基于区域协调发展的黄河流域经济空间开发

促进区域协调发展是黄河流域经济空间二次开发的立足点,黄河流域经济开发的突出问题是开发缺乏整体性、开发轴线实力弱,发展不平衡、缺乏强大的经济增长核心区。促进黄河流域协调发展,应建立与主体功能区相适应的区域开发模式,培育壮大经济增长核心区,加快重点开发轴线的建设。只有建立生态补偿机制,构建与主体功能区相适应的政绩考核体系;充分利用国内产业转移步伐加快的契机,提升经济增长核心区的发展能力;充分利用国家扩大内需的宏观经济政策,加快经济增长核心区和开发轴线建设步伐,完善并创新农村土地流转制度,才能促进人口与产业聚集,加快推进城镇化。

区域协调发展战略下的淮河流域经济空间开发研究

现阶段的淮河流域经济空间开发不利于促进我国区域协调发展战略目标的实现。淮河流域经济空间开发应当遵循整体开发与各省开发相统一、经济开发与淮河治理相统一的原则,把徐州与蚌埠培育为区域经济增长极,把淮河干流沿岸打造为经济发展轴线。国家加快内河水运发展、推动文化产业跨越式发展、稳妥推进城镇化与加快农村改革发展的政策有利于淮河流域区域经济增长极的形成与开发轴线的构建。淮河流域应当把握机遇,壮大徐州与蚌埠的经济实力,提高淮河通航能力,促进人口与产业聚集并加快推进城镇化进程。

金融危机背景下的黄河流域经济空间开发策略

现阶段的黄河流域经济空间开发存在着整体性与前瞻性不足、轴线实力较弱且缺乏强大的经济增长核心区等问题。黄河流域经济空间开发的总体方案是:把济南经济圈(城市群)、中原城市群与关中城市群培育为经济增长核心区,把陇海-兰新沿线、黄河干流沿岸作为重点开发轴线。全球性金融危机为这一方案的实施提供了历史性机遇,即国内产业转移步伐的加快以及国家扩大内需、保增长的宏观政策有利于黄河流域经济增长核心区的形成与重点开发轴线的构建。基于此,黄河流域在经济空间开发中,应当把握机遇、充分利用国家的各种宏观政策。

黄河流域经济空间开发研究——评《黄河流域经济空间分异与开发》

黄河流域经济空间开发是我国区域总体发展战略中十分重要的一环。现阶段黄河流域经济空间开发存在开发过度、环境破坏、资源浪费、经济发展差距不断扩大等问题,为了实现区域协调发展,尤其是应对近年来市场经济发展不断深入的冲击,国家颁布了诸多推动经济发展的宏观政策,为黄河流域经济空间开发创造了有利契机。《黄河流域经济空间分异与开发》一书全面阐述了黄河流域经济空间分异与空间开发模式,系统分析了黄河流域经济空间分异的形式、过程及成因,阐述了黄河流域经济空间分异的规律;并基于此,研究总结了黄河流域经济空间开发的模式,提出了选择空间开发模式、优化空间开发的建议。

新形势下黄河流域经济空间开发存在的问题与对策

黄河流域经济空间开发的突出问题是经济空间开发缺乏整体性,开发轴线实力弱且发展不平衡,缺乏强大的经济增长核心区。在目前的经济形势下,国内产业转移步伐的加快、国家扩大内需的宏观政策、国家促进农业和农村发展的政策等为黄河流域经济空间开发带来了良好的机遇。黄河流域在经济空间开发中应把握机遇,培育壮大经济增长核心区,加快重点开发轴线建设。

Measuring Chinese Marine Environmental Efficiency:A Spatiotemporal Pattern Analysis

Environmental efficiency standards are often used to evaluate the costs of oceanic economic development. A variety of statistical analyses were applied in this study to quantify the marine environmental efficiency of 11 Chinese coastal provinces and municipalities between 2000 and 2014. Results initially reveal that environmental efficiency measures that incorporate undesirable outputs are more consistent with real production conditions and thus the use of marine economic and environmental efficiencies supplement and complement one another. Second, overall marine environmental efficiency across China tends to be low and can be spatially characterized by a transformation such that the inefficiencies noted in 2000 have subsequently been transformed to comprise a three-tiered structure that encompasses northern, central, and southern cores. Third, variation in absolute and relative marine environmental efficiency differences for the coastal regions of China have been consistent over time; values initially decreased before increasing again in a fluctuating manner over the time period of this analysis. Fourth, data show that the Pearl River Delta area has experienced the highest rate of change in marine environmental efficiency over time when economic zones are used as basic research units, although values have nevertheless fluctuated significantly. Fifth, values for total factor productivity as well as technical efficiency and change across the Chinese marine economy all fluctuated over time but increased. Data show that changes in marine environmental efficiency across China can primarily be attributed to progress in marine science and technology. Finally, levels of capital investment and marine industrial pollution intensity are not significantly correlated with marine environmental efficiency. Indeed, both marine industrial structural levels and environmental protection technologies have had a positive effect on environmental efficiency while levels of investment in marine scientific research as well as the scale of economic development, the marine economy, and the degree of external openness have all exerted negative effects on this key variable.

Spatial and Temporal Pattern of Urban Smart Development in China and Its Driving Mechanism

Smart urban development is an inevitable choice, and is essential to overall strength improvement. It is important to explore an urban smart development path which unites smart growth with driving shrinkage perfectly in forming scientific and sustainable development concept and responding to new normal strategic opportunities. Based on statistic data of 294 prefecture-level cities and above in China from 2000 to 2015, we analyzed spatial and temporal evolution of urban smart development in China by constructing a dynamic fitting model of urban land expansion, population growth, and economic development as well as the coefficient of variation of urban smart development(CVSD). Further efforts were then made to consider differential distribution regularity of urban smart development so as to understand the driving mechanisms of heterogeneous classification of urban smart development in China from different scales and scale variation. Our results indicate that: 1) the disordered growth tendency of urban cities in China is overall well controlled in the middle, and late research and it mainly presented a doublet coexistence of shrinkage disordered cities and smart developing cities. It is particularly obvious that Northeast China and East China have regarded shrinkage disordered cities and smart developing cities as main development tendency separately. 2) Areas with basic stability and relative variation were relatively dispersed across the time period, but the proportion was far beyond areas with significant variation. It demonstrates a relative equilibrium spatial and temporal differential evolution pattern of prefecture-level cities and above in China, except for Tongling, Lanzhou and Chaoyang. 3) prefecture-level cities and above in China are mostly characterized by shrinkage disordered and smart development classification under the background of different scale and scale variation from 2000–2015; however, the spatial resonance relation is not obvious. 4) There are many interaction factors forming an important driving mechanism in developing the spatial and temporal pattern of urban smart development in China, including natural geographical factors, industrial structure adjustment, human capital radiation, regional traffic accessibility, and government decision-making intervention.

A New Typhoon Bogus Data Assimilation and its Sampling Method:A Case Study

In this study,the authors introduce a new bogus data assimilation method based on the dimension-reduced projection 4-DVar,which can resolve the cost function directly in low-dimensional space.The authors also try a new method to improve the quality of samples,which are the base of dimension-reduced space projection bogus data assimilation (DRP-BDA).By running a number of numerical weather models with different model parameterization combinations on the typhoon Sinlaku,the authors obtained two groups of samples with different spreads and similarities.After DRP-BDA,the results show that,compared with the control runs,the simulated typhoon center pressure can be deepened by more than 20 hPa to 30 hPa and that the intensity can last as long as 60 hours.The mean track error is improved after DRP-BDA,and the structure of the typhoon is also improved.The wind near the typhoon center is enhanced dramatically,while the warm core is moderate.

Regional differences of border geo-cities in China and the surrounding countries

As the improvement of international status and the implementation of China's neighboring diplomacy, the development of border regions and the security of border cities, as well as their spatial structure and regional differences are gaining more attention from academic circle. Based on the interdisciplinary perspectives of urban geography, regional economics and geopolitics, this paper explores the regional differences of border geo-cities in China and the surrounding countries with the help of remote sensing information acquisition and Arc GIS spatial analysis. Three primary results are found as follows:(1) The border geo-cities in China and surrounding countries are divided into five geographical regions: geo-cities in South China Sea, geo-cities in Southeast Asia, geo-cities in South Asia, geo-cities in Central Asia and geo-cities in Northeast Asia.(2) In the spatial structure system of China's border geo-cities, the importance of geo-cities in five major regions is fairly different. In terms of the security and economic development, the rank of priority is geo-cities in Northeast Asia, geo-cities in South China Sea, geo-cities in Central Asia, geo-cities in South Asia, geo-cities in Southeast Asia.(3) Considering China's geo-setting for the development of border geo-cities, the east region is significantly better than the west, and the north region is slightly better than the south.