Exploratory Spatial Data Analysis Method Research Articles

The Effect of Energy Consumption on China’s Regional Economic Growth from a Spatial Spillover Perspective

Under the downward pressure of the economy, China’s extensive economic growth relying on factor input is difficult to sustain, and adjusting the total amount and structure of energy consumption to promote high-quality economic development has become an urgent problem to be solved. Based on the exploratory spatial data analysis method (ESDA) and the partial differential method of the spatial regression model, the paper measures the spatial spillover effect of energy consumption on economic growth by taking 30 provincial units in China as the research object. The results are as follows: (1) There is a significant spatial agglomeration in the spatial distribution of regional economic growth and energy consumption, that a core-periphery model has been formed with the eastern region as the core. Therefore, in view of the imbalance in regional development, more attention should be paid to the rationality of industrial spatial distribution when formulating policies, so as to achieve the sustainable development of China’s economy. (2) Through further empirical testing of spatial metrology, it has been found that regional energy consumption has a significant spatial spillover effect on economic growth, and this effect varies according to region and type of energy consumption. Among them, although coal consumption accounts for the largest proportion of energy consumption structure, the economic effect is very limited, which provides a possibility for the government to optimize the allocation of energy resources and shift to a green economy. Therefore, more energy policies should be developed to encourage the development of clean energy.

Spatio-Temporal Pattern of Green Agricultural Science and Technology Progress: A Case Study in Yangtze River Delta of China.

Green agricultural science and technology progress (GASTP) plays an important role in the green transformation of agriculture. This study calculates the contribution rate of GASTP by using the Super-SBM model in the Yangtze River Delta (YRD) from 2011 to 2020. The exploratory spatial data analysis (ESDA) method and the Fixed Effect (FE) panel data model method were adopted to empirically analyze the spatio-temporal patterns of GASTP and its driving mechanism in the YRD. The results showed that: (i) except for Shanghai from 2011 to 2015, the contribution rate of GASTP in the YRD was generally lower than 1 in Anhui Province, Jiangsu Province, and Zhejiang Province, (ii) the level of GASTP had a positive spatial correlation with the study period, except for 2017, and (iii) per capita GDP, agricultural mechanization level, agricultural financial support, and planting structure are four influencing factors of GASTP in the YRD, while total retail sales of social consumer goods and total exports did not have significant effects on GASTP in the YRD. Therefore, we need to increase the opportunities to exchange GASTP experience between cities, improve the environment for agricultural technology extension, and develop follow-up monitoring mechanisms.

Regional Differences and Convergence of Carbon Emissions Intensity in Cities along the Yellow River Basin in China

Since the ecological protection and high-quality development of the Yellow River Basin (YRB) in China have become a primary national strategy, the low-carbon economy is crucial. To formulate effective emission mitigation policies for the YRB, we need to comprehensively understand the characteristics of the spatial agglomeration of the carbon emissions intensity in the YRB and its regional heterogeneity. Therefore, based on the relevant data from 2005 to 2017, we first scientifically measure the carbon emissions intensity of 57 cities along the YRB. Then, we analyze the spatial agglomeration characteristics and long-term transfer trends of carbon emission intensity using exploratory spatial data analysis methods and Markov chains. Finally, the Dagum Gini coefficient and the variation coefficient method are used to study the regional differences and differential evolution convergence of the carbon emissions intensity in the YRB. The results show that the carbon emissions intensity of the YRB has dropped significantly with the spatial distribution characteristics “high in the west and low in the east”, and there is a significant spatial autocorrelation phenomenon. In addition, the probability of a shift in urban carbon intensity is low, leading to a “club convergence” and a “Matthew effect” in general and across regions. Inter-regional differences have always been the primary source of spatial differences in carbon emissions intensity in the YRB, and the intra-regional differences in carbon emissions intensity in the lower YRB show a significant convergence phenomenon. The research results may provide a reference for the regional coordinated development of a low-carbon economy in the YRB, and serve to guide the win-win development model of ecological environment protection and economic growth in the YRB.

Spatial Distribution and Driving Forces of the Vegetable Industry in China

Based on the ArcGIS geostatistical analysis method, this study offers a visualization of the spatial distribution pattern and spatial trend of vegetable production in China. The research also examines the degree of spatial agglomeration patterns of vegetable production by using the standard deviation ellipse technique and exploratory spatial data analysis method. In addition, we employ the spatial regression model partial differential method to explore the driving factors leading to the changing layout of vegetable production. The findings unveil that vegetable production in China exhibit strong spatial non-equilibrium characteristics, with “high-high” and “low-low” types as the main agglomeration patterns. Furthermore, the location distribution shows a northeast–southwest orientation with the center of gravity of distribution gradually directed toward the southwest. Regarding driving factors, the results show that the effective irrigated area of natural factors had a facilitating effect on the layout of vegetable production, while the affected area had an inhibiting effect on it. Climate indicators such as temperature, precipitation and light show different degrees of influence on the layout of vegetable production. The level of urbanization and transportation conditions have a negative impact on the layout of production in the region. Market demand has a positive spillover effect on the layout of local vegetable production, while it has a negative spillover effect on other regions. Technological progress shows positive spillover effects on the layout of vegetable production in the region and other regions. Financial support policy also shows positive effects from an overall perspective.

Coupling Coordination Development of the Logistics Industry, New Urbanization and the Ecological Environment in the Yangtze River Economic Belt

Achieving the coordinated development of the logistics industry, new urbanization and the ecological environment is significant for improving the efficiency of the logistics industry, urbanization level and environmental quality, but there are few studies that consider all three together. Based on the panel data of 11 provinces and municipalities in the Yangtze River Economic Belt from 2009 to 2019, this paper constructs the evaluation index system of the logistics industry, new urbanization and the ecological environment. The entropy method, coupled coordination degree model and exploratory spatial data analysis method are used to empirically analyze the spatial and temporal evolution patterns of the coupled coordination of the three systems in the Yangtze River Economic Belt. The results show that: (1) during the study period, the comprehensive development level of each subsystem and composite system in the Yangtze River Economic Belt has been improving, but there are some differences in the development among the systems in each region; (2) the coupling coordination degree of the three shows a smooth upward trend and an overall transition from near incoordination or basic coordination to the level of primary coordination or intermediate coordination, showing a spatial distribution pattern of downstream > midstream > upstream; (3) the spatial agglomeration characteristics show significant positive spatial correlation and agglomeration and are dominated by high–high agglomeration and low–low agglomeration. These reflect the enormous spatial and temporal differences in the coupling coordination of the three systems in the Yangtze River Economic Belt. The study takes the perspective of the coupled and coordinated development of the logistics industry, new urbanization and the ecological environment, which can provide references for enterprises and governments to make sustainable industrial and urbanization development strategies.

Community-level trachoma ecological associations and the use of geospatial analysis methods: Asystematic review.

BackgroundTrachoma is targeted for global elimination as a public health problem by 2030. Understanding individual, household, or community-associated factors that may lead to continued transmission or risk of recrudescence in areas where elimination has previously been achieved, is essential in reaching and maintaining trachoma elimination. We aimed to identify climatic, demographic, environmental, infrastructural, and socioeconomic factors associated in the literature with trachoma at community-level and assess the strength of their association with trachoma. Because of the potential power of geospatial analysis to delineate the variables most strongly associated with differences in trachoma prevalence, we then looked in detail at geospatial analysis methods used in previous trachoma studies.MethodsWe conducted a systematic literature review using five databases: Medline, Embase, Global Health, Dissertations & Theses Global, and Web of Science, including publications from January 1950 to January 2021. The review protocol was prospectively registered with PROSPERO (CRD42020191718).ResultsOf 35 eligible studies, 29 included 59 different trachoma-associated factors, with eight studies also including spatial analysis methods. Six studies included spatial analysis methods only. Higher trachomatous inflammation—follicular (TF) prevalence was associated with areas that: had lower mean annual precipitation, lower mean annual temperatures, and lower altitudes; were rural, were less accessible, had fewer medical services, had fewer schools; and had lower access to water and sanitation. Higher trachomatous trichiasis (TT) prevalence was associated with higher aridity index and increased distance to stable nightlights. Of the 14 studies that included spatial methods, 11 used exploratory spatial data analysis methods, three used interpolation methods, and seven used spatial modelling methods.ConclusionResearchers and decision-makers should consider the inclusion and potential influence of trachoma-associated factors as part of both research activities and programmatic priorities. The use of geospatial methods in trachoma studies remains limited but offers the potential to define disease hotspots and areas of potential recrudescence to inform local, national, and global programmatic needs.

Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China

Although China has a high rate of economic development, it still faces the problems of unstable industrial structure, low industrial level, and large carbon emissions, which pose huge challenges to China’s sustainable development. China is working hard to develop producer services to achieve industrial transformation and reduce carbon emissions. In this context, there is an extremely urgent need to conduct academic research on changes in producer service agglomeration and carbon emissions. Whether the producer service industry has agglomeration characteristics, and whether the producer service industry affects carbon emissions through multiple paths, are issues worthy of attention. Therefore, this paper takes China as the research area, selects the provincial administrative unit as the research sample, and conducts research on these issues by using exploratory spatial data analysis methods and various spatial econometric models under the guidance of the relevant theories of new economic geography. Our research finds that producer services have significant spatial agglomeration characteristics, and they also have significant spatial differentiation patterns. In addition, the agglomeration of producer services can significantly promote the reduction of carbon emissions and can show obvious spillover effects. Finally, in response to the research conclusions of this paper, we also put forward countermeasures and suggestions from the perspective of the common development of producer service industry and manufacturing industry, hoping to promote China’s transformation from industrial economy to service economy, to maximize the use of producer service industry accumulated dividends.

Ecological Efficiency Evaluation and Spatiotemporal Characteristics Analysis of the Linkage Development of the Logistics Industry and Manufacturing Industry

The logistics and manufacturing industries are basic industries that support social development. First, this study classifies the carbon emissions from the logistics industry and pollution emissions from the manufacturing industry as undesirable outputs and evaluates the ecological efficiency of the logistics and manufacturing industries in the Yangtze River Delta from 2006 to 2016 by using the unexpected slacks-based measure (SBM) model. Second, the study analyzes the spatial differences in industrial correlation efficiency by using the exploratory spatial data analysis method. Third, the spatial econometric model is used to analyze the driving factors of the linkage ecological efficiency between logistics industry and manufacturing industry. Finally, the neural network model is used to predict the linkage ecological efficiency. The results show that the ecological efficiency of the manufacturing industry has steadily improved. The ecological efficiency of the logistics industry presents the rising trend in fluctuation. The level of the linkage development between the logistics and manufacturing industries is high. The results of the spatial heterogeneity analysis show that the spatial differentiation of high–high agglomeration and low–low agglomeration is obvious. The spatial agglomeration characteristics are relatively stable, and the spatial diffusion effect is strong. In space, the linkage ecological efficiency shows a trend of development from multiple agglomeration areas to one agglomeration area. The results of driving factor analysis show that foreign direct investment (FDI), government intervention (GI), and human capital (HP) have positive effects on linkage ecological efficiency, while industrial structure (IS), environmental regulation (ER), and energy intensity (EI) have negative effects on linkage ecological efficiency. The results of the linkage development trend analysis show that the linkage ecological efficiency of the two industries will tend to be stable in the future.

Temporal and Regional Differences and Empirical Analysis on Sensitive Factors of the Corn Production Cost in China

The corn production cost (CPC) in China is related to national food security. However, there are few studies on the temporal and regional differences (TRD) and sensitive factors in the CPC. In this paper, the TRD of the corn production cost across various regions, as well as over the entirety of the country from 2008 to 2018, is presented. It is based on the GIS exploratory spatial data analysis method (ESDA). Simultaneously, a spatial panel model is established to conduct an empirical analysis of the main factors affecting the CPC. The results from the period in question show that the CPC in China and the three major production regions present a fluctuating growth trend, mainly associated with the increase in labor prices. Moreover, the CPC exhibits significant spatial differences, and demonstrates an overall trend of gradual increase from the east to the west. Over time, the number of relatively high-cost provinces has increased. All are located in southern mountainous and hilly corn areas. In addition, the CPCs of various regions are spatially correlated. Factors such as the scale of land management, the degree of mechanization, and socioeconomic conditions have a significantly negative impact on the CPC in China. Furthermore, the labor structure has a notably positive impact on the CPC.

Sustainable international competitiveness of regional construction industry: Spatiotemporal evolution and influential factor analysis in China

In the context of increasingly fierce competition in the international construction market and gradually saturated Chinese domestic construction industry, regional construction industries urgently need to develop differentiated strategies to achieve sustainable development of international competitiveness. This research combines sustainability perspective with incremental perspective to propose the concept of sustainable international competitiveness and explores the impact of sustainability on the spatiotemporal evolution and influential factors of international competitiveness based on exploratory spatial data analysis and quantile regression method. Analysing panel statistical data from provincial regions in China, we find that most regions develop overseas construction projects independently, resulting in random distribution of competitiveness nationwide; meanwhile, spatiotemporal differentiation characteristics present geographical expansion of competitiveness decline in aggregation regions as well as robust polarization effect and path-dependent effect in dispersion regions. Additionally, determinant influential factors of sustainable international competitiveness vary with competitiveness increasing. These findings manifest unbalanced sustainability performances in strategy-driven and demand-driven regions resulting from single driving mode of competitiveness; reveal heterogeneous development mode of influential factors with inverted U-shaped effect, marginal effect, and escape-competition effect; and provide policy makers at national, regional, and firm levels with improvement strategies tailored to local conditions in extended industrial chain, integrated contracting mode, and updated strategy deployment; promoting high-quality and sustainable development of overseas construction projects with unified policy planning in the regional construction industry.

Environmental Protection, Industrial Structure and Urbanization: Spatiotemporal Evidence from Beijing–Tianjin–Hebei, China

The Beijing–Tianjin–Hebei urban agglomeration (BTH) is striving to realize the transformation process from a low-efficiency to a high-quality development mode; however, it still has problems regarding reducing energy consumption and ecological environment pressure. Based on panel data from 2013 to 2017, this paper proposes an evaluation index system based on BTH’s “environmental protection–industrial structure–urbanization” system. In the course of applying the coupling degree model (CDM) and the coupling coordination degree model (CCDM) with exploratory spatial data analysis (ESDA) methods, this paper discusses the spatiotemporal process, development level, and spatial agglomeration characteristics of the environmental protection–industrial structure–urbanization system in each city of the BTH area. The findings reveal that the coupling degree of the BTH system is gradually increasing, and that the development level of the BTH subsystem is unbalanced: the coupling coordination level of BTH shows a positive evolution process; however, it is in a stage of low-level collaborative development, and there are obvious differences in the level of BTH coupling coordination in space, revealing the convergence of low–high and high–low types. This paper concludes by putting forward the strategy of optimizing the regional spatial pattern of urban agglomeration and implementing integrated development in order to achieve the desired coupling and coordination effects.

Spatial Spillover Effects of Agricultural Transport Costs in Peru

The role of agricultural transport costs in core-periphery structures has habitually been ignored in New Economic Geography (NEG) models. This is due to the convention of treating the agricultural good as the numéraire, thus implying that agricultural transportation costs are assumed to be zero in these models. For more than three decades, this has been the standard setting in spatial equilibrium analysis. This paper examines the effects of agricultural transport costs on the spatial organisation of regional structures in Peru. In doing so, Krugman’s formulation of iceberg transport costs is modified to introduce agricultural transport costs into the dynamic of the NEG models. We use exploratory spatial flow data analysis methods and non-spatial and spatial origin-destination flow models to explore how the regional spatial structure changes when real transportation data for agricultural goods are included into the iceberg transport costs formulation. We show that agricultural transport costs generate flows that are systematically associated with flows to or from nearby regions generating thus the emergence of spatial spillovers across Peruvian regions. The results of the paper support the contention that NEG models have overshadowed the role of agricultural transport costs in determining the spatial configuration of economic activities.

Coordinated relationship between urbanization and grain production in China: Degree measurement, spatial differentiation and its factors detection

The entropy method was used to measure urbanization, and applied the coupled coordination degree model (CCDM) to calculate the coupled coordination degree of urbanization and grain production (CCDUG) of 330 cities at the prefecture-level and above from 2000 to 2018 in China, the exploratory spatial data analysis method (ESDA) is used to reveal their spatial-temporal differentiation, and the geographical detector (GeoDetector) is used to identify the dominant factors and their interaction affecting this spatial differentiation. Although the CCDUG has entered a high coordination interval, there is still much room for improvement. The spatial differentiation and agglomeration characteristics of CCDUG are significant, showing a "center-periphery" distribution pattern with the east as the center gradually decreases towards the west. The CCDUG with high-high cluster is mainly distributed in the main grain-producing areas of Northeast Plain and North China Plain. Low-low cluster is primarily distributed in the grain balance and main marketing areas of Northwest, Southwest, and South China coast. Elevation, urban-rural income gap, opening to the outside world, economic growth, and cropland resources per capita are the dominant factors affecting the spatial differentiation of CCDUG. The interaction between factors will enhance this spatial differentiation, among which the interaction between basic endowment conditions and socio-economic factors is the strongest. Understanding and portraying the interactive relationship between urbanization and grain production and its spatial laws have important theoretical references for achieving balanced coordination between high-quality economic-social transformation and food security.

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.

Spatial Distribution of Public Housing and Urban Socio-Spatial Inequalities: An Exploratory Analysis of the Valencia Case

The urban spatial distribution of public housing is not a widely addressed issue in Spain, from a geographical perspective. This paper analyses the spatial distribution of public housing in the city of Valencia (Spain), as well as to identify its relationship with other socio-residential characteristics of the urban environment. Different techniques of spatial point pattern analysis, exploratory spatial data analysis (ESDA) and clustering methods are implemented. We analyse both the univariate spatial patterns of public housing and its relationship with two variables: a low-income population and median monthly rent. Analysis has revealed that public housing follows a pattern of partial agglomeration and mostly peripheral dispersion in its spatial distribution. However, there does not seem to be a univocal and immanent relationship between such distribution patterns and the characteristics of the socio-residential environment. Conversely, it is possible to point to the existence of multiple local forms of association. The lack of a clear pattern may be due to many reasons: the heterogeneity of profiles eligible for public housing, the size of the projects and the spatial dispersion in their location.

Spatiotemporal evolution of ecosystem service values in an area dominated by vegetation restoration: Quantification and mechanisms

The Grain to Green Program (GTGP) has directly led to large-scale transformational land use/cover changes and has impacted ecosystem services. Therefore, it is of great importance to determine the relationship between the land use change and ecosystem services in retired farmland areas. Based on multi-temporal land use data (1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2018), the benefit transfer method was used to calculate the ecosystem service value (ESV), and the willingness to pay and ability to pay were then integrated into the evaluation model. In addition, we used the exploratory spatial data analysis method to systematically analyze the change characteristics of the land use and ESV. Finally, the spatial differentiation mechanisms of the ESV were identified using geographical detectors. The results showed that the transformation of the land use mainly occurred in the farmland, forestland, and grassland; the farmland showed a decreasing trend from 1980 to 2018, whereas the areas of the forestland and grassland showed an increasing trend. The ESV demonstrated an increasing trend during 1980–2018 and formed a spatial pattern with the distribution of high and low values. There was a significant positive spatial correlation in the ESV, and this spatial aggregation became increasingly intense with time. The order of different ecosystem services in the study area was as follows: regulating service > supporting service > provisioning service > cultural service, and the ecosystem service values showed an upward trend, except for the provisioning service that showed a downward trend. The spatial differentiation of the ESV is the result of a combined effect of natural factors and socioeconomic factors. With the restoration of vegetation in the retired farmland area, natural factors such as slope and elevation play a crucial role in the differentiation of the ESV.

Measurement of Investment Potential and Spatial Distribution of Arable Land among Countries within the “Belt and Road Initiative”

The belt and road initiative (BRI) is a mutual development approach projected by China, which delivers exceptional opportunities for multi-phased communication and cooperation across Asia, Africa, and Europe. It opens ample opportunities for China to easily invest in overseas arable land. Based on the macro data of 119 countries in 2010 and 2016 before and after the BRI, the study comprehensively uses fuzzy C-means clustering and the entropy method to evaluate the potential of arable land investment from four dimensions, which existing literature has not fully grasped. Moreover, the study uses the exploratory spatial data analysis methods (ESDA), kernel density estimation, and trend surface analysis to study the spatial pattern characteristics. The results show that: (i) there are noticeable regional differences in the investment potential of arable land in BRI countries. Asian countries, led by Kazakhstan and Indonesia, and African Unions, led by Ethiopia, South Africa, and Tanzania, generally have higher investment potential. However, South America and European countries are relatively lower. (ii) Resource endowment and production conditions significantly impact overseas arable land investment potential. Asia and Africa have advantages in resource endowment and production conditions, while European countries generally have better economic and political environments. (iii) From the perspective of time evolution, the investment potential in 2016 is generally higher than in 2010, and the negative correlation and dispersion are lower than in 2010. Based on these findings, it is recommended that Chinese enterprises should comprehensively consider the differences in resource endowments and agricultural development levels in various countries, optimize investment layout, and reduce investment risks. Chinese companies should collaborate with host nations on modernization and promote the long-term viability of arable land investments.

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China’s economic transformation: spatial-temporal characteristics and influencing factors

At present, China's economic development has entered a "new normal." Exploring industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the super-efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that the IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend, whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

Structural characteristics and proximity comparison of China’s urban innovation cooperation network

How to promote and improve the level of urban innovation cooperation is a major issue in China’s current high-quality economic development. Thus, enhancing innovation ability is essential to achieving high-quality economic growth under the "new normal". Based on the data of Chinese invention patents from 1985 to 2017, this paper analyzes the characteristics of China’s urban innovation cooperation network and the different roles of proximity by using social network analysis and exploratory spatial data analysis methods. The analysis results show that: (1) On the whole, the development of China’s urban innovation cooperation network is characterized by stages (initial development stage, rapid development stage, and gradual decline stage); The urban innovation cooperation network has strong connectivity and centripetal concentration but its imbalance needs to be further improved; The degree of urban participation has gradually increased, consolidating the stability of the network structure. (2) The centrality of urban innovation cooperation network has obvious characteristics of administrative center orientation, coastal areas orientation, and ‘strong east and weak west’; Beijing is the center and bridge of the network, and the network flattening characteristics are obvious; A hierarchical ‘core-edge’ structure is gradually formed for the urban innovation cooperation network, and the pyramid structure with Beijing standing at the top is being consolidated. (3) The geographical proximity presents a significant global spatial positive correlation, while the network proximity and pure network proximity have a more significant global spatial negative correlation; The local spatial autocorrelation of China’s urban innovation cooperation system based on network proximity is more obvious and identifiable than that based on the geographical proximity, which better reflects the new development model of "relationship economy".

Study on environmental performance evaluation of different linkage development types of the logistics and manufacturing industries considering the unexpected output

ABSTRACT According to the different organizational forms of logistics in manufacturing, the types of linkage development between the logistics and manufacturing industries include two different logistics service modes: logistics outsourcing and logistics self-operation. This study considers two manufacturing clusters for comparison: the northeast region in China, mainly state-owned and self-operated, and the Yangtze River Delta region in China, mainly focusing on logistics outsourcing services. First, we construct a parallel network system considering heterogeneity and an unexpected super-efficiency slacks-based measure: the Grey Relational Analysis model. Second, considering the unexpected output, this study evaluates the environmental performance of the linkage development of the logistics and manufacturing industries and explains the types of linkage development between them. Finally, the spatial and temporal characteristics of linkage efficiency under different linkage modes are studied using an exploratory spatial data analysis method. The research results show that the efficiency of the two-industry linkage in different linkage types that consider the unexpected output presents different development characteristics. Regarding time characteristics and trends, we compare the development efficiency of the two-industry linkage at the overall level of the northeast and the Yangtze River Delta regions from 2009 to 2016. The Yangtze River Delta region showed a higher state of linkage while the northeast region showed a medium linkage state. Regarding spatial pattern evolution, the effect of the technical efficiency of the linkage between the two industries in the three northeastern provinces showed continuous evolution from the medium linkage stage to the weak linkage stage, and the Yangtze River Delta showed continuous evolution from the medium linkage to the strong linkage stage. Implications: Due to data availability, references to the relevant achievements of the research on the linkage between the manufacturing and logistics industries, the transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data are replaced by industrial data. The manufacturing industry is the core industry of industrial systems. Its output value accounts for more than 90% of the industrial output. Thus, it can reflect the development trend of the manufacturing industry in general.