The marine economy of China is growing rapidly in recent years. Due to the differences in terms of the regional natural resource endowment, technology, industrial foundation, development and policy etc., there are large economic disparities among coastal regions. It takes 11 coastal provinces and municipalities as the objects to study the differences in characteristics of marine economy development in China from the two aspects of space and time in the article. The article uses Variation Coefficient and Weighted Variation Coefficient to represent the differences of coastal regions from 1996 to 2010. It adopts the Location Entropy, Lorezn Curve and Gini Coefficient to analyze the marine industrial structure and the evolution of its spatial distribution. Principal component analysis is used to evaluate the comprehensive marine economic development of the coastal provinces and the R/S analysis is used to forecast the disparities of the coastal regions. Then some reasonable suggestions are put forward to the existing problems in the development of marine economy. The results show that: marine economy in coastal regions has multiplied while there is a significant regional disparity and the overall difference decreases in volatility. Marine industrial structure is evolving into the advanced stage, the proportion of marine secondary and tertiary industry exceeds the primary industry and the marine secondary industry develops slowly on the whole. The scale of marine industrial agglomeration is reducing and tending to the balanced development. The marine industry with resource attribute has the highest concentration degree and the marine tertiary industry with spatial accessibility has the lower cluster. The coastal provinces and cities are divided into three echelons through comprehensive evaluation. Zhejiang, Shandong, Shanghai, Guangdong and Fujian are in the first echelon of integrated development of the marine economy, Liaoning, Tianjin and Jiangsu are in the second, Hainan, Hebei and Guangxi are in the third. The forecast demonstrates that gaps between coastal provinces and cities gradually decrease over the next 15 years and the differences are in the trend to increase slowly in the later time. It can be seen from the analysis that the development of marine economy in China has made considerable achievements but there are still some shortcomings such as low marine economy contribution rate, unreasonable industrial structure and lack of regional differences compared with ocean world powers, which will affect the goal of Chinese marine power construction. Finally, in order to solve the issues of space and time disparities in Chinese marine economy to optimize regional marine industry structure, narrow the differences of regional marine economy and provide a scientific reference to promote the sustainable development of Chinese marine economy, the study brings forward some concrete suggestions as improving the marine resources development ability and the quality of marine economy.
In order to optimize the location and structure of regional cities, it is important to realize the city size distribution and the evolution mechanism of urban system. According to primacy index, Gini coefficient of city size and Markov transfer matrix, this article describes the size distribution of 81 prefectural-level cities in China′s middle area over the period 1985-2010. Then self-organization and organization mechanism of urban system′s evolution are analyzed. Results show that the largest city grew more quickly than the others, leading to a increasing primacy index from 1985 to 2000. But the primacy index has continued to fall since 2000 because of the fast growth of Zhengzhou. With the change of urban population in middle area of China, Gini coefficient of city size decreased after increasing, and increased again. The gap between city-size indicated the weak trend of widening since 2002. The Markov Chains analysis shows a low interclass mobility, the large cities display higher persistence than the medium and small-sized cities. Transformations between different classes of cities are stable and the importance of major cities is strengthened. The evolution of urban system in middle area is affected by the mechanism of self-organization and organization. Self-organization is mainly represented by agglomeration of production factors, industrial adjustment, and technological innovation. Organization is reflected in the role of government. Based on the above analysis, some recommendations are put forward. The orientation of city function should be appropriate to size distribution of cities. Both the mechanism of self-organization and organization should be considered to facilitate the harmonious development of urban system.
伴随着亚太地区逐步成为国际政治、经济焦点地区,周边国家在中国国家战略中的地位逐渐提升。周边国家是维护主权权益、发挥国际作用的首要依托,中国同周边国家地缘相近,文化相联,彼此在重大的国家和地区问题上有较多共识,经济合作便利,且成本较低潜力巨大。中国地处沟通亚太沿海与内陆的重要通道位置,与周边国家的经贸往来发展较早已具备一定规模,在近年国际重大事件的影响下,中国对周边国家的商品出口呈现出较为复杂的发展态势。研究表明：① 国际经济危机爆发后,国际贸易环境呈现出全球化日益明显、国际经贸发展恢复缓慢、国际货币体系初现变革以及全球经济重心转移加快的新形势。② 通过贸易结合度指标分析周边国家在中国对外贸易中所处地位,并通过地理集中度指标分析出中国对周边国家商品出口空间格局在国际贸易环境新形势下出现了商品出口规模差距显著、主要出口商品地理集中程度较高、区域性较为明显以东南北为主,沿地缘通道展开辐射的特征。③ 预测中国对周边国家商品出口空间格局发展态势为,中国对中等贸易规模国家商品出口发展迅速、主要商品出口地理集中程度减弱、中国对东盟国家出口主要商品份额增大。
The important role played by neighboring countries has appeared to be more and more significant in our national strategies as Asia-Pacific Region becomes the hot issue in international politics and economy gradually. Neighboring countries are the primary reliance to China in maintaining the rights and interests of sovereignty and exerting international role. China and its surrounding countries are geographically close and associated in culture, sharing lots of consensuses in major national and regional issues. In addition, they enjoy convenient economic cooperation with low cost while enormous potential. China is located at the main channel position linking the coastal and inland areas of Asia-Pacific, and its economic and trade contact with surrounding countries have possessed certain scale earlier. Under the influence of international historic events in recent years, the commodity export of China to its surrounding countries have exhibited relatively complex development trend. Major conclusions are shown as follows: 1) After the global economic crisis, the international trade environment presents the increasingly globalized. The recovery of international economic and trade development is slow. The reform of the international monetary system has appeared. And the global economic centerpiece has been shifted rapidly. 2) Trade combined degree is employed to analyze the trade status of neighboring countries to China. By Geographic concentration analysis, it is shown that there are four characteristics in spatial pattern of export commodities between China and its neighboring countries under the new international trade situation: the gap of export difference becomes significant, the main export commodities appears a high degree of geographic concentration, it obviously revolves around east, north and south regions and radiates along the geo-channel. 3) It is of great significance to predict the development trend of the spatial pattern of export commodities between China and its neighboring countries for the development of Chinese commodity export trade with medium trade scale countries, for weakening the degree of geographical concentration of main export commodities and for increasing share of primary commodities in Chinese exports to ASEAN countries. The employment of geographical thought highlights the primary cause of exhibiting regional characteristics in the trade between China and its surrounding countries, which also embodies the complex influence of multiple geographical relations such as politics, economy and culture to regional trade, which supports the selection of our foreign trade strategy and benefits for China to achieve the target of national security.
The quantification of landscape pattern gradient change along urban expansion axis was not only an important method to understand oasis urbanization process, but also was the way prerequisite for the evaluation of ecological processes and mechanism that affect landscape pattern change dynamics. On the basis of remote sensing and GIS technology, jointing methods of landscape pattern metrics with gradient analysis was used to study the spatial-temporal change of landscape pattern along different road expansion axes of Jiuquan City. The expansion axes included Jiujia road and Jiuxi road transects with 12.5 km length and 5 km width and transects along Jiuqing road and Jiujin road with width of 5 km and length of 17.5 km and 20 km, separately. Comparison analysis was made among these roads transects from 1996 to 2010. The results showed that landscape pattern changed greatly along different road expansion axes. Constructed land and urban green land increased rapidly while unused land and farmland decreased gradually, and promptly converted to constructed land in the Jiujia road transect and Jiuxi road transect. The character of urbanization was “clumps - axial” along road expansion axes of Jiuquan city. Due to urbanization, landscape heterogeneity and landscape diversity were increase, the landscape shape become more complex along Jiujia road and Jiuxi road. The landscape pattern dominated by the unused land was converted to an urban landscape dominant pattern gradually. However, along the Jiujin road, urbanization didn't affected obviously on landscape shape complexity and fragmentation. With the increase of distance from the city center, the landscape patch density, landscape shape index and the landscape heterogeneity decreased along the Jiujin road and Jiuqing road. And with the decreasing of dominance of farmland, the landscape converted into a uniformly coexistent pattern of constructed land, farmland, green land and unused land. Summarily, there were different characters of urbanization and urban landscape gradient change of different road gradesthe extent of landscape changing were Jiujia road>Jiuxi road>Jiuqing road>Jiujin road. The landscape indices of urban suburbs changed more apparently than oasis agriculture area in the time scale.
以江苏省为例,利用1985年、1995年、2005 年和2008 年4期遥感影像解译获取的土地利用变化数据,按照“生产-生态-生活”土地利用主导功能分类,通过土地利用转移矩阵、重心转移、区域生态环境质量指数、土地利用变化类型生态贡献率等方法,定量研究江苏省土地利用功能结构转型、空间转型特征及其生态环境响应规律。研究表明：① 1985~2008年,江苏省土地利用主导功能结构变化主要表现为生产用地面积的减少,生态用地、生活用地面积的增加。主要的转化类型为农业生产用地转化为农村生活用地、城镇生活用地,牧草生态用地转化为农业生产用地。② 1985~2008年,农业生产用地、生态用地、农村生活用地空间分布的不均衡性进一步加剧,工矿生产用地空间分布的不均衡性有所缓和,城镇生活用地空间分布经历了先加剧后有所缓和的阶段。③ 1985~2008年,江苏省的生态环境质量稍有下降,其中农业生产用地被农村和城镇生活用地大量占用是生态环境质量退化的主导因素,农业生产用地转化成水域生态用地是区域生态环境改善的主要因素。
According to land use classification based on leading function of production, ecology and living, we took Jiangsu Province as a case study and made use of land use change/cover data in 1985, 1995, 2005 and 2008 respectively by remote sensing interpretation obtained from Landsat TM and ETM+. And then we quantitatively analyzed the characteristics of land functional structural and spatial transformation as well as its eco-environmental impacts. The methods discussed in the article include land use transfer matrix, gravity center model, index of regional eco-environmental quality and ecological contribution ratio of different kinds of land changes. The results show: 1) Land use changes in Jiangsu Province is chiefly manifested as the area decrease of productive land and increase of ecological and living land. Main types of transformation are the conversion from agricultural productive land into rural and urban living land, and that from water and pasture ecological land into agricultural productive land. 2) From 1985 to 2008, the imbalance of agricultural productive land, ecological land and the distribution of rural living space are further sharpened while that of mining productive land is weakened. And the imbalance of spatial distribution of urban living land is intensified at the beginning while then slow down. 3) From 1985 to 2008, the eco-environment quality of Jiangsu Province declines slightly. The critical factor of eco-environmental degradation is the large occupation of agricultural productive land by rural and urban living land and that of eco-environmental improvement is the conversion from agricultural productive land into water.
The 21st century is the century of the urbanization. With the rapid progress of urbanization in China, the city development is facing the problem of the severe resources and environmental expansion. Vulnerability problems during the urban development become more serious. Urban sustainable development is the objective and basis of improvement the level of the urbanization. Obvious vulnerable stages exist in coupling system of the urban-ecological environment. Therefore, it seems more important and urgent to carry out the research on the coordination and vulnerability between regional urban and eco-environment. Interaction of the urbanization and ecological environment is always the hot issue in academia, and there have been many great achievements at present. But it is still need to further explore and discuss the interactive mechanisms for the coordination and vulnerability between regional urban and eco-environment. To realize the coordinated development of the system and vulnerability reduction requires a certain amount of internal and external forces. Starting from the connotation of the coordination and vulnerability of the coupling system of urban and ecological environment, this paper discusses the characteristics of coordination and vulnerability. It elaborates that development of the urban–ecological environmental coordinated system depends on all kinds of interactive mechanisms such as natural condition and disaster-pregnant environment, the quality of the population and urban civilization, upgrading of industrial structure and technological progress, institutional innovation and management sciences, the resilience of urban-ecological systems. For high level of development between Regional Urban and Eco-environment, this study focuses on effects on the coordination and vulnerability of urban-ecological systems by various mechanisms, so as to provide a scientific reference for solving urban and regional sustainable development.
运用DEA模型测度了2000~2010年黄淮海平原县域单元的农业综合效率,并将之分解为纯技术效率和规模效率,探讨农业效率的时空特征及其演变因素,为优化农业生产要素组合,提高农业的投入产出效率提供参考。研究发现：① 综合效率总体上处于中低层次,存在着较大的区域差异,高效率县域多分布在城郊县,这可能与城郊县域多发展都市农业有关。② 研究时段总体上下波动明显,无增长趋势;变异系数在沿黄两岸的一些县域较大,其值在0.060以上。③ 综合效率受纯技术效率和规模效率共同作用。多数县域规模效率达到有效状态,且规模效率大于纯技术效率;纯技术效率对综合效率的影响及制约能力略强于规模效率。④ 纯技术效率较高的县域逐渐由东向西转移,城郊县的纯技术效率相对较高;纯技术效率较低的县域逐渐由北向南转移;纯技术效率一般的县域逐步转移至海河平原上游区域。⑤ 规模效率较高,且分布较为均衡。⑥ 黄淮海平原农业生产效率大多处于规模报酬递增阶段。
This article measured agricultural efficiency of the Huang-Huai-Hai Plain in 2000-2010 by using the method of data envelopment analysis(DEA).Then it classified the agricultural efficiency into pure technological efficiency and scale efficiency for exploring the spatial and temporal characteristics of the agricultural efficiency and its evolution factors. The results suggest that: 1) The overall efficiency on the whole is on medium and low level and there exists large regional differences with high efficiency county located in the suburban. This may be attributed to development of urban and suburban agriculture in these areas. 2) The overall study period fluctuated significantly and there is no growth trend coefficient of variation is larger in some of the county along the Huanghe River, reaching above 0.060. 3) Overall efficiency is influenced by the combined effect of pure technical efficiency and scale efficiency. Scale efficiency of most counties are efficient, with scale efficiency greater than pure technical efficiency. Pure technical efficiency of the overall efficiency is slightly stronger than the scale efficiency. 4) The counties with higher pure technical efficiency gradually shift from east to west, and in suburban the pure technical efficiency is relatively high. The counties of lower pure technical efficiency gradually transfer from north to south. The counties of general pure technical efficiency are gradually transferred to the upstream region of the Haihe Plain. 5) Scale efficiency is higher, and distributed in a balanced way. 6) Most of the efficiency of agricultural production is in the developmental stage of increased scale returns in the Huang-Huai-Hai Plain.
研究以多时序土地利用、遥感数据为基础,以大连市金石滩国家旅游度假区为例,通过景观生态学和CA-Markov模型模拟方法,系统分析了1998~2009年研究区景观格局的时空演变特征,并对2020年景观格局的情景进行了模拟预测。结果表明：① 1998~2009年金石滩的景观格局变化主要表现为：整体上,旅游景观总面增加2.30 km2,辅助性旅游景观总面积增加2.22 km2,非旅游景观总面积减少5.27 km2,非旅游景观向旅游景观和辅助性旅游景观的转变趋势明显;② 金石滩景观格局变化过程表现为,逐步从单一的村民居住型向能够满足旅游者需求的娱乐、观赏、商业等复合型景观转变;变化区域主要分布在研究区中部自西向东的龙山村、满家滩村、陈家村以及东南部的庙上村,变化类型以“传统农业→人造娱乐休憩”类型为主;③ 在对CA-Markov模型的可利用性进行分析与检验后得到金石滩2020年景观格局模拟结果：人造娱乐休憩景观、公共基础设施景观、传统工业与居民用地景观面积增加,同时自然态生物景观、传统农业景观、其他景观面积也相应减小,此外,水域景观和交通运输用地景观面积基本保持稳定;变化显著区域主要集中在研究区中部自西向东的龙山村、满家滩村、陈家村以及东南部的庙上村。
Based on multi-temporal land use data and remote sensing data , and also using Dalian Jinshitan National Tourist Holiday Resort as an example, through quantitative analysis of landscape ecology and the simulative method of CA-Markov model, this research systematically analyzes evolution characteristics of the landscape pattern in study area from 1998 to 2009, and simulates and predicts the landscape pattern in 2020. The results show that: 1) From 1998 to 2009, the main performance of landscape pattern changes is that the area of the tourism landscape increased 2.30 km2, auxiliary tourism landscape area increased 2.22 km2, the tourism landscape area reduced 5.27 km2; 2) The process of landscape pattern change shows that a gradual change from a single type of villagers living to various types which can satisfy the need of tourism’s entertainment, sightseeing, business and other complex landscape. And change areas are mainly distributed in Longshan village, Manjiatan village, Chenjia village and Miaoshang village in southeast, from west to east in the study area. In addition, the priority change type is the "traditional agriculture→artificial entertainment recreation" model. 3) After analyzing and testing the availability of CA-Markov model, we get the landscape pattern simulation result of Jinshitan in 2020 that the area increased in artificial entertainment leisure landscape, public infrastructure landscape, traditional industry and resident landscape. By contrast the area of natural biological landscape, traditional agricultural landscape and other landscape decreased. In addition, the area reminded stable of the water landscape and transportation land use landscape. Furthermore distinct areas are mainly concentrated in Longshan village, Manjiatan village, Chenjia village and Miaoshang village in southeast from west to east in the study area.
在流域粮食增产与水环境约束双重背景下,采用DEA模型、Malmquist生产率指数和GIS空间分析方法,对2000~2011年淮河流域35个地市的农业生产效率及时空变化特征进行分析。结果发现：① 2000~2011年淮河流域农业生产效率普遍较高,技术效率在流域综合效率提升方面发挥着积极作用,但决定综合效率最优的规模效率却有微弱下降;② 农业生产效率空间分析发现,流域生产效率呈现东部高于中部,中部高于西部的空间梯度分布格局;③ 农业生产效率变化时空特征研究表明,2000~2011年流域综合效率变化和规模效率变化呈现微弱的下降趋势,其中流域西部下降最为显著,而技术效率与生产率却呈现提高趋势,其中东部提升最为显著;④ 依据综合效率计算结果和时间变化特征,将农业生产效率变化划分为5种基本类型,发现综合效率持续不变的a类型地市最多,是流域农业生产效率变化的主要类型。
Under the background of grain production increase in basin and the restriction of water environment. DEA model, Malmquist productivity index and GIS spatial analysis methods were used to analyze the agriculture production efficiency and its temporal and spatial variation characteristic in 35 prefectures of Huaihe River basin from 2000 to 2011. The results demonstrated: 1) The agricultural production efficiency was generally higher in the Huaihe River Basin in 2000 to 2011. Although the technical efficiency played a positive role in increasing the basin comprehensive efficiency, there was a slight decrease in the scale efficiency which decides the optimal comprehensive efficiency; 2) Spatial analysis of agricultural production efficiency showed the spatial gradient of the distribution of production efficiency in this basin. Production efficiency in the east was higher than that in the middle part while the efficiency in the middle part was higher than that in the west; 3) The spatial-temporal variation patterns of agricultural production efficiency showed that from 2000 to 2011, both the comprehensive and scale efficiency had a slight decreasing trend in this basin, especially in the west of basin, while the technical efficiency and productivity had an increasing trend, especially in the east; 4) According to the calculation result of the comprehensive efficiency and temporal variation characteristic, the variation of agricultural production efficiency was divided into five basic types. Most prefectures were “A” type whose comprehensive efficiency remained steady, and the “A” type was also the main type of agricultural production efficiency variation in this basin
In special poverty-stricken rural areas, the primary problem of poverty alleviation is effective targeting and identifying of the poor and their distribution area. In recent years, researchers around the world focused on grasping the essence of multidimensional poverty and measurement. Based on systematic design of multidimensional poverty identifying indices system and algorithm flow, this article takes key country in Nanyang, Henan Province from national contiguous special poverty-stricken areas as the study area, constructs algorithm based on the “dual cutoff” and "dimension aggregated/decomposition" to measure and analyze the multidimensional poverty of the poor at "county-village"scale,uses Kriging method to interpolate results of multidimensional poverty measurements and systematically analyze spatial distribution pattern of multidimensional poverty at village scale in study area.The result shows: in the study area,the trend of multidimensional poverty headcount ratio and multidimensional poverty index(MPI) is that the value of west is higher than that of the east;the MPI of Neixiang country and Xichuan country is the highest,that of Zhenping country is the lowest.Their primary factors contributing to poverty are income and health,contribution of income index to the poverty appears as strip from "northwest-southeast",healthy problem mainly concentrates around Zhenping country. Their secondary factors contributing to poverty are education,schooling and fuel.Besides,the multidimensional poverty incidence is the highest in mountainous area in Xichuan country,MPI is relatively lower around the center of the country.
深入认识农户生活能源选择与区域地理特征的关系,有助于科学制定农村生活能源政策,合理引导家庭用能发展。基于临渭区的问卷调查数据与遥感影像资料,利用ArcGIS软件及贡献率模型,探究了渭河下游农户生活用能对区域地理特征的响应,结果表明：① 地理特征影响区域用能结构。平原型地区交通便利,化石能源使用量大;台塬地区种植业发达,清洁生物能源沼气利用多;丘陵地区林木丰富,薪柴使用占绝对主导地位。② 不同能源对地理特征响应不一。作物秸秆利用与公路覆盖、地形（高程、地形起伏与坡度）负相关,与耕地相关性弱;薪柴消费对地形、建设用地、耕地等因素响应明显;煤炭与公路覆盖、建设用地、耕地等因素正相关,坡度大、林地多,用煤受影响;沼气在交通好的地方或林草地多、高程与地形起伏大的山区发展受限,在耕地与人口多的地方,有采用优势。③ 优势能源类型的功能分区可有效服务于政策调控。山区内部单独使用薪柴的可能性高,平原与台塬地区更适于发展沼气或煤炭与沼气混用。④ 农户生活用能受多方面因素影响,地理特征具有重要的基础性作用,尤其对于不同类型区域。土地利用、交通条件等发生变化可直接引起区域用能的结构调整。
It is helpful for scientifically drawing up the energy policy and reasonably guiding the rural household development to study the impacts of the regional geographical features on the energy consumption. Taking Linwei District, Shaanxi Province as a case, the responses on the rural household energy use to the regional geographical features in the lower reaches of the Weihe River are studied by questionnaire survey face to face, remote sensing image, GIS technology, and contribution rate method. The conclusions are as follows. Firstly, the rural household energy use in different regions is depended on the regional geographical features. In the plain the more fossil energy is used because of the convenient transportation conditions, in the loess tableland the clean energy biogas is utilized more as a result of the advanced and developed farm industry, while in the hill the firewood is the absolutely dominant energy type on account of the rich forest resources. Secondly, the response of each kind of energy to the geographical features is different. Straw is dramatically impacted by the factors of the road cover and terrain, while it is irrelevant to the cultivated land. Firewood consumption is influenced obviously by the factors of the terrain, construction land and cultivated land. Coal consumption is closely related to the road cover, construction land and cultivated land, while it is restricted in the hilly and wooded regions. Biogas is possibly popular to be used in the regions which possess more cultivated land or larger population, while it is restrained in the regions where there is convenient transportation or hilly land. Solar energy consumption is obviously advantageous in the regions which have more cultivated or construction land. On the contrary, it is disadvantageous to be utilized in the regions, where there is higher altitude or more forest land. Honeycomb briquette and electricity on the whole belong to the common energy types and are utilized here and there in the study area, so they are not influenced obviously by the geographical features. Thirdly, the functional zoning of the dominant energy type is instructive to work out the policy of rural energy use. The firewood is more possible to be used in the hill while the biogas and coal are easier to be adopted in the plain or the loess tableland. The coal or firewood is possible to be displaced partly by the electricity, liquefied petroleum gas or natural gas in the plain, where its transportation is convenient and the economy development level is high. Finally, the paper points to that the rural household energy use is comprehensively affected by many factors, but the factors of the geographical features are primary and essential, especially to the different types of regions. Of course, change of the land use or transportation condition will result in the adjustment of regional energy use structure.
在全球气候变化、海平面上升背景下,全球许多海岸已经成为承受巨大压力的生态系统。应用海岸敏感性指数(Coastal Sensitivity Index, CSI)对中国环渤海海岸进行敏感性分析,采用岩性、海岸坡度、地貌、岸线变化速率、相对海平面上升水平、平均波高以及平均潮差多种变量的不同组合计算环渤海海岸479个单元格的敏感性数值。结果表明,增加变量数目或以岩性代替岸线变化能有效提高敏感性指数的区分能力,但不同组合下环渤海海岸敏感性宏观空间格局无较大差异。总体上,胶辽隆起带与大兴安岭-太行隆起带的山地丘陵基岩海岸敏感性相对较低,而以辽东湾辽河口附近沿岸平原海岸和渤海湾-黄河三角洲-莱州湾南岸平原海岸为代表的渤海、华北沉降带表现的敏感性相对较高。研究结果有助于海岸管理与规划人员在全球变化背景下识别海岸敏感区域,从而有选择性地采取应对措施缓解海岸带压力,并且为开展河口海岸生态系统脆弱性研究奠定科学基础。从长远来看,海岸敏感性分析如果与社会因子相结合更能有效提升海岸带系统整体的脆弱性研究水平。
Under global climate change and sea level rise, many coastal areas have become ecosystems withstanding great pressure around the world. This article presents an analysis of sensitivity along Bohai coast using costal sensitivity index(CSI), rock type, coastal slope, geomorphology, shoreline change, sea level rise, mean wave height and mean tide range were adopted and combined to calculate sensitivity value for 479 grid cells. In this study, rock type, coastal slope, geomorphology, shoreline change representing coastal structure variables, sea level rise, mean wave height and mean tide range as coastal process variables were ranked and combined to compute CSI indexes using an equal contribution product model, and the resulted index values were presented in a form of five quarter classification. Results showed that the general relative sensitivity spatial pattern were similar to each other for the three combination methods although some places have minor differences. The lowest grid appeared at the steep rocky coast in the Changxing island of Liaodong Peninsula while the highest grid is located at the muddy plain of Yellow River Delta in the Bohai Bay. The number of discrete values was increasing and the sensitivity range was also enlarged when the variables increased or replacing rock type with shoreline change. Therefore, increasing the number of variable and replacing rock type with shoreline change could enhance the discriminate power of sensitivity index, but there were no significant changes to general relative sensitivity spatial patter. On the whole, rocky cliff coast in Jiao uplift and Daxinganling-Taihangshan uplift zone have relative low sensitivity, while plain coast along Liaodong bay and Bohai bay in Bohai, Huabei subsidence area have relative high sensitivity. This paper could not only enable coastal planner and manager to identify high sensitivity area for adopting measures to relief coastal pressure, but also provide a foundation for further vulnerability research of estuary ecosystem. If social variable are considered to be combined, the vulnerability research of coastal system could be further improved in the long run.
Nowadays with the rapid development of China's economy, the urbanization process has been accelerating significantly. Due to the obvious advantages of geographical location and led by large cities, the urbanization process of many satellite cities located in urban fringes of large cities have been speeding up. How to achieve the sustainable utilization of land resources has become the key issue that must be addressed to keep the urban development. Locating in the middle of the Sichuan basin, Yanjiang District, Ziyang is in Chengdu one-hour economical circle. Taking Yanjiang District as the study area and using the CLUE-S model (The conversion of Land Use and its Effects at Small Region Extent), this article made a dynamic simulation of spatial-temporal pattern of land use from 2005 to 2020 in study area and analyzed the characteristics of its land use and cover change. In the simulation research, by using ETM image and ALOS image as data source, land use data of 2005 and 2009 was obtained through decision tree classification. Thereinto, the data of 2005 was land use data of simulation initial year, and data of 2009 was verification data of simulation result. Gray model is applied to predict the demand for land use types of future years based on the data of land use in previous years.On this basis, combined with the general plan of regional land use, the paper selected socio-economic drive factors and physical geography drive factors which are relating to land use and land cove change, and simulated the spatial-temporal pattern of study area land use from 2005 to 2020 by using CLUE-S model.Logistic model is used to calculate the correlation between types of land use of study area and socio-economic, physical geography drive factors.Then the validity of the prediction results in the study area is gotten through the ROC test.Afterwards based on the land use data of 2005, by inputting correct parameters into the CLUE-S model, The map of land use spatial pattern distribution of Jianyang County in 2009 and 2020 is simulated.Then the paper verified the accuracy of 2009 land use simulation result, of which the Kappa coefficient is 0.887. That showed the simulation results are of high precision. By using GIS（Geographic Information System）, the analysis of simulation results of 16 years(2005-2020) showed that in the study area ,cultivated land area reduced significantly, garden plot area decreased slightly, and construction land and woodland area increased. Due to the strict water protection, the water area changed little. The simulation results and the land layout of general land use planning coincided highly with each other. That meant the CLUE-S model could simulate the land use and cover change of Sichuan basin hilly area more accurately, and could be used for reference in research of land use change of other cities in this area.
Associated with agricultural growth, industrial development, as well as urban population growth in the Nashina Lake region, pollutants from agricultural, industrial and domestic activities have led to the deterioration of water quality. Nashina Lake is in the center of the Lianhuan Lake, an important natural resource protection zone with a geographical area approximately 533 km2. In order to reveal the evolution process of eutrophication of the Nashina Lake, sediment core samples were collected from the lake using a gravity sampler in July 2010. Contents of total nitrogen (TN), total phosphorus (TP) and organic matters (OM) of the sediments samples were determined and the vertical distribution characteristics of TN, TP, OM, and TOC/TN were analyzed. The results show that: TN concentrations in the sediments were ranged from 1 156 mg/kg to 4 191 mg/kg, with an average of 2 891 mg/kg. The TP concentrations in the sediments were ranged from 358 mg/kg to 509 mg/kg, with an average of 442 mg/kg. The concentrations of OM in the sediments were ranged from 1.29% to 3.64%, with an average of 2.03%. Comparatively, the concentrations of OM hada lower variation level, while those of TN and TP fluctuated significantly. The concentrations of TN, TP, and OM generally showed an ascendant trend, especially in1999 -2009. The TOC/TN ratios in the lake sediments were ranged from 2.50 to 7.94, with an average of 4.35. Analysis of results also indicates that the OM and nutrients were derived from aquatic plants, zooplankton, and phytoplankton and alga. Based on the examinations of organic indices, we also found that the impacts of human activities were insignificant from 1831 to 1999, as the lake was continuously in the natural state, with organic matters mainly authigenicand the water belonging to the less clean category. With the rapid economic development from 1999 to 2009, associated with the intensified activities of agricultural, fisheries, animal husbandry and tourism, higher levels of feed and animal manure resulted in higher levels of OM and TN contents. As a result, the organic indices were much higher in 1999-2009. Finally, the organic nitrogen indices were very high for the whole period, indicating the high risk of organic pollution.
为了解近年来陕西省碳足迹的状况,采用《2006年IPCC国家温室气体清单指南》和中国《省级温室气体编制指南》推荐的方法测算了陕西省及其各市的碳足迹。结论如下：1995~2009年,陕西省碳足迹从4 129.38×104t上升到22 460.23×104t,增加了443.91%。从空间上全省碳足迹可分为3类：陕南始终为负值;陕北和渭南市较高;关中除渭南市以外的地区较低。14 a间全省人均碳足迹由1.18 t增加到5.95 t;各市人均碳足迹,陕北较高,关中较低,陕南为负值。全省碳足迹密度从2.00 t/hm2增高到10.90 t/hm2。陕西省的人均和单位面积的碳足迹远高于应对全球气候变化的目标,但空间上差异很大。以2009年为例,全省人均碳足迹是应对全球气候变化目标的2.98倍,而陕北则超过10倍,高于美国;关中为1.29~4.57倍。全省2009年碳足迹密度是应对全球气候变化目标的4.89倍;渭南、咸阳、西安3市高达9.63~16.95倍;榆林、铜川、延安和宝鸡4市为3.54~7.10倍;陕南植被的固碳作用消除了当地的碳排放外,还有剩余碳汇,但尚不能抵消陕北及关中的较高的碳足迹,因此总体看,对气候变化有负面影响,陕西的碳减排任务仍较重。
Global warming caused by carbon emissions would cause severe natural environment and social problems. Extensive work has been done in the area of carbon footprint and quite a few researches about Carbon footprint has been reported home and abroad as well as some improvements. However, there are also some shortcomings such as mainly focusing on carbon emissions, whereas, less involving in carbon sequestration. Shaanxi Province is an important province of western China, in recent years its economy has developed rapidly, but the carbon footprint of this province has not been reported. In this article, based on the method that recommended by "the 2006 IPCC Guidelines for National Greenhouse Gas Inventories" and Chinese" Guidelines for Provincial Greenhouse Gas Inventories", the study on spatio-temporal changes of carbon footprint is performed in Shaanxi Province. Results of the study show that during the period from 1995 to 2011,Shaanxi′s carbon footprint increased from 4 129.38×104t to 22 460.23×104t, or rose by 443.91%. The carbon footprints of 10 cities in Shaanxi Province can be divided into three types. It is always a carbon sink in southern Shaanxi, higher in northern Shaanxi and Weinan City, and yet lower in Guanzhong region excluding Weinan City. The carbon footprint per capita grew from 1.18 t to 5.95 t in the past 14 years in Shaanxi Province. Concerning the carbon footprint per capita of cities in Shaanxi Province, it is higher in northern Shaanxi, lower in Guanzhong regions, and negative in southern Shaanxi. For example, it ranges as Yulin City,Yan'an City, Weinan City, Tongchuan City, Xianyang City, Baoji City, Xi'an City, Hanzhong City, Ankang City and Shangluo City in descending order according to the carbon footprint per capita in 2009. As regards the carbon footprint per unit area, it increased from 2.00 t/hm2 to 10.90 t/hm2 in the whole province. Besides, the carbon footprint per capita and per unit area in Shaanxi Province is higher than the target which set for control of global climate change, whereas widely different in space distribution. For instance, the carbon footprint per capita is 2.98 times higher than the target set for control of global climate change in Shaanxi Province in 2009, and furthermore the data in northern Shaanxi is more than 10 times, which is higher than that of the United States, yet 1.29-4.57 times in Guanzhong region. In 2009, the carbon footprint per unit area in Shaanxi Province is 4.98 times higher than the target set for control of global climate change. In particular, it is up to 9.63-16.95 times in Weinan City, Xianyang City and Xi'an City, and yet 3.45-7.10 times in Yulin City, Tongchuan City, Yanan City and Baoji City. Vegetation carbon sequestration is strong in southern Shaanxi, so there is a surplus of carbon sinks besides eliminating the local carbon emissions. Due to the high carbon footprint in Guanzhong regions and northern Shaanxi, it plays a negative impact on climate change, leaving a tough task of carbon emission reduction in Shaanxi Province.