中国人口老龄化空间格局演变及其驱动因素

基于第五、六次人口普查数据,采用ESDA和空间计量模型分析中国地级单元人口老龄化的空间格局演变特征及驱动因素。结果表明：① 胡焕庸线是中国人口老龄化呈东高西低的显著分割线,随着老龄化不断加深,最初自西向东阶梯上升的空间格局逐渐模糊;② 各地级单元间人口老龄化存在较强的空间正相关性,呈现出高-高和低-低相似值集聚的空间分布特征。其中高-高集聚区主要分布在长三角、成渝、山东半岛、辽中南等地区,低-低集聚区主要分布在西北地区、西南部分地区、东北地区西部和珠三角部分地区,10 a间这种正向的空间集聚特征减弱;③ 中国人口老龄化空间格局演变是由人口因素和社会经济因素共同作用决定的,人口因素是直接原因,根本原因是地区社会经济发展的不平衡所致。对此,分别从人口和社会经济角度提出应对人口老龄化对策建议。     

苏锡常地区制造业污染减排综合分析与评价

以太湖流域苏锡常为例,通过构建制造业污染物排放强度、制造业污染物排放量、制造业集聚度、水环境健康水平4个指数对制造业污染物排放特征进行综合评价和分析,通过前3个指标的叠加,得到污染综合程度指数,反映制造业的污染物排放程度和减排效应,然后与水环境健康指数进行叠加,得到制造业污染程度与水环境健康之间的作用关系。结果显示：① 研究区污染程度“高”“较高”“中等”“较低”“低”区域面积分别为816.12、3 314.56、7 628.25、2 500.67和2 861.75 km ²,分别占研究区总面积的4.77%,19.36%,44.55%,14.61%和16.71%,并在空间上存在明显的分异性,太湖上游地区和环太湖地区主要为污染程度“低”和“较低”区域,中游和下游环湖区域主要为污染程度“中等”区域,中下游远离太湖的地区主要污染程度为“高”和“较高”区域;② 制造业的污染程度对水环境的健康存在重要影响作用,在很大程度上呈现强烈的空间正相关性,主要表现为较健康-轻污染、中健康-中污染和低健康-重污染等分布面积广泛（约45%）,在空间上呈现太湖上游地区主要为较健康-轻污染和中健康-轻污染区域,中下游地区则主要为较健康-中污染、中健康-重污染和低健康-重污染分布区域。     

苏南太湖流域污染企业集聚与水环境污染空间耦合关系

以苏南太湖流域为例,利用核密度分析法对分行业污染密集型制造业的空间集聚及与水环境污染之间的空间耦合关系进行研究,结果显示：① 污染密集型企业主要集中在无锡市的惠山区、江阴市的东部、常州市与武进区相邻部分区域、昆山市等地区,常州市整体上污染密集型企业分布较少;② 将制造业集聚与水环境污染空间耦合关系分为高集聚-高污染、低集聚-高污染、低集聚-低污染、高集聚-低污染4种类型,4种类型区面积分别为781.25、2 993.75、12 187.50、1 187.50 km²,分别占研究区总面积的4.56%,17.46%,71.06%,6.92%,两者间正向耦合效应明显,说明制造业在空间上的集聚是造成水环境污染的重要原因之一。     

中国国家级经济技术开发区及其产业空间格局演化——基于地级及以上市面板数据实证研究

选取1989~2015年地级市国家级经济技术开发区数量及主导产业面板数据,运用探索性空间数据分析（ESDA）技术,探讨国家级经济技术开发区及其产业空间格局演化特征。研究发现：① 国家级经济技术开发区整体经历探索阶段、推广阶段、完善阶段和升级阶段等4个阶段;空间格局演化体现出“点轴结构、T型结构、π型结构和网状结构”4种空间形态;空间扩散方式有“点-线-面”扩散、梯度扩散、等级扩散、邻近扩散、集聚扩散和升级扩散6种扩散模式;空间相关性整体呈波动起伏且逐渐上升态势,高-高集聚区从青岛-烟台逐渐演变为长三角、京津冀和福建沿海。② 五大主导产业集中稳定,装备制造始终处于集聚态势,其余4个主导产业呈“分散-集聚”态势;三大类型产业空间布局演化与上述空间格局演化态势基本一致,空间集聚性整体呈“N型”变化态势,技术密集型产业集聚特征最为明显。     

沿海生态地区空间均衡内涵界定与状态评估——以黄河三角洲高效生态经济区为例

以沿海生态地区黄河三角洲高效生态经济区为例,运用组合类型方法测度空间均衡状态,得出以下结论：① 黄河三角洲高效生态区开发强度呈现中部和东翼高、西翼低的空间格局,资源环境承载能力呈现由东翼向西翼递减的空间格局;② 开发不足型、均衡型、过度开发型区域并存,东翼多为均衡型区域,中部和西翼多为过度开发区域,西翼偏北地区多为开发不足区域;③ 区域本底条件、区域发展阶段、产业集聚状态、空间集约程度、管控制度设计、区际关系结构等是影响空间均衡的重要因素。     

低碳约束下中国物流业效率的空间演化及影响因素

以交通运输、仓储和邮政业表征物流业,引入SBM-Undesirable模型,考虑低碳约束下的物流效率,对中国（除港澳台和西藏）的30个省（市、自治区）2003~2014年物流业效率进行测度,基于格局-过程-机理框架系统揭示了低碳约束下物流业效率的空间演化特征及其影响因素：① 低碳约束下物流业效率总体偏低,空间分异呈现出东部>中部>西部的地带性分异态势。② 空间分布具有一定地方依赖性,效率高值区集中于东部沿海,呈现由“大集中、小分散”向“条带状集聚”变化的特征,而低值区高度锁定于西北和西南半壁。③ 空间集聚性较弱,不断趋于收敛,高效率集聚区表现出“北扩西移”的演化趋势。④ 物流业高效率区与高产值区呈现显著的空间同配性。⑤ 物流业效率的空间演化受多种因素综合作用,经济发展、市场环境、产业集聚、信息化水平、政府调控对物流业效率具有显著的正向影响,能源强度则具有负向影响,而对外开放程度、环境规制对物流业效率的提升作用不明显,研究结果可为区域物流协调发展、提高物流业效率提供参考。     

中国绿色发展效率的空间特征及溢出分析

提高绿色发展效率是建设生态文明、促进经济转型发展的重要途径。基于Super-SBM模型对中国（除港、澳、台、西藏地区）2005~2015年绿色发展效率进行测度,从空间异质、空间关联与空间机理3个方面分析绿色发展效率的空间特征,运用空间杜宾模型验证绿色发展效率的溢出效应并探讨各要素的空间传导机制。结果表明：① 2005~2015年,中国绿色发展效率表现为“先平稳再快速再稳定”的阶段性变化规律,地区间差异较大,形成了“东-中-西”阶梯式递减和“南-中-北”对称式分布的空间分异特征,“T”字型发展格局逐渐凸显。② 绿色发展效率存在显著的空间正相关,空间集聚程度逐步降低,热点区域增加,东部沿海地区形成稳定的热点区,中西部形成稳定的冷点区。③ 绿色发展效率的空间自组织性逐渐增强,空间差异不断扩大,由空间自相关导致的结构化分异更加明显,随机成分引起的空间异质性正逐渐减弱,西北-东南是空间差异的主要方向。④ 绿色发展效率存在较强的空间溢出效应,经济水平、技术创新和能源强度产生明显正向效应,产业结构则具有显著负向效应。     

集聚经济、政策激励与中国计算机制造业空间格局——基于贸易数据的实证研究

利用中国海关贸易库数据,对计算机上游和下游出口源地的空间格局演变进行对比分析,以面板数据方法揭示产业集聚和政策激励的作用。研究发现：① 计算机上游出口集中在长三角和珠三角,下游出口集中于长三角和成渝,上游出口格局更分散;② 从动态看,上游出口从珠三角逐步向内陆扩散呈现集聚-分散-集聚的过程,内陆地区承载大部分出口增长;下游出口增长态势更为明显,最初集中分布在长三角和珠三角,2010年以后内陆地区增长迅速,成渝成为继长三角后最为重要的集聚区;③ 研究时段集聚经济对计算机上下游制造业的推动作用降低,政策激励的影响显著增加;④ 沿海和内陆计算机制造业发展模式不同,前者的兴起主要是市场化条件下的集聚经济,后者则受到集聚经济和政策激励的共同影响;⑤ 由于产业特征的差异,集聚经济对计算机上游的影响高于下游。在计算机制造业的发展中,要素成本增加造成的集聚不经济和地方政府的政策激励使得集聚效应逐步让位于政策支持。不同区域不同阶段产业对政策的敏感程度并不相同,政策制定应顺应产业演变规律。     

中国高校创新产出的空间溢出效应与区域经济增长——基于省域数据的空间计量经济分析

采用空间计量经济学方法,基于2003~2012年中国省域高校创新产出及经济增长的统计数据,对中国省域高校创新产出的空间溢出效应及其对区域经济增长的影响进行测算与分析。结果表明：① 中国省域高校创新产出存在一定的空间依赖性,空间分布的不平衡性逐步加剧,从2003年的“沿海-沿江”分布进一步演变至2012年明显集聚在长江三角洲及山东、湖北、重庆等少数省域;② 空间计量模型结果表明,研究期内高校创新产出对区域经济增长的拉动作用不断提升;③ 计量结果显示,高校通过基础研究及产学研合作获得的创新产出对促进区域经济增长的贡献显著。     

基于DEA-ESDA的农业生态效率评价及时空分异研究

采用考虑非期望产出的SBM(Slack-Based Measure)模型,结合探索性空间数据分析方法（ESDA）,对甘肃省各县（区）2000~2014年的农业生态效率及空间分布格局进行实证分析。结果表明：① 考虑非期望产出和不考虑非期望产出的农业生态效率之间存在着明显差异,而考虑非期望产出的农业生态效率结果更符合实际情况。② 在研究期内甘肃省农业生态效率呈持续上升态势,但整体效率偏低;空间变化趋势呈现自西向东、自南向北递减态势,但南北差异小于东西差异。③ 从全局空间分异来看,除2001~2003年外,甘肃省各县（区）农业生态效率呈现正相关特征,空间集聚现象显著;从局部空间分异来看,处于空间正相关地区数量在研究期内逐渐下降,区域农业生态效率的集聚现象减弱。农业生态效率高高（HH）集聚主要分布在河西走廊及甘南地区,空间上具有明显的向邻接地区扩散的趋势;低低（LL）集聚主要分布在甘肃省中部地区,空间分布较为稳定。     

1990-2014年泛长三角地区能源利用碳排放时空格局及影响因素

区域碳排放时空格局及其关键影响因素是近年来学者们关注的热点。本文以中国经济发达、经济关联密切、产业格局变化剧烈的泛长三角地区为案例,分析1990年以来典型年份碳排放的空间分异、时间演变,解析碳排放空间分异的关键影响因素。结果表明：①区域碳排放总量快速增长,总体格局稳中有变,核心区16个城市排放量占比大都超过50%。②以2005年为拐点,之前外围城市增长幅度较低,之后外围城市碳排放量快速增长;外围地区碳排放量占比从2005年的33%快速增加至2014年的47%,区域碳排放量的空间集聚度呈现先增后减的态势。③碳排放格局变化受多种因素影响,不同变量对碳排放的影响各异。其中,工业生产、城镇化建设及人口集聚仍是现阶段泛长三角地区最主要的碳排放来源;固定资产与外商投资对区域碳排放的作用呈增强趋势,但其作用强度较工业生产、城镇化建设、人口集聚要小。地区生产总值对碳排放影响存在倒U型关系,随着经济发展水平的提高,碳排放与经济发展呈现脱钩趋势。研究结果可为揭示经济发展格局变化的环境效应、制定节能减排政策提供参考。     

中国地市工业集聚与污染排放的空间特征及计量检验

采用空间分析和计量模型等方法,研究中国285个地级及以上城市工业集聚和污染排放的空间特征及内在关系,结果表明：① 工业集聚与污染排放的重心分布、轨迹、距离、速度差异较大。东部的工业集聚水平高于中西部,工业污染中西部更突出。工业集聚与污染排放总体呈负相关关系,局域上分为低高、高低、低低3种关联类型。② 工业集聚在全国及东中西部均有利于降低工业污染,但是对不同区域减排作用的大小有所不同,作用由大到小依次是西部、中部和东部。③ 对于工业污染的防治,应继续走集聚化道路。东部要优化产业结构,控制合理的集聚规模;中西部应加强产业集聚,提高技术溢出的污染减排效应。     

The impact of economic agglomeration on water pollutant emissions from the perspective of spatial spillover effects

Whether economic agglomeration can promote improvement in environmental quality is of great importance not only to China’s pollution prevention and control plans but also to its future sustainable development. Based on the COD (Chemical Oxygen Demand) and NH3-N (Ammonia Nitrogen) emissions Database of 339 Cities at the city level in China, this study explores the impact of economic agglomeration on water pollutant emissions, including the differences in magnitude of the impact in relation to city size using an econometric model. The study also examines the spillover effect of economic agglomeration, by conducting univariate and bivariate spatial autocorrelation analysis. The results show that economic agglomeration can effectively reduce water pollutant emissions, and a 1% increase in economic agglomeration could lead to a decrease in COD emissions by 0.117% and NH₃-N emissions by 0.102%. Compared with large and megacities, economic agglomeration has a more prominent effect on the emission reduction of water pollution in small- and medium- sized cities. From the perspective of spatial spillover, the interaction between economic agglomeration and water pollutant emissions shows four basic patterns: high agglomeration-high emissions, high agglomeration-low emissions, low agglomeration-high emissions, and low agglomeration-low emissions. The results suggest that the high agglomeration-high emissions regions are mainly distributed in the Beijing-Tianjin-Hebei region, Shandong Peninsula, and the Harbin-Changchun urban agglomeration; thus, local governments should consider the spatial spillover effect of economic agglomeration in formulating appropriate water pollutant mitigation policies.

     

20世纪末中国迁移人口空间分布格局——基于城市的视角

基于人口普查资料,对20世纪末中国迁移人口在不同行政级别、不同规模城市的分布格局进行分析,并运用探索性空间数据分析技术进一步对地级及以上城市迁移人口的空间分布特征进行研究。结果表明:地级及以上城市在迁移人口流动中占主要地位,其中百万规模级别以上城市在人口迁移中地位尤为显著;县级市间迁移人口规模差异显著,东部地区县级市平均迁移规模要大于西部和中部;省际迁移中城市空间分布大体呈现3个梯状层次;省内迁移中城市空间分布总体表现出极大的相似性,均以各省会城市作为省内迁移人口的第一流入地;不论是省内迁移还是省际迁移,东部沿海地区的珠三角和长三角地区城市群都是迁移人口的高度集聚地。     

中国东部沿海地区四大城市群生态效率评价

东部沿海地区城市群是国家经济发展的战略核心区和国家新型城镇化的主体区之一,其经济—生态可持续发展问题是近年来地理研究领域的前沿主题。首先对城市群生态效率进行定义并将其作为城市群可持续发展测度指标;随后运用传统DEA模型及非期望产出SBM模型,对东部沿海地区四大城市群在2005年、2011年、2014年三个时间截面中的经济效率、生态效率进行比较评价并分析了城市群生态效率时空演变特征;最后通过松弛度分析,为改善沿海地区四大城市群生态效率提供了对应建议。结果表明：① 山东半岛城市群、长三角城市群、珠三角城市群经济综合效率呈先下降后上升的V字型演变格局,而京津冀城市群经济综合效率始终呈下降趋势,2011年前其受纯技术效率影响较大,2011年后受规模效率影响较大;② 京津冀城市群受污染影响带来的效率损失最大,山东半岛城市群相对较小,总体而言,四大城市群生态效率在2005-2011年为下行期,2011-2014为修复期;③ 沿海四大城市群内城市生态效率时空格局具有不同的演化特征,核心城市周边以及沿海沿江地区城市生态效率相对较优,内陆城市则较低;④ 京津冀城市群、长三角城市群、珠三角城市群核心城市资源消耗强度高、经济效益产出高、生态效率高,从整体来看,城市群内多数城市呈污染物排放量下降趋势,污染物治理效果较为明显,环境影响问题有所改善;⑤ 影响东部四大城市群经济生态效率的原因不同,本文从城市群角度进行了差异性分析,并提出了合理化建议。     

Evaluation of the eco-efficiency of four major urban agglomerations in coastal eastern China

Urban agglomerations in China have become the strategic core of national economic development and the main component of the new type of urbanization. However, they are threatened by a series of eco-environmental problems and challenges, including the severe overexploitation of natural resources. Eco-efficiency, which is defined as accomplishing the greatest possible economic benefit with the least possible resource input and damage to the environment, is used as an indicator to quantify the sustainability of urban agglomerations. In this work, a traditional data envelopment analysis (DEA) model with a slack-based measurement (SBM) model of undesirable outputs, was used to assess and compare the economic efficiency and eco-efficiency of four major urban agglomerations in eastern China (UAECs) in 2005, 2011, and 2014. The spatio-temporal characteristics of the evolution of urban agglomerations were analyzed. Based on the results of a slack analysis, suggestions for improving the eco-efficiency of the four UAECs are provided. The overall economic efficiency of urban agglomerations located in the Shandong Peninsula, Yangtze River Delta, and Pearl River Delta displayed a V-shaped pattern (decreased and then increased). In contrast, the overall economic efficiency of the Beijing-Tianjin-Hebei urban agglomeration declined during the study period. The Beijing-Tianjin-Hebei urban agglomeration had a considerable loss of economic efficiency due to pollution, whereas the Shandong Peninsula urban agglomeration was less impacted. Overall, the eco-environmental efficiency of the four UAECs declined from 2005 to 2011 and then increased from 2011 to 2014. In addition, the urban eco-efficiency in the four coastal UAECs was characterized by different evolution patterns. The eco-efficiency was higher in the peri-urban areas of the core cities, riverside areas, and seaside areas and lower in the inland cities. The core cities of the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations were characterized by high resource consumption, economic benefit output, and eco-efficiency. In most of cities in the urban agglomerations, the emission of pollutants declined, leading to a reduction of pollutants and mitigation of environmental problems. In addition, a differential analysis, from the perspective of urban agglomeration, was performed, and concrete suggestions for improvement are proposed.

     

中国东部沿海地区四大城市群生态效率评价（英文）

Urban agglomerations in China have become the strategic core of national economic development and the main component of the new type of urbanization. However, they are threatened by a series of eco-environmental problems and challenges, including the severe overexploitation of natural resources. Eco-efficiency, which is defined as accomplishing the greatest possible economic benefit with the least possible resource input and damage to the environment, is used as an indicator to quantify the sustainability of urban agglomerations. In this work, a traditional data envelopment analysis(DEA) model with a slack-based measurement(SBM) model of undesirable outputs, was used to assess and compare the economic efficiency and eco-efficiency of four major urban agglomerations in eastern China(UAECs) in 2005, 2011, and 2014. The spatio-temporal characteristics of the evolution of urban agglomerations were analyzed. Based on the results of a slack analysis, suggestions for improving the eco-efficiency of the four UAECs are provided. The overall economic efficiency of urban agglomerations located in the Shandong Peninsula, Yangtze River Delta, and Pearl River Delta displayed a V-shaped pattern(decreased and then increased). In contrast, the overall economic efficiency of the Beijing-Tianjin-Hebei urban agglomeration declined during the study period. The Beijing-Tianjin-Hebei urban agglomeration had a considerable loss of economic efficiency due to pollution, whereas the Shandong Peninsula urban agglomeration was less impacted. Overall, the eco-environmental efficiency of the four UAECs declined from 2005 to 2011 and then increased from 2011 to 2014. In addition, the urban eco-efficiency in the four coastal UAECs was characterized by different evolution patterns. The eco-efficiency was higher in the peri-urban areas of the core cities, riverside areas, and seaside areas and lower in the inland cities. The core cities of the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations were characterized by high resource consumption, economic benefit output, and eco-efficiency. In most of cities in the urban agglomerations, the emission of pollutants declined, leading to a reduction of pollutants and mitigation of environmental problems. In addition, a differential analysis, from the perspective of urban agglomeration, was performed, and concrete suggestions for improvement are proposed.     

长江三角洲城市群发展展望

长江三角洲城市群是中国最大的城市群,其发展和演变对中国城市发展、中国沿海经济乃至亚太地区的发展均具有举足轻重的地位和作用。改革开放以来,中国经济的迅速增长,经济全球化过程的加速,均为长江三角洲城市群的发展提供难得的机遇与挑战。重点论述长江三角洲城市群发展展望。     

长三角城市群重心移动及其驱动因素研究

长三角城市群在整个中国的区域发展战略中具有十分重要的地位,为深入把握这一地区发展的空间变化态势、更好地指导长三角地区的发展实际,在测算和分析1994～2004年间长三角城市群重心位置及其变化趋势的基础上,深入探讨了近些年长三角城市群重心移动的驱动因素。结果显示,1994年以来,长三角地区的城市群重心向西北方向移动了超过20 km,重心所在位置由苏州和嘉兴的交界处移至苏州市中心附近。而从其变化的趋势来看,未来的城市群重心仍将偏向西北方向,落到苏州和无锡的交界地带。这种态势的出现主要是由于长三角内部各城市受到其自然和区位条件、经济发展以及社会环境等诸多因素的影响,使得其发展呈现出一定的差异性,进而驱动了各城市不同程度的发展及其在整个长三角地区地位的转变,并最终致使城市群重心向自然区位条件优越、经济社会发展较快的西北方向移动。     

Driving factors and spatiotemporal effects of environmental stress in urban agglomeration: Evidence from the Beijing–Tianjin–Hebei region of China

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.