation center.

基于2000和2010年全国人口普查分县数据和地级行政单元数据,采用不均衡指数和空间自相关分析等方法,刻画中国高技能劳动力和一般劳动力的空间分布格局及其变化,并采用空间滞后模型,识别高技能劳动力与一般劳动力空间分布的决定因素及其变化。结果表明：① 两类劳动力总体的空间特征均为东南密集,西北稀疏;② 2000—2010年,高技能劳动力在空间分布上呈集中化的趋势,而一般劳动力呈分散化的趋势;③ 十年间高技能劳动力集聚区虹吸作用加强,其分布越发集中于沿海特大城市群,而一般劳动力集聚区回波作用加强,其分布越发均衡;④ 空间回归分析结果表明,职工平均工资、是否直辖市或省会、每万人在校高校学生、是否沿海城市、常住人口数和空间溢出效应在十年中一直都是决定高技能劳动力和一般劳动力空间分布的主要因素,而失业率、中学生师比、二氧化硫排放量和绿地率在2010年也成为影响两类劳动力空间分布的主要因素。对比两类劳动力的模型结果可得,高技能劳动力的空间分布受行政因素、高校因素和地区生活舒适度的影响更大,而一般劳动力的空间分布受劳动力市场因素的影响更大。

基于第五、六、七次全国人口普查数据,本文分析了2000—2020年中国人口分布的空间格局、集散态势和影响因素。结果表明,中国人口分布依然保持东密西疏的基本格局,胡焕庸线两侧的自然地理环境差异性决定了这一格局的长期稳定性;人口空间集中化趋势在21世纪初的10年内主要发生在东南半壁,但在2010—2020年间成为各区域的共同特征。省会城市的人口集聚能力持续增强,2010—2020年的优势更加凸显,但这种行政力量主导的首位度提升效应将随着全省发展阶段的演进而逐渐弱化。人口变动的区域分化明显：沿海城市群的空间连绵化逐渐形成了强吸引力、高承载力的沿海人口增长带,东北地区几乎转向全面的人口收缩,中部各省依托省会展开的人口竞争依然激烈,川渝黔地区普遍实现了人口的止降回增,西北地区则面临人口收缩风险。此外,区域人口增长的驱动因素逐渐从经济要素主导转为经济要素和舒适性要素并重,各地区舒适性的差异可能将成为影响未来人口空间格局变化的重要因素。

Using data of the latest three decennial censuses, this paper systematically analyzes the overall spatial pattern, changing trends, and influence mechanisms of China's population distribution since 2000. The study reveals that the general population distribution pattern of "dense east and sparse west" in China has not changed, which is determined by the differences in natural geographic environments on the two sides of Hu Line. In the first decade of the century, population concentration was mainly found in the southeastern area, but it has been observed in all regions in the recent decade. Provincial capitals have become the winners of population competition and the advantages of population growth of provincial capitals have been further strengthened in the recent decade compared with 2000-2010. However, the administrative power-led effects of the increasing dominance of provincial capitals will gradually weaken with the development of their provinces. The regional differentiation of population changes is also apparent: coastal region gradually forms a dense belt of population and towns with solid population attraction based on the development of urban agglomerations; the northeast region almost turns to a complete population shrinking status; central provinces still compete fiercely for population based on their capitals; the population of Sichuan, Chongqing, and Guizhou has stopped the decline and returned to growth; and the northwest region faces the risk of population loss. In addition, this paper finds that the driving factors of regional population growth have gradually changed from economic factors to both economic and amenity factors, and the difference of amenities among regions may become an important factor for future changes in China's population geography.

就近城镇化是中国中西部地区破除异地城镇化模式弊端、推进新型城镇化健康发展的现实路径选择。通过构建就近城镇化率、贡献力和贡献势指标,从本省、本县和本镇3个城镇化尺度,系统分析2000—2015年中国中西部地区就近城镇化空间分异特征,并深入剖析其驱动机制。研究发现：① 2000—2015年中西部地区就近城镇人口总数从1.96亿增加到3.47亿,就近城镇化率从28.16%上升到46.89%。② 从贡献力看,中西部地区就近城镇化的主要来源是本镇户籍人口,但本镇贡献力呈下降趋势,本县贡献力波动上升,本省贡献力变化不大。③ 从贡献势看,本县贡献势>本省贡献势>本镇贡献势,县域尺度是中西部大部分地区就近城镇化的优势空间单元。④ 中西部地区就近城镇化空间分异是自然环境背景、经济增长和非农产业发展、城市公共服务供给、交通运输条件和地方行政力量等多重因素的综合作用结果。

Considering the new-type urbanization background, the nearby urbanization model provides a practical approach toward eliminating the disadvantage of trans-regional urbanization and promoting urbanization. This study establishes the indexes of the nearby urbanization rate (NU), contribution to nearby urbanization (CNU) and trend of contribution to nearby urbanization (TNU), to analyze the spatial differentiation features of nearby urbanization in central and western China from 2000 to 2015. Moreover, this study explores the determinant factors and influencing mechanism of the spatial differentiation of nearby urbanization in the study region. Results show that: (1) From 2000 to 2015, the population of nearby urbanization increased from 196 million to 347 million and the NU increased from 28.16% to 46.89%. (2) Based on the analysis of the CNU, we found that the main source of nearby urbanization was the population registered in the local town, but the contribution of local town to nearby urbanization (CNUt) gradually declined over the study period. The areas with high CNUt were widely distributed in central and western China in 2000, but by 2015, they were restricted to Sichuan province. Meanwhile, the contribution of local county to nearby urbanization (CNUc) increased and the contribution of local province to nearby urbanization (CNUp) varied slightly. Most areas with high CNUp were provincial capital cities, resource-based cities or pastural cities. (3) Based on the analysis of the TNU, the trend of the contribution of local county to nearby urbanization (TNUc) exceeded the trend of the contribution of local province to nearby urbanization (TNUp), while the trend of the contribution of local town to nearby urbanization (TNUt) exhibited the least influence. Therefore, examining nearby urbanization based on the county-level spatial unit was advantageous in most regions; however, in the northwestern border regions and provincial capital cities, the provincial level was more applicable. (4) The spatial differentiation of nearby urbanization was determined based on the integrated results of various influencing factors, including natural environment, economic development, non-agricultural industries, urban public services, transportation accessibility, and the influence of local administration.

在长江三角洲区域一体化上升为国家战略背景下,论文运用核密度、重心、集中指数和偏移—分享等方法,研究2000—2018年长江三角洲人口时空演变规律,并采用定量方法探讨人口偏移增长影响因素。结果表明：① 部分城市主导下的人口集散趋势转变,并未显著改变区域人口不均衡格局,且集中度呈现稳步增长态势。② 基于偏移—分享法的研究发现,人口增长格局转变主要表现为部分欠发达地区人口增速加快,如三省一市上安徽省转变为正偏移增长、城市尺度上核心发达城市为主的正偏移增长向部分欠发达城市为主的正偏移增长转变、县域尺度上正偏移市辖区由核心区向边缘区的转移,但人口向少数大城市、市辖区集聚的态势显著,而多样化的人口偏移增长态势表征了未来城市发展策略的差异化需求。③ 经济因素、社会发展和财政水平等是长江三角洲人口偏移增长的重要驱动力,而影响因素的时空演变表明,未来人口均衡化政策的制定不仅要因地制宜,更要与时俱进。

Population distribution is an important indicator of regional development patterns. Also, regional coordinated development is closely related to the balanced distribution of the population. In the context of the regional integration of the Yangtze River Delta as a national strategy, the population distribution across the Yangtze River Delta has undergone significant changes. Using Kernal density analysis, gravity center analysis, concentration index, and shift-share analysis methods, this study examined the spatio-temporal change characteristics of the population in the Yangtze River Delta from 2000 to 2018, and explored the influencing factors as well as internal mechanisms of population growth shifts by utilizing quantitative methods. The main conclusions are as follows: 1) The shift in the population distribution trend led by some cities did not significantly change the imbalanced pattern of regional population distribution, and the increase in the concentration index indicates that the regional population concentration showed a steady growth trend. 2) The research based on shift-share analysis found that the shift in population growth pattern was mainly manifested in the acceleration of population growth in some underdeveloped areas. For example, Anhui Province was transformed into positive growth shift at the provincial scale. On the city scale, the positive growth shift dominated by core developed cities has changed to the positive growth shift dominated by some less developed cities. The spatial distribution of positive growth shift of municipal districts changed from the core areas to the peripheral areas. However, the population was still attracted to a few large cities and municipal districts. The diversified population growth shift trend characterizes the differentiated demands for future development strategies. 3) Economic factors, social development, and financial resources were important driving forces of population growth shift in the Yangtze River Delta. However, the temporal and spatial change of influencing factors shows that the formulation of future population balancing policies not only needs to be adapted to local conditions, but also should evolve through time. This research not only provides a reference for the formulation of relevant population policies, but also enriches the empirical findings on the spatial and temporal change of population.

中国生态文明建设和“健康中国”战略强调切实治理影响人口健康的环境问题,建设健康人居环境。论文基于2000年和2010年中国人口普查资料以及2005年和2015年各省级行政单元1%人口抽样调查等数据资料,论文使用探索性空间分析方法刻画中国城市人口死亡率的时空变化特征,并采用空间回归方法,揭示城市PM_2.5的平均浓度对人口死亡率的影响及其空间溢出效应,以及社会经济因素对PM_2.5—人口死亡率关联的调节效应。结果表明：① 中国城市人口死亡率的空间分布特征呈现明显的异质性,高死亡率地区早期集聚分布于西南地区,2005年后在西南地区、华北地区、华东地区和华中地区呈现逐渐集聚分布态势。低死亡率地区长期集中分布于西北地区、东北地区、长三角地区、珠三角地区和京津两市。② 人口死亡率的分布存在空间关联性,高—高类型地区早期集中分布于西南地区,后期向东扩展;低—低类型地区主要分布于北疆、内蒙古西部和广东省及其周边地区。③ 城市PM_2.5浓度对人口死亡率具有显著的正向影响,并且对邻近地区的人口死亡率具有显著的空间溢出效应。④ 中国城市PM_2.5浓度对人口死亡率的影响存在学历差异和城乡差异,地区高学历人群集聚可降低PM_2.5的健康风险,城镇化发展进程缓慢则会加重PM_2.5的健康风险。研究旨在为防范空气污染暴露导致的健康风险、建设健康人居环境提供科学依据。

土地利用问题日益成为中国经济发展的重要约束力之一。快速城镇化背景下城镇和乡村的快速扩张吞噬了宝贵的土地资源,粗放非集约的利用方式更加剧了土地资源的浪费。从国土空间集约利用的影响机理出发分析中国县域国土空间集约的影响机理对指导集约利用实践以及宏观集约利用政策瞄准和政策矫正都具有特殊意义。综合运用OLS模型、空间面板滞后模型和空间面板自相关模型以GIS和Matlab为技术平台,构建中国县域发展基础数据库 (1992-2010年),定量刻画中国2286个县级单元的空间集约利用度时空变化格局,计量分析社会经济发展、自然环境本底、区位交通地理、宏观战略政策和历史基础5大类变量17项具体因素的影响机理。研究结果表明,空间面板数据模型的整体显著性和可信度检验略高于一般面板数据OLS模型;在固定相关效应后对各因素的影响机制进行了检验,表明工业化、城镇化、经济发展水平、区位、交通和宏观战略政策等因素对县域国土空间集约利用的影响较为明显。自然环境因素弱于社会经济因素。被忽略的历史因素对县域国土空间集约利用具有极显著的影响。未来县域国土空间集约利用应因势利导,强化有利因素,减小不利因素影响。提高工业化和城镇化发展水平和质量。发挥市场的主导作用,完善土地市场和运行机制。优化国土空间集约利用调控政策和管治手段,制定差别化的空间集约利用政策。以资源环境承载力为基础和约束最大限度地提高投入和产出水平。

Land use issue is an important constraining force for economic sustainable development of China. Urban and rural rapid expansion depletes valued land resources under the background of rapid urbanization. An extensive use pattern might cause a serious waste of land resources. The study on influencing mechanism of land intensive use (LIU) in China at the county level is an important tool for effective LIU practice and policy-making. This paper uses OLS model, Spatial Panel Lagged model and Spatial Panel Error model to characterize the influencing mechanisms of five class factors and 17 variables supported by GIS (Geographic Information System) and MATLAB. And a comprehensive data set, including physical geography attributes and socio-economic information with 2286 counties, was developed. Meanwhile, the spatiotemporal pattern of LIU has been discussed by means of GIS. The results show that Spatial Panel Data models are slightly superior to OLS model in terms of significance and confidence level. Regression results of these models indicate that industrialization, urbanization, economic development level, location, transportation and policy have significant impact on LIU of counties. The variables of physical geography are less significant than socio-economic variables. An ignored variable of historical factor, however, became the most significant factor. In the future, the LIU at the county level should enhance favorable factors and reduce disadvantageous ones, which can be acquired by improving the entire level and quality of industrialization and urbanization. We argued that an efficient and complete land market and operating system should be built to reflect market-oriented activities at the first place. Then, according to regional differences, differential LIU regulation policies and measurements should be optimized. Meanwhile, we should pay close attention to the carrying capacity of local resources and environments when conducting LIU practices.

探索建设用地扩张对景观格局变化的影响机理是理解人类活动对生态系统功能、格局和过程变化影响机制的关键。但中国全国尺度建设用地扩张过程对景观格局演化的影响研究明显不足。基于此,突破传统行政区划单元的分析方法,以覆盖全国的10 km格网为基本单元,基于1975-2014年建成区比率数据来表征建设用地扩张水平,运用同期土地利用数据计算相应的景观指数,定量刻画了建设用地扩张及景观格局的时空演化过程,并运用空间计量经济模型定量解析了建设用地扩张过程对景观格局演化的影响效应。同时,基于全样本、不同时间点、不同规模城市和不同区域城市的对比揭示建设用地扩张过程对景观格局演化影响的时空差异性。研究结果表明：40年间建成区比率值增长了3倍,高于同期的人口城镇化增长水平,东、中和西部间呈现出显著的梯度差异性,而且差异程度逐步增加,大、中、小城市之间的差异也较为显著。1975-1990年、1990-2000年和2000-2014年3个时间段的年均增速分别为5.87%、2.32%和2.32%。景观格局总体上也经历了显著变化,优势景观类型的主导性逐步降低,景观格局趋于破碎化,复杂度增强,邻近度趋于离散,聚集度逐步降低,结合度渐高,景观多样性和均匀度均有所提高。随着建设用地扩张水平的提高,景观越来越趋于破碎化,人类活动对景观的干扰强度和频率也不断增强。例如,建设用地扩张水平每提高1%将导致景观斑块数量提高0.45%左右。但是建设用地扩张水平与景观复杂度的变化在空间上存在错位。建设用地扩张水平的提高在一定程度上使同类型斑块间的距离变近,景观聚集度出现降低趋势,景观越来越呈现多种要素的密集格局,斑块聚合和集群性增强,相应的结合度也越来越高,景观均衡化趋势和均匀分布越来越明显。同时发现,不同规模城市和不同区域的影响差异显著,除地理区位因素差异性不显著外,其他控制要素也对景观格局演化具有差别化的影响。

Exploring the impact mechanisms of construction land expansion on landscape pattern changes is one of the critical tactics to understanding how human activities affect ecosystem function, pattern, and process change. However, the lack of macro-scale data and spatial analysis methods on construction land expansion and landscape has hindered China's national scale study. In response to this, here we use the recently available built-up area ratio data and the corresponding land use data to characterize the spatiotemporal process of construction land expansion and landscape metrics between 1975 and 2014 based on the 10 km grids at the national scale. And the spatial econometric model was adopted to quantitatively investigate the influence mechanism of construction land expansion process on landscape pattern evolution. At the same time, the spatial-temporal heterogeneity of the influence of construction land expansion process on the evolution of landscape pattern is examined based on the comparisons, different scale cities and different regions of China. The study found that the value of built-up area ratio has increased by threefold in the past 40 years, which is higher than the growth level of demographic urbanization in the same period. The construction land expansion has significant gradient differences between the eastern, the central and the western regions, and the gradation of difference is gradually increasing. Average annual growth rates are 5.87%, 2.32% and 2.32%, respectively, in periods 1975-1990, 1990-2000 and 2000-2014. Meanwhile, the differences between large, medium-sized and small cities are also significant. Owing to the effects of frequent human activities, the landscape pattern has also experienced significant changes. For example, the dominance of leading type has gradually decreased, the landscape pattern has become fragmented, the complexity has increased, the proximity has become more discrete, the contagion has gradually decreased, the cohesion has increased, and landscape diversity and evenness have increased. With the improvement of construction land expansion level, the landscape is becoming more and more fragmented, and the intensity and frequency of human interference to the landscape are also increasing. Every 1% increase in the expansion of construction land, for example, caused a 0.45% increase in the number of patches. However, the changes in construction land expansion level and landscape complexity are spatially mismatched. To the extent that the improvement of the level of construction land expansion has brought the distance between the same types of patches and the contagion index has decreased, and the landscape became more and more dense and aggregating, and the corresponding cohesion degree is also getting higher and higher, and the landscape equalization and even distribution are more and more obvious. At the same time, it is found that the influences of different time periods, different scale cities and different regions are significant for impact mechanisms of construction land expansion on landscape pattern changes. Besides geographical location, the other control factors have different influences on the evolution of landscape pattern.

基于全国城市的PM_2.5监测数据,识别PM_2.5的时空分布特征,并着重利用地理加权回归模型分析自然和社会经济因素对PM_2.5影响的空间异质性。结果显示：2015年全国PM_2.5的年均浓度为50.3 μg/m3,浓度变化呈现冬高夏低,春秋居中的“U型”特征;PM_2.5的空间集聚状态明显,其中京津冀城市群是全国PM_2.5的污染重心。地理加权回归结果显示：影响因素除高程外,其余指标均呈现正负两种效应,且影响程度具有显著的空间差异性特征。从回归系数的贡献均值来看,自然因素对城市PM_2.5浓度影响强度由高到低依次是高程、相对湿度、温度、降雨量、风速、植被覆盖指数;各类社会经济指标对城市PM_2.5浓度影响强度排名依次是人口密度、研发经费、建设用地比例、产业结构、外商直接投资、人均GDP。由于各指标对城市PM_2.5浓度变化的影响程度存在着空间异质性,因此在制定大气治理对策时可以考虑不同指标影响程度的空间差异,从而使得治霾对策更具针对性。

Based on the PM2.5 monitoring data of China's cities, we identified the spatial and temporal distribution characteristics of PM2.5 concentrations, and used the geographically weighted regression (GWR) model to analyze emphatically the spatial heterogeneity of the influence of natural factors and socio-economic factors on PM2.5 concentrations. The results showed that: in 2015, the average annual concentrations of PM2.5 in China was 50.3 μg/m 3, and the monthly concentration change presented a "U-shaped" pattern with a higher level in autumn and winter while a lower one in spring and summer. In addition, PM2.5 concentrations were high in cities of eastern and northern China, but low in cities of southern and western China. Beijing-Tianjin-Hebei urban agglomeration was the center of PM2.5 pollutions in China. The results of geographically weighted regression showed that: (1) in terms of natural factors, elevation had a negative correlation with the urban PM2.5 concentrations, while positive and negative correlations exist for other indexes, and negative correlation effect dominated, which is conducive to reducing PM2.5 concentrations in most cities. Thus it can be seen that the influence indexes of PM2.5 concentrations have significant spatial difference characteristics. From the mean contribution of the regression coefficient, the ranking of the influence intensity of natural indexes on PM2.5 concentrations were: digital elevation model, relative humidity, temperature, rainfall, wind speed, normalized difference vegetation index. (2) In terms of socio-economic factors, all the indicators showed positive and negative effects, with significant spatial heterogeneity. Among them, the build-up and GDP per capita were conducive to reducing PM2.5 concentrations in most cities, while population density, foreign direct investment, industrial structure and research and development expenditure can aggravate the air pollution in regions. The ranking of the influence intensity of socio-economic factors on PM2.5 concentrations were: population density, research and development expenditure, built-up, industrial structure, foreign direct investment, GDP per capita. (3) Due to the spatial heterogeneity of the influence of various factors on urban PM2.5 concentrations, the spatial difference of the influence of various indexes can be taken into account in the formulation of atmospheric governance countermeasures. Moreover, although natural factors have a more significant influence on PM2.5 concentrations, since it is difficult to change the natural conditions of cities artificially, specific strategies should be proposed from the perspective of social and economic factors in tackling haze.