The green development strategy is not only an important part of realizing the coupling and coordination of regional economy-society and resource environment, but also an important path to promote regional man-earth coordination. British economist David Pierce's book "Green Economy Blueprint" first introduced the concept of "green economy" in 1989. Since the financial crisis, green development has become an important consensus for improving economic competitiveness, coping with financial crisis and solving resource and environmental problems. Many developed countries and regions such as the European Union, the United States and Japan have begun to formulate a series of strategic plans centered on green energy technologies and green manufacturing industries. These plans actively promote the green new policy and open a new era of the green industrial revolution, with a view to achieving a green economy. The economy is recovering and is in a dominant position in the new round of global fierce competition. China faces more severe resource and environmental pressures than other countries. The special national conditions determine that China must implement sustainable development strategy. The “13th Five-Year Plan” (2016-2020) also clearly stated that “green is a necessary precondition for sustainable development and an important manifestation of the people’s pursuit of a better life”. After more than half a century of theoretical and practical evolution, green development is moving from one-dimensional to multi-dimensional, from simple to complex, and is a useful exploration of the sustainable development model. The study uses the projection pursuit evaluation model (PPM) and Gini coefficient, coefficient of variation, spatial autocorrelation and spatial measurement to analyze the temporal and spatial evolution trajectories and impact mechanisms of green development in 30 provincial areas of China, and draws the following conclusions: (1) Green development includes the three dimensions of “green growth-green welfare -green wealth”. Synergistic and balanced development of the three dimensions is the essential feature and inherent requirement of green development; (2) China's green development index is on the rise year by year, but the growth rate is low. Although the green development has achieved remarkable results, problems and pressures still exist. Both the green growth and the green welfare index showed an increasing trend, with an average annual growth rate of 5.77% and 11.74% respectively. The overall green wealth index is relatively low and slowly grows. To a certain extent, it reflects that the growth of the green wealth index is an arduous and long-term task; (3) The regional differences in green development and its composition index are gradually shrinking, but regional differences are still large, featured by eastern region > northeast region > western region > central region. The spatial distribution of green wealth and green development and other branch index is misplaced, and the characteristics of high-high and low-low spatial agglomeration are obvious; (4) Urbanizational level, industrial structure, population density, technological innovation, marketization, and international trade are the main factors affecting China's green development. The research proposes countermeasures from the aspects of ecological culture value, ecological economic construction, ecological environmental protection, resource and environmental carrying capacity and space governance system. It has certain reference value for China's ecological civilization construction and green development.

绿色发展战略既是实现区域经济社会与资源环境耦合协调的重要内容,也是推进区域人地协调的重要路径。研究运用投影寻踪评价模型（PPM）以及基尼系数、变异系数、空间自相关和空间计量等方法分析中国30个省市区绿色发展时空演变轨迹与影响机理,得出以下结论：① 构建涵盖“绿色增长-绿色福利-绿色财富”三个维度的绿色发展系统,三个维度的协同一致和均衡发展是绿色发展的本质特征和内在要求;② 中国绿色发展指数呈逐年上升趋势,但增长速度较低,绿色发展虽然取得了显著成效,但问题与压力依然严峻。绿色增长和绿色福利指数均呈现增长趋势,年均增长率分别为5.77%、11.74%,绿色财富指数整体相对较低且增长缓慢,一定程度上反映了绿色财富指数增长的艰巨性和长期性;③ 绿色发展及其构成指数的区域差异呈逐渐缩小趋势,但差异程度依然较大,总体呈东部地区>东北地区>西部地区>中部地区的特征,绿色财富与绿色发展及其他构成指数空间分布错位,高高、低低空间集聚特征明显;④ 城镇化水平、产业结构、人口密度、科技创新、市场化、国际贸易是影响中国绿色发展的主要因素。研究从塑造生态文化理念、发展生态高效产业、提升资源环境承载能力和优化空间治理体系等方面提出对策建议,对中国生态文明建设和绿色发展推进具有一定的借鉴参考价值。

Green development is pivotal to resolving natural environmental constraints, solving national economic transition, and supporting and realizing the United Nations sustainable development goals. It is gradually growing into a crucial guideline for China's ecological civilization construction, "Beautiful China" development, and global economic transition and restructure. Based on a thorough review of the concept of green development, this paper accurately depicts a full picture of China's spatio-temporal patterns of urban green development efficiency (UGDE) in 2005-2015 by using SBM-Undesirable, the Theil index and the Spatial Markov Chain methods. Moreover, the influencing mechanism has been further discussed based on a quantitative analysis of both natural and human factors. Our results demonstrate that: (1) UGDE increased steadily by 10% from 0.475 in 2005 to 0.523 in 2015. And temporally, it shows a pattern of "W"-shaped fluctuated growth. (2) Spatially, UGDE decreased from eastern to central China, and further to western China. Besides, there is an inverted pyramid pattern of "national level > regional level > local level" urban agglomeration in UDGE growth. Moreover, there is a steady urban scale structure from super city to small city in UDGE. (3) There is an evident concentration of cities with high-level and low-level UDGE, indicating a significant influence of path dependence. (4) Quantitatively speaking, compared to natural factors, human factors such as economy size, industry structure, and openness level play a more important role in influencing the UDGE.

绿色发展作为化解自然环境约束、破解经济转型难题、支撑和实现全球可持续发展目标（SDGs）关键。正逐渐成为中国生态文明建设、美丽中国建设和全球经济转型与重构的重要指导理念。在梳理绿色发展概念与内涵基础上,采用SBM-Undesirable模型、泰尔指数和空间马尔科夫链等方法,对2005-2015年中国城市绿色发展效率时空分异特征及其演变过程进行了测度与刻画,并进一步耦合自然与人文因素定量探讨了人地关系地域系统下的影响机制。研究表明：① 2005-2015年中国城市绿色发展效率稳步提升,由0.475增加到0.523,总体提高了10%,时序上呈现“W”型波动增加的阶段性演变特征。② 中国城市绿色发展效率呈现出“东中西”阶梯状递减的区域差异规律,不同类型城市群具有“国家级>区域性>地方性”倒金字塔式集群增长特征,形成了“超大城市>特大城市>大城市>中等城市>小城市”稳定等级规模结构。③ 中国城市绿色发展效率空间集聚特征显著,高效率城市存在正向溢出效应,低效率城市则负向溢出影响,“高高集聚、高带动低”的空间俱乐部趋同现象较为凸显,不同类型城市演化存在显著的路径依赖与时空惯性。④ 人地关系地域系统视角下,人文社会因素对城市绿色发展效率影响程度大于自然本底要素,其中经济实力、产业结构、开放程度和城市气温呈现积极促进作用。

：The concepts of innovation, coordination, green, open and sharing are the keys to the development of China and even the future. Green development is the main tone of the 13th Five-Year Plan of China, and it emphasizes on the mutual unity and coordinated development between economic growth and environmental protection. It is a kind of human-oriented way of sustainable development. Improving the efficiency of green development is an important way to achieve the ecological civilization construction and transformation of economic development the important way. This study used the spatial analysis methods, such as the Super-SBM model, spatial autocorrelation, spatial variation functions and spatial durbin model to measure the green development efficiency from 2005 to 2015 in China (Tibet, Hong Kong, Macao and Taiwan are excluded), by building an input and output index system of green development efficiency. In addition, from the perspective of geography space, it revealed the spatial pattern and spillover effects of green development efficiency in China. The results showed that: 1) From 2005 to 2015, the efficiency of China’s green development is characterized by the stage characteristics of ‘stable at beginning, then fast and last stable again’. It shows an overall upward trend with large differences among regions. The regional differentiation of the ‘East-Central-West’ stepwise decreasing and the ‘South-Central-North’ symmetrical distribution, and ‘T’ shaped shaft development pattern is particularly evident. 2) There is a positive correlation between green development efficiency, the degree of spatial agglomeration gradually decreases, the hot spots increase, the eastern coastal areas form stable hot spots, and the central and western parts form stable cold spots. 3) The spatial self-organization of green development efficiency is more and more strong, the space difference is gradually increased, the structural differentiation caused by spatial autocorrelation is more obvious, the spatial heterogeneity caused by random components is gradually weakened, and the space between northwest and southeast Significant difference. 4) There is a significant spillover effect of green development efficiency, a significant positive effect on the level of economic development, and a significant negative effect on industrial structure, urbanization and technological innovation. Trying hard to explore the law of spatial evolution of green development and provide a reference for the coordinated green development of the three systems of regional economy, society and environment.

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。

The “T” structures in China’s territorial development and economic layout remain to be the two areas with the greatest potential for economic growth in China, among which the Yangtze River Economic Belt is the only first-class development axis across the three plates of East, Central and West. Over the years, in the excessive pursuit of rapid economic growth, the Yangtze River and its two sides of the great lakes and major tributaries have suffered serious environmental pollution, ecosystem imbalance, and frequent water security problems. To pay attention to the great protection together and not to engage in the great development is the guideline for the construction of the green Yangtze River Economic Belt, and the environmental innovation is the core support of the green development of the economic belt of the Yangtze River. In this paper, the spatial and temporal characteristics of environmental innovation in the Yangtze River Economic Belt are explored by taking the number of green patent applications as the evaluation index, and the influencing factors are also revealed. The results are as follows. First, from 2007 to 2017, the number of green patent applications in the Yangtze River Economic Belt increased from 35422 to 47563, among which the number of patent applications in the field of green building technology always ranked first. In addition, the environmental innovation of Yangtze River Economic Belt has significant spatial correlation effect. Second, in terms of spatial pattern, the environmental innovation of the Yangtze River Economic Belt was dominated by the eastern region, and shows the characteristics of development from the East to the West. Third, environmental innovation in the Yangtze River Economic Belt has significant spatial spillover effects. The intensity of environmental regulations, technological innovation level, city scale, the proportion of tertiary industry, and urban air quality all have a significant positive role in promoting the urban environmental innovation capacity of the Yangtze River Economic Belt. The intensity of environmental regulation does not have obvious spatial spillover effects, and the level of urban technological innovation has significant negative spillover effects.

以绿色专利申请量为评价指标对长江经济带环境创新的时空特征进行了多维度挖掘，并揭示了其影响因素，研究发现：① 时序发展上，长江经济带环境创新空间集聚特征显著，绿色建筑技术始终主导长江经济带环境创新演化发展；② 空间演化上，长江经济带环境创新格局整体表现为由东部地区主导，并呈现出由东向西的阶梯推进的发展特征，且呈现出显著的空间关联性；③ 长江经济带环境创新具有显著的空间溢出效应，环境规制强度、技术创新水平、城市规模、第三产业比重和环境质量皆对长江经济带城市环境创新能力有着显著的正向促进作用，但环境规制强度不具有明显的空间溢出效应，且技术创新水平具有显著的负向溢出效应。

Technological innovation and environmental protection are two major themes of urban development at present. However, it is still controversial question that whether technological innovation can reduce environmental pollution and improve the ecological environment. This article holds that this debate is meaningless without considering spatial scale and factors. Therefore, it is necessary to study the spatial differentiation and mechanism of the impact of urban technological innovation on environmental pollution. In this study, based on the data of 285 cities (excluding those in autonomous prefectures, Hong Kong, Macau and Taiwan) at prefecture level and above in China, the spatial autocorrelation model is used to analyze the spatial correlation between technological innovation and environmental pollution at global and local scales, geographically weighted regression model is used to analyze the regional differences of the impact of technological innovation on environmental pollution, and Geodetector is used to analyze its influence mechanism. The results show that: 1) Both technological innovation and environmental pollution have significant spatial agglomeration, they have positive spatial correlation on the global scale, while positive and negative correlation coexist on the local scale. And there are 4 agglomeration types: high innovation-high pollution, low innovation-low pollution, high innovation-low pollution, low innovation-high pollution. 2) Technological innovation of all cities in the study area has a negative impact on environmental pollution, and the impact degree presents a spatial differentiation pattern with increasing impact degree from east to west. The calculation results of the existing samples conditionally verify the EKC curve, that is, technological innovation can promote the healthy development of economy and environment, and technological innovation in the eastern region can accelerate the arrival of EKC inflection point. 3) Environmental pollution is the result of many factors. Economic development, industrial structure, human capital, foreign direct investment and environmental regulation can all strengthen the effect of technological innovation on improving environmental pollution. The impact of technological innovation on environmental pollution is more to optimize industrial structure through technological progress, thereby reducing environmental pollution.

基于中国285个地级及以上城市的数据，利用空间自相关模型，在全域和局域尺度分析技术创新与环境污染的空间关联，在此基础上，使用地理加权回归模型分析技术创新对环境污染影响程度的地区差异，并借助地理探测器分析其影响机理。研究结果表明:① 技术创新与环境污染均存在显著的空间集聚性，二者在全域尺度上存在正向的空间关联，在局域尺度上正向和负向关联并存，具有高创新-高污染、低创新-低污染、高创新-低污染、低创新-高污染4种集聚类型。② 所有城市的技术创新对环境污染均存在负向影响，影响程度呈现出由东向西渐次递增的空间分异格局，现有样本计算结果有条件地支持EKC曲线，技术创新能够促进经济与环境的良性发展。③ 环境污染是多种因素共同作用的结果，经济发展、产业结构、人力资本、外商直接投资、环境规制均能强化技术创新对环境污染的改善作用，技术创新对环境污染的影响更多的是通过技术进步优化产业结构，从而减少环境污染。

The transfer of polluting enterprises/industries has reshaped regional development pattern and changed the distribution of environmental pollution, which has a significant impact on regional sustainable development. The research on this transfer is of great significance for pollution control and regional coordinated development. Based on literature review, the article generalizes systematically the concepts and categories of polluting enterprises/industries and summarizes their transfer characteristics, laws, patterns and dynamic mechanisms. The review of the literature showed that: (1) official documents, pollution emission intensity, pollution emission scale are the main basis of polluting enterprises/industries definition and classification. (2) Polluting enterprises/industries form different transfer patterns on different geographical scales, and they transfer in municipal, provincial, and national scales. Three modes (decentralized peripheral areas transfer, centralized functional areas transfer, and moving away from sensitive areas) are formed within the city. The polluting enterprises/industries transfer both from developed cities to less developed cities and from areas with strong environmental regulations to marginal areas with weak environmental regulations among the cities in the province. Neighboring province has the transfer mode that is centered on developed provinces with stronger environmental regulations and spread to neighboring provinces. This transfer pattern has three paths that tend to move to the boundary of multiple provinces, move from the upstream provinces of the river to downstream provinces and move to secondary cities along the traffic line. Inter-provincial transfer of the polluting enterprises/industries forms three modes of cost-oriented, market-expanding and policy-driven factors. ③ The transfer direction of the polluting enterprises/industries presents certain characteristics and laws. We conclude fifteen kinds of transfer directions based on regional, geographic, economic, functional areas, and policy perspectives. (4) The transfer modes of polluting enterprises includes overall transfer and partial transfer. The partial transfer includes migration of production base, process outsourcing, green mergers and acquisitions, establishment of subsidiaries or branches, etc. (5) Economic, social, geographical and policy factors not only affect the transfer of polluting enterprises/industries separately, but also comprehensively form the complex transfer mechanism. In the multi-dimensional complex influence mechanism, the government plays an important role and enterprise heterogeneity, spatial agglomeration effect and system have a significant impact. Finally, the shortcomings of relevant domestic research are discussed, and some thoughts and policy recommendations for polluting enterprise/industry transfer are proposed.

污染企业/产业的转移不仅重塑了区域发展格局,也改变了环境污染的分布,对区域可持续发展产生了显著影响。探讨污染企业/产业转移对于污染治理和区域协调发展具有重要意义。基于对相关文献的梳理,系统归纳了污染企业/产业的概念和类别,总结了污染企业/产业的转移特征、模式和动力机制。研究表明：① 官方文件、污染排放强度、污染排放规模是污染产业界定和分类的主要依据。② 污染企业/产业在不同的地理尺度上形成不同的转移模式：在城市内部形成分散式外围区转移、集中式功能区转移、远离敏感区转移三种模式;在省内城市间呈现出由发达城市向欠发达城市、由环境规制强的核心区向环境规制弱的边缘区的转移特征。邻省转移模式以经济较发达和环境规制较强的省份为中心,向邻近省份扩散,并形成向多省交界处、由河流上游省份向下游省份、沿交通线向次级城市的三种转移路径。跨省转移形成成本导向型、市场扩张型和政策推动型三种模式。③ 污染企业/产业的转移方向呈现出一定的特征和模式,包括区域、地理、经济、功能区、政策5种研究视角和15种转移方向。④ 污染企业的迁移方式包括整体迁移和部分迁移两种模式,后者包括迁移生产基地、工序外包、绿色并购与对外收购、建立子公司或分厂等。⑤ 影响污染企业/产业转移的因素既包括经济、社会、地理、政策等单因素,也包括多种因素的交互作用。在多维的复杂影响机制中,政府扮演了重要角色,企业的异质性、空间集聚效应、制度等都产生了重要的影响。最后讨论了国内相关研究的不足,并在此基础上提出一些思考和建议。

Pollution-intensive industries can promote regional economic growth. They also pose a great threat to regional environmental system. Polluting industries may be protected by regional policies due to their contribution to economic growth, but the local governments may also impose some restrictions and controls on them by means of the environmental regulations. From 1980 to 2014, the development period of pollution-intensive industries in China can be divided into three stages: slow growth, rapid expansion, and structural transition. The spatial distribution of pollution industries changed from dispersal to concentration and then to decentralization. The central provinces such as Henan, Hubei, Hunan and Jiangxi have become the major areas of pollution-intensive industries transfer. By establishing the regional attribute model and the regional-industrial interaction model, this paper quantitatively analyzes the influence of environmental regulation and local protection on the spatial distribution of the pollution-intensive industries in China during 2001-2014, and tests whether there exist the pollution haven hypothesis and the Porter hypothesis. Environmental regulation and local protection are important factors that influence the spatial distribution of polluting industries, but there exists obvious industrial and regional heterogeneity. Industries with a high degree of pollution are more likely to be affected by environmental regulations, and industries with high share of state ownership are more likely to be protected by local governments. Environmental regulation and local protection are two opposite forces, which have the effect of mutual inhibition and weakening. The roles of environmental regulation and local protection in coastal and inland areas have significant differences. The effect of environmental regulation is more prominent in eastern China, while that of local protection in central and western China is more significant. In order to prevent the central and western regions from being the "pollution haven" and to realize the win-win between economic growth and environmental protection, a differentiated policy should be formulated according to local conditions and the development stage.

污染密集型产业在促进区域经济增长的同时会对区域生态环境带来巨大威胁,其发展布局会受到地方保护和环境规制的双重影响。1980-2014年中国污染密集型产业的发展经历了缓慢增长、快速扩张和结构转型3个阶段,总体呈现“分散—集中—分散”的空间特征,中部省份是现阶段承接污染密集型产业转移的主要地区。通过建立区域属性模型、区域—产业交互项模型,定量分析环境规制、地方保护对污染密集型产业布局空间变化的影响,检验“污染避难所假说”和“波特假说”,发现环境规制和地方保护已成为污染密集型产业布局的重要影响因素,但存在显著的产业异质性和区域差异性;污染程度高的产业易受到环境规制的影响;相比高税收产业,高国有比重的产业更易受到地方的保护;环境规制和地方保护作为两种相反的力量,彼此之间相互抑制;环境规制的作用在东部地区比较突出,而中、西部地区地方保护的作用更为明显。为防止中西部地区成为污染密集型产业的“避难所”,应因地因时制宜制定差异化政策,促进经济与环境保护的协调发展。

Foreign direct investment (FDI) is an important driving force for economic growth and technological innovation, but it also brings environmental pollution problems along with economic development. From the perspective of technological innovation, the impact of FDI on China's environmental pollution deserves further study. With the spatial econometric tools employed to account for the potential spatial dependence of environmental pollution, this study uses the panel data of 30 province-level units in China from 1998 to 2016 to investigate the impact of FDI and technological innovation on environmental pollution. The results show that increased FDI can reduce environmental pollution, confirming the existence of the "pollution halo hypothesis"; technological innovation can reduce the emissions of sulfur dioxide and smoke dust but increase the chemical oxygen demand. Therefore, vigorous introduction of foreign capital is good for sustainable development for government, but it is also necessary to pay attention to screening and identifying environment-friendly enterprises with advanced production technology and management experience and to reject high-pollution and high-energy-consuming enterprises eliminated by developed countries.

The issue of air pollution in China has already surged in recent years, attracting public concerns, even great anxiety. This paper employs a unique cross-sectional data set of 150 Chinese cities in 2014 and adopts air quality index (AQI), an indicator newly-developed by the ministry of environmental protection of China, to measure air quality. Then, from a spatial heterogeneity perspective it applies ordinary least square (OLS) technique and geographically weighted regression (GWR) model to analyze the impact of foreign direct investment (FDI) on air pollution controlling for other five explanatory variables, namely, per capita GDP, population density, sulfur dioxide emissions, PM2.5 concentration, and the number of vehicles. Meanwhile, it also investigates if there is an inverted U-shaped curve between per capita GDP and air quality index, specifically environmental Kuznets curve (EKC). The findings are the following. Firstly, there is no evidence of EKC, but an increasing linear relationship between income and air pollution. Secondly, the variable of FDI is found to be significant and negative, indicating that an increase of foreign capital may reduce air quality index, in other words, improve air quality. This is because foreign capital brings advanced technologies, improving air quality effectively. Besides, sulfur dioxide emissions and PM2.5 concentration are found to have significant and positive impacts on AQI while population density, as an indicator of environmental awareness, is found to have a negative impact on AQI. However, from the results of the OLS model, the variable of private cars is not statistically significant, even found to be positive. It hence was deleted from the model. Thirdly, the GWR model results suggest that the impacts of FDI of 150 cities on air pollution greatly vary from city to city. Specifically speaking, foreign capitals of the northeastern cities, Guangzhong plain urban cities and cities of the middle reaches of the Yangtze River play a more important role in reducing air pollution than those of other cities. On the contrary, the contribution of foreign capitals of Shandong peninsula cities and Sichuan-Chongqing urban cluster is found to be insignificant. Finally, from the above analysis, a series of policy handles and suggestions are given.

空气污染问题引起了人们极大的关注。以中国2014年150个地级市作为样本数据,采用空气质量指数（AQI）作为全面衡量空气污染的指标,运用地理加权回归模型从空间异质性视角出发,分析了不同城市外商直接投资与空气污染之间的关系。研究结果表明：总体上,外商直接投资由于带来了先进的技术,有利于空气质量的改善。此外,人均地区生产总值的增加、二氧化硫和PM_2.5浓度的提高均是导致空气污染加剧的重要因素;而环保意识的提升则有利于空气质量的改善。民用汽车保有量变量在统计上不显著。从地理加权回归模型估计结果来看,不同城市的外商直接投资对环境的改善作用存在显著的空间异质性。其中,东北城市群、关中城市群和长江中游城市群外商直接投资对空气质量的改善作用最大,山东半岛城市群和川渝城市群外商直接投资对空气质量的改善作用不明显。

基于Fujita和Thisse提出的理论框架,引入制度环境变量,研究地区制造业集聚和制度环境通过地区技术创新影响空间技术溢出的机制。在此基础上,采用2003-2012年中国内地30个省、自治区和直辖市的面板数据,运用面板工具变量两阶段最小二乘法（IV-TSLS）对其机制进行实证检验。研究表明：一方面,从全国总体样本来看,加入制度环境变量后,制造业集聚与法律制度的交互项对地区技术创新和空间技术溢出有显著的正向促进作用;另一方面,从分地区的样本来看,东部地区与全国样本检验结果一致,而中西部地区的制造业集聚并未产生显著的空间技术溢出效应。进而,提出通过优化制造业集聚提高空间技术溢出效应的政策建议。

In today's world, the innovation of science and technology has become the key support for improving comprehensive national strength, also the strong lead for changing the mode of social production and lifestyle. Which country has world-class scientific and technological innovation cities maximizes the attraction to global innovation factors. Which country maximizes the attraction to global innovation factors wins strategic initiative in international competition. Based on urban ZIP code spatial database, the evaluation system of urban innovation was established in the perspective of innovation output, and the spatial evolutionary mode, which is concerning the structure of innovation space of Shanghai and Beijing from 1991 to 2014, was discussed. The results of the research indicated that ZIP districts provided a fresh perspective to study the growth of spatial structure of urban innovation. And the result, which is of the evaluation of spacial structure of urban innovation using urban ZIP code spatial database established by connecting random edge points and Voronoi, was relatively ideal. So the promotional value exists. During the 25 years, the growth of spatial structure of innovation of Shanghai and Beijing demonstrated a lot of common features: with the increase of urban space units participated in innovation year by year, although the overall gap of regional innovation output has narrowed, the trend of spatial agglomeration has strengthened. The growth of spatial structure of innovation of Shanghai and Beijing demonstrated the differences among common features during the 25 years as well: in the trend of the suburbanization of innovation resources, the spatial structure of innovation of Shanghai indicated that the driver has evolved from the single-core driver to the multi-core resonance evolution. Radiation effect using traffic arteries as spatial diffusion corridors was prominent. Accordingly, the spatial correlation effect of its innovation output also indicated the city center hollowness; the spatial structure of innovation of Beijing was single-core (the city center) oriented structure all the way. In the trend that innovation resources were agglomerated in the center, the spatial correlation effect of innovation output indicated the characteristics of the evolutionary feature where "rural area encircles cities". The spatial structure of innovation of Shanghai and Beijing has intrinsic consistency with the spatial structure of their respective regions (Yangtze River Delta urban agglomeration and Beijing-Tianjin-Hebei urban agglomeration), which suggested that the principle of proportional and disproportional distribution of city-scale pattern of technological and innovational activities is closely related to its regional innovation pattern.

基于城市邮编区划空间数据库,从创新产出的视角建构城市创新评价指标体系,对1991-2014年上海市和北京市的创新空间结构的空间演化模式进行了探讨。研究发现：① 邮政区划为研究城市创新空间结构的生长提供了全新的视角,基于随机边缘点连线以及泰森多边形法构建的城市邮编空间数据库评价城市创新空间结构的结果较为理想,因此具有推广价值;② 25年间,上海市和北京市的创新空间结构生长体现出了诸多的共性特征：随着参与创新的城市空间单元逐年增加,区域创新产出虽总体差距在缩小,但空间集聚趋势在加剧;③ 25年间,上海市和北京市的创新空间结构生长也体现出了共性上的差异性,其中上海市创新空间结构在创新资源郊区化转移的趋势下,呈现出由单核驱动向多核共振演进,以交通干道为空间扩散廊道的辐射效应凸显,相应的,其创新产出空间关联效应也显现出了市中心空心化现象;而北京市创新空间结构始终为市中心单核主导型,并在创新资源不断向中心集聚趋势下,其创新产出空间关联效应呈现出“农村包围城市”的演化特征;④ 上海市和北京市创新空间结构与其所在的区域创新空间结构（长三角城市群和京津冀城市群）具有内在的一致性,表明城内尺度科技创新活动空间分布的均衡与非均衡规律与其所处的区域创新格局密切相关。

<p>Based on the records of patent transfer from the patent retrieval and analysis platform in the State Intellectual Property Office of China, this research built an assessment index and model for technology transfer in China's city system in terms of agglomeration and dispersion, using big data mining technology, geo-coding technology, spatial autocorrelation model and multiple linear regression model. Then we studied the spatial-temporal pattern, agglomeration model and influencing factors of technology transfer in China's city system from 2001 to 2015, and obtained the following results. Firstly, with the increasing capability of city's technology transfer and the growing number of cities involved in transferring technology, the polarization and strong agglomeration of technology transfer in China's city system have been intensified. Secondly, technology transfer in China's city system has experienced a process of constant spatial polarization, the three-pole pattern led by the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region has been gradually prominent. Thirdly, technology transfer system from global to local scale in China's city system has initially taken shape. Beijing, Shanghai and Shenzhen have become the three global centers of China in technology transfer. Fourthly, technology transfer in China's city system has produced an obvious spatial correlation and agglomeration effect. The four types are mainly in the cluster, and the geographical proximity of technology transfer in China's city system is significant. Last but not least, the influencing factors of technology transfer in China's city system were also verified by multiple linear regression model. We found that the demand and supply capacity respectively represented by the scale of tertiary industry and the number of patent applications has a great influence on the growth of technology transfer capability. In addition, the number of R & D employees is an important factor, but its correlation is low. The findings further confirm that the scale of primary industry has a significant impedance effect on city's technology transfer capability.</p>

以国家知识产权局专利检索及分析平台中历年专利转让记录为数据源，采用大数据挖掘技术、地理信息编码技术、空间自相关模型和多元线性回归模型，并从集聚和扩散两个方面构建城市创新技术转移能力评价指标体系及评估模型，对2001-2015年中国城市技术转移的时空格局、集聚模式及影响因素进行了研究。结果发现：① 2001-2015年，随着城市创新技术转移能力的不断上升，且在参与创新技术转移的城市数量不断增加情境下，中国城市创新技术转移能力的两极分化及强集聚特征持续发育;② 中国城市创新技术转移格局经历着空间不断极化的历程，由京津冀、长三角和珠三角主导的三极格局逐渐凸显;③ 中国城市创新技术集散体系不断完善，从全球至地方的中国创新技术集散体系已初步形成;④ 中国城市创新技术转移呈现出显著的空间关联与集聚效应，4种类型基本呈“抱团”分布，城市创新技术转移的地理邻近性显著;⑤ 多元线性回归模型发现，城市创新技术的需求能力和供给能力决定其转移能力，第三产业产值规模和专利申请量对城市创新技术转移能力影响较大。另外，研发人员数量也是影响城市技术转移能力的重要因素，但是相关性较低，而城市第一产值规模对城市创新技术转移能力具有显著的阻抗作用。（注：①考虑到专利技术从申请至授权以及转移的期限较长，因此本文城市吸收、转出的专利速度主要基于1年转移量、2年转移量和5年转移量来综合评定。）

In this study, the principles of sustainable development motivated the validation of an original model, in which cooperation in R & D and the green route of eco-innovations satisfactorily explain companies' socioeconomic performance. Data were collected from 221 electrical and electronic manufacturers operating in Brazil and processed via SmartPLS (R) 3 using the Structural Equation Modeling technique. We originally demonstrated that the knowledge synergies released from cooperation in R & D with heterogeneous agents are advantageous to the introduction of multidimensional types of eco-innovations, including both technological (product and process) and organizational. We also succeeded in covering a gap in the literature that stems from the fragmented investigation of the well-known Triple Bottom Line paradigm. We filled this gap by showing that eco-innovations, which could be considered the environmental line, are able to positively influence both the social and economic performances of companies. These findings reverse the logic that companies must first have leftover profits to invest in environmental sustainability. Further, our novel contributions allow us to suggest that cooperation in R & D does not have the ability to influence companies' socioeconomic performance, which confirms our departure model premises in that, although the relation between cooperation in R & D and socioeconomic performance does not directly exist, it is mediated by the introduction of eco-innovations. Future studies may investigate how biomimicry could inspire radical eco-innovations and how digital transformations, such as Industry 4.0 and IoT could boost the efficiency of eco-innovations. (C) 2018 Elsevier Ltd.

The increasing awareness of sustainability issues in industrialized countries has resulted in an unprecedented rise in the number of environmental innovations produced worldwide. However, while this is a general tendency, the amount and importance of environmental innovations is far from consistent across industrialized countries. The influence of countries' policy network styles referring to which and how interest groups participate in making public policy choices is a dimension that has been subject to growing interest and controversy. We propose that companies from pluralistic countries will generate more environmental innovations, but the importance of environmental innovations will be higher in neo-corporatist countries. Our analysis, based on a longitudinal analysis of 84,274 environmental patents from the United States, Japan, Germany, and the UK during the period 1976-2003, indicates support for our prediction that neo-corporatist countries tend to generate more important environmental innovations than pluralistic countries. Yet, our findings do not support our hypothesis suggesting that pluralistic countries are more likely to generate a higher number of environmental innovations. (C) 2019 Elsevier Ltd.

The impact of environmental regulations on firms' productivities is a hot topic at present, but little attention has been paid to the geographic location aspect. As the literature show two kinds of theories named "Cost hypothesis" and "Porter hypothesis" according to the impact of environmental regulations on firms' productivities, this paper empirically analyzes the influences of spatial difference and interdependence of environmental regulations on firms' productivity based geographic data and micro-firm-level data for the period of 2004-2007. Our results show that spatial difference of environmental regulations has a significant impact on firms' total factor productivity with an inverted U-shaped relationship. That is to say, appropriate intensity of environmental regulations could promote firms' productivities, but if the environmental regulations are too intensive, firms' productivity might decrease. The effect of spatial correlation of environmental regulation is significant. The environmental regulation of neighborhoods can promote the productivity of firms in native city. Environmental regulation has a significant impact on the firms with high efficiency, but no significant influence on firms with low efficiency. Besides, the impact of environmental regulation on productivity of pollution-intensive firms is variant according to geographic location. The promotion of environmental regulation on firms' productivity is stronger in the eastern region than in the central and western regions. Our research suggests different mechanisms for the Cost hypothesis and Porter hypothesis in the context of geographic location. The governments shall make different regulations in different parts of the country instead of one policy.

环境规制对企业生产率的影响是当前学术界的研究热点,但基于地理区位视角的研究较少。应用中国环境规制地理空间数据和微观企业数据,实证分析了环境规制空间差异与环境规制空间相关性对企业生产率的影响。结果表明,环境规制空间差异与企业生产率之间存在显著的倒U型关系;环境规制空间相关性作用显著,邻近城市环境规制提升有利于本城市企业生产率增长;环境规制对生产效率高的企业具有显著促进作用,但对生产效率低的企业影响不明显;不同的地理区位,环境规制对企业生产率的影响不同,相对于中西部地区,东部地区环境规制促进企业生产率增长的作用更为显著。本文揭示了在地理区位视角下,针对环境规制对企业生产率的影响,“成本假说”和“波特假说”具有各自的解释空间。

Green innovation has attracted worldwide attention in the past decades. In this paper, we develop series of game models to address the effects of green loans and government subsidies on green innovation activities of enterprises. We derive a threshold value for loaning interest rate. If the interest rate of the green loan is lower than this threshold value, then the enterprises are willing to accept the loan from the bank and implement green innovation. By defining a measure for environmental effect of productive activities, we obtain a threshold value for the loaning scale. If the loaning scale is larger than this threshold value, then it meets the purpose of green loan to improve the environmental quality. We prove the effectiveness of government subsidies as an intervention way in supporting green innovation and environmental protection.