中部地区大城市还能增加人口吗——以合肥市为例

增加中部地区大城市人口是十分必要的。经计量城市短期和中期人口容量,增加城市人口是可行的。城市人口的增加在短期内要受住房条件的制约,在中期受城市供水能力的影响,在长期上受可利用水资源量的制约。部分中部地区大城市人口容量大于现实人口数,因此鼓励其增加人口乃是目前合理的政策取向。     

跨世纪浙江产业布局的回顾与展望

本文在简要回顾浙江过去２０年产业布局变迁特点的基础上，根据跨世纪浙江产业布局的特点，提出了以中心城市发展战略为主的跨世纪产业布局的基本原则、思路和实施建议。     

近代烟台漕运的特点及现实意义

烟台自古以来就是一个天然良港,在历史上南北漕运中扮演着重要的角色。开埠后,烟台成为我国近代最早的有规模的海运港埠之一,在近代中国经济发展中起着重要的作用,也为近代烟台城市的兴起,作出了重要的贡献。因此,探寻近代烟台漕运发展脉络,对研究制定当代烟台临港产业、临港经济发展战略有着十分重要的现实意义。     

刍议现阶段农民增收问题

党的十六大规划了2020年全面建设小康社会的宏伟蓝图，农民收入低下的问题亟待解决。本文从农民收入现状入手，进而分析造成这种局面的原因，最后提出相应的对策，以期平日实现小康目标。     

nd Foreign Pressure Vessel CodePAN

eveloping status and industry status,this paper proposes revising suggestion for the GB 150—1998 corresponding items.radiograph test;contrast;analysis0压力容器Pressure Vessel Technology63-66TH49C029C7C;C029_C7潘勇琨;60-63工程建设项目信息0压力容     

An agent-based learning-embedded model (ABM-learning) for urban land use planning: A case study of residential land growth simulation in Shenzhen,China

A forward-looking urban land use plan is crucial to a city’s sustainability, which requires a deep understanding of human-environment interactions between different domains, and modelling them soundly. One of the key challenges of modelling these interactions is to understand and model how human individuals make and develop their location decisions by learning that then shape urban land-use patterns. To investigate this issue, we have constructed an extended experience-weighted attraction learning model to represent the human agents’ learning when they make location decisions. Consequently, we propose and have developed an agent-based learning-embedded model (ABM-learning) for residential land growth simulation that incorporates a learning model, a decision-making model, a land use conversion model and the constraint of urban land use master plan. The proposed model was used for a simulation of the residential land growth in Shenzhen city, China. By validating the model against empirical data, the results showed that the site-specific accuracy of the model has been improved when embedding learning model. The analysis on the simulation accuracies has proved the argument that modelling individual-level learning matters in the agent’s decision model and the agent-based models. We also applied the model to predict residential land growth in Shenzhen from 2015 to 2035, and the result can be a reference for land-use allocation in detailed planning of Shenzhen. The ABM-learning is applicable to studying the past urban growth trajectory, aiding in the formulation of detailed residential land and public service facility planning and assessing the land use planning effectiveness.     

创业活力对城市工业经济增长的影响——基于多维视角的实证研究

运用Sys-GMM 面板方法来实证研究创业活力对中国城市工业经济增长的影响。研究显示：创业活力显著促进了城市工业增长；东部城市创业活力对工业经济的促进效应最高，中部和西部城市次之，东北城市则不显著；创业活力对城市工业经济的促进效应随着城市规模的递增而提高，即由小城市、中等城市、大城市至特大城市依次递增；在总体上，拥有经济开放政策的城市创业活力促进效应更大，但是当城市开放政策超过一定数量后，这种促进效应反而下降。     

互联网金融对城市商业银行盈利影响研究——基于上市城商行的实证分析

互联网金融迅速崛起，使我国商业银行面临巨大挑战，尤其是相对弱势的城市商业银行。依据我国16家上市（上证）城商行2011-2018年的数据，结合百度指数，使用主成分分析法构建互联网金融指数，继而通过面板数据回归分析互联网金融对我国城市商业银行盈利的影响。研究表明我国互联网金融的发展与我国城商行的盈利呈现出微弱负相关关系，对我国城商行盈利造成一定程度的影响，然后通过具体分析其影响渠道来对我国城商行效益的提升和发展提出可行建议。     

Brexit,the City and the Contingent Power of Finance

Brexit poses a profound challenge to the economic fortunes of the financial services sector in the United Kingdom (UK) because it threatens to sever access to the single market in the European Union (EU). Recognising this, the City of London’s largest financial firms and main representative bodies supported a Remain vote in the June 2016 referendum, and subsequently lobbied for a ‘soft’ Brexit policy to preserve the City’s lucrative passporting rights. Despite this, the government led by Theresa May pursued a ‘hard’ Brexit policy which threatened to leave the UK outside the single market. How can we explain the City’s apparent failure to influence the UK’s Brexit policy? We argue that whilst the UK financial sector wielded formidable latent structural power, its capacity to translate this into instrumental influence in the policy process was constrained by three factors: the political statecraft of Brexit, leading the government to downgrade the concerns of the financial industry; the reconfiguration of institutional structures, which undermined the City’s voice within government; and constraints on business organisation, caused by collective action problems and heterogeneous preferences. These three factors constitute important scope conditions which highlight the contingent power of finance in liberal market economies.