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1.
北京城市湿地时空演变及驱动力定量分析(英文) 总被引：4，自引：1，他引：3
The decision tree and the threshold methods have been adopted to delineate boundaries and features of water bodies from LANDSAT images. After a spatial overlay analysis and using a remote sensing technique and the wetland inventory data in Beijing, the water bodies were visually classified into different types of urban wetlands, and data on the urban wetlands of Beijing in 1986, 1991, 1996, 2000, 2002, 2004 and 2007 were obtained. Thirteen driving factors that affect wetland change were selected, and gray correlation analysis was employed to calculate the correlation between each driving factor and the total area of urban wetlands. Then, six major driving factors were selected based on the correlation coefficient, and the contribution rates of these six driving factors to the area change of various urban wetlands were calculated based on canonical correlation analysis. After that, this research analyzed the relationship and mechanism between the main driving factors and various types of wetlands. Five conclusions can be drawn. (1) The total area of surface water bodies in Beijing increased from 1986 to 1996, and gradually decreased from 1996 to 2007. (2) The areas of the river wetlands, water storage areas and pool and culture areas gradually decreased, and its variation tendency is consistent with that of the total area of wetlands. The area of the mining water areas and wastewater treatment plants slightly increased. (3) The six factors of driving forces are the annual rainfall, the evaporation, the quantity of inflow water, the volume of groundwater available, the urbanization rate and the daily average discharge of wastewater are the main factors affecting changes in the wetland areas, and they correlate well with the total area of wetlands. (4) The hydrologic indicators of water resources such as the quantity of inflow water and the volume of groundwater are the most important and direct driving forces that affect the change of the wetland area. These factors have a combined contribution rate of 43.94%. (5) Climate factors such as rainfall and evaporation are external factors that affect the changes in wetland area, and they have a contribution rate of 36.54%. (6) Human activities such as the urbanization rate and the daily average quantity of waste-water are major artificial driving factors. They have an influence rate of 19.52%. 相似文献
2.
China has witnessed unprecedented urbanization over the past decades.The rapid expansion of urban population has been dominantly contributed by the floating population from rural areas,of which the spatiotemporal patterns,driving forces,and multidimensional effects are scrutinized and evaluated in this study by using the latest national censuses conducted in 2000 and 2010.Analysis based on the county-level data comes to conclusions as follows.The spatial pattern of floating population has remained stable over the first decade of the new century.The top 1%cities with the largest floating population received 45.5% of all migrants in China.As the rapid development of mega-city regions,the coastal concentration areas of floating population tended to geographically united as a whole,whereas the spatial distribution of migrants within each region varied significantly.The migrant concentration area in the Yangtze River Delta was the largest and its expansion was also the most salient.However,the floating population has growingly moved into provincial capitals and other big cities in the inland regions and its gravity center has moved northward for around 110 km during the study period.The spatial pattern of floating population has been formed jointly by the state and market forces in transitional China and the impacts of state forces have been surpassed by those of market forces in the country as a whole.The attractiveness of coastal cities and counties to the floating population comes mainly from the nonagricultural employment opportunities and public services,reflecting that long-distance and long-term migrants have moved coastward not only to gain employment but also to enjoy city life.By contrast,in the central and western regions,places with a higher economic development level and at a higher administrative level are more attractive to floating populations,demonstrating that the state remains to play an important role in allocating economic resources and promoting regional development in inland China.As the main body of new urban residents,the floating population has contributed substantially to the elevation of the urbanization levels of migrant-sending and-receiving places,by 20.0% nd 49.5% respectively.Compared with extensively investigated interprovincial migrants,intra-provincial migrants have higher intention and ability to permanently live in cities and thus might become the main force of China’s urbanization in the coming decades.The internal migration has also reshaped China’s urban system in terms of its hierarchical organization and spatial structure. 相似文献
3.
《地理学报(英文版)》2019,(4)
Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size and quantity, which is of great significance for farmland protection and food security. This study took Qingyun and Wudi counties in Shandong Province as a study area and used CART decision tree classification to compile land use maps of 1990–2017 based on Landsat and HJ-1 A data. We developed rules to identify abandoned farmland, and explored its spatial distribution, duration, and reclamation. CART accuracy exceeded 85% from 1990–2017. The maximum abandoned farmland area was 5503.86 ha during 1992–2017, with the maximum rate being 5.37%. Farmland abandonment rate was the highest during 1996–1998, and abandonment trend decreased year by year after 2006. Maximum abandonment duration was 15 years(1992–2017), mostly within 4 years and only a few exceeded 10 years. From 1993–2017, the maximum reclaimed abandoned farmland was 2022.3 ha, and the minimum ～20 ha. The maximum reclamation rate was 67.44%m, with annual average rate being 31.83%. This study will help analyze farmland abandonment driving forces in the study area and also provide references to identify abandoned farmland in other areas. 相似文献
4.
The simulation of LUCC based on Logistic-CA-Markov model in Qilian Mountain area,China 总被引：2，自引：0，他引：2
The Qilian mountain area was examined for using the Logistic-CA-Markov coupling model combined with GIS spatial analyst technology to research the transformation of LUCC, driving force system and simulate future tendency of variation. Results show that:(1) Woodland area decreased by 12.55%, while grassland, cultivated land, and settlement areas increased by 0.22%, 7.92%, and 0.03%, respectively, from 1986 to 2014. During the period of 1986 to 2000, forest degradation in the middle section of the mountain area decreased by 1,501.69 km~2. Vegetation cover area improved, with a net increase of grassland area of 38.12 km~2 from 2000 to 2014.(2) For constructing the system driving force, the best simulation scale was 210m×210m. Based on logistic regression analysis, the contribution(weight) of composite driving forces to land use and cover change was obtained, and the weight value was more objectively compared with AHP and MCE method.(3) In the natural scenarios, it is predicted that land use and cover distribution maps of Qilian mountain area in 2028 and 2042, and the Lee-Sallee index test was adopted. Over the next 27 years(2015–2042), farmland, woodland, grassland, settlement areas show an increasing trend, especially settlements with an obvious change of 0.56%. The area of bare land will decrease by 0.89%. Without environmental degradation, tremendous structural change of LUCC will not occur, and typical characteristic of the vertical zone of the mountain would remain. Farmland and settlement areas will increase, but only in the vicinity of Qilian and Sunan counties. 相似文献
5.
《地理学报(英文版)》2019,(10)
The cultivation of mountainous land results in water loss and soil erosion. With rapid urbanization and industrialization in China, labor emigration relieves the cultivation of mountainous areas in regions with high poverty and leads to a significant land use transition. This research built an analysis framework for "land use transition – driving mechanism – effects –responses" for mountainous areas of China undergoing land use transition and then proposed the direction of mountainous land consolidation. The results showed that the turning point of land use morphology was the core of rural land use transition in mountainous areas. The expansion of cropland and the contraction of forestland have transitioned to the abandonment of cropland and the expansion of forestland; this transition was the main characteristic of the dominant land use change. Land marginalization and land ecological functional recovery were the main characteristics of the recessive land use transition in mountainous areas. Socioeconomic factors were the primary driving forces during land use transition in mountainous areas, with labor emigration being the most direct force. The rising costs of farming and the challenging living conditions causing labor emigration were fundamental driving forces. Rural land use transition in mountainous areas reduced the vulnerability of the ecological function of land ecosystems. The advantages and disadvantages of the socioeconomic effects should focus on rural development of mountainous areas as well as the livelihood of farmers; this should be further supported by empirical and quantitative research. Rural land use transition of mountainous areas improves natural restoration and is related to socioeconomic development. Rural land consolidation of mountainous areas should conform to land use transition,with the goal of shifting from the increase of cultivated land to the synergies of ecological and environmental protection. 相似文献
6.
《地理学报(英文版)》2017,(9)
Since the 1950 s, noteworthy farmland abandonment has been occurring in many developed countries and some developing countries. This global land use phenomenon has fundamentally altered extensive rural landscapes. A review of global farmland abandonment under the headings of "land use change – driving mechanisms – impacts and consequences – policy responses" found the following:(1) Farmland abandonment has occurred primarily in developed countries in Europe and North America, but the extent of abandonment has varied significantly.(2) Changing socio-economic factors were the primary driving forces for the farmland abandonment. And land marginalization was the fundamental cause, which was due to the drastic increase of farming opportunity cost, while the direct factor for abandonment was the shrink of agricultural labor forces.(3) Whether to abandon, to what extent and its spatial distributions were finally dependent on integrated effect from the physical conditions, laborer attributes, farming and regional socio-economic conditions at the village, household and parcel scales. With the exception of Eastern Europe, farmland abandonment was more likely to occur in mountainous and hilly areas, due to their unfavorable farming conditions.(4) A study of farmland abandonment should focus on its ecological and environmental effects, while which is more positive or more negative are still in dispute.(5) Increasing agricultural subsidies will be conductive to slowing the rate of farmland abandonment, but this is not the only measure that needs to be implemented. Due to China's rapid urbanization, there is a high probability that the rate of abandonment will increase in the near future. However, very little research has focused on this rapid land-use trend in China, and, as a result, there is an inadequate understanding of the dynamic mechanisms and consequences of this phenomenon. This paper concludes by suggesting some future directions for further research in China. These directions include monitoring regional and national abandonment dynamics, analyzing trends, assessing the risks and socio-economic effects of farmland abandonment, and informing policy making. 相似文献
7.
The reservoir wetland, which is the largest artificial wetland in Beijing, constitutes one of the important urban ecological infrastructures. Considering two elements of natural environment and socio-economy, this paper established the driving factor indexing system of Beijing reservoir wetland evolution. Natural environment driving factors include precipitation, temperature, entry water and groundwater depth; social economic driving factors include resident population, urbanization rate and per capita GDP. Using multi-temporal Landsat TM images from 1984 to 2010 in Beijing, the spatial extent and the distribution of Beijing reservoir wetlands were extracted, and the change of the wetland area about the three decade years were analyzed. Logistic regression model was used to explore for each of the three periods： from 1984 to 1998, from 1998 to 2004 and from 2004 to 2010. The results showed that the leading driving factors and their influences on reservoir wetland evolution were different for each period. During 1984-1998, two natural environment indices： average annual precipitation and entry water index were the major factors driving the increase in wetland area with the contribution rate of Logistic regression being 5.78 and 3.50, respectively, and caused the wetland growth from total area of 104.93 km 2 to 219.96 km 2 . From 1998 to 2004, as the impact of human activities intensified the main driving factors were the number of residents, groundwater depth and urbanization rate with the contribution rate of Logistic regression 9.41, 9.18, and 7.77, respectively, and caused the wetland shrinkage rapidly from the total area of 219.96 km 2 to 95.71 km 2 . During 2004-2010, reservoir wetland evolution was impacted by both natural and socio-economic factors, and the dominant driving factors were urbanization rate and precipitation with the contribution rate of 6.62 and 4.22, respectively, and caused the wetland total area growth slightly to 109.73 km 2 . 相似文献
8.
《地理学报(英文版)》2018,(8)
The question of how to generate maximum socio-economic benefits while at the same time minimizing input from urban land resources lies at the core of regional ecological civilization construction.We apply stochastic frontier analysis(SFA)in this study to municipal input-output data for the period between 2005 and 2014 to evaluate the urbanization efficiency of 110 cities within the Yangtze River Economic Belt(YREB)and then further assess the spatial association characteristics of these values.The results of this study initially reveal that the urbanization efficiency of the YREB increased from 0.34 to 0.53 between 2005 and2014,a significant growth at a cumulative rate of 54.07%.Data show that the efficiency growth rate of cities within the upper reaches of the Yangtze River has been faster than that of their counterparts in the middle and lower reaches,and that there is also a great deal of additional potential for growth in urbanization efficiency across the whole area.Secondly,results show that urbanization efficiency conforms to a"bar-like"distribution across the whole area,gradually decreasing from the east to the west.This trend highlights great intra-provincial differences,but also striking inter-provincial variation within the upper,middle,and lower reaches of the Yangtze River.The total urbanization efficiency of cities within the lower reaches of the river has been the highest,followed successively by those within the middle and upper reaches.Finally,values for Moran’s I within this area remained higher than zero over the study period and have increased annually;this result indicates a positive spatial correlation between the urbanization efficiency of cities and annual increments in agglomeration level.Our use of the local indicators of spatial association(LISA)statistic has enabled us to quantify characteristics of"small agglomeration and large dispersion".Thus,"high-high" (H-H)agglomeration areas can be seen to have spread outwards from around Zhejiang Province and the city of Shanghai,while areas characterized by"low-low"(L-L)patterns are mainly concentrated in the north of Anhui Province and in Sichuan Province.The framework and results of this research are of considerable significance to our understanding of both land use sustainability and balanced development. 相似文献
9.
《地理学报(英文版)》2019,(4)
Farmland marginalization has become the main trend of land-use change in the mountainous areas of China. Using annual survey data of major agricultural production costs and earnings at national and provincial levels in China, this study aims to analyze the reasons and mechanism behind farmland marginalization in mountainous areas. We find that farmers on plains are able to reduce their per mu labor input effectively through intensive use of agricultural machinery, which has minimized the impact of the increase in labor price. However, it is extremely challenging for farmers in mountainous areas to use the same method owing to the rough terrain. Thus, per laborer farming area in these areas has increased relatively slowly, causing a widening gap in agricultural labor productivity between the two regions. With the rapid rise in labor costs since 2003, the marginalization of cultivated land in mountainous areas is evident. In 2013, the net profit of agricultural production in mountainous China fell below zero. Since 2000, the land-use and land-cover change in these areas was characterized by the reduction of farmland area, reforestation, and the enhancement of the NDVI value. The high correlation between the NDVI change rate and the ratio of change in farmland(r = –0.70) and forest(r = 0.91) in mountainous areas at provincial level further attests to the trend of farmland marginalization there. Finally, we summarize the mechanism of such marginalization against the backdrop of the rapid increase in the opportunity cost of farming and the rapid fall of agricultural labor forces in mountainous areas. This study contributes to a deep understanding of the development process of farmland marginalization and abandonment as well as forest transition in Chinese mountainous areas. 相似文献
10.
In a blowing sand system,the wind provides the driving forces for the particle movement while the moving particles exert the opposite forces to the wind by extracting its momentum.The wind-sand interaction that can be characterized by shear stress and force exerted on the wind by moving particles results in the modification of wind profiles.Detailed wind pro-files re-adapted to blown sand movement are measured in a wind tunnel for different grain size populations and at differ-ent free-stream wind velocities.The shear stress with a blowing sand cloud and force exerted on the wind by moving par-ticles are calculated from the measured wind velocity profiles.The results suggest that the wind profiles with presence of blowing sand cloud assume convex-upward curves on the u(z)-ln(z) plot compared with the straight lines characterizing the velocity profiles of clean wind,and they can be better fitted by power function than log-linear function.The exponent of the power function ranging from 0.1 to 0.17 tends to increase with an increase in wind velocity but decrease with an increase in particle size.The force per unit volume exerted on the wind by blown sand drift that is calculated based on the empirical power functions for the wind velocity profiles is found to decrease with height.The particle-induced force makes the total shear stress with blowing sand cloud partitioned into air-borne stress that results from the wind velocity gradient and grain-borne stress that results from the upward or downward movement of particles.The air-borne stress in-creases with an increase in height,while the grain-borne stress decreases with an increase in height.The air-borne shear stress at the top of sand cloud layer increases with both wind velocity and grain size,implying that it increases with sand transport rate for a given grain size.The shear stress with a blowing sand cloud is also closely related to the sand transport rate.Both the total shear stress and grain-borne stress on the grain top is directly proportional to the squ 相似文献
11.
中国边境地区的城镇化格局及其驱动力 总被引：1，自引：0，他引：1
边境地区是内陆对外开放重要门户,也是全面建成小康社会、优化城镇空间格局的重要组成。本文深入分析2000—2015年中国134个边境县域的城镇化发展格局,基于边境地区城镇化特征构建驱动力体系,并运用地理探测器探析边境地区城镇化驱动机制。研究发现:① 2000—2015年边境地区城镇化水平长期低于全国平均水平且差距不断拉大,面临城镇化发展滞后、动力不足的困境。② 同期,西北、西南边境的城镇化水平偏低、但增速较快;西藏边境的城镇化水平最低、增速平缓;东北、北部边境的城镇化水平稍高、但城镇化速度缓慢甚至停滞。③ 交通力和产业力对边境地区城镇化贡献最大,市场力作用较小,各种驱动力之间存在明显的相互加强作用。④ 西北、西南边境分别形成了以产业力和交通力、市场力和行政力为主要驱动力的城镇化发展机制;西藏边境的自然、社会、经济条件薄弱,尚未形成具有较强贡献作用的城镇化驱动力;东北、北部边境的交通力对城镇化贡献较大,与市场、产业的交互增强效果明显。 相似文献
12.
基于分位数回归的江苏省城市化动力因子分析 总被引：1，自引：1，他引：0
识别城市化动力机制对城市化持续、合理发展有着至关重要的作用。文章对国内外研究进行综述发现,以往对城市化动力机制的计量研究多采用最小二乘法(OLS),对城市化的发展动力判断过于粗略的问题。文章首先通过变异系数、核密度估计方法,描绘出江苏省城市化发展分布状况,发现江苏省历年城市化相对差异基本稳定,城市化高速发展的同时,呈现出“双峰趋同”的态势。分位数回归方法能精确地描述解释变量对被解释变量在特定分位点的边际效应,因而可使用分位数回归与OLS回归对比分析影响城市化发展的动力因子。以2005年为界,将研究时段分为两个阶段,前后城市化动力因子存在明显差异OLS回归发现,行政力对城市化的影响作用越来越小,而市场力量、外向力等因素对城市化的影响相对增大。分位数回归结果表明,行政力量对城市化高的地区影响较弱,外向力对城市化水平高的地区的影响要大于城市化水平低的地区,市场力对城市化的影响随着城市化水平的提高则经历了一个先增大后减小的过程,产业结构的影响复杂多变。由此发现,江苏省城市化在不同阶段发展路径有不同的侧重点。 相似文献
13.
县域城镇化的有序发展和优化布局,对于推进城乡要素平等交换、产业结构优化调整、资源与市场优化配置、促进城乡融合发展与乡村振兴具有重要战略意义。研究表明：① 2000—2020年中国县域城镇化快速发展且区域差异显著,东部沿海地区、中部城郊地区和北方边境地区的县域城镇化率相对较高,而西南地区、中部农区、西藏及新疆大部分地区的县域城镇化率偏低;2020年沿“胡焕庸线”东侧的东北地区、冀北及晋陕豫地区、川东及云贵地区成为县域城镇化率高值区,珠三角地区、长三角地区、京津冀地区、成渝地区、长江中下游地区、内蒙古边境地区,以及江浙闽东南沿海一带城镇化率超过50%的县域达716个,占比38.3%。② 县域城镇化是人口、经济、社会、文化等要素综合作用的结果,经济发展水平、自然环境条件、基础设施建设、地理区位条件、公共资源供给、专业创业集群与宏观政策等影响县域城镇化的方向、范围和深度。预测到2035年中国城镇化率为76.04%,县域城镇化率为64.38%。未来要重视强化县域人口、经济、社会和空间组织结构的调整优化。③ 县域城镇化发展可分为大城市周边县域要素集聚型城镇化、专业功能县域产业集聚带动型城镇化、农产品主产区县域农业现代化引领型城镇化、重点生态功能区县域生态保育型城镇化、人口流失县域异地转移集中型城镇化等5种类型。④ 县域城镇化以县城和重点镇作为要素集聚的重要空间载体,形成居业协同与产城融合的多级体系。通过构建产业全价值链、主导产业关联集群、公共服务融合配置体系等创新路径,将推动城乡要素平等交换与公共资源均衡配置、城乡产业融合与等值化发展,促进城乡融合网络化、城乡治理体系化与资源利用集约化,为优化县域人地关系和城镇化空间体系提供持久动力。 相似文献
14.
川滇黔接壤地区城镇化空间特征及驱动力分析 总被引：1，自引：0，他引：1
川滇黔接壤地区经济落后,整体城镇化水平较低,采用川滇黔接壤地区2008年各区县(市)的城镇化率作为测度指标,运用探索性空间数据分析(ESDA)方法,分析了本区域的城镇化水平的空间分布特征。通过对城镇化率的空间自相关系数Moran’s I的计算,发现各区县城镇化率的空间分布并非表现出完全随机性,而是具有明显的空间自相关性,具体表现为低值空间聚集。利用空间计量模型研究了城镇化水平的驱动机制。结果表明:空间误差模型优于空间滞后模型和OLS模型;相邻地区各因素的空间扩散效应对城镇化水平存在显著的影响,其弹性系数为0.416 1;第三产业增加值是城镇化率的最主要驱动因子,其弹性系数为0.777 7。因此,未来川滇黔接壤地区在推进城镇化时,应考虑城镇化水平的空间相关性,建立区域一体化开发政策,并积极发展第三产业,提高第三产业在国民经济中的比重。 相似文献
15.
除了工业发展的动力之外,旅游作为一种市场动力,已被证明可以有效引导乡村城市化的实现。因此,在旅游发展的引导下,乡村城市化背后的驱动力以及各动力主体之间的作用关系亟待新的探索和突破。文章引入“国家―市场―社会”作为分析城市化驱动力的三元框架,以云台山景区内的乡村为例,通过观察、访谈等定性研究方法,认为在旅游发展所引导乡村城市化的背后,国家、市场、社会这三元动力在动力主体、作用发挥、角色扮演上和原本框架中各个动力的所指相比都已经发生了转变,主要体现在市场动力从市场供给一方转变为市场需求一方,社会动力从村民组织转变为村民个体。通过案例的实证分析,总结出了地方政府、需求市场、村民个体作为旅游发展引导下乡村城市化的三元驱动力,“地方政府―需求市场―村民个体”的分析框架也是对“国家―市场―社会”分析框架针对具体研究问题的有效修正和补充。 相似文献
16.
黑龙江垦区城镇化动力机制分析 总被引：8，自引：0，他引：8
垦区是中国农业现代化发展最为迅速的地区之一,也是中国中西部农村工业化和城镇化的参鉴模式之一。以黑龙江垦区为例,分两个层次深入分析垦区城镇化的动力机制的演变过程及与其他农区城镇化的差异。按照城镇化的资金来源和发展主体进行划分,黑龙江垦区城镇化的动力机制过程分为两个阶段,即1947-1978 年自上而下的城镇化阶段,1979-至今自下而上为主的快速城镇化新阶段。从动力因子视角对垦区的动力机制进行了研究,结果表明1989-2010 年间影响垦区城镇化的主要动力依次是市场力、内源力、行政力和外向力。与沿海地区的小城镇相比,黑龙江垦区远离大都市区,城镇化动力表现为典型的“内生性增长”,其发展模式可为我国广大中西部地区村镇发展提供很好的借鉴。但垦区的城镇化方式有其特殊性和先天不足,应加强体制改革,转变经济增长方式,合理规划农垦小城镇,实现垦区农业现代化、工业化、城镇化的协调发展。 相似文献
17.
21世纪以来中国城镇化动力机制分析 总被引：14，自引：0，他引：14
以省区为基本单元,借鉴欧向军等人提出的四维分析视角,构建综合指标体系,结合面板数据分析、时序分析、空间分析等方法,对2000年以来我国城镇化发展的动力机制加以考察。面板数据分析发现市场力、行政力以及内源力作用较强,而外向力作用较弱,反映了我国城镇化动力机制的内生性特点。时序分析发现新世纪以来城镇化动力机制具有较强的动态性与趋势性,虽然金融危机会对动力组成结构产生一定的扰动,但是在长时间尺度上,内源力作用逐渐上升而行政力作用逐渐下降将成为必然的趋势。而空间分析显示,市场力与内源力具有一定的历史依赖性,优势地区将继续保持领先,落后地区继续维持落后状态。随着市场力的不断稳固和内源力的持续增长,城镇化不平衡发展的趋势将加剧。增加行政力的宏观调控作用,促进城镇化动力的多元化是缓解这一不平衡的重要策略。 相似文献
18.
源于时代之变而构筑的双循环新发展格局是市场经济条件下多尺度空间输出理论与劳动地域大分工的“叠加”,为新型城镇化发展带来新的动力源泉。为了揭示这种影响,首先将科技创新和制度改革这2个城镇化发展动力外生变量内生化,构建基于“四维驱动力模型+创新力+制度力”的城镇化动力因子分析框架,结合主成分回归模型对2000年以来重庆市城镇化发展动力进行分析,并据此引入波特的“钻石模型”对其城镇化发展路径进行系统思考。结果表明：1）纳入动力因子体系的创新力和制度力对重庆市城镇化发展产生显著的正向驱动效应,其中创新力表现最为明显。2）重庆市城镇化发展是“六维力”共同作用的结果,但市场力和行政力依然是最主要的驱动力;其次是创新力、内源力和外向力,其中创新力对城镇化发展的驱动效应已在阶段2（2010—2018年）超越行政力的作用效应;最后是制度力;间接反映重庆市城镇化本身是一个内生作用的过程,构筑“以国内大循环为主体、国内国际双循环相互促进”新发展格局具有客观必然性和前瞻性。3）基于波特“钻石模型”分析指出：重庆市存在增长极体量小、市场购买力不足、产品低端化、同质化现象严重等问题,并据此从发展高级生产要素、创造统一的要素自由流动市场,通过“扩内需、调结构、深化对外开放”扩大市场需求,促进城市发展,培育与发展主导产业及其配套产业,抓住机遇“走出去”和建设高质量的服务型政府6个维度对其城镇化高质量发展的实现路径展开具体对策与建议的思考。 相似文献
19.
运用耦合协调模型和基于主成分的多元线性回归法,对2005—2017年间中国西南经济核心区中心城市成都和重庆的城镇化结构质量及其驱动力展开对比分析。结果表明：① 重庆与成都的城镇化结构质量整体水平较高且在持续增长中;成都的城镇化结构质量明显高于重庆,这与因地形、发展阶段、城市定位而限定的重庆“主城区现代化大都市+非主城区广大农村地域”、成都现代化都市圈的空间结构组织形式密切相关;② 城镇化驱动因子呈现多元化、阶段性与差异性特征,行政力和市场力起主要作用;其次是内源力和外向力,但外向力正处在一个不断弱化的过程,反映中国城镇化发展的内生性特征;③ 基于取平均值的城镇化子系统及其系统耦合协调性分析,可有效避免当前城镇化耦合分析中因各子系统指标选取个数不等而带来的失真、不准确问题。 相似文献
20.
Environmental stress is used as an indicator of the overall pressure on regional environmental systems caused by the output of various pollutants as a result of human activities. Based on the pollutant emissions and socioeconomic databases of the counties in Beijing–Tianjin–Hebei region, this paper comprehensively calculates the environmental stress index(ESI) for the urban agglomeration using the entropy weight method(EWM) at the county scale and analyzes the spatiotemporal patterns and the differences among the four types of major functional zones(MFZ) for the period 2012–2016. In addition, the socioeconomic driving forces of environmental stress are quantitatively estimated using the geographically weighted regression(GWR) method based on the STIRPAT model framework. The results show that:(1) The level of environmental stress in the Beijing–Tianjin–Hebei region was significantly alleviated during that time period, with a decrease in ESI of 54.68% by 2016. This decrease was most significant in Beijing, Tangshan, Tianjin, Shijiazhuang, and other central urban areas, as well as the Binhai New Area. The level of environmental stress in counties decreased gradually from the central urban areas to the suburban areas, and the high-level stress counties were eliminated by 2016.(2) The spatial spillover effect of environmental stress increased further at the county scale from 2012 to 2016, and spatial locking and path dependence emerged in the cities of Tangshan and Tianjin.(3) Urbanized zones(development-optimized and development-prioritized zones) were the major areas bearing environmental pollution in the Beijing–Tianjin–Hebei region in that time period. The ESI accounted for 65.98% of the whole region, where there was a need to focus on the prevention and control of environmental pollution.(4) The driving factors of environmental stress at the county scale included population size and the level of economic development. In addition, the technical capacity of environmental waste disposal, the intensity of agricultural production input, the intensity of territorial development, and the level of urbanization also had a certain degree of influence.(5) There was spatial heterogeneity in the effects of the various driving factors on the level of environmental stress. Thus, it was necessary to adopt differentiated environmental governance and reduction countermeasures in respect of emission sources, according to the intensity and spatiotemporal differences in the driving forces in order to improve the accuracy and adaptability of environmental collaborative control in the Beijing–Tianjin–Hebei region. 相似文献