中关村科技园区智慧产业集群的演化过程、动力因素和集聚模式

论文以高科技园区如何促进智慧产业集聚为研究导向,总结智慧产业集群演化、集聚模式等的研究进展,先基于信息产业的已有分类标准,界定高科技园区智慧产业集群的概念和产业分类;然后基于中关村科技园智慧产业2002—2016年的统计数据,分析中关村科技园智慧产业的空间布局演化和产业增长格局,以波特经典钻石模型为理论框架,细化分析演化过程背后的动力因素,得出高科技园区智慧产业集群的细分动力因素模型,继而以主要动力因素为依据,提炼中关村科技园智慧产业集群演化的主要集聚模式,得出结论:① 中关村科技园区智慧产业集群的演化呈现“一核多园”的先政策引导、后自然溢出的协同发展格局,从海淀核心区向周边园区先辐射集聚,然后基于各园区间的交通、楼宇等生产要素,逐步在非园区范围连贯成片;② 海淀作为核心区集聚了各细分智慧产业,其他各分园则主要在个别细分智慧产业形成集聚优势;③ 中关村科技园区智慧产业的演化主要受生产要素、政府影响变数、相关及支持产业等3大因素影响;④ 中关村科技园区智慧产业集群有生产要素驱动、产业平台驱动、商业地产企业驱动等3种集聚模式。     

京津冀城市群高科技园区协同发展动力机制与合作共建模式——以中关村科技园为例

在推进京津冀城市群高科技园区的协同发展进程中,三地园区之间是否深度融合,决定京津冀协同发展的质量和深度。目前京津冀城市群的天津与河北高科技园区与中关村科技园签有政府间合作协议的有10家,其对接的产业类型依次为电子信息、先进制造、新能源与节能、环境保护、生物医药产业,与中关村科技园总收入占比居前5位的产业类别恰好相对应。三地园区协同发展动力机制可分为产业梯度转移机制、市场需求吸引机制、科技产业孵化转化机制、政府引导驱动机制、市场合作驱动机制5类。合作共建模式主要有：政府引导驱动机制为主的中关村海淀园秦皇岛分园模式、市场合作驱动的类似于固安工业园的产业新城模式、科技产业孵化转化机制为主的保定中关村创新中心模式等。     

中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素分析（英文）

Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.